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VetCalc Exposure Modelling Tool for

Veterinary Medicines

N. Mackay, P. Mason, A. Di Guardo

DEFRA Research Project VM02133

User’s Manual July 2005

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Contents

1.0 INTRODUCTION............................................................................................. 5

2.0 DOSAGE REGIMES ....................................................................................... 9

2.1 Treatment types......................................................................................... 9

2.2 Relevant animal category ....................................................................... 10

2.3 Dosage of active substance ................................................................... 10

2.4 Duration of treatment.............................................................................. 10

2.5 Proportion of herd treated...................................................................... 10

3.0 ANIMAL CATEGORIES................................................................................ 11

3.1 Livestock Production Systems .............................................................. 14

3.2 Bovine: Beef ............................................................................................ 15

3.3 Bovine: Dairy ........................................................................................... 18

3.4 Ovine ........................................................................................................ 21

3.5 Swine ........................................................................................................ 26

3.6 Poultry ...................................................................................................... 29

3.7 References ............................................................................................... 32

4.0 CHEMICAL INPUT DATA............................................................................. 34

4.1 Standardisation of Criteria for Selection of Input Parameters Under FOCUS................................................................................................................. 35

4.1.1 General Approach ....................................................................................... 35 4.1.2 Degradation Data......................................................................................... 35 4.1.3 ‘Normalisation’ procedure .......................................................................... 36 4.1.4 Reference temperature ............................................................................... 36 4.1.5 Reference soil moisture.............................................................................. 37 4.1.6 Adjustment of degradation rate at different soil depths ......................... 40 4.1.7 Parameters relating degradation rate to soil temperature ...................... 41 4.1.8 Parameter relating degradation rate to soil moisture.............................. 41 4.1.9 Soil Sorption ................................................................................................ 41 4.1.10 Summary of Main FOCUS Recommendations.......................................... 41

4.2 Advanced Environmental Fate............................................................... 42 4.2.1 Degradation during storage ....................................................................... 42 4.2.2 Behaviour in water/sediment systems ...................................................... 43 4.2.3 Potential for metabolism prior to excretion.............................................. 45 4.2.4 User-defined sorption ................................................................................. 45 4.2.5 Soil degradation/dissipation ...................................................................... 46 4.2.6 Simulation of metabolites........................................................................... 47

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4.3 References ............................................................................................... 49

5.0 SCENARIO PROFILES ................................................................................ 51

5.1 Strategy for Regional Scenario Selection............................................. 52

5.2 Relevance of Regions to Livestock Sectors......................................... 61

5.3 Regional Characterisation – Development of Detailed Scenarios...... 63

5.4 Zone 1 – Sandy silt loam ........................................................................ 68

5.5 Zone 1 - Clay Loam ................................................................................. 72

5.6 Zone 2 – Sand .......................................................................................... 76

5.7 Zone 2 – Loamy sand.............................................................................. 80

5.8 Zone 2 – Sandy loam 1............................................................................ 85

5.9 Zone 2 - Sandy loam 2 ............................................................................ 89

5.10 Zone 2 – Sandy clay loam 1.................................................................... 98

5.11 Zone 2 – Sandy clay loam 2.................................................................. 102

5.12 Zone 2 – Sandy silt loam ...................................................................... 106

5.13 Zone 2 - Clay loam................................................................................. 110

5.14 Zone 2 - Clay .......................................................................................... 116

5.15 Zone 3 - Sandy Loam ............................................................................ 124

5.16 Summary ................................................................................................ 128

5.17 References ............................................................................................. 135

6.0 MANURE/SLURRY MANAGEMENT.......................................................... 137

6.1 Belgium .................................................................................................. 140

6.2 Denmark ................................................................................................. 143

6.3 Finland.................................................................................................... 146

6.4 France..................................................................................................... 149

6.5 Germany................................................................................................. 152

6.6 Ireland..................................................................................................... 155

6.7 Italy ......................................................................................................... 157

6.8 The Netherlands .................................................................................... 160

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6.9 Portugal.................................................................................................. 163

6.10 Spain................................................................................................... 166

6.11 Sweden............................................................................................... 169

6.12 The United Kingdom ......................................................................... 171

6.13 Summary ............................................................................................ 176

6.14 References ......................................................................................... 180

7.0 MODELLING FRAMEWORK ........................................................................... 182

7.1 Simulation of loadings to soil .............................................................. 183 7.1.1 Excretion by grazing animals ....................................................................... 184 7.1.2 Spreading of slurry or FYM........................................................................... 186

7.2 Simulation of fate and behaviour in soil ............................................. 191 7.2.1 Model capability......................................................................................... 191 7.2.2 Enhancements to the functionality of Leach-P ...................................... 192

7.3 Simulation of fate and behaviour in surface water ............................ 195 7.3.1 Introduction .................................................................................................... 195 7.3.2 Model development ....................................................................................... 197 7.3.3 Model verification .......................................................................................... 201 7.3.4 Input and Output ............................................................................................ 201

7.4 References ............................................................................................. 202

8.0 REFINEMENTS........................................................................................... 204

8.1 Dosage regimes..................................................................................... 204

8.2 Animal characteristics .......................................................................... 205

8.3 Chemical characteristics ...................................................................... 205

8.4 Agricultural practices ........................................................................... 205

8.5 Environmental characteristics ............................................................. 206

8.6 Interaction with FOCUS Modelling framework ................................... 206

8.7 References ............................................................................................. 207

9.0 TUTORIAL .................................................................................................. 208

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1.0 INTRODUCTION As a component of the regulatory process there is thus a clear need to evaluate the risks posed to treated animals, human consumers and the environment from veterinary medicines, and in particular those products finding their way into the environment in animal excreta and manures. Three environmental compartments are considered at greatest risk: soil, surface waters and groundwater. In evaluating risks associated with veterinary medicines the potential for exposure is a key driver that is variable and subject to a degree of management and/or mitigation. There are two separate challenges when assessing exposure potential:

• To develop approaches that are robust, both in technical and regulatory terms. • To develop a basis for estimates that is realistic in both agricultural and

environmental terms. The UK is not unique in having to address these challenges. Increasingly, it is important within the environmental risk assessment framework for veterinary medicines to consider exposure and risk potential associated with usages, agricultural practices and environmental situations throughout Europe (e.g. centralised versus decentralised applications). In recognition of the need to develop regulatory support tools to facilitate more robust exposure assessment, Defra and VMD initiated a research project in late 2002 to develop a modelling framework that would be available to both the regulatory community and industry. The VetCalc tool, described in this report, addresses a wide variety of agricultural and environmental situations including:

• Animal characteristics for major food-producing animals; • Associated manure characteristics; • Local agricultural practices; • Characteristics of the destination environment; • Fate and behaviour within three critical compartments.

The user’s manual provides the user with background information on key drivers such as treatment regimes (both bodyweight and non-body-weight related), animal characteristics and husbandry practices, manure characteristics and management regimes, environmental characteristics (soil, hydrology, weather), agricultural practices and chemical parameters. The conceptual framework employed within the VetCalc model is summarised in Figure 1.1. The framework can be divided into four major modelling tasks:

• Provision of input on dosage regime and chemical characteristics • Calculation of maximum/initial predicted environmental concentrations (PEC) in

excreta and soil • Simulation of subsequent fate in soil (including potential for run-off, leaching and

degradation and estimation of PEC values in shallow groundwater) • Simulation of subsequent fate in surface water (including potential for

dilution/advection, degradation and partitioning and estimation of PEC values in the water column)

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Figure 1.1. Conceptual framework of VetCalc model Scenario-based assessment framework The conceptual framework described in Figure 1.1 confirms that the need to balance realism and regulatory conservatism in simulation of agricultural practices and environmental processes is a key technical challenge within this project. Therefore, a scenario-based assessment scheme was proposed for use alongside a set of robust modelling tools. A set of 12 scenarios representing a diverse range of agricultural practices and pedoclimatic combinations were developed. These scenarios are not intended to be site-specific or even Member State-specific but broadly representative for consideration alongside typical Member State manure management practices. Data for the following Member States are provided for use with these scenarios:

• Belgium • Denmark • Eire • Finland • France • Germany • Italy • The Netherlands • Portugal • Spain • Sweden • The United Kingdom

Modelling tools In order to carry out the required calculations three modelling components were developed as described in Figure 1.2:

• Graphic User Interface (including standardised regulatory calculations of PECexcreta and PECsoil)

• Modified LEACHP model: Simulation of fate in soil (including estimation of PECgroundwater)

• Fugacity model: Simulation of fate in surface waters (including estimation of PECsurface

water )

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Figure 1.2. Illustration of critical modelling components within VetCalc model Graphic User Interface The first of these comprises the graphic user interface, input functions for definition of treatment regime (summarised in Section 2), chemical characteristics (summarised in Section 4 – including guidance for parameter selection) and associated animal, environmental and manure management defaults (summarised in Section 3, 5 and 6, respectively). The VetCalc model draws heavily upon certain default parameters such as body weight and manure characteristics. In recognition of the importance of these parameters attempts have been made to allow a high degree of flexibility in setting up simulations including the potential for overriding defaults where justifications can be provided. Standard calculations of PECexcreta and PECsoil draw heavily upon standardised calculations proposed by Montforts et al. (1999). These calculations are explained in detail in Section 7.1. Modified LEACHP model In order to simulate behaviour in soil (including degradation, run-off to surface water and potential for leaching to drains or groundwater) a robust Ricardian leaching model developed by Hutson (2003) was adapted to allow for simulation of movement to tile and mole drains. The choice of Ricardian hydrology model ensures a relatively robust basis for simulation of water and chemical movement. Hydrological definition of scenarios was based upon a combination of high quality model scenario precedents in the form of FOCUS (standard scenarios for evaluation of plant protection products under Directive 91/414/EEC; FOCUS, 2000; 2001) and ad hoc scenarios for additional pedoclimatic combinations. The capabilities and functions of the modified LEACHP model are summarised in Section 7.2. Fugacity model In order to simulate behaviour in surface waters, a relatively simple compartmental model was devised that conducts calculations of potential for dilution/advection,

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degradation and partitioning into air, sediment and suspended sediment employing fugacity principles (Mackay, 2001). The fugacity model is driven by output (in terms of soil temperature, water and chemical loadings) from the modified LEACHP model. The capabilities and functions of the modified fugacity model are summarised in Section 7.3. Refinement options The VetCalc model was designed to provide flexibility in the simulation of certain processes that may provide a degree of mitigation in more realistic exposure assessment, where warranted. As such, the model provides the user with the opportunity to define both a ‘Basic’ set of environmental fate and physico-chemical parameters for the compound under assessment (minimum dataset required at VICH Phase 2) or an ‘Advanced’ option including opportunity to include:

• More accurate simulation of behaviour in water-sediment systems • Metabolism • Degradation during storage • pH-dependant sorption • Field dissipation versus lab degradation rates (influence of soil moisture and

temperature conditions) • Behaviour of metabolites

Guidance on refinement options are provided in Section 8. Step by step guidance Step by step guidance on the operation of the model is provided in Section 9. Short guidance notes on operation of the model as well as a copy of the user’s manual are provided as PDF documents within the help function of the software. References FOCUS (2000) FOCUS groundwater scenarios in the EU review of active substances, The report of the work of the Groundwater Scenarios Workgroup of FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe), Version 1 of November 2000. EC DG Sanco/321/2000 rev.2 FOCUS (2001) “FOCUS Surface Water Scenarios in the EU Evaluation Process under 91/414/EEC”. Report of the FOCUS Working Group on Surface Water Scenarios, EC Document Reference SANCO/4802/2001-rev.2. 245 pp. Hutson, J. (2003) Leaching estimation and chemistry model: Model description and user’s guide. January 2003 revision. Mackay, D. (2001) Multimedia environmental models – The fugacity approach, 2nd edition, CRC Press, Boca Raton, FL, USA Montforts, M.H.M.M. (2003) Environmental risk assessment for veterinary medicinal products, Part 1. Non-immunological drug substances – second update, RIVM report 320202001/2003

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2.0 DOSAGE REGIMES The user is initially asked to provide background information on the dosage regime. This includes:

• Compound or product name • Treatment type • Relevant animal categories • Dosage of active substance • Duration of treatment • Proportion of herd treated

An illustration of the treatment definition screen in VetCalc is provided in Figure 2.1.

Figure 2.1. Treatment definition screen in VetCalc 2.1 Treatment types Treatment types are divided into two major classes; bodyweight and non-bodyweight related treatments. For bodyweight related treatments the user may choose between:

• Oral administration via feed additive • Oral administration via tablet or capsule • Drinking water treatments • Oral administration via drops (graduated pipette) • Oral administration via dispensing/drenching gun • Injections

For non-bodyweight related treatments the user chooses between:

• Injections

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• Bolus treatments • Pessaries • Products administered via inhalation • Topical products: Pour-on products • Topical products: Sprays

The definition of the treatment type simply establishes any link to bodyweight in subsequent calculations of concentration in excreta. 2.2 Relevant animal category Four categories of animals are considered (bovine, ovine, swine and avian). The user can choose to define a single treatment regime that is relevant to an entire category of animals or can define treatments of individual animal growth stages. Further details on the animal growth stages considered in the model are provided in Chapter 3.0. 2.3 Dosage of active substance If the user defines a bodyweight related treatment for the active substance (a.s.) they are asked to provide a dosage rate in units of mg a.s./kg bodyweight. If a non-bodyweight related treatment regime is defined the dosage is simply in units of mg a.s. total dosage. 2.4 Duration of treatment The duration of treatment is simply defined as number of daily treatments. This is used with the dosage rate in order to provide a total dosage rate. 2.5 Proportion of herd treated Many treatments that will require Phase 2 evaluation under the VICH scheme are whole herd treatments. However, in certain cases it has been recognised that only a proportion of the herd or flock may be treated and this may provide a degree of mitigation. The user has the option to define a percentage of the herd or flock treated. Further information on typical herd/flock sizes in the United Kingdom can be found in Chapter 3 (Sections 3.3-3.7). The default setting is 100% of herd/flock treated. Where users choose to override this default they should provide a detailed justification.

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3.0 ANIMAL CATEGORIES Four livestock categories have been identified – including both housed and grazing livestock. A number of sub-categories are identified for each major grouping as follows: Group 1: Bovine Dairy cow Cow of a breed specifically defined as being for milk production, as

distinct from beef or dual purpose breeds. Main dairy breeds in the UK are Friesian, Holstein, Ayrshire, Shorthorn, Guernsey andJersey.

Dairy heifer replacement Young female bovine animal up to birth of first calf or in lactationfollowing the first calving. Replacement heifers enter the dairy herdas a replacement for a culled cow.

Beef suckler cow A real beef type animal is solid in its body, well covered with flesh,particularly in the hindquarters and back. Breeds include SouthDevon, Welsh Black, Hereford, Aberdeen Angus, Sussex.

Grower fattener (>2 yr) Grower fattener (12-24 mo) Grower fattener (6-12 mo) Calf (0-6 mo) Group 2: Ovine Adult sheep (>1 yr) Rams (male animal that has reached sexual maturity at around six

months) and ewes (female sheep of breeding age, may bedesignated as maiden ewes, not yet bred).

Lamb (6 mo) Young sheep still with its dam (mother) or up to five months ofage. Designated as ewe lamb or ram lamb.

Group 3: Swine Maiden gilts Young female pig, has not produced first litter (up to first

farrowing).

1 sow place + litters Sow refers to a female pig after she has had her first litter. Thiscategory comprises the place required for a sow, plus its litter ofpiglets before weaning.

Weaners (3-7.5 weeks) Piglet undergoing the transition from a liquid diet to one that is usually based on dry feed ingredients.

Growers (7.5-11 weeks) Light cutter (11-20 weeks) Bacon (11-23 weeks) Bacon liquid feed (11-23 weeks)

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Group 4: Avian Laying hens Broilers Young (normally eight weeks old) male or female birds weighing 3

to 3.5lbs (1.36kg - 1.59kgs) especially bred in large quantities for the meat market. Sold as male, female or as 'hatched'; with apotentially good food conversion and quick finishing facility.

Replacement pullets Young female bird of 12 months or less (up to first moult).

Broiler breeders Young (normally eight weeks old) male or female birds weighing 3to 3.5lbs (1.36kg - 1.59kgs) especially bred in large quantities for the meat market. Sold as male, female or as 'hatched'; with apotentially good food conversion and quick finishing facility.

Turkey male There are two general breeds, the 'American Bronzewing' and the'White Holland'. Bred entirely for meat for human consumption.The male turkey is known as the stag.

Turkey female As above, females are called hens.

Ducks The characteristics of each animal that are critical within the risk assessment are summarised in Table 3.1 and 3.2. Users have the opportunity to override defaults when setting up simulations as illustrated in Figure 3.1. Unless otherwise stated, the data has been obtained from UK-based, Defra-funded research undertaken by ADAS that considered nutrient excretion (nitrogen and phosphorus) in different livestock production systems (Smith et al., 2000a, 2000b, 2000c, 2000d, 2000e) or a report prepared by Montforts (2003) that outline risk assessment techniques for veterinary medicines in the Netherlands. Because of the importance of obtaining good animal husbandry data in order to establish a meaningful basis for exposure assessment a questionnaire was prepared for completion by ADAS livestock consultants who are experts in beef, dairy, pig and poultry husbandry. The results of the first set of questionnaires are reproduced in subsequent sections. These questionnaires represent UK practices alone but may have broader relevance to other Member States. In the case of Group 3 (swine), there are relatively large body weight ranges for certain sub-categories. This reflects differences between breeds and production systems. It is considered neither realistic nor sufficiently conservative to simply base assessments upon an ‘average’ body weight for each sub-category where such a large range in body weights occurs. As a consequence this category is uniquely defined with an upper and lower body weight range. It is proposed that for prophylactic treatments the lower body weight range is employed in calculations. For all other treatments the upper body weight range is employed.

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Table 3.1- Summary of Characteristics of Simulated Systems

Body weight

(kg) Number of

cycli per yearN release rate (kg/place/yr)

P release rate (kg/place/yr)

Duration of cyclus

Group 1: Bovine Dairy cow 450-650 1 76 17.10 365 Dairy heifer replacement 500 1 58 13.05 365 Beef suckler cow 500-600 1 58 14.91 365 Grower fattener (>2 yr) 500 0.7 - 1 58 14.91 365/521Grower fattener (12-24 mo) 400 0.7 47 12.09 521 Grower fattener (6-12 mo) 180 0.7 12 3.09 521 Calf (0-6 mo) 50 - 140 1.8 7 1.80 203 Newborn 30 - 50 1.8 7 1.80 203

Group 2: Ovine Adult sheep (>1 yr) 27 - 80 1 9 1.52 365 Lamb (6 mo) 10 - 50 1 1.2 0.16 365 Newborn 3 - 6 1 1.2 0.16 365

Group 3: Swine Maiden gilts 90-130 1 13 4.09 365 1 sow place + litters 130-225 1 19.5 6.13 365 Weaners (3-7.5 weeks) 7-18 11.5 3 0.94 32 Growers (7.5-11 weeks) 18-35 6.5 6.1 1.92 56 Light cutter (11-20 weeks) 35-85 3 9.4 2.95 122 Bacon (11-23 weeks) 35-105 2.6 10.5 3.30 140 Bacon liquid feed (11-23 weeks) 35-105 2.6 10.5 3.30 140 Newborn ca 7 17.4 3 0.94 21

Group 4: Avian Laying hens (1000) 2200 0.85 660 239.80 429 Broilers (1000) 2200 6.5 495 179.85 52 Replacement pullets (1000) 3400 0.85 975 354.25 429 Broiler breeders (1000) 1600 2.4 125 45.42 152 Chick (1 day old) (1000) 35-40 NA* NA* NA* NA* Turkey male (1000) (1000) 13500 2.7 1390 505.03 135 Turkey female (1000) 6500 2.7 650 236.17 135 Turkey Chick (1000) 50 - 60 NA* NA* NA* NA* Ducks (1000) 1600 - 3400 7.4 900 327.00 49

* Characteristics are unavailable. Users may default to employing information for adults where considered relevant In the case of Group 4 (avian) where there is a significantly larger scale of production (per head) characteristics are reported on a per thousand head basis.

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For grazing animals the following data are also required: Table 3.2 Summary of characteristics of grazing animals Cattle Sheep Number of grazing days (d) 190 d Ewe: 320 d

Lamb: 160 d Dung production (pasture; kgwwt/animal/d)

Dairy: 52 Suckler cow: 52 Beef: 11

Ewe: 1.025 Lamb: 1.758

Stocking density (pasture; animals/ha)

Dairy: 3.5 Suckler cow: 3.5 Beef: 9.5

Ewe: 15 Lamb: 25

Dung air fraction (m3/m3) Dairy: 0.025 Beef: 0.03

0.07

Dung water fraction (m3/m3) Dairy: 0.90 Beef: 0.88

0.67

Dung solid fraction (m3/m3) Dairy: 0.075 Beef: 0.09

0.26

Number of excretions per day 10.5 10.5

Figure 3.1. Illustration of simulation definition screen in VetCalc 3.1 Livestock Production Systems In the previous section default characteristics are provided for each animal category. These may be applied throughout the European Union as a basis for initial risk

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assessments. Under certain circumstances, notifiers may wish to refine exposure assessments to better reflect local production systems. Important considerations may include:

• Alternative body weights for specialised treatments (e.g. prophylactic treatments) • Alternative body weights for regional or specialised production systems (e.g. breed

preferences) • Focusing certain treatments on timing of reproduction cycles (e.g. calving, lambing)

for pasture animals • Recognition of any housing periods (e.g. non-grazing periods) • Alternative stocking densities for regional or specialised production systems • Herd demographics as indicators for treatment timing • Herd demographics as indicators for potential proportions of herds treated at different

points in the year. To assist with development of refined exposure assessments the remainder of this chapter provides brief overviews of production systems for each animal category. These discussions are focused primarily upon systems in the United Kingdom, although commentary is provided on key differences between UK and other European production practices.

3.2 Bovine: Beef UK production questionnaires have been prepared on two subsets – suckler cows and beef cattle finishing systems. The results are summarised below. Livestock Production Systems - Suckler Cows The information listed in Table 3.2.1 assumes that calves are removed from the system/farm at weaning and enter a finishing system. Table 3.2.1- Suckler Cow Production System System 1 System 2 System 3 System 4 Geography Hill/Upland Hill/Upland Lowland Lowland Calving period Spring Autumn Spring Autumn Housing period (if any) Nov-May Oct-May Nov-April Oct-April Stocking density – category 1 (cows/ha)

< 1.0 cows/ha < 1.4 cows/ha < 1.8 cows/ha < 2.2 cows/ha

Stocking density – category 2 (calves/ha)

<1.0 calves/ha

<1.4 calves/ha

<1.8 calves/ha

<2.2 calves/ha

Stocking density – category 3 (replacements/ha)

<0.2 replacements/ha

<0.3 replacements/ha

<0.4 replacements/ha

<0.5 replacements/ha

Stocking density – category 4 (bulls/ha)

~0.02 bulls/ha ~0.03 bulls/ha ~0.04 bulls/ha ~0.05 bulls/ha

Stocking density Typical herd size (actually calving)

50 70 60 80

Young reared per female served by bull

0.89 0.90 0.91 0.91

Demographics – Suckler Cows Figures given in Table 3.2.2-5 relate to a herd of 100 calving cows.

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Table 3.2.2- Numbers on farm through year - System 1: i.e. Hill/Upland - Spring calving Suckler Cows Suckling

Calves Female replacements

Breeding Bulls

Jan 83 17 2 Feb 83 17 2 Mar 83 17 2 Apr 83 20 2 May 100 100 20 2 Jun 99 99 20 2 Jul 98 98 20 2 Aug 97 97 20 2 Sep 97 97 20 2 Oct 97 97 20 2 Nov 97 97 17 2 Dec 83 17 2 Table 3.2.3- Numbers on farm through year - System 2: i.e. Hill/Upland - Autumn calving Suckler Cows Suckling

Calves Female replacements

Breeding Bulls

Jan 97 97 20 2 Feb 97 97 20 2 Mar 97 97 20 2 Apr 97 97 17 2 May 97 97 17 2 Jun 83 17 2 Jul 83 17 2 Aug 83 17 2 Sep 100 100 20 2 Oct 99 99 20 2 Nov 98 98 20 2 Dec 97 97 20 2 Table 3.2.4- Numbers on farm through year - System 3: i.e. Lowland - Spring calving Suckler Cows Suckling

Calves Female replacements

Breeding Bulls

Jan 83 17 2 Feb 83 17 2 Mar 83 17 2 Apr 100 100 20 2 May 99 99 20 2 Jun 98 98 20 2 Jul 97 97 20 2 Aug 97 97 20 2 Sep 97 97 20 2 Oct 97 97 20 2 Nov 97 97 17 2 Dec 83 17 2

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Table 3.2.5- Numbers on farm through year- System 4: i.e. Lowland - Autumn calving Suckler Cows Suckling

Calves Female

replacements Breeding

Bulls Jan 97 97 20 2 Feb 97 97 20 2 Mar 97 97 20 2 Apr 97 97 17 2 May 83 17 2 Jun 83 17 2 Jul 83 17 2 Aug 100 100 20 2 Sep 99 99 20 2 Oct 98 98 20 2 Nov 97 97 20 2 Dec 97 97 20 2 Notes on differences in livestock systems and practices – Suckler cows Within the UK Grass growth occurs approximately 1 month earlier in the South-West than in the North-East or Scotland so turnout times and therefore spring calving times tend to be earlier in the South. Between the UK and other European countries Systems on mainland Europe tend to be based on pure-bred beef breeds whilst in the UK suckler cow systems are mainly based on cross-bred animals. As a result of hybrid vigour, cross-bred animals may be less susceptible to disease pathogens but the cross-bred nature of UK production systems means that many animals are transferred between farms at regular intervals. This transfer of animals between farms can act as a vector for the spread of disease. Livestock Production Systems - Beef cattle finishing systems The information that follows is applicable to 4 different beef cattle finishing systems regardless of whether the cattle originate from the dairy cow herd or the suckler cow herd after calves have been weaned. Table 3.2.6 Beef Cattle Production Systems Finishing

bulls Finishing

steers/heifers Finishing

steers/heifers Finishing

steers/heifers Age at slaughter 12 months 18 months 24 months 30 months Main feeds Conc (Grass (summer) + Silage/conc (winter)) Housing period (if any) Housed all

year Oct-April Nov-April Nov-April

Stocking density – finishing cattle (animals per forage ha) & (LSU/forage ha)

No forage ha used.

> 2.7 animals > 1.8 LSU

> 2.2 animals 1.8 - 1.4 LSU

>1.8 animals < 1.4 LSU

Typical herd size (no sold/year)

200 100 100 100

LSU = livestock units, Conc = concentrate feeds.

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Demographics – Beef cattle finishing systems Figures given relate to a finishing unit selling 100 animals per year. Table 3.2.7- Beef cattle demographics Finishing bulls

(12 mo. finishing)

Finishing steers/heifers

(18 mo. finishing)

Finishing steers/heifers

(24 mo. finishing)

Finishing steers/heifers

(30 mo. finishing) Jan 100 200 140 200 Feb 100 200 120 200 Mar 100 200 110 200 Apr 100 200 100 200 May 100 200 200 300 Jun 100 200 200 300 Jul 100 180 200 280 Aug 100 160 200 260 Sep 100 120 200 240 Oct 100 200 180 220 Nov 100 200 170 200 Dec 100 200 160 200 Notes on differences in livestock systems and practices – Beef cattle Within the UK Grass growth occurs approximately 1 month earlier in the South-West than in the North-East or Scotland so turnout times tend to be earlier in the South. Between the UK and other European countries Intensive 12-month bull beef is less common in the UK than many EU countries. Most UK beef is produced from steers or heifers in grassland based systems and many cattle are traded as store animals from rearing farms (either dairy or suckler herds) to finishing farms. This transfer of animals between farms can be a vector for the spread of disease. 3.3 Bovine: Dairy The distribution of the national dairy herd (by farm) is summarised in Table 3.3.1. The five most significant regions for dairy production are the Southwest, the Midlands, Northern Ireland, Wales and Yorksire & Lincolnshire. Table 3.3.1 Distribution of farms by region (% of category) Wales 12 Scotland 9 Northern England 9 Midlands 14 South-west 18 Yorks/Lancs 10 Eastern 2 South-east 4 South Central 8 Northern Ireland 13

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Table 3.3.2 Summary of dairy production systems System 1

Conventional System 2 Storage Feeding

System 3 Intensive Grazing

Housing period (if any) Oct-Apr Sep-May Full Jun-Aug by night

Dec-Mar

Stocking density (individuals/ha)

Overall 2.1/haPeak 8/ha

Overall 3/ha Peak 8/ha

Overall 2/ha Peak 7.5/ha

Typical herd/flock size 65 175 125 Demographics – Dairy systems Table 3.3.3 Age structure through year (Percentage change in herd size by month) Conventional dairy

system Forage feeding

system Intensive grazing

system Jan -1.0% -1.0% 0.6% Feb -1.6% -1.6% 0.7% Mar -1.1% -1.1% 1.0% Apr -1.1% -1.1% 0.4% May 0.4% 0.4% 0.3% Jun -0.4% -0.4% 0.3% Jul 0.6% 0.6% 0.2% Aug 0.7% 0.7% -0.4% Sep 1.0% 1.0% -1.0% Oct 0.3% 0.3% -1.1% Nov 0.3% 0.3% -1.6% Dec 0.2% 0.2% -1.1% Notes on differences in livestock systems and practices – Dairy systems Within the UK Southern England

• Long grazing season with good rainfall in the West • Forage and concentrates with little use of food by-products • Maize silage has significant role in diets • Strongly conventional system with a small increase in intensive grass operators • Below average herd size

Northern England

• Short grazing season therefore tendency to more storage style system • Greater use of food by-products • Maize and grass silage used • Tends to be above average genetics • Above average herd size

Scotland

• Located mostly in South West Scotland • Above average herd size • Above average yields per cow • Predominance of grass silage as main forage • Whole-crop silage growing in importance

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• Use of food by-products common place Wales

• Coastal bias with strong presence in South West • Long grazing season and grass based production • Increasing intensive grass production • Greater bias to spring milk production • Generally lower milk prices and higher feed costs • Grass and maize silage • Lower yields • Smaller herds

Between the UK and other European countries Denmark

• Rapid restructuring in the last 10 years. • 2nd to UK for Herd size • High yielding • Storage feeding due to continental climate • Product orientated milk processing • Mixed breeds used

France

• Diverse industry • Low herd size • Several different breeds used • Strong regional product portfolio • Maize silage is the main forage

Eastern Germany

• Former state farm structure of large farms and herds • Storage feeding similar to USA. • Maize silage is the predominate forage • High yields

Ireland

• Grassland based milk production • Restricted quota flexibility • EU average herd size • Low milk yields • Seasonal production pattern with strong spring peak

Northern Italy

• Storage feeding with USA style systems • Maize and lucerne are main forages • Holstein dominates • High yields • High domestic price

The Netherlands

• 2nd highest yields to Sweden • Holstein predominates in Red/White and Black/White forms • Mostly grass silage as forage • Use grass in a UK style conventional system

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3.4 Ovine Sheep numbers and regional distribution The sheep flock in the UK, had just over 20 million breeding ewes in December 1998, but after Foot and Mouth Disease (FMD) in 2001, this was reduced to 16 million breeding ewes. Details are presented in Table 3.4.1. In 1980 there were 14.0 million ewes (over 1 year old) mated and this rose to 17.4 million by 1987, increased to 20.3 million by 1998 and dropped to 16.1 million by 2001. The ewe to ram ratio was 37:1. Table 3.4.1. UK sheep numbers (December census) 1980

(millions) 1987 (millions)

1998 (millions)

2001 (millions)

Ewes mated 14.04 17.38 20.33 16.08 Ewe lambs mated 0.85 1.76 1.55 1.13 Rams used 0.38 0.49 0.56 0.46

(Source: MAFF/MLC) The distribution of sheep farms and breeding ewe numbers in the UK is shown in Table 3.4.2 below. Over 50% of the sheep population is in Wales and Scotland, with Northern England and the Midlands (Welsh Borders) being the most important sheep regions in England. Table 3.4.2. Distribution of farms with breeding ewes and ewe numbers by region (% of category) Holdings with breeding ewes Numbers of breeding ewes Wales 21 29 Scotland 23 23 Northern England 9 12 Midlands 14 10 South-west 11 8 Yorks/Lancs 10 8 Eastern 6 4 South-east 3 3 South Central 3 2 (Source: MAFF Census figures, 1996) Typical sheep performances The performance data of hill, upland and lowland ewes is presented in Table 3.4.3. The Scottish Blackface is the heaviest of the three hill breeds and the Welsh Mountain the lightest. Upland ewes are heavier and more prolific than the hill breeds. The lowland ewes have a wide range of prolificacy from the Romney at 138% to the Lleyn at 196%. The performance of cross-bred ewes from lowland flocks is presented in Table 3.4.4. The litter size and lamb losses of the Welsh Halfbred are lower than the other crosses.

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Overview of UK systems of sheep production (i) Early lambing (around 15% of UK production)

• Ewes put to rams in August. • Housed in late-November. • Pregnant ewes fed silage diet plus increasing amounts of compound (up to 0.75

kg/day) during last 6 weeks of pregnancy. • Ewes lamb indoors in January and are either kept indoors or turned out to pasture. • If indoors after lambing – ewes fed silage plus up to 1.25 kg/day until weaning at 7

weeks of age. • Lambs creep fed, weaned at 7 weeks and finished for slaughter at 10-14 weeks of

age. • Ewes turned out to pasture after weaning and stocked at 25 ewes per hectare • If outdoors after lambing – ewes fed up to 0.5 kg/day compound for 6-8 weeks • Stocked at 10 ewes/hectare. • Lambs given access to creep fed and sold from 12-16 weeks of age.

Table 3.4.3. Performance of mature pure-bred ewes by breed Breed

Weight at mating (kg)

Lambs born per ewe

Birth weight (kg)

Eight-week weight (kg)

Hill

Scottish Blackface 54 1.46 3.8 17.3 Swaledale 48 1.49 3.4 18.2 Welsh Mountain 35 1.18 2.9 13.5 Upland

Clun 60 1.72 4.3 18.1 Kerry Hill 61 1.60 4.9 18.3 Cheviot 73 1.81 5.4 22.1 Lowland

Dorset Horn 72 1.57 4.3 19.3 Leicester 94 1.51 5.8 19.6 Romney 71 1.38 4.7 19.8 Llanwenog 50 1.80 4.6 16.3 Lleyn 53 1.96 4.0 16.8 (Source: MLC) Table 3.4.4. Performance of cross-bred ewes in lowland flocks by breed Breed Mature Lambs Per 100 ewes put to ram weight

(kg) born per ewe

Ewes lambing

Lambs born

Lambs reared

Lambs Lost

Masham 71 1.79 92.8 174 151 23 Greyface 70 1.79 93.5 175 150 25 Mule 73 1.78 93.2 177 151 26 Welsh Halfbred 58 1.56 93.6 154 136 18 Scottish Halfbred 77 1.76 92.6 175 149 26 Suffolk x Mule 77 1.79 92.8 178 146 32 (Source: MLC)

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(ii) March lambing (around 75% of UK production) • Ewes put to rams in October. • Housed in early-January. • Pregnant ewes fed silage diet plus increasing amounts of compound (up to 0.75

kg/day) during last 6 weeks of pregnancy. • Ewes lamb inside in March and turned out to grass within 48 hours. • Stocked at 15 ewes/hectare. • Lambs finished over a long period from June-December.

(iii) Late lambing (around 10 % of UK production)

• Ewes put to rams in December. • Stocked at 7 ewes/hectare outside from January until April (winter). • No winter feeding or housing. • Ewes lamb outside in May. • Stocked at 15-20 per hectare in summer. • Lambs finished during period November -March period off grass or from inside on

silage plus compound diets. Typical sheep performances - by system of production The information summarised below is from MLC recorded flocks Early lambing Lowland flocks Average flock size 241 ewes Lambs reared per 100 ewes 144 lambs Stocking rates 13.9 ewes/hectare overall (16.9 for summer grazing) N fertiliser use 102 kg/hectare Spring (Mar/Apr) lambing Lowland flocks Average flock size 532 ewes Lambs reared per 100 ewes 155 lambs Stocking rates 12.8 ewes/hectare overall (12.3 for summer grazing) N fertiliser use 104 kg/hectare Spring (Mar/Apr) lambing Upland flocks Average flock size 768 ewes Lambs reared per 100 ewes 141 lambs Stocking rates 11.3 ewes/hectare overall (10.7 for summer grazing) N fertiliser use 92 kg/hectare Hill flocks (April lambing) Average flock size 933 ewes Lambs reared per 100 ewes 110 lambs Stocking rates highly variable N fertiliser use moderate levels applied to small proportion of total farm (i.e.

mainly to land for silage) No comparative information is available on late lambing flocks Demographics – Sheep flocks Most flocks are of a regular age structure, that is, a proportion of young breeding females will be brought into the breeding flock each year. This will normally be around 25% of the total flock size. Such a policy will give an age distribution of approximately 25%, 23%, 20%, 18% and 14% of ewes of 1, 2, 3, 4 and >4 years of age respectively. The lower proportion of ewes in the older age groups represent natural wastage/deaths, which occur throughout the productive life of ewes. Ewes are retained for as long as they are productive, but few will be retained past 7 years of age.

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Replacement females will be sourced either from their own breeding flock (normal for all hill and many upland flocks) or will be purchased from other breeding flocks (normal for lowland flocks). Approximately half of young females on farms will be mated at 7 months of age, lambing for the first time at 1 year old. However on most hill farms and a large proportion of upland farms, ewes are not normally mated until 18 months of age (lambing at 2 year old). Ewes are retained as long as they are capable of breeding and rearing lambs, and this means they are in good health, fertile, sound in udder and teeth, and have no gross abnormalities which will affect their ability to produce and rear lambs successfully. The only exception to this is the sale of draft ewes (at 5-6 years of age) from hill farms to lower ground farms where they are used to produced crossbred breeding stock for a further 1-2 years. The culling of unsuitable ewes from a flock can amount to around 5% per year, depending on the age spectrum of the flock (it will be higher in older age ewes. Rams are purchased as required to provide a mating ewe:ram ratio of around 40:1. These are purchased from pedigree flocks at specialist ram sales held in Aug/Sept each year. The productive life of a ram is around three years in most flocks. Seasonal variation in sheep numbers This is a complex issue to summarise because every farm is different and there is wide variation between farms of a similar location and sheep system, depending on their choice of finishing system, availability of feed, and whether they sell any breeding stock. A summary of the presence of different classes of sheep on UK farms is attempted in Table 3.5.5 below, beginning in September, the start of the sheep production calendar. Table 3.4.5. Presence of different classes of sheep on farms during the year for different production systems Month of Ewe Mature Lambs Year lambs + breeding

ewes Early lambing

Spring lambing

Late lambing

September

✓ ✓ ✓

October ✓ ✓ (✓ ) * ✓

November ✓ ✓ (✓ ) * ✓

December ✓ ✓ ✓

January ✓ ✓ ✓ (✓ ) *

February ✓ ✓ ✓ (✓ ) * March ✓ ✓ ✓ ✓ April ✓ ✓ (✓ ) * ✓ May ✓ ✓ ✓ ✓ June ✓ ✓ ✓ ✓ July ✓ ✓ ✓ ✓ August ✓ ✓ ✓ ✓ + These are breeding replacement females, retained from own flock or purchased * The symbol in brackets denotes that more than 50% of lambs have left the farm An explanation for the replacement breeding stock (‘ewe lambs’) is necessary. Ewe lambs, if the are home-bred will be selected at weaning (Aug/Sept). If the are

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bought-in from other breeding farms, they will be purchased at specialist sales in Sept/Oct. These may be mated that same autumn (Oct/Nov) or 12 months later depending on whether the decision to lamb at 1 or 2 years of age has been taken. Alternatively if the decision not to lamb until 2 years of age is taken, many farms will purchase yearling females (at 18 months of age), again at specialist sales in September, immediately before mating them in October. Lowland, Upland and Hill sheep systems Hill systems

• Generally large flocks and sheep is the predominant (often only) enterprise on the farm.

• Almost entirely Spring lambing (mainly in April) • Majority of the ewes kept are hill breeds with a large proportion bred pure as self-

replacing flocks. • However in recent years, increasing numbers of crossbred sheep are being produced

from hill farms to cash in on the lucrative breeding female demand from lowland sheep farms.

• The exact proportion of pure and crossbreeding depends on farm type, and in particular the amount of inbye land (hay/silage ground) and improved pasture,

• Almost 100% closed flocks, i.e. they breed all their replacement breeding females, only buying in a small number of breeding rams each year.

• Mainly outwintered sheep apart from a small proportion (10-25%) of more productive ewes (e.g. those carrying twin lambs) which will be housed for 6-8 weeks before lambing.

Upland systems

• Medium size flocks and often kept in conjunction with a beef enterprise, and sometimes with variable amounts of arable cropping grown mainly to provide cereals for livestock feeding during winter.

• Some will keep draft (>5 year old) hill ewes to produced crossbred lambs for sale as breeding female replacements, but the majority are crossbred ewes mated to terminal sire rams to produce finished lamb.

• Mainly inwintered for the last 6-8 weeks of pregnancy and turned out to pasture 48 hours after lambing.

• Moderately heavy stocking rates. Lowland systems

• Smaller flocks and these account for a high proportion of the pedigree flocks of the UK (producing rams for sale)

• Often sheep are a secondary enterprise on the farm (to other livestock like dairying, or to a large arable enterprise).

• The main output from lowland flocks is lambs for slaughter and these are mainly by terminal sire breeds (Suffolk, Texel and Charollais).

• Flocks are almost entirely all inwintered for 8-10 weeks before lambing. • The majority of early lambing flocks (Dec-Jan) are kept on low ground farms • Wide range of indoor feeding systems adopted.

Northern vs. Southern English systems

• The majority of early lambing flocks will be in the South of the country due to the milder climate and wider range of feeding options

• Late (May) lambing flocks, with their associated lower labour and production variable costs tend to be concentrated in the South, and are often on farms where the predominant enterprise is arable crops

• Thus sheep production in the North of the country is almost entirely (>95%) from spring lambing flocks, many of these being hill or upland systems

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• Northern sheep flocks are larger and sheep are often the primary enterprise of the farm, whereas in the South, flocks are smaller and sheep are often secondary to other farm enterprises.

• The North of the country (Northern England and Scotland), along with Wales, produce most of the crossbred sheep required by lowland farms in the South and East of the country.

• Northern flocks are generally self-replacing flocks, whereas southern flocks rely heavily on buying in breeding stock each year.

• Northern flocks rely almost entirely on grass and forage feeding systems, whereas in the South although heavily dependant on grass in summer, a wider range of arable by-products and straw are fed during the indoor winter housing period.

Further reading Cooper, M. and Thomas R.J. (1991). Profitable Sheep Farming. Farming Press Brown, D. and Meadowcroft, S.C. (1990). The Modern Shepherd. Farming Press. Meat and Livestock Commission . Sheep Yearbooks (produced annually). 3.5 Swine The June census prepared by Defra captures all holdings registered with any pigs ie 1 or 10,000. Although the Eastern region and combined South-east/South-central region would appear to have broadly similar numbers of holdings with pigs, the two regions have widely contrasting pig populations. For example, the Eastern region has the second highest pig population, all based on large units. In this region alone, there are 60 vertically-integrated finishing units, which have in excess of 2000 finishing places each. Whereas, the South-East has a large number of registered domestic pet pig owners and holdings with very small numbers. The same comment can also apply to the South-West, where alongside a limited number of large pig production operations are a large number of smallholders. Pig numbers, related to region, would represent a truer picture of pig population density. Frequency of holdings with pigs for Wales and Scotland does not give breakdown of types of pigs on each holding. Table 3.5.1 Distribution of farms by region Wales 97 Scotland 396 Northern England 121 Midlands 926 South-west 1118 Yorks/Lancs 1227 Eastern 670 South-east South Central

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Livestock Production Systems Typical swine production systems in the United Kingdom are summarised in Table 3.5.2. Demographics Pig production is a continuous process, rather than a seasonal process, hence the numbers indicated above would hold good during the year. The only exception might be when an outdoor unit de-stocks, typically every 3 years, although this event would generally be planned for the same time of the year, the newly established herd moving onto undersown stubbles or established leys.

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Table 3.5.2 Swine Production Systems Housing period (if any)

Indoor Breeder –weaner, (ca 1 ha site)

Indoor Breeder-finisher (ca 1 ha site)

Indoor-finishing only, (ca 1 ha site)

Outdoor Breeder-weaner

Stocking density Piglet/weaner (individuals/ha)

1090 0 Reared in kennels on field (<1000 per ha)

Stocking density Piglet/grower (individuals/ha)

1080 1250 Finished off site

Stocking density Fattening pig (individuals/ha)

2160 1250 Finished offsite

Stocking density Adult pig (individuals/ha)

500 0 20 adults per ha

Stocking density Sow & litter (individuals/ha)

90 adults plus 1000 piglets

0 24 adults plus 200 piglets per ha

Typical herd /flock size

450 sows 450 sows 2,500 finishing pigs

400/800 sows

Young reared per female

21 per sow per year

21 per sow per year

21 per sow per year

Notes on differences in livestock systems and practices Within the UK June census results suggest Outdoor Pig keeping accounts for 27% of the national herd ie approx 140,000 sows are kept outdoors in the UK. Principal areas for outdoor pig keeping are East Anglia, North-east midlands, South-Central, areas of the South-West, North-East Scotland, North-East England. Particular requirements are for low rainfall (ideally 500 mm max), freely draining soil, availability of large acreages of land available for rent, gently sloping or flat sites, siting away from large towns/cities. Large integrated production/processing companies, who either rent land, or pay through management agreements for breeding herds, own/control production from many herds. Weaned piglets from these predominantly outdoor herds are either reared in kennels sited on adjacent fields or transferred at 4 weeks of age to contract nursery units where they are reared for a further 8 weeks before a final transfer to a contract finishing unit for approx. 12-16 weeks before slaughter. Current slaughter weights are 80-kg dead-weight (105 kg liveweight). These multi-site production systems have taken over as the ‘norm’ as they offer health advantages to pigs by reducing numbers kept together on any one site. This all-in all out approach follows the successful broiler industry in maintaining health control. Essentially it is a terminal production system, with the opportunity to clean rest and disinfect buildings providing a clean break before re-occupation. The traditional system always relied on

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continuous production on one site, with no opportunity for cleaning, allowing disease build-up to unacceptable and unsustainable levels. Two-thirds of the Scottish National herd is situated in the Grampian region of North-East Scotland. Many of these herds are situated along a strip of sandland on the Moray Firth, an area warmed by the gulf stream and having low rainfall. The South-West region has a number of outdoor units (around the Salisbury plain, also Exeter, Cullompton areas), but typically features indoor units, based on slats, with the minimum of straw bedding, and fed via liquid pipeline systems a diet, often based on by-products from the dairy industry. South-Central region would have a large number of outdoor units and also indoor units with straw bedding. These would be based in the Thames Valley/ Newbury/Oxford areas Northern England (Yorkshire) features both outdoor units as well as highly intensive indoor units, based on slats and slurry with the minimum of straw bedding. It is widely regarded as the region with the highest density of pigs (East Anglia following close behind as the second most densely pig populated region). East Anglia typically features both indoor and outdoor systems with heavy reliance on straw bedding. Wales has a very small pig population, centred in the north-east or the south-central region. There are a number of large-scale contract finishing/rearing operations in the Eastern-Central region also. As the pig industry has contracted over the last 5 years, the residual production capacity has been centred around the main pig producing regions ie Yorkshire/E Midlands and East Anglia. However, preoccupation with maintaining health of pigs has led to emergence of new pig production centres away from those regions more traditionally associated with pig keeping. The emergence of large integrated production companies has led to the trend of contract production in general purpose buildings (eg cattle buildings), often sited in non-pig dense areas to maintain stock health. Between the UK and other European countries UK : (*4% of EU pigs) The feature that separates UK from most of the other countries named is the extensive nature of it’s production ie use of outdoor pig keeping methods, group housing of sows in yard systems, kept completely unrestrained for the duration of pregnancy and the widespread use of straw in many parts of the UK for rearing/finishing. Denmark; (11% of EU pigs) Minimal reliance on straw-most of production based in intensive stall/tether systems. France; (13% of EU pigs) Large scale production in North-West Brittany region, particularly outdoor production. Eastern Germany; Mixture of small holdings and former large scale industrial-sized farms, set up within the Communist regime, minimal welfare standards compared to UK.

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Ireland; Large scale production, especially dense around the Kilkenny area. Typical herd size 800 sows, all based on slats with no straw bedding-single site system, pigs wet fed via pipeline. Heavy investment in climate control for home built buildings with high insulation standards. Northern Italy; Large scale units- intensive systems of production with little or no use of straw. Pigs bred with heavy muscling characteristics and reared to uniquely heavy weights (145kg liveweight) for use in the Parma ham trade. Netherlands; (9% of EU pigs) Recently contracted industry due to application of National Environmental measures, particularly aimed at reduction of nitrates and phosphates. Traditionally exported many weaners across the border to Germany, Belgium and Italy. Again no reliance on straw-in fact the opposite-the whole industry set up on slatted systems with no bedding. Recent national welfare legislation ensures that slats for growing pigs are replaced by solid floors, albeit containing slatted void area. Southern Spain; Massive expansion programme, part financed by large production companies unable to expand production in their own countries eg Netherlands. There appears to be minimal environmental or planning restrictions in expanding production here. New build operations are based on a large scale intensive model eg 1500 sows on one site. The production system features are fully slatted/ sows in stalls and tethers/ very hygienic/ accent on disease control/new feed mills/processing factories in close proximity to production base. Spain is about to become the largest pig producer in the EU (20% of all sows) with over 2.5 million sows, overtaking Germany. 3.6 Poultry Laying Hens – percentage of national flock, from Defra statistics. North East = 1% North West = 9% Yorkshire = 8% East Midlands = 20% West Midlands = 13% East Anglia = 10% London = 1% South East = 15% South West = 21% Broilers – Percentage of national flock, from Defra stats. North East = Not Available North West = 7% Yorkshire = 12% East Midlands = 19% West Midlands = 15% East Anglia = 23% South East = 7% South West = 14%

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Laying Hens -Background At any one time there are around 31million laying hens on poultry farms in the UK. According to the latest Defra statistics, 70% of these are kept in cages, 26% in free range conditions, and the remaining 4% in barn systems of production. There are two phases to the production system. First is the rearing phase where birds are reared from day old to around 16 to 17 weeks of age. Second, when the birds have reached sexual maturity they are transferred from the rearing farm to the laying unit. In the majority of cases the rearing is undertaken by specialist pullet rearers, though there are examples of the egg producer themselves rearing their own replacement birds. The birds then remain in lay for around 55 weeks – generally from between age 17 weeks to 52 weeks of age. The bird has then effectively reached the end of its useful productive live and needs to be culled. However there are some producers who believe in moulting their birds. This has the effect of extending their laying ability for a further few months once the moulting process has been completed. These timeframes are identical for all laying hens irrespective of the production system that the birds are kept in. Types of production system Laying cages This is still the most popular method of production for eggs in the UK. The advantage of the system is that birds are kept in small groups of 4 or 5 hens and are separated from their droppings which fall through the bottom of their wire cages. EU legislation will ban the production of eggs in cages by the end of 2011 unless the legislation is revoked during the review of the Directive scheduled to take place in 2005. There has been a ban on the installation of new “traditional” cages in the EU from the 1 January 2003. From that date of 1 January 2003 the space requirement for layers in existing “traditional” cages has been increased to 550 cm2 per bird. Any new cages now being installed must meet additional requirements, including the provision of a perch, a nest box, a scratching area and an increased space allowance of 750 cm2 per bird. Only a few of these new “enriched” cages have so far been installed on farms due to some uncertainty as to whether the UK legislators feel happy that they actually improve the welfare of the laying hen. It is difficult to say what size an average intensive unit might be as the y range in size from just a few thousand birds to over 1 million kept on the same holding. However, it could be said that the majority of units might fall into the 50 – 100 thousand bird range. Barn systems In this system, which is a half-way house between cage and free rage, birds are not kept in cages and are free to roam around the shed. They do not have access to outdoors. Again EU rules stipulate the length of feed trough, drinker provision and perching space. Unlike the cage system where the bird lays its egg on the wire floor of the cage and it rolls away for collection, the bird lays its egg in nest boxes, often with an astroturf floor. EU legislation stipulates the amount of nest box space that each bird must be provided with. Prior to the EU legislation being adopted, birds in these systems were generally stocked at 15.5 birds per square metre. Birds previously stocked at this density will have to reduce to 9 birds per square metre on 1

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January 2007. Buildings erected after I January 2002 will have been required to stock at this density. Free Range The structure of the buildings employed in the free range sector are very similar to those for the barn system, with the obvious difference that the birds must be allowed access to ranging areas which must be mostly covered with vegetation. The same internal stocking densities as far barn are used, however the UK has a derogation to allow a higher stocking density of 12 birds per square metre for units which were in operation prior to the EU directive coming into force in August 1999. However, this will be reduced to 9 birds per square metre from1 January 2012. Rules also dictate the outside stocking density. Under EU Egg Marketing Regulations the outside stocking density must not exceed 2500 birds a hectare. However, the majority of UK free range egg production comes within the scope of the RSPCA’s Freedom Food scheme, which sets a maximum stocking density of 1000 birds a hectare. Whilst the distribution of intensive egg production is spread evenly across the country, there are areas where free range egg production is more popular than others. These areas include parts of East Anglia and the North West Midlands, but is particularly important in the South West region. Proportionally less free range egg production occurs in Northern England, Scotland and Wales as climatic factors such as heavy rainfall and winds make this a more hostile environment for the birds. Broiler chickens Unlike the laying sector, production systems for broiler chickens follow basically the same model, though there is a very small amount of organic chicken meat produced in extensive systems. The vast majority is produced in sheds with litter floors and automatic ventilation to keep the birds cool. Birds are brought onto the holding as day old chicks, and are kept in the same shed until they are sent for processing at around 44 days of age weighing 2.2 kg liveweight. Accounting for clean out times, times, a producer would have 6 six crops of birds through his shed in a year. Again it is difficult to give an average size of producer, 70,000 to 130,000 birds would be the size of an “average” producer. Each year around 900 million broilers produced in the UK. Amount of manure produced With the majority of layers being kept in cages, most manure produced from the laying sector has a fairly low dry matter content – around 30%. Manure defecated by the birds in cages falls through the wire cage floor onto a belt underneath the cage. It is then scraped off the belt every 2 or 3 days and taken to a central storage point before being spread on the land. In some systems of intensive egg production, there are no belts under the cages. In these “deep pit” systems the manure falls straight from the cage floor into a large pit situated beneath the laying house. This is then emptied once a year and spread on the land. In free range systems, the manure is either deposited whilst the birds are grazing the range area, or whilst the birds are in the house. When in the house, it can either be deposited on the small litter area, or more likely it falls through slats where the birds

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perch to feed, drink water and roost. Manure from under the slatted area is removed once a year when the flock is depleted. With broilers, all of the manure produced by the birds throughout their lifetime falls directly onto the litter. It is cleared at the end of each flock. The disease situation of the birds at any given time can affect the dry matter content of the broiler litter. If birds are unhealthy and are suffering from a disease challenge, the litter is often wetter. Typical dry matter contents for broiler litter is around 60%. 1000 intensively housed layers will produce 115 kg of manure a day, whereas a 1000 broilers will produce around 3 tonnes of manure over their 44 day lives. A tonne of layer manure contains 16 kg of nitrogen, and a tonne of broiler manure contains 30 kg of nitrogen. Production in other European Countries As far as the broiler sector is concerned, the production system is common to all countries within the EU with the only significant changes being based on ventilation rates, feed composition etc. The vast majority of chicken meat production is indoors, though a small amount of extensive production, such as the Label Rouge scheme in France, can be found. For the egg sector though a more complicated picture exists. Denmark has 61% of its production in intensive laying systems, and 39% in alternative systems. The 39% is made up as follows: 17% barn, 9% free range and 13% organic. France has 88% in intensive systems, and 12% in extensive systems. ( Free range 8%, barn 1% and organic 3%.) Germany (unfortunately there is no split for East/West ) has 85.4% in cages, and 14.6% in alternative systems ( 6.2% barn, 8.2% free range.) Ireland has 90% of egg production in cages, with the remaining 10% split across the other extensive sectors. Italy, again no separate figures for the north of the country, though their production pattern would closely follow that of the country as a whole. This is 96.73% cage, 3.27% free range. The Netherlands have 75% of their layers in cages. The remaining 25% are split as follows: 12% free range, 12% deep litter – a type of extensive indoor system which will be phased out due to EU legislation, and 1% perchery.) Southern Spain again will follow the pattern for the rest of Spain. Virtually all of Spanish egg production comes from intensive cage systems. Less than 1% comes from free range or barn systems. 3.7 References Montforts, M.H.M.M. (2003) Environmental risk assessment for veterinary medicinal products, Part 1. Non-immunological drug substances – second update, RIVM report 320202001/2003 Smith, K.A., Brewer, A.J., Dauven, A., Wilson, D.W. (2000a) A survey of the production and use of animal manures in England and Wales. I. Pig Manure, Soil Use and Management, 16, 124-132

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Smith, K.A., Brewer, A.J., Crabb, J., Dauven, A. (2000b) A survey of the production and use of animal manures in England and Wales. II. Poultry Manure, Soil Use and Management, 17, 48-56 Smith, K.A., Brewer, A.J., Crabb, J., Dauven, A. (2000c) A survey of the production and use of animal manures in England and Wales. III. Cattle Manures, Soil Use and Management, 17, 77-87 Smith, K.A., Frost, J.P. (2000d) Nitrogen excretion by farm livestock with respect to land spreading requirements and controlling nitrogen losses to ground and surface waters. Part 1: Cattle and sheep, Bioresource Technology, 71, 173-181 Smith, K.A., Charles, D.R., Moorhouse, D. (2000e) Nitrogen excretion by farm livestock with respect to land spreading requirements and controlling nitrogen losses to ground and surface waters. Part 2: Pigs and poultry, Bioresource Technology, 71, 183-194

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4.0 CHEMICAL INPUT DATA The user is required to supply the following basic physico-chemical and environmental fate data on the active substance:

• Molecular Mass (g/mol) • Vapour pressure (Pa) • Solubility (mg/l) • Aqueous hydrolysis DT50 at pH 7 (d) • Log octanol-water partition coefficient • Dissociation constant (pka) • Degradation half life in soil (d) • Soil sorption coefficient (Koc; L/kg) • Freundlich exponent (1/n)

The chemical definition screen (basic option) is illustrated in Figure 4.1. As noted on this screen, more refined data on environmental fate and metabolism can be provided in the ‘Advanced’ option. This is described in further detail in Section 4.2.

Figure 4.1 Basic chemical properties definition screen in VetCalc

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4.1 Standardisation of Criteria for Selection of Input Parameters Under FOCUS Recent comparative modelling exercises have shown that the modeller can be a significant variable in the range of output data obtained from the same available information for input (Brown et al., 1996, Boesten, 2000). It is, therefore, important to attempt to reduce the amount of variation introduced by providing guidance on standardization of strategy for input parameter selection. The following description is generally based upon the recommendations of the FOCUS Groundwater Working Group which have been used to assist in the standardization of pesticide fate and exposure modeling in regulatory submissions under EC Directive 91/414/EEC (FOCUS, 2000). Excerpts of this guidance are provided below. In certain cases (where basic environmental fate dataset requirements differ) guidance has been adapted (in all cases deviations are identified and rationale discussed). 4.1.1 General Approach Physico-chemical property data are generally available as single values from standard experiments conducted as part of the registration package. For the remaining parameters, such as degradation rate and soil sorption, a number of experimental values are generated as part of the registration package. Determining which single value should be used as input for each parameter may be contentious since the relevant output data can vary significantly depending on which of the range of possible values are used as input. A German group consisting of Regulatory and Industry representatives has provided recommendations for use in the German regulatory process (Resseler et al., 1997). Where a range of degradation rates are available, they have proposed that mean kinetics from field tests or laboratory studies should be used in preference to the worst case value. However, they note that if there are few results which are too scattered to make an average meaningful, then a single value from a field test comparable with the intended field of use should be used. The general approach recommended as a basis for modeling of veterinary medicines within VetCalc is consistent with these recommendations and is summarised in brief below:

• Where a range of data are available that fulfill or exceed the minimum regulatory requirement the use of a mean value (degradation, sorption of physico-chemical parameters) may be justified;

• Where the minimum regulatory requirement has not been met the use of a worst-case value is recommended;

• Under certain circumstances (highly scattered or skewed datasets) the use of median data instead of means may be justified;

• Under certain circumstances (highly scattered or skewed datasets) field dissipation data may be justified.

Further detail is provided below for the two most critical aspects of environmental fate characterisation; selection of degradation and sorption endpoints. 4.1.2 Degradation Data The FOCUS group recommend that where the parent compound has been studied in a minimum of four soils it is generally acceptable to use the mean degradation rate as input into the model. Similarly, the FOCUS group recommend that where the relevant metabolite has been studied in a minimum of three soils it is generally acceptable to use the mean degradation rate as input into the model. In cases where

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a large number of additional data points are available, a median value may be more appropriate. In some cases the range of the results may be too large for this to be acceptable. This should be judged on a case-by-case basis and in this situation a value from a single study should be used, with appropriate justification of the study chosen. In situations where less than the recommended number of soils have been studied it is generally appropriate to use the worst case result, which is generated in a soil of agricultural use. It is necessary to interpret this guidance within the context of standard submissions for veterinary medicines at Phase II, Tier ‘A’. Within submissions under 92/18/EEC notifiers of veterinary medicines are obliged to only submit data on degradation in three soils, whereas, for plant protection products (under Directive 91/414/EEC) a minimum of four soils is typical. However, FOCUS has recognized that in certain circumstances where less data is available (i.e. submission of data on three soils for metabolites) it may still be appropriate to use a mean DT50 value. Taking into account this guidance, and the data available within standard submissions for veterinary medicines, it is proposed that the use of a mean DT50 (where degradation of veterinary medicines has been evaluated in at least three soils) is appropriate as a basis for modelling. 4.1.3 ‘Normalisation’ procedure Chemical degradation rates vary with time in response to changing soil moisture and temperature conditions. By employing a set of standard assumptions the VetCalc model has the capability of adjusting the rate of degradation to reflect ambient conditions within the soil profile relative to standard modeling input conditions (20°C and pF 2.0 soil moisture content). It is important when carrying out modeling that the user recognizes that input must either:

1) Reflect field dissipation behaviour – in which case moisture and temperature dependency and volatilization algorithms in VetCalc must be switched off, or;

2) Reflect standardised conditions (20°C, pF 2.0) – in which case default moisture and temperature dependency factors apply.

The process of recalculating DT50 values to reflect standard conditions is known as ‘normalisation’. Incubation conditions within laboratory-based soil degradation studies are typically 20°C and pF 2.0 and, as a consequence, in many cases normalization may not be necessary. However, where these incubation conditions have not be chosen adjustments are required. The following explanation is taken from Generic Guidance for FOCUS Groundwater Scenarios (Version 1.1). 4.1.4 Reference temperature Where laboratory data have been obtained in line with modern guidelines (e.g. SETAC, 1995), the reference temperature will be 20°C. Where older studies are used, degradation may have been studied at a range of temperatures and care should be taken in the use of both the reference temperature and the degradation rate. The degradation rate can be manually normalised to 20°C by use of the temperature dependence correction equations (see relevant section of this guidance). When attempting to determine an appropriate degradation rate for input into a model, a realistic comparison of the range of available results can only be undertaken if they were all obtained under the same temperature conditions. It is therefore essential to

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ensure that a correction to a common temperature has been undertaken prior to any comparison. 4.1.5 Reference soil moisture Current EU guidelines for laboratory degradation studies require that these are undertaken at a moisture content of 40-50% MWHC (maximum water holding capacity) (SETAC, 1995). Additional data provided in study reports may include the actual moisture content of the soil during the study as volumetric (% volume/volume), or as gravimetric (% mass/mass). Other studies may define the reference soil moisture in terms of; % field capacity (FC), or as matric potential values such as pF, kPa or Bar. The availability of water within a soil profile, and therefore its effect on the rate of pesticide degradation, depends on the texture of the soil. Heavier soils contain a larger percentage of water before it becomes "available" than do lighter soils. For this reason studies are usually undertaken at defined percentages of the MWHC or FC, or at defined matric potentials, to attempt to ensure that experimental conditions are equivalent. However, by strict principles of soil physics some of these values have no definition (and some have no consistent definition), hence it is very difficult to relate them to each other directly. It is only via the actual water contents associated with some of these terms that comparisons can be made between values. There is however, little advantage in simply using an actual water content from the experimental study as input into the model, as the DT50 used is likely to be an average from a number of soils. The solution to this problem is not straightforward but, since the concept of matric potential is independent of soil type and can be related to volumetric water content, it is recommended that a reference moisture content of 10kPa (pF2) should be used with the scenarios. It is further recommended that for the purposes of this guidance, this value be considered as field capacity and in any study report where field capacity is specified without any reference to the matric potential or actual moisture content. This requires that a complex procedure is undertaken to normalise the DT50 values from all laboratory studies before an average value can be calculated. (i) The moisture content of each soil must first be converted to a volumetric or gravimetric value (The soil moisture correction is based on a ratio (θ/θREF) and hence the actual water content units are unimportant as long as they are consistent). If these values are not available in the study report then Tables 4.1.5.1 & 4.1.5.2 provide guidance on conversion methods based on average properties for the stated soil types (Wösten et al., 1998). If more than one of the available methods of measurement is given in the study report then it is recommended that the value that appears first in Table 4.1.5.1 be used for the conversion process. It is important to note that the optimal data to use are the specific moisture content at which the experiment was undertaken and the moisture content at 10kPa for the given soil as stated in the study report. All conversions stated in Table 4.1.5.1 are approximations based on generic properties of soil types and these could, on occasion, produce anomalous results. Therefore the user should also consider any transformed water contents in comparison to the original study data to ensure the derived data provide reasonable results.

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Table 4.1.5.1. Generic methods for obtaining soil moisture contents for subsequent DT50 standardisation Units provided Require unit for soil moisture normalisation %v/v (volumetric) % g/g dry weight (gravimetric) Value used in

experiment Value at field capacity (10kPa)

Value used in experiment

Value at field capacity (10kPa)

% FC (assumed 10kPa)

Conversion to volumetric or gravimetric water content unnecessary since fraction of FC can be input directly into Walker equation (i.e. = θ/θREF)

% g/g (gravimetric)

As stated Use default gravimetric value at field capacity for texture type given in Table 4.1.5.2

% v/v (volumetic)

As stated Use default volumetric value at field capacity for text type given in Table 4.1.5.2

kPa In reality the only values are likely to be 5 or 10 kPa is the defined value of field capacity and therefore no correction is required. 5 kPa is slightly wetter than field capacity but the assumption is made that degradation rates do not change at water contents between filed capacity and saturation therefore these values also do not need a moisture correction. Note: If water contents are given as fractions of 5 or 10 kPa they can be treated in the same manner as fractions of field capacity.

pF In reality, the only values are likely to be 2 or 2.5 (10 and 33 kPa respectively). pF2 (10 kPa) is the defined value if field capacity and therefore no correction is required.

For pF 2.5 (also given as 33 kPa or 1/3 Bar) Use default gravimetric value at pF 2.5 for texture type given in Table 4.1.5.2.

Use default gravimetric value at field capacity for texture type given in Table 4.1.5.2.

Bar In reality the only values are likely to be 75% of 1/3 bar. Use default

gravimetric value for texture type at 1/3 Bar given in Table 4.1.5.2. Calculate % gravimetric at give % of 1/3 Bar.

Use default gravimetric value at field capacity for texture type given in Table 4.1.5.2.

% MWHC (Maximum water holding capacity; assumed 1kPa i.e. pF 1)

Use default gravimetric value for texture type at MWHC given in Table 4.1.5.2. Calculate % gravimetric at given % of MWHC.

Use default gravimetric values at field capacity for texture type given in Table 4.1.5.2.

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Table 4.1.5.2 Default values for moisture contents for soils at field capacity, maximum water holding capacity and 1/3 Bar (based on HYPRES; Wösten et al., 1998) USDA classification

Proposed UK/BBA equivalent classification

Volumetric water content at 10 kPa (field capacity) ( θv10) (%)

Gravimetric water content at 10 kPa (field capacity) (W10) (%)

Gravimetric water content at 1/3 Bar (pF 2.5, 33kPa) (W33) (%)

Gravimetric water content at MWHC (1kpa) (%)

Sand Sand 17 12 7 24 Loamy sand Loamy sand 20 14 9 24 Sandy loam Sandy loam 27 19 15 27 Sandy clay loam

Sandy clay loam

31 22 18 28

Clay loam Clay loam 38 28 25 32 Loam Sandy silt

loam 34 25 21 31

Silt 36 26 21 32 Silty clay loam

Silty clay loam

40 30 27 34

Silt Silt loam 37 27 21 31 Sandy clay Sandy clay 40 35 31 41 Silty clay Silty clay 46 40 36 44 Clay Clay 50 48 43 53 * The pedotransfer functions from HYPRES were used to determine the soil water content at the given matric potentials based on bulk density, organic carbon content and particle size characteristics. It has been assumed that these data from undisturbed soil profiles provide an acceptable approximation to disturbed profile data which are generally stated in regulatory reports (water contents in disturbed soil profiles are likely to be higher and hence the generic data provided above would lead to more conservative [longer] standardisations of the DT50) (ii) The water content at 10 kPa (pF2) for the given soil is also determined. For the purposes of regulatory modelling this can be considered equivalent to field capacity. If this information is not provided it can be approximated as shown in Tables 4.1.5.1 & 4.1.5.2. (iii) Once the moisture content data are converted to water contents (ensuring units are the same), then the DT50 can be manually corrected to that at 10 kPa (pF2) using the same moisture dependent correction equation as used in the models. The correction factor is expressed as (f ) = (θ/θREF)B (see relevant section of this guidance). Each DT50 is then multiplied by this factor to obtain values normalised to 10 kPa (pF2). In cases where the water content of the experimental soil is calculated to be above field capacity then the DT50 should be considered to be the same as that at field capacity (i.e. no correction required) (iv) The average DT50 can then be calculated from each individual value normalised to 10 kPa. The default option in VetCalc implies that the degradation rate was measured at a matric potential of –10 kPa (-100 hPa). To provide some clarity to this normalisation procedure an example is given as follows. A study is undertaken in 4 soils at 45% MWHC and 20°C and the results are shown in Table 4.1.5.3:

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Table 4.1.5.3 Illustrative results from soil degradation assessments

Soil type (USDA classification)

DT50 Gravimetric water content at MWHC

Sandy loam 100 34 Sand 150 27 Clay loam 85 47 Silt 80 41

1. Since the gravimetric water content at MWHC is measured it is most appropriate to use

these soil specific values as the basis of the normalisation process. 45% MWHC (the moisture content under study conditions) is therefore 15.3, 12.2, 21.2 and 18.5% g/g in the sandy loam, sand, clay loam and silt soils respectively

2. No data regarding the water content at 10 kPa is provided and therefore the default data

from Tables 4.1 & 4.2 are used to obtain approximated values for these soil types i.e. 19, 12, 28, 26% g/g for the sandy loam, sand, clay loam and silt soils respectively

3. Using the Walker equation, a correction factor (f ) for the degradation rate at 10 kPa can

be worked out as follows (f ) = (θ/θREF)0.7 . f = (15.3/19) 0.7 = 0.86 for the sandy loam soil The default data suggest that the sandy soil is above field capacity therefore a value of 1 (i.e. no correction for moisture content) is used f = (21.2/28) 0.7 = 0.82 for the clay loam soil f = (18.5/26) 0.7 = 0.79 for the silt soil

4. Multiplying the DT50 values by the appropriate factors gives values of 86, 150, 70 and 63 days for the sandy loam, sand, clay loam and silt soils respectively at 10 kPa. The average of these values is 92 days.

5. The input onto the relevant model would be a DT50 of 92 days at the field capacity (10

kPa, pF 2) of the soil. 4.1.6 Adjustment of degradation rate at different soil depths This parameter can have a large effect on the amount of substance simulated to leach to groundwater or drains. Unfortunately experimental data are rarely available and hence estimation methods are usually required. Consideration should be given to whether degradation is predominantly chemical or microbial. If the substance degrades solely (or predominantly) by chemical processes (i.e. hydrolysis) then the rate of degradation does not need to change dramatically down the profile (unless degradation is pH sensitive, in which case further consideration may be required). In this case the modeller should provide a justified argument and proceed to more specific modelling. The scenarios provided in VetCalc have assumed that degradation is microbially mediated and have provided default factors which should not be altered by the user. In the light of current understanding and existing regulatory precedents, the most appropriate factors by which to multiply the degradation rate with depth (i.e. increase the half life) are as follows (Boesten & van der Pas, 2000; Di et al, 1998; Fomsgaard, 1995; Helweg, 1992; Jones & Norris, 1998; Koch et al, 1979; Kruger et al, 1993 & 1997; Lavy et al, 1996; Smelt et al, 1978a&b; Vaughan et al, 1999): 0-30 cm 1 30-60 cm 0.5 60-100 cm 0.3 >100 cm no degradation

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Due to slightly varying horizon depths in the twelve soils selected, there are some minor adjustments to these values and these are provided with the soils data for the scenarios. The degradation rates at each depth can be input directly but default values are provided automatically. 4.1.7 Parameters relating degradation rate to soil temperature For temperature correction the equation is: fT = Q10

((T-Tref)/10) fT = the correction factor (this will vary throughout the simulation, reflecting changes in the

ambient temperature conditions) Q10 = Constant. The default value is 2.2 (consistent with FOCUS), although this may be

redefined by the user (an advanced option). Value does not vary throughout the simulation.

T = ambient temperature condition (this will vary throughout the simulation) Tref = the temperature at which the DT50 was derived 4.1.8 Parameter relating degradation rate to soil moisture The recommended default B value from the Walker equation (f = (θ/θREF)B; Walker, 1974) is 0.7, which is the geometric mean of a number of values found in the literature (Gottesbüren, 1991). The moisture dependency algorithm summarised below allows for daily adjustment in degradation kinetics based upon ambient soil moisture conditions.

fM = (θ/θref) B (the Walker equation)

Where: fM = the correction factor (this will vary throughout the simulation, reflecting changes in the

ambient moisture conditions) θ = ambient moisture condition (this will vary throughout the simulation) θref = the moisture condition at which the DT50 was derived (defined by the user, remains

constant throughout the simulation) B = Constant. The default value is 0.7 (consistent with FOCUS), although this may be redefined by the user (an advanced option). 4.1.9 Soil Sorption Soil sorption results (Kfoc, Koc or Kfom, Kom) are also required in four soils for parent compound and in three soils for relevant metabolites according to the environmental fate annexes to Directive 91/414/EEC (95/36/EC). Where these are all agricultural soils, the FOCUS group recommend that it is generally acceptable to use the mean value of the sorption constant normalised for organic carbon (Kfoc, Koc, Kom or Kfom) to derive the input to the model, unless the sorption is known to be pH-dependent (note pKa value). In situations where there are results from less than the recommended number of agricultural soils then it is generally appropriate to use the worst case result (lowest sorption). In cases where a large number of additional data points are available, a median value may be more appropriate. 4.1.10 Summary of Main FOCUS Recommendations This section contains detailed guidance on the input of substance-specific parameters for four different models that are recommended for use with some or all of the FOCUS scenarios. Much of this guidance is based upon a number of more

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general principles and recommendations. To help the modeller be aware of these, they are summarised below: • The scenarios are intended for tier one (e.g. initial) risk assessment, and therefore the

guidance on the substance-specific input parameters aims to provide a degree of standardisation. This inevitably leads to over-simplification in some cases and hence, where more detailed data may be appropriate for higher tier modelling (e.g. the change of degradation rate with depth), this has been noted.

• Simulations with the worst case intended use pattern requested for review must be undertaken but simulations can additionally be undertaken using the most typical intended use pattern.

• Where there are a number of experimental values (e.g. degradation rate, sorption constants etc.) then the mean/median value should generally be used rather than the extreme value. This is because the vulnerability of the scenarios has been shared between the soil and weather data, and so should not rest also with the substance properties.

• Decisions on the use of laboratory or field degradation/dissipation rates can only be made on a case by case basis. However, when deciding which rate to use, particular attention should be paid to whether the method of determining the rates is compatible with the method assumed by the model (e.g. first order) and whether any other model sub-routines should be disabled (e.g. volatilisation).

• The increase of sorption with time is a phenomenon that is widely accepted to occur, however data to quantify this are not generally available. If specific data are available for the substance then this can be taken into account during the modelling but otherwise a default of “no increase with time” should be used.

4.2 Advanced Environmental Fate In addition to the basic options for parameterisation (and associated guidance on parameterisation) it was recognized that more sophisticated modelling may be necessary. Where the user has further data or can justify alternative definitions of environmental fate and behaviour there is the opportunity to access the ‘Advanced Data’ option for defining chemical properties (see Figure 4.2.1). As can be seen the model has been established with the opportunity to override basic input assumption for four aspects of environmental fate:

• Potential for degradation in slurry/manure during storage • Behaviour in sediment and water systems • Potential for metabolism prior to excretion • Definition of soil degradation versus dissipation behaviour • Simulation of metabolites

Each option is discussed in further detail later in this section of the user manual. 4.2.1 Degradation during storage The default assumption is that no degradation occurs during storage. Where appropriate data is available (or justifications can be made) the user has the opportunity to provide a DT50 value that will provide a degree of mitigation where storage may reduce exposure potential. No validated or standardised methods exist for assessing the fate of Veterinary Medicinal Products (VMPs) in manure or slurry at either the field or laboratory scale (Montforts et al., 1999). As a consequence, users should take care when considering justifications for this mitigation option – particularly where the results of the risk assessment are heavily dependent upon such risk mitigation and/or management options.

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A number of issues should be considered by users. These include influence of factors such as manure type (slurry versus farm yard manure (FYM)) and how storage conditions (OC content, water content, pH, temperature and redox conditions) may vary between storage systems. Wherever possible users should make reference to physico-chemical (hydrolytic stability, pKa) and environmental fate chatacteristics (sorption behaviour and knowledge of degradability in other matrices) when presenting justifications for employing degradation during storage as a mitigation option. It should be particularly noted that behaviour in soil may not be considered directly relevant to behaviour in slurry and FYM where different degradation processes may dominate.

Figure 4.2.1.- Illustration of advanced chemical data definition option with VetCalc 4.2.2 Behaviour in water/sediment systems No specific data are required within Phase II Tier A on behaviour in water/sediment systems. As a consequence it is necessary within the basic parameterization to rely upon evidence of hydrolysis at neutral pH (as a surrogate for degradation within the water column). However, it is recognized that the user may have access to additional sources of data or can provide justifications for alternative definition of behaviour in water/sediment systems. As a consequence, the user has the opportunity within the

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advanced option to override the initial default assumptions with DT50 values for each compartment under standardized temperature conditions. These will then be adjusted within the model to reflect degradation potential under simulated conditions. In certain cases users may consider the possibility of conducting ad hoc assessments of behaviour in water/sediment systems. Guidance for the conduct of water/sediment studies has been published by several groups (BBA, 1990; MAFF PSD, 1992; Agriculture Canada, 1987; US-EPA, 1982; SETAC-Europe, 1995) and a consensus summary of this guidance has been compiled in a recent OECD guideline 308 (OECD, 2001). Key experimental and interpretive considerations are summarised in Table 4.2.2.1. Table 4.2.2.1 Key experimental elements and required analyses of test results for water/sediment studies (based in part on draft OECD Guideline 308)

Key experimental elements 1. Use of appropriate sediments, water/sediment ratios and sediment depths 2. Use of both aerobic and anaerobic sediment layers 3. Application of a single, environmentally relevant pesticide concentration 4. Use of radio-labelled test substance to allow determination of degradation pathways as

well as mass balance 5. Duration of test should normally not exceed 100 days and should continue until 90% of

the test substance has been transformed 6. A minimum of five to six data points (including zero time) should be collected

Required analyses of test results 1. To support aquatic fate modelling, first-order degradation rates (i.e. half-life values)

should be determined for parent and major metabolites using appropriate regression methods (e.g. Heinzel, et al, 1993; Model Manager, 1998)

2. Specific kinetic endpoints that should be calculated from the water/sediment data include:

• DT50,wa = degradation half-life in water phase

• DT50,sed = degradation half-life in sediment phase

• DT50,sys = degradation half-life in the overall water/sediment system

It is common within such studies to report a whole water/sediment system degradation half-life (DT50,sys) alongside an aqueous phase dissipation half-life (DT50,wat dis). The use of dissipation half-lives as distinct from degradation half-lives should be avoided as dissipation considers losses from the water column due to partitioning into sediment and volatilization that are already considered in the model. Careful selection of degradation endpoints will avoid the potential for double counting processes. Under certain circumstances it may be possible to increase the accuracy of modeling by carrying out a more detailed kinetic analysis of water/sediment studies in order to derive degradation half-lives in both water and sediment compartments (DT50,wat deg and DT50,sed deg). Guidance on the conduct of such kinetic analyses is currently under development and will be reported towards the end of 2004 by the FOCUS Degradation Kinetics Group. To provide greater flexibility for the user in how they set up modeling a distinction is made between the degradation DT50 value for the compound in the aqueous phase

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(DT50,wat deg), the DT50 value in the sediment phase (DT50,sed deg), and the degradation DT50 value for the whole water/sediment system (DT50,sys). 4.2.3 Potential for metabolism prior to excretion The default assumption is that no degradation occurs between administration and excretion. It is recognized that prior to excretion the active substance may undergo adsorption or metabolism. Where this is significant a factor can be provided by the user to replace the default assumption of conservative behaviour by a factor representing combined adsorption and metabolism. Pharmacokinetic studies with veterinary medicinal products can be carried out to support studies on clinical efficacy, tolerance in the treated animal and safety for the consumer. The principal objectives are to estimate the factors involved in absorption, metabolism and elimination of active substance and product formulation. Where such studies have been carried out it is possible to interpret results to provide evidence of absorption and/or metabolism prior to excretion. Further guidance on these studies is outlined within EMEA guideline EMEA/CVMP/133/99-FINAL. 4.2.4 User-defined sorption Sorption can be defined in two ways within VetCalc. In the first option (basic option) users can supply a single Koc value. This assumes sorption is primarily dependant upon organic carbon content in soil as follows:

oc

doc f

KK =

Where Koc is the organic carbon partition coefficient

Kd is the bulk sorption coefficient foc is the fraction of organic carbon in soil.

However, under certain circumstances it may be appropriate to consider an alternative basis for defining sorption behaviour to the default that ties sorption to organic carbon content in soil via a Koc value. The user may then choose to directly define a sorption Koc value for each soil series in the simulated scenario based upon soil pH conditions. Soil-specific Kd values can then be calculated for each scenario. Immediately prior to writing the input file for the simulation the user will be provided with the opportunity to replace both default Kd values for each soil in the simulation. An example of where sorption may need to be carefully considered is where a dependency of sorption upon pH occurs. An attempt is made within VetCalc to highlight where this may occur for ionisable substances through input of a dissociation constant (pKa). After entry of a pKa value the VetCalc model will check to determine whether ionisation may be significant at environmental pH values and will warn the user of the need to consider sorption with care. In brief, where such circumstances occur the user has two options;

• The most straightforward and conservative option is to simply replace the mean Koc value by a worst-case Koc value on the basic data sheet (assuming a broad enough range of soil pH conditions were considered in sorption studies), thereby creating a worst-case simulation; or

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• The user can define soil-specific sorption Koc values, thereby allowing customized simulations to be conducted for each scenario reflecting soil pH conditions.

Figure 4.2.4.1- Example of soil properties table illustrating opportunity to redefine sorption characteristics

4.2.5 Soil degradation/dissipation As a default, the VetCalc model simulates degradation in soil based upon a user-defined, laboratory-based degradation DT50 under standard incubation conditions (soil moisture = pF 2.0 and temperature = 20°C). The model then adjusts the degradation rate in soil to reflect simulated soil moisture and temperature conditions. Where field data may be available it is possible to disable the moisture and temperature dependency functions. This should be considered with great care and justifications provided for replacement of laboratory endpoints with field endpoints. Care should also be taken to ensure climatic relevance of field studies to European conditions. Results of field studies conducted in North America may not be easily demonstrated to be transferable to Europe. Similarly field studies conducted in Northern Europe will generally not be considered transferable to Southern Europe. A precedent that has been established for pesticides under Directive 91/414/EEC has divided Europe into Northern and Southern zones for the purposes of residue and environmental fate assessment purposes (SANCO Document 7525/VI/95-rev.7, SANCO, 2001). This is illustrated in Figure 4.2.5.1.

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In order to disable the moisture and temperature dependency functions the user should replace the defaults as follows

• Moisture dependency: Replace 0.7 factor by 0.0 • Temperature dependency: Replace default Q10 value of 2.2 by 1.0

4.2.6 Simulation of metabolites Concerns surrounding metabolites may arise as a consequence of three possibilities:

• Potential occurrence as a consequence of metabolism prior to excretion • Potential occurrence as a consequence of degradation during storage • Potential occurrence as a consequence of degradation in soil after application/release

to land In some cases, it is recognised that all three processes may occur. The significance of each process is instructive when preparing modelling assessments. The following suggestions are provided on modelling strategy:

Figure 4.2.5.1.- Northern and Southern Europe Residue Zones (SANCO Document 7525/VI/95-rev.7, SANCO, 2001)

Option 1: When formation of metabolite as a consequence of metabolism prior to excretion is significant Because the VetCalc model does not specifically simulate adsorption and metabolism processes it is necessary to account for formation prior to excretion in a pragmatic manner. Under these circumstances it is suggested that users simulate metabolites on their own - as if they were parent compound. It is recommended that the evidence provided from ADME studies is carefully interpreted and used to derive a likely kinetic fraction that characterises the formation of the metabolite following degradation of parent. A default assumption of 1.0 (or 100%) can be applied for simplicity. In this case it is assumed that 100% of the parent undergoes degradation

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to form the metabolite. Care should be taken to avoid confusing the kinetic fraction with the maximum occurrence in excreta. The user can then set up modelling for the metabolite by treating it as if it were parent compound. The user will need to carry out a simple calculation based upon relative molecular mass in order to derive a ‘metabolite equivalent’ dosage rate. For example: Example background information

• Parent Compound is dosed at 5.1 mg/kg bodyweight to a 650 kg dairy cow • Parent Compound has a molecular mass of 300 g/mol • Metabolite1 has a molecular mass of 200 g/mol • The ADME study suggests 100% of parent is likely to be metabolised to form

Metabolite1 • The mass balance in the ADME study suggests that Metabolite1 in excreta accounts

for a maximum of 40% of the radiocarbon dose of Parent Compound. How is a metabolite equivalent dose estimated for modelling? Input of parent:

• Dose x Bodyweight = 5.1 mg/kg x 650 kg = 3,315 mg • On a molar basis this represents (3,315 mg / 1,000 mg/g) / 300 g/mol = 0.011 mol

Conversion of parent to metabolite:

• Estimated kinetic fraction characterising route of conversion of Parent Compound to Metabolite1 = 1.0 (complete potential conversion – no other products are likely to be formed from degradation of Parent Compound).

• Potential mass of Metabolite1 that may be formed = 1.0 x 0.011 mol = 0.011 mol • On the basis of the molecular mass of Metabolite1, the equivalent total dose of

Metabolite1 to be considered in the assessment is 0.011 mol x 200 g/mol = 2.2 g. Equivalent metabolite dose for modelling:

• The dosage rate referenced to bodyweight is then = (2.2 g x 1000 mg/g) / 650 kg = 3.4 mg/kg

This can be more directly estimated as follows: (Dosage rate of Parent Compound) x (Molecular mass of Metabolite1)/(Molecular Mass of Parent Compound) = 5.1 mg/kg x 200 g/mol / 300 g/mol = 3.4 mg/kg The maximum proportion occurring in excreta can then be taken into account in the ‘Advanced Data’ input option by setting ‘Fraction of Chemical Excreted’ = 0.4 Option 2: When formation of metabolites following degradation of parent during storage is significant If the metabolite is likely to occur in significant quantities as a consequence of degradation during storage it is recommended that simulations are conducted as described in Option 1 (above) with an amendment as follows: In Option 1, the maximum proportion of Metabolite1 occurring in excreta can be modified by a fraction considered appropriate following interpretation of the ADME study. Because Metabolite1 may continue to form as a consequence of degradation of Parent Compound during the storage phase it may be appropriate for the user to revise the ‘fraction excreted’ by either;

• Employing the default setting (‘Fraction Excreted’ = 1.0), or; • Revising the Fraction Excreted value upwards to take into account the additional

proportion likely to be formed during storage It is acknowledged that these are simplistic treatments of a potentially highly complex process. Care and attention should be taken when adjusting the ‘Fraction Excreted’

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to accommodate this behaviour and suitable justifications provided. In reality there is rarely any evidence available on behaviour during storage and modelling based upon Options 1 or 3 are most likely to be carried out. Option 3: When formation following degradation in soil is likely to be significant Under these circumstances it is recommended that modelling be established with parent compound and the metabolite is defined under the ‘Advanced Data’ option. This will allow simulation of loadings of parent compound (but not metabolite) to soil. The metabolite will then be formed following degradation of parent (a 100% conversion is assumed for simplicity). The behaviour of the metabolite is defined by the user under the ‘Advanced Data’ option (a set of basic physico-chemical and environmental fate data are then required for the metabolite). 4.3 References Agriculture Canada (1987). Environmental chemistry and fate. Guidelines for registration of pesticides in Canada. Aquatic (Laboratory) - Anaerobic and aerobic. Canada. pp 35-37. BBA (1990). Guidelines for the examination of plant protectors in the registration process. Part IV, Section 5-1: Degradability and fate of plant protectors in the water/sediment system. Germany. Boesten, J.J.T.I. (1986) Behaviour of herbicides in soil: Simulation and experimental assessment. Doctoral thesis, Institut for Pesticide Research, Wageningen, The Netherlands. Boesten, J.J.T.I. (2000) Modeller subjectivity in estimating pesticide parameters for leaching models using the same laboratory data set. Agricultural Water Management (in press). Boesten J.J.T.I., van der Pas, L.J.T., (2000) Movement of water, bromide ion and the pesticides ethoprophos and bentazone in a sandy soil: the Vredepeel data set. Agricultural Water Management 44: 21-42. Brown, C.D., Baer, U., Günther, P., Trevisan, M. and Walker, A. (1996) Ring test with the models LEACHP, PRZM-2 and VARLEACH: Variability between model users in Prediction of Pesticide Leaching Using a Standard Data Set. Pestic Sci. 47 p249-258. Di, H.J., Aylmore, A.G. and Kookana, R.S., (1998). Degradation rates of eight pesticides in surface and subsurface soils under laboratory and field conditions. Soil Science, 163, 404-411. FOCUS (2000) FOCUS groundwater scenarios in the EU review of active substances, The report of the work of the Groundwater Scenarios Workgroup of FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe), Version 1 of November 2000. EC DG Sanco/321/2000 rev.2 Fomsgaard, I.S., (1995). Degradation of pesticides in subsurface soils, unsaturated zone - a review of methods and results. Intern. J. Environ. Anal. Chem., 58, 231-245 Gottesbüren, B. (1991) Doctoral thesis. Konzeption, Entwicklung und Validierung des wissenbasierten Herbizid-Beratungssystems HERBASYS. Helweg, A., (1992). Degradation of pesticides in subsurface soil. Proceedings of International Symposium on Environmental Aspects of Pesticide Microbiology, August 1992, Sigtuna, Sweden), pp 249-265. Jones, R. L., and F. A. Norris. (1998). Factors Affecting Degradation of Aldicarb and Ethoprop. Journal of Nematology 30(1):45-55.

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Koch W, Baumeister P & Hurle K (1979). Freiland- und Laborversuche zum zeitlichen Verlauf des Abbaus einiger Herbizide in verschiedenen Boden und Bodentiefen. In H Boerner et al. (ed.) Herbizide: Abschlussbericht zum Schwerpunktprogramm "Verhalten und Nebenwirkungen von Herbiziden im Boden und in Kulturpflanzen", p. 72-77. Harald Bolt, Boppard, Germany. Kruger, E.L., Rice, P.J., Anhalt, J.C., Anderson, T.A. and Coats, J.R., (1997). Comparative fates of atrazine and deethylatrazine in sterile and nonsterile soils. J. Environ. Qual., 26, 95-101. Kruger, E.A., Somasundaram, L., Kanwar, R.S. and Coats, J.R., (1993). Persistence and degradation of [14C]atrazine and [14C] deisopropylatrazine as affected by soil depth and moisture conditions. Environmental Toxicology and Chemistry, 12, 1959-1967. Lavy, T.L., Mattice, J.D., Massey, J.H., Skulman, B.W., Sensemen, S.A., Gbur, E.E. Jr. and Barrett, M.R., (1996). Long-term in situ leaching and degradation of six herbicides aged in subsoils. J. Environ. Qual., 25, 1268-1279. MAFF PSD, (1992). Pesticides Safety Directorate. Preliminary guideline for the conduct of biodegradability tests on pesticides in natural sediment/water systems. Ref No SC 9046. United-Kingdom. OECD (2001). Aerobic and Anaerobic Degradation in Water / Sediment Systems. OECD Test Guideline 308 (Adopted 21 April 2002). Resseler, H., Schäfer, H., Görlitz, G., Hermann, M., Hosang, J., Kloskowski, R., Marx, R., Sarafin, R., Stein, B. and Winkler, R. (1997) Recommendations for conducting Simulation Calculations for the registration procedure. Nachrichtenbl. Deut. Pflanzenschutzd. 49 (12) 305-309. SETAC (1995) Procedures for Assessing the Environmental Fate and Ecotoxicity of Pesticides. Society of Environmental Toxicology and Chemistry. ISBN 90-5607-002-9. Brussels, Belgium pp1-54. Smelt JH, Leistra M, Houx NWH & Dekker A (1978a) Conversion rates of aldicarb and its oxidation products in soils. I. Aldicarb sulphone. Pesticide Science 9:279-285. Smelt JH, Leistra M, Houx NWH & Dekker A (1978b) Conversion rates of aldicarb and its oxidation products in soils. I. Aldicarb sulphoxide. Pesticide Science 9:286-292. US-EPA (1982). Pesticide assessment guidelines, Subdivision N. Chemistry: Environmental fate. Section 162-3, Anaerobic aquatic metabolism. Vaughan, P.C., Verity, A.A., Mills, M.S., Hill, I.R., Newcombe, A.C. and Simmons, N.D., (1999). Degradation of the herbicide, acetochlor in surface and sub-surface soils under field and laboratory conditions. Proceedings of the XI Symposium Pesticide Chemistry: Human and Environmental Exposure to Xenobiotics, Editors: Del Re, A.A.M., Brown, C., Capri, E., Errera, G., Evans, S.P.and Trevisan, M., September 11-15th, 1999, pp 481-490. Walker, A., (1974) A simulation model for prediction of herbicide persistence. J. Environ. Qual. 3 p396-401. Wösten, J.H.M., Lilly, A., Nemes, A. and Le Bas, C. (1998) Using existing soil data to derive hydraulic parameters for simulation models in environmental studies and in land use planning. Final Report on the European Union Funded project. Report 156. DLO-Staring Centre, Wageningen.

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5.0 SCENARIO PROFILES Exposure potential for veterinary medicines in soil, groundwater and surface water is influenced by a wide range of factors. Many of these are regionally specific and include:

• Soil characteristics • Site hydrological management practices • Climatic profile • Cropping practices • Animal husbandry • Manure management practices • Local/regional restrictions

Together these dictate the potential for transformation and transport into other compartments. This includes potential for leaching to groundwater and drains as well as run-off to surface waters. There is significant variation in these factors at a regional scale (climate) and potential also at a local scale (soil characteristics) or from farm to farm (hydrological management, cropping practices, animal husbandry, manure management). Accordingly, it is impractical to assess behaviour under every potential set of circumstances that may be encountered. Instead, a pragmatic alternative involves assessing behaviour in a set of scenarios that represent a range of situations. Scenario based assessments have been employed with success in environmental risk assessments for pesticides under Directive 91/414/EEC and have led to the development of a set of standardised modelling scenarios under the FOCUS initiative (FOCUS, 2000; 2001). A similar philosophy is employed here in the development of a set of standard scenarios. In fact, several of the scenarios developed within this exercise have close parallels within the FOCUS groundwater and surface water framework. In developing a set of standard scenarios it was, however, recognised that differences exist between the agricultural contexts considered by FOCUS (arable agriculture) and livestock production. In many cases regions where there are significant arable production are also significant in terms of livestock production. However, there are a number of notable exceptions (mainly dairy and sheep grazing) where arable agriculture is considered marginal and, as a consequence, intentionally disregarded by FOCUS. Surveys of agricultural production statistics (Eurostat) indicate that these regions may represent major production areas for certain livestock sectors (and, therefore, potentially major markets for use of certain veterinary medicine products). It was, therefore, considered appropriate to employ agricultural production statistics to assist in developing a set of scenarios that represent;

1) Major livestock production systems 2) A broad range of climate and soil conditions

Attempts were also made to ensure that scenarios had broad agricultural relevance and represented diverse production rather than being skewed by one specific form of agricultural production (e.g. sheep production). The following remit was provided by Defra and the Veterinary Medicines Directorate: A set of at least 10 representative diverse scenarios should be developed to represent pedoclimatic conditions and agricultural practices throughout the EU.

• Three of these scenarios should be based in the United Kingdom in order to provide a higher resolution basis for decentralised assessments

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• Two of these scenarios should be based in France, in recognition of significance of national production and regional variation in climatic conditions between northern and southern France

• At least one scenario should be based in Scandinavia, with additional scenarios in Southern Europe as appropriate.

The research project specifically did not consider Member States that accessed to the European Union in 2004 at the recommendation. A set of further criteria were applied in the identification of potential scenario locations, described in the next section. Scenarios were then presented to the Veterinary Medicines Directorate in draft form and amendments made where appropriate. 5.1 Strategy for Regional Scenario Selection Scenarios were initially identified at the Eurostat NUTS2 region level using livestock production data available from the CAPRI mapping tool (http://www.agp.uni-bonn.de/agpo/rsrch/capri/map/). The production data were a three year average of activity based around 1994. ‘Activity’ is defined as the absolute number of animals in a region (expressed as 1000’s Heads). The data were collated into general categories according to the main livestock groups that are likely to be treated with veterinary medicines: swine, ovine, bovine and poultry. Scenarios were chosen using a ranking system that accounts for the diversity of these livestock groups within each region, rather than the total activity that occurs. The objective of this approach was to ensure that each of the chosen scenarios would be relevant to a range of production types, and hence veterinary medicine uses/pathways, that might occur within a given environment, rather than specifically related to one livestock production type. The ranking system assigned a ranking score to each region according to their relative activity for each of the livestock categories considered. For example, the region that produced the greatest number of swine (Denmark) was given a score of 1, whilst the region characterised by the least swine production was given the highest score. The overall ranking of the regions was based on the average score achieved for the swine, ovine, bovine and poultry categories, with the most diverse regions achieving the lowest overall average score (see Table 5.1.1). In order to ensure as wide a geographical spread as possible, no adjacent NUTS2 regions were permissible in the final set of candidates for scenarios. With the exception of France and the United Kingdom, no Member States were permitted to have more than one candidate region. Total activity data (see Table 5.1.2) were also used as selection criteria to differentiate between similarly scoring regions. The scheme proposed by the German meteorologist Wladimir Köppen (1923) was employed to characterise climate in each of the most relevant regions. The categorisations employed by Köppen are based on the annual and monthly averages of temperature and precipitation. The Köppen system recognizes five major climatic types; each type is designated by a capital letter:

• A: Tropical Moist Climates: all months have average temperatures above 18ºC; • B: Dry Climates: with deficient precipitation during most of the year; • C: Moist Mid-latitude Climates with Mild Winters; • D: Moist Mid-Latitude Climates with Cold Winters; • E: Polar Climates: with extremely cold winters and summers.

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Table 5.1.1. Ranking of 83 NUTS2 EU regions by diversity of livestock production (low score = high diversity; Data from a three year average based around 1994). (Proposed scenario regions are highlighted)

Rank Region Code Swine Ovine Bovine Poultry Average Score

1 Ireland IR00 12 2 1 18 8.25 2 Castilla -Leon ES410 11 1 10 20 10.50 3 Bretagne FR520 2 51 2 1 14.00 4 Cataluna ES510 4 6 47 3 15.00 5 Andalucia ES610 13 9 24 17 15.75 6 Pays de la Loire FR510 16 46 3 2 16.75 7 Northern Ireland UKB00 30 24 6 10 17.50 8 Midi -Pyrenees FR620 31 8 11 26 19.00 9 Denmark DK000 1 64 5 7 19.25 10 Lombardia IT200 10 65 4 6 21.25 11 Noord-Brabant NL410 3 61 21 4 22.25 12 Gelderland NL220 8 56 15 12 22.75 13 Weser Ems DE940 5 72 9 9 23.75 14 Aquitaine FR610 32 31 19 16 24.50 15 Castilla-la mancha ES420 18 7 49 34 27.00 16 Poitou -Charentes FR530 40 21 28 23 28.00 17 Emilia Romagna IT400 14 60 12 29 28.75 18 Galicia ES110 28 50 20 19 29.25 19 North Yorkshire UK220 23 25 44 28 30.00 20 Cornwall, Devon UK620 47 17 26 33 30.75 21 Bord, Centr, Fife, Loth, Tay UKA10 54 15 42 15 31.50 22 West-Vlaanderen BL250 6 70 36 14 31.50 23 Aragon ES240 26 5 68 30 32.25 24 Veneto IT320 37 81 8 5 32.75 25 East Anglia UK400 15 48 55 13 32.75 26 Auvergne FR720 45 32 14 41 33.00 27 Clwyd, Dyfed, Gwynedd, Powys UK910 73 3 22 37 33.75 28 Piemonte IT110 25 59 16 36 34.00 29 Dorset, Somerset UK630 36 37 33 32 34.50 30 Communidad Valenciana ES520 21 18 76 24 34.75 31 Centre FR240 41 40 34 27 35.50 32 Basse -Normandie FR250 33 55 7 47 35.50 33 Antwerpen BL210 22 78 32 11 35.75 34 Schelswig -Holstein DEF00 17 53 13 61 36.00 35 Shropshire, Staffordshire UK720 48 33 37 31 37.25 36 Sydsverige SE040 9 82 40 21 38.00 37 Hereford-Worcs, Warwicks UK710 53 30 50 22 38.75 38 Bourgogne FR260 55 45 18 42 40.00 39 Brandenburg DE400 27 62 29 46 41.00 40 Avon, Gloucs, Wiltshire UK610 52 39 39 35 41.25 41 Extremedura ES430 51 10 35 74 42.50 42 Biera Litoral PT180 50 49 65 8 43.00 43 Dumfr, Galloway, Strathclyde UKA20 76 13 25 60 43.50 44 Muenster DEA30 7 79 31 59 44.00 45 Sardegna ITB00 49 4 46 78 44.25 46 Grampian UKA40 39 44 45 51 44.75

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47 Niederbayern DE220 20 77 30 58 46.25 48 Oestra Mellansverige SE020 19 74 38 54 46.25 49 Cumbria UK120 72 11 41 62 46.50 50 Kentriki Makedonia EL120 63 22 51 53 47.25 51 Oberbayen DE210 35 67 17 71 47.50 52 Limousin FR630 61 28 23 81 48.25 53 Lancashire UK830 60 34 57 43 48.50 54 Derby, Nottinghamshire UK310 57 47 52 38 48.50 55 Berks, Bucks, Oxfordshire UK520 44 43 59 49 48.75 56 Sterea Ellada EL240 46 19 81 50 49.00 57 Lincolnshire UK330 43 58 71 25 49.25 58 Etelae-Suomi FI120 29 83 48 40 50.00 59 Madrid ES300 42 26 67 65 50.00 60 Leics, Northamptonshire UK320 58 38 58 56 52.50 61 Schwaben DE270 34 75 27 77 53.25 62 Thessalia EL140 56 14 69 75 53.50 63 Hampshire, Isle of Wight UK560 65 54 66 39 56.00 64 Dytiri Ellada EL230 64 12 79 72 56.75 65 Humberside UK210 24 66 73 64 56.75 66 Surrey, East-West Sussex UK530 67 42 61 57 56.75 67 Cheshire UK810 66 52 54 55 56.75 68 Vaeli-Suomi FI140 38 80 43 69 57.50 69 Peloponnisos EL250 70 20 82 66 59.50 70 Northumberland, Tyne and Wear UK130 79 23 60 76 59.50 71 Puglia IT910 75 29 53 82 59.75 72 Kent UK570 77 41 74 48 60.00 73 Kriti EL430 69 16 83 73 60.25 74 Cleveland, Durham UK110 68 35 70 70 60.75 75 Gwent, Mid S-W-Glamorgan UK920 81 36 62 67 61.50 76 Highlands, Islands UKA30 82 27 56 83 62.00 77 Essex UK540 62 68 75 44 62.25 78 West Yorkshire UK240 59 57 72 68 64.00 79 Vaestsverige SE050 80 76 63 45 66.00 80 Smaaland med Oearna SE030 83 71 64 52 67.50 81 Bedfordshire, Hertfordshire UK510 74 63 78 63 69.50 82 South Yorkshire UK230 71 73 77 80 75.25 83 Greater Manchester UK820 78 69 80 79 76.50

Source: CAPRI mapping tool

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Table 5.1.2. Ranking of 83 NUTS2 EU regions by total livestock production activity (in 1000s heads) (Data from a three year average based around 1994).

Rank Region Code Total Activity

1 Denmark DK000 23634 2 Ireland IR00 17804 3 Bretagne FR520 17732 4 Cataluna ES510 16629 5 Castilla -Leon ES410 14050 6 Noord-Brabant NL410 12617 7 Clwyd, Dyfed, Gwynedd, Powys UK910 9121 8 Weser Ems DE940 8858 9 Andalucia ES610 7722 10 Castilla-la mancha ES420 7548 11 West-Vlaanderen BL250 6926 12 Gelderland NL220 6925 13 Midi -Pyrenees FR620 6755 14 Aragon ES240 6704 15 Lombardia IT200 6565 16 Pays de la Loire FR510 6536 17 Sardegna ITB00 6053 18 Muenster DEA30 6026 19 Northern Ireland UKB00 5312 20 Sydsverige SE040 5110 21 Emilia Romagna IT400 4687 22 Extremedura ES430 4645 23 Communidad Valenciana ES520 4315 24 Schelswig -Holstein DEF00 4130 25 North Yorkshire UK220 4029 26 Cornwall, Devon UK620 3986 27 Dumfr, Galloway, Strathclyde UKA20 3956 28 Cumbria UK120 3868 29 Poitou - Charentes FR530 3705 30 East Anglia UK400 3573 31 Bord, Centr, Fife, Loth, Tay UKA10 3477 32 Aquitaine FR610 3470 33 Dytiri Ellada EL230 3335 34 Thessalia EL140 3252 35 Auvergne FR720 3152 36 Piemonte IT110 2987 37 Sterea Ellada EL240 2959 38 Basse -Normandie FR250 2938 39 Limousin FR630 2910 40 Kentriki Makedonia EL120 2869 41 Kriti EL430 2795 42 Niederbayern DE220 2769 43 Galicia ES110 2737 44 Oestra Mellansverige SE020 2606 45 Veneto IT320 2606 46 Peloponnisos EL250 2553 47 Antwerpen BL210 2534

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48 Madrid ES300 2521 49 Northumberland, Tyne and Wear UK130 2306 50 Bourgogne FR260 2239 51 Shropshire, Staffordshire UK720 2222 52 Oberbayen DE210 2217 53 Dorset, Somerset UK630 2186 54 Brandenburg DE400 2163 55 Hereford-Worcs, Warwicks UK710 2160 56 Highlands, Islands UKA30 1977 57 Centre FR240 1953 58 Schwaben DE270 1906 59 Puglia IT910 1868 60 Humberside UK210 1826 61 Avon, Gloucs, Wiltshire UK610 1660 62 Grampian UKA40 1654 63 Etelae-Suomi FI120 1580 64 Lancashire UK830 1548 65 Berks, Bucks, Oxfordshire UK520 1421 66 Leics, Northamptonshire UK320 1297 67 Cleveland, Durham UK110 1221 68 Gwent, Mid S-W-Glamorgan UK920 1129 69 Derby, Nottinghamshire UK310 1117 70 Biera Litoral PT180 1097 71 Vaeli-Suomi FI140 1077 72 Surrey, East-West Sussex UK530 1057 73 Lincolnshire UK330 897 74 Kent UK570 864 75 Cheshire UK810 749 76 Hampshire, Isle of Wight UK560 645 77 West Yorkshire UK240 628 78 Essex UK540 448 79 Vaestsverige SE050 312 80 Bedfordshire, Hertfordshire UK510 300 81 South Yorkshire UK230 291 82 Smaaland med Oearna SE030 277 83 Greater Manchester UK820 206

Source: CAPRI mapping tool Each of these major regions are also broadly sub-divided according to seasons into a total of 24 sub-categories. An example is provided below for ‘C Class’, the most significant climatic class for much of the agriculturally productive area of Europe: C Class This climate generally has warm and humid summers with mild winters. Its extent is from 30 to 50 degrees of latitude mainly on the eastern and western borders of most continents. During the winter the main weather feature is the mid-latitude cyclone. Convective thunderstorms dominate summer months. Three minor types exist:

• Cfa - Humid subtropical; • Cs – Mediterranean; • Cfb - Marine.

It should be noted that within this scheme the ‘Marine’ (Cfb) classification captures a relatively large proportion of Europe including certain regions that would otherwise be

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less quantitatively or more instinctively characterised as ‘Mediterranean’ or ‘Scandinavian’. This aspect of the scheme is actually considered to be advantageous within the context of this project because it provides greater assurance that the more extreme climatic conditions within the cool/continental and warm/semi-arid regions of Europe are represented. Within the Köppen scheme much of Europe where livestock production is significant is characterised by climates that are either ‘temperate/mesothermal’ (so-called ‘type C’ climate) or continental/microthermal (so-called ‘type D’ climate) – see Figure 5.1.1 and 5.1.2. Detailed characterisation of each of the most relevant climate classes is provided below.

Figure 5.1.1. Illustration of Köppen climatic classification Climate C: Temperate/Mesothermal climates These climates have an average temperature above 10 °C (50 °F) in their warmest months, and a coldest month average between -3 °C and 18 °C. The second letter indicates the precipitation pattern - w indicates dry winters (driest winter month average precipitation less than one-tenth wettest summer month average precipitation; one variation also requires that the driest winter month have less than 30 mm average precipitation), s indicates dry summers (driest summer month less than 30 mm average precipitation, and less than one-third wettest winter month precipitation; in one variant, it is also necessary for the average annual precipitation to be not more than 890 mm [35 inches]) and f means rain in all seasons (neither above mentioned set of conditions fulfilled). The third letter indicates the degree of summer heat - a indicates warmest month average temperature above 22 °C (71.6 °F), b indicates warmest month average temperature below 22 °C, with at least 4 months averaging above 10 °C, while c means 3 or fewer months with mean temperatures above 10 °C. Climate D. Continental/microthermal climates These climates have an average temperature above 10 °C in their warmest months, and a coldest month average below -3 °C (or 0 °C in some versions).

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The second and third letters are used as for Group C climates, while a third letter of d indicates 3 or fewer months with mean temperatures above 10 °C and a coldest month temperature below -38 °C (-36.4 °F). Relevant NUTS2 regions were then generally categorised as being predominately:

• Cfa (Humid subtropical), • Cfb (Marine), • Csa (Mediterranean) or, • Dfb (Humid continental).

Figure 5.1.2. Illustration of Köppen climatic classification (Europe)

Zone 1: Mediterranean (Cfa/Csa) Two scenarios were developed for the Mediterranean zone. From Table 5.1.1 it can be seen that the three highest-ranking regions (in terms of livestock diversity) located in this zone were Cataluna (4) and Andalucia (5) (both in Spain). The next most significant region lying outside of France and Spain was Emilia Romagna (Italy; 17). To ensure a geographical spread of scenarios, only one of the Spanish regions from was chosen for development (Andalucia). This region is also the basis for a well-established FOCUS groundwater Scenario (Sevilla). Although Cataluna has a greater total livestock activity than recorded for this region (see Table 5.1.2), it was felt that, in combination with the second scenario choice (Emilia Romagna) the climate of Andalucia (relatively hot and dry) would provide greater contrast in Mediterranean climatic conditions. Emilia Romagna is also the basis of a well-established FOCUS Surface Water scenario (R3; Bologna). Zone 2: Central (Cfb) Ten scenarios were developed for this zone, since it represents the most widespread climatic type in the EU. As indicated, earlier the remit provided by VMD and Defra

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required development of at least three scenarios in the United Kingdom and a further two scenarios in northern and southern France, respectively. Table 5.1.3. Characterisation of Climatic Zones Climatic Region

Köppen Classification

Number of Required Scenarios

Most Significant Associated NUTS2 Regions*

Mediterranean Cfa and Csa 2 Cataluna (ES) Andalucia (ES) Emilia Romagna (IT) Veneto (IT)

Central

Cfb 7 3 required in UK 2 required in France

Ireland (IR) Castilla Leon (ES) Bretagne (FR) Pays de la Loire (FR) Northern Ireland (UK) Midi-Pyrenees (FR) Denmark (DK) Lombardia (IT) Noord Brabant (NL) Gelderland (NL)

Continental Scandinavian

Dfb 1 Etalae Suomi (FI) Vaeli Suomi (FI)

* Proposed Regions in Bold Based on its high ranking for diversity (2) and for total activity (1), Ireland was chosen as a candidate scenario location for this zone. The next highest ranking region (in livestock diversity terms); Castilla-Leon, was not chosen as a scenario because of it’s proximity to an already existing Spanish warm/arid zone scenario (Andalucia). The next two highest-ranking regions; Bretagne (3) and Pays de la Loire (6) (both in France) are located adjacently to each other and would be characterised by very similar climate, soil and agricultural practices. Of these regions, Bretagne was suggested as a candidate basis for a scenario since it is characterised by greater overall livestock activity than Pays de la Loire (see Table 5.1.2). The next highest diversity-ranking region is Midi-Pyrenees (8) (see Table 5.1.1). This region was proposed as a basis for the second French scenario. The choice of this region over Pays de la Loire as a second French scenario can also be justified on the basis of total livestock activity data (see Table 5.1.1). Denmark was chosen as a scenario on the basis of its diversity score (9) and total livestock activity data (1). The latter indicates that this region has greatest livestock activity in the EU, with particular focus on swine and bovine categories. This region is also the basis of a well-established FOCUS Surface Water scenario (D4; Skousbo). However, the main areas of livestock production lie to the west of this site and development of a new scenario was considered as warranted. The next highest diversity ranked region; Lombardia (Italy) was rejected as a scenario location because of its proximity to the adjacent region of Emilia Romagna, which has been proposed as a basis for a Mediterranean scenario location. The next most highly ranking region in terms of diversity was Noord Brabant (The Netherlands; 11) which could be viewed as representing livestock production and environmental conditions in the lower Rhine valley (The Netherlands, Belgium and Germany). A

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neighbouring area in the Netherlands is also the basis of a well-established FOCUS Surface Water scenario (D3; Vredepeel). A further scenario based upon Brandenburg (Germany; 39) was proposed to represent agricultural production in the former East Germany. Although several regions in the Central zone have higher diversity scores, these regions were rejected either on the basis that there were existing proposed scenarios within the Member State (e.g. Gelderland in the Netherlands) or as a consequence of proximity to already proposed scenario regions (e.g. Schleswig Holstein in Germany neighbouring Denmark). A scenario based upon the Brandenburg region may also be considered to have some general climatic relevance to certain Member States that accessed to the European Union in 2004, but were not explicitly considered in this assessment (e.g. Poland, the Czech Republic and, to a lesser extent the Slovak Republic). UK Scenarios Despite it’s high ranking for production diversity (7), Northern Ireland was rejected as a possible scenario because the adjacent Irish scenario already adequately represents this agricultural situation. The next highest-ranking regions, in terms of livestock diversity, were North Yorkshire (19), and Devon and Cornwall (20). could provide a basis for longer-term field research as appropriate. Due to their geographical diversity (representing the Northeast and Southwest of England), these regions were proposed as scenarios representing the northeast and southwest of England. A well-established FOCUS Groundwater scenario is based on Okehampton in Devon. However, it was proposed that an alternative site in Devon (based upon facilities at the Institute for Grassland Research in North Wyke) could provide a basis for future validation efforts. Similarly, a site in North Yorkshire close to ADAS High Mowthorpe was considered to potentially provide a basis for future validation research, as appropriate. Based on its high ranking for total activity (7), Clwyd, Dyfed, Gwynedd and Powys were chosen as a candidate Welsh scenario. Although East Anglia has a higher diversity ranking (25) than this region (27), it wasn’t chosen as a scenario because the Noord Brabant scenarios also represents lowland production in the Central zone. As before, it was proposed that the scenario based upon the Clwyd, Dyfed, Gwynedd and Powys region could be based upon ADAS Pwllpeiran to provide a basis for future validation research, as appropriate. Zone 3: Continental Scandinavian (Dfb) Although Scandinavian regions had consistently generally lower significance in terms of livestock production, it was recognised as being of importance to include a scenario to represent the generally cooler conditions that predominate in this region and represent this influence on environmental fate. The most significant Dfb climate region in terms of diversity and absolute production was Etalae Suomi in Finland (ranked 58th and 63rd, respectively). This region is coincidentally the basis for a long-established FOCUS groundwater scenario (Jokionen).

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Figure 5.1.3. A NUTS2 resolution map of the EU showing proposed scenario locations. Regions shaded in red represent Zone 1 (‘Mediterranean’; Csa/Cfa) locations, the green regions represent Zone 2 (‘Central’; Cfb) locations and blue region represents the Zone 3 (‘Continental Scandinanvia’; Dfb) climate. 5.2 Relevance of Regions to Livestock Sectors A summary of the relevance of each of the 12 scenarios to each major livestock class is provided in Table 5.2.1 based upon livestock production statistics abstracted from the CAPRI mapping tool ((http://www.agp.uni-bonn.de/agpo/rsrch/capri/map/). The production data were a three year average of activity based around 1994 (data summarised in Table 5.1.1). These profiles can be used to help assemble a shortlist of the most meaningful scenarios associated with each usage. The three most significant scenario are highlighted for each major livestock category. In summary, it is envisaged that users would select at a minimum the following sets of scenarios:

1. The three scenarios based upon regions associated with the major production regions for each relevant livestock category (where regions are relevant to the proposed market)

2. At least one Zone 1(Cfa; Emilia Romagna or Csa; Sevilla) scenario for each relevant livestock category (unless stipulated as very minor relevance; e.g. ovine production)

3. The solitary Zone 3 (Dfb; Etalae Suomi) scenario for each relevant livestock category (unless stipulated as very minor relevance; e.g. bovine and ovine production)

4. Additional most relevant scenarios to provide a total of at least 4 scenarios as a basis for assessment.

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Table 5.2.1 Characterisation of comparative relevance of scenarios to major livestock categories NUTS2 Region

Associated Climate

Bovine Production

Ovine Production

Swine Production

Avian Production

Zone 1: Mediterranean (Cfa/Csa) Andalucia Csa Ranked 9th Very minor Ranked 5th Ranked 4th Emilia Romagna

Cfa Ranked 5th Very minor Ranked 6th Ranked 6th

Zone 2: Central (Cfb) Ireland Cfb MAJOR

(Ranked 1st) MAJOR (Ranked 1st)

Ranked 4th Ranked 5th

Bretagne Cfb MAJOR (ranked 2nd)

Very minor MAJOR (Ranked 2nd)

MAJOR (Ranked 1st)

Midi-Pyrenees Cfb Ranked 6th MAJOR (Ranked 3rd)

Ranked 10th Ranked 7th

Denmark Cfb MAJOR (Ranked 3rd)

Very minor MAJOR (Ranked 1st)

MAJOR (Ranked 3rd)

Noord Brabant Cfb Ranked 8th Very minor MAJOR (Ranked 3rd)

MAJOR (Ranked 2nd)

North Yorkshire

Cfb Ranked 11th Ranked 5th Ranked 8th Very minor

Cornwall & Devon

Cfb Ranked 7th Ranked 4th Very minor Ranked 10th

Clwyd, Dyfed, Gwynedd & Powys

Cfb Ranked 4th MAJOR (Ranked 2nd)

Very minor Very minor

Brandenburg Cfb Ranked 10th Very minor Ranked 7th Ranked 9th Zone 3: Continental Scandinavian (Dfb) Etalae Suomi Dfb Very minor Very minor Ranked 9th Ranked 8th The shortlist of scenarios to be considered within centralised assessments based upon production statistics and climate alone are:

1. The three scenarios based upon regions associated with the major production regions for each relevant livestock category (where regions are relevant to the proposed market):

Bovine: Ireland, Bretagne and Denmark Ovine: Ireland, Clwyd, Dyfed, Gwynedd & Powys and Midi-Pyrenees Swine: Denmark, Bretagne, Noord Brabant Avian: Bretagne, Noord Brabant, Denmark

2. At least one Zone 1(Cfa; Emilia Romagna or Csa; Andalucia) scenario for each relevant livestock category (unless stipulated as very minor relevance; e.g. ovine production):

Bovine: Emilia Romagna is most relevant Mediterranean scenario for bovine category Ovine: Very minor production – can be eliminated Swine: Andalucia is most relevant Mediterranean scenario for bovine category Avian: Andalucia is most relevant Mediterranean scenario for bovine category

3. The solitary Zone 3 (Dfb; Etalae Suomi) scenario for each relevant livestock category (unless stipulated as very minor relevance; e.g. bovine and ovine production)

Bovine: Very minor production – can be eliminated Ovine: Very minor production – can be eliminated Swine: Proceed with simulation based upon Etalae Suomi region Avian: Proceed with simulation based upon Etalae Suomi region

4. Additional most relevant scenarios to provide a total of at least 4 scenarios as a basis for assessment.

Bovine: Four scenarios regions considered are Ireland, Bretagne, Denmark and Emilia Romagna

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Ovine: Four scenario regions to be considered are Ireland, Clwyd, Dyfed, Gwynedd & Powys and Midi-Pyrenees and next most relevant scenario = Cornwall & Devon Swine: Five scenario regions to be considered are Denmark, Bretagne, Noord Brabant, Andalucia and Etalae Suomi Avian: Five scenario regions to be considered are Bretagne, Noord Brabant, Denmark, Andalucia and Etalae Suomi

In order to ensure that a broad range of soil and hydrological conditions are considered, behaviour in additional scenarios may need to be assessed. Refined guidance on selection of scenarios for assessment taking into account detailed scenario characteristics is provided in Chapter 8. The recommendations provided above should simply be considered a base set. 5.3 Regional Characterisation – Development of Detailed Scenarios Within a scenario-based assessment scheme it is necessary to conduct simulations for representative sites taking into account a broad range of characteristics:

• Soil characteristics • Site hydrological management practices • Climatic profile • Cropping practices • Manure management practices • Local/regional restrictions

A series of classification schemes have been employed in order to categorise the severity of certain aspects of the scenarios, namely;

• Average autumn and spring temperature • Average annual recharge • Average annual rainfall • Slope

These schemes allow key aspects of each scenario to be classified as ‘best case’ to ‘extreme worst case’. The schemes used to categorise each scenario are summarised in Tables 5.3.1-.5.3.5, based upon approaches adopted by FOCUS SW (2001).

Table 5.3.1 Climatic temperature classes for differentiating scenarios (FOCUS SW, 2001)

AVERAGE AUTUMN & SPRING TEMPERATURE Range °C Assessment <6.6 Extreme worst-case 6.6 – 10 Worst case 10 – 12.5 Intermediate case >12.5 Best case

Appropriate soil types were then identified using broad textural, structural and organic matter characteristics. The soil characteristics used to classify relative worst cases for drainage and runoff are given in Tables 5.3.4 an 5.3.5. An illustration of the range of soil textures considered within the 12 scenarios is illustrated in Figure 5.3.1. An overall classification for the set of 12 scenarios is provided in Table 5.3.6. Details for each scenario are provided in subsequent sections. In recognition of the broader

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pedoclimatic relevance of each scenario, generic descriptors are proposed based upon climate and soil classes. For example, the scenario nominally based upon Emilia Romagna is characterised by a Mediterranean climate (Zone 1) and a clay loam soil. The generic scenario descriptor is then Zone 1 clay loam. Table 5.3.2 Climatic classes for differentiating scenarios (FOCUS SW, 2001)

AVERAGE ANNUAL RECHARGE (leaching) AVERAGE ANNUAL RAINFALL (Run-off) Range mm Assessment Range mm Assessment >300 Extreme worst case >1000 Extreme worst case 200 – 300 Worst case 800 – 1000 Worst case 100 – 200 Intermediate case 600 – 800 Intermediate case <100 Best case < 600 Best case

Table 5.3.3 Slope classes for differentiating scenarios (FOCUS SW, 2001)

SLOPE (RUN-OFF) Range % Assessment >10 Extreme worst case 4 – 10 Worst case 2 – 4 Intermediate case <2 Best case

Table 5.3.4 Relative worst-case soil characteristics for drainage potential (FOCUS SW, 2001)

Soil Characteristics Assessment Coarsely structured ‘cracking clay’ soils with extreme by-pass flow on impermeable substrates

Extreme worst case

Clays and heavy loams with by-pass flow over shallow groundwater Worst case Sands with small organic matter content over shallow groundwater Worst case Light loams with small organic matter content and some by-pass flow on slowly permeable substrates

Intermediate case

Table 5.3.5 Relative worst-case soil characteristics for runoff potential (FOCUS SW, 2001)

Soil Characteristics Assessment Soil hydrologic group D* (heavy clay soils) Extreme worst caseSoil hydrologic group C* (silty or medium loamy soils with low organic matter content).

Worst case

Soil hydrologic group B* (light loamy soils with small clay and moderate organic matter content)

Intermediate case

*Descriptions of soil hydrologic groups are according to the PRZM manual (Carsel et al, 1995)

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Figure 5.3.1 Summary of topsoil characteristics in 12 proposed scenarios

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Table 5.3.6 Overall classification of key environmental characteristics for each scenario Scenario Temperature Recharge Rainfall Slope Drainage

hydrology1 Run-off hydrology

Zone 1 Zone 1 Sandy silt loam (Andalucia)

Best case Intermediate Best case Worst case Best Case Worst case

Zone 1 Clay loam (Emilia Romagna)

Best case Intermediate Intermediate Best case/ Intermediate

Best Case Worst case

Zone 2 Zone 2 Sand (Noord Brabant)

Worst case Worst case Intermediate Best case Worst case Best case

Zone 2 Loamy sand (Denmark)

Worst case Worst case Intermediate Best case Best Case Best case

Zone 2 Sandy loam 1 (Bretagne)

Intermediate Worst case Intermediate Intermediate Intermediate Intermediate

Zone 2 Sandy loam 2 (North Yorkshire)

Worst case Extreme worst case

Intermediate Intermediate/ Worst case

Best Case Best case

Zone 2 Sandy clay loam 1 (Midi-Pyrenees)

Best case Intermediate Intermediate Worst case Best Case Intermediate

Zone 2 Sandy clay loam 2 (Ireland)

Intermediate Worst case Extreme worst case

Worst case Best Case Intermediate

Zone 2 Sandy silt loam (Brandenburg)

Worst case Extreme Worst Case

Intermediate Best Case Best Case Intermediate

Zone 2 Clay loam (Clwyd, Dyfed, Gwynedd & Powys)

Worst case Best case Worst case Worst case Best Case Worst case

Zone 2 Clay (Cornwall & Devon)

Intermediate Worst case Intermediate/Worst-case

Best case Intermediate Worst case

Zone 3 Zone 3 Sandy loam (Etalae Suomi)

Extreme worst case

Worst case Best case Best case Best Case Best case

1 Where the representative site is not drained the default classification is ‘best case’

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Overview of risk assessment implications A summary of the scenarios of greatest relevance to each route of entry to groundwater or surface water are highlighted in Table 5.3.7 for each production system: Table 5.3.7. Scenarios of likely greatest potential relevance for each route of entry to groundwater and surface water Leaching to

groundwater Run-off to surface waters

Drain discharge to surface waters

Bovine Zone 2 Loamy sand (Denmark) Zone 2 Sandy clay loam 2 (Ireland)

Zone 1 Clay loam (Emilia Romagna) Zone 2 Sandy loam 1 (Bretagne)

Zone 2 Sandy loam 1 (Bretagne)

Ovine Zone 2 Sandy clay loam 2 (Ireland)

Zone 2 Sandy clay loam 2 (Ireland) Zone 2 Clay loam (Clwyd, Dyfed, Gwynedd & Powys)

Zone 2 Clay (Cornwall & Devon)

Swine and Avian

Zone 1 Sandy silt loam (Andalucia) Zone 2 Loamy sand (Denmark) Zone 3 Sandy loam (Etalae Suomi)

Zone 1 Sandy silt loam (Andalucia) Zone 2 Sandy loam 1 (Bretagne)

Zone 2 Sand (Noord Brabant) Zone 2 Sandy loam 1 (Bretagne)

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5.4 Zone 1 – Sandy silt loam Andalucia was selected as a representative region for the Csa climate in the Mediterranean (Zone 1). Amongst the 12 scenarios it is ranked:

• 4th for avian production • 5th for swine production • 9th for bovine production • very minor ovine production

Employing the criteria outlined Section 5.2 it is proposed as a base set scenario for assessing avian and swine treatments. Site agricultural profile Sevilla was identified as the scenario location for the Andalucia region (see Figure 5.4.1). The scenario is intended to represent livestock production within the Gaudalquivir Valley. A photograph of a typical landscape is provided in Figure 5.4.2.

Figure 5.4.1. Map of southern Spain showing the location of the Andalucia scenario (Sevilla (1)). Soil characteristics The regionally dominant major soil class is the Eutric Cambisol. These soils are typically occur on level topography. They are subject to moisture stress and irrigation is necessary for satisfactory agricultural yields (FAO, 1981). The most productive agricultural soils in this region are fluvial soils associated with river valleys. Fluvisols have wide distribution throughout Europe. In the valleys of the Ebro, Tagus, Guadiana and Guadalquivir in Spain these soils are mostly devoted to arable cropping. Soils in this area have been represented within FOCUS Groundwater by the soil profile summarised in Table 5.4.1. In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The

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standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1.

Figure 5.4.2. A photograph representing the landscape class typically used for agricultural production in Andalucia (Source: Microleis, 2003). Table 5.4.1. Textural characterisation of sandy silt loam employed in Zone 1 scenario based upon Sevilla, Spain (FOCUS, 2000) Boundary

Depths (cm)

Clay Content

(%)

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(%)

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(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

0-10 14 51 35 0.93 1.21 10-30 13 52 35 0.93 1.23 30-60 15 51 34 0.70 1.25 60-100 16 54 30 0.58 1.27

100-120 16 54 30 0.58 1.27 120-180 22 57 21 0.49 1.27

As discussed earlier, the scenario is based upon a soil that is considered to be broadly representative of Fluvial soils. An illustration of the geographical extent of Fulvisols (based upon the European Soil Bureau Internet Soil Map; http://eusoils.jrc.it/msapps/Soil/SoilDB/SoilDB.phtml) is provided in the scenario overview in Section 5.16 (See Figure 5.16.3). Fluvisols are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Southern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Csa region). The simulation framework is set up to enable this scenario to be run with either Spanish or Portuguese (e.g. Alentejo) manure management defaults. Site/regional hydrology It is likely that this region is characterised by a hydrological situation as summarised below:

• Climate: Warm Mediterranean with low precipitation. • Soil type: Relatively fine textured Fluvial soil • Surface water bodies: First order streams. • Landscape: Moderately sloping hills with some terraces.

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• Crops: Winter & spring cereals, field beans, vegetables, legumes, maize, vines, pome/stone fruit, sunflower, soybean, citrus, olives.

Typical Water Management: Agricultural land normally has a water management system based around a need to alleviate both seasonal water logging and drought stress. Irrigation is common with overhead sprinkler and trickle systems being most typical. Climate profile According to the Köppen scheme, the Sevilla region is characterised by a Csa climate (Mediterranean). A summary of meteorological data for Sevilla is presented in Figure 5.4.3. The scenario represents both the warmest (annual average temperature: 17.9 °C) and most arid (average annual rainfall: 493 mm) of those developed as a component of VetCalc. Cropping practices This scenario is consistent with the Sevilla scenario developed within the FOCUS Groundwater Work Group. The report summarising the development of the FOCUS scenarios indicates that a wide variety of crops are grown in the region – ranging from Mediterranean crops such as vines, tomatoes, sunflowers and cotton to staples such as potatoes, cereals and maize. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. According to the FOCUS Groundwater report (FOCUS, 2000), winter cereals are prevalent with the following production cycle:

• Planting: 15 November • Emergence: 30 November • Harvest: 31 May

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Figure 5.4.3. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Sevilla, Andalucia, Spain during the period 1931-60 (Source: Met Office, 1996)

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It is, therefore, recommended that applications to arable land coincide with planting (e.g. on or around 15 November) or shortly after harvest (e.g. on or around 31 May). For applications to grassland it is assumed that timing would coincide approximately with the period immediately following harvest of the sward. In FOCUS Groundwater (2000) this is scheduled within the Sevilla scenario as four cycles as follows:

• First harvest: 15 April • Second harvest: 15 June • Third harvest: 15 August • Fourth harvest: 15 October

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 April). Local/regional restrictions This scenario has direct relevance to Spanish manure management practices and indirect relevance to practices in Portugal. Summaries of manure management approaches in Portugal and Spain are provided in further detail in Sections 6.9 and 6.10, respectively. In each case nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.5 Zone 1 - Clay Loam Emilia Romagna was selected as a representative region for the Cfa climate in the Mediterranean (Zone 1). Amongst the 12 scenarios it is ranked:

• 5th for bovine production • 6th for swine production • 6th for avian production • very minor ovine production

Employing the criteria outlined Section 5.2 it is proposed as a base set scenario for assessing bovine treatments. Site agricultural profile The Bologna area has been chosen as the representative site for the Emilia Romagna scenario (see Figure 5.5.1). The choice of this location was driven by the availability of an existing database developed as part of the FOCUS Surface Water risk assessment framework for plant protection products (Scenario R3). The scenario is primarily associated with dairy farming and production of fattening calves, although pork and poultry production are also represented.

Figure 5.5.1. Map of Northern Italy showing the location of the Emilia Romagna scenario (Bologna (1)). Soil characteristics The regionally dominant major soil class is the Eutric Cambisol (FAO, 1981). This major soil class occurs extensively in Southern Europe (particularly in Spain, Italy and France) under Mediterranean climate conditions but also occurs as far north as the United Kingdom on a small scale and in Southern Sweden under cool marine and cool temperate conditions, respectively. These soils occur on level topography in the Po Valley where maize, wheat and sugar beet are intensively cultivated. Stony and lithic phases are extensive in Italy. The scenario location is intended to represent the foothills of the Apennine range rather than the flat alluvial landscape of the Po valley.

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In the central Apennines land use comprises arable cropping, forestry, rough grazing and fruit and vines. Pasture yields are equivalent to 1.3 t/ha of hay while annual forage crops yield 5.4 t/ha. Over 70% of pasture production is obtained in the spring. The agricultural soils in this area are generally medium to fine in texture and are associated with hilly landscapes (slopes 8-30%). A summary of the soil characteristics employed in the FOCUS SW R3 scenario for Bologna is provided in Table 5.5.1. The geographical relevance considered with FOCUS Surface Water is illustrated in Figure 5.5.2. Table 5.5.1. Textural characterisation of clay loam employed in Zone 1 scenario based upon FOCUS R3 scenario (FOCUS, 2001)

Horizon Boundary Depths

(cm)

Clay Content

(%)

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(%)

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(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

Ap1 0-45 34 43 23 1.0 1.46 Ap2 45-75 33 42 25 1.0 1.49 Bk 75-145 35 48 17 0.35 1.52 C 145-160 36 50 14 0.29 1.54

Figure 5.5.2. Illustration of pedoclimatic relevance proposed for FOCUS SW R3 scenario (FOCUS SW, 2001) In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1.

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As discussed earlier, the regionally dominant major soil class is the Eutric Cambisol. For climatic reasons, it should be recognised that this scenario is limited in relevance to Italy (See Figure 5.1.2). An illustration of the geographical extent of Cambisols in Italy (as represented by Inceptisols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (See Figure 5.16.4).

Site/regional hydrology This scenario is consistent with the FOCUS SW Working Group scenario R3 (2003). A ‘snapshot’ summary of this scenario is provided below:

• Climate: Warm temperate with high precipitation. • Representative FOCUS Field Site & Weather Station: Bologna, Italy. • Soil type: Free draining calcareous heavy loam. • Surface water bodies: First order streams. • Landscape: Moderately sloping hills with some terraces.

Typical Water Management: Agricultural land normally has a water management system based around a need to alleviate both seasonal water logging and drought stress. Irrigation is common with overhead sprinkler and trickle systems being most typical. Climate profile A summary of meteorological data for Bologna is presented in Figure 5.5.3. The region experiences high summer temperatures and mild winters, with an annual average temperature of 14.6 °C. The scenario is considerably wetter than other ‘Mediterranean zone’ scenario (Sevilla), with an average annual rainfall of 804 mm (Met Office, 1996).

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Figure 5.5.3. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Bologna, Emilia Romagna, Italy during the period 1931-60 (Source: Met Office, 1996)

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Cropping practices This scenario is consistent with the R3 scenario developed within the FOCUS Surface Water Work Group. The report summarising the development of the FOCUS scenarios indicates that a wide variety of crops are grown in the region – ranging from Mediterranean crops such as tobacco, sunflowers and vines to staples such as potatoes, cereals and maize. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. According to the FOCUS Groundwater report (FOCUS, 2000), winter cereals are prevalent with the following production cycle:

• Emergence: 1 December • Harvest: 1 July

The planting date was assumed to be 2 weeks prior to emergence (e.g. ca 16 November). It is, therefore, recommended that applications to arable land coincide with planting (e.g. on or around 16 November). For applications to grassland it is assumed that timing would coincide approximately with the period immediately following harvest of the sward. Harvest dates are based upon the FOCUS Groundwater Piacenza scenario:

• First harvest: 15 May • Second harvest: 15 July • Third harvest: 20 September

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 May). Local/regional restrictions This scenario has direct relevance to Italian manure management practices. Summaries of manure management approaches in Italy are provided in further detail in Sections 6.7. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.6 Zone 2 – Sand Noord Brabant was selected as a representative region for the Cfb climate in the Central zone (Zone 2). Amongst the 12 scenarios it is ranked:

• 8th for bovine production • Very minor ovine production • 3rd for swine production • 2nd for avian production

Employing the criteria outlined Section 5.2 it is proposed as a base set scenario for assessing bovine, swine and avian treatments. Site agricultural profile The site location for the Noord Brabant scenario is Vredepeel, situated in the Southeast of the Netherlands (see Figure 5.6.1). An experimental farm, operated by Wageningen University and Research Centre, is located at the site, providing access to a wide range of environmental, agricultural and climatic information. The Vredepeel experimental farm is predominantly arable. However, the region is primarily associated with dairy farming, as well as pig and poultry production. The majority of dairy farms in the Netherlands are strongly specialised (62 %) i.e. more than 90 % of the farm’s activities are exclusively associated with dairy. This is especially the case in the south and east of the country (near Vredepeel) where dairy farms on sand soils are generally more intensive (milk/ha) than farms on clay or peat soils in the north and west. This scenario is based upon the FOCUS SW Working Group D3 scenario.

Figure 5.6.1. A map of the Netherlands showing the location of the Noord Brabrant scenario, Vredepeel (1). Soil characteristics The regionally dominant major soil class is the Humic Podzol. This major soil class occurs in Germany, The Netherlands, Belgium and Denmark. They are coarsely

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textured soils and occur on level topography. They are devoted to intensive arable use and livestock production (FAO, 1981). A summary of the soil characteristics employed in the FOCUS SW D3 scenario for Vredepeel is provided in Table 5.6.1. Table 5.6.1. Textural characterisation of sandy clay loam employed in Zone 2 scenario based upon Vredepeel, The Netherlands (based upon FOCUS SW, 2001)

Horizon Boundary Depths

(cm)

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(%)

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(%)

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(%)

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(%)

Bulk Density (kg/dm3)

Ap 0-30 3 6 91 2.3 1.35 Bw 30-50 3 4 93 0.5 1.46 C 50-175 2 2 96 0.1 1.67

In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Podzol soils (as represented by Spodosols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (See Figure 5.16.5). Podzols are widespread in Northern Europe. The simulation framework is set up to enable this scenario to be run with either Dutch, Belgian or German (e.g. proximity to Vlaanderen & Nord Rhein Westfalen) manure management defaults. As discussed in Section 5.7, in textural and climatic terms, this scenario has similarities to the Zone 2 Sandy loam scenario. However, the sand loam scenario is intended to represent a hydrological situation in which groundwater is vulnerable. The Zone 2 Sand scenario is intended to represent a hydrological situation in which vulnerability is primarily associated with the need to drain fields due to a shallow water table (surface water vulnerability). The geographical extent of the D3 scenario developed as a component of the FOCUS SW scenario development procedure is illustrated in Figure 5.6.3. It should be noted that the maps do not suggest that this scenario is relevant to 100% of the areas highlighted. Rather they indicate that in any of the areas highlighted, some part of the agricultural landscape corresponds to the general soil texture and climate combination represented within the scenario. The map illustrated in Figure 5.6.2 primarily illustrates the following range of conditions:

• >65% sand and <18% clay • 6.6 – 10ºC Average spring and autumn temperature

Site/regional hydrology This scenario is based upon the FOCUS SW Working Group scenario D3 (2003). A ‘snapshot’ summary of this scenario is provided:

• Climate: Temperate with moderate precipitation. • Soil type: Sands with small organic carbon content and field drains. Subsoil

waterlogged by groundwater. • Surface water bodies: Field ditches. • Landscape: Level land • Crops: Grass, winter & spring cereals, winter and spring oilseed rape, potatoes, sugar

beet, field beans, vegetables, legumes, maize, pome/stone fruit.

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Typical Water Management: Agricultural land normally has a water management system, primarily to alleviate water logging. These typically constitute a network of pipe or mole drains.

Figure 5.6.2. Illustration of geographical significance of FOCUS Scenario D3 (FOCUS SW, 2001) Climate profile A summary of meteorological conditions within Noord Brabant (Zuid-Limburg) is given in Figure 5.6.3. The climate is comparable to that of Bretagne, with an annual average rainfall of 763 mm and a mean annual temperature of 9.8 ºC (Met Office, 1996). Cropping practices A wide range of crops are grown in this region – Winter & spring cereals, winter and spring oilseed rape, potatoes and sugar beet are typical. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals considering development dates proposed for the D3 (Vredepeel, The Netherlands) scenario in FOCUS SW (FOCUS, 2001):

• Planting: 7 November • Emergence: 20 November • Harvest: 14 August

As spring cereals may also be cultivated in this region, the following production cycle is proposed considering development dates proposed for the D3 (Vredepeel, The Netherlands) scenario in FOCUS SW (FOCUS, 2001):

• Planting: 14 February • Emergence: 31 March • Harvest: 19 August

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Legislation in the Netherlands prohibits application of manures between 15 September and 1 February (see Section 6.8). Accordingly, as a potential worst case (cooler temperatures, greater potential to encounter saturated soil conditions during the three month period immediately following application) it is recommended that applications to arable land coincide with latest permissible application date (e.g. 15 September). For applications to grassland, it is assumed that timing would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed taking into account dates reported within the FOCUS Groundwater Hamburg scenario:

• First harvest: 31 May • Second harvest: 15 July • Third harvest: 31 August

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 31 May).

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Figure 5.6.3. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Zuid-Limburg, representing Vredepeel, Noord Brabant (NL) during the period 1931-60 (Source: Met Office, 1996) Local/regional restrictions This scenario has direct relevance to Dutch manure management practices and indirect relevance to practices in Belgium (Vlaanderen) and certain areas of Nord Rhein Westfalia in Germany. Summaries of manure management approaches in Belgium, Germany and the Netherlands are provided in further detail in Sections 6.1, 6.5 and 6.8, respectively. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.7 Zone 2 – Loamy sand Denmark was selected as a representative region for the Cfb climate in the Central zone (Zone 2). Amongst the 12 scenarios it is ranked:

• 3rd for bovine production • Very minor ovine production • 1st for swine production • 3rd for avian production

Employing the criteria outlined Section 5.2 it is proposed as a base set scenario for assessing bovine, swine and avian treatments. Site agricultural profile NUTS2 level resolution livestock production data were used to assess suitability of candidate regions for development of detailed scenarios. Where higher resolution data are available this can guide the development of more relevant scenarios. Based on an analysis of livestock production data (see Table 5.7.1), it appears that the highest production density (for combined cattle, pigs and poultry) occurs in Ringkøbing county, located in the west of Denmark. This scenario location is shown in Figure 5.7.1. For illustrative purposes, photographs of the typical landscape used for livestock grazing in this region are shown in Figure 5.7.2. Table 5.7.1. Summary of regional livestock production data, areas and livestock density for Denmark, 2002 (Source: Statbank, 2003).

Total Livestock (heads)* Area (ha)

Density (heads/ha)

Copenhagen region 39721 59366 0.67West Zealand county 109382 112802 0.97Storstrøm county 97473 112846 0.86Bornholm 34209 24611 1.39Funen county 199603 146833 1.36South Jutland county 321478 220000 1.46Ribe county 207285 145366 1.43Vejle county 189598 127541 1.49Ringkøbing county 351992 224573 1.57Århus county 230269 183460 1.26Viborg county 319706 209425 1.53North Jutland county 411810 286159 1.44

Soil characteristics The regionally dominant major soil class is the Orthic Podzol. The parent materials are mostly sands of glacial origin. Large areas of this soil type have been identified in Denmark where they are associated with Cambisols and have an origin in moraine deposits (FAO, 1981). The area is characterised by a gently sloping (1-3°) topography and sandy soils with relatively shallow water tables. A summary of textural characteristics of this soil is provided in Table 5.7.2. In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1.

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An illustration of the geographical extent of Podzol soils (as represented by Spodosols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (See Figure 5.16.5). Podzols are widespread in Northern Europe. The simulation framework is set up to enable this scenario to be run with either Danish or German (e.g. proximity to Schleswig Holstein) manure management defaults. Figure 5.7.1. Location of the proposed Ringkøbing scenario (1) representing Danish agriculture Figure 5.7.2. Photographs of landscape typically used for livestock grazing in the Ringkøbing region of Denmark (Source: MFAF, 2003)

In textural and climatic terms, this scenario has similarities to the FOCUS SW D3 scenario which is also represented in the Zone 2 Sand scenario. However, the sand scenario is intended to represent a hydrological situation in which surface water is

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vulnerable as a result of drainage. The scenario based in Denmark is intended to represent a hydrological situation in which vulnerability is primarily associated with leaching to groundwater. The geographical extent of the D3 scenario developed as a component of the FOCUS SW scenario development procedure is also considered relevant to the Danish scenario and is provided within Figure 5.7.3 It should be noted that the maps do not suggest that this scenario is relevant to 100% of the areas highlighted. Rather they indicate that in any of the areas highlighted, some part of the agricultural landscape corresponds to the general soil texture and climate combination represented within the scenario. The map illustrated in Figure 5.7.3 primarily illustrates the following range of conditions:

• >65% sand and <18%clay • 6.6 – 10ºC Average spring and autumn temperature

Table 5.7.2. Textural characterisation of sandy clay loam employed in Zone 2 scenario based upon Ringkøbing, Denmark (based upon MFAF, 2003)

Horizon Boundary Depths

(cm)

Clay content

(%)

Silt Content

(%)

Sand Content

(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

Ap 0-30 4 13 83 1.58 1.52 E 30-47 5 9 86 0.94 1.54 Bs 47-81 4 6 90 0.29 1.50

Cg1 81-105 3 2 95 0.12 1.56 2Cg2 105-140 21 44 35 0.23 1.60

Figure 5.7.3 Illustration of geographical significance of FOCUS Scenario D3 (FOCUS SW, 2001)

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Site/regional hydrology It is likely that this region is dominated by a hydrological situation similar to that described by the FOCUS SW Working Group (2003) for scenario D3. A ‘snapshot’ summary of this scenario is provided below, amended to represent the Danish scenario context where relevant:

• Climate: Temperate with moderate precipitation. • Soil type: Sands with small organic carbon content. Free drainage to groundwater. • Surface water bodies: Field ditches. • Landscape: Level land • Crops: Grass, winter & spring cereals, winter and spring oilseed rape, potatoes, sugar

beet, field beans, vegetables, legumes, maize, pome/stone fruit. Typical Water Management: No specific water management practices. Excess water is discharged to surface water. Climate profile A summary of meteorological conditions within Ringkøbing county (Studsgard) is given in Figure 5.7.4. The scenario experiences relatively low winter temperatures and has an average annual temperature of 7.5 °C. Average annual rainfall is 782 mm (Met Office, 1996).

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Figure 5.7.4. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Studsgard, representing Ringkøbing county (DK) during the period 1931-60 (Source: Met Office, 1996) Cropping practices A wide range of crops are grown in this region – Winter & spring cereals, winter and spring oilseed rape, potatoes and sugar beet are typical. However, a significant proportion of the agricultural landscape is also devoted to pasture. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established

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based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals considering development dates proposed for the D4 (Skousbo, Denmark) scenario in FOCUS SW (FOCUS, 2001):

• Planting: 7 September • Emergence: 21 September • Harvest: 20 August

As spring cereals may also be cultivated in this region, the following production cycle is proposed considering development dates proposed for the D4 (Skousbo, Denmark) scenario in FOCUS SW (FOCUS, 2001):

• Planting: 11 April • Emergence: 25 April • Harvest: 25 August

In Demark, restriction exist that prohibit applications between harvest and 1st February. In recognition of these restrictions it is recommended that applications to arable land coincide with planting of spring cereals (e.g. on or around 11 April). For applications to grassland, it is assumed that timing would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed taking into account dates reported within the FOCUS Groundwater Hamburg scenario:

• First harvest: 31 May • Second harvest: 15 July • Third harvest: 31 August

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 31 May). Local/Regional restrictions This scenario has direct relevance to Danish manure management practices and indirect relevance to practices in Germany (Schleswig Holstein). Summaries of manure management approaches in Denmark and Germany are provided in further detail in Sections 6.2 and 6.5, respectively. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.8 Zone 2 – Sandy loam 1 Bretagne was selected as a representative region for the Cfb climate in the Central zone (Zone 2). Amongst the 12 scenarios it is ranked:

• 2nd for bovine production • Very minor ovine production • 2nd for swine production • 1st for avian production

Employing the criteria outlined Section 5.2 it is proposed as a base set scenario for assessing bovine, swine and avian treatments. Site agricultural profile Information on livestock distribution (see Figure 5.8.1) indicates that the region is primarily associated with poultry (granivore) production. The scenario is loosely based upon the St. Brieuc region (Cotes du Nord), where considerable cattle production (dairy and beef) also occurs (see Figure 5.8.2).

Figure 5.8.1. The distribution of agricultural activity in Bretagne, France during 2000 (Source: Agreste, 2003). Soil characteristics The regionally dominant major soil class is the Orthic Luvisol. In Western Europe they are found on flat and undulating terrain on loess, glacial till or residual silts from various parent geology (FAO, 1981). Soils in this area are generally of medium texture within rolling to hilly landscapes (8-30% slope). Unfortunately, it was not possible to obtain detailed soil data from INRA. Instead, it was necessary to rely upon an illustrative Orthic Luvisol soil profile obtained from the FAO Soil Map of the World (1981) for a site in Belgium (Zottegem). The dominant soil unit in this area is coded as Lo73-2ab, a code shared by soil units in Bretagne in a region to the west of St. Brieuc. The dominant soil unit in the St. Brieuc area is Lo72-2b. Both soil units are dominated by medium textured soils dominated by Orthic Luvisols but including associations with Dystric Cambisols (Lo72-2b) or Gleyic Luvisols (Lo73-2ab). The agricultural context of both sets of soil units is similar being based upon arable cultivations and grassland. However, the lithology of the soils differ somewhat with soil unit Lo72-2b being derived from crystalline and metamorphic rock whereas Lo73-2ab is mainly derived from loess and tertiary sands and clays. It should be kept in mind that scenarios are not intended to be site specific but provide general pedoclimatic assessments of behaviour. A summary of textural characteristics for this soil is provided in Table 5.8.1.

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Table 5.8.1. Textural characterisation of sandy loam employed in Zone 2 scenario based upon St. Brieuc, France (FAO, 1981)

Horizon Boundary Depths

(cm)

Clay Content

(%)

Silt Content

(%)

Sand Content

(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

Ap 0-30 11.0 16.0 73 0.73 1.58 Bt1 30-95 22.9 19.8 57.3 0.24 1.60 Bt2 95-170 17.1 15.2 67.7 0.05 1.63

Figure 5.8.2. Location of the proposed St. Brieuc scenario (1) representing agriculture in Bretagne, Northwest France In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Luvisols (as represented by Alfisols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (See Figures 5.16.5). Luvisols are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Cfb region).

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Site/regional hydrology A ‘snapshot’ summary of this scenario is provided below:

• Climate: Warm temperate with moderate precipitation. • Soil type: Medium loam with field drains. Hard, impermeable rock at depth.

Seasonally waterlogged by water perched over the impermeable substrate. • Surface water bodies: First order streams and ponds. • Landscape: Gently to moderately sloping, undulating land. • Crops: Grass, winter & spring cereals, winter and spring oilseed rape, legumes,

maize, pome/stone fruit, sunflowers. Typical Water Management: It is typical for agricultural land in this region to have drainage systems based around a need to alleviate both seasonal water logging and drought stress and salinity issues. Drainage systems constitute a combination of ditches and networks of pipe and/or mole drains. As the soil at this site is relatively free draining it is assumed that drainage networks operate only under prolonged or intense periods of rainfall. It is assumed that due to the regional geological conditions, recharge water is not directed ultimately at groundwater but re-emerges as surface water as a result of seepage. Climate profile A summary of meteorological conditions within the Cotes du Nord region (Dinard) is given in Figure 5.8.3. The region receives an annual average rainfall of 726 mm and has a mean annual temperature of 10.9 ºC (Met Office, 1996).

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Figure 5.8.3. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Dinard, Bretagne, France during the period 1931-60 (Source: Met Office, 1996) Cropping practices A range of crops are grown in this region – winter and spring cereals, winter and spring oilseed rape, legumes and maize are typical. However, a significant proportion of the agricultural landscape is also devoted to pasture. As described in Section

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7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals:

• Planting: 14 October • Emergence: 22 October • Harvest: 24 July

As spring cereals may also be cultivated in this region, the following production cycle is proposed:

• Planting: 9 March • Emergence: 21 March • Harvest: 28 July

As a potential worst case (cooler temperatures, greater potential to encounter saturated soil conditions during the three month period immediately following application) it is recommended that applications to arable land coincide with planting of winter cereals (e.g. on or around 14 October). For applications to grassland, it is assumed that timing would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed taking into account dates reported within the FOCUS Groundwater Châteaudun scenario:

• First harvest: 15 May • Second harvest: 30 June • Third harvest: 15 August • Fourth harvest: 30 September

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 May). Local/regional restrictions This scenario has direct relevance to French manure management practices. Summaries of manure management approaches in France are provided in further detail in Section 6.4. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.9 Zone 2 - Sandy loam 2 North Yorkshire was selected as a representative region for the Cfb climate in the Central zone (Zone 2) with specific relevance to the UK. Amongst the 12 scenarios it is ranked:

• 11th for bovine production • 5th for ovine production • 8th for swine production • Very minor avian production

Employing the criteria outlined Section 5.2 it is not considered a base set scenario but could be employed to investigate specific issues within a UK context. Site agricultural profile The nominal site location for the North Yorkshire scenario is the area surrounding High Mowthorpe (see Figure 5.9.1). An ADAS research station is located on the site, which will enable potential for longer-term research including validation efforts. The site is primarily associated with beef production on permanent grassland, although other livestock farming (e.g. sheep) take place. To illustrate this further, the geographical prevalence of different livestock production groups in the England is illustrated in Figure 5.9.2-5.9.6.

Figure 5.9.1. A map of North Yorkshire showing the location of the High Mowthorpe scenario site (1). Soil characteristics The regionally dominant major soil class is the Eutric Cambisol. This major soil class occurs extensively in Southern Europe (particularly in Spain, Italy and France) under Mediterranean climate conditions but also occurs as far north as the United Kingdom on a small scale and in Southern Sweden under cool marine and cool temperate conditions, respectively. Within the UK these soils also occur in Suffolk, The

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Cotswolds, Lincolnshire, Derbyshire and Nottinghamshire. In Yorkshire, Derbyshire and Nottinghamshire dairy production and other forms of livestock production are significant (FAO, 1981).

Figure 5.9.2. Geographical prevalence of bovine production in England (Defra Agricultural Atlas)

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Figure 5.9.3. Geographical prevalence of ovine production in England (Defra Agricultural Atlas)

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Figure 5.9.4. Geographical prevalence of swine production in England (Defra Agricultural Atlas)

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Figure 5.9.5. Geographical prevalence of poultry production in England (Defra Agricultural Atlas)

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Figure 5.9.6. Representation of the landscape class typically used for livestock production in North Yorkshire (High Mowthorpe) (Source: UK Aquatic Landscapes Project; Anon, 2003). ADAS High Mowthorpe is based within a landscape of chalk and limestone plateaux, with rolling 'wolds' and plateaux with 'dry' valleys (see Figure 5.9.6). Soils are typically shallow to moderately deep loams overlying chalk and limestone. Three main soil types dominate the area surrounding ADAS High Mowthorpe: the Landbeach, Arrow and Newport series. These soils are typically very shallow and were considered to represent a very specific set of circumstances associated with chalk plateaux. Instead, alternatives that would be more closely associated with the edge of the Wold valleys were considered that may better represent a more generalised set of conditions. The soil class that was selected for this assessment was the Hunstanton series. The physical and chemical properties of this soil is summarised in Tables 5.9.1. Table 5.9.1. Textural characterisation of sandy loam employed in Zone 2 scenario based upon High Mowthorpe, England (based upon SEISMIC, Hallett et al., 1995)

Horizon Boundary Depths

(cm)

Clay Content

(%)

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(%)

Sand Content

(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

A 0-35 17 18 65 1.9 1.34 E 35-60 18 20 62 0.7 1.41 Bt 60-120 31 29 40 0.4 1.39 BC 120-150 28 41 31 0.2 1.54

In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Cambisols (as represented by Inceptisols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is

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provided in the scenario overview in Section 5.16 (See Figure 5.16.4). Cambisols are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Cfb region). This scenario is considered to be solely relevant to the UK. The prevalence of sandy loam soils in the UK is also illustrated in Figure 5.9.8, employing the SEISMIC soils database (SEISMIC; Hallett et al., 1995). Site/regional hydrology The hydrological conditions associated with this site are summarised below:

• Climate:Temperate with moderate precipitation. • Soil type: The scenario is intended to represent Wolds valleys that are characterised

by relatively free draining light sandy loam with small organic matter content at depth. Chalk plateaux consist of similar soils but with very shallow profiles.

• Surface water bodies: First order streams and ponds. • Landscape: Gently to moderately sloping, undulating land. • Crops: Winter cereals, winter & spring oilseed rape, sugar beet, potatoes, field

beans, vegetables, legumes, maize. Typical Water Management: This scenario has no specific water management practices. A more extensive summary of hydrological conditions abstracted from the SEISMIC database is provided in Table 5.9.2.

Figure 5.9.8. SEISMIC Medium loam soils (Hallett et al., 1995)

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Table 5.9.2. Summary of Hydrological Characteristics of Soils Relevant to the region surrounding ADAS High Mowthorpe Soil HOST Class Leaching Class Runoff Class Plateaux soils Arrow 7: Free draining

permeable soils in unconsolidated sands or gravels with groundwater at less than 2 m from the surface.

‘A3’: Soils with low adsorption/retention potential over sands or gravels containing shallow aquifers with relatively short travel times to the groundwater surface.

‘S4v’: Soils with low run-off potential but very low adsorption potential

Landbeach 7: Free draining permeable soils in unconsolidated sands or gravels with groundwater at less than 2 m from the surface.

‘A3’: Soils with low adsorption/retention potential over sands or gravels containing shallow aquifers with relatively short travel times to the groundwater surface.

‘S4m’ Soils with low run off potential and moderate adsorption potential.

Newport 5: Free draining permeable soils in unconsolidated sands or gravels with relatively high permeability and high storage capacity.

‘A6’: Soils with a moderately high adsorption/retention potential over loamy substrates that may contain shallow aquifers with relatively short travel times to the groundwater surface.

‘S5v’ Soils with very low run-off potential but very low adsorption potential

Representative edge of valley soil Hunstanton 1 Free draining

permeable soil on chalk and chalky substrates with relatively high permeability and moderate storage capacity

‘A11’ Moderate shallow soils with a moderate adsorption/retention potential but over rocks which normally contain deep aquifers with long travel times to the groundwater surface

‘S5m’ Soils with very low run-off potential and moderate adsorption potential

Climate profile A summary of meteorological conditions within the North Yorkshire region (Scarborough) is given in Figure 5.9.8. Annual average rainfall at the location is 647 mm, making it the driest of the UK scenarios. The average temperature is 9.8ºC (Met Office, 1996).

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Figure 5.9.8. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Scarborough Hill during the period 1931-60 (Source: Met Office, 1996) Cropping practices A wide range of crops are grown in this region - Winter cereals, winter & spring oilseed rape, sugar beet and potatoes are typical. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. Advice from ADAS arable consultants was taken in order to define the following production cycle for winter cereals in this area:

• Planting: 30 September • Emergence: 14 October • Harvest: 15 August

It is, therefore, recommended that applications to arable land coincide with planting of winter cereals (e.g. on or around 30 September). For applications to grassland, it is assumed that timing would coincide approximately with the period immediately following harvest of the sward:

• First harvest: 31 May • Second harvest: 15 July • Third harvest: 31 August

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 31 May). Local/regional restrictions This scenario has direct relevance to manure management practices in the UK. Summaries of manure management approaches in the UK are provided in further detail in Section 6.12. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.10 Zone 2 – Sandy clay loam 1 Midi-Pyrenees was selected as a representative region for the Cfb climate in the Central zone (Zone 2). Amongst the 12 scenarios it is ranked:

• 6th for bovine production • 3rd for ovine production • 10th for swine production • 7th for avian production

Employing the criteria outlined Section 5.2 it is proposed as a base set scenario for assessing ovine treatments. Site agricultural profile Information on the distribution of agricultural activity in the region is presented in Figure 5.10.1. The scenario is loosely based upon the Rodez region in Aveyron (See Figure 5.10.2). This region is typically characterised by sheep, goat and beef production.

Figure 5.10.1. The distribution of agricultural activity in Midi-Pyrenees, France during 2000 (Source: Agreste, 2003). Soil characteristics The regionally dominant major soil class is the Dystric Cambisol. These soils occur on rolling to hilly topography in the Limousin area of France where they are devoted mainly to arable crops and livestock production on good quality pastures and rough grazing. There is some small scale production of fruit and vines. Pasture production potential is approximately 10 t DM/ha with current stocking rates of 75-125 livestock units/100 ha utilised agricultural area. A stony phase on hilly topography occurs in the Massif Central in France where agricultural land consists mainly of livestock production from pastures capable of exceeding 10 t DM/ha under a mean annual rainfall in excess of 1,000 mm. In the Pyrenees region land use is restricted to livestock production for rough grazing. This major soil class dominates a large area of Western and Central Europe. They are developed from a wide variety of geology and are found in a great diversity of agricultural landscapes (FAO, 1981). The agricultural soils in this area are generally coarse to medium in texture and are associated with rolling to hilly landscapes (slopes 8-30%). Unfortunately, it was not possible to obtain detailed soil data from INRA. Instead, it was necessary to rely upon related soil profiles. Within the FAO Soil Map of the World the Rodez region is characterised a number of general soils that are dominated by medium to finely texture Dystric and Eutric Cambisols that is shared with the Roujan region that is the

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basis of the FOCUS Surface Water R4 scenario. In the absence of more detailed, site-specific soil data, this high quality modelling dataset was substituted. It should be kept in mind that scenarios are not intended to be site specific but provide general pedoclimatic assessments of behaviour. A summary of textural characteristics of this soil is provided in Table 5.10.1.

Figure 5.10.2. Location of the proposed Rodez scenario (1) representing agriculture in Midi-Pyrenees, Southwest France Table 5.10.1. Textural characterisation of sandy clay loam employed in Zone 2 scenario based upon Rodez, France (based upon FOCUS SW, 2001)

Horizon Boundary Depths

(cm)

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(%)

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(%)

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(%)

Bulk Density (kg/dm3)

A1 0-30 25 22 53 0.60 1.52 A2 30-60 25 22 53 0.60 1.50

60-170 7 24 69 0.08 1.49 In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Cambisols (as represented by Inceptisols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (See Figure 5.16.4). As can be

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seen, the Cambisols are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Cfb region). The simulation framework is set up to enable this scenario to be run with either French or Spanish (e.g. proximity to Aragon) manure management defaults. . Site/regional hydrology It is likely that this region is characterised by a hydrological situation similar to that described by the FOCUS SW Working Group (2003) for scenario R4. A ‘snapshot’ summary of this scenario is provided below: Climate: Warm Temperate/Mediterranean with moderate precipitation. Soil type: Free draining calcareous medium loam over loose calcareous sandy

substrate. Surface water bodies: First order streams. Landscape: Moderately sloping hills with some terraces. Crops: Winter & spring cereals, field beans, vegetables, legumes, maize,

vines, pome/stone fruit, sunflower, soybean, citrus, olives. Typical Water Management: Water management systems are typically based upon discharge of excess water into networks of ditches feeding streams. Irrigation is typical in certain cropping regimes via sprinkler and trickle systems. Climate profile The region receives slightly less rainfall than Bretagne (average annual total: 659 mm), and is characterised by considerably warmer summers (mean annual temperature: 12.6ºC; Met Office, 1996). A climatic summary is provided in Figure 5.10.3.

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Figure 5.10.3. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Toulouse, Midi-Pyrenees, France during the period 1931-60 (Source: Met Office, 1996)

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Cropping practices A wide range of crops are grown in this region – Winter & spring cereals, field beans, vegetables and maize are typical. However, a significant proportion of the agricultural landscape is also devoted to rough grazing. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals:

• Planting: 27 October • Emergence: 10 November • Harvest: 15 July

As spring cereals may also be cultivated in this region, the following production cycle is proposed:

• Planting: 1 March • Emergence: 15 March • Harvest: 20 July

As a potential worst case (cooler temperatures, greater potential to encounter saturated soil conditions during the three month period immediately following application) it is recommended that applications to arable land coincide with planting of winter cereals (e.g. on or around 27 October). For applications to grassland, it is assumed that timing would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed taking into account dates reported within the FOCUS Groundwater Châteaudun scenario:

• First harvest: 15 May • Second harvest: 30 June • Third harvest: 15 August • Fourth harvest: 30 September

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 May). Local/regional restrictions This scenario has direct relevance to French manure management practices and indirect pedoclimatic relevance to areas in Spain (Cataluña and Aragon). Summaries of manure management approaches in France and Spain are provided in further detail in Section 6.4 and 6.10. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.11 Zone 2 – Sandy clay loam 2 Ireland was selected as a representative region for the Cfb climate in the Central zone (Zone 2). Amongst the 12 scenarios it is ranked:

• 1st for bovine production • 1st for ovine production • 4th for swine production • 5th for avian production

Employing the criteria outlined Section 5.2 it is proposed as a base set scenario for assessing bovine and ovine treatments. Site agricultural profile NUTS2 level resolution livestock production data were used to assess suitability of candidate regions for development of detailed scenarios. Where higher resolution data are available this can guide the development of more relevant scenarios. In the case of Ireland further data have been obtained from the Irish Central Statistics Office. Based on an analysis of livestock production statistics (see Table 5.11.1), it appears that the highest production density (for combined cattle, sheep and pigs) occurs in Southeast Ireland. Potential scenario locations included Clashmore (West Waterford) and Clonroche (Wexford) where detailed soil data were available from Teagasc. These sites are specifically associated with intensive dairy and pig production on shallow sandy free-draining soils (Humphreys, et al., 2003). The locations of each site are summarised in Figure 5.11.1. The Clashmore site was selected on the basis of lower soil organic carbon content and closer proximity to Cork, for which weather data was expected to be more readily available. Table 5.11.1. Summary of regional livestock production data, pasture and grazing areas and livestock density for Ireland, 2002.

2002 Border Midland West Mid-east & Dublin

Mid-West South-East

South-West

Total Cattle

970.9 769.6 988.5 578.3 1031.6 1242.7 1410.7

Total Sheep

1285.6 558.1 1923.6 1056.4 248 1249.9 888.1

Total Pigs 532 234.9 56.3 87.6 101.1 347 410.5Other 13 9.2 14.2 14.4 12.9 19.2 13.4Total 2801.5 1571.8 2982.6 1736.7 1393.6 2858.8 2722.7Total pasture and grazing area (ha)

489.5 263.4 534.8 226.6 354.7 359.3 505.6

Density (heads/ha)

5.7 6.0 5.6 7.7 3.9 8.0 5.4

(Source: Central Statistics Office, 2003) Soil characteristics The regionally dominant major soil class is the Dystric Cambisol. This major soil class dominates a large area of Western and Central Europe. They are developed from a wide variety of geology and are found in a great diversity of agricultural landscapes (FAO, 1981). The agricultural soils in this area are generally coarse to medium in texture and are associated with level to hilly landscapes (slopes 0-30%). The Clashmore soil is relatively shallow, free-draining and coarse in texture (Humphreys, et al., 2003). Relief at these locations is typically 3-4º. A summary soil

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profile based upon information obtained from Teagasc (Diamond, Pers. Comm.) is provided in Table 5.11.2.

Figure 5.11.1. Location of the Clashmore (1) and Clonroche (2) candidate scenarios representing agriculture in Southwest Ireland Table 5.11.2. Textural characterisation of sandy clay loam employed in Zone 2 scenario based upon Clashmore, Ireland (Diamond, Pers. Comm)

Horizon Boundary Depths

(cm)

Clay Content

(%)

Silt Content

(%)

Sand Content

(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

A1 0-20 18 31 51 2.0 1.42 A2 20-32 19 31 50 1.4 1.47 E 32-50 19 37 44 0.5 1.45 Bs 50-90 17 35 48 0.4 1.57 C >90 23 33 44 0.2 1.77

In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Cambisols (as represented by Inceptisols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (see Figure 5.16.4). As can be seen, the Cambisols are widespread throughout Europe. However, for climatic

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reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Cfb region). The simulation framework is set up to enable this scenario to be run with either Irish or UK (e.g. Northern Ireland) manure management defaults. . Site/regional hydrology There are no counterparts within the scenario development exercise of the FOCUS Surface Water or Groundwater Working Groups. The Clashmore site is hydrologically characterised as follows:

• Climate: Temperate with high precipitation. • Soil type: Coarsely textured • Surface water bodies: First order streams. • Landscape: Gently sloping pasture surrounded by moderately sloping hills. • Crops: Grass

Typical Water Management:

o No specific water management practices. This scenario has a number of similarities with the scenario based in Wales (Zone 2 – Clay loam). However, the Welsh scenario is characterised by shallow hard rock geology in which run-off losses are of greatest significance. By contrast, this scenario is intended to provide a more relevant basis for assessing vulnerability of groundwater. Climate profile A summary of meteorological conditions within the region (Cork) is given in Figure 5.11.2. The region represents a worst-case scenario in terms of rainfall, with an annual average rainfall of 1048 mm (Met Office, 1996). In addition, the region is relatively mild, with a mean annual temperature of 10.1ºC (Met Office, 1996).

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Figure 5.11.2. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Cork, County Cork, Ireland during the period 1931-60 (Source: Met Office, 1996)

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Cropping practices In comparison with other scenarios a more limited range of crops are grown in this region – of which potatoes, sugar beet, winter and spring cereals and oilseed rape are typical. However, the majority of the agricultural landscape is devoted to pasture. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals:

• Planting: 7 October • Emergence: 17 October • Harvest: 1 August

As spring cereals may also be cultivated in this region, the following production cycle is proposed:

• Planting: 25 March • Emergence: 1 April • Harvest: 20 August

As a potential worst case (cooler temperatures, greater potential to encounter saturated soil conditions during the three month period immediately following application) it is recommended that applications to arable land coincide with planting of winter cereals (e.g. on or around 7 October). In reality, in Ireland the majority of manure and slurry is applied to grassland. It is assumed that timing would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed:

• First harvest: 15 May • Second harvest: 15 July • Third harvest: 15 September

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 May). Local/regional restrictions This scenario has direct relevance to Irish manure management practices and indirect pedoclimatic relevance to areas in the United Kingdom (Northern Ireland). Summaries of manure management approaches in Ireland and the United Kingdom are provided in further detail in Sections 6.6 and 6.12. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.12 Zone 2 – Sandy silt loam Brandenburg was selected as a representative region for the Cfb climate in the Central zone (Zone 2). Amongst the 12 scenarios it is ranked:

• 10th for bovine production • Very minor ovine production • 7th for swine production • 9th for avian production

Employing the criteria outlined Section 5.2 it is not considered a base set scenario but could be employed to investigate specific issues within a German context. This scenario may be considered to have broader pedoclimatic context that may also be of relevance to Poland and the Czech Republic. Site agricultural profile A map of Northern Germany showing the location of the Brandenburg region is given in Figure 5.12.1. No type-site has been identified for this scenario. This region is dominated by arable agriculture but has a significant contribution to regional dairy, beef, pork and poultry production.

Figure 5.12.1. A map of Northern Germany showing the location of the Brandenburg region Soil characteristics The regionally dominant major soil class is the Dystric Cambisol. These soils occur on level topography in the south centre of the central uplands of Germany. Stony and lithic phases can occur on rolling to hilly topography in Germany west of Brandenburg where they may be subject to erosion. Land use comprises varying proportions of arable, pasture, forestry and rough grazing. Cattle stocking rates vary from 50-125 livestock units per 100 ha of utilisable agricultural area. With intensive fertilisation, including an application of 220 kg/ha nitrogen stocking rates of 200

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livestock units per 100 hectare utilisable agricultural area are possible. Barley, wheat and potatoes are the main crop. In Brandenurg, these soils occur mainly on rolling to hilly topography. A high proportion of the soils are under arable cropping, including barley, wheat, rye, potatoes and some vines and fruit. Meadows and pastures are also important with forestry occupying upper slopes (FAO, 1981). A summary soil profile based upon information obtained from FAL Braunschweig (Rogasik, Pers. Comm.) is provided in Table 5.12.1. This soil profile was not based in Brandenburg (obtained from Braunschweig in Nieder Sacshen) but was used as a representative Dystric Cambisol soil for this region. Table 5.12.1. Textural characterisation of sandy silt loam employed in Zone 2 scenario based upon Brandenburg, Germany (Rogasik, Pers. Comm)

Horizon Boundary Depths

(cm)

Clay Content

(%)

Silt Content

(%)

Sand Content

(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

Ah1 0-35 6.3 46.7 47.0 1.6 1.48 Ah2 35-50 5.5 45.5 49.0 0.5 1.53 E3 50-70 5.2 48.8 46.0 0.4 1.54 E4 70-90 4.6 7.4 88.0 0.1 1.64

CV3 C35 90-120 5.9 8.1 86.0 0.1 1.64 CV3 C36 120-150 4.1 2.9 93.0 0.04 1.66

C4 7 150-180 3.8 3.2 93.0 0.03 1.66 In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Cambisols (as represented by Inceptisols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in section 5.16. (see Figure 5.16.4). Cambisols are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Cfb region). The simulation framework is set up to enable this scenario to be run solely with German manure management defaults. However, this scenario could be viewed as having broader pedoclimatic relevance that could include Poland and the Czech Republic. Site/regional hydrology A general hydrological characterisation for this scenario is provided below:

• Climate: Temperate with moderate precipitation.. • Soil type: Medium loam, greater permeable at depth. • Surface water bodies: First order streams and ponds. • Landscape: Gently sloping, undulating land. • Crops: Grass, winter & spring cereals, winter and spring oilseed rape, potatoes, sugar

beet, field beans, vegetables, legumes, maize, pome/stone fruit. Typical Water Management: No specific water management practices are assumed.

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Climate profile A summary of meteorological conditions within the Brandenburg region (Berlin) is given in Figure 5.12.2. The region experiences a continental climate, with relatively low winter temperatures but comparatively warm summers, resulting in an average annual temperature of 9°C. Average annual rainfall is relatively low at 603 mm.

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Figure 5.12.2. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Berlin, representing Brandenburg (D) during the period 1931-60 (Source: Met Office, 1996) Cropping practices In common with many other northern European scenarios the typical profile of arable agriculture typically includes cultivation of potatoes, sugar beet, winter and spring cereals and oilseed rape. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals:

• Planting: 12 October • Emergence: 1 November • Harvest: 10 August

As spring cereals may also be cultivated in this region, the following production cycle is proposed:

• Planting: 10 March • Emergence: 1 April • Harvest: 20 August

As a potential worst case (cooler temperatures, greater potential to encounter saturated soil conditions during the three month period immediately following application) it is recommended that applications to arable land coincide with planting of winter cereals (e.g. on or around 12 October). It is assumed that timing of applications to grassland would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed:

• First harvest: 31 May

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• Second harvest: 15 July • Third harvest: 31 August

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 31 May). Local/regional restrictions This scenario has direct relevance to Germany manure management practices. Summaries of manure management approaches in Germany are provided in further detail in Section 6.5. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.13 Zone 2 - Clay loam The Clwyd, Dyfed, Gwynedd and Powys region was selected as a representative region for the Cfb climate in the Central zone (Zone 2). Amongst the 12 scenarios it is ranked:

• 4th for bovine production • 2nd for ovine production • Very minor swine production • Very minor avian production

Employing the criteria outlined Section 5.2 this is considered a base set scenario for ovine treatments but could also be employed to investigate specific issues within a UK context. Site agricultural profile The site location for the Clwyd, Dyfed, Gwynedd and Powys scenario is Pwllpeiran (see Figure 5.13.1 [location 2]). An ADAS research station is located on the site, which will enable access to long-term meteorological data as well as provide high quality advice on local agricultural conditions and practices. The site is primarily associated with sheep farming on permanent grassland. To illustrate this further, the geographical prevalence of different livestock production groups in the Wales is illustrated in Figure 5.13.2-3.

Figure 5.13.1. Map of Wales and Southwest England, showing the locations of the Zone 2 Clay (Site 1) and Zone 2 Clay loam (Site 2) scenario sites. Soil characteristics The regionally dominant major soil class is the Dystric Cambisol. This major soil class dominates a large area of Western and Central Europe. In the United Kingdom they are most extensive in Wales, Devon and Cornwall and also in the southern uplands of Scotland and in Aberdeenshire. Land use consists of pasture, rough

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grazing and forestry. They are developed from a wide variety of geology and are found in a great diversity of agricultural landscapes. Also of immediate local relevance are Placic Podzols, which occur within the United Kingdom and Ireland under a mean annual rainfall in excess of 1,500 mm. In the Scottish Highlands, the English Pennines, the Welsh Mountains and the west Donegal mountain regions of Ireland, land use is limited to extensive sheep grazing on rough pasture (FAO, 1981).

Figure 5.13.2. Geographical prevalence of bovine production in Wales (EDINA Agcensus) ADAS Pwllpeiran is based within a hard rock landscape with gentle to moderately sloping hills and valleys with moderately deep, free-draining loams over hard rock. Slope steepness typically ranges from 2-5º. The area is characterised by the Manod series soil. A summary soil profile based upon information obtained from SEISMIC (Hallett et al., 1995) is provided in Table 5.13.1. According to the SEISMIC database this soil series is typically characterised by a C horizon at a depth between 0.6 and 1.3 m in depth over hard rock. A representation of this landscape can be found in Figure 5.13.4. This soil has been characterised within SEISMIC as follows:

• Host Class 17: Relatively free draining soils with a large storage capacity over hard impermeable rocks with no storage capacity

• Leaching Class ‘N’: Soils over hard non-aquifer bearing rocks • Run-off Class ‘S3m’: Soils with moderate run-off potential and moderate adsorption

potential This scenario cannot be employed as a groundwater scenario and is solely used to provide simulations of run-off to surface waters.

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Figure 5.13.3. Geographical prevalence of ovine production in Wales (EDINA Agcensus) Table 5.13.1. Textural characterisation of clay loam employed in Zone 2 scenario based upon Pwllpeiran, Wales (Manod series; SEISMIC; Hallett et al., 1995)

Horizon Boundary Depths

(cm)

Clay Content

(%)

Silt Content

(%)

Sand Content

(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

A 0-25 27 43 30 5.6 0.99 Bpodz 25-60 20 43 37 2.8 0.89

C 60-130 23 40 37 1.0 1.24 Table 5.13.2. Summary of Hydrological Characteristics of Manod Soil Relevant to the region surrounding ADAS Pwllpeiran Soil HOST Class Leaching Class Runoff Class Manod 17 Relatively free

draining soils with a large storage capacity over hard impermeable rocks with no storage capacity

‘N’ Soils over hard non-aquifer bearing rocks

‘S3m’ Soils with moderate run-off potential and moderate sorption potential

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In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Cambisols (as represented by Inceptisols within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (see Figure 5.16.4). Cambisols are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Cfb region). The simulation framework is set up to enable this scenario to be run solely with UK manure management defaults.

Figure 5.13.4. A photograph representing the landscape class typically used for livestock production in Clwyd, Dyfed, Gwynedd and Powys (Pwllpeiran) (Source: UK Aquatic Landscapes Project). Site/regional hydrology A general hydrological characterisation for this scenario is provided below:

• Climate: Temperate with high precipitation. • Soil type: Finely textured soil over impervious substrata • Surface water bodies: First order streams. • Landscape: Gently sloping pasture surrounded by moderately sloping hills. • Crops: Grass

Typical Water Management: No specific water management practices. Climate profile A summary of meteorological conditions within the Clwyd, Dyfed, Gwynedd and Powys region (Aberporth) is given in Figure 5.13.6. Annual average rainfall at the location is 990 mm (Met Office, 1996). The average annual temperature is 10ºC (Met Office, 1996).

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Cropping practices Essentially, the entirety of manures in this region would be likely to be spread on grassland. In common with other scenarios it is assumed that timing of applications to grassland would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed:

• First harvest: 15 May • Second harvest: 15 July • Third harvest: 15 September

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 May).

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Figure 5.13.6. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Aberporth (representing Pwllpeiran) during the period 1931-60 (Source: Met Office, 1996) Local/regional restrictions This scenario has direct relevance to manure management practices in the UK. Summaries of manure management approaches in the UK are provided in further detail in Section 6.12. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.14 Zone 2 - Clay The Cornwall & Devon region was selected as a representative region for the Cfb climate in the Central zone (Zone 2) with specific relevance to the UK. Amongst the 12 scenarios it is ranked:

• 7th for bovine production • 4th for ovine production • Very minor swine production • 10th for avian production

Employing the criteria outlined Section 5.2 it is not considered a base set scenario but could be employed to investigate specific issues within a UK context. Site agricultural profile The site location for the Devon & Cornwall scenario is North Wyke near Okehampton (see Figure 5.13.1). An IGER (Institute of Grassland Ecology and Research) research station is located on the site, which will enable access to long-term meteorological data as well as advice on local agricultural conditions and practices. The area is particularly important for dairy and beef production. The agricultural context of production for this location is illustrated in Figure 5.14.2-5. Soil characteristics The regionally dominant major soil class is the Dystric Cambisol. However, the soils in the immediate vicinity of IGER North Wyke are considered Eutric Gleysols. Eutric Gleysol. This major soil class are largely confined to the United Kingdom (where livestock production is the main enterprise) but also occur in Italy within the Po valley. In the coastal zone of the southern United Kingdom arable cropping can be important in addition to livestock production from pastures. These soils are generally susceptible to cattle treading damage because of excess wetness and poor soil structure (FAO, 1981). IGER North Wyke is based within a landscape is a mixed, hard, fissured rock and clay landscapes with gentle to moderately sloping hills and ridges with moderately deep, free-draining loams mixed with heavy loams and clays in vales. This landscape is represented in Figure 5.14.5. Four main soil types dominate the area: the Hallsworth, Hallstow, Denbigh and Cherubeer series. The Hallstow and Denbigh series are relatively shallow soils and have not been considered as candidates for simulation. The Hallsworth series soil was selected as a basis for this scenario. Characteristics are summarised in Table 5.14.1. Further background information on the Hallsworth series soil is summarised in Table 5.14.2. In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Gleysols (as represented by Aquic suborders within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (see Figure 5.16.7). As can be seen, the Gleysols are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Cfb region). The simulation framework is set up to enable this scenario to be run solely with UK manure management defaults.

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Figure 5.14.2. Geographical prevalence of bovine production in England (Defra Agricultural Atlas)

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Figure 5.14.3. Geographical prevalence of ovine production in England (Defra Agricultural Atlas)

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Figure 5.14.4. Geographical prevalence of swine production in England (Defra Agricultural Atlas)

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Figure 5.14.5. Geographical prevalence of poultry production in England (Defra Agricultural Atlas)

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Figure 5.14.6. Representation of the landscape class typically used for livestock production in Devon and Cornwall (North Wyke) (Source: UK Aquatic Landscapes Project). Table 5.14.1. Textural characterisation of clay loam employed in Zone 2 scenario based upon North Wyke, England (Hallsworth series; SEISMIC; Hallett et al., 1995)

Horizon Boundary Depths

(cm)

Clay Content

(%)

Silt Content

(%)

Sand Content

(%)

Organic Carbon Content

(%)

Bulk Density (kg/dm3)

A 0-15 36 35 29 5.1 1.03 Bg1 15-50 46 38 16 0.8 1.36 Bg2 50-80 48 30 22 0.6 1.43 BC 80-150 41 43 16 0.5 1.42

Table 5.14.2. Summary of Hydrological Characteristics of Hallsworth Soil Relevant to the region surrounding IGER North Wyke Soil HOST Class Leaching Class Runoff Class Hallsworth 24: Slowly permeable,

seasonally waterlogged soils over slowly permeable substrates with negligible storage capacity.

‘C2’: Impermeable soils with a moderate to high adsorption/ retention potential, overlying non-aquifers that may conceal groundwater-bearing strata at depth.

‘S2m’:Soils with high run off potential but moderate adsorption potential.

Site/regional hydrology A ‘snapshot’ summary of this scenario is provided below:

• Climate: Temperate with moderate precipitation. • Soil type: Light loam, slowly permeable at depth and with field drains. Seasonal

water logging by water perched over the slowly permeable clay substrate. • Surface water bodies: First order streams and ponds. • Landscape: Gently sloping, undulating land. • Crops: Grass, winter cereals, winter oilseed rape, field beans.

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Typical Water Management: Agricultural land normally has a water management system to alleviate water logging. This includes drainage systems (pipe drains and mole drains) running to ditches.

Figure 5.14.7 Prevalence of clay soils in SEISMIC (Hallett et al., 1995) Climate profile A summary of meteorological conditions within the Devon and Cornwall region (Exeter) is given in Figure 5.14.8. Annual average rainfall at the location is 800 mm (Met Office, 1996). The site is the warmest of the UK scenarios, with an average annual temperature of 10.2ºC (Met Office, 1996). Cropping practices Although a large proportion of the landscape is devoted to pasture the cultivation of winter cereals, winter oilseed rape, field beans are typical. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals:

• Planting: 7 October • Emergence: 17 October • Harvest: 1 August

As a potential worst case (cooler temperatures, greater potential to encounter saturated soil conditions during the three month period immediately following application) it is recommended that applications to arable land coincide with planting of winter cereals (e.g. on or around 7 October).

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In reality the majority of manures in this region would be likely to be spread on grassland. In common with other scenarios it is assumed that timing of applications to grassland would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed:

• First harvest: 15 May • Second harvest: 15 July • Third harvest: 15 September

As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 May).

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Figure 5.14.8. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Exeter (representing North Wyke) during the period 1931-60 (Source: Met Office, 1996) Local/regional restrictions This scenario has direct relevance to manure management practices in the UK. Summaries of manure management approaches in the UK are provided in further detail in Section 6.12. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.15 Zone 3 - Sandy Loam The Etalae Suomi region was selected as a representative region for the Dfb climate in the Continental/Scandinavian zone (Zone 3). Amongst the 12 scenarios it is ranked:

• Very minor bovine production • Very minor ovine production • 9th for swine production • 8th for avian production

Although this scenario is not very significant in terms of production, it is representative of a cooler climate situation in which greater persistence of compounds may be expected. As a consequence, employing the criteria outlined Section 5.2, it is considered a base set scenario for swine and avian treatments but could also be employed to investigate potentially persistent bovine and ovine treatments. Site agricultural profile The site location for the Etalae Suomi scenario is Jokioinen (see Figure 5.15.1). This site is also a basis for a FOCUS Groundwater scenario. The scenario is considered to be broadly representative of livestock production in parts of Finland and Sweden but may also have climatic relevance to the Baltic States (Estonia, Latvia and Lithuania)

Figure 5.13.1. Map of Finland, showing the locations of the Zone 3 Sandy loam scenario site. Soil characteristics The regionally dominant major soil class are the Vertic Cambisols and Orthic Podsols. Vertic Cambisols occur on level topography under cold marine conditions and are mainly confined to southern Finland and Sweden. They constitute important agricultural soils in these regions although a high proportion of them are afforested.

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In southern Finland the Orthic Podsols are extensively forested with limited arable and pasture uses. In this region grass swards are liable to severe winter damage (FAO, 1981). The soil employed within this scenario is based upon the same soil profile employed in the FOCUS Groundwater Jokioinen scenario. This provides a relevant, high quality basis for modelling. Further background information on the Jokioinen soil can be found in Table 5.15.1. Table 5.15.1. Textural characterisation of clay loam employed in Zone 3 scenario based upon Jokioinen, Finland (FOCUS groundwater, 2000)

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Organic Carbon Content

(%)

Bulk Density (kg/dm3)

Ap 0-30 3.6 23.2 73.2 4.06 1.29 Bs 30-60 1.8 12.2 86.0 0.84 1.52

BC1 60-95 1.2 14.9 83.9 0.36 1.64 BC2 95-100 1.7 18.9 79.4 0.29 1.63 BC2 100-120 1.7 18.9 79.4 0.29 1.63 Cg 120-150 1.9 8.6 89.5 0.21 1.66

In order to develop a dataset required to support modelling with LEACHP the data provided above have been reinterpreted or supplemented as necessary employing pedotransfer functions embedded within the SOILPAR (v.2.0) soil properties software package (Acutis and Donatelli, 2003). The standard soil characterisation required to support simulations within VetCalc is provided in Appendix 1. An illustration of the geographical extent of Cambisols and Podsols (as represented by Inceptisols and Spodosls, respectively, within the Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003) is provided in the scenario overview in Section 5.16 (see Figures 5.16.4 and 5.16.5). Both soils are widespread throughout Europe. However, for climatic reasons, it should be recognised that this scenario is limited in relevance to Northern Europe (See Figure 5.1.2 – Climatic relevance can be considered to be limited to the Dfb region). The simulation framework is set up to enable this scenario to be run with Finnish and Swedish manure management defaults. Site/regional hydrology A ‘snapshot’ summary of this scenario is provided below:

• Climate: Cool continental with moderate precipitation. • Soil type: Permeable sandy loam • Surface water bodies: First order streams and ponds. • Landscape: Gently sloping, undulating land. • Crops: Grass, winter and spring cereals, winter oilseed rape, field beans, potatoes,

sugar beet. Typical Water Management: No specific water management strategies are associated with this scenario. Climate profile A summary of meteorological conditions within the Etalae Suomi region (based upon Tampere) is given in Figure 5.13.2. Annual average rainfall at the location is 573 mm

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(Met Office, 1996). The site is the coolest of the scenarios, with an average annual temperature of 4.1ºC (Met Office, 1996).

0

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Figure 5.13.2. A summary of monthly average daily maximum and minimum temperatures and average monthly rainfall at Tampere (representing Jokioinen) during the period 1931-60 (Source: Met Office, 1996) Cropping practices Cultivation of winter and spring cereals, winter oilseed rape, field beans, potatoes and sugar beet are typical. As described in Section 7.1.2, for pragmatic reasons, simulations have been consistently established based upon either pasture situations or applications of manures and slurries to cereals. The following production cycle is proposed based upon winter cereals (Jokioinen scenario, FOCUS 2000):

• Planting: 10 September • Emergence: 20 September • Harvest: 15 August

As spring cereals may also be cultivated in this region, the following production cycle is proposed considering development dates proposed for the Jokioinen scenario in FOCUS GW (FOCUS, 2000):

• Planting: 7 May • Emergence: 18 May • Harvest: 25 August

As a potential worst case (cooler temperatures, greater potential to encounter saturated soil conditions during the three month period immediately following application) it is recommended that applications to arable land coincide with planting of winter cereals (e.g. on or around 10 September). In common with other scenarios it is assumed that timing of applications to grassland would coincide approximately with the period immediately following harvest of the sward. The following dates are assumed:

• First harvest: 15 June • Second harvest: 15 July • Third harvest: 25 August

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As a pragmatic recommendation it is suggested that applications to grassland coincide with the first harvest period (e.g. on or around 15 June). Local/regional restrictions This scenario has direct relevance to manure management practices in Finland, and indirect pedoclimatic relevance to areas of Sweden. Summaries of manure management approaches in Finland and Sweden are provided in further detail in Sections 6.3 and 6.11. Nutrient management restrictions need to be kept in mind when developing simulations that deviate from the manure management defaults incorporated within VetCalc.

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5.16 Summary A summary is provided of the climatic characteristics of each of the 13 candidate sites in Figures 5.13.1 and 5.13.2. As can be seen, the candidate scenarios represent a wide range of potential climatic situations in Europe. The geographical extent of the soils orders (major soils classes) considered within these scenarios are summarised in a series of maps (see Figures 5.16.3-5.16.7). An overall summary of the geographical extent of four major soil classes considered within VetCalc scenarios is provided in Figure 5.16.8. It should be noted that Luvisols are subsumed within this map as secondary associations with Cambisols and Gleysols. This map is intended to convey general information on prevalence and dominance of soil classes. They are not necessarily indicative of where specific scenarios have broader pedoclimatic relevance which will be defined to a greater extent by textural and climatic constraints. Associations of scenarios and major soil classes were as follows: Fluvisols Zone 1 – Sandy silt loam Cambisols Zone 1 - Clay loam Zone 2 - Sandy loam 2 Zone 2 - Sandy clay loam 1 Zone 2 - Sandy clay loam 2 Zone 2 - Sandy silt loam Zone 2 - Clay loam Zone 3 - Sandy loam Podzols Zone 2 - Sand Zone 2 - Loamy sand Zone 3 - Sandy loam Luvisols Zone 2 – Sandy loam 1 Gleysols Zone 2 - Clay

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Annual Average Precipitation (mm)

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Figure 5.16.1- Summary of Annual Average Precipitation Conditions in 12 Scenarios

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Figure 5.16.2- Summary of Annual Average Temperature Conditions in 12 Scenarios

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Figure 5.16.3. Distribution of Fluvisols in Europe (European Soil Bureau Internet Soil Map)

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Figure 5.16.4. Map of Europe showing the distribution of Cambisols as represented by Inceptisols (Northern & Mid-Latitude Soils Database; Cryosol Working Group, 2003)

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Figure 5.16.5. Map of Europe showing the distribution of soils where Podzols (FAO definition) constitute at least 40% occurrence

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Figure 5.16.6. Map of Europe (excluding Italy) showing the distribution of soils where Luvisols (FAO Definition) constitute at least 40% occurrence (Cryosol Working Group, 2003)

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Figure 5.16.7. Map of Europe showing the distribution of soils where Gleysols (as represented by Aqu-suborders) constitute at least 40% occurrence (Cryosol Working Group, 2003)

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Figure 5.16.8. Summary map of geographical extent of dominant soil classes (European Soil Bureau Internet Soil Map) 5.17 References Acutis M., Donatelli M., 2003. SOILPAR 2.00: software to estimate soil hydrological parameters and functions. Eur. J. Agron., 18, 373-377. Agreste (2003) http://www.agreste.agriculture.gouv.fr/default.asp?rub=reg&hauteur=405 Anon (2003) Aquatic ecosystems in the UK agricultural landscape (Defra project PN0931), The Ponds Conservation Trust Cryosol Working Group (2003) Northern and Mid Latitude Soil Database, Version 1. National Soil Database, Research Branch, Agriculture and Agri-Food Canada, Ottawa, Canada. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. Defra Agricultural Atlas (2003) http://farmstats.defra.gov.uk/cs/farmstats_data/MAPS/agricultural_atlas/map_select_large.asp EDINA Agcensus (2003) http://edina.ac.uk/agcensus/ FAO (1981) FAO-UNESCO Soil map of the world (1:5,000,000), Volume V; Europe, UNESCO, Paris FOCUS (2000) “FOCUS groundwater scenarios in the EU review of active substances” Report of the FOCUS Groundwater Scenarios Workgroup, EC Document Reference Sanco/321/2000 rev.2, 202 pp.

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FOCUS (2001). “FOCUS Surface Water Scenarios in the EU Evaluation Process under 91/414/EEC”. Report of the FOCUS Working Group on Surface Water Scenarios, EC Document Reference SANCO/4802/2001-rev.2. 245 pp. Hallett, S.H., Thanigasalam, P. and Hollis, J.M. 1995. SEISMIC: A Desktop Information System for Assessing the Fate and Behaviour of Pesticides in the Environment. Computers and Electronics in Agriculture, 13, 3, 229-244. Köppen, W. (1923) Die Klimate der Erde. Walter de Gruyter, Berlin Met Office (1996) Tables of temperature, relative humidity, precipitation and sunshine fo the world, Part III, Europe and the Azores, UK Meteorological Office MFAF (2003) Danske jordbundsprofiler; http://web.agrsci.dk/jbs/jordbund/index_uk.html Microleis (2003) http://leu.irnase.csic.es/microlei/microlei.htm

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6.0 Manure/Slurry Management The incorporation of realistic worst-case and typical manure and slurry management strategies into exposure assessment calculations is critical to the development of meaningful risk assessments. Strategies of manure and slurry management have a significant influence on exposure potential because they define when, how and where excreta is applied. Among the key considerations incorporated into the model database system are:

• Proportion of production systems involving management of excreta as farm yard manure (FYM) versus slurry

• On-farm storage capacity • Storage time • Dominant application systems (arable versus grassland) • Potential for incorporation following application to land • Typical application rates

A summary of the manure and slurry management strategies employed in each of the Member States where scenarios are considered to have relevance are summarised in the following sections. These Member States are divided into two categories; those with direct (site) relevance to scenarios and those that have a regional pedoclimatic association (indirect association). These are summarised in Table 6.1 Table 6.1 Summary of Direct and Indirect Relationships between Member States and VetCalc scenarios Scenario Directly Associated

Member States Indirectly Associated Member States

Zone 1 - Sandy silt loam Spain (Andalucia) Portugal (Alentejo) Zone 1 - Clay loam Italy (Emilia Romagna) Zone 2 - Sand The Netherlands (Noord

Brabant) Belgium (Vlaanderen) Germany (Nord Rhein Westfalia)

Zone 2 - Loamy sand Denmark Germany (Schleswig Holstein)

Zone 2 - Sandy loam 1 France (Bretagne) Zone 2 - Sandy loam 2 UK (North Yorkshire) Zone 2 - Sandy clay loam 1 France (Midi-Pyrenees) Zone 2 - Sandy clay loam 2 Ireland UK (Northern Ireland) Zone 2 - Sandy silt loam Germany (Brandenburg) Zone 2 - Clay loam

UK (Clwyd, Dyfed, Gwynedd & Powys)

Zone 2 - Clay

UK (Cornwall & Devon)

Zone 3 – Sandy loam Finland (Etalae Suomi) Sweden (Västmanlands, Sodermanlands, Stockholms and Uppsala län)

Much of the data summarised in the following sections has been obtained from a MATRESA nutrient management survey undertaken by Harald Menzi at the Swiss College of Agriculture under the RAMIRAN research programme and summarised by Burton and Turner (2003). In many cases the surveys are very generalized (e.g. FYM and slurry practices associated with cattle and pig wastes. Tabulated summaries (reflecting input files) have been set up to allow for higher resolution definition of

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waste practices for certain production systems targeted at specific animal growth stages – particularly relevant for pig production (e.g. weaners, growers, light cutters, bacon etc.). This has allowed more detailed definitions to be established where data is available (or may become available in future). This is relevant where further data reflecting UK practice has been obtained from detailed farm practice surveys undertaken by ADAS on behalf of Defra (Smith, 2000a, 2000b, 2000c, 2000d, 2000e). On the basis of expert advice (Chambers, Pers. Comm.) considering experience in the UK it has been estimated that typical slurry storage periods would equate to roughly half of storage capacity. Because slurry would be expected to be relatively well mixed with older slurry this is considered to provide a relatively conservative basis for exposure and risk assessment. For each livestock category the most typical manure/slurry management practice is highlighted in yellow. It is envisaged that users would initially base assessments on these practices. Livestock categories associated with practices that are very minor (account for 20% or less of management practices) are highlighted in red. These are not recommended for standard risk assessments. Nonetheless, the user will have the opportunity to employ both typical and less commonly employed practices as well as the opportunity to override assumptions and prepare customized schemes. Further, for each scenario the NVZ (Nitrate Vulnerable Zone) status acts as a nitrogen immission standard and, in effect, acts a restriction upon the quantity of manure (and associated active substances or metabolites) that can be applied to land. When carrying out a simulation the user is prompted to make choices regarding manure management as described in the decision tree in Figure 6.1. At each point the user is provided with background information on typical practices for the Member State livestock production and manure management system under consideration. The decision tree is implemented with a simulation toolbox form within VetCalc. An example is provided in Figure 6.2. As choices are made working through the toolbox relevant options are enabled in dropdown boxes or tickboxes. Once choices are made, options are locked and dropdown boxes are disabled. If users make errors the decision tree can be cleared and redefined. Once the decision tree is completed the user can then proceed to consider the loadings to soil and PEC values in excreta and/or soil as relevant.

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Figure 6.1. Schematic of manure management decision-tree employed when establishing simulations

Figure 6.2. Illustration of the implementation of the decision tree within VetCalc

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6.1 Belgium Belgian dairy and pig farms are dominated by liquid/slurry production. In both cases liquid/slurry systems account for >95% of waste production. No data was available from surveys on storage capacity. It was assumed that storage capacity in Belgium would be similar to that in the Netherlands (typically on the order of approximately 6 months in both dairy and pig farms). The majority of both FYM and slurry is applied to arable systems where there is the possibility of incorporation providing further mitigation of soil exposure. No specific data are available on potential for incorporation in Belgium. It has been assumed that, consistent with good agricultural practice, it would be typical to considered incorporation. Summaries of slurry and FYM management approaches in Belgium are provided in Tables 6.1.1 and 6.1.2. Table 6.1.1- Summary of Typical FYM and Solid Manure Management Strategies in Belgium

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Dairy heifer replacement FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Beef suckler cow FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Grower fattener (>2 yr) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Grower fattener (12-24 mo) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Grower fattener (6-12 mo) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Calf (0-6 mo) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Swine Production Maiden gilts FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha 2 sow place + litters FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Weaners (3-7.5 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Growers (7.5-11 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Light cutter (11-20 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Bacon (11-23 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Bacon liquid feed (11-23 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 20 t/ha 30 t/ha Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (100%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks YES Arable (100%) NA

NA

Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Ducks Solid (100%) No Limit 6 mo YES Arable (75%) 20 t/ha 30 t/ha

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Table 6.1.2- Summary of Slurry Management Strategies in Belgium

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Dairy heifer replacement Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Beef suckler cow Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Grower fattener (>2 yr) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Grower fattener (12-24 mo) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Grower fattener (6-12 mo) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Calf (0-6 mo) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Swine Production Maiden gilts Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha 2 sow place + litters Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Weaners (3-7.5 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Growers (7.5-11 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Light cutter (11-20 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Bacon (11-23 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha Bacon liquid feed (11-23 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (80%) 30-40 m3/ha 30-40 m3/ha NVZ Status A large component of environmental legislation are devolved to the regions of Belgium. The following summary is relevant to Wallonia, the region closest to Noord Brabant, the basis for the Zone 2 - Sand scenario. Two vulnerable zones have been designated in the Walloon region of Belgium; the subsoil basin of the Cretaceous of Hesbaye (300 km2) and the subsoil basin of Brussels sands (1240 km2). Two additional vulnerable zones are planned in Sud Namurois and Comines-Warneton. Le Payes de Herve will be designated a ‘zone with particular environmental constraints’ (ZCEP). Together, these zones cover 20% of the Walloon region. Maximum application rates in Wallonia are summarised in Table 6.1.3. Further temporary derogations requiring management plans can be granted for five years within which applications of up to 280 kg N/ha in grassland, 175 kg N/ha in cereals and 200 kg N/ha in maize and other crops are permitted. Simulations within VetCalc have been established with typical rates for non-vulnerable zones of 120 and 210 kg N/ha for arable and grassland, respectively (De Clercq et al., 2001). Table 6.1.3 Maximum N application rates (kg/ha) from organic fertilisers in Wallonia (De Clercq et al., 2001).

Vulnerable zones ZCEP Remainder of Wallonia Arable Grassland Arable Grassland Arable Grassland

Standard application

rates

80 210 120 210 120 210

Derogation limits

130 250 (Crop dependant)

(Crop dependant)

130 250

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Closed period and restricted soils: Spreading of organic fertilisers is forbidden on soils covered with snow, on saturated soils and soils cultivating leguminous crops. On arable land application of slow release organic fertilisers is restricted between July and October. During this period, a maximum application of 210 kg N/ha is only allowed when a winter crop is sown, or when a catch crop is sown before the 15th of September and removed after 30th November. A maximum application of 80 kg N/ha is allowed when the straw is incorporated on fields that do not have a winter crop or a catch crop (De Clercq et al., 2001). Surface water protection measures: Applications within 4 m of surface waters are forbidden (De Clercq et al., 2001)..

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6.2 Denmark Danish dairy and pig farms are dominated by liquid/slurry production. In the case of pig production liquid/slurry systems account for between 80 and 95% of waste production. Storage capacity is typically on the order of approximately 9 months in both dairy and pig farms. The majority of both FYM and slurry is applied to arable systems where there is the possibility of incorporation providing further mitigation of soil exposure. In Denmark liquid manure and silage effluent spread to soils without a crop must be incorporated into soil as quickly as possible (within 12 hours). Solid manure must be incorporated in the soil immediately after application (Ambus et al., 2001). Within VetCalc the default arable scenario assumes application prior to a crop and, as a consequence, incorporation would be recommended for scenarios considering Danish practices. Summaries of slurry and FYM management approaches in Denmark are provided in Tables 6.2.1 and 6.2.2. Table 6.2.1- Summary of Typical FYM and Solid Manure Management Strategies in Denmark

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (35-50%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Dairy heifer replacement FYM (35-50%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Beef suckler cow FYM (35-50%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Grower fattener (>2 yr) FYM (35-50%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Grower fattener (12-24 mo) FYM (35-50%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Grower fattener (6-12 mo) FYM (35-50%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Calf (0-6 mo) FYM (35-50%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Swine Production Maiden gilts FYM (5-20%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha 2 sow place + litters FYM (5-20%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Weaners (3-7.5 weeks) FYM (5-20%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Growers (7.5-11 weeks) FYM (5-20%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Light cutter (11-20 weeks) FYM (5-20%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Bacon (11-23 weeks) FYM (5-20%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Bacon liquid feed (11-23 weeks) FYM (5-20%) No limit 7 mo YES Arable (75%) 30 t/ha 30 t/ha Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (100%) ? NA Broilers Solid (100%) 8 wks 8 wks YES Arable (100%) ? NA Replacement pullets Solid (100%) 8 wks 8 wks YES Arable (100%) ?

NA

Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (100%) ? NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (100%) ? NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (100%) ? NA Ducks Solid (100%) No Limit 6 mo YES Arable (75%) 30 t/ha 30 t/ha

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Table 6.2.2- Summary of Slurry Management Strategies in Denmark

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (50-65%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Dairy heifer replacement Slurry (50-65%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Beef suckler cow Slurry (50-65%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Grower fattener (>2 yr) Slurry (50-65%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Grower fattener (12-24 mo) Slurry (50-65%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Grower fattener (6-12 mo) Slurry (50-65%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Calf (0-6 mo) Slurry (50-65%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Swine Production Maiden gilts Slurry (80-95%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha 2 sow place + litters Slurry (80-95%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Weaners (3-7.5 weeks) Slurry (80-95%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Growers (7.5-11 weeks) Slurry (80-95%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Light cutter (11-20 weeks) Slurry (80-95%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Bacon (11-23 weeks) Slurry (80-95%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha Bacon liquid feed (11-23 weeks) Slurry (80-95%) 9 mo 4.5 mo YES Arable (75%) 30-40 m3/ha 40 m3/ha NVZ Status The whole of Denmark has been designated NVZ status (Ambus et al., 2001). However, rules implemented by the Water Environmental Action Plan (I and II) are more stringent than the NVZ restrictions: From August 2002 the maximum application rates of animal manure and other organic fertiliser is: 140 kg total N/ha (1.4 animal units per hectare) Exception #1: Until August 2004: on farms with poultry or fur-animals: 170 kg N/ha Exception #2: On non-organic farms with cattle, sheep or goats: 170 kg N/ha Exception #3: Until August 2004: On farms with cattle if more than 70 % of the land is sown with grass, beet or under-sown grass: 230 kg N/ha This restriction translates into the following (from Knudsen, 2003): Animal Maximum number per ha Dairy cows (excluding heifers) 1.4 Sows 6.0 Pigs (7.5-30 kg) 245 Pigs (30-100 kg) 50 Hens 289 Chickens, 40 days old 4930 There are also extensive rules regarding minimum distance to neighbours from animal housings, storage capacity and coverings for animal manure, application methods and timings (Knudsen, 2003).

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Closed period and restricted soils: In the period from harvest to 1st February, spreading slurry or urine is not permitted. An exception is made for land covered with grass and land that is going to be covered with winter rape during the following winter. Here it is allowed to spread slurry or urine during the period from harvest to 1st October. It is permitted to spread solid manure in the period from harvest to 20th October if a crop is grown in the following winter. Liquid manure and silage effluent spread on soils without a crop must be incorporated into the soil as soon as possible (with 12 hours). Solid manure must be incorporated immediately after application (Ambus et al., 2001). Surface water protection measures: No specific protection measures are cited.

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6.3 Finland Finnish dairy and pig farms are dominated by liquid/slurry production. In the case of pig production liquid/slurry systems account for between 65 and 80% of waste production. Storage capacity is typically on the order of approximately 8-12 months in both dairy and pig farms. The majority of both FYM and slurry is applied to arable systems where there is the possibility of incorporation providing further mitigation of soil exposure. No specific data are available on potential for incorporation in Finland. Within VetCalc the default arable scenario assumes application prior to a crop with the assumption that in arable situations incorporation would be typical, in line with Good Agricultural Practice. Summaries of slurry and FYM management approaches in Finland are provided in Tables 6.3.1 and 6.3.2. Table 6.3.1- Summary of Typical FYM and Solid Manure Management Strategies in Finland

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (35-50%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha Dairy heifer replacement FYM (35-50%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Beef suckler cow FYM (35-50%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha Grower fattener (>2 yr) FYM (35-50%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Grower fattener (12-24 mo) FYM (35-50%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Grower fattener (6-12 mo) FYM (35-50%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Calf (0-6 mo) FYM (35-50%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Swine Production Maiden gilts FYM (20-35%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha 2 sow place + litters FYM (20-35%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Weaners (3-7.5 weeks) FYM (20-35%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Growers (7.5-11 weeks) FYM (20-35%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Light cutter (11-20 weeks) FYM (20-35%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Bacon (11-23 weeks) FYM (20-35%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Bacon liquid feed (11-23 weeks) FYM (20-35%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (90%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks

YES Arable (90%) NA NA

Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Ducks Solid (100%) No limit 8-12 mo YES Arable (90%) 15-20 t/ha 15-20 t/ha

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Table 6.3.2- Summary of Slurry Management Strategies in Finland

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (50-65%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha Dairy heifer replacement Slurry (50-65%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Beef suckler cow Slurry (50-65%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha Grower fattener (>2 yr) Slurry (50-65%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Grower fattener (12-24 mo) Slurry (50-65%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Grower fattener (6-12 mo) Slurry (50-65%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Calf (0-6 mo) Slurry (50-65%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Swine Production Maiden gilts Slurry (65-80%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha 2 sow place + litters Slurry (65-80%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Weaners (3-7.5 weeks) Slurry (65-80%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Growers (7.5-11 weeks) Slurry (65-80%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Light cutter (11-20 weeks) Slurry (65-80%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Bacon (11-23 weeks) Slurry (65-80%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

Bacon liquid feed (11-23 weeks) Slurry (65-80%) 8-12 mo 4 mo NO Arable (80%) 20-30 m3/ha 20-40 m3/ha

NVZ Status The whole of Finland has been designated as ‘N sensitive’ and, under the Nitrate Directive as ‘vulnerable’. Fixed upper limits are enforced as follows (Bäckman and Vermeulen, 2001): Autumn crops: 200 kg N/ha for entire year (30 kg N/ha in autumn) Potatoes: 130 kg N/ha for entire year Grass (hay, silage): 250 kg N/ha for entire year Spring crops, sugar beet, oilseeds: 170 kg N/ha for entire year These figures represent total mineral and organic N rates, whereas the Nitrate Directive only indicates 170 kg N/ha as manure. The ceiling of permitted application rates in Finland is the sum of mineral and organic fertilisers. This is different than in most Member States where the maximum is set as 170 kg N/ha as manure. For simplicity, simulations within VetCalc are established assuming a maximum of 170 kg N/ha (Bäckman and Vermeulen, 2001). Closed period and restricted soils: Soil organic matter, texture, slope and permeability and sensitivity to flooding are taken into account in determining N application rates (Bäckman and Vermeulen, 2001). According to the Finnish regulation based on the nitrogen directive manure must not be spread on any field between 15th October and 15th April. In exceptional cases manure can be spread in the autumn until 15th October and start spreading from 1st April if the soil is not frozen and it is dry so that no leaching to surface waters is. For grassland manure must not be spread after 15 September unless it is fully

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mixed into the soil during the next day (which is therefore seldom done). The maximum permissible use rates of manure are in the autumn 30 ton/ha of dry manure, 20 ton/ha of cow manure slurry, 15 ton/ha of pig manure slurry and 10 ton/ha poultry or fur animal manure (Kitamori, Pers. Comm. – based upon data submitted to OECD in support of EUBEES Emission Scenario Documents for Veterinary hygiene biocidal products) Surface water protection measures: No specific protection measures are cited.

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6.4 France French pig farms are dominated (>95%) by liquid/slurry production. In the case of dairy farms, liquid/slurry production is apparently far less common and FYM systems dominate. Storage capacity in slurry systems is typically relatively short (on the order of approximately 4-6 months in both dairy and pig farms). The majority of both FYM and slurry is applied to arable systems (>80%) where there is the possibility of incorporation providing further mitigation of soil exposure. No specific data are available on potential for incorporation in France. It has been assumed that in many cases manure application practices would be similar in France and the UK. Within VetCalc the default arable scenario assumes application prior to a crop with the assumption that in arable situations incorporation would be typical, in line with Good Agricultural Practice. Summaries of slurry and FYM management approaches in France are provided in Tables 6.4.1 and 6.4.2. Table 6.4.1- Summary of Typical FYM and Solid Manure Management Strategies in France

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (>50%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA Dairy heifer replacement FYM (>50%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Beef suckler cow FYM (>50%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA Grower fattener (>2 yr) FYM (>50%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Grower fattener (12-24 mo) FYM (>50%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Grower fattener (6-12 mo) FYM (>50%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Calf (0-6 mo) FYM (>50%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Swine Production Maiden gilts FYM (<5%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA 2 sow place + litters FYM (<5%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Weaners (3-7.5 weeks) FYM (<5%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Growers (7.5-11 weeks) FYM (<5%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Light cutter (11-20 weeks) FYM (<5%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Bacon (11-23 weeks) FYM (<5%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Bacon liquid feed (11-23 weeks) FYM (<5%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (100%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Ducks Solid (100%) No limit 4 mo YES Arable (95%) 40-80 t/ha NA

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Table 6.4.2- Summary of Slurry Management Strategies in France

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (<50%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha Dairy heifer replacement

Slurry (<50%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Beef suckler cow Slurry (<50%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha Grower fattener (>2 yr)

Slurry (<50%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Grower fattener (12-24 mo)

Slurry (<50%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Grower fattener (6-12 mo)

Slurry (<50%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Calf (0-6 mo)

Slurry (<50%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Swine Production Maiden gilts Slurry (>95%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha 2 sow place + litters

Slurry (>95%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Weaners (3-7.5 weeks)

Slurry (>95%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Growers (7.5-11 weeks)

Slurry (>95%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Light cutter (11-20 weeks)

Slurry (>95%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Bacon (11-23 weeks)

Slurry (>95%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

Bacon liquid feed (11-23 weeks)

Slurry (>95%) 4-6 mo 2 mo YES Arable (80%) 40-80 m3/ha 30 m3/ha

NVZ Status Vulnerable zones have been established in regions throughout France including the northeast, southwest in Aquitaine and large areas to the northeast of Ille de France (e.g. Picardie). The basis for the Zone 2 - Sandy clay loam 1 scenario does not lie in a vulnerable zone (Rapion et al., 2001). In the case of Bretagne (the basis for the Zone 2 – Sandy loam 1 scenario), the whole region has been designated a nitrate vulnerable zone. In addition, much of the Cotes du Nord, including the area around St Brieuc has been designated a Structural Surplus Zone (ZES), where more restrictive nitrogen regulations are in operation. ZESs are specific vulnerable zone where average annual N disposal from animal waste exceeds 170 kg/ha of agricultural area on which livestock waste can be spread. The regulation specifies an upper limit on organic N amount produced by a farm above which treatment or export of animal waste is obligatory (e.g. 15,000 kg N). This threshold corresponds to a maximum surface where manure spreading is possible (e.g. 15,000 kg / 170 kg/ha = 88 ha). In addition, when a farmer chooses treatment, this surface is reduced (e.g. 88 ha reduced to 40 ha) to take account of the decrease in N in manures and thus to release additional areas to manure spreading for neighbouring farmers (Rapion et al., 2001). For simplicity Zone 2 – Sandy loam 1 scenario based upon production practices in Bretagne have been established with a maximum application rate of 170 kg N/ha. A default maximum N application rate of 250 kg N/ha was been established in the Zone 2 – Sandy clay loam 2 scenario, recognising it is not in a vulnerable zone. Closed period and restricted soils: No specific protection measures are cited.

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Surface water protection measures: No specific protection measures are cited.

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6.5 Germany German dairy and pig farms are dominated by liquid/slurry production. In the case of pig production liquid/slurry systems account for between 80 and 95% of waste production. Storage capacity is typically on the order of approximately 5 and 7 months in both dairy and pig farms, respectively. The majority (ca 85%) of both FYM and slurry is applied to arable systems. Dairy FYM and slurry is generally applied to potatoes and beets in spring and autumn. In the case of pig and poultry FYM and slurry applications are most typically made to cereals, maize, potatoes. Dominant application to arable systems provides the possibility of incorporation as a means of mitigating soil exposure. Little specific data are available on potential for incorporation in Germany. It is known that incorporation is practiced although surveys suggest that this is no carried out immediately after application. It has, therefore, been assumed that, where relevant, practices would be similar in Germany and the UK. Summaries of slurry and FYM management approaches in Germany are provided in Tables 6.5.1 and 6.5.2. Table 6.5.1- Summary of Typical FYM and Solid Manure Management Strategies in Germany

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (20-35%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Dairy heifer replacement FYM (20-35%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Beef suckler cow FYM (20-35%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Grower fattener (>2 yr) FYM (20-35%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Grower fattener (12-24 mo) FYM (20-35%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Grower fattener (6-12 mo) FYM (20-35%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Calf (0-6 mo) FYM (20-35%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Swine Production Maiden gilts FYM (5-20%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha 2 sow place + litters FYM (5-20%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Weaners (3-7.5 weeks) FYM (5-20%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Growers (7.5-11 weeks) FYM (5-20%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Light cutter (11-20 weeks) FYM (5-20%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Bacon (11-23 weeks) FYM (5-20%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Bacon liquid feed (11-23 weeks) FYM (5-20%) No limit 4 mo YES Arable (85%) 15-30 t/ha 15-30 t/ha Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (100%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Ducks Solid (100%) No limit 4 mo YES Arable (85%) 15-30 t/ha NA

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Table 6.5.2- Summary of Slurry Management Strategies in Germany

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (65-80%) 5 mo 2.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Dairy heifer replacement Slurry (65-80%) 5 mo 2.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Beef suckler cow Slurry (65-80%) 5 mo 2.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Grower fattener (>2 yr) Slurry (65-80%) 5 mo 2.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Grower fattener (12-24 mo) Slurry (65-80%) 5 mo 2.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Grower fattener (6-12 mo) Slurry (65-80%) 5 mo 2.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Calf (0-6 mo) Slurry (65-80%) 5 mo 2.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Swine Production Maiden gilts Slurry (80-95%) 7 mo 3.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha 2 sow place + litters Slurry (80-95%) 7 mo 3.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Weaners (3-7.5 weeks) Slurry (80-95%) 7 mo 3.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Growers (7.5-11 weeks) Slurry (80-95%) 7 mo 3.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Light cutter (11-20 weeks) Slurry (80-95%) 7 mo 3.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Bacon (11-23 weeks) Slurry (80-95%) 7 mo 3.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha Bacon liquid feed (11-23 weeks) Slurry (80-95%) 7 mo 3.5 mo YES Arable (85%) 10-40 m3/ha 10-40 m3/ha NVZ Status Application of FYM and slurry is subject to a range of legislative restrictions (national and regional (Länder)) in Germany. These are summarised below (Happe et al., 2001):

• General water protection regulations (liability for changes in water quality, handing of substances, water protection)

• Penal law (water contamination, soil contamination, waste disposal) • Special regulations in water protection and spring areas • Agricultural regulations on solid manure storage • Fertilizer decree (Düngerverordnung)

The Fertiliser Law serves as the legal basis for the Fertiliser decree (BGBI, 26/01/1996), which implements the EU Nitrate Directive (91/676). In this respect, the whole German territory is designated as a vulnerable zone. The Fertiliser decree regulates the application of organic fertilisers for the whole of Germany. Key rules to be followed by farmers are (Happe et al., 2001):

• Organic fertilisers should be applied in such a way that there is no runoff into surface waters and the emissions of ammonia are minimised.

• Organic fertilisers are to be applied closely to the soil and on unplanted land they should be incorporated immediately

• Application after harvest is only allowed if there is a demand for N by the crop. The application rate is not to exceed 80 kg total N/ha

In implementing the Nitrate Directive, upper limits for the total N applications with liquid manure are defined at a farm level. These limits are fixed at no more than 170 kg N/ha on arable land and 210 kg N/ha on grassland (Happe et al., 2001).

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Simulations within VetCalc have, therefore, been established on the basis of the nominal maxima. Closed periods and restricted soils Between November 15 and January 15, application is generally prohibited (Happe et al., 2001). Surface water protection measures: In Nord Rhein-Westfalia (and other regions of Germany) local agreements are managed between farmers and water authorities which define restrictions and compensation payments. Other Länder pursue a more centralised management programme (Happe et al., 2001).

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6.6 Ireland Irish pig and dairy farms are dominated by liquid/slurry production. In the case of pig farms slurry/liquid systems account for >95% of production. Storage capacity in slurry systems is typically very short (on the order of approximately 4 months in both dairy and pig farms). Unlike most other Member States the majority of both FYM and slurry is applied to grassland systems (between 95 and 100%). This precludes the potential for incorporation providing further mitigation of soil exposure. Summaries of slurry and FYM management approaches in Ireland are provided in Tables 6.6.1 and 6.6.2. Table 6.6.1- Summary of Typical FYM and Solid Manure Management Strategies in Ireland

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production

Dairy cow FYM (35-50%) No limit 5 mo NO Grassland (100%) NA 30 t/ha

Dairy heifer replacement FYM (35-50%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Beef suckler cow FYM (35-50%) No limit 5 mo NO Grassland (100%) NA 30 t/ha

Grower fattener (>2 yr) FYM (35-50%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Grower fattener (12-24 mo) FYM (35-50%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Grower fattener (6-12 mo) FYM (35-50%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Calf (0-6 mo) FYM (35-50%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Swine Production

Maiden gilts FYM (<5%) No limit 5 mo NO Grassland (100%) NA 30 t/ha

2 sow place + litters FYM (<5%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Weaners (3-7.5 weeks) FYM (<5%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Growers (7.5-11 weeks) FYM (<5%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Light cutter (11-20 weeks) FYM (<5%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Bacon (11-23 weeks) FYM (<5%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Bacon liquid feed (11-23 weeks) FYM (<5%) No limit 5 mo NO

Grassland (100%) NA 30 t/ha

Avian Production

Laying Hens Solid (100%) 1 yr 1 yr NO Grassland (100%) NA NA

Broilers Solid (100%) 8 wks 8 wks NO Grassland (100%) NA NA

Replacement pullets Solid (100%) 8 wks 8 wks NO

Grassland (100%) NA NA

Broiler breeders Solid (100%) 8 wks 8 wks NO Grassland (100%) NA NA

Turkey male Solid (100%) 8 wks 8 wks NO Grassland (100%) NA NA

Turkey female Solid (100%) 8 wks 8 wks NO Grassland (100%) NA NA

Ducks Solid (100%) No limit 5 mo NO Grassland (100%) NA 30 t/ha

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Table 6.6.2- Summary of Slurry Management Strategies in Ireland

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production

Dairy cow Slurry (50-65%) 4 mo 2 mo NO Grassland (95%) 22 m3/ha 33 m3/ha

Dairy heifer replacement Slurry (50-65%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Beef suckler cow Slurry (50-65%) 4 mo 2 mo NO Grassland (95%) 22 m3/ha 33 m3/ha

Grower fattener (>2 yr) Slurry (50-65%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Grower fattener (12-24 mo) Slurry (50-65%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Grower fattener (6-12 mo) Slurry (50-65%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Calf (0-6 mo) Slurry (50-65%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Swine Production

Maiden gilts Slurry (>95%) 4 mo 2 mo NO Grassland (95%) 22 m3/ha 33 m3/ha

2 sow place + litters Slurry (>95%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Weaners (3-7.5 weeks) Slurry (>95%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Growers (7.5-11 weeks) Slurry (>95%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Light cutter (11-20 weeks) Slurry (>95%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Bacon (11-23 weeks) Slurry (>95%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

Bacon liquid feed (11-23 weeks) Slurry (>95%) 4 mo 2 mo NO

Grassland (95%) 22 m3/ha 33 m3/ha

NVZ Status Although Nitrate Vulnerable Zones (NVZs) have been identified, at the time of writing these have not yet been implemented. Ireland is, however, planning towards a system where the whole-territory is defined as a NVZ (D'Alton 2003). In Northern Ireland (UK) Nitrate Vulnerable Zones have been designated near Cloughmills, Co Antrim and Comber, Co Down. Closed periods and restricted soils Prohibition of application of slurry after October 31st (Carton, 2001) Surface water protection measures: Additional voluntary measures exist within Codes of Good Agricultural Practice for protection of surface waters (Carton, 2001).

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6.7 Italy Italian pig farms are dominated by liquid/slurry production (>95%). By contrast, in dairy farms slurry/liquid systems account for <50% of production and FYM systems dominate. Storage capacity in slurry systems is typically very short (approximately 4 months). The majority of both FYM and slurry is applied to arable systems (>90% in the case of FYM and >70% in the case of slurry/liquid) where there is the possibility of incorporation providing further mitigation of soil exposure. Little specific data are available on potential for incorporation in Italy. Within VetCalc the default arable scenario assumes application prior to a crop with the assumption that in arable situations incorporation would be typical, in line with Good Agricultural Practice. Summaries of slurry and FYM management approaches in Italy are provided in Tables 6.7.1 and 6.7.2. Table 6.7.1- Summary of Typical FYM and Solid Manure Management Strategies in Italy

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (35-50%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Dairy heifer replacement FYM (35-50%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Beef suckler cow FYM (35-50%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Grower fattener (>2 yr) FYM (35-50%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Grower fattener (12-24 mo) FYM (35-50%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Grower fattener (6-12 mo) FYM (35-50%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Calf (0-6 mo) FYM (35-50%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Swine Production Maiden gilts FYM (<5%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA 2 sow place + litters FYM (<5%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Weaners (3-7.5 weeks) FYM (<5%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Growers (7.5-11 weeks) FYM (<5%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Light cutter (11-20 weeks) FYM (<5%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Bacon (11-23 weeks) FYM (<5%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Bacon liquid feed (11-23 weeks) FYM (<5%) No limit 3 mo YES Arable (90%) 40-60 t/ha NA Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (100%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks

YES Arable (100%) NA NA

Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Ducks Solid (100%) No limit 3 mo YES Arable (100%) NA NA

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Table 6.7.2- Summary of Slurry Management Strategies in Italy

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production

Dairy cow Slurry (50-65%) 4 mo 2 mo YES Arable (70%) 100-120 m3/ha 80-100 m3/ha

Dairy heifer replacement Slurry (50-65%) 4 mo 2 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Beef suckler cow Slurry (50-65%) 4 mo 2 mo YES Arable (70%) 100-120 m3/ha 80-100 m3/ha

Grower fattener (>2 yr) Slurry (50-65%) 4 mo 2 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Grower fattener (12-24 mo) Slurry (50-65%) 4 mo 2 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Grower fattener (6-12 mo) Slurry (50-65%) 4 mo 2 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Calf (0-6 mo) Slurry (50-65%) 4 mo 2 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Swine Production

Maiden gilts Slurry (>95%) 6 mo 3 mo YES Arable (70%) 100-120 m3/ha 80-100 m3/ha

2 sow place + litters Slurry (>95%) 6 mo 3 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Weaners (3-7.5 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Growers (7.5-11 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Light cutter (11-20 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Bacon (11-23 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

Bacon liquid feed (11-23 weeks) Slurry (>95%) 6 mo 3 mo YES Arable (70%)

100-120 m3/ha 80-100 m3/ha

NVZ Status Application of FYM and slurry is subject to a range of legislative restrictions (national and regional) in Italy (Bonazzi, 2001). These are summarised below:

• The Po valley region issued regulations concerning spreading period, storage capacity and application rate of N contained in solid manure.

• Storage of solid manure is controlled by laws RD 27.07.1934 n. 1265 and RD 1482 01.12.1930.

• These oblige the “storage in beds which avoid percolation or/and loss of the liquid fraction”.

Additional national legislation controls application and discharge of liquid manures. The Emilia Romagna regional government has identified vulnerable zones based upon hydrogeological criteria. In this region, the maximum N application rate is 170 kg N/ha per year (210 in the first four years after the application of regional regulations) in vulnerable zones and 340 kg N/ha per year in non-vulnerable zones. In addition, in order to obtain a spreading permit, farmers must demonstrate a given ratio between N in livestock manure or number of animals (tonnes of live weight) and agricultural land. As a result, a detailed fertiliser plan must be prepared by:

• Pigs farms in vulnerable zones with more than 160 tonnes of live weight • Farms with a N load (from manure) exceeding 170 kg N/ha in vulnerable zones or

340 kg N/ha in non-vulnerable • Farmers who want to apply more than 170 kg N/ha in vulnerable zones(maximum

210 kg) if a particular high crop requirement can be demonstrated (Bonazzi, 2001). Simulations within VetCalc have, therefore, been established on the basis of a nominal maximum of 170 kg N/ha.

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Closed periods and restricted soils No closed periods are known. Applications to arable land are generally associated with winter cereals, maize and sugar beet production. Applications of solid manure are generally carried out in July through to September. Solid dairy manures are most often applied to maize and sugar beet just before ploughing. In the case of poultry manure, applications are most commonly made to cereals before ploughing. It is forbidden to apply slurry on poorly drained soils, uncultivated soils and soils with a slope of over 15% (Bonazzi, 2001). Surface water protection measures: It is forbidden to apply slurry within a radius of 200 m from drinking water and within a strip of 5 m width from water courses (Bonazzi, 2001).

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6.8 The Netherlands Dutch dairy and pig farms are dominated by liquid/slurry production (>95% of waste production). Storage capacity is typically relatively short (2-3 months for slurry, 6 months for FYM). In the Netherlands slurry must be applied is such a way that ammonia volatilisation is minimised. The timespan between spreading and incorporation kept as short as possible. In legal terms this is substantiated by stating that slurry must only be visible on the soil surface whilst it is being applied and must be incorporated immediately. Although FYM production is very minor, surveys suggest that incorporation is frequently practiced where applications are made to arable systems. FYM incorporation is generally carried out within 14 days of application. This is consistent with the default arable scenario in VetCalc that assumes application prior to a crop followed by incorporation. Summaries of slurry and FYM management approaches in The Netherlands are provided in Tables 6.8.1 and 6.8.2. Table 6.8.1- Summary of Typical FYM and Solid Manure Management Strategies in The Netherlands

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production

Dairy cow FYM (<5%) No limit 2-3 mo YES Grassland (90%) 20 t/ha 30 t/ha

Dairy heifer replacement FYM (<5%) No limit 2-3 mo YES Grassland

(90%) 20 t/ha 30 t/ha

Beef suckler cow FYM (<5%) No limit 2-3 mo YES Grassland (90%) 20 t/ha 30 t/ha

Grower fattener (>2 yr) FYM (<5%) No limit 2-3 mo YES Grassland

(90%) 20 t/ha 30 t/ha

Grower fattener (12-24 mo) FYM (<5%) No limit 2-3 mo YES Grassland

(90%) 20 t/ha 30 t/ha

Grower fattener (6-12 mo) FYM (<5%) No limit 2-3 mo YES Grassland

(90%) 20 t/ha 30 t/ha

Calf (0-6 mo) FYM (<5%) No limit 2-3 mo YES Grassland

(90%) 20 t/ha 30 t/ha

Swine Production Maiden gilts FYM (1.7%) No limit 2-3 mo YES Arable (90%) 20 t/ha 30 t/ha 2 sow place + litters FYM (1.7%) No limit 2-3 mo YES Arable (90%) 20 t/ha 30 t/ha

Weaners (3-7.5 weeks) FYM (1.7%) No limit 2-3 mo YES Arable (90%) 20 t/ha 30 t/ha

Growers (7.5-11 weeks) FYM (1.7%) No limit 2-3 mo YES Arable (90%) 20 t/ha 30 t/ha

Light cutter (11-20 weeks) FYM (1.7%) No limit 2-3 mo YES Arable (90%) 20 t/ha 30 t/ha

Bacon (11-23 weeks) FYM (1.7%) No limit 2-3 mo YES Arable (90%) 20 t/ha 30 t/ha

Bacon liquid feed (11-23 weeks) FYM (1.7%) No limit 2-3 mo YES Arable (90%) 20 t/ha 30 t/ha

Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (80%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (80%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks YES Arable (80%) NA NA Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (80%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (80%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (80%) NA NA Ducks Solid (100%) 6 mo 2-3 mo YES Arable (100%) NA NA

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. Table 6.8.2- Summary of Slurry Management Strategies in The Netherlands

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production

Dairy cow Slurry (>95%) 6 mo 3 mo YES Grassland (90%) 30-40 m3/ha 30-40 m3/ha

Dairy heifer replacement Slurry (>95%) 6 mo 3 mo YES Grassland

(90%) 30-40 m3/ha 30-40 m3/ha

Beef suckler cow Slurry (>95%) 6 mo 3 mo YES Grassland (90%) 30-40 m3/ha 30-40 m3/ha

Grower fattener (>2 yr) Slurry (>95%) 6 mo 3 mo YES Grassland

(90%) 30-40 m3/ha 30-40 m3/ha

Grower fattener (12-24 mo) Slurry (>95%) 6 mo 3 mo YES Grassland

(90%) 30-40 m3/ha 30-40 m3/ha

Grower fattener (6-12 mo) Slurry (>95%) 6 mo 3 mo YES Grassland

(90%) 30-40 m3/ha 30-40 m3/ha

Calf (0-6 mo) Slurry (>95%) 6 mo 3 mo YES Grassland

(90%) 30-40 m3/ha 30-40 m3/ha

Swine Production Maiden gilts Slurry (98.3%) 6 mo 3 mo YES Arable (90%) 30-40 m3/ha 30-40 m3/ha 2 sow place + litters Slurry (98.3%) 6 mo 3 mo YES Arable (90%) 30-40 m3/ha 30-40 m3/ha

Weaners (3-7.5 weeks) Slurry (98.3%) 6 mo 3 mo YES Arable (90%) 30-40 m3/ha 30-40 m3/ha

Growers (7.5-11 weeks) Slurry (98.3%) 6 mo 3 mo YES Arable (90%) 30-40 m3/ha 30-40 m3/ha

Light cutter (11-20 weeks) Slurry (98.3%) 6 mo 3 mo YES Arable (90%) 30-40 m3/ha 30-40 m3/ha

Bacon (11-23 weeks) Slurry (98.3%) 6 mo 3 mo YES Arable (90%) 30-40 m3/ha 30-40 m3/ha

Bacon liquid feed (11-23 weeks) Slurry (98.3%) 6 mo 3 mo YES Arable (90%) 30-40 m3/ha 30-40 m3/ha

NVZ Status The total area of the Netherlands is designated as a vulnerable zone and is required to comply with the regulation not to apply more than 170 kg manure N/ha per year. However, the country operates a mineral accounting system (MINAS), which is seen by the commission as being an addition to the nitrates directive rather than an alternative. MINAS is a surplus oriented system, which requires farms with more than 2.5 livestock units per hectare to register all N and P passing through the farm gate. Inputs may include purchased fertiliser, concentrate, forage, manure and animals. Outputs include sold crop produce, manure and animals, animal products such as milk, meat, eggs etc., and NH3 losses from animal houses, manure storage and grazing. When inputs exceed outputs as defined by MINAS, the surplus is charged with a levy. There are also a number of regulations regarding application timings and techniques (Neeteson et al., 2001). The Netherlands have applied for a derogation to widen the application rate on grassland from 170 to 250 kg manure-N/ha, arguing that the MINAS system promotes efficient utilisation of manure such that the higher rate will have no unacceptable environmental impact (Neetson, et al., 2001). Uniquely in the EU, the Netherlands set a maximum ceiling on application of manures on the basis of P content, as a more challenging basis for nutrient management than N content. The maximum permissible manure application rates on arable land and grassland is 80 kg P2O5.ha/year .

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Closed period and restricted soils: On sandy soils there is a spreading ban for manure, including slurry from 15th September to 1st February. On clay soils the spreading ban applies to grassland only. Arable clay soils were not included because the relationship between spreading and nitrate emissions to groundwater is not as clear as for sandy soils. On all soil types applications are not permitted on snow covered or frozen soils (Neeteson et al., 2001). Surface water protection measures: No specific restrictions associated with applications. Measures to prevent surface water contamination associated with storage have been recommended.

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6.9 Portugal Just over half of Portuguese dairy and pig farms are characterised by liquid/slurry production (50-65-% of waste production). Storage capacities in Portugal are typically very limited (1-3 months for slurry). No data was available for FYM storage capacity. Therefore, FYM storage figures were based upon neighboring Spanish practice. According to surveys, the entirety of FYM is applied to arable land. A large proportion (80%) of slurry wastes in Portugal are discharged or exported from farms. It is not clear how these are managed off-site. However, according to surveys, the remainder is applied to arable land. Slurry utilization profiles prepared in Table 6.9.2 assumed that the entirety of the tracked waste stream is applied to arable land. Little specific data are available on potential for incorporation in Portugal. Within VetCalc the default arable scenario assumes application prior to a crop with the assumption that in arable situations incorporation would be typical, in line with Good Agricultural Practice. Summaries of slurry and FYM management approaches in Portugal are provided in Tables 6.8.1 and 6.8.2. Table 6.9.1- Summary of Typical FYM and Solid Manure Management Strategies in Portugal

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (20-35%) No limit 2-3 mo YES Arable (100%) NA NA Dairy heifer replacement FYM (20-35%) No limit 2-3 mo YES Arable (100%) NA NA

Beef suckler cow FYM (20-35%) No limit 2-3 mo YES Arable (100%) NA NA Grower fattener (>2 yr) FYM (20-35%) No limit 2-3 mo YES Arable (100%) NA NA

Grower fattener (12-24 mo) FYM (20-35%) No limit 2-3 mo YES Arable (100%) NA NA

Grower fattener (6-12 mo) FYM (20-35%) No limit 2-3 mo YES Arable (100%) NA NA

Calf (0-6 mo) FYM (20-35%) No limit 2-3 mo YES Arable (100%) NA NA

Swine Production Maiden gilts FYM (<5%) No limit 2-3 mo YES Arable (100%) NA NA 2 sow place + litters FYM (<5%) No limit 2-3 mo YES Arable (100%) NA NA

Weaners (3-7.5 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) NA NA

Growers (7.5-11 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) NA NA

Light cutter (11-20 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) NA NA

Bacon (11-23 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) NA NA

Bacon liquid feed (11-23 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) NA NA

Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (100%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA

Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (100%) NA NA Ducks Solid (100%) No limit 2-3 mo YES Arable (100%) NA NA

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Table 6.9.2- Summary of Slurry Management Strategies in Portugal

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (65-80%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Dairy heifer replacement Slurry (65-80%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Beef suckler cow Slurry (65-80%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Grower fattener (>2 yr) Slurry (65-80%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Grower fattener (12-24 mo) Slurry (65-80%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Grower fattener (6-12 mo) Slurry (65-80%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Calf (0-6 mo) Slurry (65-80%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Swine Production Maiden gilts Slurry (>95%) 1-3 mo 0.5 mo YES Arable (100%) NA NA 2 sow place + litters Slurry (>95%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Weaners (3-7.5 weeks) Slurry (>95%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Growers (7.5-11 weeks) Slurry (>95%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Light cutter (11-20 weeks) Slurry (>95%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Bacon (11-23 weeks) Slurry (>95%) 1-3 mo 0.5 mo YES Arable (100%) NA NA Bacon liquid feed (11-23 weeks) Slurry (>95%) 1-3 mo 0.5 mo YES Arable (100%) NA NA NVZ Status Decree 235/97 and 68/99 transposes the European Nitrate Directive (91/67/EEC) into national legislation. Consequent to these Decrees, the vulnerable zones were identified (Cabral et al., 2001):

• Free aquifer between Esposende and Vila do Conde, situated in the North littoral of Portugal

• Quaternary aquifer in Aveiro located in the Centre littoral of Portugal • Miocenic aquifer of the Campina de Faro in the Southern littoral of Portugal

The third of these vulnerable zones has greatest relevance to the Zone 1 – Sandy silt loam scenario. The maximum amounts of total N that can be applied within this vulnerable zone on various crops is summarised in Table 6.9.3. It should be noted that there is no specific restriction associated with uses on cereals in this vulnerable zone. For simplicity, within VetCalc, a maximum N rate of 250 kg N/ha is assumed. Closed periods and restricted soils Fertiliser applications must be adjusted considering crop needs during vegetative growth and take into account risk of leaching, mainly during the winter. The application of certain fertilisers is banned during certain periods as summarised in Table 6.9.4. Application of fertilisers during heavy rain should be avoided to minimise nitrate leaching losses in bare or lightly covered soils. Application of slurries or manure is forbidden between December and January and if at any time of the

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vegetative growth, the soil is flooded, the normal soil moisture must be reached before application (Cabral et al., 2001). Table 6.9.3. Maximum amounts of total N (kg/ha) applied to different crops in the vulnerable region associated with the Miocenic aquifer of the Campina de Faro (Cabral et al., 2001) Crop Maximum N rate (kg/ha) Cereals, ryegrass, legumes, forage maize, maize, onion, carrot, garlic, peas, faba bean

No restriction

Potato 200 Green beans 220 Brassicas 170-250 Strawberries 250 Pepperoni 200 Lettuce 85 Watermelon 85 Tomato 280 Cucumber 280 Melon 280 Table 6.9.4 Ban on application of fertilisers to Miocenic aquifer of the Campino de Faro, in Portugal (Cabral et al., 2001) Crops Organic fertilisers Slurries and wet sludge Vegetables Until one before sowing or

plantation Until 15 days before sowing or plantation

Arboreous crops During dormancy and until one month before re-growth

During dormancy and until one month before re-growth

Surface water protection measures: No specific restrictions associated with applications. Measures to prevent surface water contamination associated with storage have been recommended.

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6.10 Spain Spanish pig farms are dominated by liquid/slurry production (>95%). By contrast, in dairy farms slurry/liquid systems account for between 65-80%. Storage capacity in slurry systems is typically very short (approximately 4-6 months). According to recent surveys the vast majority (or entirety in the case of solid manures), of both waste is applied to arable systems where there is the possibility of incorporation providing further mitigation of soil exposure. Little specific data are available on potential for incorporation in Spain. Within VetCalc the default arable scenario assumes application prior to a crop with the assumption that in arable situations incorporation would be typical, in line with Good Agricultural Practice. Summaries of slurry and FYM management approaches in Spain are provided in Tables 6.10.1 and 6.10.2. Table 6.10.1- Summary of Typical FYM and Solid Manure Management Strategies in Spain

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (20-35%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Dairy heifer replacement FYM (20-35%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0

Beef suckler cow FYM (20-35%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Grower fattener (>2 yr) FYM (20-35%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Grower fattener (12-24 mo) FYM (20-35%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Grower fattener (6-12 mo) FYM (20-35%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Calf (0-6 mo) FYM (20-35%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0

Swine Production Maiden gilts FYM (<5%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 2 sow place + litters FYM (<5%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Weaners (3-7.5 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Growers (7.5-11 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Light cutter (11-20 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Bacon (11-23 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0 Bacon liquid feed (11-23 weeks) FYM (<5%) No limit 2-3 mo YES Arable (100%) 25-35 t/ha 0

Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (95%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (95%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks

YES Arable (95%) NA NA

Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (95%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (95%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (95%) NA NA Ducks Solid (100%) No limit 2-3 mo YES Arable (95%) NA NA

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Table 6.10.2- Summary of Slurry Management Strategies in Spain

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (65-80%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Dairy heifer replacement Slurry (65-80%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0

Beef suckler cow Slurry (65-80%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Grower fattener (>2 yr) Slurry (65-80%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Grower fattener (12-24 mo) Slurry (65-80%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Grower fattener (6-12 mo) Slurry (65-80%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Calf (0-6 mo) Slurry (65-80%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0

Swine Production Maiden gilts Slurry (>95%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 2 sow place + litters Slurry (>95%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Weaners (3-7.5 weeks) Slurry (>95%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Growers (7.5-11 weeks) Slurry (>95%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Light cutter (11-20 weeks) Slurry (>95%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Bacon (11-23 weeks) Slurry (>95%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0 Bacon liquid feed (11-23 weeks) Slurry (>95%) 4-6 mo 2 mo YES Arable (75%) 30-50 m3/ha 0

NVZ Status Nine regions have designated nitrate vulnerable zones. These are summarised in Table 6.10.3 (Soler-Rovira et al., 2001). Specific information on the restrictions employed in Andalucia are summarised with codes of good agricultural practice (BOJA, 1998). The maximum N rate from manure established in 170 or 210 kg N/ha (arable and grassland, respectively). Closed periods and restricted soils It is recommended not to fertilise from the end of autumn until spring. Specific advice can be found in Table 6.10.4. On completely frozen soils, mineral N fertilisers, manure, compost and sludge must be spread only in limited cases. Applications of slurry or liquid manure is not permitted. It is not advisable to spread fertilisers (mineral or organic) on flooded or waterlogged fields, except for rice. Surface water protection measures: Because of limitations in the scope of the research project, simulations have been established with non-irrigated (rain-fed) winter cereals. However, good agricultural practice programmes stress the importance of controlled irrigation and fertigation. Irrigation systems should be sprinkler or drip systems. Irrigation water requirements should be calculated considering crop evapotranspiration requirements and effective rain. On sloping land fertilisers should be incorporated and the lower end of the field, drainage points and perimeters should be kept grassed. A buffer zone without applied fertiliser must be kept near rivers, fountains and water wells (between 2 and 10 m for water bodies; 30-50 m from fountains and water wells). Further advice on

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approaches to taking into account mitigation associated with buffer zones is discussed in Chapter 8.0. Table 6.10.3. Summary of Vulnerable Zones in Spain (Soler-Rovira et al., 2001) Region Vulnerable zones Andalucia Guadalquivir Valley (Sevilla)

Guadalquivir Valley (Córdoba and Jaén) Antequera detritic (Málaga) Granada Valley (Granada) Atlantic coast (Cádiz) Mediterranean coast (Almeria, Granada and Málaga)

Aragón Jalón-Huerva Gallocanta

Asturias Not designated Baleares Designated Canarias Gran Canaria

La Gomera La Palma Tenerife

Cantabria Not designated Castilla-La Mancha Mancha Occidental

Campo de Montiel Castilla y Leon Five vulnerable towns in Segovia Cataluña Alt Empordà, Baix Empordà, Pla del l’Estany, Ginorès

Maresme Osona Alt. Camp, Baix Camps. Tarragonès Baix Penedès Noguera, Segarra, Urgell, Pla d’Urgell, Segrià

Extremadura No vulnerable zones Galicia Not designated La Rioja Not designated Madrid Not designated Murcia Not designated Navarra Not designated Pais Vasco Vitoria-Gasteiz Valencia Alicante province

Castellón province Valencia province

Table 6.10.4. Periods when fertiliser applications to soil are not advisable (Soler-Rovira et al., 2001) Crops Manure Slurry, compost or sludge Uncultivated soils Whole year Whole year Winter cereals From sowing until harvest From sowing until tillering Spring cereals From sowing until harvest From sowing until next crop

soil tillage Industrial crops From sowing until harvest From sowing until next crop

tillage Vegetables From one month before

sowing until harvest From 15 days before sowing until harvest

Tree crops During winter dormancy until one month before dormancy breaks

During winter dormancy until 15 days before dormancy breaks

Ungrazed meadows During winter growth stop During winter growth stop

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6.11 Sweden A small majority of dairy farms and larger proportion of pig farms are systems based upon liquid/slurry production. In the case of pig production liquid/slurry systems account for up to 80% of waste production. Summaries of slurry and FYM management approaches in Sweden are provided in Tables 6.11.1 and 6.11.2. Table 6.11.1- Summary of Typical FYM and Solid Manure Management Strategies in Sweden

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production

Dairy cow FYM (35-50%) No limit 6-10 mo NO Grassland (90%) 20-30 t/ha 15-25 t/ha

Dairy heifer replacement FYM (35-50%) No limit 6-10 mo NO Grassland

(90%) 20-30 t/ha 15-25 t/ha

Beef suckler cow FYM (35-50%) No limit 6-10 mo NO Grassland (90%) 20-30 t/ha 15-25 t/ha

Grower fattener (>2 yr) FYM (35-50%) No limit 6-10 mo NO Grassland

(90%) 20-30 t/ha 15-25 t/ha

Grower fattener (12-24 mo) FYM (35-50%) No limit 6-10 mo NO Grassland

(90%) 20-30 t/ha 15-25 t/ha

Grower fattener (6-12 mo) FYM (35-50%) No limit 6-10 mo NO Grassland

(90%) 20-30 t/ha 15-25 t/ha

Calf (0-6 mo) FYM (35-50%) No limit 6-10 mo NO Grassland

(90%) 20-30 t/ha 15-25 t/ha

Swine Production Maiden gilts FYM (20-35%) No limit 6-10 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha 2 sow place + litters FYM (20-35%) No limit 6-10 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha

Weaners (3-7.5 weeks) FYM (20-35%) No limit 6-10 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha

Growers (7.5-11 weeks) FYM (20-35%) No limit 6-10 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha

Light cutter (11-20 weeks) FYM (20-35%) No limit 6-10 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha

Bacon (11-23 weeks) FYM (20-35%) No limit 6-10 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha

Bacon liquid feed (11-23 weeks) FYM (20-35%) No limit 6-10 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha

Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES Arable (90%) NA NA Broilers Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Replacement pullets Solid (100%) 8 wks 8 wks

YES Arable (90%) NA NA

Broiler breeders Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Turkey male Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Turkey female Solid (100%) 8 wks 8 wks YES Arable (90%) NA NA Ducks Solid (100%) No limit 8-12 mo YES Arable (90%) 20-30 t/ha 15-25 t/ha

Since 1996 in three counties in southern Sweden (Skåne, Halland and Blekinge) solid manures, slurry or urine must be incorporated within 4 hours after land application when spreading on bare soils (Steinecke et al., 2001). No specific data are available on potential for incorporation elsewhere in Sweden. It is, nonetheless, assumed to be typical. Within VetCalc the default arable scenario assumes application prior to a crop with the assumption that in arable situations incorporation would be typical, in line with Good Agricultural Practice.

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Table 6.11.2- Summary of Slurry Management Strategies in Sweden

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production

Dairy cow Slurry (50-65%) 6-10 mo 3 mo NO Grassland (90%) NA NA

Dairy heifer replacement Slurry (50-65%) 6-10 mo 3 mo NO Grassland

(90%) NA NA

Beef suckler cow Slurry (50-65%) 6-10 mo 3 mo NO Grassland (90%) NA NA

Grower fattener (>2 yr) Slurry (50-65%) 6-10 mo 3 mo NO Grassland

(90%) NA NA

Grower fattener (12-24 mo) Slurry (50-65%) 6-10 mo 3 mo NO Grassland

(90%) NA NA

Grower fattener (6-12 mo) Slurry (50-65%) 6-10 mo 3 mo NO Grassland

(90%) NA NA

Calf (0-6 mo) Slurry (50-65%) 6-10 mo 3 mo NO Grassland

(90%) NA NA

Swine Production Maiden gilts Slurry (65-80%) 6-10 mo 3 mo YES Arable (90%) NA NA 2 sow place + litters Slurry (65-80%) 6-10 mo 3 mo YES Arable (90%) NA NA

Weaners (3-7.5 weeks) Slurry (65-80%) 6-10 mo 3 mo YES Arable (90%) NA NA

Growers (7.5-11 weeks) Slurry (65-80%) 6-10 mo 3 mo YES Arable (90%) NA NA

Light cutter (11-20 weeks) Slurry (65-80%) 6-10 mo 3 mo YES Arable (90%) NA NA

Bacon (11-23 weeks) Slurry (65-80%) 6-10 mo 3 mo YES Arable (90%) NA NA

Bacon liquid feed (11-23 weeks) Slurry (65-80%) 6-10 mo 3 mo YES Arable (90%) NA NA

NVZ Status Since 1999, new measures regarding land application of manure have been implemented in Southern Sweden and coastal areas and on farms where there are more than 100 livestock units (Steinecke et al., 2001). Additional vulnerable zones have been identified in Västmanlands, Sodermanlands, Stockholms and Uppsala län. Within VetCalc, simulations based on applications to land in Sweden have, for simplicity been based on a maximum ceiling of 170 kg N/ha for both arable and grassland. Closed periods and restricted soils Manure and other organic wastes may not be applied to land in Sweden from the 1st December to 28th February, unless they are incorporated in the soil on the same day to a depth of at least 10 cm. Manure and other organic fertilisers can be applied from the 1st of August to the 30th of November only to a growing crop or before autumn sowing. Applications should take into account soil condition, soil texture and slope. Applications should not be made to water-saturated, flooded ground, snow-covered or deeply frozen ground (Steinecke et al., 2001). Surface water protection measures: Buffer zones along rivers and ditches have shown considerable reductions in nutrient losses. These buffer zones should be vegetated to reduce erosion and surface run-off. The size of these areas depends upon topography and soil type (Steinecke et al., 2001)..

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6.12 The United Kingdom Although the balance between production of liquid and solid manures in cattle farms appears to similar in the United Kingdom to France and Italy, practices associated with pig production appear to be very different in the United Kingdom compared with other European countries. This is well illustrated in Figures 6.12.1 and 6.12.2. In most European countries slurry production in pig farms dominates (often accounting for >95% of production). By contrast, in the United Kingdom, liquid/slurry production accounts for <50%. Storage capacity in slurry systems is typically 3-4 months in the case of pig farms and 3-6 months in the case of dairy farms. The majority of waste (both FYM and slurry) is generally applied to arable systems where there is the possibility of incorporation providing further mitigation of soil exposure. Detailed surveys undertaken by ADAS for the purposes of evaluating nutrient management strategies and nitrogen losses have demonstrated that in certain systems incorporation is routinely practiced although there may be delays between application and incorporation. An important exception appears to be broiler waste, which is rarely incorporated. The majority of poultry manure is added to sugar beet, potatoes, especially in spring on light land. The majority of cattle FYM are employed on cereal stubbles and potatoes. Summaries of slurry and FYM management approaches in the United Kingdom are provided in Tables 6.12.1 and 6.12.2. Figure 6.12.1- Slurry/Liquid Waste as a Proportion of Total Cattle Manure Production Application of FYM and slurry is subject to a range of legislative restrictions (national and regional) in the United Kingdom. These are summarised below:

• No regulations regarding storage of solid manures in heaps in the field. • If solid manures are stored on concrete (around buildings), then run-off must be

collected and treated as slurry. Any new structure must comply with the Control of Pollution act (Slurry, Silage and Agricultural Fuel Oil Regulations, 1991).

• Environmental Protection Act could enable members of the public to object to odours from housing, stored or spread manures.

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• Advice for farmers is contained in MAFF Code of Good Agricultural Practice of Water and similar code for the protection of air.

Figure 6.12.2- Slurry/Liquid Waste as a Proportion of Total Pig Manure Production NVZ Status A total of 55% of England was designated as a Nitrate Vulnerable Zone (NVZ) in October 2002 (this includes the 8% originally designated in 1996). Nitrate Vulnerable Zones in the UK were initially based upon Nitrate Sensitive Areas, mainly on vulnerable limestone chalk and sandstone aquifers. These have subsequently been expanded to include other areas (more than 70 in total in England and Wales, Scotland and Northern Ireland). Detailed maps can be obtained from Defra (http://www.defra.gov.uk/environment/water/quality/nitrate/nvz.htm). The Nitrates Directive provides for some discretion over the content of Action Programmes but there are certain measures which must be included. There are four key aspects to the measures:

1. Limit inorganic nitrogen fertiliser application to crop requirements, after allowing fully for residues in the soil and other sources.

2. Limit organic manure applications to 210 kg/ha of total nitrogen each year averaged over the area of the farm not in grass (reducing to 170 kg after four years), and 250 kg/ha of total nitrogen each year averaged over the area of grass on the farm.

3. On sandy or shallow soils do not apply slurry, poultry manures or liquid digested sludge between 1 September and 1 November (grassland or autumn sown crop) or 1 August and 1 November (fields no in grass without autumn sown crop). The storage capacity available for those animal manures which cannot be applied during the autumn closed period must be sufficient to cover these periods unless other environmentally acceptable means of disposal are available.

173

4. Keep adequate farm records, including cropping, livestock numbers and the use of organic manures and nitrogen fertilisers.

All farmers within the NVZs have been required to implement these measures from since 19 December 2002. The Zone 2 - Sandy loam 2 and Zone 2 - Clay scenarios are located within regions designated an NVZ (the former in a limestone chalk region and the latter region associated with sandstone aquifers). Simulations have been established for these scenarios with a maximum N rate of 210 kg N/ha (arable) and 250 kg N/ha (grassland). The Zone 2 – clay loam scenario is not located within an NVZ. Simulations have been established for this scenario with a maximum N rate of 250 kg N/ha (grassland). Table 6.12.1- Summary of Typical FYM and Solid Manure Management Strategies in The United Kingdom

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow FYM (35%) No Limit 6 mo YES (90%) Arable (>90%) 50 t/ha 30 t/ha Dairy heifer replacement FYM (35%) No Limit 6 mo YES (90%) Arable (>90%) 50 t/ha 30 t/ha

Beef suckler cow FYM (80%) No Limit 6 mo YES + NO (Both 50%)

Grassland (60%) 50 t/ha 30 t/ha

Grower fattener (>2 yr) FYM (80%) No Limit 6 mo YES + NO (Both

50%) Grassland (60%) 50 t/ha 30 t/ha

Grower fattener (12-24 mo) FYM (80%) No Limit 6 mo YES + NO (Both

50%) Grassland (60%) 50 t/ha 30 t/ha

Grower fattener (6-12 mo) FYM (80%) No Limit 6 mo YES + NO (Both

50%) Grassland (60%) 50 t/ha 30 t/ha

Calf (0-6 mo) FYM (35%) No Limit 6 mo YES (90%) Arable (>90%) 50 t/ha 30 t/ha

Swine Production Maiden gilts FYM (55%) No Limit 6 mo YES (>90%) Arable (80%) 50 t/ha 30 t/ha 2 sow place + litters FYM (55%) No Limit 6 mo YES (>90%) Arable (80%) 50 t/ha 30 t/ha

Weaners (3-7.5 weeks) FYM (55%) No Limit 6 mo YES (>90%) Arable (80%) 50 t/ha 30 t/ha

Growers (7.5-11 weeks) FYM (55%) No Limit 6 mo YES (>90%) Arable (80%) 50 t/ha 30 t/ha

Light cutter (11-20 weeks) FYM (55%) No Limit 6 mo YES (>90%) Arable (80%) 50 t/ha 30 t/ha

Bacon (11-23 weeks) FYM (55%) No Limit 6 mo YES (>90%) Arable (80%) 50 t/ha 30 t/ha

Bacon liquid feed (11-23 weeks) FYM (55%) No Limit 6 mo YES (>90%) Arable (80%) 50 t/ha 30 t/ha

Avian Production Laying Hens Solid (100%) 1 yr 1 yr YES (>85%) Arable (56%) 10-15 t/ha 10-15 t/ha Broilers Solid (100%) 8 wks 8 wks YES (>80%) Arable (56%) 4-10 t/ha 4-10 t/ha Replacement pullets Solid (100%) 8 wks 8 wks

YES (>80%) Arable (56%) 4-10 t/ha 4-10 t/ha

Broiler breeders Solid (100%) 8 wks 8 wks YES (>80%) Arable (56%) 4-10 t/ha 4-10 t/ha Turkey male Solid (100%) 8 wks 8 wks YES (>80%) Arable (56%) 4-10 t/ha 4-10 t/ha Turkey female Solid (100%) 8 wks 8 wks YES (>80%) Arable (56%) 4-10 t/ha 4-10 t/ha Ducks Solid (100%) No Limit 6 mo YES (>90%) Arable (80%) NA NA

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Closed periods and restricted soils Applications on waterlogged, flooded or frozen soils should be avoided under Codes of Good Agricultural Practice (Scholefield, 2001). Timing restrictions are established within vulnerable zones on specific soil types. On sandy or shallow soils do not apply slurry, poultry manures or liquid digested sludge between 1 September and 1 November (grassland or autumn sown crop) or 1 August and 1 November (fields no in grass without autumn sown crop). The storage capacity available for those animal manures which cannot be applied during the autumn closed period must be sufficient to cover these periods unless other environmentally acceptable means of disposal are available. The textural restriction is not applicable to the Zone 2 – Sandy loam 2 soil as it does not fulfil the coarseness criteria. Surface water protection measures: Applications close to water courses should be avoided under Codes of Good Agricultural Practice (Scholefield, 2001).

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Table 6.12.2- Summary of Slurry Management Strategies in The United Kingdom

Subset System Storage Capacity

Storage Time Incorporation

Dominant Application System

Typical Applic. Rate (Arable)

Typical Applic. Rate (Grassland)

Bovine Production Dairy cow Slurry (65%) 3-4 mo 1.5 mo YES (60%) Arable (>60%) 40-60 m3/ha 40 m3/ha Dairy heifer replacement Slurry (65%) 3-4 mo 1.5 mo YES (60%) Arable (>60%) 40-60 m3/ha 40 m3/ha

Beef suckler cow Slurry (20%) 3-4 mo 1.5 mo NO (70%) Grassland (80%) 40-60 m3/ha 40 m3/ha

Grower fattener (>2 yr) Slurry (20%) 3-4 mo 1.5 mo NO (70%)

Grassland (80%) 40-60 m3/ha 40 m3/ha

Grower fattener (12-24 mo) Slurry (20%) 3-4 mo 1.5 mo NO (70%)

Grassland (80%) 40-60 m3/ha 40 m3/ha

Grower fattener (6-12 mo) Slurry (20%) 3-4 mo 1.5 mo NO (70%)

Grassland (80%) 40-60 m3/ha 40 m3/ha

Calf (0-6 mo) Slurry (65%) 3-4 mo 1.5 mo YES (60%) Arable (>60%) 40-60 m3/ha 40 m3/ha Swine Production

Maiden gilts Slurry (45%) 3-6 Months 1.5 mo YES (>60%) Arable (55%) 40-60 m3/ha 40 m3/ha

2 sow place + litters Slurry (45%)

3-6 Months 1.5 mo YES (>60%) Arable (55%) 40-60 m3/ha 40 m3/ha

Weaners (3-7.5 weeks) Slurry (45%)

3-6 Months 1.5 mo YES (>60%) Arable (55%) 40-60 m3/ha 40 m3/ha

Growers (7.5-11 weeks) Slurry (45%)

3-6 Months 1.5 mo YES (>60%) Arable (55%) 40-60 m3/ha 40 m3/ha

Light cutter (11-20 weeks) Slurry (45%)

3-6 Months 1.5 mo YES (>60%) Arable (55%) 40-60 m3/ha 40 m3/ha

Bacon (11-23 weeks) Slurry (45%)

3-6 Months 1.5 mo YES (>60%) Arable (55%) 40-60 m3/ha 40 m3/ha

Bacon liquid feed (11-23 weeks) Slurry (45%)

3-6 Months 1.5 mo YES (>60%) Arable (55%) 40-60 m3/ha 40 m3/ha

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6.13 Summary Certain manure and slurry practices appear to be relatively consistent throughout the eight Member States (e.g. dominance of slurry systems in pig production). However, for other systems there is a greater degree of variation in practice. Three Member States stand out as having significantly different agricultural practice to the remaining five (Ireland, The Netherlands and the United Kingdom). In the case of Ireland and the Netherlands a significant proportion of manures are applied to grassland – a practice that is, by contrast, relatively rare in other Member States. The United Kingdom is noteworthy because of the significance of solid manures (FYM) for certain livestock categories where slurry systems dominate elsewhere. In order to present a very simple summary of how practice varies from Member State to Member State the data presented in previous sections for practices in each Member State has been condensed into 7 figures (Figures 6.13.1-7). These figures are based upon maximum quoted rates or percentages for illustrative purposes. The figures suggest that identification of obvious best or worst case scenarios is complex. As a consequence, the PECsoil values must be generated for each relevant scenario identified in Section 5.2 as a first step in order to determine the clear worst case for terrestrial exposure. Figure 6.13.1- Comparative Significance of Slurry Systems in Dairy Farms (Menzi, Pers. Comm.)

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Figure 6.13.2- Comparative Significance of Slurry Systems in Housed Pig Production (Menzi, Pers. Comm.)

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Figure 6.13.7- Comparative arable utilisation of poultry wastes (Menzi, Pers. Comm.; Selected data published by Burton and Tuner, 2003) NOTE: Dark blue histograms represent scenarios considered in VetCalc 6.14 References Ambus, P., Sorenson, F.L., Lillelund, D., Nielsen, G.G. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Denmark, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers BOJA (1998) Law Gazette from Andalucia, (08/01/1998) Resolution of 12th December 1997 that publishes the Code of Good Agricultural Practice of Andalucia, General Directorate of Agricultural Production, Ministry of Agriculture and Fisheries of Andalucia, pp 215. Bonazzi, G. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Italy, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Burton, C.H., Turner, C. (2003) Manure management – Treatment strategies for sustainable agriculture, 2nd Edition, Silsoe Research Institute, Silsoe, UK Bäckman, S., Vermeulen, S. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Finland, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Cabral, F., Ribeiro, H.M., Vasconcelos, E., Pinto, F.C., Cordovil, C. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Portugal, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Carton, O. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Ireland, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers De Clercq, P., Salomez, J., Hofman, G. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Belgium, In: Nutrient

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management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Happe, K., Kilian, B., Kazenwadel, G. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Germany, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Menzi, H. (2003) Pers. Comm. Neeteson, J.J., Schröder, J.J., Hassink, J. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – The Netherlands, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Rapion, P., Martinez, J., Le Bozec, G. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – The Netherlands, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Scholefield, D. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – United Kingdom, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Smith, K.A., Brewer, A.J., Dauven, A., Wilson, D.W. (2000a) A survey of the production and use of animal manures in England and Wales. I. Pig Manure, Soil Use and Management, 16, 124-132 Smith, K.A., Brewer, A.J., Crabb, J., Dauven, A. (2000b) A survey of the production and use of animal manures in England and Wales. II. Poultry Manure, Soil Use and Management, 17, 48-56 Smith, K.A., Brewer, A.J., Crabb, J., Dauven, A. (2000c) A survey of the production and use of animal manures in England and Wales. III. Cattle Manures, Soil Use and Management, 17, 77-87 Smith, K.A., Frost, J.P. (2000d) Nitrogen excretion by farm livestock with respect to land spreading requirements and controlling nitrogen losses to ground and surface waters. Part 1: Cattle and sheep, Bioresource Technology, 71, 173-181 Smith, K.A., Charles, D.R., Moorhouse, D. (2000e) Nitrogen excretion by farm livestock with respect to land spreading requirements and controlling nitrogen losses to ground and surface waters. Part 2: Pigs and poultry, Bioresource Technology, 71, 183-194 Soler-Rovira, J., Soler-Rovira, P., Soler-Soler, J. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – España, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers Steinecke, S., Jakobsson, C., Åkerhielm, H., Carlson, G. (2001) Environmental pressures and national environmental legislation with respect to nutrient management – Sweden, In: Nutrient management legislation in European countries (Editors: P. De Clerq, A.C. Gertsis, G. Hofman, S.C. Jarvis, J.J. Netteson, F. Sinabell), Wageningen Pers

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7.0 MODELLING FRAMEWORK The conceptual framework employed within the VetCalc model is summarised in Figure 7.1. The framework can be divided into four major modelling tasks:

• Provision of input on dosage regime and chemical characteristics • Calculation of maximum/initial predicted environmental concentrations (PEC) in

excreta and soil • Simulation of subsequent fate in soil (including potential for run-off, leaching and

degradation and estimation of PEC values in shallow groundwater) • Simulation of subsequent fate in surface water (including potential for

dilution/advection, degradation and partitioning and estimation of PEC values in the water column and sediment)

Figure 7.1. Conceptual framework of VetCalc model In order to carry out the required calculations three modelling components were developed as described in Figure 7.2:

• Graphic User Interface (including standardised regulatory calculations of PECexcreta and PECsoil)

• Modified LEACHP model: Simulation of fate in soil (including estimation of PECgroundwater)

• Fugacity model: Simulation of fate in surface waters (including estimation of PECsurface

water and PECsediment) Each of the modelling components are discussed in further detail in the following chapter.

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Figure 7.2. Illustration of critical modelling components within VetCalc model 7.1 Simulation of loadings to soil Phase I of the risk assessment process for a veterinary medicine requires an assessment of the potential for environmental exposure to the product, its ingredients or relevant metabolites. There are a number of potential exposure pathways to consider, since it is widely accepted that residues of veterinary medicines can be secreted into urine and faeces and may be excreted onto the land or into the water. However, in intensive livestock production systems the greatest potential releases of veterinary medicines into the environment will result from the application of manures to land. As a consequence, the critical endpoint at Phase I is the estimation of Predicted Environmental Concentration in the soil compartment resulting from spreading of drug residues in slurry (PECSoil). The risk assessment framework states that substances that have a PECSoil below 100 µg/kg do not present an unacceptable risk to the environment (EMEA, 2000). The Technical Guidance Document (EMEA, 1997) indicates that suitable methods for estimating the PEC for intensive livestock treatments which are excreted in urine or faeces where the slurry is to be spread onto land should consider the following components:

• Total dose/animal/year, i.e. the total dose given to the number of animals that occupy a defined space in the farm during a year;

• Excreta production/animal/year; • Percentage of animals treated; • Degradation during storage; • Maximum slurry application rates/year; • Plough depth (an estimate should also be made based on slurry not being ploughed

into soil, assuming even distribution of residues to 5cm); • Soil density (1.5 g/cm3).

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Methodology - Model Selection Predicted Environmental Concentrations for the soil compartment are estimated within VetCalc according to the methodology developed by Montforts et al. (1999). The methodology was developed to assist with Environmental Risk Assessments for Veterinary Medicines in the Netherlands. However, it is generally accepted that the principles upon which the calculations are based are applicable in regulatory estimations of environmental exposure elsewhere in Europe provided that appropriate parameters are chosen to represent regional agriculture practices. Montforts et al. outline two general categories of situations that can be represented mathematically:

• Excretion by grazing animals • Spreading of slurry or FYM

In each case Montforts et al. provide a mathematical framework for estimating PECSoil in grassland and in arable land. Each is elaborated in further detail below: 7.1.1 Excretion by grazing animals Residues of veterinary medicines are excreted onto land via urine and faeces. Montforts et al. (1999) has provided a framework for assessing dung exposure. The following framework is based upon this approach within minor amendments which are noted:

animal

excretiondmetabolisedungexcretedtreatmentanimalcproductdung Pdung

NFFTmCQPEC

××××××= .max

Where: Qproduct Dosage of product used (kg/kgbw/day) Cc Concentration of chemical (c ) in product (mgc/kg) manimal (Averaged) body weight (kgbw/animal) Ttreatment Duration of treatment (days) Fexcreted dung Maximum fraction excreted in dung in one day Pdung animal Dung production animal in field (kgwwt/animal/day) Nexcretion Number of dung excretion events per day (day-1) PECdung Predicted environmental concentration in dung (mgc/kgwwt) The parameter Fmetabolised (fraction of treatment that undergoes metabolism prior to excretion) has been introduced to allow more sophisticated simulations. This is consistent with similar frameworks presented later for slurry or FYM management systems. Defaults can be applied as follows in the absence of more detailed information: Fexcreted dung = 1.0 Nexcretion = 10.5 In VetCalc Qproduct is expressed in terms of mg of product per kg of bodyweight, thereby obviating the need for parameter Cc. For treatments that are not based upon bodyweight (e.g. bolus treatments, certain injections etc.) an amendment of this equation is employed in which Qproduct is reported simply in terms of mg (total dose) and manimal is omitted. For topical treatments it is assumed that PECdung is 0. The PECdung value is not taken forward into any further calculations but is reported by VetCalc for completeness and to facilitate terrestrial risk assessments. In order to estimate exposure to soil calculations are carried out as follows:

185

( )DEPTHfieldCONVRHOsoilNanimalQQ

PECfieldarea

fielddungleachedurineexcretedsoil ××

+=

The parameters Qexcreted urine and Qleached dung are estimated as follows:

treatmentdmetaboliseurineexcretedanimalcproducturineexcreted TFFmCQQ ×××××=

treatmentdmetabolisedungleacheddungexcretedanimalcproductdungleached TFFFmCQQ ××××××=

As before, the parameter Fmetabolised (fraction of treatment that undergoes metabolism prior to excretion) has been introduced to allow more sophisticated simulations. This is consistent with similar frameworks presented later for slurry or FYM management systems. In common with the framework presented above for PECdung VetCalc Qproduct is expressed in terms of mg of product per kg of bodyweight, thereby obviating the need for parameter Cc. For treatments that are not based upon bodyweight (e.g. bolus treatments, certain injections etc.) an amendment of this equation is employed in which Qproduct is reported simply in terms of mg (total dose) and manimal is omitted. Fexcreted urine and Fexcreted dung have default settings of 1.0 and 0.0 respectively. These defaults can be overridden where experimental evidence of justifications allows. In the case of ‘pour on’ topical treatments the use of a Fexcreted urine default of 1.0 is equivalent to assuming that 100% of the treatment is lost to soil (a ‘worst-case’ assumption of no rainfastness). The same assumption is also employed for topical applications that are formulated as sprays with the exception that the proportion considered likely to be lost to soil is lower. A default factor of 0.2 is introduced that assumes a cumulative 20% lost via drift or via rain. This is broadly consistent with the recommendations of Montforts (1999). The user may consider alternative losses to soil by amending the defaults presented in the grazing calculations. The parameter Fleached dung can be estimated as follows:

waterdung

dungdungleached K

FwaterF

−

=

Default values are provided for Fwaterdung for each relevant livestock category, as summarised in Table 7.1.1.1 below. Default Pdung values are provided for a number of animal categories in Table 7.1.1.2: Table 7.1.1.1- Fwaterdung and Fsoliddung characteristics Fairdung (m3 m-3) Fsoliddung (m3 m-3) Fwaterdung (m3 m-3) Dairy cow 0.025 0.075 0.90 Beef cattle 0.03 0.09 0.88 Sheep 0.07 0.26 0.67 Kdung-water is estimated as follows:

dungdung

dungdungwaterdung RHOsolidKp

FsolidFwaterK ××+=− 1000

186

RHOsoliddung is assumed to be 1675 kgdwt/m Kpdung is estimated as follows:

KocFocKp dungdung ×= Focdung is estimated as follows:

dungdung FomFoc ×= 59.0 Where Fomdung is assumed to be 0.75 kg/kg Table 7.1.1.2- Summary of default dung production values for livestock categories Livestock category Pdung (kg wwt animal-1 d-1) Dairy cow 52 Suckler cow 52 Beef cattle 11 Ewe 1.025 Lamb 1.758 All parameters required in the estimation of PECsoil are summarised below Qproduct Dosage of product used (kg/kgbw/day) Cc Concentration of chemical (c ) in product (mgc/kg) manimal (Averaged) body weight (kgbw/animal) Ttreatment Duration of treatment (days) Fexcreted urine Fraction excreted in urine Nanimalfield Stocking density animals (animals/ha) RHOsoil Dry bulk density of soil (kg/m) DEPTHfield Mixing depth with soil (m) CONVarea field Conversion factor for the area of the field (m2/ha) RHOsoliddung Density of dung solids (kg/m) RHOwater Density of water (kg/m) Fwaterdung Fraction of water in dung (m3/m3) Fsoliddung Fraction solids in dung (m3/m3) Focdung Weight fraction organic carbon in dung (kg/kg) Koc Partition coefficient organic carbon – water (dm3/kg) Qexcreted urine Quantity of active ingredient excreted with urine (mgc/animal) Qleached dung Quantity of active ingredient leached with dung (mgc/animal) Fexcreted dung Fraction excreted in dung Fleached dung Fraction leached from dung Kdung-water Partition coefficient solids and water in dung (m3/m3) Kpdung Partition coefficient solids and water in dung (dm3/kg) PECsoil Predicted environmental concentration in soil (mgc/kg/soil) 7.1.2 Spreading of slurry or FYM For a number of intensively produced animal categories the major route of entry of veterinary medicines into the environment is by spreading of stored slurry or FYM. Farmers will apply slurry or FYM to fields up to a maximum threshold defined generally on the basis of nitrate legislation. In most regions manure is applied to land at rates up to a maximum of 170 kg nitrogen equivalent. In certain regions further restrictions exist that effectively reduce the quantities of nitrogen (and, therefore,

187

manure) that can be applied. These are discussed in greater detail in this report alongside additional application timing restrictions (closed periods for spreading; see Section 6.0). In certain other regions (e.g. The Netherlands) phosphorus standards are more restrictive and are applied instead. Setting aside any closed periods, farmers may apply manures at any time of the year provided that on individual fields they do not exceed the nitrogen or phosphorus standards. For certain animal manures (e.g. poultry) that are particularly rich in nitrogen or phosphorus the thresholds defined by these standards are reached easily with relatively small applications. A few observations are appropriate for a number of livestock categories based upon results of surveys conducted in the UK (Smith et al., 2000, 2001a,b). These trends are expected to be broadly reproduced elsewhere in Europe: Pig manures (after Smith et al., 2000) Most farmers responding to surveys indicated that they applied manures to more than one crop. The application of FYM was much reduced in summer months (May – June) as opportunities for application are limited in that period and expected peak applications in August – October, on cereal stubbles and prior to root crops in autumn and winter, were evident. Slurry spreading on grass was fairly evenly distributed throughout the year, but there was a high peak in activity of >50% pig slurry applied in the autumn on cereal stubbles. Farmers were asked to make their own estimates of percentages of total FYM or slurry applied in each quarter of the year with consistent responses noted (peak in activity was noted for both slurry and FYM applications in the period between August and October). Supporting information from an independent survey (Dauven et al., 1998) indicated that within the same time period the majority of slurry and FYM was applied to support subsequent production of winter cereals. The availability of sufficient land for environmentally acceptable spreading of pig manure nutrients is a recognised problem in the UK. Surveys indicated that 22% of farms producing FYM exported almost 80% to neighbouring farms. A further 16% of respondents whose farms produced slurry exported almost 75% of manure production. Poultry manures (after Smith et al., 2001a) As with pig manures, most farmers apply poultry manures to more than one crop. Applications to grazing land and grazing + silage areas were distributed more evenly through the year relative to applications to arable land. It was noted that a particularly high proportion of poultry manure was applied between August and October, before cereals (onto stubble) or land scheduled for potatoes or sugar beet. The proportions of poultry manure applied to various crops was estimated as follows:

• 41% to cereals • 30% to grazing land • 18% to silage land • 4% to forage maize • 4% to sugarbeet • 3% to potatoes

It was, however, noted that these estimates differed slightly (although a similar temporal pattern was observed with a peak in application in the autumn to cereals) compared with those in an earlier survey (Dauven et al., 1998) in which the following trends were summarised:

• 11% to long leys (mainly for silage) • 13% to potatoes • 18% applied to permanent grass (mainly grazing) • 21% to cereals

188

• 37% to sugarbeet The availability of sufficient land for environmentally acceptable spreading of pig manure nutrients is a recognised problem in the UK. It has been estimated that maximum stocking densities of 505, 425 and 345 broilers per hectare are permitted to ensure N loadings do not exceed 250, 210 and 170 kg N/ha, respectively. Large units, with many thousands of birds, will often need to export manures to neighbouring farms. It was concluded that it should come as no surprise that on 39% of layer units and 57% of broiler units, an average of almost 90% of the manure production was exported from the farm. Cattle manures (after Smith et al., 2001b) As with other livestock categories, most farmers responding to surveys indicated that they applied manures to more than one crop. Typical application regimes are summarised below: Table 7.1.2.1- Summary of typical cattle manure spreading regimes in UK Management System Most significant spreading

period Most significant spreading target

Dairy slurry February - April Annual: Silage grass Feb – Apr: Forage maize

Dairy FYM August - October Annual: Cereals Aug – Oct: Cereals

Beef slurry February - April Annual: Forage maize and/or grazing/silage grass Feb – Apr: Grazing

Beef FYM February - April Annual: Forage maize and/or grazing/silage grass Feb – Apr: Cereals

Because of the persistence of manure solids within the sward and the contamination risk of cut herbage (and grazed swards), the application of FYM is much reduced in the summer months (May – July). A high proportion of dairy FYM is applied in the August – October period, when access to fields and suitable spreading conditions are likely to present little difficulty. However, it was noted as being of concern that a high proportion of dairy slurry is also spread on grazing land during this period (almost 60%) and silage fields (26%) since nitrate losses from slurry, overwinter are likely to be high (Chambers et al., 2000). The proximity of applications to a period of vulnerability to leaching suggests autumn applications may provide a realistic ‘worst-case’ basis for regulatory risk assessments for veterinary medicines. A high proportion of FYM and dairy slurry is also applied in the period February – April, before forage maize. Results also indicated that 40% of dairy and 46% of beef slurry were applied in February – April. A preference for spring application of beef slurry is apparent, but it should be recognised that in the UK this relates to a relatively small proportion of material (at the time of the survey only 18% of excreta from beef production units were managed as slurry systems). Unlike pig and poultry production in the UK, the availability of sufficient land for spreading manure nutrients are not usually a problem for dairy or beef units. Exportation of manures from beef and dairy units was found to be very minor (3% of dairy and beef FYM units, 8% of dairy slurry units and less than 1% of beef slurry units exported manures).

189

Development of pragmatic scenarios In light of these observations, and in recognition of the potential for applications to be carried out at almost any time of the year (excluding any closed periods) it is appropriate to consider a set of pragmatic recommendations that could form the basis of a starting point for risk assessments. Several issues need to be addressed:

• How many applications are typical to a specific field? • Is the application typically made to grassland or arable systems? • If the application is made to arable systems, which crop is most typical? • What is most typical associated timing for application?

Each of these issues is addressed below with a set of recommendations that act simply as a starting point for risk assessment. It is typical in these cases for one or two annual applications to be made to a specific field. Under rare circumstances the standard may be met through a number of applications. Because there is a high degree of variability in agricultural practices a pragmatic and conservative approach has been adopted within VetCalc model reflecting typical practice. Two application scenarios are considered;

• Application to arable land • Application to grassland

For applications to arable land it is assumed that the nitrogen or phosphorus standard is fulfilled by a single annual application. This is most typically carried out in the early autumn or spring associated with drilling. For pragmatic reasons (in order to ‘standardise’ assessments) timing of application is assmed to coincide with drilling of spring or winter cereals. Timing recommendations are provided for each scenario within the model. For applications to grassland a single application in the spring is recommended with timing recommendations provided within VetCalc. However, it is recognised that the nitrogen or phosphorus standard may be achieved more typically through two applications in spring and early summer (harvest of the sward). The ‘split’ scenario has the effect of reducing peak PEC values in soil by dividing the mass applied between two events (there is often significant potential for degradation between application events). The single annual application scenario is considered more conservative and is recommended as a starting point for risk assessment. However, the user is also provided with the option of considering the impact of split applications. The framework of calculations recommended by Montforts et al. (1999) are outlined below. The framework refers to a phosphate standard (the more restrictive standard applied in the Netherlands) but can equally be applied to considering nitrogen standards elsewhere in Europe (e.g. 170 kg N/ha):

DEPTHfieldCONVRHOsoilQC

PECfieldarea

OPPsoil ××

×= 52205

QP2O5 (phosphate immission standard) and RHOsoil are scenario-specific parameters. CONVarea field is simply a conversion factor (10,000 m2/ha). In arable simulations DEPTHfield (dilution depth) can be defined as either 0.2 m (assumes incorporation) or 0.05 m (surface applied – no incorporation). In grassland simulations DEPTHfield is assumed to be 0.05 m. CP2O5 (concentration in phosphate terms) is estimated as follows:

190

2/deg

5252 1

1 Trestslurryk

rsl

onNapplicatirsl

OPstorage

excretedOP e

FF

PTQ

C ×−×−

−×

×=

Tstorage (average storage time) is a scenario-specific parameter. Napplication is assumed to be:

• Worst-case arable or grassland = 1.0 (single annual application up to maximum nitrogen or phosphorus standard)

• Typical case grassland = 2.0 (‘split’ applications totalling the maximum nitrogen or phosphorus standard)

PP2O5 (phosphate production of animal in stable) is an animal-specific parameter. Qexcreted (amount of substance excreted) is estimated as follows:

treatmentdmetaboliseexcretedanimalcproducturineexcreted TFFmCQQ ×××××= As before, the parameter Fmetabolised (fraction of treatment that undergoes metabolism prior to excretion) has been introduced to allow more sophisticated simulations. Frsl (fraction of the concentration remaining in slurry after a given time interval) is defined as:

Tcyclusslurrykrsl eF ×−= deg

Tcyclus (duration of cyclus) is calculated simply as:

cycluscyclus NT /365= Where Ncyclus is the number of production cycli per year. kdegslurry (the rate of degradation in slurry) is defined as:

slurryslurry DT

kdeg50

2lndeg =

Where DT50degslurry is the degradation half-life in slurry. Trest (duration of storage after last treatment) is estimated as follows:

cyclusstoragerest TonNapplicatiTT ×−−= )1( Where Tstorage (average storage time of slurry) is a scenario-specific parameter All parameters required in the estimation of PECsoil are summarised below: Qproduct Dosage of product used (kg kgbw/day) Cc Concentration of chemical (c) in product (mgc/kg) manimal Average body weight (kgbw/animal) Ttreatment Duration of treatment (days) Fexcreted Fraction excreted Ncyclus Number of cycli per year (animal/place/year) PP205 Phosphate production of animal in stable (or nitrogen production;

kg/animal/place/day) Tcyclus Duration of cyclus (days)

191

Tstorage Average storage time for slurry applied to grassland/ arable land (days) Trest Realistic worst case duration of storage after last treatment (days) Napplication Number of applications per storage period kdegslurry Reaction constant transformation in manure (day-1) QP205 Phosphate immission standard (can be substituted by nitrogen immision

standard; kgP205/ha/year) RHOsoil Bulk density of soil (kg/m3) DEPTHfield Mixing depth with soil (m) CONVarea field Conversion factor for the area of the agricultural field (m2/ha) Frsl Fraction of concentration remaining in manure after time Tcyclus Qexcreted Amount of substance excreted (mgc/place/year) CP205 Concentration in phosphate (or concentration in terms of nitrogen; mgc/kgP205) PECsoil Predicted Environmental Concentration in the soil (mgc/kgsoil)

7.2 Simulation of fate and behaviour in soil Leach-P is primarily a soil and chemical leaching model, developed by Hutson (2003). In the context in which it is used in this project, it predicts the transport of water and solute through a soil column, and the partitioning of chemical into solid, dissolved and gaseous phases. 7.2.1 Model capability The major processes included in the model are shown schematically in Figure 7.2.1.1, taken from the documentation for LeachM (Hutson, 2003).

Figure 7.2.1.1 The major processes represented in Leach-P Water is added to the soil column through rainfall and may either evaporate from the soil surface or seep through the soil column. If plants are present, water may also be lost through transpiration. Chemical may be added either in solution with rainfall, or in dry form. If added in dry form it may be incorporated through a specified depth at the top of the column or applied directly to the soil surface, from where it may be transported in solution into the soil profile. Importantly, the transport of water through the soil column is determined by the solution of Richard’s equation.

192

Once in the soil profile, chemical is subject to decay to child metabolites and uptake by plants or may be lost through volatilisation at the surface or drainage beyond the base of the modelled soil column. The model considers chemical in solution, gaseous, solid and adsorbed forms. Figure 7.2.1.2, again taken from the model documentation, shows the major partitioning, transport and transformation processes included in the model.

Figure 7.2.1.2 Major chemical processes included in Leach-P 7.2.2 Enhancements to the functionality of Leach-P As previously mentioned, Leach-P is designed to simulate the transport of water and associated chemicals through a soil column. There are several processes, however, which are important in the context of this project but are not represented in Leach-P in its basic form. Parametrisations of these processes, based on published research, were added to the model as described in the following sections. Transport of water and chemical by drains Flow to tile drains or mole drains can be an important drainage pathway, particularly in impermeable or slowly permeable soils. We implemented the parametrisation of Hooghoudt (1940) to represent this pathway. The parametrisation is sensitive to the spacing of the drains, their depth and internal diameter. Since it is these factors that differentiate tile drains from mole drains, the scheme can be used to represent either type of drain with no modification other than the selection of appropriate values for these quantities. The Hooghoudt scheme considers flow to drains from above and below, whenever the local water table rises above the level of the drains. It assumes the presence of an impermeable layer at the base of the modelled soil profile. In practice, drains are likely to be installed only in situations where the soil profile is not highly permeable, and so this assumption is not unreasonable. The user is required to specify whether underdrains are present in the soil profile and, if they are, their depth, spacing and internal diameter.

193

Although the model now diagnoses the mass of dissolved chemical lost in flow to drains, in reality drains may also transport sediment, which may include adsorbed chemical. This chemical pathway is not represented in the model. Surface runoff and associated chemical losses Leach-P, in basic form, does not attempt to represent surface runoff. Since this is an important pathway for water and chemicals, it has been included in the model by the process of infiltration excess. Local storage is estimated, after Onstadt (1984), as a function of random roughness and hill slope angle. The model uses input daily rainfall amounts with an assumed event duration to calculate a constant rainfall intensity. During the course of the event, the total rainfall and the total infiltration since the start of the event are calculated on every model timestep. The difference between the two figures is assumed to be potential runoff. Any potential runoff in excess of the local storage capacity is assumed to actually run off. Runoff water may also contain chemical in solution. Since runoff water may reach local water courses, it is important that any associated chemical is accurately represented. It is assumed that runoff water interacts with the soil water near the surface, which contains a known concentration of chemical, to a specified depth (equal to 1mm). Any chemical in solution in this layer is assumed to be mixed homogeneously in runoff and soil water, and hence knowing the volume of runoff water and the concentration of chemical in the runoff water, the mass of tracer lost in solution in runoff can be calculated. Soil erosion and associated chemical losses Runoff may transport mobilised sediment from the soil surface. This sediment may contain adsorbed chemical and in addition, precipitated chemical may be present in the soil profile. In order to estimate the quantity of sediment lost with runoff, we implement the USLE (Universal Soil Loss Equation) parametrisation (Wischmeier and Smith, 1978). Following the notation used in the EPIC model (Sharpley and Williams, 1990), this model uses the following equation to estimate soil loss:

ROKFLSPECEKKEY .....= , in which Y = sediment yield, KE is a rainfall erosivity factor, K a soil erodibility factor, CE a crop management factor, PE an erosion control practice factor, LS a slope factor and ROKF a coarse fragment factor. Again, following the EPIC model, these factors are calculated as follows.

( )( )[ ]

( )

+−+

−

−+

−

+−−+=

)19.2251.5exp(117.00.1

.95.272.3exp

25.00.1

.100/10256.0exp3.02.03.0

SNSNSN

CCOC

CZZZSK

194

in which S, Z and C are the percentage fractions of sand silt and clay in the soil, OC is the percentage organic carbon in the soil and SN1 = 1-S/100. Two possible crop scenarios are catered for: bare soil and permanent grassland. The factor CE takes values of 1.0 for bare soil or 0.05 for permanent grassland. PE takes a value of 1.0 in all cases; it is assumed that no erosion control practises are in place. LS is calculated as follows:

( )065.056.441.651.22

2 ++

= LL SSLS

ξλ

in which SL is the surface slope (m/m), λ is the slope length (m) and the factor ζ is calculated from:

( )[ ] 2.009.6147.1exp

3.0+

−−+=

LL

L

SSS

ξ

The factor ROKF takes a value of 1.0 in all cases; we assume that there are no coarse fragments in the surface layer. Finally, the factor KE is calculated based on the rainfall incident on the surface during each model timestep. Here, we depart from the methodology of the EPIC model, and use a regression relationship taken from the Morgan Morgan Finney model (Morgan, 2001). The exact relationship used is a function of the location being considered, and a choice is made based on one of three climate regimes: NW Europe, Mediterranean and Western Mediterranean. The regime is selected by the user during the setup of the run. Temperature and moisture sensitivity of chemical degradation rates Although Leach-P does allow diagnosed rates of chemical transformation between parent and child species to vary with ambient temperature and moisture conditions, we have implemented the scheme developed by Walker (1978), used in the PERSIST model. In this scheme, the user is required to specify a reference half-life for each parent species, together with the temperature and moisture conditions at which this half-life pertains. A half-life appropriate for the ambient temperature and moisture conditions is then calculated for each parent species, and this applied to calculate the mass of parent species which metabolises on each model timestep. The rate of degradation of a parent species is calculated as:

kCdtdC

−=

where C is the concentration of chemical present and k is a rate constant found from:

τ693.0

=k

195

where τ is the half-life of the species. The temperature- and moisture-corrected half-life is calculated as:

TMref FF ..ττ = where τref is the reference half-life of the substance (days), FM is the moisture correction factor and FT the temperature correction factor. FM is calculated from:

B

refMF

=

θθ

,

in which θ is the moisture content of the soil (%w/w) and θref the reference moisture content. The factor B is supplied by the user. FT is found from:

1010

refTT

T QF−

= , in which the factors Tref and Q10 are user-supplied, and T is soil temperature. Representation of the relative masses of parents and metabolites. The scheme described in (5) enables the model to calculate the mass of parent species that has metabolised at any point during a model run. This is not necessarily the same as the mass of the resulting child species, however. The mass of a child species that results from the degradation of a mass Mp of a parent species is:

=

p

cpc m

mMM .

where mc is the molar mass of the child species and mp the molar mass of the parent species. The model now outputs the mass of child species resulting from chemical degradation rather than the mass of parent that has decayed. 7.3 Simulation of fate and behaviour in surface water The surface water module embedded within VetCalc was prepared by Antonio DiGuardo of the University of Insubria, Italy. Contact details are provided below: Antonio Di Guardo Environmental Modelling Group, DBSF, University of Insubria, Via Dunant 3, 21100 Varese, Italy 7.3.1 Introduction There is considerable interest in developing models and scenarios to predict fate of chemicals such as pesticides towards surface water: at EU level a forum (FOCUS) was established as a joint initiative of the Commission and industry in order to develop guidance on the use of mathematical models in the review process under Council Directive 91/414/EEC of 15 July 1991 concerning the placing of plant protection products on the market and subsequent amendments. The FOCUS

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Steering Committee mentions the need for guidance on the estimation of Predicted Environmental Concentrations (PECs) using mathematical models. The current FOCUS Working Group on "Surface Water Scenarios” developed a series of standard agriculturally relevant scenarios for the European Union that can be used with these models to fulfil the requirements for calculating PECs (FOCUS, 2001). Whilst standard scenarios are not available for the assessment of PECs in surface water and sediment, FOCUS (2001) recommended that all model calculations make careful and reasoned consideration of the definition of the scenario(s). Standard scenarios for the European Union should be developed. Among the model available the model TOXSWA (Adriaanse, 1996; Beltman and Adriaanse, 1999; Ter Horst et al. 2003) was suggested for implementing the PEC calculation in surface water. Mathematical models for prediction and interpretation of chemical fate in surface waters and sediment have proven to be valuable tools in the process of exposure assessment. Among them a large number of existing surface water models is based on QWASI models, a series of models initially developed by Mackay et al. (1983a,b) to predict the fate of a chemical in a surface water-sediment system both in a steady and unsteady state situation. New models were developed from QWASI to underline specific environmental phenomena not considered in the “original model”; to reach this goal the basic structure was modified changing, for example, the number of compartments or adding additional parameters representative for the selected environment (Freitas et al., 1997, Lun et al., 1998, Southwood et al., 1999). The original calculation scheme is maintained in all these models; this means that every compartment is defined by a mass balance equation written in terms of fugacity (Mackay, 2001) and the main dissipation and transport processes are expressed in a D-Value form. For most of the above mentioned models only steady state model formulations are available. Some of them contain an unsteady-state version, which can be used to evaluate the time to reach steady state at a constant chemical emission or the time to reach a certain concentration level when emission is ceased. However, they are “static” in terms of scenario, which means that variations in environmental parameters are not taken into account during the simulation. This approach is not suitable when the complexity of the system increases. Many environmental factors such as temperature, water inflow and outflow, water volume may markedly vary with time; in addition, chemical discharge may be not continuous and often the applied amount may vary daily or hourly; in this case an average value of these parameters is not correct to obtain good predicted results. This is the case of agricultural situations in which chemical loadings (pesticides and or veterinary pharmaceuticals) may reach the water compartments mainly driven by water (surface and subsurface runoff) movement. The SWM (Surface Water Module) model presented here is a “dynamic” water model which is capable to handle unsteady state calculations in dynamic situations where both chemical emissions and environmental scenarios vary. This model is able to simulate environmental systems subjected to seasonal climatic variations and occasional discharges of chemicals. SWM is coded using Microsoft Visual Basic 6.

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7.3.2 Model development The original QWASI Lake is a mathematical model composed by two main compartments, water and sediment, connected each other by fluxes of chemical such as sediment resuspension and diffusion from water and from sediment (Figure 7.3.2.1). SWM retains the scheme of compartments, processes and state variables. Every compartment is defined by a mass-balance equation (Mackay 2001); discharge of chemical in the system occurs only in the water compartment and in the original QWASI model it has to be constant during the simulation time. Results are calculated in terms of fugacity instead of concentration in order to simplify the algebra but the resulting equations can however be treated in terms of concentration. Fugacity (f) is defined as the tendency of a chemical to escape from a phase; the units are Pascal (Pa). It is related to concentration, C (mol/m3) by the equation:

C = Z*f (1)

where Z represents the “fugacity capacity”, (mol/m3Pa), which is specific for every phase. Figure 7.3.2.1. A schematic of the QWASI two compartment model. Arrows represent fluxes of chemical expressed in terms of D-values. The same processes appear in SWM. All the transport and transformation processes considered in QWASI Lake are expressed in form of D-values; they are parameters with units of mol/Pa*h and for each phenomena a characteristic D-value is defined. Rates of degradation and advection are calculated from transformation half lives kW and kS (h-1) and advective flow rates G (m3/h) of water and sediment (in this case the advective phenomenon considered is sediment burial). G values are calculated using the transport parameters of Table 7.3.2.1, surface areas and densities, when required.

DB sediment burial

DV DCDQ

air deposition

DCDQ

sediment deposition

DR sediment resuspension

DI, DX

inflow

Ds

sediment transformation

DJ, DY

outflow

DW

water transformation

DTsediment/water diffusion

Chemical Application

volatilization

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The calculation is then performed using degradation and advection D-Values (Mackay, 2001)

DDEG = ViZiki (2) DADV = GiZi (3)

where DDEG is the degradation D-Value for the compartment i and DADV is the corresponding advection D-Value. D-Values have dimensions of mol/Pa·h. The rates of transport water/sediment and air/water are calculated by using intermedia transport D-Values (DVs and DTs) calculated from the intermedia transport parameters (Table 7.3.2.1). A list of the D-Values used in the model is shown in Table 7.3.2.4. Table 7.3.2.1. Intermedia transfer Parameters (MTC stands for Mass Transfer Coefficient)

Parameter Units Example Value Rain rate m/y 1 Aerosol Dry Deposition m/h 7.2 Scavenging Ratio - 200,000 Volatilization MTC (air side) m/h 1 Volatilization MTC (water side) m/h 0.01 Deposition Rate of Solids* g/m2 d 18 Resuspension Rate of Solids* g/m2 d 6 Burial Rate of Solids* g/m2 d 12 Sediment/Water Diffusion MTC m/h 0.0004

SWM was developed using the fugacity approach similarly to the QWASI model. Both QWASI Lake and SWM have mass-balance equations written in D-value format; all the D-values are constant in QWASI model while most of D-values change in SWM, according to the variations of the environmental system. SWM requires two different groups of input parameters; the first dataset considers the physical-chemical properties of a chemical at 25°C and the second dataset is to define the application scenario. Physical Chemical properties The parameters required by SWM are those illustrated in Table 7.3.2.2. Some of those parameters (water solubility and vapour pressure) are recalculated at each input environmental temperature, using the Clausius-Clapeyron equation and standard delta heat of solubilization and vaporization (Mackay et al., 1996). This affects the calculation of the fugacity capacities (Z-values) for water and sediment, Table 7.3.2.2 - Physical chemical properties required by SWM

Parameter Units Molecular Weight g/mol Temperature of Property Data Measurement °C Water Solubility mg/L Vapour Pressure Pa Log Kow - Half Life in water h Half Life in Sediment h

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Environmental properties In order to properly describe the surface water scenario, a number of properties must be gathered. These are summarised in Table 7.3.2.3. Among the properties of the environment, “fixed” and time-dependent parameters can be distinguished. The first group comprises all the parameters that remain constant during the time of simulation; they are the surface of water and sediment, sediment depth, % of organic carbon, concentrations of particles in inflow water and in water column, density of particles in water, density of sediment and aerosol particles, mass transfer coefficients. The second group includes all the parameters that are dependent from the variation of the environmental system, such as temperature, water inflow and outflow, water level and chemical discharge. Table 7.3.2.3. Parameters needed by SWM for the surface water scenario description

Parameter Units Example value Water Surface area m2 10,000 Average Water Depth m 0.3 Sediment Active Layer Depth cm 5 Water Inflow Rate m3/h 10 Water Outflow Rate m3/h 10 Concentration of Particles in Water Column mg/L 0.000407 Concentration of Particles in Inflow Water mg/L 0.000407 Concentration of Aerosol Particles µg/m3 0.8 Volume Fraction of Solids in Sediment - 0.5 Density of Particles in Water kg/m3 2400 Density of Sediment Solids kg/m3 2400 Density of Aerosol Particles kg/m3 1500 Fraction of Organic Carbon in Water column particles - 0.013 Fraction Organic carbon in sediment solids - 0.00018 Fraction of Organic Carbon in resuspended Sediment particles - 0.00018 Fraction of Organic Carbon in Inflow Suspended Sediment Solids - 0.00018

The D-values shown in Table 7.3.2.4 are therefore time-variable because of these changes: the parameters with a (t) are the time variable input parameters, which may vary hourly. Additional information on D-Values can be found in Mackay (1983a, 2001).

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Table 7.3.2.4. D-values in SWM. G (m3/h) represents flow of a phase, C (mol/m3) is concentration, Z is fugacity capacity, A is area (m2), V is volume (m2) of a compartment, kS and kW are sediment and water transformation rate constant (h-1), kV and kT are respectively the overall (water-side) air-water mass transfer coefficient and the sediment-water mass transfer coefficient (m/h).

D-values Calculated as Water inflow DI GI(t)*ZW(t) Water particle inflow DX GX(t)*ZP(t) Water outflow DJ GJ(t)*ZW(t) Water particle outflow DY GY(t)*ZP(t) Absorption DV kV*AW*ZW(t) Volatilization DV kV*AW*ZW(t) Rain dissolution DM GM*ZW(t) Wet particle deposition DC GC*ZQ(t) Dry particle deposition DQ GQ*ZQ(t) Sediment-to-water diffusion DT kT*AS*ZW(t) Water-to-sediment diffusion DT kT*AS*ZW(t) Sediment deposition DD GD*ZP(t) Sediment resuspension DR GR*ZS(t) Sediment burial DB GB*ZS(t) Water transformation DW VW(t)*ZW(t)*kW(t) Sediment transformation DS VS*kS*ZS(t)

B = burial, S = sediment, R = resuspension, W = water, D = deposition, P = water particles, V = volatilization, J = water outflow, Y = particle outflow, I = water inflow, X = particle inflow, M = rain dissolution. Chemical Mass Balance Each compartment is described by a time-dependent mass-balance equation written in differential form solved using Runge Kutta numerical integration procedure (2nd order Euler modified method, Lambert, 1991);. Equations used to define both water and sediment compartment are written in differential form and time represents the independent variable. The general equation used to define both compartments is written in the form:

yn+1 = yn+∆t*f(tn+∆t/2; yn+∆t/2*f(tn; yn)) (2) where ∆t represents the integration step and t is the variable (independent variable). Both water and sediment are defined by a specific mass balance equation in which fugacity is the unknown time-dependent parameter. Mass balance for both compartments may be written in the general form:

fW’= a+b*fs-c*fW –c*fS for water compartment (3) fS’= d*fW –e*fS for sediment compartment (4)

where fW is fugacity in water, fS is fugacity in sediment and letters a, b, c, d, e group the terms evolved in the mass balance, as shown in Table 7.3.2.2. These equations are solved using 2nd order Euler modified numerical method (Lambert, 1991):

fW(n+1) = fW(n)+∆t*(a+b*fS(n)-c*( fW(n)+∆t/2*(a+b*fS(n)-c*fW(n)))) water compartment (5)

fS(n+1) = fS(n)+∆t*(dfW(n)-e*( fS(n)+∆t/2*(dfW(n)-e*fS(n)))) sediment compartment (6)

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Table 7.3.2.5. Groups of variables involved in the mass balance equations and groups definition. Letter Terms Group Definition

a IW + (fwI* (DI + DX)) + (fA* (DV + DQ + DC + DM)) / (VW* ZWT) Emission of chemical in water

b (DR + DT) / (VW * ZWT) Flux of chemical from sediment to water

c (DV + DW + DJ + DY + DD + DT) / (VW * ZWT) Losses of chemical from water

d (DD + DT) / (VS * ZST) Flux of chemical from water to sediment

e (DR + DT + DS + DB) / (VS * ZST) Losses of chemical from sediment

Fugacities and concentrations results are shown daily according to the variations of input data. The time step defined for the calculations is 1 hour. At every time-step SWM updates the parameters in bold shown in table 1 and recalculates D-values according with these new data. Chemical loadings are based upon summed contributions from runoff and drainage at each hour. Water mass balance Volume of water is calculated adding base flow to input from drains and runoff at each time step and inflow is assumed to be equal to outflow (no water is retained). Water is considered to be well mixed at each calculation. 7.3.3 Model verification SWM algorithms were verified in a round-robin comparison with the published QWASI Model. This procedure was suggested as initial verification for multimedia models (Cowan et al., 1995). The SWM.dll and QWASI model (steady state version) were fed with the same input parameters (chemical and environmental scenario), and were run at 25°C. Emission was set at 8760 kg/y for EQC and 1 kg/h for SWM (in order to simulate the same annual loading). Chemicals with different physical-chemical properties were run and results of steady state concentrations were observed. SWM converged to the same steady state concentrations of QWASI as expected. 7.3.4 Input and Output SWM is built as a standalone DLL which reads a txt file produced by the soil model (LeachP) and launches the calculation according to the number of simulation hours in the input file. SWM produces for each simulation an output file, located in the same folder as the input file. The name of the output file is obtained from that of the input file the suffix “.swo” is added (.swo stands for Surface Water Output file). This file is then retrieved by the VetCalc shell and table and graphs are produced with the results. The output file of the SWM.dll contains a heading as a first row (see Table 7.3.4.1 for the legend), and numerical values in CSV format. The data listed are part of the input data (day, our, year etc) and the concentrations in water and sediment phases, fluxes (degradation, volatilization, water outflow etc) and Fugacities (water and sediment).

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Table 7.3.4.1. Format of SWM output file (inputname.swo)

Column Number

Heading in .swo file

Description Units

1 Day Day - 2 Month Month - 3 Year Year - 4 DayNumber Day number of run - 5 Hour Time of Day - 6 TempDeg_C Input Temperature ºC 7 TotWaterC Concentration in bulk water (dissolved + particulate) ng/L 8 TotSedC Concentration in wet sediment ng/g 9 PureWatC Concentration in dissolved water ng/L 10 PartWatC Concentration on particles in Water (dry weight) ng/g 11 SedSolC Concentration in sediment solids (dry weight) ng/g 12 SedPoreWC Concentration in sediment pore water ng/L 13 Input_ug Input from LeachP (total : runoff + drains) µg/h 14 WatOutf Output from water body (dissolved in water) µg/h 15 WatPrtOut Output from water body (absorbed on particles) µg/h 16 WatTrans Transformation in water µg/h 17 SedTrans Transformation in Sediment µg/h 18 SedDep Sediment Deposition µg/h 19 SedBur Sediment Burial µg/h 20 SedRes Sediment Resuspension µg/h 21 WatSedD Water-sediment Diffusion µg/h 22 SedWatD Sediment water Diffusion µg/h 23 Volatil Volatilization µg/h 24 Absorp Absorption from Atmosphere µg/h 25 RainDis Rain Dissolution µg/h 26 WetPrt Wet Particle Deposition µg/h 27 DryPart Dry Particle Deposition µg/h 28 FugWater Fugacity in Water Pa 29 FugSed Fugacity in Sediment Pa

7.4 References Adriaanse, P.I., (1996). Fate of Pesticides in Field Ditches: the TOXSWA Simulation Model, Report 90, DLO Winand Staring Centre, Wageningen, The Netherlands. Beltman, W.H.J. & P.I. Adriaanse., (1999). User’s Manual TOXSWA 1.2, Technical Document 54. DLO Winand Staring Centre, Wageningen, The Netherlands. Cowan C. E., Mackay D., Feijtel T. C.J., van de Meent D., Di Guardo A., Davies J., Mackay N. (Eds) (1995): The Multi-Media Fate Model: A Vital Tool for Predicting the Fate of Chemicals, SETAC PRESS, Pensacola, FL, USA, pp. 100 FOCUS (2001). “FOCUS Surface Water Scenarios in the EU Evaluation Process under 91/414/EEC”. Report of the FOCUS Working Group on Surface Water Scenarios, EC Document Reference SANCO/4802/2001-rev.2. 245 pp. Freitas H., Diamond M., Semkin R., Gregor D. (1997) Contaminant Fate in High Arctic Lakes: Development and Application of a Mass Balance Model. The Sci. Total Environ. 201, 171-187. Hooghoudt, S.B., (1940) In: Smedema, L.K. and Rycroft, D.W., 1983. Land Drainage. Batsford Academic and Educational Limited, Essex. 376pp.

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Hutson, J., (2003) Leaching estimation and chemistry model: Model description and user’s guide. January 2003 revision. Lambert J.D.: Numerical Methods for Ordinary Differential Systems, John Wiley, New York, 1991. Lun R., Lee K., De Marco L., Nalewajko C., Mackay D. (1998) A model of the fate of polycyclic aromatic hydrocarbons in the Saguenay Fjord, Canada. Environ. Toxicol. Chem. 17 (2), 333-341. Mackay D. (2001) Multimedia Environmental Models: the Fugacity Approach, Lewis publishers, Boca Raton FL, USA, p. 261. Mackay D., Di Guardo A., Paterson S., Kicsi G., Cowan C. E. (1996): Assessing the Fate of New and Existing Chemicals: a Five Stage Process. Environ. Toxicol. Chem.,15,1618-1626 Mackay D., Joy M., Paterson S. (1983a) A Quantitative Water, Air, Sediment Interaction (QWASI) Fugacity Model for Describing the Fate of Chemicals in Lakes. Chemosphere, 12, 981-997. Mackay D., Paterson S., Joy M. (1983b) A Quantitative Water, Air, Sediment Interaction (QWASI) Fugacity Model for Describing the Fate of Chemicals in Rivers. Chemosphere, 12, 1193-1208. Morgan, R.P.C., (2001) A simple approach to soil loss prediction: A revised Morgan-Morgan-Finney model. Catena, 44, p305-322. Onstadt C.A., (1984) Depressional storage on tilled soil surfaces. Trans ASAE, 27, 3, p729-732. Sharpley A.N. and Williams, J.R. (eds.), (1990) EPIC – Erosion / Productivity Impact Calculator: 1. Model Documentation. US Department of Agriculture Technical Bulletin No. 1768. 235pp. Southwood J. M, Muir D. C. G., Mackay D. (1999) Modelling Agrochemical Dissipation in Surface Microlayers Following Aerial Deposition. Chemosphere, 38 (1), 121-141. Ter Horst, M.M.S., P.I. Adriaanse, W.H.J. Beltman and F. Van den Berg, (2003). User’s guide for the TOXSWA User Interface. Manual of TOXSWA in FOCUS, version 1.1.1. Alterra report 586. Walker, A., (1978) Simulation of the persistence of eight soil applied herbicides. Weed Research, 18, p305-313. Wischmeier, W.H. and Smith, D.D., (1978) Predicting rainfall erosion losses, a guide to conservation planning. US Department of Agriculture Handbook No. 537, 58pp.

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8.0 REFINEMENTS Having carried out simulations employing the defaults employed within VetCalc and within the constraints of the modelling framework it may be appropriate to consider refinements that may provide a more accurate basis for predicting environmental exposure potential. A number of refinements can be considered as follows:

• Dosage regimes • Animal characteristics • Chemical characteristics • Agricultural practices • Environmental characteristics • Interaction with FOCUS Modelling framework

Each is discussed, in brief, within this Section. 8.1 Dosage regimes A number of bodyweight and non-bodyweight related treatment regimes are considered within VetCalc. There is a default assumption that 100% of the herd is treated. However, it is recognised that for certain treatments this may be unnecessarily conservative. As a consequence the VetCalc framework includes the option to carry out a refined calculation that assumes only a proportion (user-defined) of the herd is treated. Table 8.1.1 may be used as guidance in the establishment of:

• Suitability of product to mitigation in this manner • Where relevant, appropriate mitigation factors.

Table 8.1.1 Typical proportions of herds treated with different products (CVMP, 2004) Product group % herd treatment Anthelmintics 100 Products for treatment of diarrhoea in calves, lambs and pigs

50

Coccidiostatics (with exception of poultry) 100 Ectoparasiticides 100 Intramammary preparations: - for drying off - in lactating animals

100 25

Antibiotics (feed and water medication) 100 Antibiotics (injectable pigs) 50 Antibiotics(injectable, specific indications): - respiratory infections in cattle - footrot in sheep

50 100

Teatdip and sprays 100 All products for poultry 100 For pragmatic reasons it is not possible to represent all potential treatment types. Two generalised treatment regimes are considered – those in which treatment is related to bodyweight and another set in which the active substance has a fixed dosage (e.g. bolus treatments, certain injections etc.). It is recognised that while many products may be accommodated within VetCalc, the releases to the environment associated with certain treatments will not be as easily represented. Provided that some estimate of loadings to soil can be made (e.g. washoff of pour on products etc…) it is possible within VetCalc to directly define the PECsoil value which will then be taken forward into more complex calculations.

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8.2 Animal characteristics A series of default characteristics are provided for animal classes and growth stages. These include bodyweight, characterization of production cycli and N and P content of manure. It is recognized that in certain situations (treatments, production practices etc.) it may be appropriate to consider replacing these assumptions. For example, for prophylactic treatment types it may be appropriate to replace the bodyweight defaults with lower bodyweight ranges. Accordingly, VetCalc has been designed to allow the user to replace each of the critical parameters prior to definition of the PECsoil (certain text boxes in the simulation toolbox can be edited). Care should be taken to justify alternative choices for key parameters. 8.3 Chemical characteristics VetCalc has been designed in such a manner that simulations can be constructed based upon ‘Basic data’ (e.g. minimum data that should be available within a Phase II risk assessment). Where data are available, more sophisticated calculations can be carried out by accessing the ‘Advanced data’ tab in the chemical definition form. This allows the user to provide an alternative basis for defining:

• Potential for degradation in slurry/manure during storage • Behaviour in sediment and water systems • Potential for metabolism prior to excretion • User-defined sorption parameters (Kd definition) • Definition of soil degradation versus dissipation behaviour • Simulation of metabolites

Each of these options is discussed in more detail in Section 4.2. Where ‘Advanced’ options are considered, care should be taken to provide justifications for definition of relevant processes. Wherever possible this should make reference to available laboratory or field data. 8.4 Agricultural practices The user has the opportunity of choosing a range of agricultural practices associated with the management of manures. These include the form of manure under consideration (FYM (solid) or slurry), storage periods, the destination environment (arable or grassland) and potential for incorporation. The VetCalc modelling framework has been designed in such a way to reduce the amount of guess work about what constitutes a ‘typical’ combination of parameters for each livestock category and Member State under consideration. However, each of these options highlighted as typical can be overridden and alternative combinations can be considered. As with other refinements care should be taken to justify alternative simulations that deviate from typical profiles. Where further, more detailed, data are available that characterise manure management practices in each Member State it is possible to manually override the options highlighted as typical in VetCalc. The user also has the longer term option of carrying out customised simulations by accessing the ‘User defined’ scenario options. Two sets of template Excel spreadsheets are provided which the user can edit in order to develop highly customised simulations. Detailed explanations of how such amendments should be carried out are described in Appendix 2.

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8.5 Environmental characteristics As explained earlier, a set of 12 scenarios have been defined to assist in the development of a broadly based assessment of run-off, drainage and leaching potential in a range of soil and climate combinations. The modelling framework is not, however, limited to these scenarios alone. Where the user has sufficient data and relevant justifications it is possible to develop further ‘user-defined’ scenarios. Establishing alternative environmental scenarios requires definition of a set of general soil characteristic, a number of critical hydrological parameters and manipulation of the LEACHP input files. This requires familiarity of LEACHP and should only be attempted with great care. Two sets of template input files are provided which the user can edit in order to develop highly customised simulations. Detailed explanations of how such amendments should be carried out are described in Appendix 2. 8.6 Interaction with FOCUS Modelling framework The VetCalc modelling framework has a number of key advantages in assisting in the development of more accurate assessments of environmental exposure potential for veterinary medicines. These draw heavily upon a set of spreadsheets defining default characteristics for animals and agricultural practices (manure management) in a range of Member States. It is, however, recognised that other modelling tools can be used to assess degradation, partitioning and transport potential within regulatory exposure assessments. One such set of tools are the FOCUS Groundwater and Surface Water models developed to assist with pesticide exposure assessment under Directive 91/414/EEC. The FOCUS modelling framework is also scenario based but has greater flexibility in terms of how simulations can be set up:

• Very wide range of crops • Range of water bodies • Longer-term weather datasets for groundwater assessments • More sophisticated management of application timing in surface water assessments • Multiple application capability

However, FOCUS does not include any tool that allows the user to estimate how dosage translates to a loading to soil. This is considered a major limitation as the ‘up front’ calculations of the PECsoil are often critical and may be easily miscalculated. In order to maximise the flexibility of options available to the user, the VetCalc framework has been designed to provide an estimate of loading to soil (in kg a.s./ha) that can be directly employed within FOCUS simulations, taking at least some of the guesswork out of set up of these alternative simulations. In addition, to make this procedure more straightforward, a summary file can be printed out (for reference purposes) by the user after definition of scenarios but before embarking on LEACHP simulations. This summary file can provide a framework for setting up FOCUS simulations. Certain VetCalc scenarios have parallels within FOCUS that may assist in the establishment of such alternative simulations, these are summarised in Table 8.6.1. In addition, the Member State manure management defaults within VetCalc can be used to establish application rates for specific scenarios within FOCUS. Although FOCUS scenarios are not intended to be Member-State specific, certain examples have been provided in Table 8.6.2 of potential applicability of Member State manure management defaults within VetCalc to a number of additional FOCUS scenarios. In this respect,

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Table 8.6.1 Summary of Relationships between VetCalc scenarios and FOCUS scenarios Scenario Directly Associated

Zone 1 - Sandy silt loam FOCUS GW: Sevilla Zone 1 - Clay loam FOCUS SW: R3 Zone 2 - Sand FOCUS SW: D3 Zone 2 - Loamy sand NA Zone 2 - Sandy loam 1 NA Zone 2 - Sandy loam 2 NA Zone 2 - Sandy clay loam 1 NA Zone 2 - Sandy clay loam 2 NA Zone 2 - Sandy silt loam NA Zone 2 - Clay loam NA Zone 2 - Clay NA Zone 3 – Sandy loam FOCUS GW: Jokioinen Table 8.6.2 Potential Relationships Between Member State Defaults Incorporated Within VetCalc Scenarios and FOCUS Scenarios. Member State Practices FOCUS Scenarios Most

Closely Associated with Member State

Associated VetCalc Scenarios

Belgium FOCUS SW: D3 Zone 2 - Sand Denmark FOCUS SW: D4 Zone 2 - Loamy sand Eire NA Zone 2 - Sandy clay loam 2 Finland FOCUS GW: Jokioinen Zone 3 – Sandy loam France FOCUS SW: D5, R4

FOCUS GW: Chateaudun Zone 2 - Sandy loam 1 Zone 2 - Sandy clay loam 1

Germany FOCUS SW: D3, R1 FOCUS GW: Hamburg

Zone 2 – Sand Zone 2 - Loamy sand Zone 2 - Sandy silt loam

Italy FOCUS SW: R3 FOCUS GW: Piacenza

Zone 1 - Clay loam

The Netherlands FOCUS SW: D3 Zone 2 - Sand Portugal FOCUS SW: R4

FOCUS GW: Oporto Zone 1 - Sandy silt loam

Spain FOCUS GW: Sevilla Zone 1 - Sandy silt loam Sweden FOCUS SW: D1 Zone 3 – Sandy loam United Kingdom FOCUS SW: D2

FOCUS GW: Okehempton Zone 2 - Sandy loam 2 Zone 2 - Clay loam Zone 2 - Clay

8.7 References CVMP (2004) CVMP Working Party on Environmental Risk Assessment

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9.0 TUTORIAL On opening the VetCalc software, the cover screen appears as illustrated in Figure 9.1. In the menu at the top of the form are a series of options. Two of these options are disabled (‘Define Scenario’ and ‘Execute’) until the user supplies background information on the project under the heading ‘Define Product’. The ‘View’ option allows the user to view previous results and documentation, including a copy of the user’s manual and summary information on associations of scenarios and livestock production. The ‘Help’ option provides background information on the contributors to the research project, acknowledgements and contact details for model developers and VMD. There is also the opportunity to access a website where VetCalc is hosted and further updates can be obtained. To continue, the user should select ‘Define Product’.

Figure 9.1. Start up form in VetCalc On entering the product label definition form (see Figure 9.2) the user is asked, in turn, to provide information on:

• Product name • Treatment regime • Treated livestock groups • Dosage information

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The user can add a number of additional uses (maximum of one per livestock class) by selecting ‘Additional Use’. On completion the user should select ‘Continue’.

Figure 9.2 Product Label Definition form The user is then directed to provide background information on the chemical There are two options;

• The user can provide basic physico-chemical and environmental fate data available at Phase 2 (‘Basic data’ option – see Figure 9.3).

• The user can supplement the basic data with further more sophisticated background information on the behaviour of the chemical (‘Advanced data’ option – see Figure 9.4)

Brief explanatory comments are provided for each input option in a comment box at the base of each form. Guidance on the interpretation of data and selection of input parameters is provided in Section 4 of the user’s manual.

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Figure 9.3 ‘Basic data’ chemical properties form in VetCalc

211

Figure 9.4 ‘Advanced data’ chemical properties form in VetCalc

212

When there is a necessity to conduct simulations with metabolites, the user may select ‘Check box to add metabolite’ uner the metabolite heading as illustrated in Figure 9.4. The user may then provide further information on a primary metabolite as illustrated in Figure 9.5 and 9.6.

Figure 9.5 Metabolite: ‘Basic data’ chemical properties form in VetCalc

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Figure 9.6 Metabolite: ‘Advanced data’ chemical properties form in VetCalc

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The user is then directed back to the start-up form, where they can now access the enabled ‘Define scenario’ menu. This brings up a ‘Simulation Definition’ form (see Figure 9.7). The user must begin by selecting the treated animal group under consideration from the subset of target animals highlighted earlier when defining the treatment regime. A set of default characteristics (bodyweights, N&P release rates etc.) are then provided which can be overridden as justified.

Figure 9.7 Initiation of the Simulation Definition form in VetCalc

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The user is then asked to select a scenario and a manure management regime for a Member State associated with this scenario (See Figure 9.8).

Figure 9.8 Scenario definition within VetCalc

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A database is then called and the user is provided with a series of typical manure management options for the specified combination of livestock class and Member State manure management regime (See Figure 9.9 and 9.10).

Figure 9.9 Definition of manure management practices

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Figure 9.10 Definition of manure management practices

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The user is then directed to a further form (see Figure 9.11) in which background information on the scenario is provided. This includes any restrictions associated with the application regime (e.g. NVZ status) that are used to define maximum application rates and any closed periods. The user may also view soil properties. The user is also provided with loadings to soil (in kg a.s./ha both mitigated and unmitigated) that can be used to prime other modelling tools such as FOCUS. Finally, a PECsoil value is provided that can be compared against ecotoxicological thresholds (e.g. PNEC values). Before embarking on simulations the user is asked to provide a default application date which can be viewed for each year in the context of rainfall patterns and refined accordingly. Once this is defined the user can save a summary input file, print a summary or continue to execute the simulation.

Figure 9.11 Scenario characteristics form in VetCalc

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After the simulation has been executed it is possible to review results in three formats; PECsoil (See Figure 9.12), PECgw (See Figure 9.13) and PECsw (See Figure 9.14).

Figure 9.12 Illustration of PECsoil overview

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Figure 9.13 Illustration of PECgwl overview

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Figure 9.14 Illustration of PECsw overview