wq0106 wp 4: assessment of the impacts of climate...

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WQ0106 WP 4: ASSESSMENT OF THE IMPACTS OF CLIMATE CHANGE ON

DIFFUSE POLLUTION AT A RANGE OF SCALES

Specific objective in this workpackage were to:

select relevant models for simulating impacts of climate change futures on diffuse

pollution from UK land-use systems at a range of spatial and temporal scales

(objective 14)

define climate change futures and derive climate input data for pollutants models

(objective 15)

run models using sets of land-use/climate future combinations to determine changes

in emissions to water against a baseline year (objective 16)

run models for a range of land-use adaptive responses to climate change and to key

legislation and policy instruments (objective 17)

run the models to assess the effectiveness of recognised pollution mitigation

strategies under future climates with regard to water quality, pollution swapping

through atmospheric emissions (objective 18)

(develop a new model to assess the relative importance of elevated nutrient loading,

the ratio of N to P and temporal and spatial patterns of increased water temperature

and reduced oxygen levels on eutrophication risk in UK freshwaters – rural rivers

and lakes – objective 19). This objective was removed via a contract variation

(Defra letter dated xx/xx/xxxx)

use models to optimise land managements for minimal air and water pollution

under some typical climate change futures (objective 20)

provide the basis for the specification of some key validation experiments

(objective 21)

These objectives have been met through three pieces of modelling led research, and

this report is divided into the following two sections, accordingly:

1. The selection of appropriate models for simulating the impacts of climate

change on diffuse pollution at a range of spatial and temporal and scales

a. A specific assessment of climate change on mobilisation of DWPA

b. A review of international models to determine their suitability to

address climate change (mitigation and adaptation ) on diffuse

pollution

2. An assessment of the impact of climate change and adaptation on diffuse

pollution in agricultural systems using the SPACSYS model.

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1. Select relevant models for simulating impacts of climate change futures on

diffuse pollution from UK land-use systems at a range of spatial and temporal

scales.

This objective was tackled in two ways; first a specific assessment of the potential of climate

change to affect the specific process of mobilisation of diffuse water pollutants from agricultural

farming systems, and second, a review of international models to determine their suitability for

addressing the impacts of climate change (adaptation and mitigation) on diffuse pollution to

water and air from farming systems. It is important to note that the assessment of climate change

on mobilisation of diffuse pollutants was conducted when only UKCIP02 data were available

(2007-2008), whilst UKCP09 data were specifically used in modelling the impacts of land-

use/climate future combinations on emissions to water and air.

1a. Effects of climate change on the mobilization of diffuse substances from

agricultural systems (led by Macleod C.J.A. NWRes) (also meets Objective 15 and 16)

The information generated from this objective was written as a paper (submitted to The Science

of the Total Environment with input from Pete Falloon (Met Office), R Evans (Anglia Ruskin

University) and Phil Haygarth (Lancaster University) and represents an output from both

projects WQ0106 and WQ0109. In the following section we summarise the results with limited

references. However, the full draft paper (with full reference list) is provided as an Appendix.

Summary abstract

The approach was to develop a simple framework for assessing climate impacts on

diffuse substances from UK agriculture, focusing on mobilization processes. The

assessment was based on a review of projected changes in UK climate by 2020 and

three representative model farm systems (arable, lowland dairy and upland sheep).

In general, mobilization of diffuse substances is likely to be most vulnerable to

climate change in lowland dairy systems, followed by upland sheep and arable

systems respectively.

Mobilization in the form of solubilization was greater than detachment in all

systems and has the greatest levels of uncertainty, which suggests more research is

required and that targeting of nutrient levels in farm management plans may be an

effective way to reduce future losses of diffuse substances to surface and ground

water bodies.

There is a need for research that combines experimentation and modelling to

compare the response of different agricultural systems to a wider set of diffuse

substance mobilization, along with the subsequent transport to enable a more

holistic understanding of the potential impacts of climate change.

The framework is potentially applicable to different systems in other regions of the

world, given appropriate information on which to base the assessment.

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Background Diffuse pollution of surface and ground waters from agricultural land is a significant global

environmental problem, associated with a wide range of impacts in surface and ground water

bodies. The study of diffuse pollution involves a multiplicity of chemical, biological and

physical processes operating across a range of temporal and spatial scales. Granger et al. (2010)

recently provided a useful approach to assessing diffuse pollution from managed grasslands.

This has been usefully described using the source-mobilization-transport-impact ‘transfer

continuum’ conceptual model (Haygarth et al., 2005). The mobilization and subsequent

transport (sometimes called ‘delivery’) of diffuse substances are critical steps to understanding

the potential impacts of land-based activities on surface and ground water bodies.

Changes in the climate of temperate regions is expected to increase the mobilization and delivery

of sediment, particle associated pollutants and soluble substances e.g. nitrate. Changes in surface

runoff and a rise in temperature due to climate change are likely to alter water quality affecting

ecosystems and human health. In areas where the intensity of rainfall can be expected to

increase, pollutants will be increasingly washed from soils to water bodies. Across northern

Europe, climate change is thought to lead to an increase in nutrient leaching and an increase in

the rate of soil organic matter breakdown. Increased losses of pesticide occur with higher levels

of rainfall. Linkages between changes in weather patterns and infectious disease transmission

have been established.

The management of diffuse pollution impacts is complicated by projected changes in climate

meaning that our current models and understanding of mobilization processes needs to be

urgently assessed and, if necessary revised in order to enable planning of adequate adaptation

measures. What is clearly lacking is a means of translating these changes in climate into a

meaningful tool for assessing, and adapting to future changes in the mobilization of diffuse

substances from the dominant agricultural systems in the UK.

For this objective we reviewed our state of knowledge of the impact of climate change on key

mobilization processes using three defined model farm systems (MFS) that are typical to the UK

– arable, lowland dairy and upland farm systems. Specifically, we addressed the following aims:

To develop and test a framework for assessing climate change impacts on mobilization of

diffuse substances from UK agriculture, and

To identify the state of scientific knowledge and the gaps and uncertainties around

climate change and diffuse substance mobilization.

Approach

To assess how changes in the UK climate (by 2020) may affect the mobilization of diffuse

substances from UK agricultural systems we developed and tested a prototype framework that

integrates a systemic expert assessment and a systematic review of the literature to help predict

changes. The first step in our systems assessment involved defining generic changes in annual

and seasonal climate and extreme weather events which characterise the main aspects of climate

projections for the 2020s for three dominant farming bioclimatic areas of the UK. Next we

characterized the three representative model farm systems from these bioclimatic areas, which

capture the main features of dominant farming practices in the UK. These are arable, lowland

dairy and upland sheep (Tables 2 and 3). We drew on inter(national) literature to assess the

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likely impact of future climate on solubilization and detachment mobilization processes from

these model farm systems.

In Table 1 we set out the changes in climate we have used from the UKCIP02 projections

(Hulme et al., 2002) for the three model farm systems and our relative confidence levels (Table

4). We have used regional details of these predictions to inform our scoping study. The

projected changes in climate for the 2020s under the UKCIP02 scenarios can be broadly

characterised by an annual warming by the 2020s of up to 1.5 degree Celsius with greater

summer warming in the south east than the north west and greater warming in summer and

autumn than in winter and spring. Winters are projected to become wetter by up to 15%, while

summers could become up to 20% drier. These changes vary, depending on region and scenario.

The UKCIP02 projections also indicate an increase in extreme events such as very hot days and

heavy rainfall events, plus increases in growing season length. We used each of the broad

climate changes described in Table 1 as the basis of our assessment for each of the model farm

systems.

Mobilization of diffuse pollutants Mobilization is a key step in the movement of diffuse substances from land to water bodies

(Bryan, 2000) via the source-mobilization-transport-impact ‘transfer continuum’ conceptual

model (Haygarth et al., 2005). Sources specifically refer to the inputs of potential substances to

the farm, such as the application of inorganic fertilizer or plant protection product. Mobilization,

the focus of this study, describes the ‘start of journey’ of, for example, a microbial pathogen or a

plant protection product associated with a particle of soil from a hillslope, whereas transport

(sometimes called delivery by other authors e.g. (Beven et al., 2005) entails the pathways of

substance transfer, connecting across a slope or through the soil profile from point of

mobilization to the water body. Finally, impacts denote the resulting connected biological,

chemical or physical effects that influence the quality of life, in streams, rivers, estuaries and the

ocean e.g. (Galloway et al., 1996).

In our approach to assess climate driven changes in mobilization, in general we do not refer to

specific diffuse pollutants per se, rather we use the generic system of considering substances that

have characteristics that are solubilized or detached. Typical substances that may be solubilized

include nitrate, dissolved phosphorus compounds such as phosphate and organic phosphorus,

dissolved organic carbon, water soluble plant protection products and veterinary medicines.

Typical substances that might be detached include soil particles and colloids (both organic and

inorganic), washed-off plant, faecal and manure debris, faecal indicator organisms and attached

chemicals such as particulate phosphorus compounds or pesticides (and low solubility plant

protection products. Heavy metals will associate with soluble or particulate phases depending on

their chemical properties and the redox conditions. The selective enrichment of eroded material

with heavy metals from arable fields has been recently discovered (Quinton and Catt, 2007).

Description of model farm systems To assess the implications of climate change on the mobilization of diffuse substances we have

chosen three model farm systems that are representative of UK agriculture (Tables 2 and 3). In

the UK, a total of 17,323,400 hectares of land are currently in agricultural use, with 35%

(6,048,500 ha) in arable land including grass < 5 years old, 35% (5,977,300 ha) in grassland > 5

years old excluding rough grazing and 25% (4,332,600 ha) in sole rights rough grazing (Defra,

2008b). These model farm systems have been defined to be representative of arable, lowland

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dairy and upland sheep agricultural systems and are based on national UK agricultural statistics

(Defra, 2004, 2008b) and Soil Survey information (Findlay et al., 1984b; Jarvis et al., 1984b).

Assessment of changes in mobilization processes We used expected changes in annual and seasonal climate and extreme weather events (Table 1)

and the model farm system described to estimate the direct climatic impacts on mobilization

processes based on the literature available and expert assessment. We assigned a simple scale of

0 to 3, i.e. 0- no change, 1- small, 2- moderate, 3- large change. Positive scores indicate an

increase in mobilization processes, whereas negative scores may highlight potential decreases

from the current rates and extents. The results of the scoring exercise are provided in Table 1.

When assessing the impact of an increase in temperature and number of very hot days, we have

only considered the temperature effects (including its impact on evapotranspiration and soil

drying) and disregarded the effect of changes in precipitation (or other factors). The following

sections set out the results from our expert assessment and literature review of the potential

impacts of climate change on the mode farm systems by the 2020s.

Review of climate impacts on the mobilization of diffuse pollutants from model farm

systems

Model Arable Farm system

Higher year round temperatures

The annual temperature increases may lead to a longer growing season and vegetative

growth and cover, decreasing detachment of soil particles.

On the other hand, higher evapotranspiration rates could dry out soils and increase

disaggregation and the potential for particle detachment.

The main effect of an increase in temperature will be to speed up the rate of biological

processes and their losses, although there may also be a change in the rates of chemical

processes.

Over the last three decades organic carbon in UK arable top soils has been shown to

decline and warmer temperatures may speed up this rate.

Within the annual cycle, an increase in temperature should increase the rates of nutrient

cycling and increase solubilization for carbon, nitrogen and phosphorus.

As organic carbon in soils declines soil aggregate stability decreases, especially at levels

lower than 2% soils become more at risk of water erosion.

The projected decrease in the number of very cold days in the year will lead to less frost

heave and soil disturbance, reducing detachment

increasing plant growth and nutrient uptake, and less freezing damage to plant cells could

reduce solubilization.

A longer growing season length is likely to reduce the number of days with bare soil, thus

reducing detachment.

The effects of lengthening the growing season on solubilization are more complex and will

depend on the uncertain balance between increasing growth and turnover rates and

changing plant uptake of nutrients.

Increases in nitrogen mineralization with a rise in soil temperature are well established.

Warmer, wetter winters

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Higher winter temperatures should give better vegetative cover for early sown crops and

protect the soil, reducing detachment.

However, for late sown crops, the risk of detachment is likely to increase because of the

lack of crop cover (less than 25%).

Raised winter temperatures will also increase biological activity and leakage via warmer

soil solutions, increasing solubilization.

Increasing winter precipitation will increase hydrological energy and activity thus greater

levels of detachment

increased flushing of water and solutes through the soil will also increase solubilization

and leaching.

Hotter, drier summers

Higher summer temperatures will dry the soil surface and, where soils are dominantly bare,

can increase the risk of detachment, particularly for sandy and peat soils.

The soil surface will become especially vulnerable to raindrop splash impact and wind

erosion.

As with year-round temperature increases, rising summer temperatures will likely increase

rates of chemical and biological activity, although we consider these effects to be small in

summer unless the crop is irrigated, when solubilization could increase.

Drier summer conditions will reduce hydrological energy and reduce detachment, and

reduced water availability for biological and chemical activity will also decrease

solubilization.

Increase in intense rainfall events

The projected rise in the number of intense rainfall events will significantly increase

hydrological energy and thus detachment of material from the soil surface. This will also

increase soil flushing, but in summer this is likely to have a greater impact on

solubilization than in winter.

Increase in number of very hot days

The increasing occurrence of extremes temperatures will have complex impacts on soil-

plant systems. For example, during the summer heat wave of 2003, soil temperatures

increased to a level that microbial activity was reduced resulting in lower rates of carbon

mineralization.

Lowland dairy


In general, the impacts of climate change on mobilization of diffuse substances from dairy

systems are likely to be similar to those in arable systems – here we only discuss where the

trends are different due to the presence of animals and year round vegetation cover.

High levels of vegetation cover are known to significantly reduce soil erosion rates.

Increasing annual average temperatures are likely to have a smaller effect on detachment of

soil particles in dairy systems than on arable land due to greater soil protection by grass

cover, although impacts on solubilization are likely to be similar.

Although the reduced number of very cold days and increasing growing season length will

likely reduce detachment as in arable land, the overall effect will be to increase detachment

because of the presence of organic materials (such as slurry, farmyard manure and excreta)

which are not present in arable systems.

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Reduced very cold days are likely to have a similar impact on solubilization as in arable

systems.

Slurry is sometimes applied in the winter when the ground is frozen, this material may be

readily mobilized.

Lengthening growing seasons will slightly increase soil protection as in arable systems, but

the dominant impact will be to extend the period when animals are grazing and defecating

on the land thus increasing the likelihood of detachment and solubilization.


As previously discussed, warmer winter temperatures will extend the period of animal

activity and grazing and thus increase background detachment from soils, and significantly

increase solubilization.

Wetter winters will have significant impacts on mobilization in pastures with increased

rainfall on compacted, pugged and poached soils enhancing detachment and more frequent

flushing from fields and wash off from hard standings increasing solubilization.


Compared to non-irrigated arable land, higher summer temperatures are likely to have

only very small impacts on detachment from pastures due to continuous vegetative cover

but solubilization may increase slightly as a result of more rapid chemical and biological

activity.

Drier summer conditions will be likely to reduce detachment and mobilization in pastures

since the soil is protected by vegetative cover and there will be reduced hydrological

energy and flushing.


As in arable systems, the projected increase in intense rainfall events will significantly

increase detachment via greater hydrological energy, and this could have a larger impact

on solubilization than in arable soils because wetting and drying of exposed fecal

material will increase.

The form of the animal waste will influence the likelihood that nutrients are lost

following rainfall events.


The increasing occurrence of very hot days might have only a very small impact on

detachment due to the continuous presence of grass cover on the soil.

As in arable systems, more very hot days will likely increase biological activity and

direct defecation to watercourses may occur where animals are present nearby.

Upland sheep


Warmer year-round temperatures could decrease detachment in upland systems due to a

lower number of frosts and associated heave. This is in contrast to likely increases in

arable systems because although the soils may become drier they will mostly remain

protected by continuous vegetative cover.

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Where soils are bare, however, especially peats, increased temperatures could make the

soil warmer and drier, especially in summer, and less conducive to seedling germination

and hence more vulnerable to soil particle detachment.

Solubilization may increase significantly as a result of annual average warming because

biological and chemical activity will increase as a result – both for organic and mineral

soils.

Reductions in the number of very cold days will be likely to decrease frost heave, a

primary driver of detachment of particles on bare soil in the uplands, to a greater extent

than in lowland systems, so a significant reduction in detachment could result.

As in arable systems, increasing plant growth and nutrient uptake, and less freezing

damage to plant cells could reduce solubilization particularly at higher altitudes.

Longer growing seasons will be likely to decrease detachment as in lowland dairy

because protection of vulnerable areas will increase and patches of bare soil created by

animals will have longer to recover.


As with year-round temperature increases, warmer winters will be likely to increase

chemical and biological activity, increasing solubilisation, but less than in pasture

systems. This is because bare soils are very vulnerable to rainfall in winter, wetter winter

conditions present a significant increase in the likelihood of detachment. Whereas

solubilization may decrease compared to pastoral systems arising from lower overall

temperatures and chemical and biological activity.


As in pastoral systems, warmer summer conditions (and associated drying via increased

evapotranspiration) are unlikely to affect detachment significantly due to continuous

vegetative cover.

However where soil is bare, drier conditions may make the surface more vulnerable to

particle detachment by rain splash and wind.

Organic-rich upland soils are very vulnerable to rising temperatures which could lead to

significant increases in nutrient release and mineralization especially of organic carbon,

leading to increased levels of solubilization.

More dissolved organic carbon (DOC) is released from peat soils following droughty

periods in summer not only because of increased aeration of the surface layer but also

because a greater depth of soil is affected by weathering as the water table is lowered

because of greater evapotranspiration.

Decreasing summer precipitation, especially on bare soil, is likely to result in a

significant reduction in particle detachment by rain splash, possibly balanced by an

increased propensity to detachment by wind, and as in pasture systems less flushing

could reduce solubilization.


Increasing return frequency of intense rainfall events are likely to have similar impacts in

the uplands as in pastoral systems significantly increasing both detachment and

solubilization.

Intense rainfall after a drier period may speed up re-wetting of the soil and a greater

release of DOC.

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An increase in the number of very hot days in the uplands is also likely to have similar

impacts to those in pastoral systems – little impact on detachment and a small increase in

solubilisation.

However, the occurrence of fires, both accidental and deliberate, may become more

frequent and detachment and mobilization of soil particles will be greatly increased with

severe impacts.

Results Lowland dairy is potentially the most sensitive farm type to the projected changes in the UK

climate, with nearly all changes expected to increase the mobilization of diffuse pollutants

(Table 1). Decreased summer precipitation will result in reduced mobilization and a smaller

number of very cold days may reduce solubilization processes. Very significant and significant

increases in detachment and solubilization processes are due to an increase in the length of the

growing season, increased winter precipitation and increase in the intensity of precipitation

events (Table 1). Increases in winter average temperature are expected to lead to significant

increases in the solubilization of diffuse substances from dairy the model dairy farm system.

In terms of overall impact of expected changes in climate on all mobilization processes the

arable and upland sheep systems scored equally (Table 1). However, the upland sheep system is

expected to have a larger number of significant or very significant increases in mobilization

processes (Table 1). Increases in annual and summer temperature will lead to significant

increases in solubilization of diffuse substances. Increased winter precipitation and the number

of intensive rainfall events along with an increase in the number of hot days will significantly

increase the detachment of diffuse substances from upland model sheep system. Potentially off-

setting these increases are expected significant decreases in detachment due to the decrease in the

number of very cold days and decrease in summer precipitation (Table 1). Mobilization due to

similar increases in solubilization and detachment processes from the model arable system will

be enhanced (Table 1). Significant increases in detachment due to increased winter precipitation

and the number of intense rainfall events are likely along with significant increases in

solubilization due to increased winter average temperature.

The confidence in the annual and seasonal climate and extreme weather events and our

assessment of changes in mobilization processes has been provided in Table 4. The UKCIP02

assessment had a very high level of confidence in all these predicted changes apart from the

increase in winter average temperature and decrease in summer average precipitation. In relation

to our own level of confidence, in general less is known about solubilisation processes especially

due to higher temperatures (Table 4). The greatest knowledge gaps and uncertainties were

associated with the lowland dairy MFS (Table 4).

With regard to the gaps and uncertainties in the present state of our scientific knowledge, our

knowledge is low with regard to the field based measurement of the mobilization of substances,

especially for intensive lowland dairy systems. There is also a need to focus on the response of

upland systems to climate change, because these complex systems are likely to be sensitive to

changes. Solubilization was found to be subject to the largest changes for all systems, which

suggests that targeting nutrient levels in farm management plans may be an effective way to

reduce future losses of diffuse substances to surface and ground water bodies.

References – see full paper for references

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Table 1. Predicted changes in UK climate (UKCIP02 2020 scenarios) and implications for detachment and solubilization mobilization processes for diffuse substances from model farm systems Climate change Climate change Arable Lowland dairy Upland sheep

UKCIP02 Detach Solub UKCIP02 Detach Solub UKCIP02 Detach Solub

Higher year-

round temperatures

Increase in annual average temperature

1.0 to 1.5ºC +1 +1 0.5 to 1.0ºC 0 +1 0.5 to 1.0ºC -1 +2

Decrease in very cold days

[Qualitative only]*

-1 -1 [Qualitative only]*

+1 -1 [Qualitative only]*

-2 -1

Increase in growing season

[45 to 55 days per year ]*

-1 0 [40 to 100 days per year ]*

+2 +2 [35 to 100 days per year ]*

-1 0


Increase in winter average temperature

Up to 1ºC ESC -1 LSC +1

+2 0.5 to 1.0ºC +1 +2 0.5 to 1.0ºC -1 +1

Increase in winter precipitation

Increase of up to 10%

+2 +1 Increase 10%

+3 +2 Increase 10% +2 +1


Increase in summer average temperature

1.0 to 1.5ºC +1 +1 0.5 to 1.5ºC 0 +1 0.5 to 1.5ºC 0 +3

Decrease in summer average precipitation

Decrease of 10 to 20%

-1 -1 Decrease 10 to 20%

-1 -1 Decrease 20% -2 -1

Increase in intense rainfall events ("intense" rainfall days per season)

[Up to 0.75 more]*

+3 +1 [0.25 1.5 ]* +3 +2 [0.25 to 1.5 ]* +3 +2

Increase in number of very hot days [30 to 60]* +1 +1 [30 to 60 ]* 0 +1 [24 to 60]* +2 +1

Footnote: where ranges are shown, these encompass both differences across emissions scenarios (Low to High) and geographic variation. *Values are for the 2080s Relative level of change:-3 very significant decrease; -2 significant decrease; -1 some decrease; 0 no change; +1 some increase; +2 significant increase; +3 very significant increase. ESC – Early season cultivation and drilling; LSC – Late season cultivation and drilling

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Table 2. Summary of model farm system bioclimatic conditions used in our assessment Model farm system

Category Specific Arable Lowland Dairy Upland

Robust (main farm) type

A

Cereal (Cereal) Dairy (Dairy lowland) LFA grazing (Specialist sheep)

Location S and E England SW England Scottish Borders

Topography Altitude <250m <250m >350m

Slope < 12 deg ≥ 12 deg, variable ≥12 deg, more variable,

Soils General Mineral Mineral Mineral (steeper) and peat (flatter)

Texture Mostly coarse loamy to clayey, mostly freely to imperfectly drained (many soil associations).

Steep-freely draining, fine loamy (Denbigh association). Flatter-poorly draining, clayey (Hallsworth asociation).

Mineral – coarse loamy, often thin peaty top, podzolic (Belmont association). Peat- often waterlogged (Winter Hill association).

OC <5% >5% Mineral – peaty top. Organic

Vegetation Crop Crop Lolium perenne and Trifolium Mineral- acid grassland, Calluna. Organic- Eriophorum with Molinia caerulea, and Brophyta

Bare ground Extensive, autumn and spring 10% reseed, concentrated areas Discrete patches to extensive areas due to overgrazing and severity of climate

Climate Total precipitation (mm)

<800 > 800 (often >1000) >1200 (often >1600)

Deg days (deg C)

>1350 <1500 <1150

A (Defra, 2004). NB Soil and climate data from SSEW Bulletins 10 (Jarvis et al., 1984b); 13 (Hodge et al., 1984); 14 (Findlay et al., 1984a); and 15 (Jarvis et al., 1984a).

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Table 3. Summary of model farm system management Model farm system

Category Specific Arable Lowland Dairy Upland

Robust (main farm) type

Cereal (Cereal) Dairy (Dairy lowland) Least favorable grazing (Specialist sheep)

Management Farm size (ha) 300 75 + 75 Hundreds ha, large estate or access to common land

Field size (ha) 8 8 None

Cultivation Most (every) years, high proportion conservation tilled

Occasionally reseeded grass None

Machinery Large and land often traveled; tram lines Smaller, land less often traveled None or light quad bike

Drainage Heavier soils often underdrained Yes, 2/3 of fields underdrained Surface grips

Crops Dominantly cereals with oilseed rape ¼ under CC

Grass Non

Livestock 0 (or under cover) 150 adult, 120 followers ≤ 2 Sheep/ha

Manure/slurry None (unless access to FYM) Excreta deposited in housing and field Deposited on land

British Survey of Fert Practice

N (kg/ha) Ammonium nitrate 165 Ammonium nitrate 190 None

2008 P (kg/ha) 60 P2O5 35 P2O5 None

(Defra, 2008a) Plant Protection Products

Widely used Limited None (except bracken control)

Vet medicines No Yes Yes

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Table 4. Level of knowledge in UK climate change (UKCIP02 2020 scenarios) and detachment and solubilization mobilization processes for diffuse substances from model farm systems Arable Lowland dairy Upland sheep

Study scenarios UKCIP02 Detach Solub UKCIP02 Detach Solub UKCIP02 Detach Solub

Higher year-

round temperatures

Increase in annual average temperature

1 2 3 1 3 3 1 1 3

Decrease in very cold days

1 1 3 1 2 3 1 1 3

Increase in growing season

1 1 2 1 2 2 1 1 2


Increase in winter average temperature

3 1 3 3 2 3 3 1 3

Increase in winter precipitation

1 1 2 1 1 2 1 1 2


Increase in summer average temperature

1 2 2 1 2 2 1 2 2

Decrease in summer average precipitation

2 2 1 2 2 1 2 2 1

Increase in intense rainfall events ("intense" rainfall days per season)

1 1 2 1 1 2 1 1 2

Increase in number of very hot days 1 2 2 1 3 2 1 2 2

1 Low estimate of gaps and uncertainties (good level of knowledge), 2 medium, 3 large level of gaps and uncertainty

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1.b A review of international models to determine their suitability for

addressing the impacts of climate change (adaptation and mitigation) on

diffuse pollution to water and air from farming systems (objective 14.2) (Shepherd,

Wu, Chadwick and Bol).

We reviewed a large number of international models to determine their suitability for addressing

the impacts of climate change (adaptation and mitigation) on diffuse pollution to air and water.

This review has been published as a paper (Anita Shepherd, Lianhai Wu, David Chadwick and

Roland Bol (2011). A Review of Quantitative Tools for Assessing the Diffuse Pollution

Response to Farmer Adaptations and Mitigation Methods Under Climate Change. Advances in

Agronomy 112, 1-54), and the paper is provided. Here we briefly summarise the information

with limited cited references.

Summary abstract In an era of global climate change the agricultural sector faces the challenge of increasing the

production of safe and nutritious food supplies to meet a growing world population whilst

safeguarding the environment. Farmers will adapt their agricultural practises to a changing

climate to safeguard against loss of production and to take advantage of any positive climatic

conditions. Certain management practices have been found to reduce the effects of agricultural

practices on the environment and a key question is how efficient these are under the current

climate, and will these management practices still be relevant under a changing climate?

Mathematical modelling is the only tool available to assess the potential efficacy of proposed

agricultural management practices to help evaluate their impacts on the environment in a future

climate. This review attempts to evaluate a range of published models for their capability to

simulate agricultural production systems and associated environmental system losses under a

changing climate, and their ability to introduce farmer adaptation and mitigation methods. The

review focuses on the applicability of the models given a set of essential criteria related to scale,

biophysical processes and land management. Thirty models were initially examined, based on

details found in published papers, against specific criteria, viz: 1) spatial scale and temporal

scale, ease of use, and ability to consider a change in climate; 2) ability to simulate nutrient

cycling processes, specifically carbon and nitrogen dynamics with microbial turnover,

mineralization-immobilisation, nitrification and denitrification, plant nutrient uptake, and

phosphorus cycling; 3) ability to consider a water balance and water movement through soil; and

4) ability to introduce and modify agricultural practises relating to crop and livestock

management. The review did not compare any actual model simulations. It was concluded that

albeit no single model incorporates all above stated requirements, there were 3 models,

DAYCENT, PASIM and SPACSYS which will accommodate most features. These models may

therefore be considered in the context of this review to be the most suitable for a general

assessment of the effects of on farm mitigation and adaptation on environmental losses under a

changing climate.

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2. Assess the impact of climate change and adaptation on diffuse

pollution in agricultural systems using the SPACSYS model (objectives 16, 17, 18 and 20) Lianhai Wu, Anita Shepherd and David Chadwick

Introduction

Global average surface temperature has increased by ca. 0.7°C in the last century and is

projected to increase by another 1.1-6.4°C in this century. Long-term trends in precipitation

amount were spatially variable in the last century and precipitation amount is likely to

increase in high latitudes and decrease in most subtropical land regions this century (IPCC,

2007b). As such, climate change will affect agricultural activities. Farming adaptation may be

required to avoid negative effects of climate change and take advantage of opportunities that

it may provide.

Because the climate controls the processes of plant growth and development, plant response

to climate change is not only determined solely by photosynthesis, but also the partitioning of

photosynthesate among plant organs and the progress of its development. On the other hand,

climate change may control decomposition of organic matter in soils, and other

biogeochemical processes of e.g. the nitrogen (N) cycle that may cause diffuse water

pollution or intense greenhouse gas (GHG) emissions. Field management, e.g. time of

cultivation, fertilizer application and grazing intensity could divert the direction of N cycle.

Appropriate field management could lessen diffuse pollution or GHG emissions or increase

plant N uptake. Previous studies for Defra (NT2511 Cost curve of nitrate mitigation options;

PE0203 Cost curve assessment of phosphorus mitigation options relevant to UK agriculture;

ES0121 COST-DP: Cost effective diffuse pollution management and ES0203: The Cost-

effectiveness of integrated diffuse pollution mitigation measures) have identified a range of

methods that could be adopted to reduce diffuse water pollution from agriculture and

provided the estimates of the cost and effectiveness of the various pollution control methods

at the farm scale. However, those studies have not addressed the potential effect of climate

change scenarios on the mitigation measures developed to reduce diffuse water pollutants,

GHG and ammonia emissions under our current climate.

Impacts of climate change on crop productivity are generally assessed with crop models

(IPCC, 2007a). There are many simulation models of agricultural systems, which have been

developed for various purposes. In order to assess the effectiveness of current recognised

pollution mitigation strategies under future climates and land-use adaptation to climate

change, a particular type of model is required. This model would ideally: (i) address

appropriate spatial and temporal scales to reflect management decisions on farms, at a

minimum of a monthly time-step; (ii) be capable of simulating processes, transformations,

and losses relevant to climate change; (iii) be capable of simulating mitigation methods (e.g.

a tool kit for diffuse water pollution – Cuttle et al., 2007, recently superseded by the

Inventory of Mitigation Methods and Guide to their Effects on Diffuse Water Pollution,

Greenhouse Gas Emissions and Ammonia Emissions from Agriculture, 2011), and be able to

investigate secondary effects (‘win-win’s’ and ‘pollution swapping’); (iv) accommodate

farmer adaptations to climate change and (v) be relatively easy to use.

It is clear that to predict the impact of climate change effects on both diffuse water pollution

and gaseous emissions, that an integrated, flexible model which links the carbon (C) and N

cycles is required. We have reviewed a large number of models (see previous section) to

16

determine which meet some or all of these criteria. The SPACSYS modelling framework was

deemed the most suitable and accessible process based model meeting these criteria.

To actually use the model effectively requires reliable location-specific projections of climate

change. Scenarios available under the UK Climate Projections (UKCP09) provide the best

available current information for the UK on possible future climatic regimes. UKCP09 gives

projections for each of three of the IPCC’s Special Report on Emissions Scenarios (SRES)

scenarios (A1FI (called High in UKCP09), A1B (Medium) and B1 (Low)) to show how

different emissions pathways affect future climate. The data sets cover the UK at 5 × 5 km

resolution and span the period 1914–2006 (Jenkins et al. 2008). They are available for daily,

monthly and annual timescales, as well as long-term averages for the 1961–1990 climate

baseline period, which makes it possible to use a process-based simulation model to assess

the impact of climate change on diffuse pollution in agricultural systems.

Although there are some studies on adaptation to climate change to climate venialities

(Reidsma et al. 2010), the objective of this study was to : 1) to evaluate nitrogen (N) loss

under different pollution control methods and 2) to assess the responses of agricultural

systems to the future climatic scenarios with different time slices and the selected control

measures (adaptation) for a selection of contrasting farming typologies (grass, pea, wheat,

etc).

Materials and methods

Brief description of SPACSYS

SPACSYS is a 3-dimensional, field-scale, weather-driven and daily-step process-based

dynamic simulation model. It includes a plant growth and development submodel with

detailed representation of the root system, in addition to components for C and N cycling in

the soil with links to the plant, a soil water component, and a heat transfer component (Wu et

al. 2007b). Both soil water and heat components are inherited from the SOIL model

(Johnsson et al. 1991). Since its original publication, the model has been modified slightly in

order to minimise information flows among the components. The components of

aboveground plant C and N have been merged so that the model now consists of six modular

components; plant aboveground, root system, soil C, soil N, soil water and heat. Parallel to

the detailed representation of the root system, a simplified root system that is represented by

rooting depth, the vertical distribution of root length density and root biomass has been

implemented in the modified version (Wu et al. 2007a).

Scenarios of adaptations and mitigation

Cuttle et al (2007) listed 44 methods to control diffuse water pollution from agriculture in the

UK and linked these to a series of policy measures to enable policy makers to explore the

cost effectiveness of policy scenarios. The Cuttle et al (2007) list has since been superseded

with a more comprehensive Inventory of Mitigation Methods and Guide to their Effects on

Diffuse Water Pollution, Greenhouse Gas Emissions and Ammonia Emissions from

Agriculture. Some of these methods can be simulated using the SPACSYS model under

baseline and future climate change scenarios. Therefore, we selected those methods from the

list and transferred the agricultural practices from typical farming typologies into quantitative

model parameters and scenarios (Table 1). Simulations for each time slice and baseline were

set up in the way that there is a control simulation (without a mitigation or adaptation

measure) to compare the results with that produced from the correspondent mitigation or

adaptation measure considered whenever appropriate. The parameter values and field

17

management of the pair simulations are identical except those that the measure is

distinguished. The use of nitrification inhibitor could limit nitrate build-up in the soil and

subsequent nitrate leaching in agricultural systems. Although the measure is not included in

the policy scenarios published by Cuttle et al. (2007), published research has demonstrated

that inhibitors are effective in limiting the nitrification of ammonium to nitrate in soil for in

excess of 100 days (Smith et al. 2005) and highly effective in decreasing nitrate leaching (Di

et al. 2009). In this study, it is added as one of the mandatory methods on grassland. It is

assumed that inhibition of nitrification starts on the day when artificial fertilizer is applied

and will last about 120 days.

18

Table 1. List of simulated adaptation and mitigation methods (with abbreviations in simulations) under future climate (methods are based on Cuttle et al., 2007)

Land use

type

Method Brief description

Far

mer

ad

apta

tion

Ara

ble

Establish autumn cover crops Establish cover grass immediately post-harvest of spring barley (or pea) and

removed before spring crops sown next year

Cultivate arable land in the spring Cultivate arable land for spring crops (barley, peas and potato) in the spring rather

than the autumn

Spring establishment of cover Change spring crops with over-winter crops (spring barley -> winter barley)

Gra

ssla

nd Longer period of livestock housing Changing grazing periods from 2 months grazing period with cutting for forage to

grazing around a year

Man

dat

o

ry

mit

i

gat

i

on Arable No till Minimal tillage practice for spring barley, spring peas, winter barley, winter wheat

and winter oilseed rape

Grassland Nitrification inhibitor Apply nitrification inhibitor to limit NO3-N build-up in the soil and subsequent nitrate leaching and nitrous oxide emission

19

Location of model farms for climate change / diffuse water pollution mitigation &

adaptation scenarios

Two sites were chosen to represent typical agro-eco-climatic zones for the arable and

grassland agricultural systems; (i) arable - sandy loam soil in Eastern England, and (ii)

grassland - clay soil in SW England. The grassland site was based at North Wyke (latitude

50°45’36”N; longitude 03°52’05’’W) with soil of the Hallsworth series i.e., slowly

permeable, seasonally waterlogged soils over slowly permeable substrates with negligible

storage capacity. The arable site was based at Gleadthorpe (latitude 53°13’25”N; longitude

01°06’53’’W) with soil of the Cuckney soil series i.e., free draining permeable soils on soft

sandstone substrates with relatively high permeability and high storage capacity. Soil data for

the North Wyke site was collated from various sources (Harrod et al. 2008; Armstrong et al.

1991) while the data for the Gleadthorpe site was based at Beulke et al. (1998) (Table 2).

Field management for all simulations were based on the farm typology, as defined in Work

Package 5 of this project (Robust Farm Typologies) (Tables 3 and 4).

20

Table 2. Soil properties with soil depth at the sites

Gleadthorpe (sandy soil) North Wyke (clay soil)

Soil depth (m) 0 - 0.1 0.1-0.3 0.3-0.65 > 0.65 0-0.15 0.15-0.3 0.3-0.66 >0.66

Soil bulk density (g cm-3) 1.51 1.51 1.53 1.60 0.99 0.99 1.31 1.55

Residual water content 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1

Saturated water content (vol%) 40.7 40.7 42.3 39.8 63.3 63.3 50.4 41.5

Water content at wilting point (vol%) 4.4 4.4 1.3 3.4 29.3 29.3 29.1 18.6

Saturated matrix conductivity (mm d-1) 1493 1493 1997 1778 100 100 130 5

Saturated total conductivity (mm d-1) 1993 1993 2497 2278 5333 222 89 3

Macro pore volume 4 4 4 4 4 4 4 4

Pore size distribution index 0.16 0.13 0.11 0.17 0.16 0.13 0.11 0.17

Air entry pressure 1.4 1.4 0.1 0.9 2.2 2.5 2.2 3.1

Tortuosity factor 3 3 1 1 3 3 1 1

pH value 6.6 6.6 6.3 5.6 5.3 5.3 6 6.1

21

Table 3. Field and crop management in arable land (all settings are same for each simulation year)

Crop Measure Cultivation

Fertilizer application* (kgN/ha) Crop sowing date

date amount

Spring barley Control early-March early March 28 barley: mid-March

early April 37

early May 11

over-winter cover early-March early March 28 barley: mid-March

early April 37 over-winter cover crop: mid-July

early May 11

Spring peas Control mid-February early March 28 mid-February

over-winter cover mid-February early March 28 peas: mid-February

over-winter cover crop: mid-July

Potato Control mid-October mid-April 38 mid-April

mid-May 14

mid-June 9

cultivation in spring early-April mid-April 38 mid-April

mid-May 14

mid-June 9

Spring barley Control mid-October early March 28 mid-March

early April 37

early May 11

cultivation in spring early-March early March 28 mid-March

early April 37

early May 11

no till

early March 28 mid-March

early April 37

early May 11

Spring peas Control mid-October early March 28 mid-February

cultivation in spring mid-February early March 28 mid-February

22

no till

early March 28 mid-February

Winter barley Control end-August early March 38 end-October

early April 59

early May 29

no till

early March 38 end-October

early April 59

early May 29

Winter oilseed rape Control mid-August early March 42 end-August

early April 64

early May 32

no till

early March 42 end-August

early April 64

early May 32

Winter wheat Control end-September early March 39 end-October

early April 60

early May 30

no till

early March 39 end-October

early April 60

early May 30

*split in N fertiliser requirement is for model simulation and hence appears more precise than would occur on farm

23

Table 4. Grass cut (C) events and grazing (G) periods and fertilizer applications (kg N/ha) in grassland

property Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Cut/grazed C C G G (dairy) Fertilizer

N 4 27 32 19 12 9 5 1

Manure + excreta N*

77 77

Cut/grazed G G G G G G (dairy) Fertilizer

N 4 27 32 19 12 9 5 1

Manure + excreta N*

77 77 77 77 77 77

Grazed G G G G G G G G G G G G (sheep) Fertilizer

N 6 30 45 23 2

Manure + excreta N*

8 8 8 8 8 8 8 8 8 8 8 8

* split in N loadings via fertiliser and manure+excreta is for model simulation and hence appears more precise than would occur on farm.

Climate change scenarios

The former climate projection series by the Met Office/Hadley Centre, UKCIP02, was a

deterministic projection, a best guess of what the future should look like. The advantage of

UKCP09 climate projections is that they now provide probabilistic projections, so users can,

via an interface, obtain statistics of probability. The uncertainty in the projections is then

transparent. The data extracted from the UKCP09 was applied to this project. However, the

UKCP09 projections do not provide a single value for a projection, providing one hundred

variants of daily data. This makes data input for model application problematic. Out of

necessity to make data applicable because we cannot manually run our model one hundred

times, the one hundred climate files were processed into one representative file.

Daily scenarios in the UKCP09 were extracted for North Wyke and Gleadthorpe, requesting

30 year timeslices based around the 2020s, 2050s and 2080s, for medium and high emission

scenarios. Medium and high emission scenarios are based on future projections of greenhouse

gas and aerosol levels according to IPCC determined storylines; SRES A1B (medium, with

‘m’ as a suffix thereafter) and SRES A1F1 (high, with ‘h’ as a suffix thereafter) represent a

global economy based on A1B decarbonization and a mix of energy sources, and carbon

intensive energy, respectively. A suite of short simple programs were written to reformat

data, obtain mean meteorological values from a hundred files per scenario downloaded and

convert units. For each 30-year daily scenario, mean values across one hundred files were

calculated for maximum and minimum temperature, humidity and radiation.

Various problems and solutions were found to obtaining representative data; for example,

mean values are not possible for precipitation, a stochastic model is needed. Also, wind speed

data are not provided by UKCP09. It was necessary to obtain historical daily precipitation for

the sites and provide monthly means and the number of rain days per month from the

UKCP09 files to generate daily precipitation. The 11-member RCM (regional climate model)

dataset for wind speed was obtained via the British Atmospheric Data Centre LINK project

24

(http://badc.nerc.ac.uk/home). Mean UKCP09 values were combined with wind speed and

stochastically produced precipitation data to generate the appropriate climate files for each

projection.

Seven UKCP09 projections were produced, 3 timeslices for medium (represented as 2020m,

2050m and 2080 respectively), 3 timeslices for high emissions (represented 2020h, 2050h

and 2080h respectively) plus historic climate (symbolized as baseline) for each site location.

Annual values of main climatic elements for all scenarios are shown in Table 5 and dynamics

of weekly values shown in Figure 1 and 2.

Table 5. Annual mean climatic characteristics in baseline and time slices at sites North Wyke Gleadthorpe

Temperature (°C) Rainfall (mm)

Temperature (°C) Rainfall (mm) Maximum Minimum Maximum Minimum

Baseline 12.8 5.8 1029 12.9 4.7 637 2020M 14.3 7.2 1051 14.5 6.1 645 2050M 15.4 8.1 1058 15.5 6.9 635 2080M 16.4 9.0 1046 16.6 8.0 609 2020H 14.3 7.1 1022 14.4 6.0 630 2050H 15.7 8.3 1025 15.8 7.2 623 2080H 17.5 10.0 1043 17.5 8.7 623

The UKCP09 weather generator is quite distinct from GCMs, which provide a mathematical

representation of the processes that control the climate system. The UKCP09 Weather

Generator learns about daily climate variable relationships from climate data and uses what

has been learned to develop statistical relationships. This means there is no guarantee that the

UKCP09 Weather Generator will always reproduce correct daily behaviour. What are

produced are time series which are indicative possible and plausible realisations of the daily

climate. Despite what its name might suggest, the purpose and design of a Weather Generator

is not to provide a weather forecast for the future. A weather forecast gives an indication of

what the weather is predicted to be on a particular day. A Weather Generator provides a

multiple plausible daily time series which are (statistically) consistent with both the baseline

climate (1961–1995) and with the UKCP09 probabilistic projections of future climate change

(UK Climate Projections online:

http://ukclimateprojections.defra.gov.uk/content/view/1100/500/). It generates many

different, but statistically equivalent time series, but these time series should be seen as

indicative of the 2020s, 2050s or 2080s, not viewed as a weather forecast for a deterministic

time series.

25

Figure 1. Average weekly precipitation (A), maximum (B) and minimum (C) temperatures in various climate change scenarios at North Wyke

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50

NW historic

NW high 2020s

NW high 2050s

NW high 2080s

NW med 2020s

NW med 2050s

NW med 2080s

A

0

5

10

15

20

25

30

0 10 20 30 40 50

NW historic

NW high 2020s

NW high 2050s

NW high 2080s

NW med 2020s

NW med 2050s

NW med 2080s

B

0

5

10

15

20

0 10 20 30 40 50

NW historic

NW high 2020s

NW high 2050s

NW high 2080s

NW med 2020s

NW med 2050s

NW med 2080s

C

week No.

26

Figure 2. Average weekly precipitation (A), maximum (B) and minimum (C) temperatures in various climate change scenarios at Gleadthorpe

Parameterizaton

Models should be tested and validated before they are used for application. SPACSYS has

been validated for the estimation of nitrate leaching, gaseous N emissions as well as plant

growth and development previously (Wu et al. 1997; 1999; 2006). Since there is insufficient

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50

NW historic

NW high 2020s

NW high 2050s

NW high 2080s

NW med 2020s

NW med 2050s

NW med 2080s

A

0

5

10

15

20

25

30

0 10 20 30 40 50

NW historic

NW high 2020s

NW high 2050s

NW high 2080s

NW med 2020s

NW med 2050s

NW med 2080s

B

0

5

10

15

20

0 10 20 30 40 50

NW historic

NW high 2020s

NW high 2050s

NW high 2080s

NW med 2020s

NW med 2050s

NW med 2080s

week No.

C

27

data for validation at both sites, the model was used directly to simulate the various scenarios

in this study. Parameters for water cycling and soil heat transformation are from McGechan

et al. (1997). Parameters to describe plant growth and development are crucial for accurate

estimation of C and N cycles. Therefore, potato and winter oilseed rape were parameterized

first in this study, while other crops including barley, wheat, peas and grass that had been

parameterized and validated elsewhere were used directly. An assumption was made that

varieties of crops and grass keep the same genetic characteristics throughout all of the

timeslices.

Simulation running

To run SPACSYS, a series of datasets were pre-prepared. Soil physical properties for each

soil type, field and crop management for each method in all the timeslices were stored in a

database. All field managements including ploughing, applied fertilizer amount and times,

crop sowing and harvest dates are kept in the same in all the timeslices and the baseline at a

particular site. The initial conditions of soil water and soil temperature were set at typical

values.

The selected management scenarios were repeated for current climate and the medium-high

and medium-low climate scenarios to produce 21 model simulations, each run for 30 years,

for the comparison of the effects of climate change and management strategies to reduce

diffuse water pollution. As the initial conditions were assumed, we used the first 3-year

running period to allow any inaccurate starting values to approach the real situation.

Therefore, the simulation results presented in the next section are the summation for 27 years

(i.e. from the fourth year onwards).

Estimation of gas nitrogen emissions in the SPACSYS simulations is based on denitrification,

and is expressed as the sum of N2O and N2. (Ammonia emissions are not currently simulated

in SPACSYS). As we are interested in N2O emission, we have developed a methodology to

estimate N2O emission from total denitrification losses. The proportions of N2O and N2 in the

emitted gases depend considerably on soil type, land use, climatic condition and other

environmental factors (Schindlbacher et al. 2004). The ratio of N2:N2O gas flux during

denitrification is extremely variable and difficult to estimate. However, previous research has

shown that N2O emissions account for about 60% of the total denitrification, which gave a

satisfactory estimation from the simulation with SPACSYS compared to sampled N2O

emission in Scotland (Wu et al. 2006). Therefore, 60% of the total denitrification loss was

estimated to be as N2O emission in this study.

Simulation results

Grain yield

Model simulations suggest that grain yields of winter wheat will increase markedly (by ca.

%) between the baseline year and 2020 under both the medium and high scenarios (see

Figure 3), mainly as a result of the increased length of growing season.

28

Figure 3. Simulated effect of climate change scenarios on Winter Wheat grain yields

Nitrate leaching

Nitrate leaching from spring barley increases with each climate change scenario, with greater

losses from the 2080 scenarios compared with the baseline scenarios. Most mitigation

measures we tested could decrease nitrate leaching loss under the baseline and all climate

change scenarios on arable land. For example, nitrate leaching loss in a spring barley field

with over-winter cover grass could be decreased by up to 50% compared to that in the field

without over-winter cover grass (Figure 4a). Similarly, reduced tillage for winter wheat

results in reduced nitrate leaching (Figure 4b). The application of a nitrification inhibitor in

grassland mitigates nitrate leaching dramatically in all climate scenarios.

The results for average annual nitrate leaching losses from a range of crops for each climate

change scenario are shown in Table 6, along with the effectiveness of the potential DWPA

mitigation methods (reduced tillage, use of cover crops, spring cultivations for arable crops;

reduced grazing season, nitrification inhibitor for grassland systems). It should be noted that

the degree of reduction varies between mitigation measures, grown crops and climate change

scenarios. Even under the same scenario, nitrate leaching loss varied between each of the

simulated 30 years, for example in the spring barley field with cultivation in early spring

(Figure 5). The variations between years are indicated with standard deviation values for each

mean (Table 6).

0.00

2000.00

4000.00

6000.00

8000.00

10000.00

12000.00

baseline 2020m 2050m 2080m 2020h 2050h 2080h

yie

ld (

kg/h

a)

winter wheat grain yield

control

no tillage

29

Figure 4a. Simulated average annual nitrate leaching loss over 30 years in spring barley field with (mitigation)/without (control) over-winter cover grass under the baseline and climate change scenarios.

Figure 4b. Simulated average annual nitrate leaching loss over 30 years in winter wheat field with cultivation in the autumn (control) and no tillage under the baseline and climate change scenarios.

0

10

20

30

40

50

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

ave

rage

nit

rate

leac

h

(kgN

/ha)

spring barley

control

mitigation

0

10

20

30

40

50

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

ave

rage

nit

rate

leac

h

(kgN

/ha)

winter wheat

control

no tillage

30

Figure 5. Annual changes of nitrate leaching loss over 30 years winter wheat field with (control) / without tillage in baseline (a) and 2020h (b) climate change scenarios (losses in other scenarios are not shown avoiding the complexity of the graph).

0

5

10

15

20

25

0 5 10 15 20 25 30

ann

ual

nit

rate

leac

hin

g lo

ss (

kgN

/ha)

year No.

winter wheat - baseline

control - baseline

no till - baseline

a

0

4

8

12

16

20

0 5 10 15 20 25 30

ann

ual

nit

rate

leac

hin

g lo

ss (

kgN

/ha)

year No.

winter wheat 2020h

control-2020h

no-till - 2020h

b

31

Table 6. Average annual nitrate leaching loss (kgN/ha) under various mitigation and adaptation scenarios

spring barley spring peas potato

control over-winter cover reduction

(%)

control over-winter cover reduction

(%)

control cultivation in

spring reduction (%) annual

loss STDEV annual loss STDEV

annual loss STDEV

annual loss STDEV

annual loss STDEV

annual loss STDEV

baseline 31 6.7 16 4.9 -49 26 4.2 7 3.0 -73 21 3.5 20 3.5 -3

2020m 36 6.5 18 4.2 -49 24 3.8 7 3.1 -70 22 3.7 21 3.7 -4

2050m 37 6.6 22 4.6 -40 25 4.0 10 3.6 -61 24 3.9 23 3.8 -3

2080m 40 8.0 28 5.9 -30 26 4.4 13 3.7 -51 26 4.2 25 4.1 -3

2020h 36 6.5 18 4.4 -50 24 4.0 7 3.1 -71 22 3.8 21 3.6 -4

2050h 39 7.5 25 5.5 -36 26 4.4 11 4.0 -57 24 4.1 24 4.0 -3

2080h 43 8.5 31 6.3 -29 28 4.9 14 4.3 -51 28 4.4 27 4.4 -2

Table 6. Average annual nitrate leaching loss (kgN/ha) under various mitigation and adaptation scenarios (cont.)

spring barley spring peas

Control cultivation in spring reduction

(%)

no tillage reduction (%)

control cultivation in spring reduction (%)

no tillage reduction (%)

annual loss STDEV

annual loss STDEV

annual loss STDEV

annual loss STDEV

annual loss STDEV

annual loss STDEV

baseline 31 6.6 31 6.7 0 31 6.7 0. 33 5.2 33 5.1 -1 32 5.1 -3

2020m 36 6.4 36 6.5 0 36 6.6 0 28 4.3 28 4.3 -1 27 4.2 -3

2050m 37 6.6 37 6.7 1 37 6.7 -1 28 4.3 27 4.3 1 27 4.3 -2

2080m 40 7.9 40 8.0 0 40 8.0 -1 28 4.6 27 4.6 1 27 4.6 -1

2020h 35 6.5 35 6.5 0 35 6.5 -1 28 4.6 28 4.5 1 27 4.5 -3

2050h 38 7.2 38 7.4 -1 38 7.5 -1 28 4.6 28 4.6 -1 27 4.6 -2

2080h 43 8.4 43 8.5 -1 43 8.5 -1 29 5.0 29 4.9 -1 29 4.9 -1

32


winter barley winter oilseed rape winter wheat

Control no tillage reduction (%)

control no tillage reduction

(%)

control no tillage reduction

(%) annual loss

STDEV annual

loss STDEV

annual loss

STDEV annual

loss STDEV

annual loss

STDEV annual

loss STDEV

baseline 28 8.6 28 8.6 -1 85 14.7 85 14.5 0 12 3.2 11 3.1 -6

2020m 11 3.4 10 3.4 -3 80 16.3 81 16.0 1 10 3.3 9 3.2 -4

2050m 11 4.0 11 3.9 -1 78 16.7 78 16.4 0 9 3.7 8 3.6 -3

2080m 14 5.3 14 5.4 0 75 17.5 75 17.7 0 9 3.8 8 3.8 -2.0

2020h 10 3.2 9 3.1 -4 79 15.8 81 16.1 2 9 3.2 9 3.1 -5

2050h 12 4.4 11 4.3 -2 77 17.3 77 17.5 0 9 3.8 8 3.7 -3

2080h 16 6.6 17 6.9 2 77 18.8 78 18.4 1 9 3.9 9 3.9 -1


dairy with two months grazing dairy with six months grazing sheep with grazing around a year

Control with inhibitor increase

(%)

control with inhibitor increase

(%)


(%) annual

loss STDEV

annual loss

STDEV annual

loss STDEV

annual loss

STDEV annual

loss STDEV

annual loss

STDEV

baseline 27 4.8 9 1.8 -67 63 12.2 19 4.4 -70 14 1.9 6 0.9 -57 2020m 28 5.3 10 2.1 -65 63 13.2 21 5.1 -67 13 2.1 6 1.0 -58. 2050m 30 5.4 10 2.2 -65 67 13.1 22 5.2 -67 15 2.3 6 1.1 -58. 2080m 29 5.8 11 2.4 -63 66 13.9 23 5.7 -65 15 2.4 6 1.1 -57. 2020h 28 5.9 10 2.4 -65 63 14.1 21 5.6 -66 13 2.4 5 1.1 -59 2050h 29 6.3 10 2.6 -64 66 15.2 23 6.4 -64 14 2.4 6 1.1 -58 2080h 31 6.2 11 2.6 -63 69 14.7 25 6.3 -64 16 2.7 7 1.3 -57

33

In grassland, with a longer grazing period for given livestock, more excreta will be loaded

onto the grazing field. The simulation is based on the assumption that excreta are spread in

the grazing field uniformly. The results indicate that this assumption may be too simple and

possibly does not reflect real situation. (SPACSYS, like most models cannot accommodate

the within field spatial heterogeneity of urine and dung deposition).

There is a trend that annual nitrate leaching loss will increase with the time slices (Figure 6).

Although sheep graze in the field all year round, less nitrate would be leached compared with

dairy system, because less excreta and manure from sheep would be added into the field.

There are fewer differences in nitrate leaching between time slices in the fields where sheep

graze compared with those where dairy cows graze.

Grass could grow all year in SW England ( North Wyke site), so more inorganic nitrogen can

be taken up if other environmental factors are favourable to grass growth; while crops grown

on arable land in E England (Gleadthorpe site) have a shorter growing season, resulting in

less nitrogen taken up and potentially more nitrate could be leached (given sufficient

hydrologically effective rainfall). In fact, SPACSYS modelling suggests that grassland has

the larger risk in diffuse N pollution than the arable land, reflecting different climate patterns

(e.g. precipitation distribution), soil physical properties and amount of fertilizer and manure

applications between the sites and farming systems.

Figure 6. Simulated average annual nitrate leaching loss over 30 years in grassland with different types of livestock, grazing period and amount of manure incorporation under the baseline and climate change scenarios.

Nitrogenous gas emission

Similar to the nitrate leaching losses, the use of the over winter cover crop (grass)

successfully reduced N2O emissions from the spring barley field under the baseline and all

climate change scenarios (Figure 7). The use of a cover crop results in a small increase in

N2O emissions in the potato field while the emissions become greater with ascending time

slices in the spring pea field.

Changing cultivation timing to spring instead of autumn in the spring sown pea field could

increase N2O emissions slightly (Table 7). There is no marked difference in N2O emissions

between the medium-high and high emissions of climate change for the same time slices in

the spring peas field. Changing cultivation timing in the spring barley field could decrease

N2O emissions greatly.

0

10

20

30

40

50

60

ann

ual

ave

rage

nit

rate

le

ach

(kg

N/h

a)

grassland with various grazing periods

dairy (2months grazing)

dairy (6months grazing)

sheep (grazing around a year)

34

In general, ‘no tillage’ increases N2O emissions in most scenarios in both the spring crop

fields and over-winter crop fields (Table 7), e.g. in winter wheat field (Figure 8). The

simulation results are consistent with field experiments conducted in a loam and a heavy clay

soil (Rochette et al. 2008).

Figure 7. Simulated average annual N2O emissions over 30 years in spring barley field without (control)/with over-winter cover grass under the baseline and climate change scenarios.

Figure 8. Simulated average annual N2O emissions over 30 years in winter wheat field with cultivation in the autumn (control) and no tillage under the baseline and climate change scenarios.

0

1

2

3

4

5

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

N2O

em

isio

n (

kgN

/ha)

spring barley

control

over-winter cover

0

1

2

3

4

5

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

N2O

em

issi

on

(kg

N/h

a)

winter wheat

control

no tillage

35

Table 7. Average annual N2O emission (kgN/ha) under various mitigation and adaptation scenarios


Control over-winter cover

increase (%)

control over-winter cover

increase (%)

control cultivation in spring

increase (%)

annual emission STDEV






baseline 2.1 0.32 1.8 0.30 -14 1.6 0.20 1.5 0.24 -10 1.5 0.20 1.6 0.21 6

2020m 2.4 0.32 2.1 0.28 -16 1.8 0.24 1.8 0.34 -1 1.7 0.21 1.8 0.23 8

2050m 2.7 0.36 2.3 0.30 -17 2.1 0.27 2.1 0.40 3 1.8 0.22 1.9 0.23 8

2080m 3.5 0.44 2.8 0.34 -20 2.5 0.31 2.7 0.52 7 2.1 0.23 2.2 0.24 7

2020h 2.5 0.33 2.1 0.30 -16 1.9 0.24 1.8 0.34 -3 1.7 0.23 1. 0.24 8

2050h 2.9 0.37 2.4 0.31 -19 2.1 0.28 2.2 0.42 3 1.9 0.22 2.0 0.23 8

2080h 3.8 0.50 3.0 0.36 -23 2.6 0.32 2.8 0.64 8 2.2 0.25 2.4 0.27 7

Table 7. Average annual N2O emission (kgN/ha) under various mitigation and adaptation scenarios (cont.)


Control cultivation in spring

increase (%)

no tillage

increase (%)


increase (%)

no tillage

increase (%)







baseline 2.1 0.34 2.1 0.32 -1 2.1 0.33 1 1.7 0.21 1.7 0.21 2 1.8 0.22 6

2020m 2.5 0.33 2.4 0.32 -2 2.5 0.32 -1 1.9 0.25 1.9 0.25 2 2.0 0.26 5

2050m 2.8 0.37 2.7 0.36 -2 2.8 0.36 -1 2.1 0.27 2.1 0.27 1 2.2 0.29 4

2080m 3.5 0.44 3.5 0.44 -2 3.5 0.44 -1 2.5 0.31 2.5 0.31 1 2.6 0.32 3

2020h 2.5 0.35 2.5 0.33 -2 2.5 0.34 -1 1.9 0.24 2.0 0.24 2 2.1 0.26 5

2050h 3.0 0.38 2.9 0.37 -2 3.0 0.38 0 2.1 0.28 2.2 0.28 1 2.2 0.29 4

2080h 3.9 0.50 3.8 0.50 -2 3.9 0.50 -1 2.6 0.32 2.7 0.32 1 2.7 0.33 3

36



Control no tillage

increase (%)

control no tillage

increase (%)

control no tillage

increase (%)







baseline 2.5 0.39 2.5 0.39 3 4.4 0.75 4.1 0.73 -5 1.6 0.23 1.7 0.23 9

2020m 2.1 0.24 2.4 0.26 11 5.2 0.89 5.0 0.91 -4 1.9 0.15 2.1 0.19 12

2050m 2.8 0.39 3.1 0.41 12 5.9 0.99 5.7 1.07 -3. 2.3 0.16 2.5 0.20 13

2080m 4.6 0.61 5.0 0.81 8 7.2 1.04 7.1 1.19 -1 3.1 0.21 3.4 0.24 12

2020h 2.2 0.22 2.4 0.23 12 5.1 0.92 4. 0.94 -4 1.9 0.15 2.1 0.18 12

2050h 3.4 0.48 3.8 0.55 12 6.4 0.97 6.23 1.07 -2 2.5 0.15 2.9 0.17 13

2080h 5.8 0.78 6.1 1.05 5 7.8 1.24 7.8 1.37 -1 3.7 0.24 4.1 0.26 11




(%)


(%)

control with inhibitor increase (%)

annual emission

STDEV annual

emission STDEV

annual emission

STDEV annual

emission STDEV

annual emission

STDEV annual

emission STDEV

baseline 1.4 0.29 1.1 0.26 -21 1.5 0.32 1.0 4.60 -36 1.5 0.38 1.3 0.36 -13 2020m 1.4 0.26 1.0 0.21 -26 1.6 0.30 1.0 0.18 -42 1.3 0.33 1.1 0.29 -15 2050m 1.5 0.31 1.1 0.24 -30 1.9 0.36 1.0 0.22 -46 1.2 0.28 1.0 0.25 -16. 2080m 1.6 0.30 1.0 0.19 -35 2.1 0.42 1.0 0.17 -51 1.1 0.21 0.9 0.17 -19 2020h 1.4 0.25 1.0 0.21 -27 1.7 0.32 0.9 0.19 -43 1.3 0.33 1.1 0.30 -15 2050h 1.6 0.34 1.1 0.25 -32 1.9 0.41 1.0 0.22 -48 1.2 0.27 1.0 0.25 -17 2080h 2.0 0.39 1.2 0.26 -39 2.7 0.52 1.2 0.22 -56 1.2 0.23 1.0 0.18 -22

37

In Grassland, however, grazing all year round with sheep produces less N2O under all the

climate change scenarios (Figure 9) than the other grassland systems. This is despite urine

deposition being all year round in the sheep system, and reflects the greater N inputs in the

dairy systems [130 vs. 280 (2months) and 360 (6months) kgN/year]. The dairy system with

the longer grazing season (6 months) produces more N2O than that with shorter grazing

season.

Figure 9. Simulated average annual N2O emissions over 30 years in grassland with different types of livestock, grazing period and amount of manure incorporation under the baseline and climate change scenarios.

Nitrogen recycling

Plant N uptake

Plant N uptake is one of the most important components in the N cycle. The more N that

plants take up, the less risk there is for nitrate leaching and N2O emissions. Some of selected

mitigations are based on extending the period of ground cover. For example, by growing an

over-winter cover crop like grass in a spring peas field, the amount of annual N uptake by the

peas and the grass is much higher than that without cover crop (Figure 10), so reducing the

risk of nitrate leaching.. Annual N uptake slightly decreases with the time slices of climate

change.

‘No tillage’ restrains plant N uptake with no marked difference (e.g. in winter wheat field in

Figure 11) with conventional tillage apart from the spring peas field where the differences

between conventional tillage and no tillage under various climate change scenarios are

between 5-10% (Table 8). Changing cultivation time from autumn to spring in spring crop

fields does not appear to change the amount of N uptake..

0

1

2

3

4

5

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

N2O

em

isio

n (

kgN

/ha)

grassland with various grazing periods

dairy (2 months grazing)



38

Figure 10. Average annual N uptake by plants over 30 years in spring peas field with (mitigation)/without (control) an over-winter cover crop (grass) under the baseline and climate change scenarios.

0

50

100

150

200

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

N u

pta

ke (

kgN

/ha)

spring peas

control

over-winter cover

39

Table 8. Average annual nitrogen uptake (kgN/ha) under various mitigation and adaptation scenarios



increase (%)


increase (%)


spring

increase (%)

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

baseline 94 6.9 124 8.0 31 52 2.8 158 7.5 201 129 3.9 129 3.9 0

2020m 88 5.9 122 7.0 38 58 2.3 164 11.1 185 126 3.9 126 3.9 0

2050m 88 5.6 120 6.2 37 57 2.2 154 12.6 170 122 3.5 122 3.5 0

2080m 84 5.3 111 5.9 32 56 2.0 141 11.5 150 116 3.3 116 3.3 0

2020h 89 6.7 123 7.5 38 58 2.5 165 10.7 186 126 4.1 126 4.1 0

2050h 86 5.8 116 6.0 36 56 2.2 144 12.3 155 120 3.4 120 3.4 0

2080h 80 3.9 109 5.0 36 55 2.0 151 11.4 177 113 2.8 113 2.8 0

Table 8. Average annual nitrogen uptake (kgN/ha) under various mitigation and adaptation scenarios (cont.)



increase (%)

no tillage

increase (%)


spring

increase (%)

no tillage

increase (%)

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

baseline 94 6.9 94 6.9 0 94 6.9 0 57 3.1 58 3.1 1 59 3.1 3

2020m 88 5.9 88 5.9 0 88 5.9 0 60 2.5 61 2.5 1 62 2.5 2

2050m 88 5.6 88 5.6 0 88 5.6 0 58 2.3 59 2.3 1 59 2.3 2

2080m 84 5.3 84 5.3 0 84 5.3 0 57 2.0 57 2.0 1 57 2.0 1

2020h 89 6.7 89 6.7 0 89 6.7 0 61 2.7 61 2.7 1 62 2.6 2

2050h 86 5.8 86 5.8

86 5.8 0 57 2.3 58 2.3 1 58 2.2 1

2080h 80 3.9 80.0 3.9 0 80 3.9 0 55 2.1 55 2.1 1 55 2.0 1

40

Table 8. Average annual nitrogen uptake (kgN/ha) under various mitigation and adaptation scenarios (cont.)


Control no tillage

increase (%)

control no tillage

increase (%)

control no tillage

increase (%)

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

annual uptake STDEV

baseline 158 18.5 156 22.3 -1 88 13.0 889 13.6 1 221 6.9 223 6.5 1

2020m 202 10.3 199 19.2 -1 96 6.8 97 6.8 1 235 4.9 236 4.7 1

2050m 200 10.1 198 15.4 -1 99 7.7 100 7.9 1 244 5.0 245 4.6 0

2080m 194 10.2 192 12.8 -1 102 9.1 104 9.2 1 247 5.0 248 4.6 0

2020h 203 9.6 200 18.8 -1 98 6.5 98 6.6 1 236 4.5 237 4.3 0

2050h 198 9.7 196 13.8 -1 100 8.3 101 8.5 1 245 4.8 246 4.1 0

2080h 187 9.8 186 11.7 -1 98 8.2 99 8.4 1 247 5.2 247 4.9 0

Table 8. Average annual nitrogen uptake (kgN/ha) under various mitigation and adaptation scenarios (cont.) should we include this table



(%)


(%)


(%) annual uptake

STDEV annual uptake

STDEV annual uptake

STDEV annual uptake

STDEV annual uptake

STDEV annual uptake

STDEV

baseline 263 14.1 263 14.1 0 431 32.6 431 32.6 0 223 8.0 223 8.0 0 2020m 288 15.8 288 15.8 0 464 33.1 464 33.1 0 228 8.3 228. 8.3 0 2050m 294 16.9 294 16.9 0 460. 31.6 460. 31.6 0 229 8.4 229 8.4 0 2080m 301 18.0 301 18.0 0 466 30.3 466 30.3 0 230 8.7 230 8.7 0 2020h 292 16.6 292 16.6 0 473 35.2 473 35.2 0 230 8.8 230 8.8 0 2050h 305 17.4 305 17.4 0 480 33.2 480 33.2 0 232 8.1 232 8.1 0 2080h 314 20.5 314 20.5 0 482 32.9 482 32.9 0 235 9.7 235 9.7 0

41

Figure 11. Average annual N uptake by plants over 30 years in winter wheat field with (control) /without tillage under the baseline and climate change scenarios.

Nitrification

Nitrification controls the nitrate and ammonium content in the soil. When the process is

suppressed because of unfavourable environmental conditions or lack of substrate, nitrate

leaching and N2O emissions could be reduced. All mitigation methods applied to arable land

have no apparent effect on the annual nitrification rate under the baseline and all the climate

change scenarios apart from the method of including an over-winter cover crop in spring

barley and peas fields (Table 9 and Figure 12). The method to include a cover crop in the

spring barley field has the largest effect among spring crop fields (Figure13).

The use of a nitrification inhibitor retards the nitrification process. The annual rate of

nitrification with this method decreases dramatically when included in the grassland systems

(Table 9).

0

50

100

150

200

250

300

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

N u

pta

ke (

kgN

/ha)

winter wheat

control

no tillage

42

Table 9. Average annual nitrification (kgN/ha) under various mitigation and adaptation scenarios – round values



increase (%)


increase (%)


increase (%)

annual nitrification STDEV





annual nitrification

STDEV

baseline 24.8 2.0 19 1.9 -25 17 1.3 37 2.1 113 26 1.0 26 1.1 -1 2020m 27.8 1.6 21 1.5 -25 18 1.1 43 3.3 133 28 1.2 28 1.3 0 2050m 29.5 1.8 23 1.5 -22 20 1.4 44 3.9 120 29 1.4 29 1.4 0 2080m 32.5 2.0 27 1.9 -18 22 1.7 45 4.4 104 31 1.6 31 1.6 0 2020h 27.9 1.7 21 1.6 -26 18 1.3 42 3.2 133 28 1.2 28 1.2 0 2050h 30.5 1.7 24 1.7 -21 21 1.5 43 4.4 105 30 1.4 30 1.5 0 2080h 34.1 2.2 28 2.1 -18 23 1.9 50 4.2 112 32 1.7 32 1.8 0

Table 9. Average annual nitrification (kgN/ha) under various mitigation and adaptation scenarios (cont.)



increase (%)

no tillage

increase (%)


increase (%)

no tillage

increase (%)





STDEV


STDEV


STDEV

baseline 245 1.8 25 2.0 0 25 1.9 0 26 1.7 26 1.7 0 26 1.7 -1

2020m 28 1.5 28 1.6 0 28 1.7 0 23 1.4 23 1.4 0 23 1.4 -1

2050m 29 1.7 29 1.8 0 30 1.7 0 23 1.6 23 1.6 0 22 1.6 -1

2080m 32 2.1 32 2.0 0 32 2.1 0 23 1.8 23 1.8 0 23 1.8 0

2020h 28 1.8 28 1.7 0 28 1.7 0 24 1.5 24 1.5 0 23 1.5 -1

2050h 30 1.8 31 1.7 0 31 1.7 0 23 1.5 23 1.6 0 22 1.6 -1

2080h 34 2.2 34 2.2 0 34 2.1 0 24 1.9 24 1.9 0 24 1.9 0

43



Control no tillage

increase (%)

control no tillage

increase (%)

control no tillage

increase (%)


STDEV


STDEV


STDEV


STDEV


STDEV


STDEV

baseline 25 2.5 25 2.9 0 44 2.8 44 2.7 1 20 1.0 20 1.0 -2

2020m 22 1.5 22 1.9 1 45 3.5 46 2.9 1 24 0.8 24 0.8 1

2050m 26 1.3 26 1.9 2 46 3.9 47 3.7 1 28 0.9 28 0.9 1

2080m 31 1.4 31 2.1 2 49 4.8 50 4.0 2 33 0.9 34 0.9 1

2020h 22 1.4 22 1.8 1 45 3.7 46 2.9 1 24 0.8 24 0.8 1

2050h 27 1.3 28 1.9 2 47 4.2 47 3.7 1 30 0.9 30 0.9 1

2080h 34 1.8 35 2.7 2 51 5.1 52 4.3 1 36 1.0 37 0.9 1




(%)


(%)


(%) annual

rate STDEV

annual rate

STDEV annual

rate STDEV

annual rate

STDEV annual

rate STDEV

annual rate

STDEV

baseline 38 2.5 10 0.7 -75 90 7.1 23 2.2 -75 20 1.5 4.5 0.2 -77 2020m 42 3.1 11 0.7 -73. 96 8.7 26 2.7 -73 20 1.8 5.2 0.2 -74 2050m 45 3.7 12 0.8 -73. 101 8.9 27 2.7 -73 21 1.8 5.6 0.2 -74 2080m 46 3.8 13 0.9 -73 103 10.1 29 3.1 -72 22 1.8 6.0 0.3 -72 2020h 43 3.0 12 0.8 -73 98 8.5 27 2.7 -73 20 1.8 5.3 0.2 -74 2050h 46 3.6 13 0.9 -72 105 9.5 30 3.1 -71 21 1.5 5.9 0.2 -72 2080h 51 4.2 1 1.0 -73 114 10.8 32 3.4 -72 24 2.0 6.8 0.3 -72

44

For grassland, the length of the grazing season in the dairy system has significant effect on the amount of annual nitrification (Figure 14).

Figure 12. average annual nitrification over 30 years in winter wheat field with (control) /without tillage under the baseline and climate change scenarios.

Figure 13. average annual nitrification over 30 years in spring barley field without (control) /with over-winter cover grass under the baseline and climate change scenarios.

0

5

10

15

20

25

30

35

40

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

nit

rifi

cati

on

(kg

N/h

a)

annual nitrification in winter wheat field with/without tillage

control

no tillage

0

5

10

15

20

25

30

35

40

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

nit

rifi

cati

on

(kg

N/h

a)

annual nitrification in spring barley field with/without over-winter cover

control

over-winter cover

45

Figure 14. Simulated average annual nitrification rate over 30 years in grassland with different types of livestock, grazing period and amount of manure incorporation under the baseline and climate change scenarios.

Mineralisation

Mitigation methods appear not to have a major effect on annual N mineralization rates in the

fields (Table 10). The only exception is in spring peas field with an over-winter cover crop,

where the rates are higher than those in the control field under the baseline and all the climate

change scenarios. There is a mixed influence on mineralization rates of the inclusion of a

cover crop in the spring barley field (Figure 15). The simulation results show that the method

stimulates the process under the baseline and the time slice of 2020 with high and medium-

high emissions of climate change, while the negative influence under other scenarios else.

Figure 15. average annual N mineralization over 30 years in spring barley field without (control) /with over-winter cover grass under the baseline and climate change scenarios

0

10

20

30

40

50

60

70

80

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

nit

rifi

cati

on

rat

e (

kgN

/ha)

annual nitrification in grassland




0

10

20

30

40

50

60

baseline 2020m 2050m 2080m 2020h 2050h 2080h

ann

ual

N m

ine

raliz

ato

n (

kgN

/ha)

annual N mineralization in spring barley field without/with over-winter cover grass

control

over-winter cover

46

Table 10. Average annual N mineralization (kgN/ha) under various mitigation and adaptation scenarios – round values


Control over-winter cover increase (%)

control over-winter cover increase (%)

control cultivation in spring increase (%)

annual mineralization

STDEV


STDEV


STDEV


STDEV


STDEV


STDEV

baseline 44 1.6 46 1.5 5 43 1.9 123 5.2 187 65 1.3 65 1.3 0

2020m 46 1.9 46 1.7 1 46 1.8 131 9.2 187 66 1.7 66 1.8 0

2050m 47 2.1 47 2.0 -1 47 2.1 126 11.1 166 66 1.8 66 1.9 0

2080m 49 2.3 48 2.4 -3 49 2.4 117 10.6 140 67 2.0 67 2.0 0

2020h 46 1.8 46 1.7 1 45 1.8 131 9.1 189 66 1.7 66 1.8 0

2050h 48 2.0 47 2.1 -2 48 2.2 117 11.3 144 66 1.9 66 2.0 0

2080h 50 2.7 48 2.7 -3 49 2.6 128 10.3 159 67 2.1 67 2.1 0

Table 10. Average annual N mineralization (kgN/ha) under various mitigation and adaptation scenarios (cont.)



increase (%)

no tillage

increase (%)


increase (%)

no tillage

increase (%)

annual mineralization STDEV






baseline 44 1.6 44 1.6 0 434 1.6 0 58 2.7 58 2.7 0 58 2.8 1

2020m 46 1.9 46 1.9 0 46 1.9 0 54 2.3 54 2.3 0 54 2.4 0

2050m 47 2.1 47 2.1 0 47 2.1 0 52 2.4 52 2.4 0 52 2.4 0

2080m 49 2.3 49 2.3 0 49 2.3 0 51 2.5 51 2.5 0 51 2.4 0

2020h 46 1.8 46 1.8 0 46 1.8 0 54 2.3 54 2.3 0 54 2.3 0

2050h 48 2.0 48 2.0 0 48 2.0 0 51 2.3 51 2.3 0 51 2.3 0

2080h 50 2.8 50 2.7 0 50 2.7 0 51 2.6 51 2.6 0 51 2.6 0



Control no tillage increase

(%)

control no tillage increase

(%)

control no tillage increase

(%) annual STDEV annual STDEV annual STDEV annual STDEV annual STDEV annual STDEV

47

mineralization mineralization mineralization mineralization mineralization mineralization

baseline 55 2.7 55 3.0 0 50 3.1 50 3.3 0 82 2.4 82 2.4 0

2020m 69 2.5 69 2.9 0-0.29 52 2.3 52 2.3 0 95 2.6 96 2.6 0

2050m 72 2.1 72 2.4 0 54 2.4 54 2.4 0 105 2.0 105 1.9 0

2080m 73 2.4 73 2.6 -0 57 2.5 57 2.5 1 109 2.0 109 1.9 0

2020h 69 2.2 69 2.6 0 53 2.3 53 2.4 0 96 2.3 96 2.3 0

2050h 72 2.0 72 2.2 0 55 2.4 55 2.4 1 106 1.8 106 1.7 00

2080h 72 2.7 72 2.8 0 57 2.6 57 2.6 0 110 2.1 110 2.0 0




(%)


(%)


(%) annual

rate STDEV

annual rate

STDEV annual

rate STDEV

annual rate

STDEV annual

rate STDEV

annual rate

STDEV

baseline 162 15.9 162 16.0 0 353 38.6 353 38.0 0 92 8.3 92 8.4 0 2020m 186 17.6 187 17.7 1 384 39.0 384 38.5 00.0 95 9.0 96 9.0 0 2050m 196 18.6 197 18.8 1 387 38.6 387 39.5 0 97 9.0 98 9.0 0 2080m 203 20.3 205 20.5 1 395 42.1 395 41.8 0 98 9.3 99 9.3 0 2020h 190 18.2 190 18.3 1 392 38.7 391 38.7 0 97 9.1 97 9.1 0 2050h 204 18.8 206 19.3 1 404 39.3 403 39.6 0 98 8.2 99 8.2 0 2080h 219 21.8 222 21.8 1 417 43.5 416 43.7 0 105 9.9 105 9.9 0

48

Conclusions

The DWPA mitigation methods to include use of an over-winter cover crop such as grass in

spring crop field could benefit the farmer through increased plant N uptake and the

environment through reduced risk of nitrate leaching loss and N2O emission under the

baseline and all climate change scenarios. The benefit gradually declines with each time

sliced for both the medium-high and high emissions of climate change. The simulation results

suggest the extension of the period of ground cover in a year, especially keeping a green

canopy in the winter on arable land, is an effective way to tackle diffuse water pollution and

N2O emissions.

Postponing ploughing from late autumn to spring the following year may reduce nitrate

leaching loss in the fields with spring crops under all climate change scenarios. The ’no

tillage’ mitigation method could be of benefit in reducting nitrate leaching in the fields with

spring crops and winter wheat but increase the risk of increasing N2O emissions. The

simulation results for N2O emissions in this case study are consistent with published

experimental results (Baggs, et al. 2003; Rochette, et al. 2008).

Diffuse pollution is controlled by various physical and biological factors. If there is not

drainage water, there would not be diffuse pollution. The amount of drainage water and its

distribution with the season is dominated by weather condition and geographical topology.

Different climatic zones have different responses of diffuse pollution to climate change

scenarios. The effect of climate change on nitrate leaching loss and GHG emissions gradually

ease off with the time sliced delayed for both the medium-high and high emissions of climate

change.

Mitigation methods may be benefit to diffuse N pollution in one climatic zone but cause other

problems, e.g. higher GHG emissions. Therefore, the methods should be assessed and

balanced among positive and negative effects in a systems level in each of climatic zones in

the UK.

Acknowledgement

Dr Aiming Qi of Brooms Barn provided help with obtaining daily precipitation from the

hundred files for all timeslices and baseline scenarios. Dr Pete Falloon of the Met Office gave

advice on sourcing climate data. Dr Eunice Lord and John Williams of ADAS provided field

data.

49

Appendix 1. Parameter values for winter oilseed rape and potato

Parameter description unit Winter oilseed rape potato

value reference value reference

Parameters related to assimilation and respiration:

extinct coefficient - 0.63 (Tang et al. 2009) 1.0 (Klepper et al. 1991)

temperature at which photosynthesis ceases oC 4.3 (Tang et al. 2009)

2 (Kooman et al. 1995)

leaf transmission coefficient - 0.046 (Tang et al. 2009) 0.2 (Klepper et al. 1991)

Leaf photosynthesis rate at saturating light levels, optimal temperature, water and nitrogen conditions

gCO2m-2s

-1 0.002 (Jensen et al. 1996) 0.0014

leaf N concentration at which the photosynthesis ceases gNg-1

DM default 0.005 (Klepper et al. 1991)

leaf N concentration at which the effect on photosynthesis is in unity gNg-1

DM 0.0463 (Habekotté, 1997b) 0.015 (Gayler et al. 2002)

Photochemical efficiency at optimal temperature, water and nitrogen conditions

gDMMJ-1

3.5475 (based on Dreccer et al. 2000)

12.5 (Gayler et al. 2002)

photosynthesis optimal temperature oC 20 (Paul et al. 1990) 16 (Klepper et al. 1991)

temperature when the Q10 function is in unity oC 25 (Tang et al. 2009)

2 (Ku et al. 1977)

Q10 value for plant maintenance respiration - 2 (Tang et al. 2009) 2.5 (Klepper et al. 1991)

specific green leaf area m2g

-1DM 0.026 (Jensen et al. 1996) 0.03 (Klepper et al. 1991;

Heidmann et al. 2008)

specific green ear area m2g-1DM 0.0024 (Jensen et al. 1996) -

specific stem area m2g-1DM 0.0038 (Jensen et al. 1996) -

Parameters about plant development:

50

accumulated temperature required from sow to emergence oCd 120 (Gabrielle et al. 1998) -

accumulated temperature required from emergence to heading oCd 1280 (Malagoli et al. 2005) 350 (Streck et al. 2007)

accumulated temperature required from heading to maturity oCd 550 (Malagoli et al. 2005) 850 (Streck et al. 2007)

accumulated temperature required from emergence to flag leaf appearance

oCd 1280 (Malagoli et al. 2005) 350 (Streck et al. 2007)

The critical photoperiod for vegetative stage below or over which plant development will not be affected by light.

hr 14.8 (Habekotté, 1997a) 10.7 (Streck et al. 2007)

Threshold temperature for emergence oC 0.3 (Habekotté, 1997a) -

Minimum temperature below which vernalisation response function is zero.

oC -3.7 (Habekotté, 1997a) -

Maximum temperature above which vernalisation response function is zero

oC 17.2 (Habekotté, 1997a) -

Optimum temperature at which vernalisation response function is maximised.

oC 3.0 (Habekotté, 1997a) -

The critical photoperiod for vegetative stage below or over which plant development will stop.

hr 5.74 (Habekotté, 1997a) 18.8 (Gayler et al. 2002)

Coefficient in the photo period response function - -0.0645 (Streck et al. 2007)

Threshold temperature during reproductive stage oC 4.9 (Habekotté, 1997a) 7 (Streck et al. 2007)

Threshold temperature for vegetative development oC 0.5 (Habekotté, 1997a) 4 (Streck et al. 2007)

Parameters with plant uptake:

critical inorganic N concentration in root zone below which that plant will take up organic nitrogen

gNm-3

0.4 0.4 default

maximum N uptake rate in Michaelis-Menten equation gNm-2

root

surfaced-1

0.0023 (Malagoli et al. 2004) 0.055 (Sharifi et al. 2006)

51

Lifetime of leaf oCd

725 (Klepper et al. 1991)

available N concentration in the root zone at which N uptake ceases gNm-3

0.02 (Abrahamsen et al. 2006)

Root ammonium uptake preference index - 0.5 assumed 0.5 assumed

Optimum stem nitrogen concentration gN/gDM 0.023 (Orlovius, 2003) 0.048 (Clutterbuck et al. 1978)

Michaelis-Menten constant gNm-3 18.59 (Malagoli et al. 2004) 0.193 (Sharifi et al. 2006)

osmotic at half uptake rate cm 200 default 200 default

power in osmotic effect part - 3 default 3 default

power in tension effect part - 3 default 3 default

tension at half uptake rate cm water 400 default 400 default

Parameters in 1D root growth:

Maximum depth where root can penetrate to m 1.8 (Barraclough, 1989) 1.0 (Parker et al. 1991)

Minimal root density that will never be below, as long as there is enough root mass

m·m-3

Root density at the potential rooting depth m·m-3

2000 (Abrahamsen et al. 2006)

potential root penetration rate cmºC-1d

-1 1.6 (Gabrielle et al. 1998) 0.3 (Heidmann et al. 2008)

Threshold temperature below which root stop growing oC 4 (Malagoli et al. 2004) 4 (Abrahamsen et al. 2006)

Average root radius cm 0.0085 (Macduff et al. 1986) 0.18 (Sattelmacher et al. 1990)

Root length per unit root dry matter mg-1

DM 585.4 (Macduff et al. 1986) 284 (Sattelmacher et al. 1990)

52

Appendix 2. Average annual nitrogen budget for all scenarios over 30 years

input output

deposition

N fixation fertiliser manure harvest leached emission

sp

ring

ba

rle

y

co

ntr

ol

baseline 20 0 76 0 59 47 4

2020m 20 0 76 0 54 52 4

2050m 20 0 76 0 53 53 5

2080m 19 0 76 0 50 56 6

2020h 20 0 76 0 54 51 4

2050h 20 0 76 0 51 55 5

2080h 20 0 76 0 47 60 6

ove

r-w

inte

r co

ver

baseline 20 0 76 0 69 26 3

2020m 20 0 76 0 61 31 3

2050m 20 0 76 0 58 35 4

2080m 19 0 76 0 53 42 5

2020h 20 0 76 0 61 30 3

2050h 20 0 76 0 55 38 4

2080h 20 0 76 0 50 45 5

sp

ring

pe

as

co

ntr

ol

baseline 20 52 28 0 78 37 3

2020m 20 50 28 0 81 33 3

2050m 20 48 28 0 78 35 3

2080m 19 45 28 0 75 36 4

2020h 20 50 28 0 81 33 3

2050h 20 48 28 0 77 36 4

2080h 20 45 28 0 73 38 4

ove

r-w

inte

r co

ver

baseline 20 44 28 0 94 16 2

2020m 20 39 28 0 95 16 3

2050m 20 39 28 0 92 20 4

2080m 19 37 28 0 88 24 4

2020h 20 40 28 0 95 16 3

2050h 20 41 28 0 92 22 4

2080h 20 35 28 0 86 25 5

pota

to

co

ntr

ol

baseline 20 0 61 0 56 34 2

2020m 20 0 61 0 54 35 3

2050m 20 0 61 0 52 37 3

2080m 19 0 61 0 49 40 3

2020h 20 0 61 0 54 34 3

2050h 20 0 61 0 51 38 3

2080h 20 0 61 0 47 42 4

ove

r-w

inte

r co

ver

baseline 20 0 61 0 56 33 3

2020m 20 0 61 0 54 33 3

2050m 20 0 61 0 52 36 3

2080m 19 0 61 0 49 38 4

2020h 20 0 61 0 54 33 3

2050h 20 0 61 0 51 37 3

2080h 20 0 61 0 47 40 4

sp

rin g

barle y

co

ntr ol

baseline 20 0 76 0 59 47 4

2020m 20 0 76 0 54 52 4

53

2050m 20 0 76 0 53 53 5

2080m 19 0 76 0 50 56 6

2020h 20 0 76 0 54 51 4

2050h 20 0 76 0 51 54 5

2080h 20 0 76 0 47 59 7

cult

ivat

ion

in s

pri

ng baseline 20 0 76 0 59 47 4

2020m 20 0 76 0 54 52 4

2050m 20 0 76 0 53 53 5

2080m 19 0 76 0 50 56 6

2020h 20 0 76 0 54 51 4

2050h 20 0 76 0 51 54 5

2080h 20 0 76 0 47 60 6

no

-till

age baseline 20 0 76 0 59 47 4

2020m 20 0 76 0 54 52 4

2050m 20 0 76 0 53 53 5

2080m 19 0 76 0 50 56 6

2020h 20 0 76 0 54 51 4

2050h 20 0 76 0 51 55 5

2080h 20 0 76 0 47 60 6

sp

ring

pe

as

co

ntr

ol

baseline 20 46 28 0 54 47 3

2020m 20 47 28 0 68 39 3

2050m 20 47 28 0 71 38 3

2080m 19 44 28 0 71 38 4

2020h 20 47 28 0 67 39 3

2050h 20 47 28 0 72 38 4

2080h 20 45 28 0 72 39 4

cul

tiv

ati

on

in

sp rin g baseline 20 45 28 0 54 46 3

no -

till ag e

2020m 20 46 28 0 67 38 3

2050m 20 46 28 0 71 38 4

2080m 19 44 28 0 71 37 4

2020h 20 46 28 0 66 38 3

2050h 20 47 28 0 72 38 4

2080h 20 44 28 0 71 39 4

baseline 20 45 28 0 54 45 3

2020m 20 46 28 0 68 38 3

2050m 20 47 28 0 72 37 4

2080m 19 44 28 0 72 37 4

2020h 20 46 28 0 67 38 3

2050h 20 47 28 0 73 37 4

2080h 20 45 28 0 72 39 5

win

ter

barle

y

co

ntr

ol

baseline 20 0 126 0 101 47 4

2020m 20 0 126 0 127 19 4

2050m 20 0 126 0 123 21 5

2080m 19 0 126 0 119 23 8

2020h 20 0 126 0 128 18 4

2050h 20 0 126 0 121 21 6

2080h 20 0 126 0 116 26 10

no -

till

ag e

baseline 20 0 126 0 99 46 4

54

2020m 20 0 126 0 125 18 4

2050m 20 0 126 0 121 20 5

2080m 19 0 126 0 118 22 8

2020h 20 0 126 0 126 17 4

2050h 20 0 126 0 120 20 6

2080h 20 0 126 0 115 26 10

win

ter

oils

eed

rap

e

co

ntr

ol

baseline 20 0 138 0 43 116 7

2020m 20 0 138 0 51 108 9

2050m 20 0 138 0 53 105 10

2080m 19 0 138 0 56 101 12

2020h 20 0 138 0 52 107 8

2050h 20 0 138 0 54 103 11

2080h 20 0 138 0 54 101 13

no

-till

age baseline 20 0 138 0 44 115 7

2020m 20 0 138 0 51 109 8

2050m 20 0 138 0 54 105 9

2080m 19 0 138 0 57 99 12

2020h 20 0 138 0 52 108 8

2050h 20 0 138 0 55 103 10

2080h 20 0 138 0 54 102 13

win

ter

wh

ea

t

co

ntr

ol

baseline 20 0 129 0 125 18 3

2020m 20 0 129 0 126 15 3

2050m 20 0 129 0 125 13 4

2080m 19 0 129 0 125 13 5

2020h 20 0 129 0 127 14 3

2050h 20 0 129 0 124 13 4

2080h 20 0 129 0 125 14 6

no

-till

age baseline 20 0 129 0 127 16 3

2020m 20 0 129 0 126 14 3

2050m 20 0 129 0 125 12 4

2080m 19 0 129 0 125 12 6

2020h 20 0 129 0 127 12 4

2050h 20 0 129 0 125 12 5

2080h 20 0 129 0 125 13 7

dairy w

ith

tw

o m

onth

s g

razin

g

co

ntr

ol

baseline 22 0 109 153 190 73 2

2020m 23 0 109 153 189 69 2

2050m 22 0 109 153 183 73 3

2080m 22 0 109 153 179 72 3

2020h 22 0 109 153 191 65 2

2050h 22 0 109 153 187 66 3

2080h 22 0 109 153 179 71 3

wit

h in

hib

ito

r

baseline 22 0 109 153 192 69 2

2020m 22 0 109 153 192 65 2

2050m 23 0 109 153 186 69 2

2080m 22 0 109 153 183 67 2

2020h 22 0 109 153 194 62 2

2050h 22 0 109 153 190 62 2

2080h 22 0 109 153 183 65 2

55

dairy w

ith

six

mo

nth

s g

razin

g

co

ntr

ol

baseline 22 0 109 230 220 94 2

2020m 23 0 109 230 219 88 2

2050m 23 0 109 230 210 95 3

2080m 22 0 109 230 206 94 3

2020h 22 0 109 230 221 84 2

2050h 22 0 109 230 215 86 3

2080h 22 0 109 230 205 92 4

wit

h in

hib

ito

r

baseline 22 0 109 230 223 90 2

2020m 22 0 109 230 222 83 2

2050m 23 0 109 230 214 90 2

2080m 22 0 109 230 210 88 2

2020h 22 0 109 230 225 79 2

2050h 22 0 109 230 219 80 2

2080h 22 0 109 230 209 86 2

sh

ee

p w

ith

gra

zin

g a

roun

d a

ye

ar

co

ntr

ol

baseline 22 0 107 100 180 46 3

2020m 22 0 107 100 183 46 2

2050m 22 0 107 100 182 47 2

2080m 22 0 107 100 180 47 2

2020h 22 0 107 100 183 45 2

2050h 22 0 107 100 184 45 2

2080h 22 0 107 100 180 49 2

wit

h in

hib

ito

r

baseline 22 0 107 100 181 45 2

2020m 22 0 107 100 184 44 2

2050m 23 0 107 100 183 46 2

2080m 22 0 107 100 181 46 1

2020h 22 0 107 100 184 44 2

2050h 22 0 107 100 185 44 2

2080h 22 0 107 100 182 47 2

56

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59

Provide the basis for the specification of some key validation experiments (objective 21)

Key validation experiments of specific components of modelling can be investigated

systematically, e.g effects of temperature and soil moisture on nitrification rates and N2O

losses via denitrification; effect of atmospheric CO2 concentrations on crop growth; use of

inhibitors or cash crops to reduce nitrate leaching; effect of rainfall intensity on mobilisation

of sediment or ammonium, and used to parameterise and validate individual model

components.

But there is real difficulty in devising experiments to validate models at the whole farming

system scale to different climate change scenarios. There is the potential to use climate

analogues, both in space and time, where appropriate. For example, spatially – one could

select similar cropping systems (soil type, cropping type and nutrient management) but in

contrasting climatic zones, thus use the natural variability in climate in different regions to

simulate climate change. The limitations to this approach are that soil type, slope, aspect etc

may not be exactly the same at the two sites, i.e. there may be more than one variable (the

climate), making interpretation of the effects of ‘simulated’ climate change difficult.

Another approach is to analyse measured and modelled data from the same site (same

cropping, nutrient management, soil type etc), but over many years, thus taking into account

extremes of weather either over the whole year or limited to extremes within different

seasons. If sufficiently similar experimental treatments have been continued during this time,

and the same parameters have been measured, e.g. nitrate concentrations in drainage water,

then an exploration of historical data could provide and insight into the ability of current

models to predict a number of parameters simultaneously form contrasting weather

conditions. Disadvantages are that long-term resources are required to continue key

measurements in the hope that extreme weather patterns can be encompassed. There are

several long-term, if not intermittently funded, experimental platforms in the UK where key

parameters have been quantified over many years, and a scoping study to collate information

about treatments, soil types, cropping and nutrient management, measurements made, inter

year variability in weather data – could be a useful exercise to assess the potential to use

historical datasets to validate whole-system climate change and adaptation agricultural

models.

However, it should be noted that whilst natural variation in current climate (across multiple

sites on similar soils) or historical weather patterns at the same experimental platform could

generate sufficient datasets to validate models, in one respect they are likely to be lacking a

key element in climate change scenarios, i.e. changes in atmospheric CO2 emissions. FACE

experiments are expensive and the area of land covered is therefore relatively small. Hence,

soils processes have been investigated (e.g. C and N cycling), as have agronomic yields

where FACE experiments have been conducted in agricultural experiments – but key model

parameters such as diffuse water pollution and nitrous oxide emissions are less likely to have

been investigated.

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