DEPARTMENT for ENVIRONMENT, FOOD and RURAL AFFAIRS CSG 15Research and Development

Final Project Report(Not to be used for LINK projects)

Two hard copies of this form should be returned to:Research Policy and International Division, Final Reports UnitDEFRA, Area 301Cromwell House, Dean Stanley Street, London, SW1P 3JH.

An electronic version should be e-mailed to resreports@defra.gsi.gov.uk

Project title Comparability of soil properties derived from different data sources     

DEFRA project code SP0515

Contractor organisation and location

National Soil Resources Instititue,Cranfield UniversitySilsoe, Bedfordshire     

Total DEFRA project costs £ 173,000

Project start date 01/01/01 Project end date 31/03/03

Executive summary (maximum 2 sides A4)

Data from four UK soil monitoring schemes are compared: the National Soil Inventory (NSI), the Representative Soil Sampling Scheme (RSSS), the Countryside Surveys (CS) and the Environmental Change Network (ECN). Data from each of these have been used in association with other surveys—for example, the Agricultural Census—to inform debates about soil quality and environmental issues. But hitherto there has been no systematic attempt to compare the usefulness of the datasets for such purposes and the errors involved. It was not seen as one of the outcomes of the project to recommend one scheme over the others.

The results confirm that each of the four schemes is valid and consistent in its aims and the achievement of those aims. Each has strengths, particularly for the target users for whom they were designed. Each scheme covers different landscape/soil/land use combinations and their sampling designs were devised to answer the specific demands created by the scheme rationales. Thus, although the conclusions reached may the same there is not necessarily symbiosis in combining the data.

Care is essential in ensuring that the measurement units are consistent when comparing vales. The data can be used to examine change over time with NSI and CS visiting the same sites. RSSS can be used in a similar way but with 5 year averages used to monitor change on a national basis.

The absence of field measured bulk density makes comparison of data less rigorous.

The absence of field or laboratory measured texture in RSSS and CS makes comparison of data less rigorous.

The absence of field assessed soil classification in RSSS makes comparison of data less rigorous.

CSG 15 (Rev. 6/02) 1

Projecttitle

Comparability of soil properties derived from different data sources     

DEFRAproject code

SP0515

The main conclusion from the field experiments is that an increase in cultivation depth since the 1960’s has lead to a substantial movement of P and other nutrients to below the standard soil sampling depth of 15 cm. In addition, where organic manures have been applied, there is also likely to have been movement of soluble organic P to below 15 cm.

The comparison of the various monitoring schemes led to the conclusion that the design of any new scheme should reflect the combined needs of England, Wales, Scotland and Northern Ireland; there should not be different schemes for different parts of the UK. Equally important is that the design of any new or revised scheme should be consistent with the spatial and temporal sampling regimes of existing schemes so that there is continuity.

The layers below 15 cm depth must be assessed in any developments of a monitoring scheme to help in the understanding and modeling of soil functions. There must be rigorous protocols for data collection, analysis and storage.

CSG 15 (Rev. 6/02) 2

Projecttitle

Comparability of soil properties derived from different data sources     

DEFRAproject code

SP0515

Scientific report (maximum 20 sides A4)

1 IntroductionNational scale soil monitoring will be implemented within the European Commission Sixth Framework. It is now timely to review soil resources within England and Wales to assess their suitability and relevance to future monitoring schemes1 alongside other policy-relevant objectives. There are four schemes in England and Wales that are either specifically for soil sampling or contain a significant component of soil sampling: the National Soil Inventory (NSI2), the Representative Soil Sampling Scheme (RSSS), the Countryside Surveys (CS) and the Environmental Change Network (ECN). Each scheme was established to meet a range of objectives. Most of these are mutually exclusive across the schemes: national soil inventory, fertiliser practice, status and change in the countryside and long term environmental change. There are, however, several areas where they appear to overlap due to the type of soil analyses made.

It is timely to determine just how comparable these data and how compatible the different schemes actually are. They differ in sampling interval, sampling strategy and reporting of results, but data from all the schemes have been used to inform debates about soil quality and environmental issues. Many environmental/soil questions are being answered by using survey and census data in various combinations or by extrapolating using GIS technology. There has been no systematic assessment of how differences between the schemes and their relevant data might affect policy-relevant conclusions drawn thereby.

2 Project aims and objectivesThe overall aims were to assess the comparability and compatibility of representative data from four soil sampling schemes in England and Wales.

An important aspect addressed is the compatibility of analytical data determined on a volume (mg l -1) and mass basis (mg kg-1). This looks at the reliability of conversion factors which rely on the measurement of soil bulk density. For this part we have converted representative data from the national datasets to a common means of expression, examined differences in the results obtained and determined if such differences can be explained.

We assess the reliability of mapping soil data from different sampling resolutions and the comparability of soil data obtained from different sampling depths and those from fixed depths as opposed to from pedological horizons. Earlier work with the National Soil Inventory suggested that the general increase in the size and power of farm machinery may have resulted in a general increase in soil cultivation depth. Such soil depth changes have significant implications in interpreting apparent changes over time of topsoil contents of, for example, nutrients.

The results from all components of the project can be used to inform the debate about practical and cost-effective design, management and implementation of a UK soil monitoring network.

1 An inventory does not imply time or change over time whereas a monitoring programme does. Based on: Cambridge International Dictionary of Englishinventorynoun [C] a detailed list of all the items in a place For example: "A set of twenty-four soil profiles appear on the inventory of the farm for 1980". monitor verb [T] to watch (something) carefully for a period of time in order to discover something about it For example: " A set of twenty-four soil profiles appear on the inventory of the farm for 1980; the same profiles appear in 1990 and 1995."

2 Details of abbreviations are given in Appendix 1.CSG 15 (Rev. 6/02) 3

Projecttitle

Comparability of soil properties derived from different data sources     

DEFRAproject code

SP0515

The specific project objectives are:1. To concisely describe the soil sampling components of the National Soil Inventory, the Representative Soil

Sampling Scheme, the Countryside Survey and the Environmental Change Network so as to indicate clearly their similarities and differences.

2. To assemble representative datasets for comparable soil parameters from each of these schemes. 3. To correct the quantitative data to common units of mass and volume.4. To use scaling factors to adjust for the different sampling depths in (2).5. To compare the results from (4) with (3).6. To extrapolate the volume based values to give national estimates of nutrient loadings using agricultural

census data.7. To compare the nutrient loads from (6) with data from the Survey of Fertiliser Practice.8. To investigate experimentally the effects of changing plough depth on measured soil properties.9. To assess the possibilities for mapping the various datasets through geostatistical techniques and to compare

the results one with another.

At the project contract stage it was suggested that there could be a significant interruption to the work through the retirement in September 2002 of Professor Loveland (the original project co-ordinator). However, it was concluded that this would not affect the overall outputs since the project team retained sufficient knowledge of each scheme. The area of staff changes did however highlight continuity issues that are relevant to all data-rich organisations, especially where there is a significant time gap between the original establishment of sampling schemes and subsequent re-sampling. Measures are required to ensure that staff losses do not result in any reduction in the reliability, accuracy and precision of soil sampling, sample storage and analyses and interpretation of the consequent data.

3 Sampling schemes to be assessed from England and WalesThere are currently four major sources of national data on soil analyses. These are: the Representative Soil Sampling Scheme (RSSS), the National Soil Inventory (NSI), the Countryside Survey (CS) and the Environmental Change Network (ECN).

These schemes are funded by a range of organisations and each scheme has its own set of objectives and specific design in terms of the selection and numbers of sites, sampling protocols, frequency of sampling, etc.

The first sampling for the NSI was carried out by the Soil Survey of England and Wales (now NSRI) between 1979 and 1983 on a 5 km orthogonal grid (Figures 1a, b and c), as part of the parallel National Soil Mapping programme. The aim was to characterise the soils of England and Wales using a statistically valid set of data. It was designed to determine the total and extractable amounts of biologically important elements in samples representing the principal soil series. Samples were taken of the topsoil, site features recorded and a full soil description made to 1.2 m depth. The data form an important component of the NSRI Land information System (LandIS). Further uses of the site network and data have included, for example erosion monitoring, organic carbon status and structural stability. Re-sampling of statistical derived subsets was carried out in 1995, 1996 and 2003 to detect changes over time in arable and ley grassland, permanent (managed) grassland and non-agricultural soils, respectively. These exercises centred primarily on organic carbon, but a full range of other chemical parameters have also been measured.

RSSS was initiated in 1969 to keep MAFF informed of the status of agricultural soils in England and Wales by obtaining truly representative soil pH and nutrient data across the range of farming types. It is managed by ADAS with Rothamsted Research responsible for storing the digital data. To minimise costs and to spread the work load evenly over the years, the strategy was adopted of sampling a relatively small number (120 to 180) of farms in each year. The results are bulked from 5 year periods to obtain enough data for statistical analysis with temporal changes assessed between these 5 year periods (Figures 1d and e).

CSG 15 (Rev. 6/02) 4

Projecttitle

Comparability of soil properties derived from different data sources     

DEFRAproject code

SP0515

The CS is a research programme jointly funded, as a partnership, by several Government departments and Agencies, and the Natural Environment Research Council (NERC). It provides a national network of sites across Great Britain and is used to obtain information necessary for reporting on biodiversity in the wider countrywide, measuring progress towards sustainable development and detecting the impacts of human activities and global environmental change (Haines-Young et al., 2000). The first survey was carried out in 1978 with subsequent surveys in 1984, 1990 and the most recent in 1998/99, which is known as Countryside Survey 2000 (CS2000) (Figure 1f). Soils were sampled during 1978 and again in CS2000 with soil mapping in 1994 by SSLRC and MLURI. Soils were sampled in CS2000 for a baseline assessment of soil biological properties and to examine long-term change in soil pH and organic matter. Soil data are also used to investigate associations between soils and vegetation, for example, eutrophication in British habitats.

The ECN is the UK’s long-term integrated monitoring programme designed to aid the detection, interpretation and forecasting of environmental changes resulting from natural and human causes. It is a multi-agency initiative co-ordinated by NERC at CEH Merlewood on behalf of a consortium of 14 sponsoring organisations. ECN currently operates a network of 12 terrestrial (and 42 freshwater sites) (Figure 1g) each of which monitors an agreed set of physical, chemical and biological ‘core measurements’ according standard published protocols. These measurements were selected to represent the main drivers of change (for example, climate, atmospheric chemistry, land use) and ecosystem responses such as the impacts of climate change and biodiversity loss, atmospheric pollution, soil degradation and water quality.

The bases on which soil samples are collected within these surveys, and the analyses performed on the soils obtained differ greatly. NSI is based on a fixed grid of sites, of which there are more than in the RSSS, but they have been sampled less frequently, but both work to a fixed depth of 15 cm. In 1978, the Countryside Survey took samples from soil profiles at 5 locations within each of 256 1 km by 1 km squares representative of the range of landscapes in Great Britain. In 1998, the number of squares in the survey had risen to 569. The original 5 points within the original 256 1 km by 1 km squares were re-sampled for soils.

In addition to the four datasets that form the main focus for this study there are other sources of systematically collected soil information. Topsoil and subsoil data for many parts of the UK have been collected by the British Geological Survey (BGS) and are included in G-BASE (for further details see http://www.bgs.ac.uk/gbase/home.html). In Scotland soil data are available from the Macaulay Land Research Institute (MLURI). The National Soil Inventory carried out in Northern Ireland (Cruickshank, 1997, Jordan et al., 1999) is maintained by DARD. In addition, Forest Research monitors soil properties at their ICP level 1 and 2 sites. Brief outlines of these schemes are included as Appendix 4. There are also long term experiments at Rothamsted Research and the University of Newcastle (Cockle Park) which include soil information.

4 Experimental workEarlier work (OC9403 - The impact of changing farm practices on the sustainability of soils in England and Wales.) raised the possibility that significant changes in soil chemistry might reflect a general deepening of ploughing depths arising from increases in the size and power of farm machinery since at least 1980. For nutrients and organic carbon, this effect would most likely result in a dilution of concentrations with the incorporation of deeper, carbon- and nutrient-poor subsoil into the plough layer. For trace elements, the picture is less clear as subsoils do not necessarily have lesser concentrations than topsoils. Thus, concentrations of some trace elements might increase, whilst others might decrease. Change in pH values could suggest an increase in acidification if the subsoil pH was lower than that of the topsoil, and a decrease in acidification if the sub-soil contained more lime than the topsoil. This deeper ploughing hypothesis has not been tested previously and it is part of objectives to test this at 40 sites with four different cropping regimes.

CSG 15 (Rev. 6/02) 5

Projecttitle

Comparability of soil properties derived from different data sources     

DEFRAproject code

SP0515

5 Description and comparison of the sampling schemes Full descriptions of the NSI, the RSSS, CS and the ECN soil protocols are presented as Appendix 2. The distributions of sampling points for each scheme are shown in Figure 1, summaries of their activities in Table 1 and summaries of the protocols in Appendix 3.

Although all the schemes have written accounts of their procedures, and the project members know their 'own' scheme, it was realised they did not have any experience of the implementation of the other schemes under review. Therefore a field day was convened at CEH Merlewood in September 2002 at which each sampling protocol, together with that for G-BASE was demonstrated in the same field and samples taken for analysis. A video was made during the event and digital photographs taken (Appendix 5). The results of those analyses give an insight into the comparability of data from the different schemes.

5.1 Data storageNSI, CS and ECN are stored in ORACLE databases with a unique identifier for each sample and OS gird references for each site visited. Long term, managed data storage is therefore assured. A rigorous data validation process was crucial to the incorporation of these data into information systems. RSSS had to refer back to paper records for some of the earliest data and these needed close checking to ensure that correct grid references had been given to each site. Data confidentiality restricts access to RSSS and CS location data. Summary data are freely available from the CS2000 report and web pages whilst certain data from earlier surveys are available from the Countryside Information System via the web. Data from the NSI is held in LandIS (Proctor et al., 1999) and can be assessed through licensing arrangements. Bona fide researchers can license the data free from royalties following an agreement between Cranfield University and Defra.

Table 1 Summary of schemes reviewedNSI RSSS CS ECN

Rationale behind establishment of scheme

Statistically valid inventory of soils in England & Wales; biologically important nutrients and trace elements.

Regular assessment of the fertility status of soils in England & Wales

Survey of the British countryside

Long term, integrated environmental monitoring in UK

First description and soil samples 1979 to 1983 1969 to 20033 1978 1992

Second soil samples at same site - arable/ley

1995 1974 to 2003 1998/99 1997

Second soil samples at same site - permanent/managed grassland

1996 1974 to 2003 1998/99 1997

Second soil samples at same site - woodland/non-agricultural

Due for completion mid 2003

- 1998/99 1997

Frequency of subsequent visits to same site

None Year 5 and 10 only

CS2006 is planned

Due 2003

Re-visits to same site or same field

Same site Same field Same 1 km square; soils are from same 2 m quadrat.

Same site

Shortest period over which data can be compared from SAME site

15 years for arable, ley, managed grassland

5 year Currently 20 years, 6 years possible within CS framework

5 years

Longest period over which data can be compared from SAME site

20 years for non-agricultural land

10 years 20 years 10 years

Compatibility with other UK soil Yes No Yes - by design Yes - by design

3 Farms are sampled on a rolling 10 year cycle which is explained in Appendix 2, Table 5.CSG 15 (Rev. 6/02) 6

Projecttitle

Comparability of soil properties derived from different data sources     

DEFRAproject code

SP0515

sampling schemes Scotland 5 or 10 km gridNorthern Ireland sample within 1 km grid.

CS is GB-wide ECN is UK-wide

Figure 1 Distribution of sampling pointsa) NSI 1979 to 1983

b) NSI re-sampling of arable, ley grassland and permanent grassland (1995 to 1996)

CSG 15 (Rev. 6/02) 7

c) NSI re-sampling of non-agricultural land (2003)

d) RSSS aggregated annual data for 1979 to 1983

e) RSSS aggregated annual data from 1995 to 1999

f) CS 1978

g) ECN terrestrial sites in England and Wales

5.2 Assembly of representative datasets from the schemesData have been chosen to investigate:

an important nutrient of environmental interest, for example, in eutrophication and plant health (available P).

CSG 15 (1/00) 9

the construction a nutrient budget (total P) in agricultural systems over time. assessment of soil acidity as an indicator of liming need, land use change or the impacts of and/or

recovery from acid rain (pH). assessment of carbon status as an indicator of soil health and greenhouse gases (organic carbon). heavy metal pollutant levels (total zinc).

The data are available as follows: available P: NSI, RSSS, ECN total P: NSI, CS, ECN pH (in water): NSI, RSSS, CS, ECN organic carbon: NSI, CS4, ECN. total Zn: NSI, CS, ECN

In addition, bulk density is required for conversion from weight to volume basis (see Section 5.3).

Data from the four schemes were assembled into spreadsheets with results from around 1980 and 1996. An example extract of the data from NSI is given in Table 2. The value of monitoring schemes lies in their ability to measure change over time and tables were compiled of the changes in five parameters between 1980 and 1996 (Table 3). Medians were compiled because of the skewed distributions of all the data sets examined (for example Figures 2a and b). However, the change data show normal distributions (Figure 2c).

The original NSI data were collected between 1979 and 1983. These have been treated as a single sampling date and are labelled as "1980". The arable and ley grass sites were re-sampled in 1995 and the permanent (managed) grassland in 1996. These are grouped together for the purposes of this exercise and labelled "1996". The same part of each field was sampled for each project and therefore change can be determined on a site by site basis.

The RSSS data have been collected annually from 1969 onwards. For the purposes of comparing these data with data from the other schemes, data from 1979 to 1983 were grouped together as were those from 1995 to 1999. Change can only be determined from the data means since the same fields were not sampled during the two selected periods.

CS data are available for 1978 and 1998/9. The same locations within each 1 km square were sampled in each survey and therefore change can be determined on a site by site basis.

ECN data collection only began in 1992; changes in parameters therefore could not be assessed over periods compatible with the other schemes.

Table 2 Example dataa) NSI 1980EAST NSI NORTH NSI LAND USE SUB GROUP 1980 PSC 1980 pH 1980 carbon 1980 avail P 1980 total P 1980 Zn136000 26000 LE 5.21 sandy 7.7 1.9 36 814 27

136000 31000 AR 6.21 coarse loamy 5.7 4.2 77 1255 35

141000 26000 LE 6.11 fine loamy 5.1 5.2 40 1239 55

141000 31000 LE 6.21 coarse loamy 5.7 6.8 48 1145 32

b) NSI 1996EASTNSI NORTH NSI LANDUSE SUB GROUP 1996 pH 1996 carbon 1996 total P 1996 avail P 1996 Zn146000 26000 LE 6.11 6.1 2.3 634 24.9 31.3

151000 36000 LE 6.11 6.2 2.7 597 11.7 44.3

156000 31000 PG 6.11 7.0 3.4 1165 80.0 104.7

156000 36000 AR 5.41 7.9 1.2 960 54.7 106.3

166000 36000 PG 6.22 5.8 4.7 526 23.0 44.0

Key:4 As loss on ignition

CSG 15 (1/00) 10

EAST NSI Ordnance Survey grid reference easting in metresNORTH NSI Ordnance Survey grid reference northing in metresLAND USE AR: arable; LE: ley short term grassland; PG: permanent (managed) grasslandSUB GROUP soil subgroup (Clayden and Hollis, 1984)1980 PSC Particle size group (Clayden and Hollis, 1984) based on measured particle size distributions from original NSI

samples 1980 pH pH in water from original NSI samples1980 carbon organic carbon % from original NSI samples1980 avail P extractable phosphorus from original NSI samples1980 total P total phosphorus from original NSI samples1980 Zn total zinc from original NSI samples1996 pH pH in water from re-sampled NSI sites1996 carbon organic carbon % from re-sampled NSI sites1996 avail P extractable phosphorus from re-sampled NSI sites1996 total P total phosphorus from re-sampled NSI sites1996 Zn total zinc from re-sampled NSI sites

Table 3 demonstrates the criteria by which the data from the three schemes can be divided to clarify change between 1980 and 1996. Soil group, land use and texture for NSI sites are based on observed data from the field sites. RSSS does not record soil type or texture and these were derived by overlaying the field site locations onto the National Soil Map (1:250,000 scale) to give the soil association at each point. It was then assumed that the dominant soil series in each association would be found at the field site. This is a “best guess” approach but is flawed in so far as the map is at reconnaissance level and the dominant soils series may only occupy 45% of the association. It is recommended that in future field visits, some attempt is made to record soil texture, and if feasible, soil type. In CS soil series is recorded from soil maps at 1:10,000 scale for each CS 1 km square. Soil texture was recorded in 1978.

It is assumed in CS and NSI that the land use recorded in 1980 is the same as in 1996, i.e. if a site changes from permanent grass to arable it would be recorded for the purposes of assessing change by land use as permanent grass. This is not the case with RSSS because the sites change and therefore the land use category is more likely to reflect real changes on the proportions of lands cover in the assessment periods. ECN data are presented for a single period covering the first “5 year sampling” and as time progresses the data for 1998 and 2003 will be included in temporal analysis.

Table 3 Change in parameters between early 1980’s and mid to late 1990’s+ indicates increase from 1980’s to 1990’s, - indicates decrease from 1980’s to 1990’sa) NSI

Carbon % 1980 Carbon % 1996 Carbon change 1980 to 1996 N pH 1980 pH 1996 pH change 1980 to 1996 N

Lithomorphic soil 3.7 3.2 -0.3 98 7.5 7.6 +0.2 98

Pelosol 2.8 2.4 -0.1 93 7.5 7.7 +0.2 93

Brown soil 2.6 2.5 -0.1 582 6.3 6.7 +0.3 581

Podzolic soil 4.4 3.8 -0.5 73 5.4 5.5 +0.2 73

Stagnogley soil 3.5 3.1 -0.3 319 5.8 6.2 +0.3 321

Ground-water soil 4.0 3.7 -0.4 145 6.3 6.7 +0.3 144

Peat soil 16.4 16.0 -1.5 14 6.3 7.0 +0.4 14

Arable 2.2 2.1 0.0 566 7.2 7.5 +0.3 566

Ley 3.2 3.1 -0.3 200 5.9 6.5 +0.4 200

Permanent grass 4.2 3.7 -0.4 580 5.6 5.6 +0.1 581

Sand 1.5 1.4 0.0 34 6.8 6.9 +0.1 34

Coarse loamy 2.4 2.4 -0.1 251 6.2 6.4 +0.2 251

Fine loamy 3.1 2.8 -0.2 492 6.1 6.4 +0.2 492

Coarse silty 3.5 3.6 +0.1 14 5.7 5.9 +0.3 14

Fine silty 3.1 2.9 -0.1 166 6.1 6.5 +0.3 166

Clayey 3.9 3.3 -0.4 290 6.7 7.0 +0.4 290

CSG 15 (1/00) 11

Peat 16.7 8.9 -9.4 39 6.3 7.1 +0.3 39

Zn mg/kg 1980

Zn mg/kg 1996

Zn change 1980 to 1996

N Avail P mg/kg 1980

Avail P mg/kg 1996

Avail P change 1980 to 1996

N

Lithomorphic soil 92.5 70.2 -23.2 98 24.1 32.0 +8.7 98

Pelosol 97.0 75.7 -19.0 93 23.9 25.5 +1.9 93

Brown soil 86.0 63.3 -21.7 582 30.6 36.7 +6.8 581

Podzolic soil 87.0 57.4 -22.3 73 24.5 30.9 +7.1 73

Stagnogley soil 80.0 60.4 -22.0 319 24.5 30.2 +5.8 321

Ground-water soil

94.0 68.3 -24.3 145 28.2 32.2 +4.1 144

Peat soil 90.5 69.7 -20.8 14 28.5 37.6 +9.1 14

Arable 82.0 65.5 -12.7 566 33.0 42.8 +10.4 566

Ley 85.5 70.5 -17.2 200 28.3 36.5 +8.6 200

Permanent grass 91.5 61.5 -28.7 580 22.7 22.9 +0.5 581

Sand 34.0 33.8 -5.8 34 43.8 53.9 +11.4 34

Coarse loamy 62.0 52.7 -13.3 251 33.5 39.3 +6.7 251

Fine loamy 87.0 65.5 -23.0 492 26.3 31.8 +5.8 492

Coarse silty 92.0 67.0 -27.5 14 30.0 27.6 -2.4 14

Fine silty 91.0 66.5 -24.7 166 24.6 30.0 +6.1 166

Clayey 105.0 75.0 -29.8 290 24.6 29.7 +5.2 290

Peat 77.0 57.3 -22.7 39 23.7 37.1 +13.4 39

b) RSSSpH 1981

Avail P mg/l 1981

N 1981

pH 1997

Avail P mg/l 1997

N 1997

pH change 1981 to 1997

Avail P change 1981 to 1997

All sites 6.5 23.3 2169 6.8 23.0 728 +0.3 -0.3

Lithomorphic soil 7.7 21.7 25 8.1 32.0 43 +0.3 +10.3

Pelosol 7.2 41.0 22 7.6 17.3 66 +0.3 -23.7

Brown soil 6.3 22.0 172 6.7 25.0 285 +0.4 +3.0

Podzolic soil 5.8 14.8 26 5.9 19.3 28 +0.2 +4.5

Surface water gley soil

6.4 22.2 124 6.6 22.5 240 +0.2 +0.3

Ground water gley 7.3 27.3 39 7.1 21.0 53 -0.2 -6.3

Peat nd nd nd nd nd nd nd nd

Sand 6.9 43.0 21 7.5 43.5 14 +0.6 +0.5

Coarse loamy 6.3 22.3 188 6.4 26.0 222 +0.1 +3.7

Fine loamy 7.6 25.3 53 7.1 20.5 303 -0.5 -4.8

Coarse silty 6.5 19.3 88 7.0 23.8 14 +0.5 +4.5

Fine silty 6.5 16.7 25 7.1 23.3 130 +0.6 +6.6

Clayey 7.9 22.7 3 7.8 23.5 10 -0.1 +0.8

Peat 6.2 23.7 31 7.1 29.5 24 +0.9 +5.8

c) CSpH 1978 pH 1998 LOI 1978 LOI 1998 pH change 1978 to 1998 LOI change 1978 to 1998 N

All soils 5.2 5.57 10 12.79 +0.3 +2.18 1030

Lithomorphic soil 5.2 5.7 19.0 22.7 +0.2 +3.6 94

CSG 15 (1/00) 12

Pelosol 7.6 7.3 6.0 6.7 +0.1 +1.0 24

Brown soil 5.9 6.1 7.0 8.7 +0.3 +2.1 317

Podzolic soil 4.4 4.5 19.0 25.0 +0.2 +4.1 149

Surface water gley soil 5.3 5.7 9.0 12.2 +0.4 +3.1 190

Ground water gley 5.9 6.3 8.0 9.6 +0.3 +1.2 119

Peat soil 4.2 4.5 93.5 94.4 +0.3 +1.0 126

Crops & weeds 6.8 7.0 5.0 6.1 +0.3 +1.2 178

Fertile grassland 5.9 6.1 8.0 9.9 +0.2 +2.3 182

Heath and bog 4.2 4.4 89.0 91.8 +0.2 +3.3 181

Infertile grassland 5.4 5.7 9.0 11.7 +0.4 +2.9 205

Lowland woodland 5.2 5.0 9.0 13.8 +0.3 +3.9 27

Moorland grass 4.4 4.6 42.0 34.4 +0.3 +2.4 127

Tall grass and herb 6.6 7.3 6.0 7.4 +0.6 +1.2 55

Upland woodland 4.1 4.3 15.0 20.2 +0.2 +5.4 65

LOI = Loss on Ignition, suggested conversion from LOI to organic carbon is: organic carbon = 0.44 (LOI) -0.39 (Bradley, 1977).

d) ECN sites (combined 1993 to 1998)

Site name Soil type Land use pH NCarbon % N

Avail P mg/kg N

Total P mg/kg N Zn mg/kg N

Alice Holt Stagnogley soil Woodland 4.5 12 3.3 12 6.8 12 435 6 42.8 6

Drayton PelosolPermanent pasture 6.7 12 4.8 12 39.2 12 881 12 74.6 12

Moor House Peat soil Heather moor 3.7 12 362 12 110.0 6

North Wyke Stagnogley soilPermanent pasture 5.7 12 4.5 12 21.9 12 701 12 29.9 12

Porton Down Lithomorphic soil Chalk downland 8.5 12 4.5 12 10.2 12 975 6 54.7 6

Snowdon Brown soil Upland grassland 5.6 12 4.2 12 9.7 12 850 6 64.7 6

Wytham Stagnogley soil Woodland 5.7 12 4.5 12 11.2 12 624 12 93.1 12

CSG 15 (1/00) 13

Figure 2 Histograms and frequency graphs for example datasetsa) pH for RSSS sites (1979 to 1983)

0

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4.4 4.6 4.8 4.9 5.1 5.3 5.5 5.6 5.8 6.0 6.2 6.4 6.5 6.7 6.9 7.1 7.3 7.4 7.6 7.8 8.0 8.1 8.3 More

Freq

uenc

y

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120%

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Cumulative %

b) pH for CS2000 sites

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60

70

3.4 3.7 4.0 4.4 4.7 5.0 5.3 5.7 6.0 6.3 6.6 6.9 7.3 7.6 7.9 8.2 8.5

Freq

uenc

y

0%

20%

40%

60%

80%

100%

120%

Frequency

Cumulative %

CSG 15 (1/00) 14

c) pH change for NSI sites (1980’s to 1990’s)+ indicates increase from 1980’s to 1990’s, - indicates decrease from 1980’s to 1990’s

0

20

40

60

80

100

120

140

160

-2.3

-2.0

-1.7

-1.3

-1.0

-0.7

-0.4

-0.1 0.2

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.8

3.1

Mor

e

pH units

Freq

uenc

y

0%

20%

40%

60%

80%

100%

120%

Frequency

Cumulative %

d) pH values for all NSI, RSSS and CS sites

0%

20%

40%

60%

80%

100%

120%

3 4 5 6 7 8

CS 78

RSSS 79-83

NSI 79-83

e) pH values for agricultural sites from NSI, RSSS and CS

0%

20%

40%

60%

80%

100%

120%

3.0 4.0 5.0 6.0 7.0 8.0

CS 78

RSSS 79-83

NSI 79-83

Of the five determinands chosen, pH is the only parameter common to all the databases and therefore it was selected to exemplify the feasibility of bringing together the different data. Statistical analysis suggests that there are significant differences, for example, between RSSS data and NSI (Table 4 and Figures 2d and e). However, in terms of land management, the differences between pH values of, for example, 6.3 and 6.6 would not be seen as significant. Reducing the number of sites by selecting by land use, texture or soil type does not bring the data sets closer together statistically.

CSG 15 (1/00) 15

When the data are brought together for comparison, the differences in sampling regimes become clear (Figure 2d). However, if only the agricultural sites are included the distribution curves become more similar. The similarity between the datasets for pH is investigated in Table 4.

Table 4 Comparison of pH using t-tests, with variables treated as independent samples* p<0.05 ** p<0.01 *** p<0.001a) all agricultural sites

Mean Group 1

Mean Group 2

t-value df p Valid N Group 1

Valid N Group 2

Std.Dev. Group 1

Std.Dev. Group 2

F-ratio variances

p variances

CS1978 vs. CS2000

6.690300 6.302261 6.6297 1759 .000000*** 434 1327 1.054580 1.059751 1.009830 .911159

CS1978 vs. RSSS_79

6.690300 6.624858 1.3669 2430 .171796 434 1998 1.054580 .867965 1.476231 .000000***

CS1978 vs. RSSS_95

6.690300 6.825865 -2.7326 2427 .006329** 434 1995 1.054580 .909014 1.345916 .000041***

CS1978 vs. NSI_80

6.690300 6.401294 4.0269 857 .000062*** 434 425 1.054580 1.048698 1.011249 .908153

CS1978 vs. NSI_96

6.690300 6.585503 1.7989 1781 .072209 434 1349 1.054580 1.056011 1.002716 .982992

CS2000 vs. RSSS_79

6.302261 6.624858 -9.5976 3323 .000000*** 1327 1998 1.059751 .867965 1.490743 .000000***

CS2000 vs. RSSS_95

6.302261 6.825865 -15.2066

3320 0.000000*** 1327 1995 1.059751 .909014 1.359147 .000000***

CS2000 vs. NSI_80

6.302261 6.401294 -1.6809 1750 .092966 1327 425 1.059751 1.048698 1.021190 .801321

CS2000 vs. NSI_96

6.302261 6.585503 -6.9251 2674 .000000*** 1327 1349 1.059751 1.056011 1.007095 .897055

RSSS_79 vs. RSSS_95

6.624858 6.825865 -7.1461 3991 .000000*** 1998 1995 .867965 .909014 1.096823 .039055*

RSSS_79 vs. NSI_80

6.624858 6.401294 4.6387 2421 .000004*** 1998 425 .867965 1.048698 1.459809 .000000***

RSSS_79 vs. NSI_96

6.624858 6.585503 1.1778 3345 .238969 1998 1349 .867965 1.056011 1.480240 .000000***

RSSS_95 vs. NSI_80

6.825865 6.401294 8.4994 2418 .000000*** 1995 425 .909014 1.048698 1.330944 .000092***

RSSS_95 vs. NSI_96

6.825865 6.585503 7.0226 3342 .000000*** 1995 1349 .909014 1.056011 1.349571 .000000***

NSI_80 vs. NSI_96

6.401294 6.585503 -3.1411 1772 .001711** 425 1349 1.048698 1.056011 1.013996 .870878

b) brown soilsMean Group 1

Mean Group 2

t-value df p Valid N Group 1

Valid N Group 2

Std.Dev. Group 1

Std.Dev. Group 2

F-ratio variances

p variances

CS1978 vs. CS2000

5.896637 6.160933 -2.8789 683 .004115**

342 343 1.206940

1.195791

1.018733

.863897

CSG 15 (1/00) 16

CS1978 vs. RSSS_79

5.896637 6.440116 -5.2960 512 .000000 342 172 1.206940

.838855 2.070132

.000000***

CS1978 vs. RSSS_95

5.896637 6.712339 -14.7088

1888 0.000000***

342 1548 1.206940

.854559 1.994744

.000000***

CS1978 vs. NSI_80

5.896637 6.345343 -5.9847 909 .000000***

342 569 1.206940

1.023294

1.391139

.000554***

CS1978 vs. NSI_96

5.896637 6.645845 -10.0782

921 .000000***

342 581 1.206940

1.016227

1.410554

.000299***

CS2000 vs. RSSS_79

6.160933 6.440116 -2.7417 513 .006326**

343 172 1.195791

.838855 2.032065

.000000***

CS2000 vs. RSSS_95

6.160933 6.712339 -9.9812 1889 .000000***

343 1548 1.195791

.854559 1.958064

.000000***

CS2000 vs. NSI_80

6.160933 6.345343 -2.4719 910 .013621* 343 569 1.195791

1.023294

1.365558

.001112**

CS2000 vs. NSI_96

6.160933 6.645845 -6.5556 922 .000000***

343 581 1.195791

1.016227

1.384616

.000621***

RSSS_79 vs. RSSS_95

6.440116 6.712339 -3.9706 1718 .000075***

172 1548 .838855 .854559 1.037793

.769305

RSSS_79 vs. NSI_80

6.440116 6.345343 1.1072 739 .268555 172 569 .838855 1.023294

1.488084

.002067**

RSSS_79 vs. NSI_96

6.440116 6.645845 -2.4217 751 .015686* 172 581 .838855 1.016227

1.467602

.002864**

RSSS_95 vs. NSI_80

6.712339 6.345343 8.2902 2115 .000000***

1548 569 .854559 1.023294

1.433893

.000000***

RSSS_95 vs. NSI_96

6.712339 6.645845 1.5160 2127 .129678 1548 581 .854559 1.016227

1.414157

.000000***

NSI_80 vs. NSI_96

6.345343 6.645845 -4.9964 1148 .000001***

569 581 1.023294

1.016227

1.013956

.867968

CSG 15 (1/00) 17

c) coarse loamy soils onlyMean Group 1

Mean Group 2

t-value df p Valid N Group 1

Valid N Group 2

Std.Dev. Group 1

Std.Dev. Group 2

F-ratio variances

p variances

RSSS_79 vs. RSSS_95

6.411287 6.485465 -1.16935

1430 .242459 189 1243 .827263 .810248 1.042440 .685863

RSSS_79 vs. NSI_80

6.411287 6.302110 1.35843 1514 .174529 189 1327 .827263 1.059765 1.641090 .000027***

RSSS_79 vs. NSI_96

6.411287 6.585474 -2.17581

1536 .029721* 189 1349 .827263 1.056012 1.629485 .000035***

RSSS_95 vs. NSI_80

6.485465 6.302110 4.90339 2568 .000001*** 1243 1327 .810248 1.059765 1.710738 .000000***

RSSS_95 vs. NSI_96

6.485465 6.585474 -2.68842

2590 .007225** 1243 1349 .810248 1.056012 1.698640 .000000***

NSI_80 vs. NSI_96

6.302110 6.585474 -6.92801

2674 .000000*** 1327 1349 1.059765 1.056012 1.007122 .896668

The data in Table 4 are visualised in the Box and Whiskers plots presented in Figure 3 and 4. They draw attention to the fact that although median values may be similar for the different categories plotted it is important to take into consideration the range of values as represented by the quartile ranges. They also emphasise the concept that although the medians may be different statistically, in terms of the soil management then criteria, such as those presented in the DEFRA “Fertiliser recommendations for agricultural and horticultural crops” (RB209), must be taken into consideration.

Figure 3 Box and whisker plots of NSI, RSSS and CS data for pHCS1978 CS data for 1978CS2000 CS data for 1978CS_CHANG difference in CS between 1978 & 1998RSSS_78 data for 1978 to 1983

RSSS_95 RSSS data for 1995 to 1999NSI_80 NSI data for 1979 to 1983NSI_96 NSI data for 1995 to 1996NSI_CHAN difference in NSI between 1980’s & 1990’s

a) All sites

Min-Max25% -75%Median value

B ox & W hisker P lot

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

CS1978CS2000

CS_CHA NGRSSS_79

RSSS_95NSI_80

NS I_96NSI_CHAN

CSG 15 (1/00) 18

b) Brown soils only

c) Coarse loamy soils only

Figure 4 Box and Whisker plot for pH at ECN sitesa) all sites

CSG 15 (1/00) 19

b) land use

5.3 Correction of the quantitative data to common units of mass and volumeIn examining the relationships between the three data sets from NSI, RSSS and CS, a first assumption is that the data can be combined. However, this assumption is erroneous in that the under lying sampling rationales of the three schemes are different (Appendix 2). Hence in this part of the project the question addressed is “despite these differences in rational do the schemes reach the same conclusions for different England and Wales scenarios”. As well as statistical differences there are analytical differences.

Although the project document describes the objective in this part of the project as "correction", many would view it as a "conversion" as there has been a long discussion as to the validity of expressing results as volume or weight. NSI, ECN and CS record the data on a weight basis - usually as milligrams per kilogram of soil (mg kg-1), whereas RSSS results are quoted as milligrams per litre (mg l-1). This reflects the origins of RSSS in the determination of fertiliser requirements, which are cited in kilograms per hectare (kg ha-1).

To convert from weight to volume basis requires the use of "bulk density", the weight of soil in a given volume, usually grams per cubic centimetre (g cm-3). Most non-humose soils have a dry bulk density of between 1.0 and 1.4 m g cm-3 (Hallett et al., 1995). Humose soils, however, often have densities below 1.0 g/cm -3 and peaty soils less than 0.4 g cm-3, therefore, if an extraction method specifies a 10 ml scoop of soil to be taken, the weight may lie between 1.0 and 1.4 g for mineral soils but less than 1.0 g for a humose soil. The more organic soils will therefore contain less of every element than would be expected if a constant weight was taken.

NSRI used a pedo-transfer function, based on over 1600 field measurements of bulk density, that takes the clay, silt and organic carbon contents of a soil sample and predicts bulk density (Mayr et al., 1999). Field measured bulk density data are held for most ECN sites. Density is calculated for RSSS samples based on air dry soil in the ADAS laboratory. Bulk density for CS2000 samples is estimated from measured sample volume and oven-dry weight of soil.

The results of converting the NSI data files are given as an example in Table 5. It should be noted in any calculations and/or conclusions drawn from these data that they are derived from pedo-transfer values of bulk density rather than measured data and that measured data can be derived either in the laboratory or the field.

CSG 15 (1/00) 20

Table 5 Weight to volume conversion using calculated bulk density (NSI).EAST NORTH LAND

USESUB GROUP

1980 avail P

1980 total P

1996 avail P

1996 total P

Clay Silt BD 1980

BD 1996

1980 avail P vol

1980 total P vol

1996 total P vol

1996 avail P vol

146000 26000 LE 6.11 26.0 1084 24.9 634 18 53 1.00 1.26 26.1 1087 799 31.4

151000 36000 LE 6.11 13.0 988 11.7 597 23 33 1.03 1.21 13.4 1019 722 14.1

156000 31000 PG 6.11 15.0 920 80.0 1165 26 46 1.03 1.14 15.5 952 1329 91.3

156000 36000 AR 5.41 48.0 693 54.7 960 24 46 0.93 1.43 44.8 647 1368 77.9

166000 36000 PG 6.22 55.0 805 23.0 526 10 30 1.08 1.10 59.6 872 579 25.3

Key:As above withClay Laboratory measured clay from original NSI sampleSilt Laboratory measured clay from original NSI sample..vol parameter converted to volume basis using pedo-transfer functions.

Figure 5 Comparison of RSSS and NSI data for available P both expressed on volume basis (mg l-1)

0%

20%

40%

60%

80%

100%

120%

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0

RSSS 79-83

NSI all sites 79-83

NSI agricultural 79-83

Available P for each soil texture group, expressed to a common base, is similar for NSI and RSSS in 1980 (NSI: 670.9 x 103 tonnes). However in 1996, while there is little change in the RSSS data for each soil group, the NSI data show consistent increases by texture group. The method of deriving available P data was examined as a further example of the conversion from weight to volume basis. In Defra Project SP0514 (“Sampling strategies and soil monitoring”) the weight of soil in 5 ml and 10 ml scoops was measured for 120 samples. They confirm that at low organic carbon values approximately 5 ml of soil weigh 5 g and 10 ml weigh 10 g. Therefore the convention of multiplying by a field derived bulk density is open to question.

If the effects of laboratory density and bulk density are compared for the same samples then the results are as in Figure 6 (based on the Merlewood exercise described in Appendix 5). Figure 6a was constructed based on estimated field bulk density whereas Figure 6b used a laboratory density of 1 g/cm3. Both graphs show how, although data may be statistically different, in terms of monitoring for agronomic use it is the relationship to fertility indices that is as important. Table 6 shows the density necessary to compare weight based data to volume based data for the same samples, again close to 1 g/cm3 rather than the estimated field value of 1.15 g/cm3 for Denbigh series topsoils.

CSG 15 (1/00) 21

Figure 6 Comparison of analyses for available P from Merlewood exercisea) Field bulk density

b) laboratory density

0

10

20

30

40

50

60

70

80

ECN 0-5

ECN 5-10

ECN 10-20

ECN 20-30

weighte

d ECN 0-

15

NSI 0-1

5

CS 0-15

RSSS 0-15

amou

nt o

f ava

ilabl

e P

mg/l

mg/kg

Table 6 Comparison of analyses for available P from Merlewood exercise

mg/l mg/kgDensity required to convert from weight to

volume basis

ECN 0-5 64 74.0 1.16

ECN 5-10 60 62.3 1.04

ECN 10-20 38 41.6 1.09

ECN 20-30 16 14.3 0.89

weighted ECN 0-15 54 59.3 1.10

NSI 0-15 55 56.4 1.03

CS 0-15 31 34.8 1.12

RSSS 0-15 31 41.0 1.32

5.4 Use of scaling factors to adjust for the different sampling depthsIf samples are taken at different depth then it is necessary to use scaling factors to adjust the data compiled in section 5.3 for different sampling depths. This scaling should be based on standard sampling depths and/or sampling by horizon, where these exist, by using transparent rules based on expert judgement. The aim

CSG 15 (1/00) 22

Comparison of analyses for available P

(VOLUME basis)

01020304050607080

ECN NSI CS RSSS

mg

per l NSRI lab

ADAS lab

Index 5

Index 4

Index 3

Comparison of analyses for available P (VOLUME basis)

01020304050607080

ECN NSI CS RSSS

mg

per l NSRI lab

ADAS lab

Comparison of analyses for available P (VOLUME basis)

01020304050607080

ECN NSI CS RSSS

mg

per l NSRI lab

ADAS lab

Index 5

Index 4

Index 3

originally was to compare changes in soil pH, as an example, over time in relation to the design of the soil monitoring schemes.

NSI, CS and RSSS all sample 0 to 15 cm depth, and ECN 0-5, 5-10, 10-20 and 20-30 cm as well a set corresponding to horizons within the top 30 cm (Appendix 2). Hence the data for NSI, CS and RSSS are directly comparable by depth. In the case of the ECN, where data are available in both ways, then weighting can be used to produce calculated data for the 0-15 cm layer (Table 7). This is the procedure adopted following the Merlewood sampling exercise to allow comparison of the results of the ECN and other samples (Appendix 5).

There is no real evidence from the literature as to the origins of 0-15 cm as a "standard" sampling depth. It is presumed that it represents the cultivated layer with maximum mixing. For arable and permanent grassland sites this is a reasonable assumption and is a realistic sampling regime. However, for non-agricultural soils it presents problems, particularly when interpreting the results and comparing sites. Such soils tend to have thin surface layers of variable thickness (Figure 7) and a sample from 0 to 15 cm can therefore include, for example, highly organic (O or Ah) material mixed with material with low organic carbon content (E).

Change statistics for these soils have to presume that the soils were sampled in the same way each time and that like is being compared with like. The profiles in Figure 7 could have similar amounts of, for example, organic carbon measured over 15 cm, but clearly the functions and dynamics of these upper layers will be very different.

Table 7 demonstrates how the calculated contents vary according to the starting point of depth or horizon sampling. Horizon sampling would appear to over-estimate the contents of all the parameters when calculated as 0 to 15 cm. However, given that the 5 to 10 cm sample and 20 to 30 cm cross horizon boundaries, it is likely that the values calculated from horizon based data give a better approximation. This analysis shows the advantage of horizon sampling and the inclusion of both horizon and depth sampling in the ECN programme. The meaning and interpretation of the sampling protocols become even more significant once sub-soil sampling is incorporated into any monitoring scheme.

Table 7 Example of data scaling by depth using data from Wytham Wood ECN (Block A, 1993)Measured Calculated Measured Calculated

0-5 cm

5-10 cm

10-20 cm

20-30 cm

0-15 cm from depth samples

Horizon 10-8 cm

Horizon 2 8-21 cm

0-15 cm from horizon samples

pH 5.5 5.4 5.3 5.2 5.4 5.6 5.9 5.7

Organic carbon

8.4 5.8 3.2 1.7 5.8 9.4 5.6 7.6

Total P 817 689 551 385 686 903 721 818

Avail P 33.0 18.7 10.0 3.8 20.6 36.2 19.4 28.3

Zn 102.2 102.8 98.3 94.0 101.1 113.1 110.2 111.7

CSG 15 (1/00) 23

Figure 7 Effect of different horizon thicknesses on standard depth sampling

5.5 Comparing the results from scaled data with the weight and volume based dataThe contract document states that the project will "compare the results from the scaled data with the weight and volume based data so as to illustrate the uncertainty in translating from one set of data e.g. on a volume basis, to the expression of the same data on a mass basis and from data based on fixed sampling depths to those based on soil horizons of differing depth". However, since no scaling has been necessary, it is possible to go straight from the weight and volume based data to look at loadings and change.

5.6 Extrapolate the soil based values to give national estimates of nutrient loadings using agricultural census data

Table 8 was prepared by assessing the content of available P, total P and total Zn from the NSI and RSSS data, using the extent of each soil type to extrapolate from the points to a spatial basis. Pedo-transfer functions were used to convert the RSSS data to a weight basis for direct comparison with the NSI data to give results in tonnes.

CSG 15 (1/00) 24

Table 8 Changes in soil loadings of available P, total P and total Zn, 1980-1996. (All values in t x 103)

Soil GroupAvailable P load Net change in available

P loadNet change in total

P loadNet addition of P fertiliser

(from SFP)Net change in total

Zn load

NSI (1980)

NSI (1996)

NSI RSSS

Sandy 49.2 70.3 21.2 6.1 -17.7 - 9.5

Coarse loamy 365.1 432.8 67.6 -4.3 -1534.8 - -179.9

Fine loamy 70.9 89.3 18.4 -7.7 -642.1 - -80.9

Coarse silty 84.1 78.3 -5.8 -6.7 -898.0 - -83.0

Fine silty 5.7 7.2 1.4 0.6 -51.6 - -5.2

Clay 95.8 116.7 20.8 2.1 -970.5 - -141.1

Total 670.9 794.7 123.8 -10.0 -4114.9 6895.4 -480.6

Total as % of baseline

18.4 -1.6 -22.7 -25.3

Total as % of P inputs

1.8 -0.1 -59.6

5.7 Comparison of the loadings of P from extrapolation with data from the Survey of Fertiliser Practice

Available P for each soil texture group, expressed to a common base, is similar for NSI and RSSS in 1980 (NSI: 670.9 x 103 tonnes) (Table 8). However in 1996, while there is little change in the RSSS data for each soil group, the NSI data show consistent increases by texture group. Therefore available P loadings appear to have increased according to the NSI, but have hardly changed according to the RSSS.

In 1980 the available P analyses, for each texture group expressed as mg/l, are quite similar with the total available P loading from the RSSS 96% that for the NSI. However, when comparing the values for 1980 and 1996, while the available concentrations for the RSSS change little, those for the NSI increase, in some cases by quite considerably with the total available P load from the RSSS for 1996 about 84% of that for the NSI.

When comparing total P (data from NSI only), there appears to have been a decrease in the loading of 23% between 1980 and 1996. This could be due to deeper cultivation as evidenced in the following section. There was a similar (25%) decrease in total-Zn loading over the same period.

The value of 6895.4 x 103 tonnes fertilizer-P addition is the gross value. The net addition, reduced mainly due to removal in crops, was 3361.5 x 103 tonnes.

Some of these differences can be accounted for by the laboratory method used to measure available P (Olsen) and some by the lack of the field measured bulk density required to reliably convert from volume to weight basis.

5.8 Experimental investigation into the effects of changing plough depth on measured soil properties

The aim of this experimental work was to undertake field work to test the plough-depth hypothesis, based on two scenarios using two pairs of ADAS/Defra farms representing a wide range of farming practice and soil type combinations - Boxworth (calcareous clay soils with the possibility of P-immobilisation) and Drayton (non-calcareous clay soils); Gleadthorpe (sandy soils) and Terrington (silty/loamy soils):

Scenario 1: a combinable crops rotation on clay soils, with straw formerly burnt, but now incorporated by ploughing.

Scenario 2: potatoes, with an increase in cultivation depth over time, due to introduction of stone separation, etc.

The workplan was:

CSG 15 (1/00) 25

To sample 10 fields from each farm in increments of 5 cm to 50 cm depth, and determine total (aqua regia) and available (ammonium bicarbonate) P, pH (water), soil organic carbon, total (aqua regia) Zn and bulk density in each sample.

To identify the 2 fields on each farm with the overall steepest change in P, pH, soil organic carbon and Zn between 0-5 cm and 40-50 cm.

To cultivate two areas of each of these two fields to c.5 cm and 10 cm below the discontinuity. To resample the areas to ‘standard’ depths of 15 and 25 cm and determine pH, available and total P, pH,

soil organic carbon, total Zn and bulk density. To evaluate the effects of changing the plough depth.

5.8.1 Results

Effect of cultivation depth on analysis of 0-15 cm layerAt each site increased cultivation depth was predicted to reduce concentrations of soil organic carbon (SOC), available P and Zn in the 0-15 cm layer (Table 9). Analysis of the results of post-cultivation sampling confirmed the decrease in concentrations of SOC and available P following cultivation, but there was no significant effect on total Zn (Table 10). Regression of the measured analyses in the 0-15 cm layer post cultivation, and those predicted from the incremental sampling and depths of cultivation, showed good agreement with p < 0.001% in all cases, with R2 of 71 to 94%. The slopes of regression for predicted and measured SOC, total Zn and pH were all reasonably close to 1.0. However, prediction of available P was less good with a slope of 0.75.

At Terrington the depth to which P and SOC enrichment occurred (D) was much deeper than expected and the project team were advised that it would be impossible to plough to a greater depth than D. In consequence, extra experiments were carried out at Drayton.

Table 9 Predicted concentrations in the 0-15 cm horizon following standard and deep cultivationSite Standard cultivation

(a)Deep cultivation (b) SE Residual df c=a-b % reduction

Gleadthorpe SOC 1.09 0.95 0.021 4 0.14 13

Boxworth SOC 2.11 1.97 0.021 4 0.14 7

Drayton SOC 2.23 2.00 0.021 4 0.23 10

Gleadthorpe Avail P 41 31 0.3 4 10 25

Boxworth Avail P 14 13 0.3 4 1 7

Drayton Avail P 16 12 0.3 4 4 23

Gleadthorpe Zn 25 22 0.3 4 3 10

Boxworth Zn 94 92 0.3 4 2 2

Drayton Zn 77 75 0.3 4 2 2

CSG 15 (1/00) 26

Table 10 Measured, log-transformed means (back-transformed means)Site Standard cultivation

(a)Deep cultivation (b)

SE Residual df

c=a-b

% reduction

Bulk density

Amount kg ha-1

Gleadthorpe SOC -0.29 (0.74) -0.49 (0.62) 0.018 4 0.12 9 1.57 2830

Boxworth SOC 0.63 (1.87) 0.66 (1.93) 0.018 4 -0.06 -3 1.26 -1130

Drayton SOC 0.70 (2.01) 0.63 (1.87) 0.016 4 0.14 7 1.27 2670

Gleadthorpe Avail P 3.4 (31) 3.5 (32) 0.031 4 -1 -3 1.57 -1.5

Boxworth Avail P 2.5 (13) 2.5 (12) 0.031 4 1 8 1.26 1.5

Drayton Avail P 2.7 (15) 2.5 (12) 0.028 4 3 20 1.27 4.5

Gleadthorpe Total-Zn

3.4 (29) 3.1 (23) 0.021 4 6 21 1.57 14

Boxworth Total Zn

4.5 (87) 4.5 (86) 0.021 4 1 1 1.26 2

Drayton Total Zn

4.3 (74) 4.3 (76) 0.019 4 -2 3 1.27 -4

Estimation of amounts of SOC, available P and Zn moved by cultivationAt Gleadthorpe deeper cultivation did not appear to affect the distribution of available P. However at Boxworth and Drayton, concentrations of available P in the 0-15 cm layer were decreased by 8 and 20% respectively. These decreases were equivalent to apparent reductions of 1.5 and 4.5 kg ha-1 in topsoil available P.

There was no overall effect on Zn in the 0-15 cm layer of cultivation depth. However, there was a significant interaction with site, with decreases at Gleadthorpe and Boxworth of 21 and 1 mg kg -1 respectively, equivalent to losses of 14 and 2 kg ha-1 respectively.

There was also an overall reduction in SOC at Drayton and Gleadthorpe of 7 and 9% respectively, equivalent to c. 2700 and 2800 kg ha-1.

Enrichment of soil layers below the standard sampling depth of 15 cm may be estimated by calculating the mean concentration in the subsoil, subtracting this from concentration in layer between 15 cm and the subsoil and multiplying the difference by the depth of the discontinuity (D) and mean bulk density (Db).

At Boxworth available P enrichment in the 0-15 cm layer and D (15.1 to 30.0 cm) was 24 and 15 kg ha -1

respectively. The equivalent amounts at Drayton were 20 and 20 kg ha-1. At Gleadthorpe, where poultry manure has been regularly applied, P enrichment was much greater; c. 61 kg ha-1 to 15 cm and c. 82 kg ha-1

between 15 and 40 cm. These amounts of available P in D were equivalent to c. 63, 67 and 134% respectively at Boxworth, Drayton and Gleadthorpe of the estimated enrichment in the 0-15 cm layer. Thus, on the heavier-textured soils, the amount of available P accumulated below the sampling depth may be c. 65% of that accumulated in the 0-15 cm layer. On lighter textured soils the amount of available-P accumulated below 15 cm may be as great as that in the sampled layer.

Shepherd and Withers (1999) concluded from an experiment at Gleadthorpe, in which large amounts of P were added to the soil as both fertilizer and poultry manure, that there was little movement of P down the soil profile, except by cultivation. Where leaching of P occurs, it is associated with acid soils, or movement of organically-bound P as may occur on fields that have been given large amounts of manure for many years (Smith et al., 1998). They reported substantial enrichment of available P below 45 cm, at sites with a history of organic manure applications. Thus, where organic manures have been applied, P enrichment below the conventional soil sampling depths of 15 cm may occur due to movement of soluble organic P, as well as by cultivation. Although none of the sites in the study reported above included pig FYM, it seems reasonable to conclude that this would be the mechanism for some of the P enrichment below standard cultivation depth at Terrington.

We conclude that an increase in cultivation depth since the 1960’s is likely to have lead to a substantial amount of P and other nutrients being moved to below the standard soil sampling depth of 15 cm. However, where organic manures have been applied there is also likely to have been movement of soluble organic P to below 15 cm.

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5.9 Assessment of the possibilities for mapping the various datasets through geostatistical techniques and comparison of the results one with another

There are many examples of mapping using the NSI dataset. To illustrate this use, maps of the krigged estimates for the four sampling grids, 5 km, 10 km, 15 km and 20 km, for Zn are shown in Figure 8 (from Loveland et al., 2000, MAFF Project SP0124 Statistical and geostatistical study of the National Soil Inventory, and the design of a soil monitoring network").

10 km grid – one site in a block of four was retained, reducing the data by 75%, to 1433 sites, 15 km grid – one site in nine was retained reducing, the data by 89%, to 637 sites, and 20 km grid – one site in 16 was retained reducing the data by 94%, to 358 sites.

The map for the 10 km sample (Figure 8b) is remarkably similar to that for the full data (Figure 8a). The pattern of variation for the 15 km grid is reasonable (Figure 8c). The degradation in detail is clear for the 20 km grid, but the large scale pattern is still evident (Figure 8d). These results show that there is a loss of detail as the sampling intensity decreases and the variation that remains becomes increasingly like that of the long-range component from factorial kriging.

Figure 8 Example of mapping from the 1980’s NSI data for Zna) 5 km b) 10 km

c) 15 km d) 20 km

A geostatistical analysis of the CS data for Zn was carried out in the same way as for the NSI data. However, it was not possible to map the data as there was no spatial structure to the interpolated data, due to the low sampling frequency across England and Wales mapping of RSSS data is being considered as part of a separate project due to report later in 2003, but initial conclusions suggest that it is possible to spatial map the RSSS data and that the results are similar to those from mapping NSI data (Oliver, personal communication, 2003).

6 DiscussionCompatibility and comparability

1. NSI, RSSS and CS use same sampling depths.2. Organic carbon, pH, total and extractable P, and Zn are directly comparable using same analytical

methods, but not measured in all schemes.3. Land use is a common criteria on which to sort data

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4. Depth of sampling is common to NSI, RSSS and CS but care needs to be taken over differences in sample strategy e.g. arbitrary(?) removal of litter and/or root mat.

5. RSSS is valuable for looking at short term (less than 10 years) temporal changes, but restricted for looking at changes longer than this because field technicians do not visit same sites.

6. NSI, ECN and CS cover nationwide ranges of values and scenarios.

Differences (not necessarily meaning incompatibility)1. Very few parameters are directly comparable in all schemes – for example pH in the sample set

considered in this project.2. Seemingly common parameters are those expressed on weight or volume basis. They can be converted

from one to the other with bulk density or laboratory density.3. Soil series and association data can not be combined – series being a classification of individual profiles

whereas associations are map units which include other series than the named dominant. Neither are directly assessed at RSSS and CS sites.

4. Care needs to be taken to identify measured as opposed to estimated properties.5. None of schemes deliberately visit the same field at same time of year, RSSS does try to minimise the

effects of ‘recent’ fertiliser additions and CS usually samples within the same 4 month window.6. NSI, RSSS and ECN take bulk samples (25 cores combined together); CS and ECN take single core

samples.

Monitoring of soil qualityThere is growing interest in, and justification for, a more systematic approach to soil monitoring (Huber et al., 2001, Commission of the European Communities, 2002). The Commission of the European Communities (CEC) is likely to commission a Technical Working Group to advise it on the most appropriate structure for a pan-European monitoring framework. This structure will then be sent to Member States. A network of sites for the monitoring of European forest soils has already been established on an approximate 16 km grid in order to comply with CEC Regulation 1696/87 (Vanmechelen et al., 1997). With French adoption of a similarly spaced network for soil quality monitoring, there is growing support for adoption of this grid as the basis of a pan-European Soil Monitoring Network. Few of the intersects of the NSI or CS coincide with 16 km intersects e.g. if a regular grid were imposed in England and Wales, then there would be 591 sites of which 1 in 80 would be coincident with NSI points, giving a maximum of 70 sites. It is logical and essential to ensure continuity between any future network and data collected to date and this issue needs urgent attention at the highest level.

Initial thinking within Europe is that the pan-European soil monitoring network should address, possibly selectively: Fundamental soil attributes such as pH and organic matter, Soil qualities such as biomass and biodiversity, erosion rates and physical condition, Soil fertility, Soil contamination with inorganic elements/compounds and radionuclides, Soil contamination with organic compounds, Soil contamination with pathogens.

The coverage of the existing schemes of these criteria is given in Table 11. Some, at least, should be measured at more than one depth within the soil, i.e. not just topsoil. The temporal frequency and spatial density of sampling and many other issues remain undecided.

Table 11 Current schemes and compatibility with requirements of a possible enhanced monitoring schemeNSI RSSS CS ECN

National statistics for change in soil parameters Yes - based on sites Yes - based on 5 year average

Yes Yes

England and Wales distribution of sites for all land uses

'1980' only. Currently arable, ley, managed grassland for '1996'; non-agricultural due autumn 2003.

No Yes Not applicable

Regional data Yes Yes Yes Not applicable

County data '1980' only No5 Not at Not applicable

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present

Fundamental soil attributes, such as particle size distribution, pH and organic matter

Yes Some organic matter and soil type

Yes Yes

Soil qualities such as biomass and biodiversity, erosion rates and physical condition

Erosion rates; physical condition from original descriptions; stability assessments

No Yes Physical condition from original descriptions

Soil fertility Yes Yes Yes Yes

Soil contamination with inorganic elements/compounds and radionuclides

Yes No Yes No

Soil contamination with organic compounds No No Yes No

Soil contamination with pathogens No No No No

Other environmental data Yes No Yes Yes

7 Conclusions and Relevance to Future Monitoring Schemes1. Each of the four schemes reviewed is valid and consistent with its own aims and in the achievement of

those aims. Each scheme has strengths, particularly for the target users for whom they were designed and are implemented. Each covers a different landscape/soil/land use combination.

2. There is complementarity in the datasets - particularly as NSI, RSSS and CS sample from 0 to 15 cm depth, and some of the analytical methods are identical.

3. Each scheme covers different landscape/soil/land use combinations and their sampling designs were devised to answer the specific demands created by the scheme rationales. Thus, although the conclusions reached may the same there is not necessarily symbiosis in combining the data.

4. None of the NSI, RSSS and CS schemes are designed to give an answer specific to a field or farm but use analysis of data from individual sites to give an overall regional or national picture or trend. In contrast the ECN scheme is designed to give detailed information about specific locations for use with other measurements.

5. The design of any new or combined scheme must reflect the needs of England and Wales, Scotland and Northern Ireland - it seems illogical to have different schemes for different parts of the UK which would result in the under-representation of particular habitats, land uses and soil types.

6. The design of any new or combined scheme must reflect the needs of the users - questions of why monitor, how do we monitor (time interval, what is sampled, sampling depths, etc.) - together with the needs and demands of policy makers in UK and EU.

7. The design of any new or combined scheme must take into careful consideration the value and continuity the existing schemes. Any changes, however small, in the sampling strategy or protocols for such a new scheme could effectively re-start the clock with the value of any historic data being lost. .

8. Standardised protocols are needed for data management just as much as data collection.9. Monitoring into the future must include biological and physical parameters as well as the current range of

chemical parameters alongside other environmental data such as land use.10. The timing of sampling is important – thought needs to be given in the protocols as to whether the time it

takes to collect samples will have any effect on their composition as influenced by fertiliser additions.11. Data from ECN show how parameters change with depth within the top 30 cm of the profile, emphasising

the need for samples below 15 cm depth.12. Care is essential in ensuring that the measurement units are consistent when comparing vales. The data can

be used to examine change over time with NSI and CS visiting the same sites. RSSS can be used in a similar way but with 5 year averages used to monitor change on a national basis.

13. The absence of field measured bulk density makes comparison of data less rigorous.14. The absence of field or laboratory measured texture in RSSS and CS makes comparison of data less

rigorous.15. The absence of field assessed soil classification in RSSS makes comparison of data less rigorous.16. The main conclusion from the field experiments is that an increase in cultivation depth since the 1960’s has

lead to a substantial movement of P and other nutrients to below the standard soil sampling depth of 15 cm.

5 Data have been presented for ‘super counties’, i.e. the largest individual counties, such as Devon and Lincolnshire and groups of smaller counties such as Nottinghamshire, Leicestershire and Derbyshire.

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In addition, where organic manures have been applied, there is also likely to have been movement of soluble organic P to below 15 cm.

8 CollaboratorsRI Bradley NSRI (Project co-ordinator from September 2002)PH Bellamy NSRIPJ Loveland NSRI (Project co-ordinator to September 2002)HIJ Black CEHM Hornung CEHA Scott CEHA Lane CEHJ Webb ADASDR Jackson ADAS

9 References Bradley, R.I. (1977) Trace elements in soils in south Ceredigion. Unpublished M.Sc. thesis, University College

of Wales, Aberystwyth.Clayden, B. and Hollis, J.M. (1984) Criteria for differentiating soil series. Technical Monograph No. 17, Soil

Survey of England and Wales, Harpenden. CEC (2002) Communication from the Commission to the Council, the European Parliament, the Economic and

Social Committee and the Committee of the Regions: Towards a Thematic Strategy for Soil Protection. COM 2002 179 final 16.4.2002. Brussels.

Cruickshank, J.G. (Ed.) (1997) Soil and environment: Northern Ireland. Agricultural and Environmental Science Department, The Queen's University, Belfast.

Jordan, C., Cruickshank, J.G., Higgins, A.J. and Hamill, K.P. (1999) The soil geochemical atlas of Northern Ireland. Department of Agriculture for Northern Ireland.

Haines-Young, R.H., Barr, C.J., Black, H.I.J. and others (2000) Accounting for nature: assessing habitats in the UK countryside. Report to DETR November 2000, pp. 134.

Hallett, S.H., Thanigasalam, P. and Hollis, J.M. (1995) SEISMIC: A Desktop Information System for Assessing the Fate and Behaviour of Pesticides in the Environment. Computers and Electronics in Agriculture, 13, 3, 229-244

Harrod, T.R., McHugh, M., Appleby, P.G., Evans, R., George, D.G., Haworth, E.Y., Hewitt, D., Hornung, M., Housen, G., Leekes, G., Morgan, R.P.C. and Tipping, E. (2000). Research on the Quantification and Causes of Upland Erosion. Final Report to MAFF, Project SP0402.

Huber, S., Syed, B., Freudenschuss, E.V. and Loveland P.J. (2001). Proposal for a European monitoring and assessment framework. European Environment Agency Technical Report 61, EEA, Copenhagen.

Mayr, T., Jarvis, N. and Simota, C. (1999). Pedotransfer Functions for Soil Water Retention Characteristics. Proceeding of the International Workshop Characterisation and Measurement of the Hydraulic Properties of Unsaturated Porous Media, Riverside, CA. 22-24 October 1998.

Proctor, M.E., Siddons, P.A., Jones, R.J.A., Bellamy, P.H. & Keay, C.E. 1998. LandIS - a land information system for the UK. In: Land Information Systems: Developments for planning the sustainable use of land resources. (eds H.J. Heineke et al.), pp. 219-233. European Soil Bureau Research Report No. 4, EUR 17729 (EN), Office for Official Publications of the European Communities, Luxembourg.

Shepherd, M.A. & Withers, P.J. (1999) Applications of poultry litter and triplesuperphosphate fertilizer to a sandy soil: effects on soil phosphorus status and profile distribution. Nutrient Cycling in Agroecosystems 54, 233-242.

Smith, K.A., Chalmers, A.G., Chambers, B.J. & Christie, P. (1998) Organic manure phosphorus accumulation, mobility and management. Soil Use and Management 14, 154-159.

Vanmechelen, L., Groenmans, R. and Van Ranst, E. (1997). Forest soil condition in Europe: results of a large scale soil survey. Technical Report EC UN/ECE. Ministry of the Flemish Community, Brussels. Geneva.

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