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Neap-N Nitrate Leaching for 1970 and 2014 December 2016

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Neap-N Nitrate Leaching for

1970 and 2014

December 2016

Joint Water Evidence Programme

Neap-N Nitrate Leaching for 1970 and 2014

Final report WT1550

Produced: December 2015

Funded by the Joint Water Evidence Programme. The Joint Water Evidence Programme comprises Defra and Environment Agency with partners including Natural England and Forestry Commission.

This is a report of research carried out by ADAS, on behalf of the Department for

Environment, Food and Rural Affairs

Research contractor: ADAS

Authors: David Lee, Richard Gooday, Steven Anthony and Isabel Whiteley

Publishing organisation

Department for Environment, Food and Rural Affairs Nobel House, 17 Smith Square London SW1P 3JR

© Crown copyright 2016

Copyright in the typographical arrangement and design rests with the Crown. This

publication (excluding the logo) may be reproduced free of charge in any format or

medium provided that it is reproduced accurately and not used in a misleading context.

The material must be acknowledged as Crown copyright with the title and source of the

publication specified. The views expressed in this document are not necessarily those of

Defra. Its officers, servants or agents accept no liability whatsoever for any loss or

damage arising from the interpretation or use of the information, or reliance on views

contained herein.

[Type here]

1 Introduction .............................................................................................................................. 1

2 Methodology ............................................................................................................................ 1

2.1 The Neap-N Model ........................................................................................................................1

2.2 Updating of the Neap-N Coefficients.............................................................................................1

2.2.1 Cropping ................................................................................................................................2

2.2.2 Pigs and Poultry .....................................................................................................................2

2.2.3 Grazing Livestock ...................................................................................................................2

2.2.4 Resulting Coefficients ............................................................................................................3

2.3 Generation of Landcover and Livestock dataset ...........................................................................4

3 Results ...................................................................................................................................... 4

3.1 Crop and Livestock Datasets ..........................................................................................................4

3.2 Nitrate Leaching ............................................................................................................................4

3.2.1 1970 .......................................................................................................................................5

3.2.2 2014 .......................................................................................................................................7

4 Validation of 2014 results .......................................................................................................... 9

4.1 Outline ...........................................................................................................................................9

4.2 Preparation of dataset for verification of NEAP-N agricultural nitrate loads ...............................9

4.3 Selection of Harmonised Monitoring Scheme sites for analysis ................................................. 10

4.4 Comparison of PARCOM measured nitrate loads with predicted nitrate loads ......................... 11

4.5 Comparison of HMS flow weighted mean measured nitrate concentrations with predicted flow

weighted mean nitrate concentrations .................................................................................................. 12

4.6 Calibration between HMS 95th percentile nitrate concentration and predicted flow-weighted

mean nitrate concentration ................................................................................................................... 13

4.7 Comparison of EA measured 95th percentile concentrations with predicted 95th percentile

nitrate concentrations ............................................................................................................................ 14

4.8 Prediction of WFD catchment nitrate 95th percentile concentrations ........................................ 16

4.9 Discussion of validation .............................................................................................................. 17

5 Discussion ............................................................................................................................... 19

5.1 Comparison of 1970 and 2014 Results ....................................................................................... 19

5.2 Mitigation Impacts ..................................................................................................................... 21

6 Conclusion .............................................................................................................................. 21

7 References .............................................................................................................................. 22

© ADAS 1

This project has delivered national estimates of long-term climate average nitrate leached (pollutant

load and concentrations) from agricultural land. These estimates have been produced for the years 1970

and 2014, to support the designation of Nitrate Vulnerable Zones by the Environment Agency.

The estimates were achieved through use of the NEAP-N (Lord and Anthony, 2000) nitrate leaching

model. The model was parameterised by preparing national data layers of agricultural land use and

livestock numbers for 1970 and 2014, scaling Neap-N model coefficients to represent long-term changes

in nitrogen fertiliser use and livestock performance and running the model for these inputs. The project

has produced national scale databases of the livestock, landcover and cropping for 1970 and 2014 at

1km2 resolution, alongside a national database of the agricultural N losses for the 1970 and 2014

scenarios.

The NEAPN model (Anthony et al., 1996; Lord and Anthony, 2000; Silgram et al., 2001) was developed

under Defra Water Quality funding as a policy tool to allow estimation of nitrate loss from agricultural

land, applicable to any catchment in England and Wales. In essence NEAPN was devised as an export

coefficient model, with adjustments for climate and soil type. Nitrate loss potential coefficients are

assigned to each crop type and livestock category. The livestock coefficients represent the short and

long-term increase in nitrate leaching risk associated with the keeping of stock and the spreading of

manures. For grassland, nitrate leaching loss is represented mainly through the coefficients for grazing

livestock, on the grounds that due to the wide variation in stocking densities, losses are much more

closely correlated with stock numbers than with area of grassland. NEAPN includes a water balance

model and a leaching algorithm, which calculates the proportion of the potential loss that is actually

leached. The NEAPN model operates at 1 km2 resolution, with the input data being the agricultural

census (for each of the cropping and livestock categories),the dominant soil type (sourced from the

Cranfield University NatMap soils dataset), mean annual rainfall (sourced from the UKCP09 1961-90

baseline climate dataset) and potential evapotranspiration for the different crop types. Output of the

model is total annual nitrate-N loss from the soil profile for agricultural land, and associated water flux.

No calculation of groundwater delays or in-river processes is included. The version of NEAPN used in this

project has been modified to better represent the impacts of atmospheric deposition (Lord et al., 2007)

The original NEAPN crop coefficients were designed to represent nitrate leached under different UK

arable crops fertilised in accordance with DEFRA guidelines, derived from Lord (1992), with revisions

based on the results of subsequent research (Lord et al., 1995; Shepherd and Lord, 1996; Webb et al.,

1997; Lord and Mitchell, 1998, Anthony et al., 1996) and the results of measurements on commercial

farms within Nitrate Vulnerable Zones. The original livestock coefficients were derived from research

under-pinning the N-CYCLE model (Scholefield et al., 1991), and subsequently modified by Lord et al

(2007), primarily to account for the typical timing of manure applications using the MANNER model

(Chambers et al., 1999). The original NEAPN coefficients are thus considered to be representative of

agricultural practice and yields circa 2000, and need to be modified to account for differences in farm

practice and yields in 1970 and 2014. When calculating these revised coefficients, the average value for

© ADAS 2

any data item was taken over a 5 year period centred on the year of interest (and thus only 3 years for

2014) in order to account for annual fluctuations.

The NEAPN coefficients for crops can be considered as the N at risk of leaching over the winter, and are

thus comparable to soil mineral nitrate (SMN) values in the autumn. The NitCat model (Lord, 1992) uses

a nitrogen balance approach to determining this N at risk of leaching. The nitrogen balance in NitCat is a

function of crop yield, crop N content, fertiliser applied, manure applied and background mineralisation.

Nitrogen losses from manure are accounted for as part of the livestock coefficients and so could be

ignored here. Crop N contents were assumed to be constant over time due to insufficient suitable data,

although Garwood et al (1995) show wheat grain N values ranging between 19.4 and 24.5 kg N t-1

between 1969 and 1994, but with no discernible trend1. The N balance was thus calculated using

fertiliser rates and crop yields only – the effective contribution to the N balance from mineralisation was

assumed to be accounted for in a scalar applied to the N balance to make the results of the calculation

for 2000 equal to the original NEAPN coefficient, and this scalar was assumed to be constant for all

years. Fertiliser rates for each crop type were taken from British Survey of Fertiliser Practice. Crop yields

were taken from Defra statistics for Agriculture in the UK2, supplemented by data for the United

Kingdom taken from the production data available from the Food and Agriculture Organisation (FAO) of

the United Nations3.

The excreta (and thus manure) produced by livestock is generally proportional to body weight, so for

pigs and poultry types (other than laying hens), the original NEAPN coefficients were simply scaled by

changes in body weight. The formula used for the scaling factor, F, was

75.0/ dCF

where C is carcass weight, d is the dressed proportion and the factor of 0.75 converts to metabolic live

weight. Dressed proportions were 0.7 for pigs and 0.75 for poultry. Scaling factors were derived for

1970, 2000 and 2014, and the changes to the coefficients derived for 1970 and 2014 from the ratio of

their scaling factors to the 2000 value. To account for potential annual fluctuations in carcass weights,

the average value over a 5 year period centred on the year of interest (and thus only 3 years for 2014)

was used. Carcass weights were taken from Defra statistics.

For laying hens, changes to the coefficients were based upon egg production per bird taken from Defra

statistics.

The NEAPN coefficients for grazing livestock represent the losses associated with excreta and manure,

and also losses due to the fertiliser used to grow the grass they consume. Therefore it was felt that

changes to the coefficients needed to account for both the changes in livestock yields over time and also

the intensity of production (i.e. the additional fertiliser required to maintain 2 cows on 1 ha of grassland

would result in more than twice the leaching than if that 1 ha of grassland supported only 1 cow of the

same size).

1 Garwood et al (1995) was calculating nutrient balances for England and Wales between 1969 and 1994 and used constant N contents for all crop types except wheat and barley. 2 https://www.gov.uk/government/statistical-data-sets/agriculture-in-the-united-kingdom 3 http://faostat3.fao.org/download/Q/QC/E

© ADAS 3

The first stage in this calculation was to determine the number of livestock units (LSU) for all grazing

livestock types. MAFF (1980) states the milk output of a one LSU dairy cow, and how the LSU changes as

milk production changes, which allowed for the calculation of the total number of LSUs for dairy cows

for 1970, 2000 and 2014 using national livestock totals and milk production figures. It also states the LSU

equivalents for a range of animals at different weights, which allowed for a relationship to be derived

between livestock weight and LSUs. Using this relationship, national livestock totals and livestock

weights derived from Defra statistics, it was possible to determine the total LSU for other categories of

grazing livestock. One LSU is defined as requiring 48,000 MJ of metabolisable energy (MAFF, 1980), so it

was possible to determine the total energy required by all grazing livestock. Using national average

fertiliser rates to grassland for the appropriate years (taken from the British Survey of Fertiliser Practice)

and the total area of grassland, it was possible to set up the NCycle model (Scholefield et al, 1991) so

that it produced the required dry matter of grass to provide the necessary amount of metabolisable

energy per hectare of grassland. For this scenario NCycle predicted a loss of nitrate per hectare. An

effective loss per unit of each livestock for each livestock category was then calculated from the energy

requirement, the energy produced per hectare and the leaching per hectare. The ratios of these

leaching values for each livestock category between 1970, 2014 and 2000 was used to scale the original

NEAPN coefficients, deemed representative of 2000, to produce values appropriate for 1970 and 2014.

Table 2.1 below shows how the coefficients have varied between 1970, 2000 and 2014 for some of the

major livestock and crop categories. For wheat, both fertiliser use and yields were lower in 1970, with

much lower potential N loss coefficients, whereas for potatoes, yields were lower in 1970, but fertiliser

rates were higher than today so the risks of N loss are greater. For non-grazing livestock, bodyweights

and production have increased over time, resulting in higher coefficients. For grazed livestock,

coefficients are highest for 2000 as this is where livestock numbers and fertiliser rates to grassland are

highest, coupled with a smaller grassland area and thus a more intensive level of production.

Table 2.1 - Changes in selected coefficients

Category 1970 2000 2014

Winter Wheat 30 45 46

Potatoes 151 120 111

Adult Dairy Cows 25.5 31.3 28.2

Adult Beef Cows 12.3 14.1 12.7

Sheep 1.7 1.7 1.5

Fattening Pigs (>110 kg) 3.68 4.8 5.21

Laying Hens 0.12 0.13 0.14

Note that there are a number of factors that have not been taken in to account when determining the

new coefficients for 1970 and 2014, either due to data availability or due to the constraints of the

coefficient based approach of the NEAPN model. Such changes include crop N contents, timing of

fertiliser applications, timing and method of manure applications, sowing and harvest dates, grazing

management and crop residue management.

© ADAS 4

The Neap-N model requires livestock and cropping data input at the 1km2 resolution. The 2014 land

cover and livestock dataset was generated using the 2014 June Survey returns provided by Defra and

Welsh Government. The methodology used to generate a 1km2 dataset from the June Survey returns is

detailed in Comber at al. (2008). This methodology has been used in previous projects to generate

appropriate datasets from survey returns from other years.

Comber at al. (2008) provides full methodological detail of the Dasymetric mapping approach, but in

brief:

Non-agricultural areas were identified using a fixed dataset derived from the CEH Land Cover

Map (1990) and supplementary Ordnance Survey Strategi datasets for urban areas, woodland

and linear features.

An iterative pycnopylatactic approach was used to distribute Parish level counts of livestock and

areas of cropping across the agricultural land present on the 1km2 cells in each parish.

Validation was performed to ensure no significant differences between the total areas and

populations for the derived 1km2 scale data and the nationally reported Defra totals.

Crop and Livestock datasets covering England and Wales have been provided for 1970 and 2014. These

spatially disaggregate the livestock and cropping data to a 1km2 scale. The two datasets have different

livestock and cropping categories, reflecting differences in the data collected in the two years. In

addition, reduced detail in the cropping data collection in Wales mean that there are not values in Wales

for all of the categories that are collected in England.

Nitrate Leaching values have been modelled for each 1km2 in England and Wales for 1970 and 2014.

They have been reported as both total losses, and also split into the following components:

Atmospheric deposition to arable land

Spreading of housed animal manure to arable land

General cropping practices, including fertiliser applications, to arable land

Atmospheric nitrogen deposition to grassland

Spreading of housed animal manure to grassland

© ADAS 5

General grassland practices, including fertiliser applications, excreta returns and spreading of

grazed animal manure, to grassland

Atmospheric nitrogen deposition to rough pasture

Excreta deposition to rough pasture

Losses from farm woodland.

Figure 3.1 shows the predicted nitrate leaching from agricultural land for 1970 livestock, cropping and

coefficients. There are peaks in northern East Anglia, the North East above Hull and in some areas of the

Welsh Marches. The lowest values are in the upland areas of Wales and Northern England.

© ADAS 6

Figure 3.1 1970 Neap-N Loads

© ADAS 7

Figure 3.2 shows the predicted nitrate leaching from agricultural land for 2014 livestock, cropping and

coefficients. The pattern appears similar to 1970, with peaks in northern East Anglia, the North East

above Hull and in some areas of the Welsh Marches. The lowest values are again in the upland areas of

Wales and Northern England.

© ADAS 8

Figure 3.2 - 2014 Neap-N Loads

© ADAS 9

The outputs from the NEAP-N (Lord and Anthony, 2000) model (parameterised with 2014 livestock and

cropping data) were combined with modelled nitrate loads from non-agricultural sources to predict

nitrate loads and concentrations in each catchment.

The predicted nitrate loads and concentrations were verified against PARCOM nitrate loads and

concentrations covering the period 2008-2012 (Harmonised Monitoring Scheme (Environment Agency,

2013) and UK Hydrometric Register (Marsh & Hannaford, 2008) data).

The regular monitoring at HMS sites and the large catchment areas for these sites make HMS

monitoring data an excellent independent set of measurements against which to compare predicted

loads and concentrations.

A regression model was calibrated using PARCOM data to convert predicted mean flow-weighted

concentrations to predicted 95th percentile nitrate concentrations. The updated tool was verified using a

larger dataset of measured 95th percentile TIN concentration data covering the period 2004-2009,

provided by the Environment Agency.

The total national modelled agricultural N load for the area of comparison was 240 kt, and average N

losses per ha were generally in the range of 5.4kg/ha to 35.6kg/ha.

The combined framework produced unbiased predictions of nitrate loads and concentrations.

For verification of the NEAP-N predicted agricultural nitrate loads, the following modelled non-

agricultural nitrate loads were used to allow prediction of total nitrate loads in each catchment:

- Modelled nitrate loads from sewage treatment works, diffuse urban sources, septic tanks, storm

tanks, combined sewer overflows, bank erosion as developed in Defra project WQ0223 (Zhang

et al., 2014).

- Modelled nitrate loads from re-charge from historical agricultural nitrate loading to

groundwater, using modelled groundwater nitrate concentrations (Wang et al, in prep).

- Modelled nitrate loads from re-charge from urban nitrate loading to groundwater (Anthony,

2005; Lerner, 2000).

Defra project WQ0223 identified catchments with significant historical loading from agriculture to

groundwater nitrate concentrations (Wang et al, in prep). In catchments which had a modelled

groundwater concentration (n=1413), the proportion of the modern agricultural nitrate load leached to

groundwater (and therefore not contributing to modern nitrate loads in rivers) was modelled as the

Baseflow Index multiplied by the NEAP-N Agricultural N load (in kg) for each WFD waterbody catchment.

In catchments without a modelled groundwater concentration (n=3072), none of the modern

agricultural N load was modelled as being leached to groundwater.

The NEAP-N model outputs as adjusted for leaching to groundwater and N loads from non-agricultural

sources were totalled and accumulated along the flow path to give a cumulative N load (in kg) for each

WFD waterbody catchment.

© ADAS 10

The average annual flow was calculated for each catchment using the NEAP-N model to estimate runoff

from agricultural land and the flow from rainfall direct to open water. Runoff from urban areas was

estimated using the Wallingford Procedure (Mitchell et al., 2001).

The effects of in-river retention of N on the total N load were modelled for each catchment, using

Behrendt & Opitz’s (1999) approach and the calculated average annual flows.

The cumulative N load, adjusted for in-river retention, was divided by the cumulative WFD catchment

area to give a predicted N load (in kg per ha) for each catchment and divided by the cumulative

modelled flow to give a predicted N concentration (in mg per l) for each catchment.

Harmonised Monitoring Scheme (HMS) sites were matched with UK Hydrometric Register gauging

stations. The coordinates of each gauging station from the UK Hydrometric Register were used to assign

the sites to WFD waterbody catchments.

Only sites with an HMS sample count >40 were used in the analysis. HMS site 1010, the River Wyre at St

Michaels, was excluded from the analysis because of a strong seasonal bias in the sampling (>40% of

samples taken in November and December and only 7.4% of samples taken in March to June).

Not all gauging stations are located close to the outflow point of the WFD waterbody catchment that

they fall within. The WFD waterbody catchment area was compared with the gauging station catchment

area from the UK Hydrometric Register and sites where the difference between the two areas exceeded

20% of the gauging station catchment area were excluded from the analysis. Sixty-six out of 88 sites

passed the selection criteria. The difference between the areas was <10% of the gauging station

catchment area for 46 out of the 66 sites.

Table 4.1 - Summary of characteristics of the 66 sites used in the analysis

Characteristic Mean Std. dev Minimum Maximum

Mean annual rainfall (mm) 1225 487 582 3049

Modelled WFD catchment area (km2) 1050 1986 50 9966

Agricultural area as % of modelled area 89.9 7.2 72.4 98.6

© ADAS 11

Figure 4.1- Map showing location of the 66 HMS sites used in the analysis

HMS measured concentrations and flows were combined with long term average flows and catchment

areas for each site from the UK Hydrometric Register to calculate the annual average measured nitrate

loads in kg/ha at each site.

The annual average measured nitrate loads were compared against predicted nitrate loads. Regression

analysis showed that there was no significant intercept term and that predicted loads on average slightly

overestimated measured loads. Certain mitigation methods currently practiced on some farms are not

explicitly represented in the NEAP-N model – for example improved manure management and fertiliser

timing, and use of buffer strips. National modelling using Farmscoper predicts that current practice of

mitigation methods accounts on average for an 8.9% reduction in nitrate loads.

© ADAS 12

Figure 4.2- Graph comparing annual average measured nitrate loads against predicted nitrate loads. Measured load = 0.8834 (±0.0247) x Predicted load, RMSE = 4.226 kg/ha/yr. Red lines show 95% prediction intervals.

HMS measured concentrations and flows were used to calculate an annual flow weighted average

observed concentration for each site. The flow weighted average observed concentrations were

compared against predicted flow weighted mean nitrate concentrations.

Regression analysis showed that there was no significant intercept term and that predicted

concentrations on average were a slight overestimate compared with measured concentrations.

© ADAS 13

Figure 4.3 - Graph comparing flow weighted average measured nitrate concentrations against predicted flow weighted average nitrate concentrations. Observed concentration = 0.8687 (±0.0265) x Predicted concentration,

RMSE = 1.069 mg/l. Red lines show 95% prediction

The 95th percentile of HMS observed nitrate concentrations over the period 2008-2012 was calculated

for each site. A regression analysis calculated the average conversion factor between predicted flow-

weighted mean nitrate concentrations and observed 95th percentile nitrate concentrations as 1.278

(±0.034) x predicted flow-weighted mean nitrate concentrations. The RMSE was 1.38 mg/l.

The largest prediction errors occurred in catchments where the agricultural nitrate load accounted for

less than 80% of the total nitrate load. When the regression analysis is restricted to catchments where

the agricultural nitrate load accounts for more than 80% of the total load, the RMSE drops to 0.76mg/l.

© ADAS 14

Figure 4.4 - Graph comparing HMS 95th percentile nitrate concentrations against predicted flow weighted mean nitrate concentrations. 95th percentile concentration = 1.278 (±0.034) x Mean concentration, RMSE = 1.375 mg/l.

Red lines show 95% prediction intervals.

The conversion factor of 1.27816 x Mean flow-weighted concentration, calculated from the regression

between HMS measurements and predicted mean concentrations, was used to convert predicted mean

flow-weighted concentrations into predicted 95th percentile concentrations.

The predicted 95th percentile concentrations were compared with measured 95th percentile TIN

concentration data covering the period 2008-2012, which were provided by the Environment Agency

(EA) and are independent of the HMS measurements used as a training dataset to calibrate predicted

flow-weighted mean nitrate concentrations with 95th percentile nitrate concentrations.

The EA measurements were filtered to select monitoring points that are within the main river channel,

within 2km of the corresponding WFD catchment outflow, and where the corresponding WFD

catchment outflow is more than 1km downstream of any sewage treatment works within the same WFD

catchment. There is a level of uncertainty, represented by confidence intervals provided with the data,

associated with the EA measured 95th percentile concentrations which are calculated as Weibull

estimates. Only measurements for which the dataset contained both upper and lower confidence

intervals were used. Following these criteria, 301 EA measured 95th percentile concentrations were

selected for use in the comparison with predicted 95th percentile concentrations.

© ADAS 15

Figure 4.5- Map showing the 301 EA monitoring points used in the comparison.

Figure 4.6 - Graph comparing EA measured 95th percentile nitrate concentrations against predicted flow 95th percentile nitrate concentrations. Measured 95th percentile concentration = 0.9272 (±0.0301) x Predicted 95th

percentile concentration + 1.153 (±0.232), R2

© ADAS 16

Figure 4.7 - Map showing predicted WFD catchment nitrate 95th percentile concentrations

© ADAS 17

Figure 4.8 - Map showing probability that nitrate 95th percentile concentration exceeds 11.3 mg/l.

There is a strong linear relationship between predicted nitrate loads and PARCOM measured nitrate

loads, with very few outliers. Similarly, there is a strong linear relationship between predicted mean

flow-weighted concentrations and HMS mean flow-weighted concentrations, with few outliers,

demonstrating that the estimated nitrate loads and concentrations consistently predict the relative

magnitude of observed nitrate loads and concentrations across England and Wales.

Predicted nitrate loads and mean flow-weighted concentrations both on average slightly overestimated

PARCOM measured nitrate loads and HMS mean flow-weighted concentrations. This may be explained

by current mitigation practice not represented in the Neap-N model.

The predicted 95th percentile nitrate concentrations, calibrated from predicted mean flow-weighted

concentrations using HMS measurements as a training dataset, compared well with an independent set

of measured 95th percentile TIN concentrations provided by the Environment Agency, demonstrating the

© ADAS 18

strong predictive ability of the modelled nitrate loads and concentrations across all parts of England and

Wales.

© ADAS 19

Table 5.1 shows the totals and apportionment of N losses for England and Wales for the two time periods and Figure 5.1 maps the changes at the water

management catchment scale.

Table 5.1 - Comparison of totals between 1970 and 2014

1970 Total (kT)

% of Total

2014 Total (kT)

% of Total

% Change

All Losses 288 256 -11.2

All Losses from ANY Contribution from Arable Land 171 60 155 61 -9.3

All Losses - ANY Contribution from Grassland 102 35 86 34 -15.5

All Losses from Grazed Animal N on Rough Pasture 8 3 8 3 -3.4

All Losses from Atmospheric N on Rough Pasture 1 0 1 0 -12.4

All Losses from Atmospheric N on Woodland and Forest 3 1 3 1 -3.1

All Losses from Atmospheric N on Open Water 2 1 2 1 -0.1

[

© ADAS 20

Figure 5.1- Mapped changes in N load between 1970 and 2014 at the water management catchment scale

© ADAS 21

The table shows an 11% decrease in total nitrate leaching from agricultural land between the two dates.

The greatest reduction is seen in the losses from grassland (15.5%), with a lesser value from arable

(9.3%).

The smallest changes are the reductions from atmospheric N on woodland and open water, which are

not impacted by changes in agricultural practice, and there is no change in modelled deposition rates for

the two time periods.

Despite some differences in the level of reduction between the two dates, the overall apportionment

has stayed consistent, with approximately 65% of the losses coming from arable land and 35% from

grassland in both 1970 and 2014.

It is inevitable that there will be a large and varied degree of change between the years at the 1km2

scale as the livestock and cropping reporting locations change. Summarising the 1km2 cell values to the

water management catchment level gives a better overall view of the regional patterns of change

(Figure 5.1) level. The map shows large areas of England and Wales with reductions in nitrate leaching,

and a number of regions where the values have stayed consistent. There are only four catchments

where the nitrate leaching is estimated to have increased over the time period, one of the main drivers

for these changes appears to be pig production. Both the Hull and East Riding WMC (red area on the

East of the map) and the Waver WMC (red catchment in the North West) are now major areas for pig

production, and both have shown a large increase in pigs over the time period (total pigs increased

430% for the Waver, 57% for Hull and East Riding from a much greater starting level). In addition, the

loss coefficients for pigs have also increased over this time period (all have increased by between 14 and

41%). This combination of large increases in pig numbers in these catchments coupled with the increase

in pig loss coefficients (due to typical pigs in 2014 being larger than 1970) has led to increases in nitrate

leaching in these areas.

The Neap-N model can only reflect the impact of mitigation that is itself reflected in a change in

livestock numbers or cropping areas reported in the June survey, or through the fertiliser rates that have

been used to calculate year-specific crop coefficients. Therefore it will reflect mitigation in the form of

changes in stocking densities or cropping types, but will not reflect in field measures such as buffer

strips.

Previous ADAS work using the Farmscoper model (Gooday et al. 2014) has shown that the magnitude of

impact of current mitigation additional to that represented in Neap-N on nitrate leaching is estimated to

be 8.9% (England only), which is within the likely error of the predicted value for any cell, given the

uncertainty in the exact location of landuse, the use of a single soil type and the application of national

coefficients to represent farm practice. The impact of this current mitigation will also vary spatially

depending on measure take up, local farming types and other conditions.

This project has delivered England and Wales livestock and cropping datasets and nitrate leaching losses

for 1970 and 2014, both at 1km2 resolution. Comparison of the two years shows an approximately 11%

reduction in nitrate leaching. Mapping of the change shows that at the water management catchment

scale, only four catchments showed an increase greater than 5%. The 2014 nitrate leaching values have

© ADAS 22

been validated against monitored data and show a strong relationship between predicted loads and

measured PARCOM values.

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Biology 46, 23-32.

Anthony, S. (2005). NVZ Designation and WFD Characteristation for Nitrate: Towards development of a

joint methodology integrating ground and surface water quality. Technical Report to Environment

Agency. ADAS, Wolverhampton, 60pp.

Behrendt, H. & Opitz, D. (1999). Retention of nutrients in river systems: dependence on specific runoff

and hydraulic load. Hydrobiologia 410: 111-122

Chambers, B., Lord, E., Nicholson, F. and Smith K. ( 1999.) Predicting the nitrogen availability and losses

following application of animal manures to arable land. Soil Use and Management 15, 137-143.

Comber A, Anthony A, Proctor C. 2008. The creation of a national agricultural land use dataset:

combining pycnophylactic interpolation with dasymetric mapping techniques. Transactions in GIS 12,

775-791.

Ellis, J.B. (1986). Pollutional aspects of urban runoff. In: Torno, H.C., Marsalek, H.C., and Desbordes, M.

Urban runoff pollution. Springer Verlag, Berlin, Germany.

Environment Agency (2013). Historic UK Water Quality Sampling Harmonised Monitoring Scheme

Detailed Data.

Gooday, RD., Anthony, SG., Durrant, C., Harris, D., Lee, D., Metcalfe, P., Newell-Price, P., Turner, A.

(2014). Farmscoper Extension. Defra Project SCF0104.

Lerner, D. N. (2000). Guidelines for estimating urban loads of nitrogen to groundwater. Defra Project

Report, NT1845, 21pp.

LORD, E.I. (1992) Modelling of nitrate leaching: Nitrate Sensitive Areas. Aspects of Applied Biology 30,

19-28.

Lord, E. I., Anthony, S. G., Miles, A., and Turner, S. (1995) Agricultural nitrogen surplus and diffuse

nitrogen losses to surface waters within England and Wales. Final Report to MAFF RMED for submission

to PARCOM. ADAS, Wolverhampton, 55pp.

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