MOF, Alm.del - 2017-18 - Endeligt svar på spørgsmål 242: Spm. om oversendelse af de andre publikationer, der ud over de fem artikler, der er nævnt i MOF alm. del - spørgsmål 710 m.m., til miljø- og fødevareministeren

Miljø- og Fødevareudvalget 2017-18
MOF Alm.del
Offentligt

Det Jordbrugsvidenskabelige Fakultet

Baggrundsnotat til Vandmiljøplan III - midtvejsevaluering

Reestimation and further development in the

model N-LES, N-LES

to N-LES

Kristian Kristensen

Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus

University

Jesper Waagepetersen

Christen Duus Børgesen

Finn Pilgaard Vinther

Department of Agroecology and Environmnet, Faculty of Agricultural Sciences,

Aarhus University

Ruth Grant

Gitte Blicher-Mathiesen

National Environmental Research Institute, Aarhus University

December 2008

MOF, Alm.del - 2017-18 - Endeligt svar på spørgsmål 242: Spm. om oversendelse af de andre publikationer, der ud over de fem artikler, der er nævnt i MOF alm. del - spørgsmål 710 m.m., til miljø- og fødevareministeren

Index

1. Introduction ..................................................................................................... 3

2. Data................................................................................................................... 3

3. Choice of model type and explanatory parameters ..................................... 4

4. Results: N-LES

model.................................................................................... 6

4.1 Parameter estimates.......................................................................................................... 6

4.2 Random effects and coefficient of determination .......................................................... 10

5. Model validation ............................................................................................ 11

6. References ...................................................................................................... 22

Appendix 1 – data used and shown in figure 2............................................... 23

Appendix 2 – data shown in figure 4 ............................................................... 24

Appendix 3 – data shown in figure 5 ............................................................... 24

Appendix 4 – data used and shown in figure 6............................................... 25

1. Introduction

This model N-LES

is the fourth version of an empirical model for prediction of nitrogen

leaching from arable lands. The first version was published by Simmelsgaard et al. (2000) and

the previous version (N-LES

) was described by Kristensen et al. (2003). The model predicts

the leaching based on nitrogen applications and crops in the year of leaching, the crops in the

previous year, the average nitrogen applications through the last five years and information on

soil type and drainage during the last two years. The model is developed in cooperation

between The Faculty of Agricultural Sciences (DJF) and National Environmental Research

Institute (NERI), both part of the University of Aarhus – and based on data collected by both

these institutions. This report describes the results of this latest version of N-LES together

with information on how it deviates from previous versions. In addition, the report describes

some of the effects and gives a preliminary evaluation of the model.

2. Data

This model version uses the same data sources as previous versions, i.e. data from the

agricultural catchment monitoring programme – the LOOP programme - (collected by NERI),

data from series of drainage water measurements collected by DJF and data from field

experiments carried out by DJF. However, previous sources have been updated with more

recent data when possible. Data from monitoring catchment 5 (LOOP5) together with one

observation from catchment 1 (LOOP1) and one from catchment 3 (LOOP3) have been

excluded because these sites are atypical for Danish agriculture and are no longer part of the

monitoring programme.

The more recent data show leaching levels and ranges that are generally somewhat lower than

those previously used. Average leaching and standard deviation of leaching used in N-LES

were thus, respectively, 74 and 62 kg N ha

-1

, those in N-LES

were 64 and 57 kg N ha

-1

respectively, while they in the present version were 52 and 45 kg N ha

-1

, respectively. The

range of leaching in the data used in the present version has become smaller because of some

very extreme observations in the excluded data, so the smallest and largest leaching levels

recorded are now 0 and 341 kg N ha

-1

compared to 0 and 446 kg N ha

-1

, respectively, in the

data used for N-LES

. In total, 1467 observations were used for estimation of the parameters,

while in the N-LES

, N-LES

and N-LES

models the numbers of observations were 598, 596

and 1299, respectively. The number of observations together with the year span and the level

of leaching for each source of data can be found in Table 5.

Calculation of drainage and thus leaching were done using rainfall corrections and

evaporation with the Makkink formula and crop coefficients (K

) that may exceed the value 1

(Plauborg et al., 2002). The monthly drainage was estimated using the model DAISY

(Abrahamsen and Hansen, 2000) and the yearly leaching, defined as from 1April in the year

of harvesting the summer crop to 31 March the following year, was then calculated.

The amount of nitrogen (N) fixed by a main crop, and any catch crop, has for the NERI data

been calculated using the Danish farm planning program “Bedriftsløsningen” from the Danish

Agricultural Advisory Service (Hvid, 2004), while nitrogen fixation for data from DJF has

been calculated according to Høgh-Jensen et al. (2004). The latter requires information on the

dry matter content of harvested legumes, which is usually included in the experiments at DJF,

but not in the NERI data from the monitoring programme. A comparison of the two methods

(Vinther, unpublished) shows that at typical clover levels (about 20 %), the same fixation

level was obtained with the two methods, while at high or low levels of clover content the

best estimate was achieved using the method of Høgh-Jensen et al. (2004).

3. Choice of model type and explanatory parameters

The basic structure in the model is the same as in previous versions (Simmelsgaard et al.,

2000 and Kristensen et al., 2003). This means that the model comprises both additive and

multiplicative parameters. The model has, however, been modified in a number of areas on

the basis of new knowledge and discussions with users of the model. The most important

modifications are:

•

The effect of crop is now included as an additive effect. The previous crop is included as

an additive effect in order to describe their effect on variation in residual N levels in the

soil. Both the crop and previous crop are now subdivided into a summer crop (main crop)

and a winter crop (sub crop). The winter crop can be the same as the summer crop and/or

the crop of the following year – or it can be an under sown grass for fodder or a catch

crop used to reduce leaching. The grouping of crops has been changed.

In the modelling process the difference between commercial farms and experimental

stations is now included as an additive effect.

The effect of organic matter in the soil is now included both as an additive effect and a

multiplicative effect. The additive effect describes the nitrogen leaching from the organic

matter taking into account the relation between N and C and how this influences the

degradation and thus release of N (Thomsen et al., 2008). The multiplicative effect

describes the extent to which organic matter retains mineral nitrogen and water in the soil

and thus reduces leaching.

•

The effect of nitrogen removed in harvested crops is no longer included in the model as

the effect was very low. This effect was only included in some of the previous models.

A technology effect, which describes any significant development in leaching over time,

is included. This effect describes the changes over time that are not included in the model,

such as changes to crop rotations, soil tillage, use of pesticides and varieties, etc.

All additive effects that directly or indirectly describe added or removed N are now

summed before they are raised to a power. This makes the effect of a specific source of N

dependent on how much is added/removed by the other sources. For example, the effect

of 1 kg spring-applied plant-available N will be larger if the N level is 200 kg N than if it

is 150 kg N. Similarly, the effect will depend on crop, previous crop, and other nitrogen

sources. If the sum of the additive effects describing added or removed N becomes

negative, it is set at a very small value and some of it is additionally transferred to the part

of the additive effects that are not raised to a power. This is done in order to ensure a

slope on the N response curve also at low N levels.

In this version of N-LES the drainage has been made up on a monthly basis instead of on

a yearly basis as used in previous versions. Effect of yearly leaching is now calculated for

the period from 1 April to 31 March and the drainage for all fields is now calculated using

the model DAISY. In previous versions the model EVACROP (Olsen and Heidmann,

1990) was used for some of the data. The Daisy model yielded lower drainage values,

hence the calculated N leaching values were lower than previously.

An effect of previous year’s drainage is included in order to incorporate the leachable N

that may remain in the soil from previous years.

The effect of both the drainage in the year of leaching and in previous year has now been

subdivided into three periods: A summer period (April-August), an autumn period

(September-December) and a winter period (January-March) in order to take into account

the differences in importance of drainage in the different periods.

The N level is calculated as the average amount of N added in the five years previous to

the actual year of leaching. When information about the five previous years is not

available, average N values from the first five years with recordings have been used.

The newest methods for calculating water drainage and N fixation have been adopted

(Plauborg et al., 2002; Høgh-Jensen et al, 2004).

•

In connection with the setup of the model, several explanatory variables were investigated but

not included. These were the effects of spring and autumn-applied organic N in animal

manure as separate parameters (they are included in the N level), but the effects were non-

interpretable and thus not retained in the model. The effect of farm type (crop, cattle and pigs

in combination with organic farming) was investigated as additive effects, but the effects were

relatively small and thus not retained. The effect of temperatures, in three-four separate

periods, was investigated as additive effects, but the effects were not so clear and seemed to

be correlated with each other and other effects already included in the model.

4. Results: N-LES

model

For given values of the explanatory variables the prediction model can be written as:

{

}

Where

is the predicted leaching

is the sum of direct and indirect N effects

is an additive effect of a temporal development (technology effect) and a mathematical

expression that ensures a slope to the N response curve also at low N levels

the positive values of T that are raised to an exponent

is a constant to ensure same mean of leaching for predicted and observed values,

ˆ ˆ

hvis

≥

⎧

år

−

)

⎪

ˆ ˆ

⎨

år

−

)

hvis

⎪

⎩

min 0

⎧

=⎨

⎩

0 (0.001) if

≤

level

(

sping

fix

)

excretion

autumn

total

−

exp(

−

)]exp(

−

) exp(

−

) exp(

−

)

and

are the drainage in the months April-August, September-December, January-March

in the actual year.

and

are the drainage in same months of the previous year

other parameters are defined in tables 1-3.

summer crop

winter crop

previous summer crop

previous winter crop

experimantal station

4.1 Parameter estimates

Tables 1, 2 and 3 show the parameter estimates for the model together with an approximate

standard error. With the chosen parameter estimates, the level of leaching,

can be predicted

for given values of the explanatory variables using the equation above.

Table 1 shows the additive effects that comprise the effect of added N,

N level, N-spring, N-

autumn, N-excretion

and

N-C in soil.

Table 1. Parameter estimates and standard errors for additive non-classification

variables.

Approximate

Parameter Description

Estimate

StdErr

Power

1.50

0.10

Intercept

175

Technology effect

2878

Technology effect

1968

Intercept in T

N-level

0.115

0.026

N-spring and N-fixation

0.094

0.023

N-excretion

0.103

0.052

N-autumn

Sandy soil

0.374

0.176

N-autumn

Clay soil

0.167

0.071

N-C in soil

0.728

0.160

Effect of negative T

0.5

Intercept:

can be interpreted as the leaching of N from a hypothetical cereal crop field

followed by bare soil with a cereal followed by bare soil as previous crop and with a clay and

organic matter content of 0%, where no N has been added and where drainage has been,

respectively, infinitely large and zero in the current and previous leaching years.

Technology effect:

The variable is the specific year when the leaching year starts. Year 2001

is, for example, the leaching year that starts in April 2001 and finishes March 2002. The

technology effect explains the changes (which cannot be explained by the input variables) in

leaching that took place over the years. This could be the effect of new crop varieties, changes

in soil management or crop protection. Changes in temperature and increasing CO

levels can,

however, also have an effect. The technology effect is largest in the 1970s and reduces over

time. As the technology effect cannot in any sensible way be extrapolated to coming years, it

is recommended that in the future application of the model the year 2004 is used – the last

year of the data material.

N-level:

Has been calculated as the average addition of N (measured in kg ha

-1

year

-1

) in the

five years of leaching prior to the actual year of leaching (where no information on the five

previous years is available, average N levels from the first five years of data are used). Added

N comprises the sum of total-N in artificial fertilizer, animal manure, deposition from animals

on grass and biological N fixation.

N-spring:

Is the amount of added mineral N in artificial fertilizers and animal manure

(measured in kg ha

-1

year

-1

) in the period between 15

February and 1

September.

N-fixation:

Is the amount of fixed N (measured in kg ha

-1

year

-1

) by crops grown in the year of

leaching. For fields without legumes a fixation of 2 kg ha

-1

year

-1

is assumed.

N-excretion:

Is the total amount of N (measured in kg ha

-1

year

-1

) deposited on the field

during grazing.

N-autumn:

Is the autumn-applied artificial fertilizer and winter-applied (1.9 – 15.2)

ammonium-N (measured in kg ha

-1

year

-1

) in animal manure on either sandy soil (Jb 1-4) or

clay soil (Jb 5-8).

N-C in soil:

Is the effect of the amount of N in soil organic matter (based on tonne C ha

-1

and

the C/N relation in the top 0-25 cm). The parameter

describes the effect after the total

amount of C has been corrected by a factor that depends on the relation between C and N

(Thomsen et al., 2008). The factor varies between 0.35 and 1.00 for the soils used here and is

given by the expression

min(56.2

−

1.69

,1.0) , where CN is the relation between C

and N in the top 0-25 cm.

Table 2 shows the additive effects that comprise the effect of crop, previous crop and

cultivation on an experimental station when compared to a commercial farm.

Table 2. Parameter estimates and standard errors for additive classification variables.

Parameter Description

Estimate Approximate

StdErr

Summer crop

Grass

+ Peas + Cereal/clover

Beets + Potatoes

Cereal + Grass for seed production +

Legume/spring cereal

Rape

Maize

18.6

-29.3

23.2

28.4

-100.6

-43.6

-11.5

-17.7

5.0

-51.6

-9.1

-15.9

-24.9

6.2

6.7

14.4

15.0

16.5

7.8

4.6

3.2

18.5

3.2

9.3

6.7

Winter crop

No crop (bare soil)

Grass for seed productions + Grass

Undersown grass + Winter rape + Autumn-sown

catch crop

Autumn-sown cereal

Previous summer crop

Grass for seed productions + Beets + Potatoes +

Peas + Maize +Legume/spring cereal

Grass

+ Rape + Fallow

Cereal + Cereal/clover

Previous winter crop

No crop (bare soil)

Grass for seed productions

Grass

+ Under sown grass + Autumn-sown cereal

Winter rape + Other autumn-sown crop

Location of observation

Experimental station

Includes pure grass as well as grass-clover mixtures

Summer crop:

The effect of a crop group is estimated as being either larger or smaller than

group 3 (

Cereal + Grass for seed production + Legume/spring cereal

). Group 5 had the largest

indirect N effect of 28.4. Crops in group 5 therefore – all other things being equal – produced

the largest level of leaching. The lowest leaching levels were obtained with crops in group 2.

Winter crop:

The effect of a winter crop group is estimated as being either larger or smaller

than group 1 (

No crop, i.e. bare soil

). All winter crops had a negative value and they thus

produced less leaching than bare soil – all other things being equal. The lowest leaching levels

are achieved when the soil was covered with a grass crop (group 2).

Previous summer crop:

The effect of a previous crop group is estimated as being either larger

or smaller than group 3 (

Cereal + Cereal/clover

Previous winter crop:

The effect of a previous winter crop group is estimated as being either

larger or smaller than group 0 (

No crop, i.e. bare soil

). All winter crops reduced leaching in the

following leaching year when compared to bare soil and with all other things being equal.

Experimental station

: The effect of the field being located on an experimental station is

estimated as being either larger or smaller than a field on a commercial farm. The estimate

shows that – all other things being equal – the leaching from a field on an experimental

station was smaller than for a similar field on a commercial farm.

Table 3 shows the parameter estimates of multiplicative variables that comprise the effect of

drainage and soil type and the correction factor.

Table 3. Parameter estimates and standard errors of multiplicative variables.

Approximate

Parameter Description

Estimate

StdErr

Drainage in year of leaching April-

0.000382

0.000112

December

Drainage in year of leaching January-

0.000659

0.000201

March

Drainage in previous year April-

0.000549

0.000390

August

Drainage in previous year

0.000424

0.000118

September-March

Amount of Humus, %

0.1866

0.0237

Amount of clay, %

0.0494

0.0064

Correction factor

1.256

Drainage in the leaching year:

The drainage is calculated using the exponential function.

Drainage has a very strong effect on leaching. At very large drainage events (where leaching

is not limited), a multiplication factor of 1 is used. If the drainage is 0 the multiplication factor

is 0 and for all other drainages the factor varies between 0 and 1. A given drainage event had

a larger effect in the winter period (January-March) than in the rest of the year.

Drainage in previous leaching year:

The drainage is calculated using the exponential

function. It has a less important effect on leaching than drainage in the leaching year. At low

drainage events (where leaching the previous year was virtually non-existent) the factor is

close to 1 and becomes smaller with increasing levels of drainage in the previous year.

Soil type:

Is characterised by percentage of organic matter and clay in the topsoil. Leaching

reduces with increasing soil organic matter content and clay content. An increase in soil

organic matter content from 2% to 4% thus reduces leaching by approx. 31%. An increase in

soil clay content from 6% to 10% correspondingly reduces leaching by approx. 18%.

Correction for skewness:

In order to obtain the same average for the predicted leaching as for

the measured values, all values are multiplied by the factor 1.256. The correction factor was

calculated after all other effects were estimated.

4.2 Random effects and coefficient of determination

Table 4 shows the contribution to the variability of ln(Y) for each of the random effects, the

coefficient of determination (R

) and the standard deviation.

Table 4. Number of observations, variance components, coefficient of determination and

standard deviation on differences between observed and predicted values.

Parameter Description

Estimate

Number of observation

1467

Location

0.0419

Year

0.0221

Residual, DJF

0.2593

Residual, DMU without grassing animals

0.3965

Residual, DMU with grassing animals

0.6350

Coefficient of determination

0.526

Standard deviation

33.3

Std

Field on farmlands or on experimental station

Based on sum of squares for predicted and observed leaching

Based on differences between observed and predicted leaching,

kg N ha

-1

year

-1

Variance:

The residual variance on ln(Y) corresponds to a coefficient of variation on leaching

of approx. 50-70%. If random effects are included, the coefficient of variation is 60-90%.

Residual variance includes several components. The most important are: 1) uncertainty

relating to concentrations in water in suction cups due to, e.g., soil variation, variation in the

application of fertilizer, etc., 2) uncertainty in the explanatory variables such as applied

fertilizer, nitrogen excretion, etc, and 3) the inability of the model to explain all the details.

The coefficient of variation therefore depends on the observation type, as many relations must

necessarily be more uncertain when the explanatory variables are based on interviews rather

than on experimental data, likewise the uncertainty relating to application of fertilizer will be

larger when some of this originates from grazing animals. The random variation of location

and year describes the additional variation from location to location (field to field or treatment

to treatment) caused by e.g. soil variation not explained by percentage organic matter and clay

and the farmers’ choice of management. In this version, this component of variation was

clearly reduced when compared to the previous versions, which is most probably mainly

caused by the exclusion of some fields (more extreme ones). The random variation of year

describes the additional variation from year to year caused by e.g. climate differences from

year to year.

Coefficient of determination

): About 50% of the variation in the observed leaching was

explained by the model. The value is about the same as in the previous model, but somewhat

lower than in N-LES

and N-LES

, which is due, among other things, to the number of

observations increasing steeply from about 600 to about 1300-1500 and the new observations

being less extreme than earlier data.

Standard deviation

(Std): describes the average deviation between recorded and predicted

leaching. The value of 33.3 corresponds to approx. 60% of the average leaching in the data.

This may seem to be a rather high value. However, even with the best models, the standard

deviation cannot be less than the standard deviation of recorded leaching. It is associated with

a large level of uncertainty and in some experiments with measurements in replicated plots in

the same field it has been found to vary between approx. 1-200% with an average value of

around 40%. On this background the model is judged to give a good description of the

utilized data.

5. Model validation

The model has not been validated using independent data or any kind of cross-validation. The

previous version was validated using cross-validation (Larsen and Kristensen, 2007) and

similar methods may be used to validate this model. The following shows a number of tables

and figures that can be used for a preliminary validation of the model.

Table 5 shows the measured and predicted leaching for each locality and/or experiment. A

good agreement between recorded and modelled leaching can be seen. However, in a few

series predicted leaching deviated considerably from measured leaching. This could be due to

the effect of variables that the model does not take account of. Some experiments may, for

example, have taken place in a period with cold autumns and winters, which is expected to

reduce mineralization in the leaching period.

Table 5. Observed and predicted leaching for each locality or experiment. Only fixed

effects have been used to predict the leaching (i.e. without including the random effect of

the individual field or year).

Locality/experiment

Years

No.

Observed

Predicted

obs.

kg N/ha

Avg.

Min

Max

Avg.

Min

Max

Loop nr 1

Loop nr 2

Loop nr 3

Loop nr 4

Loop nr 6

Drainage water

Supplementary square grid

Increasing N

Ploughing of grass-clover

Catch crop, tillage and N

Long-term catch crop

Organic matter input

Low input rotations

Fodder crop rotation

Organic cereal rotations

Organic fodder crop

rotation

Residual effect of grassland

Drainage water, continued

Slurry and catch crop

Low input fodder crops

All

1991-2004

1973-1996

1989-1993

1974-1990

1990-1994

1988-1991

1994-1996

1990-1992

1998-2004

1995-2001

1998-1999

1998-2004

1988-1989

1998-2000

1972-2004

106

103

294

168

1467

101

109

284

341

127

334

240

192

169

108

160

117

129

121

248

144

140

232

155

341

109

183

204

123

231

165

145

113

112

108

131

121

123

111

120

231

Table 6 likewise shows measured and estimated leaching for 20 groups of crop. Large

deviations were found for fields with first year and older grass, with winter rape/winter cereal,

with maize and especially with other crops, but here there were only two observations and

both were atypical agricultural crops (winter rape with under sown grass and summer rape

followed by bare soil). For the remaining crops the averages of observed and predicted

leaching seemed to be reasonably close. The maximum predicted values were in most cases

smaller than the observed maximum and the minimum predicted values were in many cases

larger than the observed minimum. This is expected because the model aims at finding the

best predicted value for a given field.

Table 6. Observed and predicted leaching for each combination of summer/winter crop.

Only systematic effects have been used to predict the leaching (i.e. excluding the random

effect of the individual field or year).

Crop

No.

Observed kg N/ha

Predicted kg N/ha

summer/winter

obs.

Avg.

Min

Max

Avg.

Min

Max

Grass for seed production

First year grass/grass

248

113

Older grass/grass

319

121

Grass/winter cereal

341

204

Winter cereal/under sown grass

Spring cereal/under sown grass

341

221

184

Fodder beets/bare soil

184

208

Sugar beets/bare soil

106

109

Potatoes/bare soil

Cereal/winter cereal

141

174

137

Cereal/other crop

127

Cereal/bare soil

378

240

172

Spring rape/winter cereal

171

143

Peas/winter cereal, other crop

201

182

Winter rape/winter cereal

162

165

Maize/winter cereal

107

Maize/bare soil

114

334

120

231

Cereal-mixture/under sown grass

185

Cereal-mixture/bare soil

235

111

Other crops

143

109

178

All

1467

341

231

Winter rape with under sown grass and spring rape with bare soil.

Figure 1 shows the observed leaching plotted against predicted leaching. The plot shows that

the variation between predicted and observed leaching was much smaller when the leaching

was small than when the leaching was large.

400

300

Observed leaching

200

100

200

300

400

Predicted leaching

Figure 1. Plot of observed leaching against predicted leaching.

Figure 2 shows the effect of N for spring cereal followed by bare soil and cereal followed by

winter cereal. The calculation assumed that N level was identical to

N spring + N fixed,

that

there were no other N allocations to the crop and that the previous crop was cereal flowed by

bare soil. The leaching depends on soil type and precipitation and the two figures show two

extremes – a coarse sandy soil in a region with high precipitation and a sandy loam soil in a

region with low precipitation. The figure shows the increasing marginal effect of applied

nitrogen as the level of nitrogen increased and that the leaching – for the same crop –

depended very much on where it was grown. The marginal effect seemed to be lower than

expected from experimental data (Simmelsgaard and Djurhuus, 1998). The average slope was

about 0.30 and 0.15 for spring cereal followed by bare soil in the figure to the left and to the

right, respectively.

150

Predicted leaching, kg N/ha

100

150

200

Applied Nitrogen in spring, kg N/ha

150

Predicted leaching, kg N/ha

100

150

200

Applied Nitrogen in spring, kg N/ha

Figure 2. Predicted nitrogen leaching for different levels of spring-applied fertilizer for

spring cereal. In each plot with (full line) and without (dashed line) autumn-sown winter

crop. Left: on sandy soil (Jb 1) and high precipitation (Jyndevad). Right: Clay soil (Jb 6)

and low precipitation (Roskilde). Data are shown in appendix 1.

Similarly rather low values for responses to nitrogen application were found for other crops.

In order to investigate whether this could be caused by defects in the model, four different

sets of data were selected. In each set the crop, previous crop and soil conditions were made

as homogeneous as possible. For each of the sets the leaching was plotted against the level of

nitrogen application to the soil, a simple regression analysis was performed and the regression

line superimposed on the plot (Figure 3). The figures seemed to show an accordance between

the low predicted leaching for increasing nitrogen and the data from which they were

estimated.

Spring sown cereal after spring sown cereal on Jb 1

Y=28+0.29x

300

Autumn sown cereal after autumn sown cereal on Jb 6, 7 or 8

Y=18+0.13x

150

250

Leaching, kg/year

150

100

Leaching, kg/year

100

150

200

250

300

200

100

150

200

250

300

350

Average level of nitrogen, kg/year

Grass after grass on Jb 1

350

Y=-60+0.41x

Maize after maize or beets

250

Y=-36+0.19x

300

200

250

Leaching, kg/year

200

Leaching, kg/year

200

250

300

350

400

450

500

550

150

100

150

100

150

200

250

300

350

400

Average level of nitrogen, kg/year

Figure 3. Plot of leaching against average nitrogen level (over five years) for four

different crops with the best linear regression line.

It was questioned whether the low response to nitrogen application could be due to a partial

confounding with the technology effect because of the decreasing nitrogen application over

time from about 1975 to 2005. This was examined by estimating the nitrogen response in a

model where the technology effect was excluded. This did not change the response to

increasing nitrogen application (Table 7), although the absolute amount of leaching was

predicted to be higher (in 2005) when the decline over years was not taken into account.

Table 7. Change in nitrogen leaching in models with and without the technology effect

incorporated for selected examples.

Leaching with N application in model

with tech.

without tech.

Summer

Winter

Precipitation

JB-

effect

crop

level

no 100* 200* 300* 100* 200* 300*

Grass

none

Jyndevad

123 157 194 133 165 201

mixture

Grass

Jyndevad

mixture

Winter

none

Jyndevad

117 151

125 157

barley

Winter

none

Roskilde

barley

Winter

Jyndevad

101 132

108 136

barley

Winter

Roskilde

barley

Maize

none

Jyndevad

117 151 188 128 161 196

Maize

* kg N/ha

none

Roskilde

The technology effect was almost the same in N-LES

as in N-LES

(Figure 4). The shifting

position of the lines is caused by the smaller response to nitrogen applications in N-LES

than

in N-LES

Predicted leached, kg N/ha

1980

1990

2000

2010

1970

1980

1990

2000

2010

Year

Predicted leached, kg N/ha

1970

1980

1990

2000

2010

Year

Figure 4. Nitrogen leaching for a cereal crops as a function of year predicted with N-

LES

(blue solid line) and N-LES

(green dashed line). Top left: Spring cereal with 50 kg

applied N. Top right: Spring cereal with 100 kg applied N. Bottom: Spring cereal with

150 kg applied N. The values are predicted means of all combinations of soil type and

precipitation. Data are shown in appendix 2.

Comparisons with N-LES

The results of the new N-LES

were compared with the previous version (N-LES

) by

predicting the leaching by both models:

The average yearly leaching was calculated for five LOOP-areas and compared

The leaching in three standard crop rotations were calculated and compared

The LOOP programme is part of the National Monitoring Programme for the Aquatic

Environment initiated in 1990. The LOOP areas consist of five small agricultural catchments

(5-15 km

) varying in soil type, rainfall, and livestock density. The monitoring consists of

yearly interviews with farmers regarding farming practices (crops, fertiliser, yields,

cultivation, and livestock) and intensive measurement of soil water and groundwater at 5-8

selected sites of each catchment as well as measurement of stream water.

The comparisons for the LOOP areas showed a clear trend of N-LES

predicting less leaching

than N-LES

in the years until about 1997, whereas in the following years the predicted

leaching values were almost identical (Figure 5). The discrepancies in the period 1990-97 are

due to high levels of N applications and a lower N response in NLES

than in NLES

Comparisons between two crop rotations, a maize rotation and a grass-clover rotation, are

shown in Figure 6, and for continuous spring barley in Table 8. No catch crops were included

and the spring whole-seed crop was under sown with grass-clover. Liquid cattle manure

equivalent to 1.5 animal units per ha was applied to all rotations and supplemented with

inorganic N according to the Danish N-norms. N removed by crops was calculated from norm

yields and standard N contents. The comparisons were made for a coarse sandy soil (JB1) and

a sandy loam soil (JB6) in combination with high and low precipitation, corresponding to a

runoff at 1 m depth of 549-575 and 251-279 mm, respectively, depending on crops.

Generally, calculations with N-LES

resulted in higher leaching than when calculated with N-

LES

, and the difference was most pronounced with high precipitation. The largest difference

between N-LES

and N_LES

, corresponding to 33 kg N/ha, was found for continuous barley

on a sandy loam soil and at high precipitation (Table 8).

LOOP1 Storstrøm

1 5

LOOP2 Nordjylland

1 0

1 5

1 0

1 9 1 9 1 9 1 9 1 9 2 0 2 0 2 0 2 06

9 0 9 2 9 4 9 6 9 8 0 0 0 2 0 4 0

H st å

90 1 2 19

94 1 6 19

98 20 0 2 2 20

04 2

006

H st å

LOOP3 Vejle

LOOP4 Fyn

175

150

125

100

90 1 2 19

94 1

996 19

98 2 0 20

02 2 4 20

H st år

1990 1992 1994 1996 1998 2000 2002 2004 2006

H st år

LOOP6 Sønderjylland

1 0 19

92 19

94 19

96 19

98 20

00 2

002 2

004 2 6

H st å

Figure 5. Predicted yearly leaching for each of five loops. Blue squares and lines are for

N-LES

while red dots and lines are for N-LES

. Data are shown in appendix 3.

180

150

120

150

120

150

120

150

120

150

120

Spring barley

Grass-clover

Maize

Average

Whole-seed

Average

Precipitation: Low

Soiltype: Sandy loam (JB6)

Precipitation: Low

Soil type: Coarse sand (JB1)

Precipitation: High

Soiltype: Sandy loam (JB6)

Precipitation: High

Soil type: Coarse sand (JB1)

Figure 6. Leaching (kg N/ha) from a maize and a grass-clover rotation at four

combinations of precipitation and soil type. Results of N-LES

calculations are shown

with grey bars and N-LES

with white bars. Data are shown in appendix 4.

Table 8. Leaching (kg N/ha) calculated with N-LES

and N-LES

from continuous

barley at four combinations of precipitation and soil type.

High precipitation

Low precipitation

Coarse sand

Sandy loam

Coarse sand

Sandy loam

N-LES

113

101

N-LES

Leaching (kg N/ha)

6. References

Abrahamsen, P. and Hansen S (2000). Daisy: an open soil-crop-atmosphere system model.

Environmental Modelling and Software. 15, 313-330

Høgh-Jensen, H., Loges, R., Jørgensen, F. V., Jensen, E. S. & Vinther, F. P. (2004) An

empirical model for quantification of symbiotic nitrogen fixation in grass-clover mixtures.

Agricultural Systems 82, 181-194.

Hvid, S. K. (2004) Beregning af kvælstoffiksering, Planteavlsorientering Nr. 07-497,

Landbrugets Rådgivningscenter.

Kristensen, K., Jørgensen,U.,Grant, R.. 2003. Genberegning af modellen N-LES

12 pp.

Baggrundsnotat til Grant, R. og Waagepetersen, J. 2003. Vandmiljøplan II - slutevaluering.

Danmarks Miljøundersøgelser, Miljøministeriet. ISBN:87-7772-776-2”. Available at

http://nywww.agrsci.dk/var/agrsci/storage/original/application/phpE1.tmp.pdf and at

http://www.dmu.dk/1_viden/2_publikationer/3_ovrige/rapporter/VMPII/Genberegning_af_m

odellen_NLES.pdf. An English translation with the title “Recalculation of the model N-LES

”

is available at: http://130.226.173.223/farmn/dokumentation/Nles3%20english.doc.

Larsen, S., Kristensen, K. 2007. Udvaskningsmodellen N-LES

– usikkerhed og validering.

DJF rapport, Markbrug. 132.

Plauborg, F., Refsgaard, J.C., Henriksen, H.J., Blicher-Mathiesen, G., Kern-Hansen, C. 2002.

Vandbalance på mark- og oplandsskala. DJF rapport, Markbrug. 70, 45 pp.

Simmelsgaard, S. E. and Djurhuus, J. 1998. An empirical model for estimating nitrate

leaching as affected by crop type and long-term N fertilizer rate. Soil Use and Management,

30-36

Simmelsgaard, S.E., Kristensen, K., Andersen, H.E., Grant, R., Jørgensen, J.O., Østergaard,

H.S. 2000. Empirisk model til beregning af kvælstofudvaskning fra rodzonen. N-LES.

Nitrate Leaching EStimator. DJF rapport, Markbrug. 32, 67 pp.

Thomsen, I. K., Petersen, B. M., Bruun, S., Jensen, L. S. and Christensen, B. T. 2008.

Estimating soil C loss potentials from C to N ratio. Soil Biol. Biochem. 40

849-852.

Appendix 1 – data used and shown in figure 2

Applied values of variables that do not depend on amount of applied N

All data

Depending on soil and

Coarse

Sandy loam

precipitation

sandy soil,

soil, low

high

precipitation

Year of

precipitation

harvest

2005

N-level

See below

C in soil

65 t/ha

55 t/ha

N-spring

See below

C/N factor

0.56

0.98

N-fixation

2 kg/ha

Drainage Apr-Dec

315 mm

109 mm

N-excretion

0 kg/ha

Drainage Apr-Dec

245 mm

138 mm

N-autumn

0 kg/ha

Drainage prev. year Apr-

54 mm

34 mm

Aug

Crop

Spring cereal Drainage prev. year Sep-

517 mm

217 mm

Mar

Previous crop

Spring cereal Amount of humus

3.2 %

2.5 %

Location of

Commercial Amount of clay

4.7 %

12.7 %

obs.

Applied N-level, N spring application and predicted N leaching

Soil type and

Winter crop

N-level and N spring application

precipitation

100

150

200

Coarse sandy soil, high

101

116

133

precipitation

Sandy loam soil, low

precipitation

Appendix 2 – data shown in figure 4

Predicted leaching as function of year and applied N for spring cereal with N-LES

and

N-LES

Year

50 kg N/ha

100 kg N/ha

150 kg N/ha

N-

LES

1975

1980

1985

1990

1995

2000

2005

Appendix 3 – data shown in figure 5

Predicted by N-LES

Year of

harvest

LOOP1 LOOP4 LOOP3 LOOP2 LOOP6 LOOP1 LOOP4 LOOP3 LOOP2

1991

145

163

113

1992

141

148

103

1993

128

152

1994

111

149

1995

112

125

1996

104

113

1997

110

1998

112

1999

2000

2001

2002

2003

2004

2005

2006

LOOP6

142

134

138

113

115

113

112

Appendix 4 – data used and shown in figure 6

High precipitation

Coarse sandy soil (JB1)

Sandy loam soil (JB6)

Crop rotation

no. and crops

N-level

for crop

rotation

271

177

166

N leaching

N-

N-level

spring

predicted with:

spring

predicted with:

for crop

rotation

fixation N-LES

N-LES

fixation N-LES

N-LES

188

290

312

159

141

110

101

115

111

171

137

114

112

137

133

128

275

174

169

182

300

322

148

133

115

103

145

118

102

100

108

105

101

1. Barley

1. Whole-seed

1. Grass-clover

2. Barley

2. Maize

3. Barley

N level corresponds to average total N applied to the crop rotation.

N-spring corresponds to the amount of inorganic N in manure plus added fertilizer N.

Low precipitation

Coarse sandy soil (JB1)

Sandy loam soil (JB6)

Crop rotation

no. and crops

N-level

for crop

rotation

271

177

166

N leaching

N-

N-level

spring

predicted with:

spring

predicted with:

for crop

rotation

fixation N-LES

N-LES

fixation N-LES

N-LES

188

290

312

159

141

110

101

102

275

174

169

182

300

322

148

133

115

103

1. Barley

1. Whole-seed

1. Grass-clover

2. Barley

2. Maize

3. Barley

a, b

N Notes a and b: see table above