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Europ. J. Agronomy 30 (2009) 110–118

Contents lists available at ScienceDirect

European Journal of Agronomy

journa l homepage: www.e lsev ier .com/ locate /e ja

Evaluation of Best Management Practices for N fertilisation in regional fieldvegetable production with a small-scale simulation model

C. Nendel ∗

Institute for Vegetable and Ornamental Crops, Theodor-Echtermeyer-Weg 1, 14979 Großbeeren, Germany

a r t i c l e i n f o

Article history:Received 9 April 2008Received in revised form 14 August 2008Accepted 18 August 2008

Keywords:Field vegetable productionNitrate leachingAgro-ecosystem modellingBest Management PracticeRegionalisation

a b s t r a c t

Assessing the environmental impact of field vegetable production on a regional basis is a challenging task,since vegetable farms are often scattered. In order to use a soil–plant–environment–economics model asan assessment tool, a method is suggested that allows Best Management Practices (BMPs) for N-efficientfield vegetable production to be tested and evaluated on different spatial entities. The model farm con-cept was exemplarily demonstrated on field vegetable production in the region of Baden-Wurttemberg,Germany. Two scenarios using different fertiliser strategies illustrate how the implementation of BMP canbe evaluated at regional, sub-regional and farm level. Simulated results show how the area-wide enforce-ment of the Nmin method in Baden-Wurttemberg, as the first step to implement BMP, would lower theN leaching potential of field vegetable production by 66%. At sub-regional level, the results reveal thatcurrent targets stipulated by German legislation may still not be achieved in smaller areas with intensivefield vegetable production.

© 2008 Elsevier B.V. All rights reserved.

1. Introduction

Three decades ago, process-based agro-ecosystem modellinginvolved single process investigations and simple models. Today,however, sophisticated and complex model systems with variousscopes and focuses are available. Many of the current models weredesigned to reproduce processes on an infinitively small spatialscale: they are point models (Kersebaum et al., 2007). Very often,these models are calibrated and tested against field data. This pro-cedure turns them into pseudo-field-scale models, and in mostcases they are applied as such (e.g. Decrem et al., 2007). However,applying simulation models on a scale for which they were notdesigned necessitates use of a scaling procedure.

A wide range of models is available to simulate nitrogen dynam-ics in agro-ecosystems. Combinations of models with geographicalinformation systems (GISs) have been applied successfully in thepast to derive conclusions at larger scales (Gimona et al., 2006;Almasri and Kaluarachchi, 2004; Vinten and Dunn, 2001; Granlundet al., 2000). If available, a GIS supplies geo-referenced input datafor the model, which can then simulate representative points in abatch operation. Subsequently, the GIS framework aggregates thesimulation outputs and presents them in the spatial context.

∗ Present address: Leibniz Centre for Agricultural Landscape Research, Institutefor Landscape System Analysis, Eberswalder Straße 84, 15374 Müncheberg, Ger-many. Tel.: +49 33432 82355; fax: +49 33432 82334.

E-mail address: nendel@zalf.de.

GIS-based aggregation procedures, however, are not appli-cable for deriving conclusions on the impact of legislation onfield vegetable production practice, economics and environmen-tal compatibility at different scales, such as at crop rotation,farm, catchment, regional or national level. There are two rea-sons for this: (i) in many regions, GIS data does not cover thetarget area sufficiently to drive a process-based point model. Com-plete sets of soil, weather and infrastructure data have only beenmade available in GIS for a few areas in Europe. Data collectionis ongoing in most countries. (ii) Although field vegetable pro-duction is concentrated in few important regions in Europe, it isstill carried out on scattered farms. The spatial pattern is patchy,making it difficult to assess the contribution of vegetable pro-duction to the total N losses of a larger catchment. For thesereasons, an alternative aggregation approach was used, based onthe work by Stoorvogel (1995), Schou et al. (2000) and Dalgaardet al. (2006). This paper will discuss Best Management Prac-tice for field vegetable production in the German federal state ofBaden-Wurttemberg in the light of regionalised simulation resultsof the integrated soil–plant–environment–economics model EU-Rotate N (Rahn et al., 2007b).

2. Material and methods

2.1. The model farm concept

The regionalisation concept for the simulation output of theEU-Rotate N model (Rahn et al., 2007b) is based on model farms.

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C. Nendel / Europ. J. Agronomy 30 (2009) 110–118 111

Model farms are theoretical concepts which represent real veg-etable farms. All model farms consist of a number of fields ofvariable size, hosting different crop rotations that are consideredtypical for the area and the farm type. At the next level, a modelregion includes a number of model farms, which represent a realregion’s variability with regard to the size of the annual vegetableproduction area (APA) and the type of crops they produce. Modelfarms are devised on the basis of vegetable production data atnational or regional level. They are designed such that the sum ofAPA for the region’s most important vegetable crops (APAcrop ≥ 3%of APAtotal) is used as a target to optimise (i) the field sizes, (ii)the farm-type distribution and (iii) the composition of the croprotations. The algorithm for this optimisation also considers thefrequency of the respective vegetable crop in the rotation and thelength of the rotation.

2.2. Model farm crop rotation design

The initial setup of model farm types, their distribution in theregion and their crop rotation design is based on the analysis ofofficial statistical data for the region. Data on the APA for differentcrops per farm exists in most European regions. Local experts havederived a sound classification of different major farm types. Thisclassification is based on (i) the average size of the APA, (ii) the maincrops produced, (iii) the soil and (iv) the market channel. An exam-ple model farm could be “A large-sized farm with intensive carrotand small radish production on sandy loam for the supra-regionalsupply of wholesale markets in the Rheinhessen-Pfalz region, Ger-many”. Another could be a “small-sized farm with white cabbage,onion and carrot production embedded in barley and early potatocrop rotations on loam for regional wholesale and direct sale sup-ply chains in Sør-Østlandet, Norway”. Any gaps in the data must befilled by expert knowledge.

Once the farm types have been classified, the local experts setup a framework of typical crops and possible rotation sequences.This classification also allows the inclusion of crops in the rotationthat were not initially considered as one of the main crops for thefarm type or as important for the region, as long as their percent-age is negligible. Fine-tuning the crop rotations, field sizes and theproportion of the single model farm types can be carried out in aparameter estimation procedure.

2.3. Model farm parameter estimation procedure

Vegetable production within a region is represented by theparameter vector:

˚ = (fm, Pr,m, cn,r,m)

where fm is the fraction of F for model farm type m, F the totalnumber of farms in the region, Pr,m the size of the field that hasrotation r on model farm type m and cn,r,m is the frequency of cropn in rotation r on model farm type m.

The criterion L(˚) for the parameter estimation problem is thesquared difference between the real annual production area for allconsidered crops in the region, as derived from national or regionalstatistics, and the estimated annual production area for the samecrops in the model region:

L(˚) =(

N∑n=1

An −N∑

n=1

An

)2

(1)

where N is the number of crops considered in the region, and

An = anAtot (2)

where An is the real annual production area for a specific crop n, an

the fraction of total annual production area for a specific crop n andAtot is the total annual production area in the region, and

An =M∑

m=1

((fmF)

Rm∑r=1

(Pr,m

cn,r,m

Cr,m

))(3)

where An is the annual production area for specific crop n in themodel region, M the number of model farms chosen to representthe region, Rm the number of crop rotations for model farm m, Cr,m

the total number of crops in rotation r on model farm m, and fm,Pr,m and cn,r,m as described above.

The identification problem is to find a parameter vector ˚ suchthat

L( ˆ ) = min˚ ∈ U

L(˚)

where U denotes the admissible parameter space for ˚.

2.4. The EU-Rotate N simulation model

The EU-Rotate N decision support system was developed as atool to optimise nitrogen use in horticultural crop rotations acrossEurope (Rahn et al., 2007b). It is based on a dynamic process-basedsimulation of the crop–soil–environment interaction, which in turnemanated from basic ideas of the N ABLE model (Greenwood et al.,1996). N movement in soil is driven by water balance and transport.Water movement in soil follows a capacity approach (Ritchie, 1998),where the water content at saturation, field capacity and wiltingpoint define the hydraulic soil properties as main parameters. Cropevapotranspiration is calculated using the FAO approach (Allen etal., 1998). The effect of water stress on plant growth assumes alinear reduction in dry matter accumulation with a reduction oftranspiration (Shani and Dudley, 2001; Hanks, 1983).

Nitrogen mineralisation from organic matter is based on theroutines used in the DAISY model (Hansen et al., 1991). Residuesof crops simulated with the target-based crop growth model areassigned a dynamic C to N ratio, which reflects the growth condi-tions of the crop with respect to N supply. Default C to N ratios andpartitioning coefficients for crop residues are derived from stepwisechemical digestion experiments (Jensen et al., 2005). Manure andslurry parameters are taken directly from DAISY (Abrahamsen andHansen, 2000). Nitrogen volatilisation from soil-applied manureand slurry is described using an empirical relation implemented inthe ALFAM model (Søgaard et al., 2002). A soil pH dependency factorwas introduced to the model by fitting Michaelis–Menten kineticsto data from He et al. (1999) and subsequently normalising the rela-tion between pH and volatilisation half-life to pH 7.0. Ammonia lossfrom applied urea fertiliser is calculated using modified routines ofthe AMOVOL model (Sadeghi et al., 1988).

Snow depth and density, water storage in snow and water melt-ing from the snow pack is calculated using the daily input of airtemperature, following the ideas of Vehviläinen and Lohvansuu(1991). The process of soil freezing is implemented according toOlsen and Haugen (1997), with thermal property values taken fromthe SOIL model (Jansson, 1991). The approach used for thawing istaken from the ECOMAG model (Motovilov et al., 1999).

Root growth is calculated by a heat sum approach and dis-tributed spatially in a 2D soil cell grid, allowing for the simulation ofspacing effects in row crops. Crop- and soil-specific rooting depthenables deep and shallow-rooted crops, and their characteristic Nexploitation from the soil, to be simulated. N uptake is calculated asa function of crop N demand and the potential root N uptake (Rahnet al., 2007b). Crop growth and the critical N content are calculatedfollowing Greenwood (2001), allowing for luxury N consumption.

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112 C. Nendel / Europ. J. Agronomy 30 (2009) 110–118

Fig. 1. Field vegetable production in the counties of Baden-Wurttemberg.

Fertility-building crops are treated differently, using differentfixed growth rates according to the user’s rating. Litter loss, Nfixation processes and winterkill are included with parameters.Undersown crops are simulated by apportioning an appropriate drymatter and nitrogen content as a starting position as soon as theoverstorey has been harvested.

A database containing European standard prices for marketablecrops and standard figures for gross margin calculation supports theeconomic output of the model, enabling the simultaneous evalua-tion of management effects on both the environment and economicreturns.

The EU-Rotate N simulation model was tested against a numberof vegetable crop rotation experiments across Europe and demon-strated fitness for purpose (Rahn et al., 2007a).

2.5. Field vegetable production in Baden-Wurttemberg, Germany

To demonstrate the scaling procedure, a regional case studywas simulated using the field vegetable production area ofBaden-Wurttemberg (Fig. 1), which covers 7382 ha (StaLa Baden-Württemberg, 2005). Asparagus, which is a main vegetable cropin the region, is not considered in this investigation, since it is aperennial crop and as such cannot be described using the cropmodelling approach implemented in the EU-Rotate N model. Atotal of 1775 farms grow vegetables with different focuses on cropsand markets. The overall variety of vegetables grown in the areais high. However, a noticeable overbalance of lettuces in the pro-duction portfolio has developed recently driven by supra-regionalmarked demands. All of the vegetables included in the simulation

each occupy at least 3% of the production acreage, totalling 75% ofall vegetables produced; lettuce varieties make up 53% (Table 1).Vegetables in this region are often grown in rotation with cerealsor other agricultural cash crops. Some smaller sub-regions inBaden-Wurttemberg contain vegetable farms that produce onlyfor the local market in intensive or extensive crop rotations. In suchfarms, cover crops over winter are the only non-vegetable cropsgrown in the rotation. With the help of local experts, eight typicalfarm types were identified, representing all Baden-Wurttembergvegetable farms. Their crop rotations and the average size of their

Table 1Baden-Wurttemberg field vegetable production in 2004/2005 (StaLa Baden-Württemberg, 2005; observed) and as result of the combination of therepresentative crop rotations in Table 1 (model farm setup), expressed in annual pro-duction acreage and as a percentage of total annual production acreage (7382 ha a−1)

Crop Observed Model farm setup

ha a−1 % ha a−1 %

Leaf lettuce 1846 25 1825 25Onion 591 8 559 8Lamb’s lettuce 591 8 686 9White cabbage 591 8 645 9Head lettuce 517 7 473 6Carrot 443 6 414 6Cucumber (pickling) 295 4 296 4French bean 221 3 213 3Red cabbage 221 3 231 3Cauliflower 221 3 275 4

Average acreage per farm 4.2 4.2

Multiple cropping is considered as separate acreage.

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Table 2Representative model farms for Baden-Wurttemberg

No. Farm type Frequency(%)

Soil Size(ha)

Crop rotation

1st year 2nd year 3rd year

1A Large Heilbronn farm,

Stuttgart district1 Dystric Cambisol 55.0 Cucumber for pickling Winter wheat Onion

B 30.0 Leaf lettuce–whitecabbage

Potato Onion

2A Medium Esslingen

farm, Stuttgart district13 Vertic Cambisol 3.0 White cabbage

(industry)3× leaf lettuce Winter wheat

B 3.0 Red cabbage 3× leaf lettuce Winter wheat

3A Medium Ludwigsburg

farm, Stuttgart district10 Vertic Cambisol 3.0 Leaf lettuce–fennel White cabbage Carrot

4A Medium Heidelberg

farm, Karlsruhe district4 Dystric Cambisol 3.0 Cauliflower–French

bean–Lamb’s lettuceFrench bean–Lamb’slettuce

Fennel–Frenchbean–Lamb’s lettuce

5A Medium Rhein-Neckar

farm, Karlsruhedistrict, field swapping

6 Dystric Cambisol 5.0 Carrot Winter wheat Winter wheat

6A Small farm, Tübingen

district9 Vertic Cambisol 0.5 Cauliflower–leaf

lettuceClover grass Potato

B 0.5 Radish–leek Onion Winter wheat

7A Medium Breisgau farm,

Freiburg district6 Gleyic-eutric Fluvisol 5.0 2× Lamb’s lettuce Cauliflower 2× head lettuce

8A Small farm, Freiburg

district20 Dystric Cambisol 0.5 Beetroot–head lettuce Leek–Lamb’s lettuce Radish–kohlrabi–head

lettuceB 0.5 Celery–fennel–spinach Carrot–savoy cabbage Spinach–kohlrabi–Lamb’s

lettuce

fields were chosen in such a way that 12 crop rotations producesimilar outputs to those given in official statistics. For simplifica-tion, only triannual crop rotations were designed. The model farmsand their crop rotations are summarised in Table 2. Soil types usedfor vegetable production in Baden-Wurttemberg were identifiedfrom the European Soil Database (European Soil Bureau, 2003).Based on the property classes given there, the required parameterswere fixed in agreement with local experts for three major soiltypes (Table 3). The soils are not affected by groundwater.

2.6. Scenario design

The scaling procedure is illustrated by comparing different sim-ulation scenarios. Here, the comparison of a traditional fertiliserstrategy with the current standard of knowledge demonstrates thebenefit of BMP. Traditional Farming Practice (TFP) represents theexperience-based, old-fashioned style of calculating fertiliser rates.By trial and error, farmers have gained experience in fertilising theircrops and use their own standard amounts for fertilising. These

farmers have no knowledge of the mineral N content in their soil,and react simply by watching the performance of their crops. Forthe model calculation, this strategy is simulated by applying thecrop-specific target values of the official Baden-Wurttemberg fer-tiliser recommendation, albeit without consideration of the soilmineral N content. A flat subtraction of 20% of the fertiliser tar-get value accounts for the farmer’s experience with soil organicmatter mineralisation (adjusted application, Table 4). The standarddate for fertiliser application is the day before planting. Fertiliserrates exceeding 110 kg N ha−1 were split into half, with a top dress-ing applied on average 30 days (20 days for short-term crops) later.Cereals receive an autumn starter dressing of 30 kg N ha−1, and twofurther dressings in spring.

Best Management Practice is adhered to if fertiliser rates fol-low state-of-art knowledge and technology. The simplest of theadvanced techniques for determining the crop fertiliser demandis based on a crop-specific target value and the soil mineral Nstatus in spring (Wehrmann and Scharpf, 1979). This method rep-resents a great improvement towards achieving ecologically and

Table 3Baden-Wurttemberg soils

Dystric Cambisol Gleyic-eutric Fluvisol Vertic Cambisol

0–30 cm 30–200 cm 0–30 cm 30–200 cm 0–30 cm 30–200 cm

Clay (%) 10 10 20 20 25 25Sand (%) 60 60 45 45 20 20SOM (%) 1.5 0.2 2.5 0.5 1.8 0.5BD (kg m−3) 1500 1550 1500 1650 1550 1650Porosity (%) 43 43 42 42 43 43Field capacity (%) 27 27 33 33 36 36PWP (%) 9 9 17 17 19 19

SOM: soil organic matter; BD: bulk density; PWP: permanent wilting point.

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114 C. Nendel / Europ. J. Agronomy 30 (2009) 110–118

Table 4Target values for automatically triggered N fertilisation (Feller et al., 2007)

Crop Lower boundary forsampling (cm)

Target value for sampledvolume (kg N ha−1)

Target value for top30 cm (kg N ha−1)

Adjusted application(kg N ha−1)

Beetroot 60 230 40 184Carrot 60 90 20 72Cauliflower 60 300 80 240Celery 30 200 40 160Clover grass 0 0 0 0Cucumber for pickling 30 190 40 152Fennel 60 200 60 160French bean 60 110 40 88Head/leaf lettuce 30 150 60 120Kohlrabi 30 230 60 184Lamb’s lettuce 15 80 40 64Leek 60 230 40 184Onion 60 130 20 104Potato 60 160 40 128Radish 30 140 40 112Red cabbage 60 220 40 176Savoy cabbage 60 250 40 200Spinach 30 180 40 144Winter wheat 90 200 100 160White cabbage 60 270 40 216White cabbage (industry) 60 340 40 272

Application adjusted to farmer’s experience (adjusted application) calculated as the target value for sampled volume less 20%.

economically sound fertilising strategies, since available soil N cantake considerable values even at the time of sowing or plantingthe crop. However, this approach does require additional effortand costs which may be the reason why it is not practised moreregularly.

Within the model, BMP strategy is applied using an automatictrigger. The trigger calculates the soil mineral N status at twodepths: 0–30 cm and a crop-specific rooting depth (0–30, 0–60 or0–90 cm). A target value is given for both depths (Table 4). Themodel calculates the crop N demand by subtracting the N contentin soil from the target value at the respective depth. The greaterdifference yielded by the two calculations is applied as N fertiliser.A maximum limit prevents too high dosage. Greater N demand issatisfied with a top dressing, delayed by a certain number of days.

All crops are irrigated automatically with 10 mm at a watercontent threshold of 35 mm in a soil volume of 30 cm depth. Irriga-tion water is assumed to contain 30.0 g l−1 NO3 (≈6.8 g l−1 NO3-N),which is the average nitrate content in irrigation water in Baden-Wurttemberg. All crop residues are ploughed into 30 cm soil depthwithin 5 days. Using CLIMGEN (Stöckle et al., 1999), 10 weatherdata sets were generated, based on real weather data (1991–2005)from Karlsruhe (farms 4, 5, 7 and 8) and Stuttgart-Echterdingen(farms 1, 2, 3 and 6) weather stations (DWD, 2005). The presentedresults were calculated from the mean of 10 simulations using theseweather data sets. Prices and production costs were adapted fromZiegler et al. (2002) and Frisch et al. (2004). Costs for Nmin sam-pling and analysis were assumed to amount to D25 ha−1 sample−1.The simulation period was 10.5 years for three rotations. The first

Table 5Summary information on Baden-Wurttemberg model farms performing Traditional Farming Practice, including calculation of total annual N loss in the region

Farm type Sum

1 2 3 4 5 6 7 8

Aa Ba Aa Ba Aa Aa Aa Aa Ba Aa Aa Ba

Average field size (ha) 50.0 40.0 3.0 3.0 3.0 3.0 5.0 0.5 0.5 5.0 0.5 0.5Assumed number of farms 18 18 231 231 178 71 107 160 160 107 355 355Total model farm area (ha) 900.0 720.0 693.0 693.0 534.0 213.0 535.0 80.0 80.0 535.0 177.5 177.5 5338.0

Nitrogen input and output per rotationLeaching below 90 cm (kg N ha−1) 290.9 615.2 564.7 530.7 598.9 1067.7 305.9 365.9 399.3 1031.2 1115.4 1559.7Uptake from below 90 cm (kg N ha−1) 12.3 36.8 33.8 18.1 32.4 4.1 35.3 10.2 10.7 45.4 14.9 9.4Mineral fertiliser (kg N ha−1) 416.0 624.0 792.0 792.0 568.0 856.0 392.0 488.0 580.0 608.0 968.0 1128.0Organic fertiliser (kg N ha−1) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Biological fixation (kg N ha−1) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0Irrigation water (kg N ha−1) 10.1 12.3 12.2 14.9 17.1 40.9 5.4 4.8 9.0 21.9 38.4 38.6SOM mineralisation (kg N ha−1) 173.5 177.6 193.6 192.0 207.8 209.4 200.8 176.6 175.0 323.1 195.0 200.0Residues mineralisation (kg N ha−1) 148.5 264.7 164.9 128.9 167.8 311.6 117.9 602.0 222.1 224.2 322.9 368.2Volatilisation and denitrification (kg N ha−1) 9.1 13.5 36.2 34.4 28.4 17.2 10.4 39.2 32.1 21.0 23.9 23.9Offtake marketable yield (kg N ha−1) 336.9 374.9 470.4 465.4 354.8 448.2 445.5 284.7 405.3 328.4 525.0 521.0

N loss summary per annumLeaching (t N a−1) 83.6 138.8 122.6 118.4 100.8 75.5 48.3 9.5 10.4 175.8 65.1 91.7 1040.6Gaseous loss (t N a−1) 2.7 3.2 8.4 7.9 5.0 1.2 1.9 1.0 0.9 3.7 1.4 1.4 38.9

Total unwanted N loss (kg N ha−1 a−1) 202.2

Leaching loss is defined as leaching below 90 cm minus N uptake from below 90 cm. SOM: soil organic matter.a Rotation.

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Table 6Summary information on Baden-Wurttemberg model farms performing Best Management Practice, including calculation of total annual N loss in the region

Farm type Sum

1 2 3 4 5 6 7 8

Aa Ba Aa Ba Aa Aa Aa Aa Ba Aa Aa Ba

Average field size (ha) 50.0 40.0 3.0 3.0 3.0 3.0 5.0 0.5 0.5 5.0 0.5 0.5Assumed no. of farms 18 18 231 231 178 71 107 160 160 107 355 355Total model farm area (ha) 900.0 720.0 693.0 693.0 534.0 213.0 535.0 80.0 80.0 535.0 177.5 177.5 5338.0

Nitrogen input and output per rotationLeaching below 90 cm (kg N ha−1) 106.0 171.5 89.8 80.9 167.7 584.0 19.0 147.9 90.8 619.6 514.0 725.0Uptake from below 90 cm (kg N ha−1) 15.6 36.9 30.5 14.0 29.8 4.4 9.7 9.2 11.5 46.6 14.3 9.9Mineral fertiliser (kg N ha−1) 228.4 227.1 239.7 232.6 139.3 444.3 174.6 154.7 217.6 317.9 501.3 503.2Organic fertiliser (kg N ha−1) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Biological fixation (kg N ha−1) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0Irrigation water (kg N ha−1) 10.1 12.3 12.2 14.9 17.1 40.9 5.4 5.2 9.0 20.2 38.4 38.6SOM mineralisation (kg N ha−1) 174.2 183.0 193.9 192.5 208.3 209.8 201.1 187.1 176.0 310.5 195.1 200.2Residues mineralisation (kg N ha−1) 146.5 221.9 164.4 128.4 166.8 307.8 117.5 551.0 220.4 213.6 322.8 365.3Volatilisation and denitrification (kg N ha−1) 1.9 1.0 14.9 13.4 12.3 5.9 3.6 23.3 24.9 9.2 12.5 10.4Offtake marketable yield (kg N ha−1) 331.5 403.5 469.3 462.5 349.7 443.9 438.9 315.8 402.8 339.4 525.0 499.2

N loss summaryLeaching (t N a−1) 27.1 32.3 13.7 15.5 24.5 41.2 18.8 3.7 2.1 102.2 29.6 42.3 352.9Gaseous loss (t N a−1) 0.6 0.3 3.4 3.1 2.2 0.4 0.6 0.6 0.7 1.6 0.7 0.6 14.9

Total unwanted N loss (kg N ha−1 a−1) 68.9

Leaching loss is defined as leaching below 90 cm minus N uptake from below 90 cm. SOM: soil organic matter.a Rotation.

two rotations were used to produce a near-equilibrium state of thesystem. All results are based on the third rotation only.

3. Results

In traditional fertiliser schemes, farmers would use 684 kgN ha−1 on average in their rotations. This would result in 704 kgN ha−1 leaching below 90 cm soil depth and a gaseous loss of24 kg N ha−1 per rotation. However, 22 kg N ha−1 would be recov-ered from below 90 cm soil depth by crop roots during a rotation.The mean annual gross margin for all rotations was calculated asD6375 ha−1 a−1. The result of the TFP scenario is summarised inTable 5. An aggregation was carried out for the Baden-Wurttembergregion, which assumes that all farmers produce their vegetablesaccording to traditional practice and that they fertilise amountsof N corresponding to the crop’s respective target value minus20%. The results show an average N loss per year, via leaching andgaseous phase, of up to 1040 t N a−1, which corresponds to 202 kgN ha−1 a−1.

Following BMP, the assumed rotations would lead to an averageuse of 281 kg N ha−1. As a result, leaching below 90 cm soil depthwould amount to 276 kg N ha−1 and gaseous losses would total 11 kgN ha−1. Recovery from below 90 cm soil depth, by crop roots, wascalculated as 19 kg N ha−1. The mean annual gross margin for allrotations under the BMP scheme was calculated as D6269 ha−1 a−1.The result of the BMP scenario is summarised in Table 6. An aggre-gation of the results suggests that, provided all farmers producetheir vegetables according to BMP and apply amounts of N corre-sponding to the crop’s respective target value minus the amountof mineral N available in the soil before sowing or planting, theaverage N loss per year via leaching and gaseous phase would total353 t N a−1 for the Baden-Wurttemberg region. This corresponds toa hectare loss of 69 kg N ha−1 a−1.

Fig. 2 compares the N losses, as leaching + gaseous losses, withthe net return of the simulated crop rotations for the TFP and BMPscenarios. For all crop rotations, the reduction in N losses thatoccurred by applying the Nmin method affects the gross margin onlymarginally. Rotations 7a and 8b are the only crop sequences thatyield noticeably less. However, rotation 8b also shows the greatest

Fig. 2. Gross margin vs. N loss (leaching + gaseous loss) for Baden-Wurttembergmodel farm crop rotations. TFP: Traditional Farming Practice scenario; BMP: BestManagement Practice scenario.

decrease in N losses. The gross margin increases slightly under BMPfor several crop rotations, due to less fertiliser use.

4. Discussion

4.1. Regionalisation methodology

The method presented here allows a regional analysis of fieldvegetable production with conclusions drawn for either the com-plete region, smaller sub-regions or even farms and fields. Newmeasures aimed at improving the N use efficiency in field vegetableproduction can be easily tested using the EU-Rotate N model (Rahnet al., 2007b). The ability to aggregate results up to the regionallevel allows users to assess the impact of changes in a larger spatialcontext. Since EU-Rotate N is a point model, it is not possible toforecast groundwater nitrate concentrations. As a consequence,the model cannot be used in relation to the EU Water Framework

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Directive (The Council of the European Communities, 2000).However, a rough calculation of nitrate concentrations in leachateemitted from the simulated farms may indicate which farm typesare most likely to contribute to nitrate pollution. If a more detailedassessment of groundwater nitrate concentration is required, agro-nomical models covering all kinds of land use need to be combinedwith hydrological catchment models (Ledoux et al., 2007; Conan etal., 2003; Wendland et al., 2001), in which sub-soil and in-streamprocesses have to be considered (Wriedt and Rode, 2006).

Dalgaard et al. (2006) showed that model farms sometimesrepresent a number of quite different farms in the target area.Differences in management strategies and fertiliser philosophyamong farms within one particular model farm category cannotbe reflected. This means that extreme nutrient balances and emis-sions are smoothed out using this approach. This is an unfortunateside effect of using statistical methods at different scales.

The strength of the approach is that results can be aggregated.This furnishes information on certain management strategies,especially when farm types are associated with sub-regions. Theimplications of the scenario runs can also be elaborated at a lowerspatial level.

Using the model farm concept allows the analysis of nutrientbalances at farm and regional levels, as will be demonstrated inthe following sections by means of the well-known relationshipbetween fertiliser dosage and leaching potential. However, it hasto be stressed that the figures derived in the example rely on thequality and applicability of the simulation model employed. Forhorticultural crop rotations, EU-Rotate N is the only simulationmodel so far that is able to provide a high level of detail. Althoughtesting revealed only average performance of the model (Rahn etal., 2007a), it is assumed that EU-Rotate N is still the best predic-tion method for the offered range of crops, management optionsand the time scale. Ultimately, the quality of the model does notaffect the method of regionalisation and analysis of the simulationresults, which is the main issue of this paper. However, the followingresults have to be understood in this context.

4.2. Nutrient balances at farm level

The results from running scenarios that describe TraditionalFarming Practice in field vegetable production in Baden-Wurttemberg show how excessively N is used, especially sinceindustrial fertiliser has become available at acceptable prices.However, many farmers have now developed a feeling for the envi-ronmental consequences of their activities, and now use fertiliserwith much more caution.

The results from farmers in the Baden-Wurttemberg regionsthat perform in the traditional manner are taken as a basis todemonstrate what has been achieved by developing codes of GoodAgricultural Practice (GAP), including methods of tailoring N fer-tiliser schemes to the demand of the crops, both with regard tothe amount and timing. Even implementation of the simple Nminmethod (Wehrmann and Scharpf, 1979) can reduce total N loss by66%, assuming that all farmers employ this method. This includesa reduction of leaching losses by 66% and of gaseous losses by62%.

A close look at the distribution of N losses between the dif-ferent farm types identifies pure vegetable rotations, with up tothree crops per season, as the highest emitters (farms 4, 7 and8 in Tables 5 and 6), while the rotations that include cereals arerevealed to be the most efficient N users (farms 1, 2, 5 and 6 inTables 5 and 6). The comparison between farms 2 and 4 underlinesthat cereals can reduce N leaching losses in crop rotations. Growinga cereal, which has deep reaching roots, after a shallow-rooted andwell-irrigated lettuce crop, will extract a considerable amount of

N from below 90 cm (standard sampling depth in Germany). Thiscan add up to almost 60 kg N ha−1 in rotation 2A (Table 6). In con-trast, the shallow rooting crops grown on farm 4 have no potentialto extract deep soil N. As a consequence, all N that has leacheddeeper than 60 cm should be considered as lost to the crop. The Nbalance of farm 3 benefits from the carrot crop, which has a lowN demand and can produce acceptable returns with little N fer-tiliser. The combination of carrot and cereals in farm 5 probablyrepresents the most efficient vegetable crop rotation in terms of Nuse.

Further results from the N balance show that in all rotationsalmost all the N added as mineral fertiliser is leached out of thesystem and the crop N demand could technically be satisfied fromN mineralised from soil organic matter and crop residues. However,in practice it is not as easy as this, since such a balance approachdoes not consider the temporal variation of plant N demand and soilN supply. However, it does demonstrate that there are still methodsby which N could be used more efficiently in crop rotations, byconsidering N release from organic N depots and synchronising itwith crop N demand.

4.3. Regional implications

Field vegetable production has the reputation of being respon-sible for leaching nitrates into groundwater. Using a regionalapproach, e.g Baden-Wurttemberg, however, opens up an inter-esting discussion. The most intensive vegetable production, whichincludes up to three crops and a potential fertiliser use of 376 kgN ha−1 per season when not following BMP (farm 8, Table 5), ismainly carried out on small farms. Although the number of farmsin this category is high, the total acreage is low compared to thesmaller number of larger farms. Moreover, the larger farms inBaden-Wurttemberg run less intensive crop rotations with cere-als and other non-vegetable cash crops, which are less leaky interms of nitrate loss. According to the model output, measures toimprove N efficiency are expected to have the highest impact whenapplied to those farms that have intensive vegetable productionon medium-sized fields, such as farm types 4 and 7. Farm type 7resulted in the highest total annual N loss because it representedintensive production on frequently appearing medium-sized farms(Table 1).

The target value, as expressed in the German Fertiliser Applica-tion Ordinance (Bundesgesetzblatt, 2007) for acceptable N surplusin agriculture beyond 2011, is 60 kg N ha−1, with additionalcrop-related allowances for vegetable production. Applying thisreasoning to the Baden-Wurttemberg region would lead to the con-clusion that, when BMP is strictly applied, the total annual N loss perhectare would amount to 69 kg N ha−1, resulting in average leachateNO3 concentrations of around 450 mg NO3 l−1. If located in an areaof otherwise diversified land use, these concentrations would bediluted by the groundwater and NO3 concentrations would proba-bly be around the current threshold value of 50 mg NO3 l−1 (GermanDrinking Water Ordinance, Bundesgesetzblatt, 2001).

However, farm types within Baden-Wurttemberg are of aregional nature and are grouped in typical areas. Farm 7, forinstance, is representative of the Breisgau area, which is close tothe river Rhine. The model calculates an annual average leachingloss of 166 kg N ha−1 and a corresponding leachate NO3 con-centration of 909 mg l−1 for farm type 7 if the Nmin method isapplied. This leaching loss is not acceptable, even if the addi-tional allowances for lettuces (50 kg N ha−1) and cauliflower (120 kgN ha−1; Bundesgesetzblatt, 2007) are considered. Consequently,the latest German nitrate report shows NO3 concentrations abovethe threshold value, and rising, downstream from the Breisgau area(BMU, 2004).

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4.4. Model farm economics

The analysis of gross margins after applying the Nmin methodto vegetable rotations (Fig. 2) clearly shows that, in most cases, aconsiderable improvement in N efficiency was achieved withoutnegatively affecting economic output. Moreover, the net returnsare expected to rise in several cases, since the profits generatedby reduced fertiliser inputs are greater than the costs of using theNmin method. It is important to stress this fact when advising farm-ers to use the Nmin method. When further reductions of N lossesare required, it is likely that yield and gross margins may be moreseverely affected. In this context, the EU-Rotate N model can helpidentify measures that promote further reductions of N losses andcan be advertised to the farmers because they are not expected toaffect economic return. Furthermore, the effect of supra-regionalmeasures on the environment and farm income can be testedat regional, sub-regional, farm and field level, which is advanta-geous to large-scale approaches (Lehtonen et al., 2007; Turpin etal., 2005). However, it is difficult to combine a regional approachwith a behavioural model (Schou et al., 2000), since the decisionstaken in field vegetable production to meet changing frameworkconditions are very much based on farmers’ philosophy and localmarket demands; the possibilities for farmers to react by changingthe crop portfolio or the cropping sequence are manifold. This iswhy a stochastic approach, as chosen by Johnston et al. (2005), is notapplicable. However, this approach offers the opportunity to testdifferent strategies out of this large set of possibilities at farm leveland to evaluate the most promising approaches that meet changingmarkets or other economic conditions (e.g. Kelly et al., 1996).

5. Conclusion

The derivation of information on the environmental impact offield vegetable production for larger regions using point model sim-ulations is hampered by the fact that vegetable farms rarely formcontinuous blocks within the agricultural landscape. However, theaggregation of simulation results can be understood by design-ing theoretical farm units, representing typical real-world farmtypes. Using the example of field vegetable production in Baden-Wurttemberg, it was demonstrated that the model farm conceptallows both the evaluation of BMPs at crop rotation level andtheir consequences at higher spatial aggregations. The EU-Rotate Nmodel can be used to simulate the economic and environmentalimplications of different measures developed to increase N use effi-ciency in field vegetable production. These simulations can be easilytested and evaluated once the model farms have been set up tomatch official regional data.

Acknowledgements

The author gratefully acknowledges the contributions of K.Rather, State Horticultural College and Research Institute Heidel-berg, Germany and R.D. Lillywhite, Warwick HRI, Wellesbourne,UK. The work was funded by the EU within the project QLK5-2002-01100—Development of a model-based decision support system tooptimise nitrogen use in horticultural crop rotations across Europe(EU-Rotate N), coordinated by C.R. Rahn of Warwick HRI, Welles-bourne, UK.

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