Using a cropping system model at regional scale: Low-data approaches for crop management information and model calibration

Agriculture, Ecosystems and Environment 142 (2011) 85–94

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Using a cropping system model at regional scale: Low-data approaches for crop management information and model calibration Olivier Therond a,∗ , Huib Hengsdijk b , Eric Casellas a,c , Daniel Wallach a , Myriam Adam b,d,e , Hatem Belhouchette c , Roelof Oomen d,g , Graham Russell f , Frank Ewert d,g , Jacques-Eric Bergez a , Sander Janssen d,h , Jacques Wery c , Martin K. Van Ittersum d a

INRA, UMR 1248 Agir, F-31320 Castanet Tolosan, France Plant Research International, Wageningen University and Research Center, P.O. Box 616, 6700 AP Wageningen, The Netherlands c INRA, UMR 1123 System, F-34060 Montpellier, France d Plant Production Systems Group, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands e CIRAD, UMR 1123 System, F-34060 Montpellier, France f School of GeoSciences, The University of Edinburgh, West Mains Road, EH9 3JN Edinburgh, United Kingdom g Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D – 53115 Bonn, Germany h Alterra, Wageningen University and Research Center, P.O. Box 47, 6700 AA Wageningen, The Netherlands b

a r t i c l e

i n f o

Article history: Received 8 September 2009 Received in revised form 12 May 2010 Accepted 19 May 2010 Available online 17 June 2010 Keywords: Scaling Calibration Crop management Impact assessment

a b s t r a c t Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes. Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Agricultural policies of the European Commission aim to improve the sustainability of agriculture in the European Union (EU). The ex-ante assessment of policies and innovations on the economic, social and environmental performance of EU agriculture contributes to better-informed decision making. The SEAMLESS project (Van Ittersum et al., 2008) has developed an integrated assessment and modelling platform for carrying out such ex-ante policy assessments. This platform includes several linked models, one of which is the Agricultural Production and Externalities Simulator (APES), a generic cropping system model (http://www.apesimulator.org/; Donatelli et al., 2010). Cropping

∗ Corresponding author. Tel.: +33 561285048; fax: +33 561735537. E-mail address: [email protected] (O. Therond). 0167-8809/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2010.05.007

system models are powerful tools for assessing the interacting effects of management practices, soils and climate on the environment and agricultural production (Van Ittersum and Donatelli, 2003). APES allows simulating the behaviour of the major cropping systems across the EU based on soil, weather, and crop information. Model outputs include information on crop growth and development, yield and externalities such as nitrogen leaching, soil erosion and the fate of pesticides. APES has been designed to simulate crop rotations (cropping systems) for a field, which is assumed uniform in soil and climate characteristics, and management activities. Scaling procedures are required to use APES, or any other cropping system model, for simulation of crop yields and externalities at regional or EU scale. General problems associated with the application of cropping system models at regional scale are discussed by Leenhardt et al. (2006). Using cropping system models at regional scale raises at least two challenges. First, the input data requirements of such models

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for large heterogeneous areas are difficult to fulfil. Soil and daily climate data are often available from existing databases at different spatial levels of detail, but spatially and temporally explicit information on crop parameters and crop management is much less readily available (Leenhardt et al., 2010; Godard et al., 2008; Maton et al., 2007; Mignolet et al., 2007; Faivre et al., 2004; Biarnès et al., 2004; Jagtap and Jones, 2002). Although there are several databases available describing the characteristics of the major agricultural systems in Europe (e.g. Russell et al., 1999), none describes the associated crop management in sufficient detail to be used in cropping system models, i.e. timing of operations, type and amount of inputs, and application methods (Janssen et al., 2009a; Godard et al., 2008). The second and related challenge is to develop a procedure to calibrate cropping system models for large areas. Spatial heterogeneity requires the collection of data at regional scale that represents the spatial variation in crop parameters to calibrate the model (Hansen and Jones, 2000; Jagtap and Jones, 2002; Xiong et al., 2008). Tackling of both challenges requires the application of various downscaling and upscaling procedures for different data types. Many studies have been carried out using cropping system models to simulate regional crop yields (Faivre et al., 2004; Van Ittersum et al., 2003; Jagtap and Jones, 2002; Bouman et al., 1996). These studies focus on simulating the effect of climate change (e.g. Reidsma et al., 2009; Xiong et al., 2008; Challinor et al., 2004, 2009; Wolf and Van Oijen, 2002; Hansen and Jones, 2000), land use, policy and technological change (e.g. Godard et al., 2008) and on yield forecasting (e.g. Jagtap and Jones, 2002; Chipanshi et al., 1999; Supit and Van der Goot, 1999; Supit, 1997). Most of these studies simulate potential, water-limited or nutrient-limited yield levels, whereas actual yields are also determined by growth reducing factors (Van Ittersum et al., 2003). Crop management is usually described by using typical or recommended practices (e.g. Godard et al., 2008; Jagtap and Jones, 2002; Wolf and Van Diepen, 1995) or management practices in experimental trials (e.g. Xiong et al., 2008). In regional studies, the spatial variation in management information is often described using statistical and probability relationships (e.g. Faivre et al., 2004; Moen et al., 1994) or assuming uniform (i.e. the same) crop management over large areas (e.g. Wolf and Van Oijen, 2002; Wolf and Van Diepen, 1995; Easterling et al., 1992). With such representations of management over large areas, important interactions between crop management and soil and weather are not well captured. The first objective of this paper is to describe a low-data approach for developing detailed crop management data required by APES and other cropping system models for application at regional level (in this case various EU regions). This approach is based on expert knowledge and easy-to-collect regional crop management information. Effects of the expert-based crop management rules on simulated yields and nitrogen leaching are illustrated. The second objective is to develop a low-data method for calibrating default crop parameters of APES applied to 12 EU regions. This method allows better simulation of regional variation in actual farm yields in order to provide more accurate estimates of actual regional production. To allow the investigation of these objectives, we also present how EU-wide data on soils and climate and the agro-management information for the regions have been integrated in a common spatial framework.

2. Material and methods 2.1. Model The APES is a simulation model system for estimating the biophysical behaviour of agricultural production systems in response

to the interaction of weather, soil and agro-technical management options. APES is a generic cropping system model that operates at the scale of an individual homogeneous field (Donatelli et al., 2010). APES is composed of two main groups of software units: the simulation engine which uses the modelling framework MODCOM (Hillyer et al., 2003), and the model components, which include a cross-component unit to compute mass balance. Model components can be grouped into agricultural management, soil components, production enterprise components, and weather. Biophysical processes are simulated in APES using deterministic approaches, which are mostly based on mechanistic representations. APES was designed with a modular modelling framework to facilitate the exchange of different simulation components and allowing model developers to work individually on different components, which can be integrated at a later stage. It is a feature of the design of APES that a process can often be simulated by different versions of a component, with the one chosen depending on the data available and the importance of particular yield-limiting or yield-defining factors in certain environments. As a consequence there is no single APES model but rather a family of modelling solutions. However, all model components conform to a standard specification in terms of their ontology, and use a daily time-step for integration and communication across components. The implementation (modelling solution) used for the present study takes into account possible water and nitrogen stress and agronomic treatments but not yield-reducing factors such as pests, disease and harvest losses that can be considered stochastic. In APES agricultural management is defined in terms of events that are described by, first, a timing rule specifying when the event takes place, and secondly, an ‘impact’, specifying the type of event such as nitrogen application. These management events are grouped into tillage, irrigation, fertilization, crop protection, sowing/planting and harvesting. The soil component uses soil descriptions to parameterize processes that affect soil carbon, nitrogen and water dynamics. The soil carbon and nitrogen component describes N mineralization, immobilisation and turnover and the interactions between C and N dynamics in decomposing plant residues and soil organic matter. It includes above- and belowground plant residue pools and three soil organic matter pools (microbial biomass, young and old soil organic matter) with different turnover times. The N available to crops thus includes both soil and fertilizer N. The soil water component simulates one dimensional water movement in the soil, and takes account of changes after a soil tillage operation. A soil profile is represented as a series of superimposed horizontal layers. For each layer, hydrological properties are provided by specifying the parameters of the appropriate hydraulic functions. The production enterprises simulate amongst others crop growth and development using a light use efficiency approach moderated by water and nitrogen limitations (Donatelli et al., 2010). In the present study, weather data were read directly into the model and only evapotranspiration required to be calculated. 2.2. Model inputs 2.2.1. Soil and climate data Various spatial databases with different regional delineations have been used to define homogenous land units (simulation units) in the EU. These so-called Agro Environmental Zones (AEnZs) are based on unique combinations of an environmental zone, a soil type, and an administrative region (Hazeu et al., 2010). The environmental zones are based on Metzger et al. (2005) who identified 13 environmental zones in the EU using selected climate variables, geomorphology and latitude. Six different soil types were defined according to the organic carbon content in the topsoil, which explains 90% of the variation in other soil variables and is the only

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Fig. 1. The 12 NUTS2 regions (shaded) for which experts provided observed average crop management information and yields: Andalucia (1); Castilla y Léon (2); MidiPyrenees (3); Poitou-Charentes (4); Champagne Ardenne (5); Schwaben (6); Northumberland and Tyne and Wear (7); Flevoland (8); Danmark (9); Brandenburg (10); Zachodniopomorskie (11); and Podlaskie (12).

soil variable for which a detailed EU-wide map is available (Hazeu et al., 2010). The EU is divided into administrative regions based on the ‘Nomenclature of Territorial Units for Statistics’ (NUTS), which is the EU standard for referencing the administrative subdivisions of countries for statistical purposes. There are three NUTS levels of which the NUTS2 level – used here – refers to sub-national regions for most countries and consists of 257 regions in the EU25. We performed our analyses using information of 12 NUTS2 regions, i.e. Andalucia (Spain); Brandenburg (Germany); Castilla y Léon (Spain); Champagne Ardenne (France); Danmark; Flevoland (Netherlands); Midi-Pyrenees (France); Northumberland and Tyne and Wear (England); Podlaskie (Poland); Poitou-Charentes (France); Schwaben (Germany); Zachodniopomorskie (Poland) (Fig. 1). These 12 regions were chosen to provide a representative sample of the wide range of biophysical conditions and farming systems across the EU25. In total 3513 AEnZs have been identified for the EU25, the 12 NUTS2 regions studied consist of 93 AEnZs. For running APES, detailed soil characteristics (e.g. water content at field capacity and wilting point, bulk density, texture and organic carbon content) and daily weather data (i.e. temperature, radiation, rainfall, wind speed and reference evapotranspiration) are required. Therefore, each AEnZ has been further characterized in terms of soil properties and climate conditions. Soil data are from the European Soil Database (ESBN, 2008), which contains soil information on a 1 × 1 km grid basis for the EU25. This database contains the Soil Geographical Database of Eurasia, the PedoTransfer Rules Database, the Soil Profile Analytical Database of Europe,

and the Database of Hydraulic Properties of European Soils (ESBN, 2008). These soil data were supplemented with selected variable values from Baruth et al. (2006) to deal with missing values and improve the estimation of the values required by the cropping system model. The predominant soil in each AEnZ is considered the representative soil in a given AEnZ. Climate data are from the European Interpolated Climate Data (JRC, 2008) which provides interpolated daily weather data from approximately 1500 meteorological stations across Europe in a grid of 50 × 50 km. Each AEnZ is characterized by a 25 years climate record (1982–2006) based on the grid-area-weighted mean. 2.2.2. Crop management data General information on crop management across the EU has been collected in the 12 NUTS2 regions according to a standardised survey protocol (Zander et al., 2009). Depending on the region, between one and ten local experts were surveyed, mainly from local agricultural advisory services. Since cropping systems and associated management practices may differ within NUTS2 regions, each region was sub-divided into homogeneous zones (or ‘site classes’) in terms of cropping systems by the experts. The experts specified representative management regimes for the major cropping systems in each site class. Site classes were linked to AEnZs using an online map interface. Although some site classes correspond to a single AEnZ, most site classes comprise several AEnZs. The numbers of NUTS regions, site classes and AEnZs used in the analyses are shown in Table 1.

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Table 1 The total number of investigated site classes, AEnZs and NUTS2 regions, their average area and the standard deviation and the number of site classes, AEnZs and NUTS2 regions in which each of the three studied crops occupied a significant proportion of the area.

Site class AEnZ NUTS regions

Total number

Average area (km2 ) [standard deviation]

Grain maize

Soft wheat

Durum wheat

27 93 12

9963 [15869] 2397 [4879] 29889 [27804]

7 38 3

26 82 11

8 42 4

As surveys collecting detailed crop management information are resource-consuming, the survey collected only average and aggregate management information for the main agricultural activities of each site class. The management information collected by site class and crop included average sowing and harvest dates, total nitrogen input, total irrigation water applied and the number of irrigations per cropping season (Zander et al., 2009). In addition, experts estimated current average yields for each crop within each site class for the period 2000–2002. Table 2 shows a selection of management data collected for soft winter wheat in Podlaskie (Poland) and Flevoland (the Netherlands) and for grain maize in Midi-Pyrenees (France). The data illustrate that management information and crop yields may differ across site classes and AEnZs. Based on the collected average and aggregated management information in the crop survey expert-based rules have been developed specifying each management event required for running APES. Authors of this manuscript acted as the experts to develop the rules. Currently, the rules determine for the major crops in the EU25 the date(s) and depth(s) of tillage event(s), the crop residue management (fraction of residues removed from the field), the sowing date and depth, number, date(s) and quantity(ies) of nitrogen fertilization, and number and date(s) of irrigation event(s) and associated amount of water applied. In this study, the expert-based

management rules specifying the date of sowing and the fertilizer application are described in detail, and their effects on selected model outputs are presented for the region Flevoland. More information on the expert-based rules for other management events can be found in Janssen et al. (2009b) and Oomen et al. (2009). 2.2.3. Crop parameters Default crop parameters for maize grain, soft wheat and durum wheat are based on above-ground biomass, yield and phenology data obtained from field trials at the INRA-Toulouse experimental farm from 1996 to 2002 (Table 3; Debaeke et al., 2006). These field trials included a range of management practices and crop varieties that represent the diversity of farmers’ practices in the Midi-Pyrenees region of France. Obviously, using crop data from one location to parameterize a cropping system model to be used across Europe is problematic due to differences in chosen crop varieties and their response to prevailing environmental conditions. Therefore, regional data from the crop survey for the 12 NUTS2 regions (Section 2.2.2) have been used in addition. These “observed” data refer to the average yields and sowing and harvest dates collected in the crop survey for each site class. Yields from agricultural statistics were not used, as they generally refer to administrative and not to biophysical regions.

Table 2 Selected data for soft winter wheat and grain maize from the survey on crop management in three NUTS2 regions, i.e. Podlaskie in Poland, Flevoland in the Netherlands and the Midi-Pyrenees in France. Site class (name)

AEnZ (name)a

Podlaskie – soft winter wheat Nemoral and Medium SOC = 2.46–3.94% Good Nemoral and Nemoral SOC = 3.94–5.66% Good Continental Continental and SOC = 5.66–8.86% Flevoland – soft winter wheat Atlantic North Loam and SOC = 3.94–5.66% Atlantic North Sand and SOC = No information Atlantic Central Clay and SOC = 3.94–5.66% Midi-Pyrenees – grain maize Mediterranean Argilo-calcaire North and SOC = 3.94–5.66% Alpine South Argilo-calcaire and SOC = 1.23–2.46% Atlantic Central Argilo-calcaire and SOC = 2.46–3.94% Mediterranean Boulbene North and SOC = 2.46–3.94% a b

Sowing date (week number)

Harvest date (week number)b

37

32

73

0

0

2.2

37

32

127

0

0

4.2

39

32

102

0

0

3.5

42

33

205

0

0

6.9

42

33

165

0

0

6.4

42

33

205

0

0

7.2

13

41

180

5

1600

6.7

13

41

180

5

1600

8.0

13

41

180

5

1600

6.7

13

41

200

6

1800

7.8

SOC stands for soil organic carbon content. Week number of soft winter wheat refers to the year after sowing.

N input (kg N ha−1 )

Irrigation (number of events)

Irrigation amount (m3 ha−1 )

Yield (t dry matter ha−1 )

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Table 3 Default APES parameters for grain maize, soft wheat and durum wheat using field trial data from the INRA-Toulouse experimental farm, 1996–2002 (Debaeke et al., 2006). Parameter definition

Unit

Light use efficiency Temperature sum from sowing to emergence Temperature sum from emergence to anthesis Temperature sum from anthesis to maturity

g DM MJ−1 ◦ C.d ◦ C.d ◦ C.d

Our study illustrates the consequences of using default crop parameters and presents a low-data method for model calibration using easy-to-collect regional yields, and sowing and harvest dates. Results and method are illustrated using soft wheat, which is grown throughout the EU, grain maize, which is grown from the south to the centre of Europe and finally durum wheat, which is more common in south-western Europe.

Grain maize 3.3 85 1150 635

Soft wheat

Durum wheat

3 148 460 810

3 146 510 800

A perfect model would have an EF of 1.0. A value of 0 means that the model does not predict better than the average of the observed values, i.e. the model is not useful for explaining the variability in observed values. Mean square error (MSE) is: MSE =

n 1

n

(Yi − Yˆ i )

2

i=1

2.3. Simulations

It can be decomposed as (Kobayashi and Salam, 2000): APES has been used to simulate yields of grain maize, soft wheat and durum wheat for all 93 AEnZs in the 12 NUTS2 regions. The model was run using the 25 years weather data (Section 2.2.1). The simulations with APES generate many output variables, but in the present study we focus on yield and temperature sum describing the phenology. Annual simulated yields and temperature sums were averaged over the 25 years simulation period to estimate average yield and temperature sum per AEnZ. To upscale the average AEnZ yields and temperature sums to site classes crop area information of the Farm Accountancy Data Network (FADN) has been used (EC, 2008). The spatial allocation procedure used for converting non-spatial FADN information into spatially explicit AEnZ information is presented in Elbersen et al. (2006) and Kempen et al. (this issue). The average simulated yield and temperature sum of a site class are the area-weighted averages of AEnZ yields and temperature sums in the given site class, respectively. The averaged simulated yields were compared to the ‘observed’ yields obtained from the survey. 2.4. Model calibration using site class data For each site class, we calculated a ratio called Kpheno, which is the observed temperature sum based on information of the crop survey divided by the temperature sum using the default crop parameters based on the INRA-Toulouse experiments. These correction factors were used to multiply the phenological model parameters that determine the degree days between emergence to anthesis and anthesis to maturity (Table 3). Model re-runs with these modified phenological model parameters provided a new set of yields. There are thus two comparisons between observed yields in the 12 regions and simulated yields, i.e. using default APES phenology parameters and using the calibrated phenology parameters. 2.5. Model evaluation Model performance has been evaluated by calculating model efficiency, mean square error, and the terms of mean square error (Wallach, 2006). Modelling efficiency (EF) is defined as:

n EF = 1 −

2 (Y − Yˆ i ) i=1 i 2 n (Y − Y¯ ) i=1 i



where Yi is the observed value in site class i, Yˆ i is the simulated value, Y¯ is the average of the observed values and n is the total number of site classes. The values can refer to temperature sum, yield before correction or yield after correction for the site class.

MSE = Bias2 + SDSD + LCS where: 2

bias2 = (Y¯ − Yˆ¯ ) SDSD = (Y − Yˆ )2 LCS = 2Y Yˆ (1 − r) in which: Y2 =  2ˆ Y

=

r=

n  1

n

2 (Yi − Y¯ )

n  1 i=1

2 (Yˆ i − Yˆ¯ )

n

i=1

n  1

n

 

(Yi − Y¯ )(Yˆ i − Yˆ¯ ) /Y2  2ˆ Y

i=1

Bias is the square of the mean deviation between observed and simulated values. SDSD stands for the Squared Difference between Standard Deviations of observed (Y ) and simulated values (Yˆ ). It measures the difference in variability between observed and simulated values. LCS stands for Lack of Correlation weighted by the Standard deviations, which measures the agreement between observations and simulations. Yˆ¯ is the average of simulated values. 3. Results 3.1. Expert-based management rules: sowing date and fertilizer N application In practice, the timing of sowing depends on many factors such as prevailing and expected weather and associated soil conditions. The collected information in the survey comprised the average week number of sowing of crops in each site class. First, a window of 10 days before and after this week number was defined, providing a ‘sowing window’ of 27 days. Second, the sowing of the crop begins as soon as the plant available water in the top soil is within a specific range (for more details see Janssen et al., 2009b). The lower threshold of this range indicates that the soil is too dry, while the upper threshold indicates that soil conditions are too wet for sowing. The plant available water content of the soil layer is simulated by APES on a daily time-step (Section 2.1). If plant available water remains too low or too high during the sowing window, sowing takes place at the last day of the window. The survey provided only information on the average nitrogen application per crop in each site class. Information on the timing

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Table 4 Timing of nitrogen fertilizer application and thresholds for splitting the total fertilizer amount as provided in the survey for selected crops. Crop/crop group

Nitrogen threshold (kg N ha−1 )

Development stage (DVS)a

Split fraction

Cereals

<100 100–150

0.7 0.2 0.7 0.2 0.7 0.9

1 0.4 0.6 0.3 0.4 0.3

>150

Potato

<150 >150

0 0 1

1 2/3 1/3

Sugar beet

<120 >120

0 0 0.5

1 2/3 1/3

a

DVS = 0.0 at emergence, DVS = 1.0 at anthesis/tuber initiation, and DVS = 2.0 at maturity.

Fig. 2. Timing of sowing, fertilizer application and harvest in sugar beet in Flevoland, the Netherlands using (a) expert-based management rules and (b) management based on fixed dates.

and possible splits of the nitrogen application was not available. In practice, farmers will often aim to temporally match the application of nitrogen fertilizers to the crop needs, and splitting of the total amount of fertilizers is common to improve fertilizer use efficiency. Therefore, for the major European crops and for each crop phenological stage, the timing of fertilizer applications and the thresholds for splitting the total fertilizer amount as provided in the survey have been defined (Table 4). For example, if the nitrogen fertilizer amount in potato is less than 150 kg per ha in the survey, the total amount is applied at crop emergence (DVS = 0.0). If the amount is more than 150 kg N ha−1 two-thirds of the total amount is applied at emergence and the remaining one-third at tuber initiation (DVS = 1.0).

(timing and impact) also simulated yields of sugar beet are affected. Average simulated sugar beet yields using expert-based management rules were 2% higher than the ‘observed’ (survey) yield in Flevoland, while the management using fixed dates resulted in an underestimation of more than 20% of the same yield. Obviously, crop management also affects the environmental impact of cropping systems. Here, we illustrate the effect of the expert-based management rules and management based on fixed dates using simulation results of APES for nitrogen leaching of a typical cropping system in Flevoland including spring soft wheat, winter soft wheat, sugar beet and potatoes (Fig. 3). In all four crops simulated N leaching is lower using the expertbased management rules compared with the fixed management

3.2. Comparison expert-based rules and management based on fixed dates The effect of expert-based management rules for the timing of selected events on simulated yields are illustrated for sugar beet in Flevoland region, the Netherlands. Simulation results are compared with the situation using fixed dates for management events (based on the survey only). The expert management rules are based on a combination of observed (survey) data and prevailing biophysical conditions such as rainfall, plant available water in the soil and crop phenological stage. The simulated timing of management events in sugar beet, therefore, varies among the years compared to using fixed dates for management events (Fig. 2). Although the timing of sowing does not differ much between both approaches, the timing of the nitrogen application and harvest is later using the expert rules. In addition, the amount of nitrogen applied (i.e. 150 kg N ha−1 ) is given in two splits based on the expert-based thresholds for fertilizer applications (Table 4). Through these differences in management

Fig. 3. Nitrogen leaching simulated with APES for a cropping system in Flevoland comprising spring soft wheat, winter soft wheat, sugar beet and potato using expertbased management rules and management using fixed dates.

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the decomposition of MSE is zero, because the simulated values are constant. For maize, the default temperature sum is 1785 ◦ C.d, whereas the average observed temperature sum is 1507 ◦ C.d. Hence, the default maize variety simulated by APES has a longer growth duration than the varieties observed in the site classes. As a result of the large bias for maize, the squared bias contributes most to MSE. Hence, reducing the bias would improve simulations of the temperature sum for maize most. For soft wheat and durum wheat, the bias is smaller and the SDSD term contributes most to MSE. Since the simulated values have no variability, SDSD is equal to the variance of the observed values. Soft wheat has the largest SDSD, because of the largest variability in observed temperature sums (Fig. 4). Improvement of the simulated temperature sum would require that simulated values vary over the range of the observed values. Results for durum wheat are best, with the least negative modelling efficiency and the smallest MSE. The phenology correction Kpheno (Section 2.4) that is proposed, corrects both for bias and differences in variance between observed and calculated values. For maize, Kpheno varies between 0.77 and 0.99 depending on the AEnZ, for soft wheat between 0.71 and 1.50, and for durum wheat between 0.51 and 1.10. The effects of Kpheno on the simulated yields across site classes are shown in Figs. 5 and 6. For maize, model efficiency for yield was −0.16 before correction of the phenology, and after correction of the phenology it was 0.46 (Table 5). Hence, correction of the phenology improved the yield simulations substantially. The lower MSE values for simulated yields, i.e. 2.20 before applying Kpheno and 1.03 after applying Kpheno are consistent with the improved EF (Table 5). The reduced bias in the yield simulation contributes most to this improvement. After the correction, the major contribution to MSE comes from the LCS term, for which there is no simple interpretation. For soft wheat, model efficiency for yield was low but positive before correction (0.19) and hardly changed after the correction (0.20). Similarly, MSE was hardly affected by correcting the phenology, but the contributions to MSE did change. Correcting the phenology eliminated the bias contribution to MSE, but it increased the LCS contribution. For durum wheat, model efficiency was high (0.71) before correction, and was further improved by correcting the phenology to 0.80. MSE was similarly improved. In this case the bias contribution to MSE was already very small before correction and remained so after correction. Correcting the phenology results in a positive modelling efficiency for maize, a relatively high modelling efficiency for durum wheat, and an intermediate result for soft wheat. Correcting the phenology substantially improves the modelling efficiency for maize but hardly improves modelling efficiency for soft and durum wheat. After the correction, the bias term of MSE is very small in all cases. This is important and encouraging, since a small bias means that average yield (averaged over all site classes and over

Fig. 4. Default temperature sums for emergence to harvest derived from the INRAToulouse experiment (y-axis) versus the temperature sums derived from the survey for the 12 regions (x-axis) for grain maize (), soft wheat () and durum wheat (䊉). The points refer to individual site classes.

events. Nitrogen leaching is structurally lower due to expert-based imposed fertilizer splits and the application of nitrogen fertilizer after sowing (Table 4). 3.3. Model calibration using site class data for phenology Comparison of the default temperature sum from emergence to harvest used in APES (Table 3) with the observed temperature sums of three crops in various site classes based on the survey shows considerable deviations (Fig. 4). The variability is particularly large for soft wheat which is cultivated over the greatest range of conditions (82 AEnZs in 11 NUTS2 regions), but it is also appreciable for maize and durum wheat present in 38 and 42 AEnZs and in three and four NUTS2 regions, respectively. A large part of the observed variability in the temperature sum is because varieties with different maturity classes are cultivated in the various site classes to maximise yield and to limit risks of adverse harvest conditions. The default model parameters implicitly assume the same variety with the same development rate in all locations. The results here show the consequences of this oversimplification. Modelling efficiency for the temperature sum is negative for all three crops (Table 5), implying that the model simulations do not capture the observed variability between site classes. The decomposition of MSE for simulating temperature sum involves only the first two terms, the squared bias and the difference in standard deviations (Table 5). The third term (LCS) in

Table 5 Values of the Goodness of fit criteria by crop type. TSUM corresponds to the temperature sum between emergence and harvest, Yield-BPC to the simulated yield before phenology correction and Yield-APC to the simulated yield after phenology correction. Bias2

Crop

Variable

Efficiency

MSE

Grain maize

Tsum Yield-BPC Yield-APC

−2.40 −0.16 0.46

108971 2.20 1.03

76953 0.88 0.01

SDSD 32019 0.32 0.13

LCS 0.00 1.01 0.89

Soft wheat

Tsum Yield-BPC Yield-APC

−0.89 0.19 0.20

91458 2.14 2.12

43017 0.38 0.00

48440 0.42 0.27

0.00 1.34 1.85

Durum wheat

Tsum Yield-BPC Yield-APC

−0.36 0.71 0.80

58835 0.74 0.51

15690 0.07 0.04

43145 0.18 0.25

0.00 0.49 0.22

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(model input data and parameters and data for model evaluation) were initially available at this scale and scaling procedures were required for each type of data. For soil input data, a representative soil type was associated with each AEnZ. For climate input data, a simple average over available climate data (from European Interpolated Climate Data, Section 2.2.1) in each AEnZ was used. Management input data from the survey were available at the scale of site classes. Since site classes include one or several AEnZs, management input data were assigned to AEnZs. These data in combination with the developed generic expert-based management rules, allowed us to simulate crop yields for each AEnZ. Subsequently, these outputs needed to be upscaled again to site classes for which observed yields (from the survey) were available. For upscaling, we used a crop area-weighted average of the AEnZ yields resulting indeed in a much closer match with observed yields than when ignoring the crop areas (data not shown). 4.2. Expert-based management rules

Fig. 5. Simulated (before phenology correction, i.e. with default phenology parameters) versus observed yield for grain maize (), soft wheat () and durum wheat (䊉). The points refer to individual site classes. Numbers above points correspond to the 12 NUTS regions: Andalucia (1); Castilla y Léon (2); Midi-Pyrenees (3); PoitouCharentes (4); Champagne Ardenne (5); Schwaben (6); Northumberland and Tyne and Wear (7); Flevoland (8); Danmark (9); Brandenburg (10); Zachodniopomorskie (11); and Podlaskie (12).

25 years) was well estimated. Average yield is particularly relevant for analyses at EU scale. 4. Discussion 4.1. Scaling of model inputs The spatial unit of analysis/simulation that we chose was the Agro Environmental Zones (Hazeu et al., 2010). None of our data

Although the presented results using expert-based management rules on simulated sugar beet yields and nitrogen leaching of four crops are indicative and need further analysis, they illustrate the effects of specifying management in different ways. Characterizing crop management for large areas in such a way that the information can be used in cropping system models is a major challenge (Leenhardt et al., 2010). The proposed method based on relatively easy-to-collect crop management information which is further detailed using expert knowledge seems a promising way to be further developed and tested in regional studies. It must be noted however, that in our expert rules we assumed similar types of management across the 12 regions. For instance, we assumed split application of nitrogen for all regions, which may not be the case in reality. Such spatial variation in management could either be included in the survey (‘Do farmers split their nitrogen fertilizer application and if so, in how many splits?’) or should be included in the expert rules which may then differ per region. 4.3. Model calibration It is generally accepted that cropping system models should be calibrated for each new range of situations with representative data. Calibration, however, requires detailed data, which are relatively scarce and difficult to collect across, for instance, the EU. In this study, APES was initially calibrated by using crop parameters from one particular site class and then phenology was corrected for regional differences based on survey data. It is likely that not only phenological parameters will vary across regions. For example, the harvest index is likely to depend on crop variety-specific straw lengths. Work is ongoing to obtain calibration data sets from several additional sites, in order to test whether variation across Europe in other crop parameters needs to be taken into account. In this study, it has been assumed that the calibration factor Kpheno applies equally to the period between emergence and anthesis and between anthesis and maturity, and this may not be the case. Moreover, the period between maturity and observed harvest date can be significant, particularly in Northern and Western Europe where crops mature at a time of year when the risk of wet conditions increases. 4.4. Application to the environmental impacts of land use

Fig. 6. Simulated (after phenology correction) versus observed yield for grain maize (), soft wheat () and durum wheat (䊉). The points refer to individual site classes. Numbers above points correspond to the 12 NUTS regions: Andalucia (1); Castilla y Léon (2); Midi-Pyrenees (3); Poitou-Charentes (4); Champagne Ardenne (5); Schwaben (6); Northumberland and Tyne and Wear (7); Flevoland (8); Danmark (9); Brandenburg (10); Zachodniopomorskie (11); and Podlaskie (12).

The methods used here to derive yield are also relevant to calculating point and diffuse pollution from agriculture and greenhouse gas emissions. Rather few experimental observations are available which means that modelling must play an important role. The use of site classes and the method for attributing agro-management

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data are as applicable to environmental impacts as they are to yield (Fig. 3). At first sight, phenology is less relevant. However, the development of the crop canopy affects ground cover which has an important effect on nitrogen leaching.

4.5. Simulated versus actual yields The model input variables do not cover all the variability that can occur in reality, and furthermore, the dependence on the input variables may not be exactly correct, i.e. the model may be mis-specified. This is true even when the model is used for simulating yields at the field scale. The major yield determining factors described by the present model are temperature, solar radiation, water and nitrogen. However, in most farmers’ fields there are important other yield-limiting and yield-reducing factors, e.g. diseases, pests, weeds, poor soil structure, adverse harvest conditions, etc. These will in general increase model error, particularly when the effects of these factors vary from year to year, as often is the case (Jamieson et al., 1999; Landau et al., 2000; Ewert et al., 2002). It might be expected that after correcting the phenology, the biggest differences between observed and simulated yields occur in those areas where such limiting or reducing factors due to poor management are less controlled (e.g. regions in Central and Eastern European countries). In such areas factors not accounted for in the model reduce observed yields and accordingly the model overestimates the simulated yields. This appears to be the case. The four site classes with the largest differences between observed yield and simulated yield after correction of the phenology for soft wheat (Fig. 6) are site classes in the Podlaskie region (12) and the site class with the poorest soil of the Brandenburg region (10). These are the site classes with low observed yields and low total nitrogen input. Under such extensive conditions observed yields may be reduced by pests, weeds, diseases and other constraints which are not simulated by the APES version used in this study.

5. Conclusions We have proposed and successfully implemented low-data approaches for generating detailed management information required for calibrating and running a cropping system model (here APES) across the EU. New regional management information has been collected according to a standardised format from experts. We were able to convert the average and aggregate management information using expert knowledge into operational management rules that can be used by APES. We have illustrated how the specification of management in APES affects simulated crop yields and environmental emissions. Model calibration was based first on comparing the calculated temperature sums and yields using default crop parameters with regionally observed yields, and calculated temperature sums from observed sowing and harvest dates. Based on this comparison, a phenology correction factor Kpheno has been defined for each AEnZ, which was the basis for the second step of the calibration. With Kpheno included, the model results are considerably improved with respect to modelling efficiency for all three crop types. Tackling of both challenges involved the development of various downscaling and upscaling procedures for various data types. Our results suggest that it is possible to capture at least the major variation in yields over Europe by operationalising a cropping system model in the way described in this paper and using only simple measures for correcting for regional differences in phenology. Future efforts should be directed at quantifying the different sources of error and reducing those that are most serious. One way of reducing error would be to identify better calibration strategies.

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Acknowledgements The work presented in this publication has been funded by the SEAMLESS integrated project, EU 6th Framework Programme for Research Technological Development and Demonstration, Priority 1.1.6.3. Global Change and Ecosystems (European Commission, DG Research, contract no. 010036-2). We wish to thank all SEAMLESS partners and regional experts who participated to this work. The authors would like to thank the anonymous referees for their extensive, detailed and constructive comments and suggestions on an earlier version of this manuscript.

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