Simulating regional winter wheat yields using input data of different spatial resolution

Field Crops Research 145 (2013) 67–77

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Simulating regional winter wheat yields using input data of different spatial resolution C. Nendel a,∗ , R. Wieland a , W. Mirschel a , X. Specka a , C. Guddat b , K.C. Kersebaum a a b

Leibniz Centre for Agricultural Landscape Research, Institute of Landscape Systems Analysis, Eberswalder Straße 84, 15374 Müncheberg, Germany Thuringia State Office for Agriculture, Department of Plant Production and Agro-Ecology, Apoldaer Straße 4, 07774 Dornburg-Camburg, Germany

a r t i c l e

i n f o

Article history: Received 27 August 2012 Received in revised form 9 November 2012 Accepted 22 February 2013 Keywords: MONICA Agro-ecosystem model Dynamic modelling Scaling Input data

a b s t r a c t The success of using agro-ecosystem models for the high-resolution simulation of agricultural yields for larger areas is often hampered by a lack of input data. We investigated the effect of different spatially resolved soil and weather data used as input for the MONICA model on its ability to reproduce winter wheat yields in the Federal State of Thuringia, Germany (16,172 km2 ). The combination of one representative soil and one weather station was insufficient to reproduce the observed mean yield of 6.66 ± 0.87 t ha−1 for the federal state. Use of a 100 m × 100 m grid of soil and relief information combined with just one representative weather station yielded a good estimator (7.01 ± 1.47 t ha−1 ). The soil and relief data grid used in combination with weather information from 14 weather stations in a nearest neighbour approach produced even better results (6.60 ± 1.37 t ha−1 ); the same grid used with 39 additional rain gauges and an interpolation algorithm that included an altitude correction of temperature data slightly overpredicted the observed mean (7.36 ± 1.17 t ha−1 ). It was concluded that the apparent success of the first two high-resolution approaches over the latter was based on two effects that cancelled each other out: the calibration of MONICA to match high-yield experimental data and the growth-defining and -limiting effect of weather data that is not representative for large parts of the region. At the county and farm level the MONICA model failed to reproduce the 1992–2010 time series of yields, which is partly explained by the fact that many growth-reducing factors were not considered in the model. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The simulation of agricultural yields has gained in importance recently due to growing concern about food security for a world population that hit the 7 billion mark and is set to continue increasing (Rötter et al., 2011), with a wide range of potential maxima having been projected based on different scenarios (UN, 2011). In addition, agriculture is highly vulnerable to changes in climate, as climate factors are the main drivers of open-field agricultural production. The potential impact of climate change on agriculture is explored in depth in the recent debate on the various challenges agriculture will have to face in a changing climate (Cassman, 2007; Foley et al., 2011; Lobell et al., 2008; Porter et al., 2010; Schmidhuber and Tubiello, 2007; Vermeulen et al., 2012). The analysis of yield gaps and the search for ways to ecologically intensify the world’s staple food production systems are key areas of current environmental research (Bennett et al., 2012; Lobell et al., 2009; Neumann et al., 2010). Also the increasing competition between bioenergy and food production for land and resources demands for

∗ Corresponding author. Tel.: +49 33432 82355; fax: +49 33432 82334. E-mail address: [email protected] (C. Nendel). 0378-4290/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.fcr.2013.02.014

model-based yield predictions for energy cropping scenario analysis (Das et al., 2012; Miguez et al., 2012; VanLoocke et al., 2012). Crop modelling as a tool to predict crop yields under assumed climate and land use scenarios is widely accepted (White et al., 2011); the predictive power of models is currently under investigation, as their prediction error adds to the range of uncertainty produced by global scale climate models and the down-scaling procedures required to drive small-scale crop simulation models (Déqué et al., 2007; Olesen et al., 2007; Zhang et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP, Rosenzweig et al., 2013) and the European Commission’s knowledge hub MACSUR (http://www.macsur.eu) are the most recent initiatives involving a wide range of scientists across the globe tasked with quantifying the uncertainty of crop yield projections obtained from simulations, and find ways of reducing them. Our study adds improved understanding on how the use of climate variables for driving crop models may influence the simulation accuracy. In this investigation real weather data is used; however, the findings can also be transferred to the use of climate scenarios. Crop models run on a single field or crop canopy scale. This is the scale used to test how a model performs against observed data and to prove the reliability of the model’s predictions for crop or soil processes (De Willigen, 1991; Diekkrüger et al., 1995; Kersebaum

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et al., 2007; Palosuo et al., 2011; Rötter et al., 2012). This process is often referred to as “validation”, although the modeller community reached consensus long ago that “validity” cannot be expected from the result of a simulation model (Oreskes et al., 1994; Rykiel, 1996). However, this is the reason why great emphasis must be placed on determining the potential error produced by the model when used to predict processes in a previously unknown – and thus unparameterised – environment. Use of crop models on a large scale – up to the global scale – requires a number of assumptions that do not apply to the scale of model validation (Faivre et al., 2004). The restriction of the input data required to drive the model and – until recently – limited computer power often led to modelling approaches that explained crop growth on planet Earth in a very simplified manner. Simulating crop growth on such large areas was mainly performed in three different ways, whereby the aim of the simulation exercise determined which approach was considered most appropriate: (1) Simple crop growth models were created and crop factors or statistical models used to transform the biomass results into data corresponding to typical observations for different crops in certain areas of the world. In some cases, groups of crops with similar characteristics (plant functional types) were considered (e.g. Bondeau et al., 2007; Deryng et al., 2011; Lobell et al., 2008). (2) State-of-the-art crop models were used to simulate crops at certain sites considered representative for a larger area (catchment, continent, agro-ecological zone; e.g. Alexandrov et al., 2002; Elsgaard et al., 2012; Parry et al., 2004; Wolf and van Diepen, 1995). (3) State-of-the-art crop models were used to simulate crops on a spatial grid in all areas where these crops typically grow (e.g. Liu et al., 2007; Moriondo et al., 2010; Stehfest et al., 2007; Supit et al., 2012). Now that computer power is able to produce high-resolution simulations of large areas using sophisticated process models, it is now possible to investigate the results generated by different approaches for simulating regional crop yields (Andersson et al., 2012; Folberth et al., 2012). Previous works already addressed scaling issues in the context of crop model application (Gimona et al., 2006; Hansen and Jones, 2000; Moen et al., 1994; Therond et al., 2011; Xiong et al., 2008), for which Ewert et al. (2011) provide a theoretical framework. A systematic investigation of the effects of input data aggregation on simulation results was presented by van Bussel et al. (2011) for winter wheat phenology. Several studies demonstrated that simulations for regional crop yields improved – within limits – with using a finer resolution of climate data (de Wit et al., 2005; Easterling et al., 1998; Olesen et al., 2000; Rivington et al., 2006). Soil data aggregation does not seem to affect simulation results much under sufficient water supply of the crop (Easterling et al., 1998; Folberth et al., 2012). However, it may well do so when water is limiting (Baron et al., 2005; Wassenaar et al., 1999). Crop yield simulations may also be strongly affected by aggregating management information, as it was shown for irrigated and rain-fed maize production (Folberth et al., 2012). We chose a federal state of Germany – the Free State of Thuringia for which a set of detailed historical yield records exists – to test how the spatial resolution of input data would affect the result of a spatial simulation. Our study aims to answer two main research questions: the first was to determine whether it is necessary to simulate a region using the highest possible level of detail, including small-scale soil, relief and crop distribution information, in order to produce a regionally representative yield figure for winter wheat (Triticum aestivum L.). For the opposite case of highest possible level of generalisation, we would end up with one carefully selected site and one equally selected crop parameter set that would give an

estimate of the annual winter wheat yield in the region good enough to be representative for the federal state, or at least two or three sub-regions. The second aim was to find out whether the crop model was able to capture the spatial and temporal variability of winter wheat yields at a lower scale. To do this the model results were evaluated also at the county level and the model was additionally tested against field data obtained from experimental stations. The subsequent discussion focuses on which effects of changing factors continue across scales and which become lost in the scaling process.

2. Materials and methods 2.1. Simulation design We simulated winter wheat as a single crop using the Model for Nitrogen and Carbon dynamics in Agro-ecosystems MONICA (Nendel et al., 2011) for the 1992–2010 period. Four different sets of input data were tested, each of which had a different information density. First, we used historical weather data from one single weather station (Erfurt-Bindersleben, 50◦ 58 49 N, 10◦ 58 12 E, 307 m), which was assumed to represent typical weather for most of the agricultural area of Thuringia. Additionally, we used the most abundant soil in Thuringia – a Vertic Cambisol with >60% clay – and conducted one simulation using this soil–weather station combination. This simulation, referred to as the single point simulation, was only analysed at state level, not at county level or below. In a further step, this simulation was repeated using the second most abundant soil – a loamy sand (Dystric Cambisol) – in order to demonstrate the effect caused by the choice of soil. We also repeated this simulation for all combinations of the five most abundant soils with the six Thuringian weather stations Artern, Erfurt-Bindersleben, Gera, Meinigen, Leinefelde and Schmücke (Fig. 1) to quantify the range of results that would arise from the choice of a non-representative soil or weather station. Second, we used the Erfurt-Bindersleben weather station and simulated a 100 m × 100 m grid covering the whole 16,172 km2 of Thuringia. MONICA was executed in a parallel application on a 48 core computer; more than 1.6 × 106 data points were calculated daily. Soil data was taken from the soil map of Germany (BÜK 1000, Hartwich et al., 1995b) in the scale of 1:1,000,000, which also provided information on average within-profile groundwater levels (Hartwich et al., 1995a). Altitude and slope information was derived from a digital elevation model with a resolution of 100 m. This simulation is referred to as the single station simulation. Third, we used the same 100 m × 100 m grid with soil, groundwater, altitude, and slope information, this time using 14 weather stations within and outside of Thuringia. Each grid cell was assigned one nearest weather station using Thiessen polygons (Thiessen, 1911). This simulation is referred to as the nearest neighbour simulation. Finally, we used the 100 m × 100 m grid with soil, groundwater, altitude, and slope information and 14 weather stations within and outside of Thuringia, but added 39 rain gauges from Thuringia. For each grid cell, weather information was interpolated from the weather station data and corrected according to the grid cell’s average altitude. This simulation is referred to as the interpolation simulation. For all simulations carried out on the 100 m × 100 m grid, sandy soils with <25% silt and <8% clay were excluded because winter wheat would not be produced on such soils in Thuringia. Before analysing the simulated yields, the grid was filtered for agricultural land use using CORINE 2000 land cover data (Keil et al., 2005). Yield statistics for each of the 17 counties were provided by the State Government of Thuringia for the 1992–2010 period. The yield statistics

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Fig. 1. Location of the six agricultural experimental stations (), 14 weather stations (䊉) and 39 rain gauges () in Thuringia, Germany.

for another six counties which mainly represent urban areas were neglected, even though there is some agricultural production there. In the framework of scaling methods classified by Ewert et al. (2011) the latter three approaches represent data manipulation methods which average the outputs of multiple model runs. 2.2. The agro-ecosystem simulation model MONICA MONICA is a dynamic, process-based agro-ecosystem simulation model developed from the HERMES model (Kersebaum, 1995, 2007) for simulating crop growth and soil processes in Central Europe (Nendel et al., 2011). It simulates the growth and development of annual crops and perennial grassland as well as the related water, nitrogen and carbon dynamics in the soil–plant–atmosphere system. A capacity approach describes water transport in the soil according to Wegehenkel (2000). Reference evapotranspiration is calculated using the Penman-Monteith method, according to Allen et al. (1998), and crop-specific potential evapotranspiration is computed using crop-specific factors (Kc ) during the growing season. Organic matter turn-over is calculated using algorithms from the DAISY model (Hansen et al., 1991). Crop growth follows the generic approach first presented by the SUCROS model (van Keulen et al., 1982). [CO2 ] affects the crop’s maximum photosynthesis rate and stomatal resistance, which in turn influences transpiration (Nendel et al., 2009). The impact of extreme heat on crop growth and yield formation is considered based on ideas put forward by Challinor et al. (2005) and Moriondo et al. (2010). Maintenance respiration is calculated separately for day and night periods using AGROSIM algorithms (Mirschel and Wenkel, 2007). Root dry matter is distributed over depth according to Pedersen et al. (2010), whereby the rooting depth increases linearly with the thermal sum. Water and N stress reduce crop growth and accelerate crop ontogenesis at specific development stages. MONICA was calibrated to predict the growth of major Central European crops, also under elevated [CO2 ], and tested at length for its ability to predict yields at the field scale in uncalibrated situations in Germany (Nendel et al., 2011), across Europe (Rötter et al., 2012; Salo et al., in preparation) and globally for major food crops and agricultural regions (Rosenzweig et al., 2013). So far, MONICA has not been used to predict crop yields for larger areas. Its original design was developed to achieve optimum

results at the field scale. Accordingly, its parameters were only calibrated to experimental data at the field level. MONICA operated with one sowing date and with one genotype parameterisation for the whole region. N fertiliser applications were modelled according to the Nmin method (Wehrmann and Scharpf, 1979) which is implemented in MONICA. This approach takes the 0–0.9 m soil mineral N stock at 30 March into account for calculation of the bestpractice N application for winter wheat and thus provides sufficient N supply to the crop in most, but not all seasons (Nendel, 2009). Irrigation was not applied. 2.3. MONICA test against field experiment data In many simulation exercises, the simulation model is first calibrated against experimental data from the region of interest – if available – and then, in a second step, used to predict yields or other variables in space or time. We postulate that MONICA has been sufficiently calibrated against field data from Germany (Nendel et al., 2011) and that no further calibration is required to improve the model. However, we use the data available from different experimental stations across Thuringia to challenge the model again on the scale it was initially designed to be applied to. Winter wheat data was provided in the form of mean yields across all varieties grown in field experiments. This is comparable to county statistics in which all varieties grown in the county are integrated. However, contrary to county data, station data provides yields obtained under optimum conditions, which can easily be secured on sufficiently small experimental plots, but rarely under the conditions of large-scale commercial agriculture. Experimental station data were provided for the 1992–2010 period, with a few missing years. The experimental stations sometimes grow their variety trials on different fields that exhibit different soil characteristics. For this reason the soil information used for simulations of the experimental stations may not be representative for parts of the trials in some cases (Table 1). 2.4. Input data for the simulation exercise Soil information was taken from the 1:106 soil map of Germany (BÜK 1000, Hartwich et al., 1995a). The spatial resolution for

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Table 1 Experimental stations in Thuringia. Station

County

Altitude (m)

Mean precipitation (mm a−1 )

Mean temperature (◦ C)

Substrate

Texture

Burkersdorf Dornburg Friemar Großenstein Heßberg Kirchengel

Saale-Orla-Kreis Sömmerda Gotha Altenburger Land Hildburghausen Kyffhäuser-Kreis

440 260 284 300 380 305

642 584 541 606 773 556

7.1 8.3 8.0 8.0 7.4 7.6

Withered rock Loess Loess Loess Withered rock Loess

Sandy loam Clayey silt Loam Loam Clay loam Loam

the simulations was chosen as being the coarsest possible resolution that still enabled the boundaries of soil classes from the soil map to be drawn smoothly. According to this map, each grid cell was assigned a soil type for which basic information is available in terms of a representative soil profile. MONICA uses horizon boundaries, soil texture, soil organic matter contents and bulk density. Soil types with signs of groundwater influence were used as indicators for shallow (within profile) groundwater levels, where the boundary between the oxidised and reduced soil horizon marked the mean groundwater level. MONICA uses the mean groundwater level for soil water content and transport calculations. For Thuringia, seasonal groundwater level fluctuations were assumed to be of minor importance. The vast majority of groundwater-influenced soils in Thuringia are alluvial soils and have their groundwater level regulated by close-by rivers, which do not show large seasonal fluctuations in water level. The digital elevation model for Thuringia provides information on the mean altitude for each grid cell. An average slope within the grid cell was calculated from this information. MONICA uses the slope information to calculate precipitation surface run-off. Daily weather data for 1992–2010 was provided by the German Weather Service (DWD) for 14 weather stations within and outside of Thuringia. Weather information includes minimum and maximum air temperatures (2 m), precipitation, global radiation, mean wind speed (2 m) and air humidity on a daily basis. Additionally, precipitation data from 39 DWD rain gauges was used. For the interpolated simulation, all weather data except precipitation was interpolated and corrected for altitude according to Watson and Philip (1985). The spatial interpolation algorithm is based on a combination of a regression function and an inverse distance algorithm. The regression estimates the response of a climate element (temperature, precipitation, etc.) X(x, y) at the location (x, y) to the altitude h(x, y). X(x, y) = n + m · h(x, y)

(1)

The residual res(xi ,yi ) (x, y) = X(xi , yi ) − X(x, y) of the measured X(xi , yi ) at the location of each climate station (xi , yi ) is used to correct the regression using the inverse distance method



X(x, y) = n + m · h +

(res(xi ,yi ) (x, y))/d(xi ,yi ) (x, y)



(xi ,yi )

(xi ,yi )

1/(d(xi ,yi ) (x, y))

(2)

with the Euclidean distance d(xi ,yi ) (x, y) between the station i and the location (x, y). The regression coefficients n, m were calculated on daily basis to take into account different weather conditions. The rain gauge data was used for the interpolation of precipitation in addition to the climate station data. 2.5. Data treatment and performance indices The yield data obtained from official statistics show a shallow but significant positive trend for each of the 17 counties. However, Gutzler et al. (in preparation) concluded from their analysis of yield trends in Germany that at least the yield trend in the Eastern states of Germany in the 1990s was mainly driven by technology improvement, whereas no further positive trend can be observed after

1998. A technology trend superposing yield data cannot easily be addressed with a biophysical process model such as MONICA. Since it is impossible to clearly mark the end of the technology trend, we decided to use the data untreated. In contrast, yield data from the experimental stations also show a significant positive trend for the period after 1999, showing the continuous improvement of winter wheat varieties towards higher potential yields. We did not attempt to address a temporal shift in varieties using the MONICA model. For this reason, we decided to detrend the data from the experimental stations with a linear model, fitted to each data set. We postulate that the trend elimination improves the comparability between the data and the simulation. For five out of six stations, the linear trend of the linear model ranged between 0.54 (Kirchengel) and 2.02 (Dornburg). For Burkersdorf the trend was −0.09, probably due to a number of missing values. This trend was also eliminated for consistency reasons. The mean winter wheat yield at the stations is also considerably higher than official county statistics. This is due to the fact that great pains are taken to ensure that trials at the station are always maintained in optimum conditions for crop growth. Furthermore, many of the high-performance varieties grown in trials are not yet widely accepted by farmers, which may be due to the effect of the varieties’ performance on many other characteristics, such as pest resistance and lodging tendency. In order to reduce the model performance evaluation to the year-to-year variability of the yield data, data from wheat variety trials were also corrected by the mean bias error MBE of the model prediction. The mean bias error (Addiscott and Whitmore, 1987) summarises the average error of model predictions and, as such, identifies any overpredictions or underpredictions generated by the model. The mean absolute error (MAE, Shaeffer, 1980) was also used as an additional performance measure. MAE measures the average magnitude of prediction errors, however, without indicating the direction of deviation. MAE can be normalised by dividing the figure by the mean of the observations, yielding the normalised mean absolute error (nMAE). Finally, modelling efficiency (ME, Nash and Sutcliffe, 1970) was used. ME is another common index based on the correlation between observed and predicted values. It ranges from −∞ to 1 and finds its optimum when ME equals 1. A value of zero indicates that the model is no better estimator than the observed mean. Here, ME was calculated for the simulation against the detrended yield data (MEd ) and against the detrended and mean-corrected yield data (MEdc ). 3. Results The mean observed winter wheat yield for Thuringia during the 1992–2010 period was 6.66 t ha−1 , with a standard error of 0.87 t ha−1 . This observation is represented by a black line and a grey bar in Fig. 2. Using only one representative weather station and the most abundant soil (single point simulation, boxplot A), MONICA calculated 5.47 ± 2.37 t ha−1 , underestimating the observed mean yield significantly by 1.19 t ha−1 . When the second most abundant soil was used instead (single point simulation, boxplot B), MONICA predicted 7.98 ± 1.18 t ha−1 , significantly overestimating the observed mean yield by 1.32 t ha−1 (Fig. 2). Other combinations of

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namely by 0.48 t ha−1 (Table 2, MBE). However, data from three relatively high-yielding years were missing at Burkersdorf, reducing the overall mean for this site (Table 2, O). The MAE of the simulation ranged between 1.52 and 2.37 t ha−1 (Table 2, MAE), an error which accounts for 17–27% of the mean (Table 2, nMAE). Consequently, the Nash-Suttcliffe modelling efficiency was negative for all sites, and thus below the acceptable range of [0;1] (Table 2, MEd ). Correcting the observed yield at each location by the respective MBE in order to limit the model evaluation to the year-to-year differences in yield slightly improved the result for two of the six locations, but worsened it for two others (Table 2, MEdc ). Additional meta information on the year-to-year conditions for cereal production, obtained from annual agricultural reports, provides insight into specific reasons for deviations between observed yields and model predictions. Table 3 summarises the years in which the wheat yield was significantly reduced at certain locations. Some of the growth-reducing factors (van Ittersum and Rabbinge, 1997) are not considered in MONICA, which may explain some of the model’s overpredictions in those years. Fig. 2. Box plot of 1992–2010 winter wheat yields of the Federal State of Thuringia, Germany, as simulated using the MONICA model for a point simulation with weather data from a single station and the most abundant soil (A, n = 19 years), a single station and the second most abundant soil (B, n = 19 years), all combinations of six weather stations with the five most abundant soils (C, n = 570 = 19 years × 5 soils × 6 weather stations) and for a high-resolution simulation across 17 counties with weather data from a single station (n = 323 = 19 years × 17 county means), with weather data from the nearest weather station (nearest neighbour, n = 323) and with interpolated and altitude-corrected data from weather stations and rain gauges (interpolation, n = 323). The black line and the grey band represent mean and standard error, respectively, derived from official yield statistics.

soil and weather data generally yielded no better results, some of them rather far off, as demonstrated by the error shown in Fig. 2 (single point simulation, boxplot C). Only two combinations came close to the observed mean, both of which included the fourth most abundant soil, a loamy Rendzic Leptosol developed from withered rock (data not shown). When additional soil information was included (single station simulation), the yield generated was 7.01 ± 1.47 t ha−1 . The nearest neighbour approach yielded 6.60 ± 1.37 t ha−1 and the interpolated weather information led to a prediction of 7.36 ± 1.17 t ha−1 (Fig. 2). The latter three simulation results differ insignificantly from the observed value. The approaches based on highly resolved soil information were additionally analysed with regard to their performance on the next lower spatial scale, at county level. The single station, nearest neighbour and interpolation simulations of the time series in each of the counties yielded very different results. Three examples of counties are highlighted (Fig. 6). For “Altenburger Land” – a county dominated by loess soils that produce the highest average wheat yields in Thuringia – all three approaches were able to reproduce the county mean and–albeit far from perfectly – inter-annual dynamics. For “Sömmerda” county the observed mean was overestimated by 1.60, 1.42 and 1.31 t ha−1 , respectively, and all three approaches – in a similar manner – failed to reproduce inter-annual dynamics. For “Hildburghausen” county the observed mean was overestimated and underestimated by the three approaches. All three approaches failed in different ways to reproduce inter-annual dynamics. The scatterplot of simulated winter wheat yields at county level against observed yields – here for the interpolation approach only – shows that year-to-year yield dynamics were not reproduced by the model (Fig. 7). Testing the model against the detrended mean data from variety trials at six different experimental stations in Thuringia (Fig. 8) revealed that the model underestimated the observed mean at five of the six stations by between 1.04 and 1.98 t ha−1 (Table 2, MBE). The model only overestimated the mean yields at Burkersdorf,

4. Discussion 4.1. Model applicability and data availability at different spatial scales Simulation models are commonly evaluated against observed data to prove that the joint functioning of all the implemented processes in the model produces a similar behaviour to that of the described system (Håkanson, 1995; Nendel et al., 2011). This data is usually obtained from field experiments, which always harbour a larger number of uncertainties compared to laboratory experiments, most of which are due to heterogeneities of the environmental conditions in the field (Kersebaum et al., 2002). Biophysical process models are unable to capture the whole range of possible conditions at the field scale because the investigated system is open (Oreskes et al., 1994). However, such models can still predict one of the conditions, which ideally should produce a mean predictor of any desired target variable. Further scaling to larger spatial units causes similar problems, albeit with fewer data to drive and evaluate the model (Hansen and Jones, 2000). The philosophy behind biophysical models is based on the assumption that biological and physical processes are the same everywhere. Once calibrated, a model should be applicable in similar ecosystems all over the world, as long as all relevant processes are considered. Accordingly, it should not be a problem to use a model calibrated for a specific crop on the field scale for the same crop in a larger area, even though the purpose-oriented design of many models may give reason for limitations, e.g. due to the consideration or non-consideration of particular processes. However, even for a perfect model the data problem remains (Hoogenboom, 2000). Driving variables derived from soil or weather information are available in much lower density and quality (Rivington et al., 2006). It could be that one or another essential driving variable is not available at all. On the evaluation side, target variables may be measured – or even only estimated – at a few points or may only be available as average figures for a larger spatial unit. In our case, winter wheat yields were averaged across a range of variety trials to produce a mean yield figure for an experimental station and across a large number of estimates provided by farmers to produce a mean yield figure for a county. 4.2. Evaluation of simulation results at different scales Beginning at the largest scale, MONICA simulations were closer to the observed federal state mean when using detailed soil

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Table 2 Performance indices for MONICA simulations of 1992–2010 winter wheat yield data from the experimental stations (average across all grown varieties), including the number of data pairs n, the mean observed winter wheat yield O, the mean bias error of prediction MBE, the mean absolute error of prediction MAE, the normalised mean absolute error of prediction nMAE, the Nash–Suttcliffe modelling efficiency against detrended yield data MEd and against detrended and mean-corrected yield data MEdc . Station

n

O (t ha−1 )

MBE (t ha−1 )

MAE (t ha−1 )

nMAE

MEd

MEdc

Friemar Burkersdorf Dornburg Großenstein Heßberg Kirchengel

18 15 18 19 15 19

8.80 7.20 9.82 9.43 8.95 8.94

−1.04 0.48 −1.98 −1.78 −1.74 −1.90

1.52 1.66 2.02 2.07 1.94 2.37

0.17 0.23 0.21 0.22 0.22 0.27

−0.23 −1.20 −0.17 −0.48 −1.64 −1.31

−0.34 −1.52 −1.20 −1.26 −0.39 −0.09

Table 3 Growth-reducing factors that occurred during the 1992–2010 period at experimental stations Burkersdorf (B), Friemar (F), Dornburg (D), Großenstein (G), Heßberg (H) and Kirchengel (K) in Thuringia. Only those years in which MONICA generated significant overpredictions were considered. Year

Observed growth-reducing factor

Location

Considered in MONICA

1992

Late emergence due to autumn drought Tiller abortion due to warm spring Second growth due to rainfall Late emergence due to autumn drought Frost damage Tiller abortion due to warm spring Lodging Late emergence due to low temperatures Frost damage Silting due to heavy rain (oxygen stress) Damage by rodents Damage by wheat bulb fly Ponding damage Tiller abortion due to warm spring Heat stress Lodging and harvest delay due to rainfall Crop withering due to April frost Summer drought Heat stress Spring drought Harvest delay due to rainfall, second growth

Alla

Yes No No Yes No No No Yes No No No No Yes No Yes No No Yes Yes Yes No

1993

1994

1998 1999 2000 2002 2003

2007 a b c

All H All F Allc D B F K F D, G, H, K B, D, F, G, K All

Allb

No data available for Friemar and Burkersdorf. No data available for Burkersdorf. No data available for Heßberg.

information. Although there were two combinations of a soil and a weather station that produced the federal state mean very well, these combinations were not amongst the first a-priori selection. No convincing results were produced by choosing the most common or second-most common soil and the most representative weather station according to local experts. In addition to failing to predict the observed federal state mean, they also produced a much higher standard error than the observations. Use of a highly resolved grid of soil and relief information improved the prediction of the federal state mean and also provided a reasonable prediction of the standard error in time and space. The interpolation approach delivered the smoothest pattern of yields, including a clear accentuation of the Thuringian Forest and the Thuringian Basin north of it (Fig. 5). The reason why the federal state mean was overpredicted using the interpolation approach is due to the fact that MONICA was calibrated to field experiments that usually produce higher yields than the average farmer’s field (Hansen and Jones, 2000). In this case, the difference between simulation and observation is 0.7 t ha−1 which could be regarded as a bias corrector for regional simulations of MONICA for winter wheat. However, the temporal relationship between simulated and observed yield is very weak (Fig. 7) and a bias correction following the example of Jagtap and Jones (2002) would not substantially improve the prediction. Concerning the spatial pattern, the single station approach performed in a similar way as the interpolation approach. However, dramatically low winter wheat yields were simulated for clay soils around the Thuringian Basin and south of the Thuringian Forest under the influence of weather recorded at Erfurt-Bindersleben

weather station (orange-red colours in Fig. 3). These low yields from clay soils dragged the simulated federal state mean down to what in the end seemed to be a very good result when compared to the observations. A similar effect was observed for the nearest neighbour approach. Here, the sharp edges of the Thiessen

Fig. 3. 1992–2010 mean winter wheat yields in Thuringia, Germany, simulated on a 100 m × 100 m grid of soil and relief information using the MONICA model with weather data from Erfurt-Bindersleben meteorological station.

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Fig. 4. 1992–2010 mean winter wheat yields in Thuringia, Germany, simulated on a 100 m × 100 m grid of soil and relief information using the MONICA model with weather data from 14 weather stations following a nearest neighbour (Thiessen polygon) approach.

polygons show through the spatial yield pattern, drawing a quite unrealistic picture in the areas further away from the weather stations. Fig. 4 reveals that the mountain weather station Schmücke, located 937 m above sea level, covered quite a large polygon, much larger than the Thuringian Forest. This means that the agricultural area at the foot of the mountain range was simulated with weather data recorded at Schmücke weather station, including the low temperatures typical for this altitude. The respective polygon clearly shows significantly lower yields than the other polygons, again dragging the simulated federal state mean down and accidently corresponding quite well to the observed federal state mean (Fig. 4). At county level, it becomes apparent that the model was able to reproduce the county mean for some counties, but overpredicted or underpredicted it for others. The three different approaches that used high-resolution soil and relief information (single station, nearest neighbour and interpolation) performed quite differently, none of them showing any clear advantage over the others. More importantly, however, the model was unable to reproduce inter-annual yield dynamics in most of the counties. However,

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Fig. 6. 1992–2010 mean winter wheat yields for three of 17 counties from the Federal State of Thuringia, Germany, observed (black line) and simulated using the MONICA model on a 100 m × 100 m soil and relief information grid with weather information from a single station (grey line), the nearest of 14 weather stations (light grey line) and interpolated and altitude-corrected from 14 weather stations and 39 rain gauges (grey scattered line).

MONICA has already shown its ability to simulate winter wheat yields in uncalibrated situations, including two long-term field trials across different winter wheat varieties in Saxony, the federal state that borders Thuringia to the east. Here, the yield level was also considerably underpredicted, but inter-annual dynamics were well reproduced (Nendel et al., 2011). The farm level, which comprises different variety trials at each location, performs similarly. However, in addition to the inability to reproduce inter-annual yield dynamics, the model also significantly underpredicted the average yield level of the trials. This finding was almost expected, as all of the trials included high-performance winter wheat varieties to which MONICA was not calibrated. The experimental data used for model calibration included varieties that are well established on the market (Nendel et al., 2011). The difference between the two yield levels amounts to between 1 and 2 t ha−1 . 4.3. Following changing factors across scales

Fig. 5. 1992–2010 mean winter wheat yields in Thuringia, Germany, simulated on a 100 m × 100 m grid of soil and relief information using the MONICA model with interpolated and altitude-corrected temperature data from 14 weather stations and additional precipitation data from 39 rain gauges.

The overall effect of weather on crops can be seen on the field, county and federal state scale. Drought and heat stress impacts during growth, the ontogenesis-accelerating effect of warm temperature, late frost as well as excessive rainfall during the harvest period are visible as significant yield reductions across all scales. In contrast, mild temperatures, high radiation input and precipitation evenly distributed over time produce bumper yields, also across all scales. The effect of a rising atmospheric CO2 concentration is likely to add to this; however, the considered time period in this study is too short to identify any CO2 effect on wheat yields. The spatial distribution of soils and relief in a landscape leads to a differentiation of weather impact on yields, even though the weather affects the crop produced on a landscape in a similar manner. Whilst loam soils with a high storage capacity for plantavailable water may maintain sufficient water supplies to the crop during shorter periods of drought, sand and clay soils may not. Hill tops dry out more quickly as soil water descends towards depressions, where it provides extra moisture to a thirsty crop or – in case of excessive water supply – drowns it (Manning et al., 2001; Priyashantha et al., 2007). The differentiation of crop growth by relief is even visible at the field scale. If high-resolution soil and

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Fig. 7. Simulated 1992–2010 mean winter wheat yields for 17 Thuringia counties using interpolated weather data vs. observed yields from official statistics (n = 323). Highlighted A: Counties Hildburghausen (×), Sömmerda (), Altenburger Land (+); B: Years 1992 (), 2000 (×), 2002 (), 2006 (䊉).

relief data is available, biophysical simulation models are good at capturing these differences (Reuter and Kersebaum, 2009; Sadler et al., 2000; Wendroth et al., 2011). However, sub-surface water flow is only considered in a few three-dimensional models (e.g. Hanson et al., 2004; Paydar and Gallant, 2008). At the field level, the difference between soils may still be small and less important than the relief effect. However, if the relief amplitude remains within a certain range across the landscape, a model can successfully predict mean crop growth for a specific soil, and soil patterns become more important. At county level, weather may be still rather homogeneous whilst soils may not. However, macro-relief may then become the most important factor (mountain ranges, valleys, long-range altitude gradients). With the example of Thuringia, some counties include parts of the Thuringian forest, a mountain ridge peaking at 983 m above sea level. For submontane areas, the lower average temperature at higher altitudes partly superimposes the differences in crop growth on different soils within a county. The presence of a macro-relief may also significantly affect rainfall patterns. Effects of smaller relief structures (undulating moraine landscape, river

terraces, etc.) become less important, also because data availability becomes insufficient. Even a 100 m × 100 m grid of relief information as used in our study is too coarse to capture the true slope angles of the relief properly. Solar angle (Reuter et al., 2005) and rainfall surface run-off calculations (Nippgen et al., 2011; Rihani et al., 2010; Saue and Kadaja, 2009) become erroneous. For higher spatial levels – national or continental – Rivington et al. (2006) demonstrated the uncertainty introduced to a simulation by using weather data via the nearest neighbour approach over larger distances. They found that meteorological stations could be easily substituted by others within a few 10 km range, but large errors were introduced by using meteorological stations from over 100 km distance. 4.4. Growth-reducing factors and processes not considered in MONICA The analysis of the meta data provided with annual yield figures for Thuringia revealed that there were many reasons why winter wheat was not produced at the maximum level of its physiologi-

Fig. 8. Difference between MONICA-simulated 1992–2010 winter wheat yields and detrended, mean-corrected observed yield data averaged across all grown varieties at different experimental stations in Thuringia, Germany. Missing data ().

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cal potential. The first group of factors relate to climate: extreme heat and frost damaged the crop and reduced its ability to produce grain. Accelerated ontogenesis due to warm temperatures shortened the grain filling period and led to lower 1000 grain weights. Drought and the associated limitation to take up soil mineral nitrogen reduced photosynthesis or halted it altogether. These processes are considered in MONICA and should be captured by the model. A second group of factors also relate to climate-influenced plant physiological processes. The only difference between this group and the first is that these processes are not considered in MONICA. This group includes tiller initiation of winter wheat in relation to the soil mineral N supply in autumn, tiller abortion due to spring drought or warmth and tiller regrowth during a rainy harvest period and the production of green grains that intermingle with matured grain and may clog the elevators of older combine harvesters; no mention is made of the subsequent problems with grain storage if a part of the grain yield is still green. The model concept of MONICA is source-driven, whilst consideration of these processes would require the additional modelling of sink physiology. Ponding and subsequent oxygen deficiency of the crop is considered in MONICA. However, the spatial distribution of ponds in a field, their extension and their infiltration rate is extremely variable and difficult to predict. A third group of factors, also related to climate, is not considered in MONICA because the weather data required to drive the respective algorithms is not available in sufficient quality and temporal resolution to deliver acceptable simulations. This includes stem lodging, for which the speed and duration of wind gusts and rain intensity are decisive factors, and crop damage from hail, the appearance of which is often so local that it cannot be measured or predicted well in scenarios. Finally, a fourth group of factors includes technological reasons for failing to gain a perfect yield. Amongst others, it includes field inaccessibility due to rain-soaked soils, machine failure or unavailability and the failure of individual farmers to protect their crops or to perform fertiliser management. So far, there has been insufficient data to feed a crop simulation model to enable it to simulate these effects. However, whole-farm modelling approaches and agentbased modelling may be of use in this respect (e.g. Rebaudo and Dangles, 2011).

5. Conclusions It is apparent from the present simulation study that the good result obtained for the reproduction of federal state-level winter wheat yields using single station and nearest neighbour simulation is based on compensation of errors that operate at the lower scale and affect yield estimates in different directions: on the one hand, the model overestimates yields at the farm level due to the calibration with data from optimum-conditioned field experiments that inadequately represent average agricultural production. On the other hand, the weather information provided with inadequate spatial distribution leads to considerable underestimation of yields in some areas. When using the single station and nearest neighbour approach, these two effects cancel each other out. Weather information is distributed much more accurately in space when the interpolation approach is used. Hence, using this approach the overprediction of yields due to inadequate calibration of the model at the federal state level prevails. We learned from this exercise that in regions with agricultural production across a significant altitude range the use of Thiessen polygons for extrapolation of weather data may include climate–soil combinations that are not representative for the production area. Same applies for the use of one representative climate station, for which the area of

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representativeness may be even smaller than the range identified by Rivington et al. (2006). The simulation result for the experimental stations and counties in Thuringia reveal a number of growth-reducing factors for which none of the currently available crop models can account for. Harvest problems related to technology are not yet considered in crop models and lodging of cereals after heavy storms, hail damage and the failure of farmers to conduct crop protection management pose a distinct challenge to modellers. Some of these growthreducing factors may be addressed in the future. Model approaches for cereal lodging have already been presented (Berry et al., 2003, 2006; Cui and Shen, 2011; Martinez-Vazquez and Sterling, 2011) and it also seems feasible to be able to develop a model approach for field accessibility. It will be interesting to see how combining remote sensing with improved bio-physical and stochastic simulation models (de Wit et al., 2012; de Wit and van Diepen, 2007; Hu and Mo, 2011; Nearing et al., 2012) will contribute to further development of model-based regional yield estimation (Moen et al., 1994; Moulin et al., 1998; Savary et al., 2006). Acknowledgement The authors gratefully acknowledge the provision of climate data by the German Weather Service. References Addiscott, T.M., Whitmore, A.P., 1987. Computer-simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring. J. Agric. Sci. 109, 141–157. Alexandrov, V., Eitzinger, J., Cajic, V., Oberforster, M., 2002. Potential impact of climate change on selected agricultural crops in north-eastern Austria. Glob. Change Biol. 8, 372–389. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Roma. Andersson, J.C.M., Zehnder, A.J.B., Wehrli, B., Yang, H., 2012. Improved SWAT model performance with time-dynamic Voronoi tessellation of climatic input data in Southern Africa. J. Am. Water Resour. Assoc. 48, 480–493. Baron, C., Sultan, B., Balme, M., Sarr, B., Traore, S., Lebel, T., Janicot, S., Dingkuhn, M., 2005. From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact. Philos. Trans. R. Soc. B: Biol. Sci. 360, 2095–2108. Bennett, A.J., Bending, G.D., Chandler, D., Hilton, S., Mills, P., 2012. Meeting the demand for crop production: the challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71. Berry, P.M., Sterling, M., Baker, C.J., Spink, J., Sparkes, D.L., 2003. A calibrated model of wheat lodging compared with field measurements. Agric. For. Meteorol. 119, 167–180. Berry, P.M., Sterling, M., Mooney, S.J., 2006. Development of a model of lodging for barley. J. Agron. Crop. Sci. 192, 151–158. Bondeau, A., Smith, P.C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze-Campen, H., Müller, C., Reichstein, M., Smith, B., 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706. Cassman, K.G., 2007. Climate change, biofuels, and global food security. Environ. Res. Lett. 2, 1–3, 011002. Challinor, A.J., Wheeler, T.R., Craufurd, P.Q., Slingo, J.M., 2005. Simulation of the impact of high temperature stress on annual crop yields. Agric. For. Meteorol. 135, 180–189. Cui, H.L., Shen, H.S., 2011. Modeling and simulation of buckling and postbuckling of plant stems under combined loading conditions. Int. J. Appl. Mech. 3, 119–130. Das, S., Priess, J.A., Schweitzer, C., 2012. Modelling regional scale biofuel scenarios – a case study for India. Glob. Change Biol. Bioenergy 4, 176–192. De Willigen, P., 1991. Nitrogen turnover in the soil-crop system – comparison of 14 simulation models. Fert. Res. 27, 141–149. de Wit, A.J.W., Boogaard, H.L., van Diepen, C.A., 2005. Spatial resolution of precipitation and radiation: the effect on regional crop yield forecasts. Agric. For. Meteorol. 135, 156–168. de Wit, A.J.W, Duveiller, G., Defourny, P., 2012. Estimating regional winter wheat yields with WOFOST through the assimilation of green area index retrieved from MODIS observations. Agric. For. Meteorol. 164, 39–52. de Wit, A.M., van Diepen, C.A., 2007. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agric. For. Meteorol. 146, 38–56. Déqué, M., Rowell, D.P., Luthi, D., Giorgi, F., Christensen, J.H., Rockel, B., Jacob, D., Kjellstrom, E., de Castro, M., van den Hurk, B., 2007. An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim. Change 81, 53–70.

76

C. Nendel et al. / Field Crops Research 145 (2013) 67–77

Deryng, D., Sacks, W.J., Barford, C.C., Ramankutty, N., 2011. Simulating the effects of climate and agricultural management practices on global crop yield. Glob Biochem. Cycles 25, GB2006. Diekkrüger, B., Söndgerath, D., Kersebaum, K.C., McVoy, C.W., 1995. Validity of agroecosystem models – a comparison of results of different models applied to the same data set. Ecol. Model. 81, 3–29. Easterling, W.E., Weiss, A., Hays, C.J., Mearns, L.O., 1998. Spatial scales of climate information for simulating wheat and maize productivity: the case of the US Great Plains. Agric. For. Meteorol. 90, 51–63. Elsgaard, L., Borgesen, C.D., Olesen, J.E., Siebert, S., Ewert, F., Peltonen-Sainio, P., Rotter, R.P., Skjelvag, A.O., 2012. Shifts in comparative advantages for maize, oat and wheat cropping under climate change in Europe. Food Addit. Contam. A: Chem. Anal. Control Expo. Risk Assess. 29, 1514–1526. Ewert, F., van Ittersum, M.K., Heckelei, T., Therond, O., Bezlepkina, I., Andersen, E., 2011. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agric. Ecosyst. Environ. 142, 6–17. Faivre, R., Leenhardt, D., Voltz, M., Benoit, M., Papy, F., Dedieu, G., Wallach, D., 2004. Spatialising crop models. Agronomie 24, 205–217. Folberth, C., Yang, H., Wang, X.Y., Abbaspour, K.C., 2012. Impact of input data resolution and extent of harvested areas on crop yield estimates in large-scale agricultural modeling for maize in the USA. Ecol. Model. 235, 8–18. Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., O’Connell, C., Ray, D.K., West, P.C., Balzer, C., Bennett, E.M., Carpenter, S.R., Hill, J., Monfreda, C., Polasky, S., Rockstrom, J., Sheehan, J., Siebert, S., Tilman, D., Zaks, D.P., 2011. Solutions for a cultivated planet. Nature 478, 337–342. Gimona, A., Birnie, R.V., Sibbald, A.R., 2006. Scaling up of a mechanistic dynamic model in a GIS environment to model temperate grassland production at the regional scale. Grass Forage Sci. 61, 315–331. Gutzler, C., Nendel, C., Mirschel, W. Yield development in Germany between 1980 and 2010: a trend analysis for winter wheat, winter rye, rapeseed and silage maize, in preparation. Håkanson, L., 1995. Optimal size of predictive models. Ecol. Model. 78, 195–204. Hansen, J.W., Jones, J.W., 2000. Scaling-up crop models for climate variability applications. Agric. Syst. 65, 43–72. Hansen, S., Jensen, H.E., Nielsen, N.E., Svendsen, H., 1991. Simulation of nitrogen dynamics and biomass production in winter-wheat using the Danish simulationmodel DAISY. Fert. Res. 27, 245–259. Hanson, P.J., Amthor, J.S., Wullschleger, S.D., Wilson, K.B., Grant, R.F., Hartley, A., Hui, D., Hunt, E.R., Johnson, D.W., Kimball, J.S., King, A.W., Luo, Y., McNulty, S.G., Sun, G., Thornton, P.E., Wang, S., Williams, M., Baldocchi, D.D., Cushman, R.M., 2004. Oak forest carbon and water simulations: model intercomparisons and evaluations against independent data. Ecol. Monogr. 74, 443–489. Hartwich, R., Behrens, J., Eckelmann, W., Haase, G., Richter, A., Roeschmann, G., Schmidt, R., 1995a. Bodenübersichtskarte der Bundesrepublik Deutschland 1:1 000 000 (BÜK1000) – Erläuterungen und Textlegende (Beiheft zur Karte). Hanover, Germany. Hartwich, R., Haase, G., Richter, A., Roeschmann, G., Schmidt, R., 1995b. Soil survey map of Germany 1:1 000 000 (BÜK1000). Hanover, Germany. Hoogenboom, G., 2000. Contribution of agrometeorology to the simulation of crop production and its applications. Agric. For. Meteorol. 103, 137–157. Hu, S., Mo, X.G., 2011. Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Ecol. Model. 222, 2530–2541. Jagtap, S.S., Jones, J.W., 2002. Adaptation and evaluation of the CROPGRO-soybean model to predict regional yield and production. Agric. Ecosyst. Environ. 93, 73–85. Keil, M., Kiefl, R., Strunz, G., 2005. CORINE Land Cover 2000 – Germany. Final Report. Oberpfaffenhofen, Germany. Kersebaum, K.C., 1995. Application of a simple management model to simulate water and nitrogen dynamics. Ecol. Model. 85, 145–156. Kersebaum, K.C., 2007. Modelling nitrogen dynamics in soil-crop systems with HERMES. Nutr. Cycl. Agroecosyst. 77, 39–52. Kersebaum, K.C., Hecker, J.-M., Mirschel, W., Wegehenkel, M., 2007. Modelling water and nutrient dynamics in soil-crop systems: a comparison of simulation models applied on common data sets. In: Kersebaum, K.C., Hecker, J.-M., Mirschel, W., Wegehenkel, M. (Eds.), Modelling Water and Nutrient Dynamics in Soil Crop Systems. Springer, Stuttgart, pp. 1–17. Kersebaum, K.C., Lorenz, K., Reuter, H.I., Wendroth, O., 2002. Modeling crop growth and nitrogen dynamics for advisory purposes regarding spatial variability. In: Ahuja, L.R., Ma, L.W., Howell, T.A. (Eds.), Agricultural System Models in Field Research and Technology Transfer. Lewis Publishers, Boca Raton, USA, pp. 229–251. Liu, J.G., Williams, J.R., Zehnder, A.J.B., Yang, H., 2007. GEPIC – modelling wheat yield and crop water productivity with high resolution on a global scale. Agric. Syst. 94, 478–493. Lobell, D.B., Burke, M.B., Tebaldi, C., Mastrandrea, M.D., Falcon, W.P., Naylor, R.L., 2008. Prioritizing climate change adaptation needs for food security in 2030. Science 319, 607–610. Lobell, D.B., Cassman, K.G., Field, C.B., 2009. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 204. Manning, G., Fuller, L.G., Eilers, R.G., Florinsky, I., 2001. Soil moisture and nutrient variation within an undulating Manitoba landscape. Can. J. Soil. Sci. 81, 449–458. Martinez-Vazquez, P., Sterling, M., 2011. Predicting wheat lodging at large scales. Biosyst. Eng. 109, 326–337. Miguez, F.E., Maughan, M., Bollero, G.A., Long, S.P., 2012. Modeling spatial and dynamic variation in growth, yield, and yield stability of the bioenergy crops

Miscanthus × giganteus and Panicum virgatum across the conterminous United States. Glob. Change Biol. Bioenergy 4, 509–520. Mirschel, W., Wenkel, K.-O., 2007. Modelling soil–crop interactions with AGROSIM model family. In: Kersebaum, K.C., Hecker, J.-M., Mirschel, W., Wegehenkel, M. (Eds.), Modelling Water and Nutrient Dynamics in Soil Crop Systems. Springer, Stuttgart, pp. 59–74. Moen, T.N., Kaiser, H.M., Riha, S.J., 1994. Regional yield estimation using a crop simulation model – concepts, methods, and validation. Agric. Syst. 46, 79–92. Moriondo, M., Bindi, M., Kundzewicz, Z.W., Szwed, M., Chorynski, A., Matczak, P., Radziejewski, M., McEvoy, D., Wreford, A., 2010. Impact and adaptation opportunities for European agriculture in response to climatic change and variability. Mitig. Adapt. Strateg. Glob. Change 15, 657–679. Moulin, S., Bondeau, A., Delecolle, R., 1998. Combining agricultural crop models and satellite observations: from field to regional scales. Int. J. Remote Sens. 19, 1021–1036. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models. Part I. A discussion of principles. J. Hydrol. 10, 282–290. Nearing, G.S., Crow, W.T., Thorp, K.R., Moran, M.S., Reichle, R.H., Gupta, H.V., 2012. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: an observing system simulation experiment. Water Resour. Res. 48, W05525. Nendel, C., 2009. Evaluation of Best Management Practises for N fertilisation in regional field vegetable production with a small scale simulation model. Eur. J. Agron. 30, 110–118. Nendel, C., Berg, M., Kersebaum, K.C., Mirschel, W., Specka, X., Wegehenkel, M., Wenkel, K.O., Wieland, R., 2011. The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecol. Model. 222, 1614–1625. Nendel, C., Kersebaum, K.C., Mirschel, W., Manderscheid, R., Weigel, H.J., Wenkel, K.O., 2009. Testing different CO2 response algorithms against a FACE crop rotation experiment. NJAS: Wageningen J. Life Sci. 57, 17–25. Neumann, K., Verburg, P.H., Stehfest, E., Muller, C., 2010. The yield gap of global grain production: a spatial analysis. Agric. Syst. 103, 316–326. Nippgen, F., McGlynn, B.L., Marshall, L.A., Emanuel, R.E., 2011. Landscape structure and climate influences on hydrologic response. Water Resour. Res. 47, W12528. Olesen, J.E., Bocher, P.K., Jensen, T., 2000. Comparison of scales of climate and soil data for aggregating simulated yields of winter wheat in Denmark. Agric. Ecosyst. Environ. 82, 213–228. Olesen, J.E., Carter, T.R., Díaz-Ambrona, C.H., Fronzek, S., Heidmann, T., Hickler, T., Holt, T., Minguez, M.I., Morales, P., Palutikof, J.P., Quemada, M., Ruiz-Ramos, M., Rubaek, G.H., Sau, F., Smith, B., Sykes, M.T., 2007. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Clim. Change 81, 123–143. Oreskes, N., Shraderfrechette, K., Belitz, K., 1994. Verification, validation, and confirmation of numerical-models in the earth-sciences. Science 263, 641–646. Palosuo, T., Kersebaum, K.C., Angulo, C., Hlavinka, P., Moriondo, M., Patil, R., Ruget, F., Rumbaur, C., Takáˇc, J., Trnka, M., Bindi, M., Caldag, B., Ewert, F., Ferrise, R., ˇ ska, B., Rötter, R.P., 2011. Simulation of winMirschel, W., Olesen, J., Saylan, L., Siˇ ter wheat yield and its variability in different climates of Europe. A comparison of eight crop growth models. Eur. J. Agron. 35, 103–114. Parry, M.L., Rosenzweig, C., Iglesias, A., Livermore, M., Fischer, G., 2004. Effects of climate change on global food production under SRES emissions and socioeconomic scenarios. Glob. Environ. Change 14, 53–67. Paydar, Z., Gallant, J., 2008. A catchment framework for one-dimensional models: introducing FLUSH and its application. Hydrol. Process. 22, 2094–2104. Pedersen, A., Zhang, K.F., Thorup-Kristensen, K., Jensen, L.S., 2010. Modelling diverse root density dynamics and deep nitrogen uptake – a simple approach. Plant Soil 326, 493–510. Porter, J.R., Challinor, A., Ewert, F., Falloon, P., Fischer, T., Gregory, P., van Ittersum, M.K., Olesen, J.E., Moore, K.J., Rosenzweig, C., Smith, P., 2010. Food security: focus on agriculture. Science 328, 172–173. Priyashantha, K.R.S., Maule, C.P., Elliott, J.A., 2007. Influence of slope position and hog manure injection on fall soil P and N distribution in an undulating landscape. Trans. Asabe 50, 45–52. Rebaudo, F., Dangles, O., 2011. Coupled information diffusion-pest dynamics models predict delayed benefits of farmer cooperation in pest management programs. PLoS Comput. Biol. 7, e1002222. Reuter, H.I., Kersebaum, K.C., 2009. Applications in precision agriculture. In: Hengl, T., Reuter, H.I. (Eds.), Geomorphometry: Concepts, Software, Applications. Elsevier, Amsterdam, pp. 623–636. Reuter, H.I., Kersebaum, K.C., Wendroth, O., 2005. Modelling of solar radiation influenced by topographic shading – evaluation and application for precision farming. Phys. Chem. Earth 30, 143–149. Rihani, J.F., Maxwell, R.M., Chow, F.K., 2010. Coupling groundwater and land surface processes: idealized simulations to identify effects of terrain and subsurface heterogeneity on land surface energy fluxes. Water Resour. Res. 46, W12523. Rivington, M., Matthews, K.B., Bellocchi, G., Buchan, K., 2006. Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data. Agric. Syst. 88, 451–471. Rosenzweig, C., Jones, J.W., Hatfield, J.L., Ruane, A.C., Boote, K.J., Thorburn, P., Antle, J.M., Nelson, G.C., Porter, C., Janssen, S., Asseng, S., Basso, B., Ewert, F., Wallach, D., Baigorria, G., Winter, J.M., 2013. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Forest. Agr. Meteorol. 170, 166–182. Rötter, R.P., Carter, T.R., Olesen, J.E., Porter, J.R., 2011. Crop-climate models need an overhaul. Nat. Clim. Change 1, 175–177.

C. Nendel et al. / Field Crops Research 145 (2013) 67–77 Rötter, R.P., Palosuo, T., Kersebaum, K.C., Angulo, C., Bindi, M., Ewert, F., Ferrise, R., Hravlinka, P., Moriondo, M., Nendel, C., Olesen, J.E., Patil, R., Ruget, F., Tacáˇc, J., Trnka, M., 2012. Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crops Res. 133, 23–36. Rykiel, E.J., 1996. Testing ecological models: the meaning of validation. Ecol. Model. 90, 229–244. Sadler, E.J., Gerwig, B.K., Evans, D.E., Busscher, W.J., Bauer, P.J., 2000. Site-specific modeling of corn yield in the SE coastal plain. Agric. Syst. 64, 189–207. Salo, T. et al. Comparing the performance of eleven agro-ecosystem models in predicting crop yield response to nitrogen under Finnish weather conditions. Field Crops Res., in preparation. Saue, T., Kadaja, J., 2009. Modelling crop yield response to precipitation redistribution on slopes. Biologia 64, 502–506. Savary, S., Teng, P.S., Willocquet, L., Nutter, F.W., 2006. Quantification and modeling of crop losses: a review of purposes. Annu. Rev. Phytopathol. 44, 89–112. Schmidhuber, J., Tubiello, F.N., 2007. Global food security under climate change. Proc. Natl. Acad. Sci. U.S.A. 104, 19703–19708. Shaeffer, D.L., 1980. Model evaluation methodology applicable to environmental assessment models. Ecol. Model. 8, 275–295. Stehfest, E., Heistermann, M., Priess, J.A., Ojima, D.S., Alcamo, J., 2007. Simulation of global crop production with the ecosystem model DayCent. Ecol. Model. 209, 203–219. Supit, I., van Diepen, C.A., de Wit, A.J.W., Wolf, J., Kabat, P., Baruth, B., Ludwig, F., 2012. Assessing climate change effects on European crop yields using the Crop Growth Monitoring System and a weather generator. Agric. For. Meteorol. 164, 96–111. Therond, O., Hengsdijk, H., Casellas, E., Wallach, D., Adam, M., Belhouchette, H., Oomen, R., Russell, G., Ewert, F., Bergez, J.E., Janssen, S., Wery, J., van Ittersum, M.K., 2011. Using a cropping system model at regional scale: low-data approaches for crop management information and model calibration. Agric. Ecosyst. Environ. 142, 85–94. Thiessen, A.H., 1911. Precipitation averages for large areas. Mon. Weather Rev. 39, 1082–1084. UN, 2011. World population prospects: the 2010 revision – highlights and advance tables. Working Paper No. ESA/P/WP.220. New York. van Bussel, L.G.J., Ewert, F., Leffelaar, P.A., 2011. Effects of data aggregation on simulations of crop phenology. Agric. Ecosyst. Environ. 142, 75–84.

77

van Ittersum, M.K., Rabbinge, R., 1997. Concepts in production ecology for analysis and quantification of agricultural input–output combinations. Field Crops Res. 52, 197–208. van Keulen, H., Penning de Vries, F.W.T., Drees, E.M., 1982. A summary model for crop growth. In: Penning de Vries, F.W.T., van Laar, H.H. (Eds.), Simulation of Plant Growth and Crop Production. PUDOC, Wageningen, pp. 87–97. VanLoocke, A., Twine, T.E., Zeri, M., Bernacchi, C.J., 2012. A regional comparison of water use efficiency for miscanthus, switchgrass and maize. Agric. For. Meteorol. 164, 82–95. Vermeulen, S.J., Aggarwal, P.K., Ainslie, A., Angelone, C., Campbell, B.M., Challinor, A.J., Hansen, J.W., Ingram, J.S.I., Jarvis, A., Kristjanson, P., Lau, C., Nelson, G.C., Thornton, P.K., Wollenberg, E., 2012. Options for support to agriculture and food security under climate change. Environ. Sci. Policy 15, 136–144. Wassenaar, T., Lagacherie, P., Legros, J.P., Rounsevell, M.D.A., 1999. Modelling wheat yield responses to soil and climate variability at the regional scale. Clim. Res. 11, 209–220. Watson, D.F., Philip, G.M., 1985. A refinement of inverse distance weighted interpolation. Geo-Processing 2, 315–327. Wegehenkel, M., 2000. Test of a modelling system for simulating water balances and plant growth using various different complex approaches. Ecol. Model. 129, 39–64. Wehrmann, J., Scharpf, H.C., 1979. Mineral nitrogen concentration of soil as a gauge of need for nitrogen-fertilizer (N-min method). Plant Soil 52, 109–126. Wendroth, O., Kersebaum, K.C., Schwab, G., Murdock, L., 2011. Spatial relationship of soil properties, crop indices, and nitrogen application pattern with wheat growth and yield in a field. In: Ahuja, L.R., Ma, L.W. (Eds.), Methods of Introducing System Models into Agricultural Research. ASA, CSSA, SSSA, Madison, WI, USA, pp. 229–259. White, J.W., Hoogenboom, G., Kimball, B.A., Wall, G.W., 2011. Methodologies for simulating impacts of climate change on crop production. Field Crops Res. 124, 357–368. Wolf, J., van Diepen, C.A., 1995. Effects of climate change on grain maize yield potential in the European Community. Clim. Change 29, 299–331. Xiong, W., Holman, I., Conway, D., Lin, E., Li, Y., 2008. A crop model cross calibration for use in regional climate impacts studies. Ecol. Model. 213, 365–380. Zhang, X.C., Liu, W.Z., Li, Z., Chen, J., 2011. Trend and uncertainty analysis of simulated climate change impacts with multiple GCMs and emission scenarios. Agric. For. Meteorol. 151, 1297–1304.