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Zhao, G., Siebert, S., Enders, A., Rezaei, E. E., Yan, C., & Ewert, F. (2015). Demand for multi-scale weather data for regional crop modeling. Agricultural and Forest Meteorology, 200, 156–171.
Abstract: A spatial resolution needs to be determined prior to using models to simulate crop yields at a regional scale, but a dilemma exists in compromising between different demands. A fine spatial resolution demands extensive computation load for input data assembly, model runs, and output analysis. A coarse spatial resolution could result in loss of spatial detail in variability. This paper studied the impact of spatial resolution, data aggregation and spatial heterogeneity of weather data on simulations of crop yields, thus providing guidelines for choosing a proper spatial resolution for simulations of crop yields at regional scale. Using a process-based crop model SIMPLACE (LINTUL2) and daily weather data at 1 km resolution we simulated a continuous rainfed winter wheat cropping system at the national scale of Germany. Then we aggregated the weather data to four resolutions from 10 to 100 km, repeated the simulation, compared them with the 1 km results, and correlated the difference with the intra-pixel heterogeneity quantified by an ensemble of four semivariogram models. Aggregation of weather data had small effects over regions with a flat terrain located in northern Germany, but large effects over southern regions with a complex topography. The spatial distribution of yield bias at different spatial resolutions was consistent with the intra-pixel spatial heterogeneity of the terrain and a log-log linear relationship between them was established. By using this relationship we demonstrated the way to optimize the model resolution to minimize both the number of simulation runs and the expected loss of spatial detail in variability due to aggregation effects. We concluded that a high spatial resolution is desired for regions with high spatial environmental heterogeneity, and vice versa. This calls for the development of multi-scale approaches in regional and global crop modeling. The obtained results require substantiation for other production situations, crops, output variables and for different crop models. (C) 2014 Elsevier B.V. All rights reserved.
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Kuhnert, M., Yeluripati, J., Smith, P., Hoffmann, H., van Oijen, M., Constantin, J., et al. (2016). Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands. European Journal of Agronomy, 88, 41–52.
Abstract: For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data.
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Salo, T. J., Palosuo, T., Kersebaum, K. C., Nendel, C., Angulo, C., Ewert, F., et al. (2016). Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization. J. Agric. Sci., 154(7), 1218–1240.
Abstract: Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.
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Angulo, C., Rötter, R., Lock, R., Enders, A., Fronzek, S., & Ewert, F. (2013). Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe. Agricultural and Forest Meteorology, 170, 32–46.
Abstract: Process-based crop simulation models are increasingly used in regional climate change impact studies, but little is known about the implications of different calibration strategies on simulated yields. This study aims to assess the importance of region-specific calibration of five important field crops (winter wheat, winter barley, potato, sugar beet and maize) across 25 member countries of the European Union (EU25). We examine three calibration strategies and their implications on spatial and temporal yield variability in response to climate change: (i) calculation of phenology parameters only, (ii) consideration of both phenology calibration and a yield correction factor and (iii) calibration of phenology and selected growth processes. The analysis is conducted for 533 climate zones, considering 24 years of observed yield data (1983-2006). The best performing strategy is used to estimate the impacts of climate change, increasing CO2 concentration and technology development on yields for the five crops across EU25, using seven climate change scenarios for the period 2041-2064. Simulations and calibrations are performed with the crop model LINTUL2 combined with a calibration routine implemented in the modelling interface LINTUL-FAST. The results show that yield simulations improve if growth parameters are considered in the calibration for individual regions (strategy 3); e.g. RMSE values for simulated winter wheat yield are 2.36, 1.10 and 0.70 Mg ha(-1) for calibration strategies 1, 2 and 3, respectively. The calibration strategy did not only affect the model simulations under reference climate but also the extent of the simulated climate change impacts. Applying the calibrated model for impact assessment revealed that climatic change alone will reduce crop yields. Consideration of the effects of increasing CO2 concentration and technology development resulted in yield increases for all crops except maize (i.e. the negative effects of climate change were outbalanced by the positive effects of CO2 and technology change), with considerable differences between scenarios and regions. Our simulations also suggest some increase in yield variability due to climate change which, however, is less pronounced than the differences among scenarios which are particularly large when the effects of CO2 concentration and technology development are considered. Our results stress the need for region-specific calibration of crop models used for Europe-wide assessments. Limitations of the considered strategies are discussed. We recommend that future work should focus on obtaining more comprehensive, high quality data with a finer resolution allowing application of improved strategies for model calibration that better account for spatial differences and changes over time in the growth and development parameters used in crop models. (c) 2012 Elsevier B.V. All rights reserved.
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van Bussel, L. G. J., Ewert, F., Zhao, G., Hoffmann, H., Enders, A., Wallach, D., et al. (2016). Spatial sampling of weather data for regional crop yield simulations. Agricultural and Forest Meteorology, 220, 101–115.
Abstract: Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50,100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.
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