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Hlavinka, P., Trnka, M., Kersebaum, K. C., Cermák, P., Pohanková, E., Orság, M., et al. (2014). Modelling of yields and soil nitrogen dynamics for crop rotations by HERMES under different climate and soil conditions in the Czech Republic. J. Agric. Sci., 152(02), 188–204.
Abstract: The crop growth model HERMES was used to model crop rotation cycles at 12 experimental sites in the Czech Republic. A wide range of crops (spring and winter barley, winter wheat, maize, potatoes, sugar beet, winter rape, oats, alfalfa and grass), cultivated between 1981 and 2009 under various soil and climatic conditions, were included. The model was able to estimate the yields of field crop rotations at a reasonable level, with an index of agreement (IA) ranging from 0.82 to 0.96 for the calibration database (the median coefficient of determination (R-2) was 0.71), while IA for verification varied from 0.62 to 0.93 (median R-2 was 0.78). Grass yields were also estimated at a reasonable level of accuracy. The estimates were less accurate for the above-ground biomass at harvest (the medians for IA were 0.76 and 0.72 for calibration and verification, respectively, and analogous medians of R-2 were 0.50 and 0.49). The soil mineral nitrogen (N) content under the field crops was simulated with good precision, with the IA ranging from 0.49 to 0.74 for calibration and from 0.43 to 0.68 for verification. Generally, the soil mineral N was underestimated, and more accurate results were achieved at locations with intensive fertilization. Simulated yields, soil N, water and organic carbon (C) contents were compared with long-term field measurements at Ne. mc. ice, located within the fertile Moravian lowland. At this station, all of the observed parameters were reproduced with a reasonable level of accuracy. In the case of the organic C content, HERMES reproduced a decrease ranging from c. 85 to 77 tonnes (t)/ha (for the 0-0.3 m soil layer) between the years 1980 and 2007. In spite of its relatively simple approach and restricted input data, HERMES was proven to be robust across various conditions, which is a precondition for its future use for both theoretical and practical purposes.
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Rötter, R. P., Palosuo, T., Kersebaum, K. - C., Angulo, C., Bindi, M., Ewert, F., et al. (2012). Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Research, 133, 23–36.
Abstract: ► We compared nine crop simulation models for spring barley at seven sites in Europe. ► Applying crop models with restricted calibration leads to high uncertainties. ► Multi-crop model mean yield estimates were in good agreement with observations. ► The degree of uncertainty for simulated grain yield of barley was similar to winter wheat. ► We need more suitable data enabling us to verify different processes in the models. In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction.
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Kollas, C., Kersebaum, C., Bindi, M., Wu, L., Sharif, B., Öztürk, I., et al. (2014). Improving yield predictions by crop rotation modelling? a multi-model comparison..
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Hlavinka, P., Kersebaum, K. C., Dubrovský, M., Pohanková, E., Balek, J., Žalud, Z., et al. (2014). Water balance and yield estimates for field crop rotations present versus future conditions based on transient scenarios..
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