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Tao, F., Palosuo, T., Roetter, R. P., Hernandez Diaz-Ambrona, C. G., Ines Minguez, M., Semenov, M. A., et al. (2020). Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Agricultural and Forest Meteorology, 281, 107851.
Abstract: Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.
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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: 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. (C) 2012 Elsevier B.V. All rights reserved.
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Moriondo, M., Ferrise, R., Trombi, G., Brilli, L., Dibari, C., & Bindi, M. (2015). Modelling olive trees and grapevines in a changing climate. Env. Model. Softw., 72, 387–401.
Abstract: The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed. (C) 2014 Elsevier Ltd. All rights reserved.
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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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Eitzinger, J., Thaler, S., Schmid, E., Strauss, F., Ferrise, R., Moriondo, M., et al. (2013). Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. J. Agric. Sci., 151(6), 813–835.
Abstract: The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.
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