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Grosz, B., Dechow, R., Hoffmann, H., Zhao, G., Constantin, J., Raynal, H., et al. (2015). The implication of input data aggregation on upscaling of soil organic carbon changes. MACSUR Science Conference.
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Wang, E., Martre, P., Zhao, Z., Ewert, F., Maiorano, A., Rötter, R. P., et al. (2017). The uncertainty of crop yield projections is reduced by improved temperature response functions. Nature Plants, 3, 17102.
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. Erratum: doi: 10.1038/nplants.2017.125
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Ewert, F., van Bussel, L. G. J., Zhao, G., Hoffmann, H., Gaiser, T., Specka, X., et al. (2015). Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments. In C. Rosenzweig, & D. Hillel (Eds.), (pp. 261–277). Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America (In 2 Parts), ICP Series on Climate Change Impacts, Adaptation, . London: Imperial College Press.
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Webber, H., Oomen, R., Gaiser, T., Teixeira, E., Zhao, G., Srivastava, A., et al. (2016). Uncertainty in future European irrigation water demand.. Berlin (Germany).
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Webber, H., Gaiser, T., Oomen, R., Teixeira, E., Zhao, G., Wallach, D., et al. (2016). Uncertainty in future irrigation water demand and risk of crop failure for maize in Europe. Environ. Res. Lett., .
Abstract: While crop models are widely used to assess the change in crop productivity with climate change, their skill in assessing irrigation water demand or the risk of crop failure in large area impact assessments is relatively unknown. The objective of this study is to investigate which aspects of modeling crop water use (reference crop evapotranspiration (ET0), soil water extraction, soil evaporation, soil water balance and root growth) contributes most to the variability in estimates of maize crop water use and the risk of crop failure, and demonstrate the resulting uncertainty in a climate change impact study for Europe. The SIMPLACE crop modeling framework was used to couple the LINTUL5 crop model in factorial combinations of 2-3 different approaches for simulating the 5 aspects of crop water use, resulting in 51 modeling approaches. Using experiments in France and New Zeland, analysis of total sensitivity revealed that ET0 explained the most variability in both irrigated maize water use and rainfed grain yield levels, with soil evaporation also imporatant in the French experiment. In the European impact study, net irrigation requirement differed by 36% between the Penman and Hargreaves ET0 methods in the baseline period. Average EU grain yields were similar between models, but differences approached 1-2 tonnes in parts of France and Southern Europe. EU wide esimates of crop failure in the historical period ranged between 5.4 years for Priestley-Taylor to every 7.9 years for the Penman ET0 methods. While the uncertainty in absolute values between models was significant, estimates of relative changes were similar between models, confirming the utility of crop models in assessing climate change impacts. If ET0 estimates in crop models can be improved, through the use of appropriate methods, uncertainty in irrigation water demand as well as in yield estimates under drought can be reduced.
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