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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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Kahiluoto, H., Kaseva, J., Balek, J., Olesen, J. E., Ruiz-Ramos, M., Gobin, A., et al. (2019). Decline in climate resilience of European wheat. Proc. Natl. Acad. Sci. U. S. A., 116(1), 123–128.
Abstract: Food security relies on the resilience of staple food crops to climatic variability and extremes, but the climate resilience of European wheat is unknown. A diversity of responses to disturbance is considered a key determinant of resilience. The capacity of a sole crop genotype to perform well under climatic variability is limited; therefore, a set of cultivars with diverse responses to weather conditions critical to crop yield is required. Here, we show a decline in the response diversity of wheat in farmers’ fields in most European countries after 2002-2009 based on 101,000 cultivar yield observations. Similar responses to weather were identified in cultivar trials among central European countries and southern European countries. A response diversity hotspot appeared in the trials in Slovakia, while response diversity “deserts” were identified in Czechia and Germany and for durum wheat in southern Europe. Positive responses to abundant precipitation were lacking. This assessment suggests that current breeding programs and cultivar selection practices do not sufficiently prepare for climatic uncertainty and variability. Consequently, the demand for climate resilience of staple food crops such as wheat must be better articulated. Assessments and communication of response diversity enable collective learning across supply chains. Increased awareness could foster governance of resilience through research and breeding programs, incentives, and regulation.
Keywords: wheat; cultivar; Europe; climate resilience; response diversity; Diversity; Weather; Growth; Shifts; Crops; Yield; Variability
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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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Brilli, L., Ferrise, R., Dibari, C., Bindi, M., & Bellocchi, G. (2017). Needs on model improvement (Vol. 10).
Abstract: The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models.
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Bellocchi, G., B., Brilli, L., Ferrise, R., Dibari, C., & Bindi, M. (2017). Model comparison and improvement: Links established with other consortia (Vol. 10).
Abstract: XC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org ) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org ). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls.
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