Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J. L., Li, T., et al. (2015). A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. Agricultural and Forest Meteorology, 214-215, 483–493.
Abstract: Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
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Tao, F., Zhang, S., Zhang, Z., & Rötter, R. P. (2015). Temporal and spatial changes of maize yield potentials and yield gaps in the past three decades in China. Agric. Ecosyst. Environ., 208, 12–20.
Abstract: The precise spatially explicit knowledge about crop yield potentials and yield gaps is essential to guide sustainable intensification of agriculture. In this study, the maize yield potentials from 1980 to 2008 across the major maize production regions of China were firstly estimated by county using ensemble simulation of a well-validated large scale crop model, i.e., MCWLA-Maize model. Then, the temporal and spatial patterns of maize yield potentials and yield gaps during 1980-2008 were presented and analyzed. The results showed that maize yields became stagnated at 32.4% of maize-growing areas during the period. In the major maize production regions, i.e., northeastern China, the North China Plain (NCP) and southwestern China, yield gap percentages were generally less than 40% and particularly less than 20% in some areas. By contrast, in northern and southern China, where actual yields were relatively lower, yield gap percentages were generally larger than 40%. The areas with yield gap percentages less than 20% and less than 40% accounted for 8.2% and 27.6% of maize-growing areas, respectively. During the period, yield potentials decreased in the NCP and southwestern China due to increase in temperature and decrease in solar radiation; by contrast, increased in northern, northeastern and southeastern China due to increases in both temperature and solar radiation. Yield gap percentages decreased generally by 2% per year across the major maize production regions, although increased in some areas in northern and northeastern China. The shrinking of yield gap was due to increases in actual yields and decreases in yield potentials in the NCP and southwestern China; and due to larger increases in actual yields than in yield potentials in northeastern and southeastern China. The results highlight the importance of sustainable intensification of agriculture to close yield gaps, as well as breeding new cultivars to increase yield potentials, to meet the increasing food demand. (C) 2015 Elsevier B.V. All rights reserved.
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Halford, N. G., & Foyer, C. H. (2015). Producing a road map that enables plants to cope with future climate change. J. Experim. Bot., 66(12), 3433–3434.
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Ingram, J. S. I., & Porter, J. R. (2015). Plant science and the food security agenda. Nature Plants, 1(11), 15173.
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Eza, U., Shtiliyanova, A., Borras, D., Bellocchi, G., Carrère, P., & Martin, R. (2015). An open platform to assess vulnerabilities to climate change: An application to agricultural systems. Ecological Informatics, 30, 389–396.
Abstract: Numerous climate futures are now available from global climate models. Translation of climate data such as precipitation and temperatures into ecologically meaningful outputs for managers and planners is the next frontier. We describe a model-based open platform to assess vulnerabilities of agricultural systems to climate change on pixel-wise data. The platform includes a simulation modeling engine and is suited to work with NetCDF format of input and output files. In a case study covering a region (Auvergne) in the Massif Central of France, the platform is configured to characterize climate (occurrence of arid conditions in historical and projected climate records), soils and human management, and is then used to assess the vulnerability to climate change of grassland productivity (downscaled to a fine scale). We demonstrate how using climate time series, and process-based simulations vulnerabilities can be defined at fine spatial scales relevant to farmers and land managers, and can be incorporated into management frameworks. (C) 2015 Elsevier B.V. All rights reserved.
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