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Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., et al. (2013). Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change, 3(9), 827–832.
Abstract: Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
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Bonatti, M., Schlindwein, S. L., De Vasconcelos, A. C. F., Sieber, S., Agostini, L. R. D., Lana, M. A., et al. (2013). Social organization and agricultural strategies to face climate variability: a case study in Guaraciaba, southern Brazil. Sustainable Agriculture Research, 2(3), 118.
Abstract: Climate scenarios and projections have suggested that the impacts of climate change on land use will be noticed particularly by the communities that depend on natural resources for their subsistence. The climate vulnerability of poor communities varies greatly, but in general, climate change combines with other threats and becomes superimposed on existing vulnerabilities. This paper presents a case study that strives to understand the social organization in a vulnerable community of Guaraciaba, in southern Brazil, to investigate aspects of an adaptation strategy to climate change based on the local development and conservation of landraces of a set of crop species. Landraces are varieties better adapted to adversities, especially drought, which is an important threat to the famers in the region. Every farmer receives annually a “kit of biodiversity”, a set of local varieties with the amount of seeds necessary to be cultivated in order to produce enough food for the family. The study had a qualitative approach and was carried out through semi-structured interviews with technicians and 30% of the rural families who farm with landraces. The study concludes that the factors that make this adaptation strategy sustainable are: the ability to undertake actions strongly based on local socio-cultural needs (a social support network), biodiversity management practices designed to reduce external economic dependence, self management of genetic resources, the establishment of priorities based on locally available resources, a work plan for community participation (field days, a community based festival), the establishment of the roles of community in the planning and implementation of programs for biodiversity management.
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Rötter, R. P., Höhn, J. G., & Fronzek, S. (2012). Projections of climate change impacts on crop production – a global and a Nordic perspective. Acta Agriculturae Scandinavica, Section A – Animal Science, 62, 166–180.
Abstract: Global climate is changing and food production is very sensitive to weather and climate variations. Global assessments of climate change impacts on food production have been made since the early 1990s, initially with little attention to the uncertainties involved. Although there has been abundant analysis of uncertainties in future greenhouse gas emissions and their impacts on the climate system, uncertainties related to the way climate change projections are scaled down as appropriate for different analyses and in modelling crop responses to climate change, have been neglected. This review paper mainly addresses uncertainties in crop impact modelling and possibilities to reduce them. We specifically aim to (i) show ranges of projected climate change-induced impacts on crop yields, (ii) give recommendations on use of emission scenarios, climate models, regionalization and ensemble crop model simulations for different purposes and (iii) discuss improvements and a few known unknowns’ affecting crop impact projections.
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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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Kowalczyk, A., & Kuźniar, A. (2012). The threats of water erosion in the Grajcarek river basin. Pol. J. Environ. Stud., 21(5a), 217–221.
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