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Müller, C., Waha, K., Bondeau, A., & Heinke, J. (2014). Hotspots of climate change impacts in sub-Saharan Africa and implications for adaptation and development. Glob. Chang. Biol., 20(8), 2505–2517.
Abstract: Development efforts for poverty reduction and food security in sub-Saharan Africa will have to consider future climate change impacts. Large uncertainties in climate change impact assessments do not necessarily complicate, but can inform development strategies. The design of development strategies will need to consider the likelihood, strength, and interaction of climate change impacts across biosphere properties. We here explore the spread of climate change impact projections and develop a composite impact measure to identify hotspots of climate change impacts, addressing likelihood and strength of impacts. Overlapping impacts in different biosphere properties (e.g. flooding, yields) will not only claim additional capacity to respond, but will also narrow the options to respond and develop. Regions with severest projected climate change impacts often coincide with regions of high population density and poverty rates. Science and policy need to propose ways of preparing these areas for development under climate change impacts.
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Bassu, S., Brisson, N., Durand, J. - L., Boote, K., Lizaso, J., Jones, J. W., et al. (2014). How do various maize crop models vary in their responses to climate change factors. Glob. Chang. Biol., 20(7), 2301–2320.
Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
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Bojar, W., Knopik, L., Żarski, J., Sławiński, C., Baranowski, P., & Żarski, W. (2014). Impact of extreme climate changes on the forecasted agriculture production. Acta Agrophysica, 21(4), 415–431.
Abstract: The paper presents general characteristics of resources and outputs of agriculture in the Kujawsko-Pomorskie and Lubelskie Regions, based on statistical databases and literature review. Some specific features of the regions, with special consideration for the predicted extreme climate changes, are also included. Next, some statistically significant dependencies between the climatic parameters and yields of selected important crops in the abovementioned regions were worked out on the basis of empirical survey conducted in the University of Technology and Life Sciences, Bydgoszcz, and the Institute of Agrophysics in Lublin. Creating an appropriate method of forecasting long series of ten days without precipitation was necessary to find the desired dependencies. Third, some efforts were taken to make integrated assessments of forecast agricultural outputs influenced by climate extreme phenomena on the basis of the yield-precipitation relations obtained and on the data coming from wide area model regional outputs such as prices of farmland and produce.
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Twardy, S., & Kopacz, M. (2014). Comparison of concentrations and loads of macronutrients brought with precipitation and leaching from the soil profile. Pol. J. Environ. Stud., 23(3a), 132–136.
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Kopacz, M., & Twardy, S. (2014). Spatial modeling as a tool supporting the management of catchment area of retention reservoir. Pol. J. Environ. Stud., 23(3a), 53–57.
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