Records |
Author |
Müller, C.; Waha, K.; Bondeau, A.; Heinke, J. |
Title |
Hotspots of climate change impacts in sub-Saharan Africa and implications for adaptation and development |
Type |
Journal Article |
Year ![sorted by Year field, ascending order (up)](img/sort_asc.gif) |
2014 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
Volume |
20 |
Issue |
8 |
Pages |
2505-2517 |
Keywords |
Africa South of the Sahara; *Climate Change; Crops, Agricultural; Environment; Hydrology; *Models, Theoretical; Uncertainty; adaptation; climate change; development; impacts; modeling; sub-Saharan Africa |
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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English |
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Series Issue |
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Edition |
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ISSN |
1354-1013 |
ISBN |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4534 |
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Author |
Müller, C.; Robertson, R.D. |
Title |
Projecting future crop productivity for global economic modeling |
Type |
Journal Article |
Year ![sorted by Year field, ascending order (up)](img/sort_asc.gif) |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
Volume |
45 |
Issue |
1 |
Pages |
37-50 |
Keywords |
climate change; crop modeling; agricultural productivity; land use; greenhouse-gas emissions; soil organic-carbon; sub-saharan africa; climate-change; elevated co2; land-use; system model; wheat yields; maize yields; agriculture |
Abstract |
Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change. |
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Series Issue |
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Edition |
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ISSN |
0169-5150 |
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Article |
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Notes |
CropM, TradeM, ft_macsur |
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no |
Call Number |
MA @ admin @ |
Serial |
4533 |
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Author |
Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
Type |
Journal Article |
Year ![sorted by Year field, ascending order (up)](img/sort_asc.gif) |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
Volume |
65 |
Issue |
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Pages |
141-157 |
Keywords |
crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
Abstract |
We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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Series Editor |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0936-577x |
ISBN |
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Article |
Area |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4754 |
Permanent link to this record |
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Author |
Ingram, J.S.I.; Porter, J.R. |
Title |
Plant science and the food security agenda |
Type |
Journal Article |
Year ![sorted by Year field, ascending order (up)](img/sort_asc.gif) |
2015 |
Publication |
Nature Plants |
Abbreviated Journal |
Nature Plants |
Volume |
1 |
Issue |
11 |
Pages |
15173 |
Keywords |
africa; maize |
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Edition |
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ISSN |
2055-026x 2055-0278 |
ISBN |
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Medium |
Editorial Material |
Area |
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Conference |
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Notes |
CropM, ftnotmacsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4705 |
Permanent link to this record |
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Author |
Wolf, J.; Ouattara, K.; Supit, I. |
Title |
Sowing rules for estimating rainfed yield potential of sorghum and maize in Burkina Faso |
Type |
Journal Article |
Year ![sorted by Year field, ascending order (up)](img/sort_asc.gif) |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
Volume |
214-215 |
Issue |
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Pages |
208-218 |
Keywords |
crop modelling; maize; sorghum; sowing; WOFOST; yield potential; semiarid west-africa; pearl-millet cultivation; soil organic-matter; climate-change; planting dates; crop model; variability; water; adaptation; tillage |
Abstract |
To reduce the dependence on local expert knowledge, which is important for large-scale crop modelling studies, we analyzed sowing dates and rules for maize (Zea mays L.) and sorghum (Sorghum bicolor (L)) at three locations in Burkina Faso with strongly decreasing rainfall amounts from south to north. We tested in total 22 methods to derive optimal sowing dates that result in highest water-limited yields and lowest yield variation in a reproducible and objective way. The WOFOST crop growth simulation model was used. We found that sowing dates that are based on local expert knowledge, may work quite well for Burkina Faso and for West Africa in general. However, when no a priori information is available, maize should be sown between Julian days 160 and 200, with application of the following criteria: (a) cumulative rainfall in the sowing window is >= 3 cm or available soil moisture content is >2 cm in the moderately dry central part of Burkina Faso, (b) cumulative rainfall in this period is >= 2 cm or available soil moisture content is >1 cm in the more humid regions in the southern part of Burkina Faso. Sorghum should also be sown between Julian days 160 and 200 with application of the following criteria: (a) in the dry northern part of Burkina Faso the long duration sorghum variety should be sown when cumulative rainfall is >2 cm in the sowing window, and the short duration sorghum variety should be sown later when cumulative rainfall is >= 3 cm, (b) in central Burkina Faso sowing should start when cumulative rainfall in this period is >= 2 cm or when available soil moisture content is >1 cm. Sowing date rules are shown to be generally crop and location specific and are not generic for West Africa. However, the required precision of the sowing rules appears to rapidly decrease with increasing duration and intensity of the rainy season. Sowing delay as a result of, for example, labour constraints, has a disastrous effect on rainfed maize and sorghum yields, particularly in the northern part of West Africa with low rainfall. Optimization of sowing dates can also be done by simulating crop yields in a time window of two months around a predefined sowing date. Using these optimized dates appears to result in a good estimate of the maximal mean rainfed yield level. (C) 2015 Elsevier B.V. All rights reserved. |
Address |
2015-10-12 |
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English |
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Original Title |
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Series Editor |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0168-1923 |
ISBN |
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Article |
Area |
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Notes |
CropM |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4702 |
Permanent link to this record |