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Waha, K.; Müller, C.; Bondeau, A.; Dietrich, J.P.; Kurukulasuriya, P.; Heinke, J.; Lotze-Campen, H. |
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Title |
Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa |
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Journal Article |
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Year |
2013 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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Volume |
23 |
Issue  |
1 |
Pages |
130-143 |
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Keywords |
multiple cropping; sequential cropping systems; crop modelling; agricultural management; adaptation options; global vegetation model; future food-production; rainy-season; west-africa; agriculture; yield; maize; soil; variability; heat |
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Abstract |
Multiple cropping systems provide more harvest security for farmers, allow for crop intensification and furthermore influence ground cover, soil erosion, albedo, soil chemical properties, pest infestation and the carbon sequestration potential. We identify the traditional sequential cropping systems in ten sub-Saharan African countries from a survey dataset of more than 8600 households. We find that at least one sequential cropping system is traditionally used in 35% of all administrative units in the dataset, mainly including maize or groundnuts. We compare six different management scenarios and test their susceptibility as adaptation measure to climate change using the dynamic global vegetation model for managed land LPJmL. Aggregated mean crop yields in sub-Saharan Africa decrease by 6-24% due to climate change depending on the climate scenario and the management strategy. As an exception, some traditional sequential cropping systems in Kenya and South Africa gain by at least 25%. The crop yield decrease is typically weakest in sequential cropping systems and if farmers adapt the sowing date to changing climatic conditions. Crop calorific yields in single cropping systems only reach 40-55% of crop calorific yields obtained in sequential cropping systems at the end of the 21st century. The farmers’ choice of adequate crops, cropping systems and sowing dates can be an important adaptation strategy to climate change and these management options should be considered in climate change impact studies on agriculture. (C) 2012 Elsevier Ltd. All rights reserved. |
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2016-10-31 |
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0959-3780 |
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CropM |
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no |
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Call Number |
MA @ admin @ |
Serial |
4823 |
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Author |
Ventrella, D.; Giglio, L.; Charfeddine, M.; Dalla Marta, A. |
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Title |
Consumptive use of green and blue water for winter durum wheat cultivated in Southern Italy |
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Journal Article |
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Year |
2015 |
Publication |
Italian Journal of Agrometeorology |
Abbreviated Journal |
Italian Journal of Agrometeorology |
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Volume |
20 |
Issue |
1 |
Pages |
33-44 |
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Keywords |
irrigation; water productivity; model simulation; climate change; climate-change scenarios; air co2 enrichment; impact; footprint; irrigation; simulation; yield; agriculture; variability; resources |
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Abstract |
In this study at the regional scale, the model DSSAT CERES-Wheat was applied in order to simulate the cultivation of winter durum wheat (WW) and to estimate the green water (GW) and the blue water (BW) through a dual-step approach (with and without supplemental irrigation). The model simulation covered a period of 30 years for three scenarios including a reference period and two future scenarios based on forecasted global average temperature increase of 2 and 5 degrees C. The GW and BW contribution for evapo transpiration requirement is presented and analyzed on a distributed scale related to the Puglia region (Southern Italy) characterized by high evaporative demand of the atmosphere. The GW component was dominant compared to BW, covering almost 90% of the ETc of WW Under a Baseline scenario the weight BW was 11%, slightly increased in the future scenarios. GW appeared dependent on the spatial and temporal distribution of rainfall during the crop cycle, and to the hydraulic characteristics of soil for each calculation unit. After considering the effects of climate change on irrigation requirement of WW we carried out an example of analysis in order to verify the economic benefit of supplemental irrigation for WW cultivation. The probability that irrigation generates a negative or zero income ranged between 55 and 60% and climate change did not impact the profitability of irrigation for WW as simulated for the economic and agro-pedoclimatic conditions of Puglia region considered in this study. |
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CropM, ft_macsur |
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MA @ admin @ |
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4653 |
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Watson, J.; Challinor, A.J.; Fricker, T.E.; Ferro, C.A.T. |
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Title |
Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model |
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Journal Article |
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Year |
2015 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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Volume |
132 |
Issue |
1 |
Pages |
93-109 |
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Keywords |
maize; yield; ensemble; impacts; design; heat |
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Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve. |
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0165-0009 1573-1480 |
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CropM, ft_macsur |
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MA @ admin @ |
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4546 |
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Author |
Müller, C.; Robertson, R.D. |
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Title |
Projecting future crop productivity for global economic modeling |
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Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
Issue |
1 |
Pages |
37-50 |
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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 |
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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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ISSN |
0169-5150 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4533 |
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Foyer, C.H.; Siddique, K.H.M.; Tai, A.P.K.; Anders, S.; Fodor, N.; Wong, F.-L.; Ludidi, N.; Chapman, M.A.; Ferguson, B.J.; Considine, M.J.; Zabel, F.; Prasad, P.V.V.; Varshney, R.K.; Nguyen, H.T.; Lam, H.-M. |
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Modelling predicts that soybean is poised to dominate crop production across Africa |
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Journal Article |
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Year |
2019 |
Publication |
Plant Cell and Environment |
Abbreviated Journal |
Plant Cell Environ. |
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42 |
Issue |
1 |
Pages |
373-385 |
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Keywords |
Climate-Change; Food Security; Sustainable Intensification; Smallholder; Farmers; Nitrogen-Fixation; Yield; Adaptation; Diversity; Impact; CO2 |
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The superior agronomic and human nutritional properties of grain legumes (pulses) make them an ideal foundation for future sustainable agriculture. Legume-based farming is particularly important in Africa, where small-scale agricultural systems dominate the food production landscape. Legumes provide an inexpensive source of protein and nutrients to African households as well as natural fertilization for the soil. Although the consumption of traditionally grown legumes has started to decline, the production of soybeans (Glycine max Merr.) is spreading fast, especially across southern Africa. Predictions of future land-use allocation and production show that the soybean is poised to dominate future production across Africa. Land use models project an expansion of harvest area, whereas crop models project possible yield increases. Moreover, a seed change in farming strategy is underway. This is being driven largely by the combined cash crop value of products such as oils and the high nutritional benefits of soybean as an animal feed. Intensification of soybean production has the potential to reduce the dependence of Africa on soybean imports. However, a successful “soybean bonanza” across Africa necessitates an intensive research, development, extension, and policy agenda to ensure that soybean genetic improvements and production technology meet future demands for sustainable production. |
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2019-01-10 |
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0140-7791 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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5215 |
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