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Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa
AbstractSmallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.
- Florida Southern College United States
- National Research Institute for Agriculture, Food and Environment France
- University of South Africa South Africa
- Institut D'Economie Rurale Mali
- Agricultural Research Service United States
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 550, Crop Modelling, 2306 Global and Planetary Change, Mali, 630, Smallholder Agriculture, Smallholder farming systems, smallholder farming systems, uncertainty, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, Uncertainty, ta4111, Rendement des cultures, model intercomparison, 2304 Environmental Chemistry, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, 2300 Environmental Science, Crop simulation model, 330, Exploitant agricole, crop simulation model, P40 - Météorologie et climatologie, Nitrogen, Climate Change, ta1172, [SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture, Zea mays, 333, Petite exploitation agricole, ensemble modelling, [SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture, Fertilizers, Sub‐Saharan Africa (SSA), Changement climatique, Agriculture faible niveau intrants, U10 - Méthodes mathématiques et statistiques, Modélisation des cultures, Engrais azoté, Modèle de simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Maize, Ensemble modelling, Système d'exploitation agricole, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, 2303 Ecology, Model intercomparison, agrovoc: agrovoc:c_5195, agrovoc: agrovoc:c_24242, agrovoc: agrovoc:c_8504, agrovoc: agrovoc:c_9000024, agrovoc: agrovoc:c_1666, agrovoc: agrovoc:c_166, agrovoc: agrovoc:c_7113, agrovoc: agrovoc:c_14343, agrovoc: agrovoc:c_2807, agrovoc: agrovoc:c_34370, agrovoc: agrovoc:c_10176
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 550, Crop Modelling, 2306 Global and Planetary Change, Mali, 630, Smallholder Agriculture, Smallholder farming systems, smallholder farming systems, uncertainty, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, Uncertainty, ta4111, Rendement des cultures, model intercomparison, 2304 Environmental Chemistry, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, 2300 Environmental Science, Crop simulation model, 330, Exploitant agricole, crop simulation model, P40 - Météorologie et climatologie, Nitrogen, Climate Change, ta1172, [SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture, Zea mays, 333, Petite exploitation agricole, ensemble modelling, [SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture, Fertilizers, Sub‐Saharan Africa (SSA), Changement climatique, Agriculture faible niveau intrants, U10 - Méthodes mathématiques et statistiques, Modélisation des cultures, Engrais azoté, Modèle de simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Maize, Ensemble modelling, Système d'exploitation agricole, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, 2303 Ecology, Model intercomparison, agrovoc: agrovoc:c_5195, agrovoc: agrovoc:c_24242, agrovoc: agrovoc:c_8504, agrovoc: agrovoc:c_9000024, agrovoc: agrovoc:c_1666, agrovoc: agrovoc:c_166, agrovoc: agrovoc:c_7113, agrovoc: agrovoc:c_14343, agrovoc: agrovoc:c_2807, agrovoc: agrovoc:c_34370, agrovoc: agrovoc:c_10176
