Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Falconnier, G N and Corbeels, M and Boote, K J and Affholder, F and Adam, M and MacCarthy, D S and Ruane, A C and Nendel, C and Whitbread, A M and Justes, E and Ahuja, L R and Akinseye, F M and Alou, I N and Amouzou, K A and Anapalli, S S and Baron, C and Basso, B and Baudron, F and Bertuzzi, P and Challinor, A J and Chen, Y and Deryng, D and Elsayed, M L and Faye, B and Gaiser, T and Galdos, M and Gayler, S and Gerardeaux, E and Giner, M and Grant, B and Hoogenboom, G and Ibrahim, E S and Kamali, B and Kersebaum, K C and Kim, S H and van der Laan, M and Leroux, L and Lizaso, J I and Maestrini, B and Meier, E A and Mequanint, F and Ndoli, A and Porter, C H and Priesack, E and Ripoche, D and Sida, T and Singh, U and Smith, W and Srivastava, A and Sinha, S and Tao, F and Thorburn, P J and Timlin, D and Traore, B and Twine, T and Webber, H (2020) Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa. Global Change Biology (TSI). pp. 1-23. ISSN 1354-1013

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Abstract

Smallholder 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.

Item Type:	Article
Divisions:	Research Program : Innovation Systems for the Drylands (ISD) Research Program : West & Central Africa
CRP:	CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC)
Uncontrolled Keywords:	Crop simulation model, Ensemble modelling, Model intercomparison, Smallholder farming systems, Uncertainty
Subjects:	Others > Crop Modelling Others > Smallholder Agriculture Others > Maize Others > Climate Change
Depositing User:	Mr Arun S
Date Deposited:	23 Aug 2020 14:51
Last Modified:	15 Mar 2021 09:00
URI:	http://oar.icrisat.org/id/eprint/11565
Official URL:	https://doi.org/10.1111/gcb.15261
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	We are grateful to the members of the AgMIP leadership team for their support and to Senthold Asseng and Pierre Martre for sharing their insights in the AgMIP Wheat Team. The lead author also thanks Sonali McDermid for her help in extracting AgMERRA data.
Links:	Google Scholar

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