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Can we use crop modelling for identifying climate change adaptation options?

Corbeels M., Berre D., Rusinamhodzi L., Lopez-Ridaura S.. 2018. Agricultural and Forest Meteorology, 256-257 : p. 46-52.

DOI: 10.1016/j.agrformet.2018.02.026

Climate model projections coupled with process-based crop models are advocated for assessing impacts of climate change on crop yields and for informing crop-level adaptations. However, most reported studies are vague on the choice of the global circulation models (GCMs) for climate projections, and on the corresponding uncertainty with this type of model simulations. Here we investigated whether climate-crop modelling can be used for identifying crop management-level adaptation options. We focused our analyses on a case study for maize in southern Africa using the APSIM crop growth model and projections from 17 individual climate models for the period 2017¿2060 for the contrasting representative concentration pathways 2.6 and 8.5. Intensification of nitrogen fertiliser use (from 30 to 90¿kg¿N¿ha-1) was simulated as an example of a crop management-level adaptation to climate change. Uncertainties in crop yield predictions were about 30 to 60%, i.e. larger than expected crop responses to most management-level interventions or adaptations. Variation in simulated yields was caused by inter-seasonal rainfall variability and uncertainty with climate models. Some GCMs resulted in significantly different maize yield predictions, without any clear pattern across sites. Given these high uncertainties, we argue that crop modellers should be cautious when informing future crop management adaptation strategies based on climate-crop model ensembles. A better use of crop models is the simulation of crop responses to current weather variability aiming at the identification of crop management practices for coping with climate variability. Promising practices can then be evaluated with farmers on their feasibility over a range of plausible future biophysical and socio-economic farming conditions.

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