Year of publication 2024

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Place of publication Florence, Italy ;

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Pages 19 p.

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Author Alvar-Beltrán, J.; Franceschini, G.;

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Publisher Firenze University Press ;

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Product type Article

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Synopsis (short abstract) Understanding the relationship between adverse weather conditions and crop productivity is the backbone of risk assessments on food security. It is paramount in countries like Bhutan, which has a limited number of impact assessment studies in agriculture. The work presented here highlights agricultural production trends under a changing climate and the attribution of yield changes to a specific weather hazard. Thus, the relationship between climate and yields is improved by running the Food and Agriculture Organization (FAO) Python Agroecological Zoning (PyAEZ) model with state-of-the-art climate projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CORE). At the national level, we analyze climate change impacts on yields for ten crops (grain maize, foxtail millet, buckwheat, wheat, wetland rice, common beans, cabbage, white potatoes, carrots, and citrus). The main simulation findings point to higher yield variations, either a gain or a loss, under rainfed conditions for the Representative Concentration Pathway (RCP) 8.5 as opposed to irrigated conditions and RCP 2.6; for example, by +17.4% (white potatoes), +15.3% (wheat), +12.8% (cabbage), -5.8% (citrus), and -6.7% (buckwheat) under RCP 8.5 by 2070-2099. Yield results show the potential of irrigation to modulate adverse weather conditions and to improve crop performance by +43.4% on average for all crops as opposed to rainfed crops which are more exposed to weather hazards (i.e., heat stress and dry spells). This study also sheds light on the most impactful weather perils describing 28% (RCP 2.6) and 33% (RCP 8.5) of the yield variability over time. Thus, our findings support smallholder farmers, decision-makers, and project formulators in developing adaptation solutions that minimize the effects of growing adverse weather conditions on crop yields.

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