4.7 Article

Potential Impact of Future Climates on Rice Production in Ecuador Determined Using Kobayashi's 'Very Simple Model'

Journal

AGRICULTURE-BASEL
Volume 12, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/agriculture12111828

Keywords

agricultural modeling; rice; climate change; RCPs; yield gaps

Categories

Funding

  1. National Council for Scientific and Technological Development (CNPq, Brazil)
  2. Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil)
  3. Division of Educational Topics of the Ministry of Foreign Affairs of Brazil

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Rice, as a staple food for over 50% of the world's population, may need to increase production by more than 70% before 2050 to meet global food requirements, despite challenges posed by environmental degradation, climate change, and extreme weather events. This study predicts that rice production in Ecuador may be negatively affected under the Representative Concentration Pathway 8.5, but may have a smaller impact under pathway 2.6.
Rice (Oryza sativa L.) is the main staple food of more than 50% of the world's population. However, global production may need to increase by more than 70% before 2050 to meet global food requirements despite increasing challenges due to environmental degradation, a changing climate, and extreme weather events. Rice production in Ecuador, mainly concentrated in lowland tropical plains, declined in recent years. In this paper, we aim to calibrate and validate Kobayashi's 'Very Simple Model' (VSM) and, using downscaled corrected climate data, to quantify the potential impact of climate change on rice yields for Ecuador's two main rice-growing provinces. The negative impact is expected to be highest (up to -67%; 2946 tons) under the Representative Concentration Pathway (RCP) 8.5, with a lower impact under RCP 2.6 (-36%; 1650 tons) yield reduction in the Guayas province. A positive impact on yield is predicted for Los Rios Province (up to 9%; 161 tons) under RCP 8.5. These different impacts indicate the utility of fine-scale analyses using simple models to make predictions that are relevant to regional production scenarios. Our prediction of possible changes in rice productivity can help policymakers define a variety of requirements to meet the demands of a changing climate.

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