Machine learning projection of climate and technology impacts on crops key to food security
Climate change poses a serious threat to every sector of the economy. Agricultural sector is particularly vulnerable, due to its exposure to extreme conditions, temperature increases and systematic precipitation redistribution. How this susceptibility impacts crop production will have substantial re...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Environmental Research: Climate |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2752-5295/adcbc9 |
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| Summary: | Climate change poses a serious threat to every sector of the economy. Agricultural sector is particularly vulnerable, due to its exposure to extreme conditions, temperature increases and systematic precipitation redistribution. How this susceptibility impacts crop production will have substantial repercussions on policies related to food security. Projecting the prospective impacts of climate change on crop production necessitates a comprehensive modelling system outlining crop responses to future conditions. Here we introduce a multivariate autoregressive econometrics model that includes a time-varying non-linear variable to account for the decreasing impact of technology on crop yields. Our model is designed to capture the relationships between technology, climate variables and the annual growth rate in crop yield across the world’s producing regions. Utilizing historical national crop production data and climate variables from 1961 to 2018, the developed model outperforms traditional panel regression methods. Additionally, a novel machine learning climate model emulator allows efficient estimation of crop production growth under a multitude of carbon equivalent emissions scenarios. Our key finding is that technological effects are prone to have a diminishing impact on wheat and rice production over time and may not adequately offset the negative effects of climate change under certain future emission scenarios. Naive assumptions surrounding technology result in overestimates of production exceeding 200% for wheat and 150% for rice. Our study implies that integrated assessment and other economic models that use oversimplified climate damage functions can compound inaccuracies in production estimates with adverse repercussions on policy decisions. |
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| ISSN: | 2752-5295 |