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Estimating the Spatially Varying Responses of Corn Yields toWeather Variations using GeographicallyWeighted Panel Regression

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  • Cai, Ruohong
  • Yu, Danlin
  • Oppenheimer, Michael

Abstract

Researchers have extensively studied crop yield response to weather variations, while only a limited number of studies have attempted to identify spatial heterogeneity in this relationship. We explore spatial heterogeneity in corn yield response to weather by combining geographically weighted regression and panel regression. We find that temperature tends to have negative effects on U.S. corn yields in warmer regions and positive effects in cooler regions, with spatial heterogeneity at a fine scale. The spatial pattern of precipitation effects is more complicated. A further analysis shows that precipitation effects are sensitive to the existence of irrigation systems.

Suggested Citation

  • Cai, Ruohong & Yu, Danlin & Oppenheimer, Michael, 2014. "Estimating the Spatially Varying Responses of Corn Yields toWeather Variations using GeographicallyWeighted Panel Regression," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(2), pages 1-23.
    • Handle: RePEc:ags:jlaare:186586
    DOI: 10.22004/ag.econ.186586
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    2. Aditya Kusuma & Bethanna Jackson & Ilan Noy, 2018. "A viable and cost-effective weather index insurance for rice in Indonesia," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(2), pages 186-218, September.
    3. Giri, Anil & Johnson, Bruce & Supalla, Raymond, 2016. "An Alternative Approach to Measuring Drought in the Corn Belt," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235628, Agricultural and Applied Economics Association.
    4. Aditya Kusuma & Bethanna Jackson & Ilan Noy, 2018. "A viable and cost-effective weather index insurance for rice in Indonesia," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(2), pages 186-218, September.
    5. Yu, Danlin & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Li, Guangdong, 2021. "The varying effects of accessing high-speed rail system on China’s county development: A geographically weighted panel regression analysis," Land Use Policy, Elsevier, vol. 100(C).
    6. Lavorato, Mateus & Braga, Marcelo José, 2021. "On the Risk Efficiency of a Weather Index Insurance Product for the Brazilian Semi-Arid Region," 2021 Conference, August 17-31, 2021, Virtual 315193, International Association of Agricultural Economists.
    7. Yu, Danlin & Murakami, Daisuke & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Wang, Xiaoxi & Li, Guangdong, 2020. "Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 21-37.

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