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Cover Crop Adoption and Climate Risks: An Application of Causal Random Forests

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  • Quigley, David T.
  • Che, Yuyuan
  • Yasar, Mahmut
  • Rejesus, Roderick M.

Abstract

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Suggested Citation

  • Quigley, David T. & Che, Yuyuan & Yasar, Mahmut & Rejesus, Roderick M., 2023. "Cover Crop Adoption and Climate Risks: An Application of Causal Random Forests," 2023 Annual Meeting, July 23-25, Washington D.C. 335586, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea22:335586
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    References listed on IDEAS

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    1. Fengxia Dong, 2022. "Cover Crops, Drought, Yield, and Risk: An Analysis of US Soybean Production," NBER Chapters, in: American Agriculture, Water Resources, and Climate Change, pages 241-267, National Bureau of Economic Research, Inc.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
    4. Benedikte Bjerge & Neda Trifkovic, 2018. "Extreme weather and demand for index insurance in rural India," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(3), pages 397-431.
    5. Chen, Shuai & Chen, Xiaoguang & Xu, Jintao, 2016. "Impacts of climate change on agriculture: Evidence from China," Journal of Environmental Economics and Management, Elsevier, vol. 76(C), pages 105-124.
    6. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    7. Cai, Jing & Song, Changcheng, 2017. "Do disaster experience and knowledge affect insurance take-up decisions?," Journal of Development Economics, Elsevier, vol. 124(C), pages 83-94.
    8. Anthony Louis D'Agostino & Wolfram Schlenker, 2016. "Recent weather fluctuations and agricultural yields: implications for climate change," Agricultural Economics, International Association of Agricultural Economists, vol. 47(S1), pages 159-171, November.
    9. Zhang, Peng & Zhang, Junjie & Chen, Minpeng, 2017. "Economic impacts of climate change on agriculture: The importance of additional climatic variables other than temperature and precipitation," Journal of Environmental Economics and Management, Elsevier, vol. 83(C), pages 8-31.
    10. Marshall Burke & Kyle Emerick, 2016. "Adaptation to Climate Change: Evidence from US Agriculture," American Economic Journal: Economic Policy, American Economic Association, vol. 8(3), pages 106-140, August.
    11. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    12. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    13. Jacob, Daniel, 2021. "CATE meets ML: Conditional average treatment effect and machine learning," IRTG 1792 Discussion Papers 2021-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    14. Carolyn Kousky, 2017. "Disasters as Learning Experiences or Disasters as Policy Opportunities? Examining Flood Insurance Purchases after Hurricanes," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 517-530, March.
    15. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
    16. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    17. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    18. Shackelford, Gorm E. & Kelsey, Rodd & Dicks, Lynn V., 2019. "Effects of cover crops on multiple ecosystem services: Ten meta-analyses of data from arable farmland in California and the Mediterranean," Land Use Policy, Elsevier, vol. 88(C).
    19. Yoder, Landon & Houser, Matthew & Bruce, Analena & Sullivan, Abigail & Farmer, James, 2021. "Are climate risks encouraging cover crop adoption among farmers in the southern Wabash River Basin?," Land Use Policy, Elsevier, vol. 102(C).
    20. Yuyuan Che & Hongli Feng & David A. Hennessy, 2020. "Recency effects and participation at the extensive and intensive margins in the U.S. Federal Crop Insurance Program," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(1), pages 52-85, January.
    21. Fan Li & Kari Lock Morgan & Alan M. Zaslavsky, 2018. "Balancing Covariates via Propensity Score Weighting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 390-400, January.
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    Keywords

    Productivity Analysis; Risk and Uncertainty; Production Economics;
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