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A statistical learning approach to pasture, rangeland, forage (PRF) insurance coverage selection

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  • Samuel D. Zapata
  • Xavier Villavicencio
  • Anderson Xicay

Abstract

This study uses statistical learning methods to identify robust coverage alternatives for the Pasture, Rangeland, Forage (PRF) insurance program. Shrinkage and ensemble learning techniques are adapted to the context of the PRF coverage selection process. The out‐of‐sample performance of the proposed methods is evaluated on 116 representative grids throughout Texas during 2018–2022. Ensemble learning methods generated more stable coverage choices compared with the other selection strategies considered. Depending on the target return, a reduction in the prediction error between 5% and 14% was observed. Furthermore, the proposed coverages can provide a broader protection than current coverage choices made by farmers.

Suggested Citation

  • Samuel D. Zapata & Xavier Villavicencio & Anderson Xicay, 2024. "A statistical learning approach to pasture, rangeland, forage (PRF) insurance coverage selection," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(4), pages 1429-1449, December.
    • Handle: RePEc:wly:apecpp:v:46:y:2024:i:4:p:1429-1449
    DOI: 10.1002/aepp.13447
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    References listed on IDEAS

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    2. Yu, Jisang & Vandeveer, Monte & Volesky, Jerry D. & Harmoney, Keith, 2019. "Estimating the Basis Risk of Rainfall Index Insurance for Pasture, Rangeland, and Forage," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 44(01), January.
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    5. Cho, Whoi & Brorsen, B. Wade, 2021. "Design of the Rainfall Index Crop Insurance Program for Pasture, Rangeland, and Forage," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 46(01), January.
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