Climate Smart Pest Management

Xiaoxue Du (), Levan Elbakidze, Liang Lu and R. Garth Taylor
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Xiaoxue Du: Department of Agricultural Economics and Rural Sociology, University of Idaho, Moscow, ID 83844, USA
Levan Elbakidze: Davis College of Agriculture, Natural Resources, and Design, West Virginia University, Morgantown, WV 26506, USA
R. Garth Taylor: Department of Agricultural Economics and Rural Sociology, University of Idaho, Moscow, ID 83844, USA

Sustainability, 2022, vol. 14, issue 16, 1-19

Abstract: This study examines the role of weather and pest infestation forecasts in agricultural pest management, taking into account potential correlation between weather and pest population prediction errors. First, we analytically illustrate the role of the correlation between weather and pest infestation forecast errors in pest management using a stochastic optimal control framework. Next, using stochastic dynamic programming, we empirically simulate optimal pest management trajectory within a growing season, taking into account correlation between weather and pest population prediction errors. We used lentil production in the Palouse area of northern Idaho and eastern Washington as a case study, where pesticide use was restricted due to environmental or health reasons. We showed that the benefit of applying pesticides later in the growing season can outweigh benefits of early application when pesticide use is restricted due to environmental or health regulations. The value of information is close to $9 per acre, approximately 8% of the expected net returns per acre, and close to $12 per acre ($106–$94), or approximately 13% of the expected net returns per acre for the baseline versus the climate change scenarios, respectively.

Keywords: stochastic optimal control; pest management; climate change; pest invasion (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Related works:
Working Paper: Climate Smart Pest Management (2018)
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