|Benedict, John - TEXAS A&M UNIV|
|Ring, Dennis - LOUISIANA STATE UNIV|
Submitted to: International Agricultural Engineering Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 28, 2006
Publication Date: October 1, 2006
Citation: Lan, Y., Benedict, J.H., Ring, D.R., Hoffmann, W.C. 2006. Economic analysis of insect control strategies using an integrated crop ecosystem management model. Agricultural Engineering International: The CIGR Ejournal. 8:1-18. Interpretive Summary: Cotton is grown on an estimated 13.5 million acres in the United States with the cost of management and loss to insects reaching $1.118 billion or $81.58 per acre. The Integrated Crop Ecosystem Management Model, ICEMM, was used to predict the effectiveness of different insect pest management decisions using actual weather data. The model demonstrated that the use of a B.t. cotton variety provides the best overall single strategy for managing tobacco budworm and other insect pests under all risks and states of nature. This study shows that computer simulation methods and economic analyses can be used to evaluate a range of insect management strategies for various states of nature for any pest and crop production system. Cotton farmers and researchers can use this model to better understand and forecast the economic impact that different insect pest management schemes will have for their production scenario.
Technical Abstract: The Integrated Crop Ecosystem Management Model (ICEMM) (stochastic simulation model) was used to predict cotton lint yields for five insect management strategies under various states of nature (i.e., weather and insect densities) using historical weather data, insecticide rates, bollworm/tobacco budworm densities, and economic inputs. The economic outcomes of these bollworm/tobacco budworm management strategies under various states of nature are presented. Based on the economic comparison of different strategies, production of transgenic cotton (B.t. cotton) expressing an insecticidal protein from a bacterium, Bacillus thuringiensis (B.t.) resulted in the highest net returns. Moreover, the B.t. cotton resulted in the best net returns for all years together compared to other more conventional insect management strategies using probabilities for different states of nature. This paper presents and discusses methods for use in the analysis and prediction of optimum insect management strategies for cotton with a stochastic system simulation model, ICEMM, of insect-plant interactions and Bayesian probability analysis.