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Cotton Yield Forecasting for the Southeastern United States Using Climate Indices

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Applied Engineering in Agriculture. 28(5): 711-723. (doi: 10.13031/2013.42423) @2012
Authors:   T. B. Pathak, J. W. Jones, C. W. Fraisse
Keywords:   Climate indices, Cotton yield, Yield forecast, Principal component regression

The United States cotton industry is one of the major economic drivers of the country accounting for more than $25 billion in products and services annually. The southeastern United States holds a major share of total cotton production in the United States. Although cotton is considered as a drought tolerant crop, climate variability may adversely impact cotton production. An effective way to reduce agricultural vulnerability to climate variability is through the implementation of effective adaptation strategies. Knowing cotton yield forecast in advance based on climate information such as using large scale climate indices, would aid the growers in making informed decisions to adapt to climate risk. The objectives of this study were to evaluate the relationships between large-scale climate indices and cotton yield and to evaluate the skill of cotton yield forecasts. Seven January and February month oceanic and atmospheric climate indices were correlated with May-September temperature, precipitation, and county average cotton yield for 64 counties in Georgia and Alabama. All climate indices were then summarized using a principal component analysis and regressed against historic cotton yield for 64 counties to obtain empirical models for cotton yield forecasting. The yield forecasts were evaluated using leave one out cross validation. Results indicated that January and February monthly climate indices exhibited statistically significant correlations with climate during the cotton growing season as well as with cotton yields. With a lead time of approximately 2 months before the typical planting period on the southeastern United States, about 77% of the counties in Georgia and 70% of the counties in Alabama showed statistically significant correlations between observed and forecasted cotton yields. Climate indices showed potential to forecast cotton yield in the southeastern United States with significant lead time.

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