Development of pre-harvest cotton fiber quality prediction equations



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Texas Tech University


Cotton (Gossypium hirsutum L.), the major crop of the Southern High Plains of Texas, serves as a major source of income in several counties of the South Plains. Pre-harvest knowledge of fiber properties is valuable to buyers, sellers, and processors of cotton. The current pre-harvest sampling technique requires considerable time, effort, and resources. This research was conducted to determine the feasibility of predicting cotton fiber length, micronaire, and strength before harvest, with regression equations based on climatic variables under both dryland and irrigated production. This study utilized the developed equations to predict fiber properties of the 1982 through 1987 Southern High Plains cotton crops and to determine their accuracy in estimating overall crop quality. Multiple-regression analysis of fiber quality properties and weather variables (solar radiation, heat units, rainfall, and rainfall plus irrigation) were used to develop these equations. Cotton fiber quality data were obtained from the High Plains Research Foundation at Halfway and the Texas Agricultural Research and Extension Center at Lubbock from 1982 through 1987. The predictive equations developed included: fiber length, micronaire, and strength under irrigated production with R values of .66, .59, and .51, respectively. Also, fiber length, micronaire, and strength under dryland production with R^ values of .76, -59, and .19, respectively. Irrigated production refers to crop development utilizing seasonal rainfall plus two irrigations (pre-plant plus one during the growing season). Dryland production refers to crop development utilizing seasonal rainfall only. Fiber quality predictions as compared to the actual USDA Cotton Classing Office averages varied by -0.42 to +1.79 32nds of an inch for fiber length, -0.10 to +0.27 for micronaire and -0.50 to +1.63 gram per tex for strength for the years 1982 through 1987. Predictions made by these equations were not consistent enough to be used for making marketing decisions. Additional field studies are needed to improve the predictive capabilities of these models.