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Cotton

Identification of Relationships between Cotton Yield, Quality, and Soil Properties

^a Dep. of Agronomy and Horticulture, Univ. of Nebraska, Lincoln, NE 68583-0915
^b Dep. of Plant and Soil Science, Texas Tech Univ., Lubbock, TX 79409-2122
^c Texas Agric. Exp. Stn., RR 3, Box 219, Lubbock, TX 79403

^* Corresponding author (cary.green{at}ttu.edu)

Received for publication September 8, 2003. Intercorrelation among soil properties can result in multicollinearity problems regarding relationships between soil properties and crop yield. The objective of this study was to compare statistical methods of examining relationships between cotton (Gossypium hirsutum L.) yield, quality, and soil properties. Soil and plant samples were collected from 1-ha grids on an irrigated production cotton field in Texas from 1998 through 2000. Ordinary least square regression (OLS), partial least square regression (PLS), and principal component regression (PCR) were compared as methods for quantifying relationships between cotton yield or quality and soil properties. The PLS method eliminated multicollinearity problems and resulted in the coefficient estimations with meaningful signs compared with their associations to cotton yield and fiber quality. Furthermore, loadings from linear combinations of variables in PLS allowed identifying soil properties that had the greatest influence on yield. While PCR identified the principal components that maximized the variance of independent variables, it did not improve the modeling of crop–soil relationships. Among the selected soil and landscape properties, sand and clay content, exchangeable Ca²⁺ and Mg²⁺, NO₃^–, Olsen-P, pH, relative elevation, and slope were important factors affecting lint yield and fiber quality. Higher lint yields were usually accompanied by higher fiber quality. Magnitudes of influence of different soil properties on yield and quality, however, varied among the 3 yr, suggesting that long-term studies are needed to establish robust relationships for site-specific management.

Abbreviations: CV, coefficient of variation • DCa, depth to caliche • DFCa, depth to free carbonate • DGPS, differential global positioning system • OLS, ordinary least square regression • PCA, principle component analysis • PCR, principle component regression • PLS, partial least square regression

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