COTTON Variability in Cotton Fiber Yield, Fiber Quality, and Soil Properties in a Southeastern Coastal Plain

correlated with soil organic matter, B, Cu, Fe, Mn, and Zn. Fiber quality was correlated with soil Mg, K, Cu, To maximize profitability, cotton (Gossypium hirsutum L.) producand As. Elms et al. (1997) reported that yield in an ers must attempt to control the quality of the crop while maximizing irrigated cotton field in Texas displayed spatial correlayield. The objective of this research was to measure the intrinsic variability present in cotton fiber yield and quality. The 0.5-ha experition. These authors also noted that production of fruitmental site was located in a producer’s field (Norfolk–Coxville soil ing sites and fruit retention was spatially correlated. association) in Florence, SC, for 2 yr (1996 and 1997). Soil (0–20 cm) Micronaire exhibited a moderate degree of spatial variand fiber samples (1-m row) were collected from a regular grid (129.2 ability, and strength showed the lowest degree of variby 45.6 m, 7.6-m interval). Soil properties determined included soil ability. moisture, soil texture, organic matter, pH, Ca, Mg, K, P, and Na. Fiber Several recent publications have attempted to quanquality was estimated by the high-volume instrumentation method and tify the extent of soil spatial variability present in soils the Advanced Fiber Information System. Fiber strength and elongaof the Southeastern Coastal Plains. Sadler et al. (1995) tion were also estimated by the stelometer procedure. All fiber and attempted to relate crop yield variations to soil map soils data were analyzed by both nonspatial statistics and geostatistical unit but found that intramap unit variance was almost techniques. Distinct patterns of spatial correlation were observed in as large as intermap unit variance. This variation was soils and fiber yield. These patterns were not equally evident in all fiber not successfully explained using statistical regression properties. Soil pH, soil P, and soil organic matter were correlated with methods or mechanistic modeling. Geostatistical techfiber yield and a number of fiber properties, including micronAFIS, niques provided a better description of the highand immature fiber fraction, fine fiber fraction, cross-sectional area, and micronaire. Factor analysis of soil properties identified four factors low-yield regions, but year-to-year variation obscured in 1996 and three in 1997. In both years, a Carolina bay factor and some of the underlying trends. This problem was adan exchangeable bases factor were obtained. These factors were not dressed by normalizing yield before the kriging procesuccessfully related to fiber yield and quality. Kriged contour maps dure. Sadler et al. (1998) reported that crop yields in of soil properties provided useful indicators of fiber yield and qualSoutheastern Coastal Plain soil were correlated with ity variation. soil map unit, but the relation was weak at best. Crop yields also displayed spatial correlation with a range varying from 57 to 252 m. However, it was noted that P agriculture is an informationand techsignificant variation occurred at distances as short as nology-based agricultural management system that 10 m, suggesting that a modification of current soilidentifies, analyzes, and manages site spatial and tempoand plant-sampling schemes might prove necessary and ral variability within fields for optimum profitability, more appropriate for precision agriculture applications. Precision agriculture offers cotton producers a mansustainability, and protection of the environment (Robagement strategy that could help to control production ert et al., 1995, 1996). Recent developments in cotton inputs so that return is maximized. Although absolute yield-sensing technology (Wilkerson and Hart, 1996) quantities of crop inputs may not be decreased, the and soil-fertility mapping (Valco et al., 1998) indicate reallocation of these inputs could result in better utilizathat precision agriculture systems show potential for tion and decreased waste (Olson, 1998). The objective widespread use in cotton production. In addition, sevof this study was to measure soil variability in relation eral studies have demonstrated that the spatial variabilto both cotton fiber yield and quality in a field under ity of cotton yield and fiber quality is sufficient to justify typical commercial crop management. site-specific management. In a study performed in Winnsboro, LA (Johnson et al., 1999), the authors found that MATERIALS AND METHODS cotton yield and all fiber quality properties measured, with the exception of short-fiber content, displayed spaSoils tial correlation. They also noted that fiber yield was A field experiment conducted in a producer’s field in Florence, SC, investigated the influence of soil spatial variability R.M. Johnson, USDA-ARS, Sugarcane Res. Unit, Houma, LA 70360; Abbreviations: AFIS, Advanced Fiber Information System; A(n), fiR.G. Downer, Dep. of Exp. Stat., Louisiana State Univ., Baton Rouge, ber cross-sectional area by number; CEC, cation exchange capacity; LA; J.M. Bradow, USDA-ARS, Southern Regional Res. Cent., 1100 CV, coefficient of variation; FFF, fine-fiber fraction; Hunter’s b, Robert E. Lee Blvd., New Orleans, LA 70124; and P.J. Bauer and yellowness; HVI, High-Volume Instrumentation; IFF, immature fiber E.J. Sadler, USDA-ARS, Coastal Plains Res. Cent., Florence, SC. fraction; L(hvi), fiber length as determined by high-volume instrumenReceived 19 June 2001. *Corresponding author ([email protected]. tation; L(n), fiber length by number; L(w), fiber length by weight; usda.gov). Rd, reflectance; SFC(n), short fiber content by number; SFC(w), short fiber content by weight. Published in Agron. J. 94:1305–1316 (2002).