Relationships between soil properties and variability of yield and quality of cotton grown on irrigated soils.

摘要

Precision agriculture provides an opportunity to increase production efficiency and reduce potential negative impacts on the environment. Successful application of precision agriculture management practices, however, will depend on the understanding of spatial variability on soil and crop yield and relationships between soil properties and crop parameters. The overall objectives of this study were to (1) explore global and local spatial variability of cotton yield, (2) evaluate performances of several common interpolation methods on selected soil properties, (3) examine spatial variability of soil properties, cotton lint yield, yield components, and fiber quality under various sampling schemes, (4) identify relationship between cotton yield, quality, and soil properties, and (5) delineate potential management zones for cotton. This research was conducted on two 49-ha production cotton fields during 1998 through 2000. Samples of soil and cotton plant were collected from regular 1-ha grid, triangular points, intensive grid and two transects. Data were analyzed with classical statistics, multivariate statistics, geostatistics, and geographic information systems.; The result showed that while global spatial statistics could describe the overall spatial association of cotton yields over whole field, local spatial statistics were useful to identify the influences from individual positions and the trends between positions. Spatial weight selections affected spatial association statistics. The accuracy, precision, and efficiency of spatial interpolation varied with soil property, soil depth, and estimation method. In comparison, soil properties that tended to have higher variations included cotton lint yields, yield components, and fiber quality. Except nitrate-N and Olsen-P, soil properties were strongly spatially dependent. Lint yields, yield components, and fiber quality tended to be weakly or moderately spatially dependent. Furthermore, soil texture, exchangeable calcium, pH, and depth to free carbonate were related to lint yield and fiber quality. The application of multivariate analyses such as partial least square, principal component regression, and cluster analysis can help to explore the relationships between cotton and soil properties and delineate potential crop management zones from inter-correlated independent variables.