Exploring spatial dependence of cotton yield using global and local autocorrelation statistics

Abstract The understanding of spatial dependence of yield and abiotic factors that influence yield plays a key role in successful application of precision agriculture. The objectives of this study were to (i) evaluate the application of both global and local autocorrelation statistics to explore the spatial dependence of cotton (Gossypium hirsutum) yield and yield pattern changes under two weather scenarios, and (ii) compare effects of weight selection on spatial autocorrelation statistics. Cotton yield was measured from 1 ha grids in a 49 ha production field from 1998 through 2000. Spatial dependence was described in terms of global Moran’s I and Geary’s C, local indicator of spatial association, and local Gi and G i ∗ statistics. While global spatial autocorrelation statistics could describe the overall spatial dependence of cotton yields over the entire field, local spatial autocorrelation statistics were useful in identifying the influences from individual positions compared to their neighbors. The application of Moran scatterplots could decompose the spatial dependence and identify influential positions. Spatial dependence of cotton yield was highly affected by weather conditions. The lint yields were significantly spatially autocorrelated in the drier years (1998 and 2000), but not in wetter year (1999) in this study. Furthermore, a trend existed with changing locations and the detrending decreased the spatial association. Additionally, spatial autocorrelation of lint yield in the drier years turned from positive into negative as contiguity order increased. Maximum spatial autocorrelation was obtained in inverse distance with power 1 and in k-nearest points with k as 4. In comparison, there were some similarities between spatial semivariogram, and global and local spatial association statistics but the latter can provide some useful spatial association to be used for management zone delineation. Based on global and local spatial statistics, three major and five minor management zones were identified, which could help decision making in site-specific management systems.

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