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Fluid Journal : Early Spring 2013
18 The Fluid Journal Early Spring 2013 Table 2. Correlations among lint yield, canopy NDVI, and leaf N concentration at Gibson in 2011. Dependent variable (Y) Independent variable (X) R2 R P Lint yield NDVI_7-5-11 0.13 0.36 <0.0001 Lint yield NDVI_7-27-11 0.18 0.42 <0.0001 Lint yield NDVI_8-4-11 0.29 0.54 <0.0001 Lint yield NDVI_8-17-11 0.26 0.51 <0.0001 Lint yield Leaf N_7-5-11 0.02 0.14 0.114 Lint yield Leaf N_7-27-11 0.01 0.1 0.193 Lint yield Leaf N_8-4-11 0.05 0.22 0.024 Lint yield Leaf N_8-17-11 0.04 0.2 0.021 Leaf N_7-5-11 NDVI_7-5-11 0.01 0.1 0.195 Leaf N_7-27-11 NDVI_7-27-11 0 0 0.994 Leaf N_8-4-11 NDVI_8-4-11 0.05 0.22 0.018 Leaf N_8-17-11 NDVI_8-17-11 0.08 0.28 0.002 and ammonium after cotton harvest, which suggests that residual nitrate and ammonium from the N treatments were ignorable in the soil after harvest. Spatial dependence. In order to examine the spatial dependence of lint yield within the test field, we conducted a quadratic regression of lint yields with sidedress N application rates using the classic model in the GeoDa software, and we observed significant spatial dependence of lint yields within the test field (data not presented). Then the spatial error model in Geoda was used to conduct the quadratic regression of lint yields with sidedress N rates; the output is presented in Table 3. In order to visualize the spatial dependence of lint yield relating to the characteristics of the test field (not to N treatments), we used residual lint yields (which were obtained in the spatial error model in GeoDa and in which N treatment effects on lint yield had been excluded) to make Moran's I statistic and scatter plot and LISA cluster map. Moran's I statistic and scatter plot and LISA cluster map are shown in Figures 1 and 2. Autocorrelation. Moran's I and scatter plot evaluate global spatial autocorrelation. Moran's I scatter plot provides a visual exploration of global spatial autocorrelation. The four quadrants of Moran's I scatter plot provide a classification of four types of spatial autocorrelation: high-high and low-low for positive autocorrelation; low- high and high-low for negative spatial autocorrelation. The value listed at the top of the graph is Moran's I statistic. Figure 1 shows that there was significant (p = 0.001) spatial autocorrelation of residual lint yields (N treatment effects on yields excluded) within the tested field. The LISA cluster map estimates local autocorrelation. It contains information on only those locations that have significant spatial autocorrelation. Four types of spatial autocorrelations are colored in four different colors: dark red for high- high, dark blue for low-low, pink for high-low, and light blue for low-high. The LISA cluster map in Figure 2 shows that there were some significant local clusters of residual lint yields (N treatment effects on yield excluded) within these significant local clusters of residual lint yields (N treatment effects on yields excluded) within this tested field. Specifically, there were eighteen sub plots with high residual yields surrounded by high Figure 2: LISA cluster map of lint yield (N treatment effects on yields excluded) at Gibson in 2011.
Late Spring 2013