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Site-Specific Soil Fertility Management

A Model for Map Quality

T. G. Mueller, N. B. Pusuluri, K. K. Mathias, P. L. Cornelius and R. I. Barnhisel

Dep. of Agronomy, N-122 Agronomy Science North, Univ. of Kentucky, Lexington, KY 40502



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Fig. 1. Sampling design. The 30.5-m grids are indicated with crosses and the validation points are indicated with circles.

 


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Fig. 2. Plots of predicted vs. measured for pH, P, and K for the four 61.0-m grids (i.e., G61a, G61b, G61c, G61d) at the Hopkins County location.

 


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Fig. 3. Plots of predicted vs. measured for pH, P, and K for the four 61.0-m grids (i.e., G61a, G61b, G61c, G61d) at the Shelby County location. PE = prediction efficiency.

 


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Fig. 4. Model semivariance minus measured semivariance of individual semivariograms of individual pairs for (a) pH in Hardin County, (b) P in Hardin County, and (c) pH in Shelby County. PE = prediction efficiency.

 


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Fig. 5. A visual representation of the contributions of the factors in the regression model (Table 4) to the final estimates. The partial contributions to the predictions (i.e., regression coefficient x repressor variable value) were plotted against the untransformed values of the regressor variables.

 


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Fig. 6. Regression model (Table 4) evaluation. Modeled vs. measured prediction efficiency for (a) the data that was used to generate the model, (b) the original data with the exception of pH and buffer pH values from the Calloway, Hopkins, Hardin, Nelson, and Michigan locations, the original data from the pH and buffer pH values from the Calloway, Hopkins, Hardin, Nelson, and Michigan locations, and (d) reported results from Kravchenko (2003). RMSEs and errors of accuracy (ACC) and precision (PREC) listed. Accuracy and precision have been defined Eq. [2] and [3], respectively.

 





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