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United States Department of Agriculture

Agricultural Research Service


item Sudduth, Kenneth - Ken
item Drummond, Scott
item Birrell, Stuart
item Kitchen, Newell

Submitted to: North Central Extension Industry Soil Fertility Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 11/20/1996
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The full benefit of site specific crop management will not be realized until the critical factors that cause variations in yield can be related to a measured parameter or identified as being caused by unknown parameters. Analysis and correlation methods that would assist in the detection of these critical factors on a point by point basis were evaluated. Methods investigated included standard linear correlation, stepwise multiple linea regression, projection pursuit regression, and back propagation neural network analysis. The data set used for analysis consisted of four field years of yield data, along with soil fertility, topsoil depth, and field topography. Standard statistical techniques such as linear correlation and stepwise multiple linear regression were unable to adequately model the spatial variability. Complex nonlinear, non-parametric methods such as projection pursuit regression and back propagation neural network analysis provided much more accurate yield predictions (up to r**2=0.77 and r**2=0.55, respectively). Nonlinear response curves were obtained from the non-parametric models for yield as a function of each of the input variables. These empirical response curves generally agreed with observed yield limitations on these fields and should prove useful for studying the interactions between multiple critical factors and crop yields.

Last Modified: 06/24/2017
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