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Yield predictability comparisons of unadjusted and adjusted means based on past performance in the Nebraska corn hybrid trials
Abstract
The Nebraska Variety Testing Program tests ten different crops for performance throughout Nebraska. Recent advances in computer software make it more feasible to adjust means used to evaluate and predict varietal performance. Adjusted mean analyses must predict future performance more effectively than unadjusted mean analyses in order to be useful. In this study, unadjusted mean analyses as well as covariance, nearest neighbor, and principle component adjusted analyses from one-, two-, and three-year combinations were used to predict future hybrid performance of maize grain yield. The average unadjusted multi-year correlation was r$\sb{\rm s}$ = 0.40. Principle component and weather covariate adjusted means did not predict future maize yield performance as well as the unadjusted means. Nearest neighbor analyses showed improvements over the unadjusted means in predicting future yield performance in Zones I and III, but only 25 percent of the multi-year and none of the one-year analyses exhibited a 10 correlation unit improvement. Zone II, a dryland-irrigation transition area, had poor prediction qualities due to the extreme variation induced by environmental stress. The results demonstrated that multi-year unadjusted mean analyses were the best predictors of future hybrid performance.
Subject Area
Agronomy|Biostatistics
Recommended Citation
Grombacher, Alan Wall, "Yield predictability comparisons of unadjusted and adjusted means based on past performance in the Nebraska corn hybrid trials" (1992). ETD collection for University of Nebraska-Lincoln. AAI9308178.
https://digitalcommons.unl.edu/dissertations/AAI9308178