Relationship between corn yield and chemical composition of the ear leaf measured with different regression models

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Abstract
Corn yields and leaf samples were obtained from experimental plots receiving varying rates of N, P and K. Yields were regressed on leaf levels of N, P, K, Ca, Mg, B, Cu, Fe, Mn and Zn as independent variables. Various polynomial regressions were fitted to the yields and “goodness of fit”; of particular mathematical models was used as a basis for evaluating particular biological and statistical concepts. It was found that no regression, where chemical elements were used as ratios, fit the observations as well as a quadratic polynomial or its square root transformation. This suggests that within the set of data used, emphasis on particular cation or anion ratios does not necessarily result in the “best”; explanation of variation in yield. “Classical”; growth equations were fitted to corn yields, but were not as precise in predicting yields as the polynomials. A modified stagewise regression procedure was used for fitting one mathematical model, but results were less satisfactory than those obtained with usual least squares procedures.