Generation of corn tissue norms from a small, high‐yield data base

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Abstract
The purpose of this study was to determine the practicality of generating plant tissue norms with analytical data from a few, extremely high‐yield observations. A set of ear leaf tissue norms for corn (Zea mays L.) were developed from a set of data consisting of 10 observations of field‐grown corn with yields greater than 18 Mg ha‐1. The norms thus generated were compared with norms generated from 8494 observations from a wide geographical area. Norms for several elements (P, K, Mg, S, Mn, and B) calculated from the two data bases were significantly different, while norms for the other nutrients (N, Ca, Fe, and Cu) were not. The norms generated from the limited data base were tested with data from an N3P3K3S3 factorial greenhouse experiment. Results of this test indicate that the norms developed from the limited, high‐yield data base were slightly better at predicting yield increases than those from the broader worldwide data base. The increase in accuracy seemed to be largely due to lower S norms and higher P norms when derived from the smaller data base, resulting in less frequent determinations of S‐induced yield limitations. Using a few, extremely high‐yield observations appears to be an efficient, accurate, and relatively inexpensive means of generating plant tissue nutrient optima.