Synopsis The negative binomial distribution function is used to characterize quantitatively the time‐space distribution of excreta from freely‐grazing animals over long periods of time; the Poisson distribution function was found to be reasonably accurate over short periods. Proportion of pasture covered by 0, 1, 2, … excretions during a grazing period appears to depend primarily on mean excretal density at the end of the period, and to be practically independent of the size and shape of the pasture.

A number of statistical tests can assist in the interpretation of the results of agronomic experiments. Examination of recent issues of the Agronomy Journal indicates that one of the more popular procedures is to use some form of a multiple comparison test to look at pairwise comparisons among the treatment means. In this paper use of these tests is examined, and alternative procedures are suggested for situations in which multiple comparison tests are inappropriate. These are illustrated with examples extracted from the Agronomy Journal. In general, multiple comparison procedures are appropriate for “data snooping” following experiments with unstructured qualitative treatments. Multiple comparison tests are almost never appropriate for experiments in which 1) the treatments are graded levels of a quantitative variable, 2) for factorial combinations of two or more factors at two or more levels, and 3) for quantitative treatments where previously formulated linear combinations of treatment means are of particular interest.

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A method of evaluating grazing trials, and of generalizing the results, is presented. Data recording forms and computational procedures are given in detail. The method of computation and interpretation is illustrated by a numerical example.

Synopsis Quantitative theory indicates that gain per animal is constant as stocking rate is increased to a “critical” point. Beyond this point gain per head is inversely related to stocking rate. Gain per acre increases linearly as stocking rate is increased to the “critical” point, then decreases linearly with further increases in stocking rate.