Cluster analysis as a tool for classifying locations in order to minimize the within‐cluster genotype by location interactions was discussed and applied to the data for lint yield per hectare in upland cotton (Gossypium hirsutum L.) obtained from the Regional Cotton Variety Tests for years 1960–1964. The distance coefficient, which was more efficient as a measure of similarity than the product‐moment correlation coefficient in preliminary analyses, was used to study the zoning of the cotton belt. Some modifications in the currently recognized zones of adaptation for cotton were suggested.

The performance of four varieties of Upland cotton (Gossypium hirsutum L.) in 101 environments was used in (1) variance analyses to estimate the components of variance among genotypes, genotype by environment interactions, and experimental error, and (2) multiple regression analyses to relate the environment and the interaction effects to several environmental variables characterizing part of the environmental complex. The analyses were performed on lint yield per hectare, boll size, lint percent, seed index, and five fiber traits. The results of the analyses of variance showed that the interaction components were important for yield, but relatively less important for the other traits studied. In the multiple regression analyses, the independent variables were temperature, elevation, and subjective evaluations of moisture availability, disease condition, insect condition, and soil fertility level. These variables were jointly relatable to a proportion of the interaction sum of squares ranging from .245 for fiber fineness to .382 for fiber length. Of the environmental variables studied, temperature had the highest association with interaction.

Uniformity trials were conducted with pure stands of three forage species and one mixture of two species. Plot variance decreased with increasing plot size in every case. The relationship was very close and almost completely linear when plot variance and plot size were converted to logarithms. Thus plot size can be used to estimate plot or error variance. In general, increasing replication number reduced error more rapidly than increasing plot size. Using green weight only to estimate dry weight left 10% of the variation in dry weight in error.

The effect on cross pollination of four directions and six distances from the marker variety in Egyptian cotton was investigated. The percentage of cross pollination did not differ appreciably in the different directions but there were significant differences in cross fertilization among the six distances. A linear relation was observed between cross pollination per cent and distance up to 8 • 8 m. from the marker variety. Implications of these findings on various aspects of cotton breeding methodology are discussed.

Formulae are given to calculate the efficiency of the split-plot design, in which whole-plot treatments are arranged in randomized complete blocks. Efficiency for each of the whole-plot and split-plot treatment comparisons is discussed relative to three simpler designs. Tables are given to facilitate calculations for experiments in which up to five whole-plot treatments and five split-plot treatments are tested, using four, six or eight blocks.Split-plot designs are frequently used in agricultural experiments when two or more factors are studied, with levels of one or more factors representing the whole-plot treatments and those of one or more other factors representing the split-plot treatments. In their simplest form, split-plot designs involve the random assignment of the whole-plot treatments to the whole plots according to the completely randomized, randomized complete-block or Latin-square designs, after which the split-plot treatments are allotted at random to the split plots within each whole plot. The use of split-plot designs is recommended in the following cases:1. If the plots required for one or more of the factors are larger than those for the remaining factor (s), in which case the larger plots (whole plots) are used for whole-plot treatments and the smaller ones (split plots) for split-plot treatments; e.g. irrigation treatments, methods of fertilizer application or land preparation require large plots, whereas varieties, seed rates or seed treatments can be compared using much smaller plots, 2. If the experimenter is interested in comparing the levels of one or more factors more precisely than those of the other factor (s), in which case higher precision is required for comparisons among split-plot treatments as well as the interaction between whole-plot and split-plot treatments; e.g. the experimenter may already have information on the effects of the wholeplot treatments, and is mainly interested in their interaction with the split-plot treatments, expecting variation among adjacent split plots to be less than among adjacent whole plots, and therefore precision to be higher for comparisons involving split-plot treatments, and 3. If the experimenter wishes to introduce one or more extra factors at a later stage of the experiment.Split-plot designs have several disadvantages when compared with ordinary factorial experiments, in which all treatment combinations of the levels of the