Wind erosion adversely affects soils, plants, animals, equipment, the environment, and people. Wind erosion can be minimized or prevented by either standing residue or flat residue cover. Our objective was to develop mathematical relationships between these two crop residue properties and soil loss ratio (SLR: soil loss from protected soil/soil loss from flat, bare soil), for more accurate predictions of wind erosion soil losses. Therefore, from a previously reported wind tunnel study (wind tunnel 1.1 m high, 0.51 m wide, and 5 m long) we took data for velocities ranging from 9.4 to 16.1 m s−1 and silhouette areas (S) of upright wood dowels (simulating plant stems) ranging from 31 to 813 cm2 m−2 of soil surface (washed sand <0.42 mm) and developed the following equation for standing residue and SLRs: SLRs = exp(−28.49 × S0.6413/V2.423) (r2 = 0.95), where S = stalk height (cm) × stalk diameter (cm) × stalk density (no. m−2) and V = wind velocity in m s−1 at a height of 0.61 m. We combined data from a second previously reported wind tunnel (0.9 m high, 0.6 m wide, and 7 m long) study in which the soil had been covered from 0.0 to 80.0% with wood dowels, artificial clods, or cotton (Gossypium hirsutum L.) gin trash with data from field studies published by other researchers for various soil types and soil coverages ranging from 8 to 95% with wheat (Triticum aestivum L.) residue or gravel, and developed the following equation for soil cover and SLRc: SLRc = exp(‐0.04380 × psc) (r2 = 0.94), where psc is the percent of the soil that is covered by nonerodible material (e.g., soil aggregates, rocks, plant material). These equations should be useful to researchers developing and evaluating wind erosion models, prediction systems, and wind erosion control practices.

New statistical methods of separating means into different groups should be brought to the attention of researchers so they can decide if the new methods can be used advantageously in their research programs. Our objective was to illustrate the use of a cluster analysis method (which we have called the Scott‐Knott method after the developers) and compare it to the commonly used Duncan's multiple range test. We applied the two methods to four sets of data. The results showed that the smaller the variance of the treatment means, the more similar were the separation groupings produced by the respective methods. However, in contrast to Duncart's procedure, the Scott‐Knott method never produced overlapping mean separation groups and distinct groups of non‐overlapping means are often desired by the researcher. The Scott‐Knott method has more complex calculations than Duncan's method, but it is readily programmable for computers and many desk top electronic calculators.

We studied the stability, adaptation, and yields of several diverse cotton (Gossypiutn hirsutum L.) cultivars that had been grown at several locations in one or more of three, 3‐year periods of testing. We used as environmental indexes the mean lint yields of three “standard” cultivars that were common to all tests. The yields of the remaining cultivars were regressed upon these indexes. The regression coefficients (6 values) were used as measures of adaptability, and the coefficients of determination (r2 values) were used as measures of stability. Analyses of variance of lint yields also were computed.In comparison with the three standards, most of the other cultivars were adapted to all environments (b = 1.0), and all but one were stable. However, there were several significant yield differences among the cultivars within each of the three periods of testing. Thus, yield level was the most divergent parameter measured, adaptation was next, the stability was the least divergent. Because we found significant differences among commercial cultivars in both adaptation and stability, we believe that the use of these two parameters in conjunction with yield would be of significant benefit in breeding material evaluation.

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