MOVING MEAN AND LEAST-SQUARES SMOOTHING FOR ANALYSIS OF GRAIN-YIELD DATA

Soil heterogeneity often decreases precision in large yield trials. Es timation of, and adjustment for, fertility trends within a trial may i ncrease precision. Two methods of estimating fertility trends were eva luated in 12 experiments with hexaploid what (Triticum aestivum L.) an d 11 experiments with tetraploid wheat (T. turgidum L. var durum). Eac h experiment consisted of a trial with two replications of 114 to 282 entries repeated at each of two local:ions in south-western Saskatchew an, Canada. With moving mean, the fertility trend for each plot was ca lculated as the average of the unadjusted yields of six neighbor plots . With least squares smoothing, the fertility trend was calculated as a weighted average of plot yields adjusted for differences among treat ments. Weights were inversely proportional to the distance from the ad justed plot and depended upon the observed fertility trend. Compared w ith unadjusted yields, adjustments by moving means increased the corre lation between the two trials within an experiment from an average of 0.41 to 0.48, whereas least squares smoothing increased the average co rrelation from 0.41 to 0.49. These increases indicate an average incre ase of within-trial precision of 20% for moving mean analysis and 24% for least squares smoothing. Either method is useful for removing fert ility trends in large yield trials.