Optimal Environments for Yield Testing¹

Abstract

This research was undertaken to obtain information about the type of yield trial environment that will foster maximum progress from selection. Theory and parameter estimates from five crops that are relevant to the question of the optimum nursery environment for yield testing are reported. The five crop species were barley, Hordeum vulgare L.; wheat, Triticum aestivum L.; oats, Avena sativa L.; soybean, Glycine max (L.) Merr.; and flax, Linum usitatissimum L. Analyses of variance of yield data (a separate analysis for each year and location) reconstructed from information in the annual reports of Uniform Nursery Trials were the sources of our parameter estimates. We defined as the value of a genotype relative to a test environment, as the value of a genotype relative to the entire population of environments in which a selected genotype would be used, and H as σ2y/(σ2y + σ2e/n) where σ2y is the variance of , σ2e, is plot error variance and n is the number of replications in the field trial comparison used as the basis for selection. We then found the proper measure of test environment value to be r√H where r is the correlation between and . This is because r√H reflects genotype ✕ environment interaction as well as ✕2e and n, appropriately. The single environment variance analyses available to us provided estimates of ✕2y and ✕2e but not of r. In general, both ✕2y and ✕2e were larger when the nursery yield level was higher. Estimates of √H from the data of low, intermediate and high yielding trials were not greatly different. The indicated advantage (given n = 3) of favorable (high yield) environments over intermediate environments were 4% and 7% for wheat and soybeans, respectively, but in the case of barley, oats and flax, there were no indications that high yield environments were superior. Our results indicate that if any class of environments is eventually established as substantially superior for testing purposes, it will be because the correlation between and is comparatively high for that class of environments.

Gains from Selection for High Grain Yield under Contrasting N Environments in F2 Populations of Wheat Diallel Crosses

“…The direct selection under abiotic stress would ensure the preservation of alleles for stress tolerance (Langer et al,1979) but direct selection under optimal environment would take advantage of high heritability (Allen et al, 1978;Blum, 1988;Smith et al, 1990 andBraun et al, 1992). A third alternative, which is currently used at CIMMYT, deploys the simultaneous evaluation under both near optimum and stress conditions, with selection of those genotypes that perform well in both environments (Calhoun et al, 1994).…”

A heritability-adjusted GGE biplot for test environment evaluation

“…1, it is clear that the usefulness of the test environment in indirect selection for the target environment has to be evaluated with regard to two aspects: (1) the heritability for the trait of interest in the environment (h j 2 ), and (2) its genetic correlation with the target environment (r g(jj 0 ) ). In the terminology of Allen et al (1978), the proper measure of the value of a test environment is r ffiffiffiffi H p where r is the correlation between genotypic performance in the test environment and the target environments and H = h 2 is the heritability in the test environment.…”

Variety-environment interactions for maize in Bajio, Mexico

“…The correlation coefficient between each test environment and the overall mean across environments, or the target population of environ ments, has been shown to be involved in the formula for expected gain in the following manner: E(A-) = kra-v^ (Allen et al, 1978), where E(A-) is the expected gain from selection; k is the standardized selec tion differential; a-is the square root of the genotypic variance;…”

“…If k and a-are considered constant, the value of any environment, or group of environments as a test environ ment, is measured by the product rt4ï (Allen et al, 1978). Under this situation, environments with higher r values are preferred as test environments (Allen et al, 1978).…”

“…The previous formula for expected gain can be expressed in a dif-2 ferent manner (Comstock and Moll, 1963): E(A-) = k(o_ + ja-^-)/o_, y y y*iy p 2 where E(a_), k, and o_ are as defined by Allen et al, (1978); j is the jth environment; "^y.yf is the covariance between genotypic effect y (mean of each entry across all environments minus mean of all entries across all environments or grand mean) and genotype x environment ef fects fy (mean across replications plus grand mean minus mean across environments and mean across genotypes). The covariance estimates be tween y and fy provide useful information to choose environments.…”