Selection Environments for Maize in the U.S. Western High Plains

Abstract

Dryland maize (Zea mays L.) production in the U.S. western High Plains is hampered by variable yields because of substantial environmental variation in this region. This study was conducted to determine the degree to which the ranking of superior maize hybrids for dryland production in the western High Plains was predictable from performance of the same hybrids in highly productive, irrigated environments in the same region. Forty‐five maize hybrids were evaluated for grain yield performance under different water regimes in western Nebraska, eastern Wyoming, and northeastern Colorado in 1998 and 1999. The value of genotypic variance was by far larger in fully irrigated test environments (0.70) than in nonirrigated test environments (0.01–0.17). The genotypic mean repeatability in fully irrigated test environments (0.63) compared with that in nonirrigated test environments (0.18–0.69, respectively), and it showed correspondence with yield performance. The genetic correlation between fully and nonirrigated environments (0.72) was lower than that observed between all‐nonirrigated environments (0.78–1.02). Thus, the proportion of direct advance in the former case (0.63) was generally lower than in the latter (0.41–0.97). However, an environmental similarity ratio (ESR) derived from crossover interaction indicated that water‐contrasting environments were as similar (ESR = 0.53) as nonirrigated environments (ESR = 0.49) in ranking the maize hybrids. Selective identification of maize hybrids in irrigated environments for production under nonirrigated environments in the western High Plains might be a useful surrogate to direct selection in the latter environments.

Multienvironmental evaluation of wheat landraces by GGE Biplot Analysis for organic breeding

“…Combined analysis of variance (ANOVA) across environments for grain yield and the other parameters were were then estimated as measure of effectiveness for differentiation among wheat cultivars to each testing environment [15]. The mean differences were estimated by LSD based on Fisher's test for P < 0.05 (*), P < 0.01 (**) and P < 0.001 (***).…”

Environment and genotype-environment interaction in maize breeding in Paraná, Brazil

“…Highest estimates of repeatability coefficient (r) and genetic variability (V G ) were obtained in NSE, followed by intermediate and highly stressful environments (SE). Guillen-Portal et al (2004) obtained a high estimate of r jj' between NSE and SE, suggesting the possibility of concentrating selection on optimal conditions. Other results, however, indicated that genetic correlations between NSE and SE tended to decrease as the level of stress increases (Hohls 2001).…”

“…The efficiency of indirect selection (IS) is also determined by the magnitude of the genetic and environmental components of variation, by the genotypic repeatability coefficient at each location and by the genotypic performances (Falconer 1987). The effectiveness of IS in stressful (SE) and non-stressful environments (NSE) differs (Byrne et al 1995, Atlin et al 2000, Hohls 2001, Bänzinger and Cooper 2001, Guillen-Portal et al 2004. Highest estimates of repeatability coefficient (r) and genetic variability (V G ) were obtained in NSE, followed by intermediate and highly stressful environments (SE).…”

Genotype by Environment Interactions on Cotton Fiber Traits and Their Implications on Variety Recommendation

“…The cultivar phenotypic variance (σ 2 p ) was partitioned to its respective components, cultivar genetic variance (σ 2 g ) and cultivar by environment interaction variance (σ 2 ge ) (McIntosh, 1983). The broad sense heritability estimation (H) was calculated as σ 2 g /σ 2 p ratio (Guillen-Portal et al, 2004). The GGE biplot and AMMI analyses for environment classification together with genotype evaluation were performed using the PB tools 1.4 software, provided by the IRRI.…”