Classification of Diverse Bread Wheat‐Growing Environments Based on Differential Yield Responses

Abstract

Bread wheat (Triticum aestivum L.) environments in West Asia, North Africa, and Mediterranean Europe are highly variable in terms of moisture, temperature, and biotic stresses. The present study attempted to divide this region into relatively uniform subregions by cluster analysis, to reduce the large magnitude of genotype (G) ✕ environment (E) interaction. Grain yield data of 21 to wheat lines in 2 yr were analyzed by a hierarchical agglomerative program with the correlation coefficient as a distance measure and average linkage as the clustering strategy. The large and significant entry ✕ trial interaction detected in the region implied that breeding for wide adaptability for this region would be difficult. Based on differential yield responses of the wheat lines, the cluster analysis showed the differences between the irrigated/high‐rainfall (IHR) and rainfed low‐rainfall (RLR) sites. Besides moisture supply, winter temperature of the sites appeared to be another determinant of the clusters. The IHR clusters consisted of sites with milder temperature than sites in the RLR clusters. The presence or absence of diseases also influenced the delineation of the clusters, but photoperiod of the sites did not appear to have a large influence. The greater variability in RLR sites relative to IHR sites indicated that breeding for RLR environments would be more difficult. We conclude that different breeding strategies should be followed within IHR and RLR environments.

Comparison of shifted multiplicative model, rank correlation, and biplot analysis for clustering winter wheat production environments

“…One method used by breeders to reduce the effect of G 9 E interactions is to combine environments or genotypes into homogeneous groups and then breed for each group (Abdalla et al 1997;Ramey and Rosielle 1983;Yau et al 1991).…”

“…The most widely used technique for stratifying environments or genotypes into groups has been cluster analysis (Ramey and Rosielle 1983;Yau et al 1991). Cluster analysis technique was first applied by Abou-El-Fittouh et al (1969) to classify cotton (Gossipium hirsutum L.) testing regions of the Cotton Belt (southern region of the US) and it was defined as a tool to classify locations in order to minimize the within-cluster genotype 9 location interactions.…”

Leaf area and crop growth in relation to phenology of barley in Mediterranean environments

“…The environmental effect on yield thus domi- nated the pattern effect. This is a common observation in the highly variable environments of the Mediterranean region (Nachit et al, 1992;Yau et al, 1991).…”

Demand-driven breeding of food legumes for plant-nutrient relations in the tropics and the sub-tropics: serving the farmers; not the crops!

“…1 Schematic presentation of breeding for optimum soil fertility level, low soil fertility stress tolerance, nutrient use efficiency and stable genotypes across all soil fertility gradients biological responses but also on edaphic and climatic factors. Alternative approaches have also been suggested and widely applied in different crops to stratify environments into homogeneous groups by cluster analysis based on the similarity of crop biological responses and edaphic and climatic factors (Campbell and Lafever 1980;Fox and Rosielle 1982;Yau et al 1991;van Oosterom et al 1993). The schematic presentation of a possible classification strategy appropriate where low, moderate and high genotype by soil fertility level interaction prevails is given in Fig.…”