LEVERAGING FROM GENOTYPE BY ENVIRONMENT INTERACTION FOR BREAD WHEAT PRODUCTION in EASTERN AFRICA

Abstract

Developing high yielding and stable genotypes for wide and specific adaptation is important in wheat ( Triticum aestivum L.) production. The objective of this study was to exploit the gains from genotype by environment interaction for increased bread wheat production in eastern Africa. Thirty-three advanced bread wheat lines, along with two check varieties (Danda\u2019a and Hidasse) were evaluated at ten locations in Ethiopia and Kenya. The experiment was laid out in alpha lattice design in three replications. The analysis of variance for AMMI model of grain yield showed that environment, genotypes and genotype by environment interaction (GEI) effects were highly significant (P<0.01), and accounted for 62.4, 4.8 and 15.8% of the total sum of squares variations, respectively. High environmental and significant GEI indicated that the environment had major influence for inconsistent performance. Grain yield of the genotypes ranged from 1.58 t ha-1 (G30) to 9.05 t ha-1 (G31). Genotypes G31, G18 and G35 were the best performing lines across environments. The AMMI biplot, using the first two principal components, showed that testing sites Njoro and Arsi-Robe highly discriminated the tested genotypes. Njoro was negatively interacting with high yielding genotypes, and was a different environment from any of the testing locations of Ethiopia for these sets of genotypes. It may be difficult to develop high yielding and stable varieties for the two countries, but one should look for specific adaptation. Genotypes G31 and G18 produced high grain yield, with low stability across locations which were favouring high yielding environments. However, G21 and G8 had above mean grain yield and good stability across locations. Therefore, wheat breeding for specific adaptability is very important to exploit the genetic advantage of specific genotypic performances across the region. However, extensive testing considering many locations across East African countries is vital for delineating and exploiting wheat environments for marked developments.Le d\ue9veloppement de vari\ue9t\ue9s stables et a rendements \ue9lev\ue9s dans le but d\u2019adoption a grande \ue9chelle, est important dans la production du bl\ue9 tendre ( Triticum aestivum L.). L\u2019objectif de cette \ue9tude est d\u2019exploiter l\u2019effet de l\u2019interaction entre g\ue9notypes et environnements (IGE) pour accroitre la production du bl\ue9 tendre en Afrique de l\u2019Est. Trente-trois lign\ue9es avanc\ue9es de bl\ue9 tendre ensemble avec deux vari\ue9t\ue9s de r\ue9f\ue9rence (Danda\u2019a and Hidasse) ont \ue9t\ue9 \ue9valu\ue9es dans dix locations. Le plan exp\ue9rimental \ue9tait en treillis alpha avec trois r\ue9p\ue9titions. La m\ue9thode de l\u2019interaction des effets additifs and multiplicative (AMMI) avait \ue9t\ue9 utilis\ue9e pour le rendement en grain. L\u2019analyse des variances selon ce mod\ue8le a montr\ue9 que l\u2019environnement, le g\ue9notype et l\u2019interaction des deux ont des effets significatifs sur le rendement en grains (P<0,01), et contribuent respectivement, 62,4\ua0; 4,8 et 15,8% \ue0 la variation totale. Un effet important de l\u2019environnement et une interaction significative indiquent que l\u2019environnement a un r\uf4le majeur dans les diff\ue9rences de rendements. Les rendements en grains des g\ue9notypes testes varient de 1.58 t ha-1 (G30) a 9.05 t ha-1 (G31). Les g\ue9notypes G31, G18 et G35 \ue9taient de fa\ue7on g\ue9n\ue9rale, les plus performants. Le biplot g\ue9n\ue8re par AMMI a montr\ue9 que les sites Njoro and Arsi-Robe discriminent nettement les g\ue9notypes test\ue9s. Njoro \ue9tait n\ue9gativement corr\ue9l\ue9 avec les g\ue9notypes a rendement \ue9lev\ue9 et constituait un environnement diff\ue9rent de toutes les autres locations de l\u2019Ethiopie ou ces g\ue9notypes ont \ue9t\ue9 testes. Il peut s\u2019av\ue9rer difficile de d\ue9velopper des vari\ue9t\ue9s \ue0 haut rendement et stable dans les deux pays, mais l\u2019on doit rechercher des vari\ue9t\ue9s adapt\ue9es \ue0 chaque milieu. Les g\ue9notypes G31 et G18 ont eu des rendements \ue9lev\ue9s mais n\u2019ont pas \ue9t\ue9 stables dans les milieux qui se sont av\ue9r\ue9s \ue0 haut rendement. N\ue9anmoins, G21 et G8 ont eu des rendements plus \ue9lev\ue9s que la moyenne et se sont montres stables d\u2019un milieu \ue0 un autre. Il s\u2019ensuit donc que le d\ue9veloppement de vari\ue9t\ue9 de bl\ue9 tendre adapt\ue9 \ue0 chaque milieu serait une bonne approche pour une exploitation efficiente des avantages g\ue9n\ue9tiques des g\ue9notypes \ue0 haute performance. N\ue9anmoins, il est important de faire des essais extensifs prenant en compte plusieurs localit\ue9s des pays de l\u2019Afrique de l\u2019Est afin d\u2019explorer et identifier les milieux propices au bl\ue9 tendre