PDF(Pubmed)
Abstract：
The high performance and stability of wheat genotypes for yield, grain protein content (GPC), and other desirable traits are critical for varietal development and food and nutritional security. Likewise, the genotype by environment (G × E) interaction (GEI) should be thoroughly investigated and favorably utilized whenever genotype selection decisions are made. The present study was planned with the following two major objectives: 1) determination of GEI for some advanced wheat genotypes across four locations (Ludhiana, Ballowal, Patiala, and Bathinda) of Punjab, India; and 2) selection of the best genotypes with high GPC and yield in various environments. Different univariate [Eberhart and Ruessll\'s models; Perkins and Jinks\' models; Wrike\'s Ecovalence; and Francis and Kannenberg\'s models], multivariate (AMMI and GGE biplot), and correlation analyses were used to interpret the data from the multi-environmental trial (MET). Consequently, both the univariate and multivariate analyses provided almost similar results regarding the top-performing and stable genotypes. The analysis of variance revealed that variation due to environment, genotype, and GEI was highly significant at the 0.01 and 0.001 levels of significance for all studied traits. The days to flowering, plant height, spikelets per spike, grain per spike, days to maturity, and 1000-grain weight were specifically affected by the environment, whereas yield was mainly affected by the environment and GEI. Genotypes, on the other hand, had a greater impact on the GPC than environmental conditions. As a result, a multi-environmental investigation was necessary to identify the GEI for wheat genotype selection because the GEI was very significant for all of the evaluated traits. Yield, 1000-grain weight, spikelet per spike, and days to maturity were observed to have positive correlations, implying the feasibility of their simultaneous selection for yield enhancement. However, GPC was observed to have a negative correlation with yield. Patiala was found to be the most discriminating environment for both yield and GPC and also the most effective representative environment for GPC, whereas Ludhiana was found to be the most effective representative environment for yield. Eventually, two NILs (BWL7508, and BWL7511) were selected as the top across all environments for both yield and GPC.
摘要：
小麦基因型对产量的高性能和稳定性，谷物蛋白质含量(GPC)，和其他理想的性状对于品种发育以及食物和营养安全至关重要。同样，每当做出基因型选择决定时，应彻底调查环境基因型（G×E）相互作用（GEI）并有利地利用。本研究计划有以下两个主要目标：1）确定四个位置（Ludhiana，Ballowal,Patiala,和旁遮普邦的Bathinda），印度；2）在各种环境中选择具有高GPC和产量的最佳基因型。不同的单变量[Eberhart和Ruessll的模型；Perkins和Jinks的模型；Wrike的生态价；以及Francis和Kannenberg的模型]，多变量(AMMI和GGE双绘图)，和相关性分析用于解释来自多环境试验(MET)的数据。因此,单变量和多变量分析在表现最佳和稳定的基因型方面提供了几乎相似的结果.方差分析显示，由于环境的变化，基因型，对于所有研究的性状，GEI在0.01和0.001的显着性水平上都非常显着。开花的日子，植物高度,每穗小穗，每穗粒数,天成熟,1000粒重特别受环境影响，而产量主要受环境和GEI的影响。基因型,另一方面,对GPC的影响大于环境条件。因此,多环境调查对于确定小麦基因型选择的GEI是必要的，因为GEI对所有评估的性状都非常重要。产量，1000粒重，每穗小穗，观察到成熟天数呈正相关，暗示了它们同时选择提高产量的可行性。然而,观察到GPC与产率呈负相关。Patiala被发现是产量和GPC最具鉴别力的环境，也是GPC最有效的代表环境。而Ludhiana被认为是产量最有效的代表环境。最终,两个NIL(BWL7508和BWL7511)在所有环境中选择作为产率和GPC两者的顶部。
