Due to environmental diversity, genotype performance for yield and stability is essential for crop improvement. The GGE biplot, and 11 parametric and non-parametric stability models were employed to evaluate 23 wheat (Triticum aestivum L.) genotypes, tested in randomized complete block trials across two contrasting fields (sandy and loamy) and four seasons. The sandy field yielded half compared to the loamy field, reflecting relatively low- and high-input environments, respectively. Analysis of variance showed significant differences between genotypes for grain yield and crossover genotype ranking across environments; the loamy field was more representative of an overall genotype performance. The stability models resulted in diverse genotype classification and were distinguished into two separate groups. The first group comprised measures that consider both G and GE focusing on the agronomic aspect of stability and high-yielding ability. The second group included tools that consider only GE focusing on the static aspect of stability and characterized most of the high-performing genotypes as undesirable. The GGE biplot highlighted genotypes that were characterized as either desirable or undesirable following most models in both groups. Therefore, the GGE biplot presented an effective statistical tool for assessing wheat genotypes in terms of general and specific adaptation without overlooking yielding ability. It is suggested the preference of favorable experimental conditions and application of the GGE model to identify genotypes that are more promising for stable performance across wide agroecosystems.

由于环境的多样性，提高产量和稳定性的基因型表现对于作物改良至关重要。GGE双线图，以及11个参数和非参数稳定性模型用于评估23种小麦（小麦）L.）基因型，在两个对比领域（沙质和壤土）和四个季节的随机完整试验中测试。相比于壤土田，沙田的产量仅为一半，分别反映了相对低投入和高投入的环境。方差分析显示，谷物产量的基因型与跨环境的交叉基因型排名之间存在显着差异。壤土领域更能代表整体基因型表现。稳定性模型导致不同的基因型分类，并分为两个独立的组。第一组包括考虑G和GE的措施，重点放在稳定性和高产能力的农艺方面。第二组包括仅考虑通用电气专注于稳定性静态方面的工具，并将大多数高性能基因型定性为不良工具。GGE双线图突出显示了根据两组中的大多数模型表征为合意或不合意的基因型。因此，GGE双线图提供了一种有效的统计工具，可根据一般和特定适应性评估小麦基因型，而不会忽视产量。建议优先选择有利的实验条件和GGE模型的应用，以鉴定更有望在整个农业生态系统中稳定运行的基因型。GGE双线图提供了一种有效的统计工具，可根据一般和特定适应性评估小麦基因型，而不会影响产量。建议优先选择有利的实验条件和GGE模型的应用，以鉴定更有望在整个农业生态系统中稳定运行的基因型。GGE双线图提供了一种有效的统计工具，可根据一般和特定适应性评估小麦基因型，而不会影响产量。建议优先选择有利的实验条件和GGE模型的应用，以鉴定更有望在整个农业生态系统中稳定运行的基因型。