Water shortages have direct adverse effects on wheat productivity and growth worldwide, vertically and horizontally. Productivity may be promoted using water shortage-tolerant wheat genotypes. High-throughput tools have supported plant breeders in increasing the rate of stability of the genetic gain of interpretive traits for wheat productivity through multidimensional technical methods. We used 27 agrophysiological interpretive traits for grain yield (GY) of 25 bread wheat genotypes under water shortage stress conditions for two seasons. Genetic parameters and multidimensional analyses were used to identify genetic and phenotypic variations of the wheat genotypes used, combining these strategies effectively to achieve a balance. Considerable high genotypic variations were observed for 27 traits. Eleven interpretive traits related to GY had combined high heritability (h² > 60%) and genetic gain (>20%), compared to GY, which showed moderate values both for heritability (57.60%) and genetic gain (16.89%). It was determined that six out of eleven traits (dry leaf weight (DLW), canopy temperature (CT), relative water content (RWC), flag leaf area (FLA), green leaves area (GLA) and leaf area index (LAI)) loaded the highest onto PC1 and PC2 (with scores of >0.27), and five of them had a positive trend with GY, while the CT trait had a negative correlation determined by principal component analysis (PCA). Genetic parameters and multidimensional analyses (PCA, stepwise regression, and path coefficient) showed that CT, RWC, GLA, and LAI were the most important interpretive traits for GY. Selection based on these four interpretive traits might improve genetic gain for GY in environments that are vulnerable to water shortages. The membership index and clustering analysis based on these four traits were significantly correlated, with some deviation, and classified genotypes into five groups. Highly tolerant, tolerant, intermediate, sensitive and highly sensitive clusters represented six, eight, two, three and six genotypes, respectively. The conclusions drawn from the membership index and clustering analysis, signifying that there were clear separations between the water shortage tolerance groups, were confirmed through discriminant analysis. MANOVA indicated that there were considerable variations between the five water shortage tolerance groups. The tolerated genotypes (DHL02, DHL30, DHL26, Misr1, Pavone-76 and DHL08) can be recommended as interesting new genetic sources for water shortage-tolerant wheat breeding programs.

Abiotic stress decreases crop production worldwide. In order to recommend suitable genotypes for cultivation under water deficit and heat stress conditions, an overall understanding of the genetic basis and plant responses to these stresses and their interactions with the environment is required. To achieve these goals, the multitrait genotype-ideotype distance index (MGIDI) was utilized to recognize abiotic-stress-tolerant wheat genotypes, and the weighted average of absolute scores (WAASB) index as well as the superiority index, which enables weighting between the mean performance and stability (WAASBY), were utilized to recognize high-yielding and stable genotypes. Twenty wheat genotypes were examined to determine the abiotic stress tolerance capacity of the investigated genotypes under nine test environments (three seasons × three treatments). Abiotic stress significantly decreased most morpho-physiological and all agronomic traits; however, some abiotic-stress-tolerant genotypes expressed a slight reduction in the measured traits as compared with the control group. G04, G12, G13, and G17 were identified as convenient and stable genotypes using the MGIDI index under all environments. Based on the scores of the genotype index (WAASB), G01, G05, G12, and G17 were selected as superior genotypes with considerable stability in terms of the grain yield (GY). G04, G06, G12, and G18 were classified as cluster (I), the productive and stable genotypes, using the WAASBY superiority index. The combined indices (MGIDI and WAASB) and (MGIDI and WAASBY) revealed genotypes G12 and G17 and genotypes G04 and G12, respectively, as the most stable candidates. Therefore, these are considered novel genetic resources for improving productivity and stabilizing GY in wheat programs under optimal conditions, water deficit, and heat stress. The genotype G12 was jointly expressed in all three indices. Stability measures using WAASB may help breeders with decision-making when selecting genotypes and conducting multi-environment trials. Hence, these methods, if jointly conducted, can serve as a powerful tool to assist breeders in multi-environment trials.

The effect of traits and the interaction of genotype × environment (GE) is one of the major challenges in detecting traits and genotypes with outstanding performance and stability through various stresses and years. The objective of this study was to identify the genetic influence traits of wheat, and genotypes with outstanding performance and stability under different environmental stress. The trials were carried out in two consecutive seasons with three treatments (optimal irrigation, limited irrigation, and heat stress), totaling six test environments at two different locations. After observing the importance of GE interaction, and the statistical significance for all studied traits, multivariate analysis was applied using stepwise regression (SR) for detecting influenced traits, and AMMI, AMMI’s stability values (ASV), yield stability index (YSI), superiority and GGE biplot methods to identify the genotype’s phenotypic stability. SR analysis showed that nine out of 22 traits have contributed significantly to grain yield (GY), which varied according to the environment. Equations of the models (GY) regression coefficient values reflected the importance seven of them have on a significant positive correlation on GY. The study confirmed the importance of AMMI and GGE biplots in decoding the GEI based on GY data. AMMI1 biplots showed that the three environments E1, E4, and E6 were the stronger interacting environments than E2, E3, and E5, in which the interaction was weak. YSI, superiority analysis, and superiority multi-trait analysis scores were largely compatible. YSI scores described the six genotypes viz, G5 (DHL26), G12 (DHL29), G10 (DHL01), G18 (Sakha-93), G2 (DHL02) and, G6 (Gemmeiza-9), these were marked by high stability and productivity. The GGE biplot analysis showed genotypes (G15 (Misr1) and G4 (DHL07)) recorded the highest grain yield in E3 and E4, whereas genotype G18 (Sakha-93) was in E6. It also showed G19 (Pavone-76) was the best genotype due to being situated in the center of the concentric circles and due to its high-yield. The methods considered were compatible with the detection of promising wheat genotypes with high mean performance and outstanding phenotypic stability across various stresses and years.

Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant’s capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.

Increasing atmospheric temperature can significantly reduce global wheat productivity; despite a mounting demand for wheat grain supplies. Developing genotypes with superior performance under current and future hot climates is a key challenge for wheat breeders. Multidimensional tools have supported plant breeders in increasing the genetic stability rate of agro-physiological indices that influence wheat productivity. We used 25 agro-physiological indices to classify 20 bread wheat genotypes for their heat stress tolerance. Agro-physiological indices and multidimensional analyses to identify differences in genetic and phenotypic were used, combining these analyses to reach selection criteria of accurate and credible. The 25 studied indices reflected high genotypic and environmental variations. We used 16 indices, which have brought together high heritability and genetic gain as indicators for screening heat-tolerant genotypes. Based on the seven principal comprehensive indices of (D value), wheat genotypes were classified into three highly heat-tolerant, four heat-tolerant, six moderately heat-tolerant, five heat-sensitive, and two highly heat-sensitive wheat genotypes. Based on four critical indices [grain yield (GY), grain-filling duration (GFD), spike length (SL) and canopy temperature (CT)] obtained from stepwise multiple linear regression (SMLR), the genotypes were grouped as four genotypes highly heat-tolerant, six heat-tolerant, two moderately heat-tolerant, four heat-sensitive and four highly heat-sensitive. The classification D value and SMLR distances were significantly correlated based on the Mantel test, with a perfect match in nine genotypes. SMLR indicated that a mathematical equation for the evaluation of wheat heat tolerance was established: GY = 0.670 + 0.504 × GFD + 0.334 × SL − 0.466 × CT (R² = 0.739; average prediction accuracy of 94.12%). SMLR-based classification of wheat genotypes for heat tolerance was further verified through discriminant analysis, which showed that prior and posterior classification was identical in eighteen genotypes. Cross-validation showed that prior and posterior classification was identical in thirteen genotypes. Based on this study, we can recommend tolerated new wheat lines (DHL25, DHL05, DHL23 and DHL08) and cultivar Pavone-76 as a promising genetic source for heat-tolerant breeding programs.

Successfully promoting drought tolerance in wheat genotypes will require several procedures, such as field experimentations, measuring relevant traits, using analysis tools of high precision and efficiency, and taking a complementary approach that combines analyses of phenotyping and genotyping at once. The aim of this study is to assess the genetic diversity of 60 genotypes using SSR (simple sequence repeat) markers collected from several regions of the world and select 13 of them as more genetically diverse to be re-evaluated under field conditions to study drought stress by estimating 30 agro-physio-biochemical traits. Genetic parameters and multivariate analysis were used to compare genotype traits and identify which traits are increasingly efficient at detecting wheat genotypes of drought tolerance. Hierarchical cluster (HC) analysis of SSR markers divided the genotypes into five main categories of drought tolerance: four high tolerant (HT), eight tolerant (T), nine moderate tolerant (MT), six sensitive (S), and 33 high sensitive (HS). Six traits exhibit a combination of high heritability (>60%) and genetic gain (>20%). Analyses of principal components and stepwise multiple linear regression together identified nine traits (grain yield, flag leaf area, stomatal conductance, plant height, relative turgidity, glycine betaine, polyphenol oxidase, chlorophyll content, and grain-filling duration) as a screening tool that effectively detects the variation among the 13 genotypes used. HC analysis of the nine traits divided genotypes into three main categories: T, MT, and S, representing three, five, and five genotypes, respectively, and were completely identical in linear discriminant analysis. But in the case of SSR markers, they were classified into three main categories: T, MT, and S, representing five, three, and five genotypes, respectively, which are both significantly correlated as per the Mantel test. The SSR markers were associated with nine traits, which are considered an assistance tool in the selection process for drought tolerance. So, this study is useful and has successfully detected several agro-physio-biochemical traits, associated SSR markers, and some drought-tolerant genotypes, coupled with our knowledge of the phenotypic and genotypic basis of wheat genotypes.