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Lentil yield is a complicated quantitative trait; it is significantly influenced by the environment. It is crucial for improving human health and nutritional security in the country as well as for a sustainable agricultural system. The study was laid out to determine the stable genotype through the collaboration of G × E by AMMI and GGE biplot and to identify the superior genotypes using 33 parametric and non-parametric stability statistics of 10 genotypes across four different conditions. The total G × E effect was divided into two primary components by the AMMI model. For days to flowering, days to maturity, plant height, pods per plant, and hundred seed weight, IPCA1 was significant and accounted for 83%, 75%, 100%, and 62%, respectively. Both IPCA1 and IPCA2 were non-significant for yield per plant and accounted for 62% of the overall G × E interaction. An estimated set of eight stability parameters showed strong positive correlations with mean seed yield, and these measurements can be utilized to choose stable genotypes. The productivity of lentils has varied greatly in the environment, ranging from 786 kg per ha in the MYM environment to 1658 kg per ha in the ISD environment, according to the AMMI biplot. Three genotypes (G8, G7, and G2) were shown to be the most stable based on non-parametric stability scores for grain yield. G8, G7, G2, and G5 were determined as the top lentil genotypes based on grain production using numerical stability metrics such as Francis’s coefficient of variation, Shukla stability value (σi2), and Wrick’s ecovalence (Wi). Genotypes G7, G10, and G4 were the most stable with the highest yield, according to BLUP-based simultaneous selection stability characteristics. The findings of graphic stability methods such as AMMI and GGE for identifying the high-yielding and stable lentil genotypes were very similar. While the GGE biplot indicated G2, G10, and G7 as the most stable and high-producing genotypes, AMMI analysis identified G2, G9, G10, and G7. These selected genotypes would be used to release a new variety. Considering all the stability models, such as Eberhart and Russell’s regression and deviation from regression, additive main effects, multiplicative interactions (AMMI) analysis, and GGE, the genotypes G2, G9, and G7 could be used as well-adapted genotypes with moderate grain yield in all tested environments.

Barley is the fourth largest grain crop globally with varieties suited to temperate, subarctic, and subtropical areas. The identification and subsequent selection of superior varieties are complicated by genotype-by-environment interactions. The main objective of this study was to use parametric and non-parametric stability measures along with a GGE biplot model to identify high-yielding stable barley genotypes in Iran. Eighteen barley genotypes (16 new genotypes and two control varieties) were evaluated in a randomized complete block design with four replications at five locations over three growing seasons (2013–2014, 2014–2015, 2015–2016). The combined analysis of variance indicated that the environment main effect accounted for > 69% of all variation, compared with < 31% for the combined genotype (G) and genotype-by-environment interaction effects. The mean grain yield of each genotype across the five test sites and three seasons ranged from 1900 to 2302 kg ha−1. Using Spearman’s rank correlation and principal component analyses, the stability measures were divided into three groups: the first included mean yield, TOP and b, which are related to the dynamic concept of stability, the second comprised θi, Wi2, σi2, CVi, \\[S_{di}^{2}\\], KR, and the non-parametric measures, S(i) and NP(i), which are related to the static concept of stability, and the third included θi and R2. The GGE biplot analysis indicated that, of the five test locations, Gonbad and Moghan had the most discriminating and representative environments. Hence, these locations are recommended as ideal test locations in Iran for the selection of superior genotypes. The numerical and graphical methods both produced similar results, identifying genotypes G12, G13, and G17 as the best material for rainfed conditions in Iran; these genotypes should be promoted for commercial production.

The phenolic compound contents and antioxidant activities of the leaf extracts of nine olive genotypes were determined, and the obtained data were analysed using chemometric techniques. In the crude extracts, 12 compounds belonging to the secoiridoids, phenylethanoids, and flavonoids were identified. Oleuropein was the primary component for all genotypes, exhibiting a content of 21.0 to 98.0 mg/g extract. Hydroxytyrosol, verbascoside, luteolin 7-O-glucoside, and luteolin 4′-O-glucoside were also present in noticeable quantities. Genotypes differed to the greatest extent in the content of verbascoside (0.45–21.07 mg/g extract). The content of hydroxytyrosol ranged from 1.33 to 4.03 mg/g extract, and the aforementioned luteolin glucosides were present at 1.58–8.67 mg/g extract. The total phenolic content (TPC), DPPH• and ABTS•+ scavenging activities, ferric reducing antioxidant power (FRAP), and ability to inhibit the oxidation of β-carotene-linoleic acid emulsion also varied significantly among genotypes. A hierarchical cluster analysis enabled the division of genotypes into three clusters with similarity above 60% in each group. GGE biplot analysis showed olive genotypes variability with respect to phenolic compound contents and antioxidant activities. Significant correlations among TPC, FRAP, the values of both radical scavenging assays, and the content of oleuropein were found. The contents of 7-O-glucoside and 4′-O-glucoside correlated with TPC, TEAC, FRAP, and the results of the emulsion oxidation assay.

The two most common styles to analyze genotype-by-environment interaction (GEI) and estimate genotypes are additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot. Therefore, the aim of this study was to find the winning genotype(s) under three locations, as well as to investigate the nature and extent of GEI effects on Bambara groundnut production. The experiment was carried out in the fields of three environments with 15 Bambara groundnut accessions using the randomized complete block design (RCBD) with three replications each in Ibadan, Osun, and Odeda. Yield per plant, fresh seed weight, total number of pods per plant, hundred seed weight, length of seeds, and width of seeds were estimated. According to the combined analysis of variance over environments, genotypes and GEI both had a significant (p < 0.001) impact on Bambara groundnut (BGN) yield. This result revealed that BGN accessions performed differently in the three locations. A two-dimensional GGE biplot was generated using the first two principal component analyses for the pattern of the interaction components with the genotype and GEI. The first two principal component analyses (PCAs) for yield per plant accounted for 59.9% in PCA1 and 40.1% in PCA2. The genotypes that performed best in each environment based on the \"which-won-where\" polygon were G8, G3, G2, G11, G6, and G4. They were also the vertex genotypes for each environment. Based on the ranking of genotypes, the ideal genotypes were G2 and G6 for YPP, G1 and G5 for FPW, G15 and G13 for TNPP, G3 and GG7 for HSW, G7 and G12 for LOS, and G10 and G7 for WOS. G8 was recorded as the top most-yielding genotype. G8, G4, G7, and G13 were high yielding and the most stable across the environments; G11, G14, and G9 were unstable, but they yielded above-average performance; G14, G12, G15, and G1 were unstable and yielded poorly, as their performances were below average. Bowen was the most discriminating and representative environment and is classified as the superior environment. Based on the performance of accessions in each region, we recommend TVSU 455 (G8) and TVSU 458 (G3) in Bowen, TVSU 455 (G8) and TVSU 939 (G6) and TVSU 454 (G1) in Ibadan, and TVSU 158 (G2) and TVSU 2096 (G10) in Odeda. The variety that performed best in the three environments was TVSU 455 (G8). They could also be used as parental lines in breeding programs.

Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.g., years) separation and/or combination of these factors. The genotype-by-environment interaction (GEI) is very important in plant breeding programs. Over the past six decades, the propensity to model the GEI led to the development of several models and mathematical methods for deciphering GEI in multi-environmental trials (METs) called “stability analyses”. However, its size is hidden by the contribution of improved management in the yield increase, and for this reason comparisons of new with old varieties in a single experiment could reveal its real size. Due to the existence of inherent differences among proposed methods and analytical models, it is necessary for researchers that calculate stability indices, and ultimately select the superior genotypes, to dissect their usefulness. Thus, we have collected statistics, as well as models and their equations, to explore these methods further. This review introduces a complete set of parametric and non-parametric methods and models with a selection pattern based on each of them. Furthermore, we have aligned each method or statistic with a matched software, macro codes, and/or scripts.

Rust caused by is a major biotic constraint to field pea ( L.) cultivation worldwide. Deployment of host-pathogen interaction and resistant phenotype is a modest strategy for controlling this intricate disease. However, resistance against this pathogen is partial and influenced by environmental factors. Therefore, the magnitude of environmental and genotype-by-environment interaction was assessed to understand the dynamism of resistance and identification of durable resistant genotypes, as well as ideal testing locations for rust screening through multi-location and multi-year evaluation. Initial screening was conducted with 250 diverse genotypes at rust hot spots. A panel of 23 promising field pea genotypes extracted from initial evaluation was further assessed under inoculated conditions for rust disease for two consecutive years at six locations in India. Integration of GGE biplot analysis and multiple comparisons tests detected a higher proportion of variation in rust reaction due to environment (56.94%) as an interactive factor followed by genotype × environment interaction (35.02%), which justified the requisite of multi-year, and multi-location testing. Environmental component for disease reaction and dominance of cross over interaction (COI) were asserted by the inconsistent and non-repeatable genotypic response. The present study effectively allocated the testing locations into various categories considering their \"repeatability\" and \"desirability index\" over the years along with \"discrimination power\" and \"representativeness.\" \"Mega environment\" identification helped in restructuring the ecological zonation and location of specific breeding. Detection of non-redundant testing locations would expedite optimal resource utilization in future. The computation of the confidence limit (CL) at 95% level through bootstrapping strengthened the accuracy of the GGE biplot and legitimated the precision of genotypes recommendation. Genotype, IPF-2014-16, KPMR-936 and IPF-2014-13 identified as \"ideal\" genotypes, which can be recommended for release and exploited in a resistance breeding program for the region confronting field pea rust.

Sugar beet is regarded as a main source of sugar supply alongside sugarcane. To evaluate new promising hybrids in terms of stability and adaptability, six distinct environments were used to sow 15 different sugar beet genotypes. The results of the combined analysis of variance showed that genotype, environment, and their interaction had a significant impact on root yield and white sugar yield. Among 15 genotypes, using AMMI biplot analysis, G5 and G14 were distinguished as adaptive and high-yielding genotypes for root yield, and G6 for white sugar yield. The interaction between the first two principal components of the AMMI 2 biplot model explained 72.3% of the variation resulting from the G + G × E interaction for root yield and 75.2% for white sugar yield. G5 stands out as the genotype with the greatest stability and yield efficiency. The GGE biplot revealed environment 3 as the ideal environment. Genotypes G7, G6 and G5 were selected as the superior stable genotypes with respect to white sugar yield based on the WAASB stability index, while G5 was determined as the most stable genotype by MSTI. Using the GGE biplot, G5’s high white sugar yield and stability within diverse environments made it the optimum genotype.

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects ( p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

Globally, sorghum is the fifth most important crop, which is used for food, feed and fuel. However, its production and productivity are severely limited by various stresses, including drought. Hence, this study aimed to determine the responses of different drought-tolerance related traits in the Ethiopian sorghum germplasm through multi-environment field trials, thereby identifying novel sources of germplasm that can be used for breeding the crop for drought-tolerance. Three hundred twenty sorghum landraces and four improved varieties were grown at three sites within drought-prone areas (Melkassa, Mieso and Mehoni) in Ethiopia. The targeted traits were chlorophyll content at flowering (CHLF), chlorophyll content at maturity (CHLM), green leaf number at flowering (GLNF), stay-green (SG), flag leaf area (FLA), peduncle length (PDL), and panicle exertion (PAE). Multi-variate analyses of the collected data revealed the presence of high phenotypic variation in all traits. The combined and AMMI Analysis of variance showed that phenotypic variation due to the genotypes was higher for SG, CHLM, CHLF and GLNF and lower for FLA, PE and PDL in comparison with variation due to the environments or genotype by environment interactions. High broad sense heritability was observed for CHLF, CHLM, SG, GLNF, FLA, and PDL, whereas PAE showed moderate heritability. Due to the high heritability of chlorophyll content and the relatively small effect of environmental factors on it, it could serve as a criterion for selecting desirable genotypes for drought-tolerant breeding in sorghum. It has been found that chlorophyll content has a significant positive correlation with stay-green and grain yield, indicating that high chlorophyll content contributes to increasing grain yield by delaying the process of leaf senescence. The analyses of AMMI, GGE biplot, and genotype selection index revealed that several sorghum landraces outperformed the improved varieties with respect to CHLF, CHLM, and SG. Such landraces could serve as novel sources of germplasm for improving drought tolerance through breeding.

Seven field experiments were conducted at three experiment stations representing major sugarcane producing regions in Egypt. Each experiment comprised a randomized complete block design with three replications. Fourteen elite breeding lines typical of those routinely generated in the three final selection stages of sugarcane breeding programs in Egypt, along with one check variety (GT54-9) were evaluated for cane and sugar yield in this study during the 2018/2019, 2019/2020 and 2020/2021 seasons. Stability parameters including cultivar stability rank and superiority index were determined. The data was also investigated using GGE-biplots, the additive main effects and multiplicative interaction model (AMMI), and the AMMI stability value (ASV). The genotype main effect was used to visualize the G x E interaction. The results of these trials are of significance in guiding the selection and recommendation of superior sugarcane varieties and more stable in sugarcane production zones. The clone G.2016–129 had a mean sugar yield and cultivar superiority index for sugar yield exceeding that of GT54-9, and hence was recommended for commercial planting. Because of local conditions in Egypt, an elite sugarcane variety would have high and stable yield and would adapt to a wide range of environments. In the present study, only one clone G.2016–129 fit that definition by producing higher and more stable sugar yield than the commercial variety GT 54–9.. At the side of multivariate analyses, the ASV (AMMI stability value) supports selection of stable varieties in the AMMI Method. Varieties with lowest ASV are stable. Therefore, the results of this study exposed that G.2016–95, F-150 and G.2016–129 with lowest ASV for cane yield by contrast, G.2009–11, G.2016–128, F-150 and G.2016–95 with lowest ASV for sugar yield, were stable clones for cane and sugar yields, respectively.