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ABSTRACT Advent of changing climatic conditions along with nutrient deficient soils adversely affects the environment for the rice production. Wild introgression lines derived from KMR3 and Oryza rufipogon population were evaluated in six environments, including optimum and low phosphorus stress condition. Significant differences among the introgression lines were observed for plant height, tiller number, biomass, grain yield per plant, days to 50% flowering and harvest index across the environments. Based on grain yield observed under optimum phosphorus and limited phosphorus (P stress condition), eight stress tolerance indices were calculated and found STI and GMP are the better indices to discriminate among tolerant and susceptible genotypes, and correlation studies also confirmed the significant association between STI and GMP. Cluster analysis based on stress tolerance indices revealed three different clusters distinguishing genotypes based on their stable performance on yield related traits. AMMI and GGE biplot analysis to identify the stable performance across environments revealed NSR60, NSR101, NSR105, NSR85 and NSR86 as high grain yielders, whereas NSR135, NSR5 and NSR88 as stable performers. WAASBY‐based stability analysis on multiple traits (MTSI) showed NSR135, NSR79 and NSR18 with lowest MTSI, indicating their high stability and high mean performance compared with parent KMR3. Further genotyping for low P tolerance gene (PSTOL1) and grain yield genes (Gn1a, SPIKE, TGW6, DEP1 and OsSPL14) using allele specific markers showed that the desirable alleles of SPIKE, Gn1a and TGW6 were derived from wild parent O. rufipogon. Low P tolerance allele PSTOL1 was absent in recurrent parent KMR3; however, the introgression lines harboured desirable alleles, which were derived from O. rufipogon. Further mapping studies will help to identify a significant potential QTLs/gene for low P tolerance from O. rufipogon. Wild introgression lines, NSR85, NSR124, NSR80, NSR54, NSR86 and NSR88, were found as the high yielding and nutrient stress tolerant genotypes, which can be used as potential donors in future breeding programmes for low P stress tolerance. [ABSTRACT FROM AUTHOR]

Background: The research focused on investigating the impact of genotype-environment interaction on the yield stability and performance of eight chickpea genotypes across three different locations (Bhatapara, Kawardha and Raipur). Methods: The study employed a randomized block design with two replications in each environment in Rabi season during 2021-22. Analysis of variance (ANOVA) revealed significant variations between environments and genotype-environment interaction (G x E) based on AMMI and GGE biplot. Result: To visualize the genotype-environment relationship, a two-dimensional GGE biplot was created using the first two principal components, which accounted for 87.8% and 11.5% of the interaction's variation. The GGE biplot indicated that genotype g4 (ICCX 161100-B-B-B-B) in environment e2 (Kawardha) and g8 (JG-24) in environment e1 (Bhatapara) exhibited high yield. Genotype g3 (ICCV 201112) in environment e3 (Raipur) also showed high yield, while genotype g5 (JG 2020-10) demonstrated stability across all three environments. Furthermore, the AMMI analysis identified genotype g2 (ICCV 201103) as a superior performer in terms of yield stability across environments. By employing the GGE biplot and AMMI analysis, the study categorized the genotypes into three groups, facilitating simplified visual evaluations. The GGE biplot highlighted that genotype g8 (JG-24) exhibited better seed index in environment e3 (Raipur), while genotype g1 (ICCV 201210) performed well in environment e1 (Bhatapara). Genotype g2 (ICCV 201103) demonstrated a good seed index in all three environments. The AMMI analysis indicated that genotype g3 (ICCV 201112) had a slightly lower seed index but maintained stability across all environments. Overall, the findings of this investigation, as represented by the GGE and AMMI biplots, visually illustrated the relationship between genotypes and environments. The identified genotypes hold potential for improving yield production and further research and breeding efforts can be based on these findings. [ABSTRACT FROM AUTHOR]

Cymbopogon flexuosus, commonly known as Lemongrass (Poaceae) is an industrially significant plant due to its fragrant compounds and ability to produce a large amount of essential oil. However, variations in chemical components, agronomical characters and essential oil output can be observed due to grown in different climatic conditions. Pertaining to the economic importance of the crop, the present study was designed to evaluate the stability indices of the developed and registered variety of lemongrass 'Jor Lab L-8'. In this experiment, cv. Jor Lab L-8 was assessed along with two check varieties at eight different locations of NE India for two years (2021-2022, 2022-2023). The analysis of variance (ANOVA) by regression method revealed that Jor Lab L-8 had a significantly higher herbage yield, essential oil yield, citral content and total essential oil yield than the other two check varieties. Furthermore, AMMI ANOVA was used to confirm the consistent performance of the recorded agro-morphological and quality characters and Jor Lab L-8 was shown to be stable in plant height, herbage yield, essential oil yield, citral content and total essential oil yield. The GC-MS study of the essential oil identified 18 constituents, with citral emerging as the predominant constituent. Additionally, majority of the locations were pinpointed as ideal environments for cultivating Jor Lab L-8, underscoring its resilience across multiple locations and highlighting its long-term industrial promise. [ABSTRACT FROM AUTHOR]

Cashew (Anacardium occidentale L.) is a predominant tree crop cultivated in tropical and subtropical regions of the world. The crop is plagued by the regular incidence of polyphagous insect pest tea mosquito bug (TMB) (Helopeltis spp.). TMB causes 30–50% nut yield loss in regular incidences and up to 100% in epidemic situations. The incidence of TMB is prevalent in most of the cashew growing regions but research on the interaction between TMB and year has not been undertaken. To this end 15 cashew germplasms were evaluated for TMB incidence from 2016 to 2022 to study the stability of TMB incidence in cashew. TMB damage scores was analyzed following additive main effect and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) methods. Years (Y) differed significantly (P ≤ 0.01) whereas genotypes (G) (P = 0.18) and G × Y interactions were not significant (P = 0.96). The variations explained by G, Y and G × Y were 7.15%, 37.05% and 17.18% respectively. The total variability captured by the first two incremental principal component analysis (IPCA) viz., IPCA 1 (49.83%) and IPCA 2 (29.49%) was 79.32%. Accessions NRC 112, 144, 75 and 176 won in the first mega environment and NRC 120, 140, 189 won in second, third and fourth mega environments respectively. Three accessions NRC 301 (0.511), V-8 (0.567) and NRC 270 (0.734) with least TMB incidences were found stable and could serve as useful candidate genotypes in cashew breeding programme for TMB tolerance. [ABSTRACT FROM AUTHOR]

Stability analysis of a panel of twenty clones of Neolamarckia cadamba was evaluated in three locations viz., Chennai, Neyveli and Dhimbum. Experiment was conducted using Randomised Block Design (RBD) with three replications. Tree height and girth were measured from the second year of planting and subjected to statistical analysis. Pooled mean of tree height varied from 4.56 (C17) to 6.11 m (C127), while girth was ranged from 21 (C17) to 31.81cm (C105) across locations. Results of the pooled analysis of variance showed that there were significant differences (p<0.01) for the studied traits across genotypes, environments and genotypes by environment interaction (GEI). Additive Main Effects and Multiplicative Interaction (AMMI) biplots constructed between the first two principal components, explained 73.5 and 69.8% of the GEI for height and girth, respectively. Neyveli was identified as the ideal location among three locations studied based on the biplots. Clones C144 and C127 were reported to be highly stable based on the different biplot models while AMMI stability indices identified C140 and C105 as more stable. Utilizing these clones in future breeding programmes could improve yield production, and these clones identified could be recommended for commercial cultivation. [ABSTRACT FROM AUTHOR]

The current study was conducted at four different locations in India in order to evaluate the genetic diversity and stability of 38 genotypes of onion (Allium cepa L.) under various climatic circumstances. The genetic diversity was evaluated through cluster analysis which divided thirty-eight genotypes into two major clusters (cluster I and cluster II) each of which had 20 and 18 genotypes. As per Additive Main-effects and Multiplicative Interaction (AMMI) model, five genotypes from cluster I, namely Accession (Acc.) 1217, 1209, Bhima Shweta, 1639, and 1640 were shown to have greater adaptation, with regressive values of 0.14, 0.74, 1.51, 2.11, and 2.17, respectively and marketable yields ranging from 176.23–227.31 q/ha. The mean performance of genotypes in individual clusters revealed that the cluster I and cluster II genotypes showed maximum marketable yield, total yield and average bulb weight. Based on the findings of the assessment study, these stable genotypes could be utilised as parent plants in future breeding programmes to develop high-yielding onion varieties that not only showcase stability but also possess widespread adaptability, thereby enhancing overall onion crop performance. [ABSTRACT FROM AUTHOR]

Diverse rice germplasm comprising 112 genotypes was evaluated for yield traits across three environments. Pooled and environmentwise analysis of variance revealed heterogeneity in the data and significant environment interactions for all the yield traits. As per AMMI (additive main effects and multiplicative interaction) and GGE (genotype and genotype x environment interaction) biplots, the influence of environment was significant and varying on all the component yield traits including grain yield and was not significant in case of flowering date. Dry season at Maruteru in 2014–15 (E1) was the most discriminative and representative environment for favourable plant growth in terms of plant height, panicle number and panicle length. None of the environments represented ideal environment for the favourable expression of grain number while all the environments were equally informative for thousand grain weight and grain yield. Panicle number, grain number and thousand grain weight were contributing to grain yield across the environments. Three genotypes Panthdhan 12, Konark and Udaygiri were the most stable genotypes for grain yield with favourable combination of associated yield genes for all the traits, viz. 1000 grain weight, the number of grains per panicle, the number of filled grains per panicle, productive tillers and plant height with higher yield, and grouped in one cluster. Genotyping using previously reported markers revealed that favourable alleles of yield genes associated with the number of productive tillers were predominantly found followed by alleles for the number of grains/filled grains per panicle correlating with the superior phenotypic value of the respective trait. The information on association of yield stability with reported yield genes from this study is useful in marker-assisted breeding studies for yield improvement and can be confirmed with various sets of genotypes under multi-environment testing. The identified superior genotypes are potential components in future breeding programmes and the development of stable adaptable varieties. The present study suggests that yield stability could be effectively achieved with targeted improvement of component yield traits associated with favourable alleles. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine the structure of GEI in the investigated parameters and to investigate the performance stability of durum wheat varieties using the GGE biplot and AMMI analysis method. The study was carried out in four different locations (Diyarbakır-1, Diyarbakır-2, Hatay, and Şanlıurfa) using 31 durum wheat genotypes under rainfed conditions. Field experiments were conducted in a randomized complete block design with three replications. As a result of the study, it was determined that there was a significant difference between locations in terms of grain yield. The highest grain yield was obtained from Diyarbakır-1 (6797 kg ha-1) location, followed by Hatay (4584 kg ha-1), Diyarbakır-2 (2512 kg ha-1) and Şanlıurfa (2026 kg ha-1), respectively. Significant variations in grain yield were detected among the examined genotypes, and Artuklu and Zühre had the highest grain yields (4774 and 4501 kg ha-1). Again, Artuklu and Zühre were the most stable varieties in terms of their performance in different locations. Diyarbakır-1 has come to the fore as the location where the variation between genotypes can be best detected. As a result, more data should be provided to plant breeders in the selection studies by considering the irrigated conditions as a factor in the studies to be carried out and determining the stable cultivars in terms of grain yield in both precipitation-based and irrigated conditions. [ABSTRACT FROM AUTHOR]

Abstract Triticale is a man-made crop produced by crossing wheat (Triticum aestivum) and rye (Secal cereal) with the aim of increasing the adaptability and potential production of its parental line (wheat) under environmental stresses and harsh conditions. Accordingly, the objectives of this study are as follow, a: compare different stability measurement methods regarding their suitability, b: identify most stable genotypes for cultivar release, and c: provide a freely available package in R programming language (named ‘Stbidx’) capable of exploiting all stability methods for similar studies. Accordingly, 30 triticale genotypes were evaluated for their stability and adaptability to changing environments during four consecutive years from 2016 to 2020 in different locations in Iran (one year in Zarghan, two years in Ghorveh, one year in Sanandaj) and under two different irrigation regimes (normal irrigation and water deficit stress conditions). All 30 triticale genotypes were cultivated in a randomized complete block design (RCBD) with three replications in each environment (12 environments: 6 trials in 2 conditions). Grain yield of triticale genotypes was measured and it applied to calculate all known stability indices (univariate methods) along with the meta-analysis models were evaluated in this study. Additionally, two novel techniques (Stability ID: SID and AMMI PC Value: APCV) were introduced and successfully tested for screening stable genotypes. These methods were introduced to improve and resolve the limitation of the previous stability methods. Meanwhile, heatmap analysis, as a proper data-mining technique, has been successfully applied for the first time to find stable genotypes in different environments in this study. The results clearly indicated a higher suitability of our introduced methods over previous multi-trials screening methods. Based on our methods and considering all environments, triticale genotypes ELTTCL21 (SID = 8.00; APCV = 28.12), ELTTCL25‌ (SID = 8.14; APCV = 30.02), ELTTCL30 (SID = 8.43; APCV = 17.00), ET-90-8 (SID = 14.00; APCV = 42.05), and Sanabad (SID = 10.57; APCV = 12.44) were among stable genotypes and they can be nominated as new cultivars. Additionally, for stress condition genotypes ELTTCL30 (stress: 380.6 g/m2) and ELTTCL18 (stress: 360.79 g/m2) and for normal condition genotypes ET-90-4 (normal: 849.4 g/m2) and ELTTCL20 (normal:845 g/m2) were respectively the most proper genotypes with the highest production to be considered for each condition separately.

The study characterized 34 Job's tears accessions from the northeastern Himalayan region using yield-related traits and simple sequence repeats (SSR) markers. Genotyping with 17 SSR markers revealed an average of 3.18 alleles per locus, varying from 2 to 4. Polymorphism information content values ranged from 0.27 to 0.52, averaging 0.41. Clustering and principal coordinate analysis (PCoA) based on SSR markers grouped the accessions into three major groups. The first three principal coordinates in the PCoA cumulatively explained 41.96% of the variability in the accessions, with the first and second principal coordinates explaining 22.51% and 10.39% of the molecular variability, respectively. The model-based genetic structure analysis detected the maximal ΔK (80.87) at K = 2, grouping the entire accessions into two subgroups. The analysis of molecular variance (AMOVA) indicated higher genetic variation within individuals (52.86%) than among individuals (47.14%). The FST value (0.2), STRUCTURE analysis, and PCoA analysis indicated moderate population differentiation. Yield-trait-based analysis resulted in four clusters. Cluster I included six accessions with short height, cluster II comprised 12 accessions with high test weight, cluster III comprised six accessions with high yield, and cluster IV had nine accessions with early flowering, respectively. Principal component analysis (PCA) extracted three components explaining 67.4% of the total variation, with PC1 emphasizing yield and growth-related traits and PC2 highlighting days to flowering and panicle length. Most traits showed positive correlations with yield per plant, especially test weight (0.65, P ≤ 0.01). Path coefficient analysis revealed a strong positive direct effect of test weight and the number of internodes per plant on yield per plant. Cluster analysis and genotype-by-trait biplot analysis showed similar patterns among accessions. However, the Mantel test indicated no significant correlation between genetic and morphological distances (r2 = 0.010, P = 0.41). Additive main-effects and multiplicative interaction (AMMI) analysis and multi-trait stability index (MTSI) identified three promising accessions (IC600638, IC540181, and IC540256) suitable for selection in future breeding programs with 10% intensity. [ABSTRACT FROM AUTHOR]

In the present experiment, 71 Indian mustard (Brassica juncea) genotypes were evaluated in six environments for two successive years (three environments in each year) to identify the influence of environment over the genotypes and to identify the stable, better-performing genotypes in respect to yield in Terai region of West Bengal. Statistical analysis was carried out to evaluate genotype × environment interaction using two different approaches, namely, additive main effect, and their multiplicative interaction model (AMMI) and genotype plus genotype by environment interaction (GGE) biplot for seed yield of mustard genotypes. AMMI ANOVA showed that genotype and environment accounted for 11.46% and 47.50% of the variation respectively, where GEI captured 36.57% of the variation for seed yield. The AMMI model and GGE biplots showed that genotype RGIN-73 was the highest yielding genotype to a specific environment but DRWR-15-9 was found to be well stable with good yielding in all environments.

Multi-environment yield trials and multi-trait stability analyses are essential to identify the superior genotype. The aim of this research was to assess the adaptation and stability of rice genotypes using additive main effect and multiplicative interaction (AMMI) and genotype plus genotype by environment (GGE) biplot analysis and multi-trait stability index (MTSI). In this study, 20 rice genotypes were evaluated for three seasons at the same location during 2020–21. Based on AMMI analysis, the genotypes G22 (Vandana), G32 (IC-0098989), G34 (IC-0135769), G60 (ADT 36), G61 (ADT 37), and G64 (ADT 45) were found to have high yield and were more stable. GGE analysis revealed that the high-yielding and stable genotypes were G22 (Vandana), G34 (IC-0135769), G60 (ADT 36), and G64 (ADT 45). The genotypes G65 (ADT 48), G60 (ADT 36), G38 (IC-0135529), and G33 (IC-0124198) were observed to be stable based on the Multi Trait Stability Index (MTSI). Based on the above, G60 (ADT 36) could be concluded as stable across all seasons, not only based on yield but also other important agronomic traits.

Abstract Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha-1) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha-1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems.

White tea is made from peko shoots, which is beneficial for human health as an antivirus including avian influenza, antidiabetic, anticancer, antimicrobial, and reduces oxidative stress. White tea production is still low due to the lack of peko shoot production as its raw material. This study aims to evaluate the stability of peko production of 9 Indonesian tea genotypes in producing peko shoots. Nine tea genotypes were observed at six different times. The research was conducted in a randomized block design with four replications. The time is treated as a location factor in the AMMI model, and can be assumed to be independent of each other. The results of the study showed that the main effect of time was significant for KUI1 and KUI2 tea genotypes. In the biplot it appears that GMB 3, GMB 4, Tambi 1, and Kiara 8 are stable genotypes. GMB 3 and Tambi 1 have higher stability, respectively with an average percentage of peko shoots over banji shoots of 38% and 39%. The stable genotypes could be recommended for further development. [ABSTRACT FROM AUTHOR]

Agronomical traits of crop plants exhibit quantitative variation that is controlled by multiple genes and is dependent on environmental conditions. The main objective of this study was to decipher the genotype-by-environment interaction (GEI) for six yield-related traits of 25 winter oilseed rape (WOSR) genotypes using the additive main effects and multiplicative interaction (AMMI) model. The genotypes chosen included canola cultivars, our newly developed WOSR breeding lines, yellow-seeded, semi-resynthesized and mutant genotypes, together with ogu-INRA F1 hybrids and their parental lines. These were tested in field trials at two locations over three growing seasons. Field experiments were conducted in a randomized block design with four replicates. We recorded the beginning of flowering, seed yield (SY) and SY components, the number of siliques per plant, the length of siliques, the number of seeds per silique, and the weight of 1000 seeds. The average SY in six environments varied from 16.55 to 41.64 dt·ha−1. The AMMI analysis showed significant effects of both G and E, as well as GEI, for the above traits. In this study, we observed that the climate condition, especially precipitation in addition to the soil type were the most influential factors on the SY and SY-trait value. Seed yield was positively correlated with: the number of siliques per plant, the length of siliques, the number of seeds per silique and the weight of 1000 seeds. We also found that our new ogu-INRA F1 hybrids, as well as cultivars Monolit, Mendel, Starter and Sherlock, showed stability for the analyzed traits. [ABSTRACT FROM AUTHOR]

Pigeonpea [Cajanus cajan (L.) Millspaugh] is a widely grown pulse with high seed protein content that contributes to food and nutritional security in the Indian subcontinent. The majority of pigeonpea varieties cultivated in India are of medium duration (<180 days to maturity), which makes it essential for breeders to focus on the development of stable high-yielding varieties. The diverse agroecological regime in the Indian subcontinent necessitates an efficient multi-environment study by taking into consideration genotype (G) × environment (E) interaction (GEI) that has a significant impact on traits like grain yield (GY) in developing high-yielding and widely adaptable varieties. In the present study, 37 pigeonpea genotypes were evaluated during the 2021 rainy season at ARS Badnapur, ARS Tandur, BAU Ranchi, GKVK Bengaluru, and ICRISAT Patancheru. The GEI was significant on the grain yield (p < 0.01), and hence, genotype + genotype × environment (GGE) and additive main effects and multiplicative interaction (AMMI) biplots along with AMMI stability value (ASV) and yield relative to environmental maximum (YREM) statistics were used to identify stable high-yielding genotypes. The interaction principal component analysis 1 and 2 (IPC1 and IPC2) explained 40.6% and 23.3% variations, respectively. Based on the rankings of genotypes, G37 (ICPL 20205), G35 (ICPL 20203), G8 (ICPL 19404), G17 (ICPL 19415), and G9 (ICPL 19405) were identified as ideal genotypes. Discriminativeness vs. representativeness identified GKVK Bengaluru as an ideal environment for comprehensive evaluation of test genotypes. However, ICPL 19405 was identified as the potentially stable highyielding genotype for further testing and release across the test environments based on its mean grain yield (1,469.30 kg/ha), least ASV (3.82), and low yield stability index (YSI) of 13. [ABSTRACT FROM AUTHOR]

In a study conducted in India, 50 Fusarium isolates were collected from pigeonpea growing regions and extensively examined for their cultural and morphological characteristics. These isolates exhibited significant variations in traits including growth rate, mycelial growth patterns, color, zonation, pigmentation, spore size, and septation. Subsequently, 30 isolates were chosen for pathogenicity testing on eight pigeonpea genotypes. Results showed distinct reactions, with four genotypes displaying differential responses (ICP8858, ICP8859, ICP8862, and BDN‐2), while ICP9174 and ICP8863 consistently exhibited resistance and ICP2376 and BAHAR remained susceptible to wilt disease. To study the interaction between Fusarium isolates and pigeonpea host differentials (HDs), an additive main effects and multiplicative interaction analysis was conducted. The majority of disease incidence variation (75.54%) was attributed to HD effects, while Fusarium isolate effects accounted for only 1.99%. The interaction between Isolates and HDs (I × HD) contributed 21.95% to the total variation, being smaller than HD but larger than I. Based on HD reactions, isolates were classified into nine variants, showing varying distributions across pigeonpea growing states, with variants 2 and 3 being prevalent in several regions. This diversity underscores the need for location‐specific wilt‐resistant pigeonpea cultivars. Furthermore, genetic analysis of 23 representative isolates, through internal transcribed spacer region of ribosomal DNA and translation elongation factor 1‐α gene sequencing, revealed three major clusters: Fusarium udum, Fusarium solani, and Fusarium equiseti. These findings hold potential for developing location‐specific wilt‐resistant pigeonpea cultivars and enhancing disease management strategies. [ABSTRACT FROM AUTHOR]

Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha-1) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha-1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems. [ABSTRACT FROM AUTHOR]

The introgression lines (ILs) derived from interspecific crosses with wild accessions were subjected to stability analysis and QTL mapping for yield related traits for three seasons. According to GGE biplot, G14 (C1-15) was identified as most stable genotype for single plant yield. The introgression line G65 (C1-101) was identified as stable and adaptable and most ideal line for single plant yield across three seasons. Similarly, G50 (C1-74) was observed to show stable and higher total tiller and productive tiller numbers; G148 (C1-225), G116 (C1-177) and G76 (C1-116) were identified with stable total grain weight as per AMMI biplot analysis. Further QTL mapping for yield traits detected seven QTLs with LOD > 2.5 for plant height, total tillers per plant, productive tillers per plant, biomass per plant and single plant yield on chromosomes 2, 5, 6 and 11. Multi-environment testing for QTL analysis detected 52 QTLs on three environments and among the QTLs, two QTLs qPTN2.1 on chromosome 2 and one QTL qBM11.1 on chromosome 11 were consistent in two environments within the same marker intervals. The allele for productive tiller number was derived from donor parent 166S and the allele for biomass per plant was derived from recurrent parent Swarna. Thus, evaluating the advanced breeding lines across multi environment help to identify stable yielding genotypes and these lines can be used in future breeding programmes. This approach of using prebreeding lines as donors will minimize the undesirable trait inheritance from wild accessions when directly used as a parent in breeding programme. [ABSTRACT FROM AUTHOR]

Effects of essential oil components obtained by hydrodistillation of Ammi genus members (Ammi majus L., Ammi visnaga L. (Lam.)), which have important chemical and active components were investigated against two important storage pests; fig borer Cadra cautella (Walker) (Lepidoptera: Pyralidae) and flour moth Ephestia kuehniella Zeller (Lepidoptera: Pyralidae). A total of 23 and 14 components were detected in A. majus and A. visnaga, respectively, and the product yield was found to be 96.05%, 82.53%. Among them, the major components for A. majus are 2 heptadecanone, benzoic acid, 2 pentadecanone while for A. visnaga they are linalol, nonadecane, carvacrol. Essential oil of A. visnaga extended the adult emergence times in E. kuehniella and C. cautella while the increase in pupation time was found statistically significant only in E. kuehniella. A. visnaga essential oil reduced the adult life span in E. kuehniella at the highest dose while a decrease was detected in both doses applied in C. cautella. Adult weight and number of eggs decreased due to the application of A. visnaga in both insects. Also, alterations were observed in the adult emergence, pupation time, and pupal period. In E. kuehniella and C. cautella, adult life spans, weights and egg production of females showed statistically significant decreases depending on the application of A. majus essential oil. The findings obtained within the scope of the current study reveal that the essential oils of A. majus and A. visnaga species have the potential to be used in the control of storage pest insects. [ABSTRACT FROM AUTHOR]

Genotype × environment interaction (GEI) poses a critical challenge to plant breeders by complicating the identification of stable variety (ies) for performance across diverse environments. GGE biplot and AMMI analyses have been identified as the most effective and appropriate statistical techniques for identifying stable and high-performing genotypes across diverse environments. The objective of this study was to identify widely adapted and high-yielding soybean genotypes from Multi-Locational Trials (MLTs) using GGE and AMMI biplot analyses. Fifteen IITA-bred elite soybean lines and three standard checks were evaluated for stability of performance in a 3 × 6 alpha lattice design with three replications across seven locations in Nigeria. Significant (p

Banana (Musa species) is the fourth most important export crop worldwide after cereals, oil crops and sugar. In spite of this socio-economic significance, the crop suffers massive postharvest losses caused by mechanical fruit damage, limited infrastructure for fruit ripening, postharvest diseases and physiological disorders. Although use of optimum postharvest environments such as packaging and storage temperatures can reduce fruit loss and improve ripening quality; information regarding the interaction between varieties and postharvest environments and stability of fruit ripening traits across postharvest environments is limited. The objectives of this study were to determine the magnitude of interaction of varieties with postharvest environments on fruit ripening traits and to identify stable banana variety for ripening across postharvest environments. Seven commonly grown banana varieties (Dwarf Cavendish, William I, Grand Naine, Poyu, Giant Cavendish, Butazu and Local variety) were laid out in a completely randomized design with five replications in ten varied postharvest environments. The result indicated that pulp and peel ratio had negative high principal component one (PCA1) score whereas the PCA1 score for postharvest period, peel weight and fruit weight were positive and high. Cluster analysis grouped Dwarf Cavendish and Grand Naine; Poyu and Butazu varieties together for postharvest traits whereas the local variety was clustered separately. This study has demonstrated that hybridization of local with the introduced varieties can be done to improve postharvest traits. AMMI depicted significant variation for genotype, postharvest environments and their interactions for all traits. The magnitude of environmental effect was higher than the genotype and interaction effects. AMMI and GGE biplot analyses identified Gran Naine, Poyu and William I as consistent for ripening traits across postharvest environments.

A total of 50 genotypes were evaluated during Kharif 2022 in three different locations in Tamilnadu, India. The data on seed yield per plant was subjected to AMMI and GGE biplot analysis for stability study. The IPCA values explained 100% and 98.8% variation in AMMI and GGE biplot respectively. Results from both analyses inferred that the genotypes viz., Mash 114, Mash 1008, VBN (Bg) 4 and VBN 10 were stable across the environments with high mean seed yield per plant. Hence these varieties can be utilized for cultivation in varied environments of Tamilnadu. Besides, the genotypes; MASH 1008, MASH 114, VBN (Bg) 4, VBN 8, VBN 10 and VBN 11 were found to be resistant to MYMV based on the field screening at National Pulses Research Centre, Vamban, Pudukkottai.

The current study investigates how genotype by environment interaction (GEI) complicates superior cultivar selection in onion breeding. Employing a randomized block design with three replications, we assessed GEI effects on onion yield and identified adaptable, stable genotypes. The sensitivity of onion genotypes to environmental changes significantly influences yield and quality, necessitating adaptability across diverse climatic conditions. We conducted multilocation trials evaluating 28 onion genotypes across four distinct locations, each representing varying environmental factors. Through phylogenetic analysis, genotypes were categorized into four clusters: cluster I (3), cluster II (4), cluster III (15), and cluster IV (6). Rigorous assessment of yield performance, employing additive main effects and multiplicative interaction models, particularly the AMMI model, revealed five genotypes demonstrating remarkable stability and potential across diverse environmental conditions: RO-1626, RO-1623, RO-1639, RO-1625, and RO-1627. Notably, genotypes from cluster II exhibited the highest marketable yield (277.06 q/ha) and total yield (295.66 q/ha), indicating adaptability to varied environmental conditions. These findings hold promise for breeding high-yielding onion varieties resilient to diverse environments, ensuring stability, adaptability, and quality in cultivation.

AbstractEvaluation of maize varieties under multiple environments, including drought and low nitrogen (N) stressed sites is an important breeding approach, to identify well adapted and stable maize varieties. This study was undertaken to identify new quality protein maize (QPM) hybrids that have good agronomic performance and assess the presence of genotype by environment (G × E) interaction and grain yield stability of QPM hybrids under different environment conditions. Forty-five hybrids, including two QPM, two non-QPM and one local check were evaluated across 34 environments under stress and non-stress conditions in Ethiopia, Zimbabwe, Zambia, Mozambique, and Malawi during 2018 to 2020. Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype main effects plus G × E interaction (GGE) bi-plots were used for stability analysis. Environment, genotype and G × E interaction effects were significant for grain yield and other traits in all management conditions. The top yielding hybrids were 44 (QS7646) 12 (CZH15099Q) under optimum; 14 (CZH15142Q), 44 (QS7646) and 23 (CZH17192Q) under random stress; 9 (CZH142237Q) and 10 (CZH142238Q) under managed drought; and 14 (CZH15142Q) and 34 (CZH17203Q) under low N conditions. Among these, 10 (CZH142238Q) and 14 (CZH15142Q) were the most stable hybrids and can be recommended for release in sub-Saharan Africa to improve food and nutritional security of smallholder farmers who depend on maize. Kwekwe (KWE), Bindura (BIN), Chokwe (CHO) and Bako (BK2) were identified as the most discriminating and representative for optimum, random stress, managed drought and low N environments, respectively and help to identify superior hybrids.

Drought stress constricts crop production around the world. Employing high-yielding cultivars with drought tolerance might be the ideal professional approach to coping with its detrimental outcomes. As a result, the current study was performed to investigate the sensitivity and tolerance of nine wheat genotypes to drought stress. In a randomized block design experiment, nine wheat genotypes were subjected to four water treatments: 100%, 85%, 70%, and 55% of the available water (AW). Four water regimes in two growing seasons were counted as eight environmental zones. The leaf's water relations and photosynthetic pigment were estimated, as well as growth and yield parameters. Univariate and multivariate statistical approaches, including the new method of multi-trait genotype–ideotype distance (MGIDI), were used for evaluation. The analysis of variance revealed that genotype, environment, and their interactions had a highly significant effect on all traits. The same trend was shown by the additive main effects and multiplicative interaction (AMMI) analysis of variance for grain yield across the environments. The AMMI biplot study indicated that the G8 genotype is the most stable in terms of water stress. The G7 genotype can withstand droughts up to 55% of the available water, while the G8 and G3 genotypes can withstand droughts up to 70% of the available water. Based on all examined traits, this index was used to identify the stable genotypes G7, G8, and G3, which can therefore be suggested for cultivation during drought conditions. Furthermore, we found a positive correlation between the MGIDI, ANOVA, and tolerance index results, indicating that the same desirable genotypes of G7 and G8 were identified by these procedures as being highly tolerant and stable across a range of soil moisture conditions. Based on MGIDI analysis, we can recommend that the G7 genotype exhibits higher grain yield and yield-related traits with the best drought-tolerant indices. [ABSTRACT FROM AUTHOR]

Maize yields perform differently in different environments, so the selection of suitable genotypes in diverse environments is essential for variety selection to enable better site-specific planting. Hence, the objective of the study was to estimate the productivity of 11 maize hybrids (G) in 10 different environments (E) and select high-yield and stable varieties for adaptive cultivation in 2022 and 2023. The combined analysis of variance showed that G (4%), E (50%), and their interaction (31%) had a significant effect (p < 0.01) on maize yield, with E factors contributing the most. In addition, the average yield ranged from 9398 kg/ha to 10,574 kg/ha, and ZF-2208 and DY-519 performed relatively well in both years. The AMMI model showed that the varieties DY-213, DY-605, and DY-519 had high and stable production in 2022, whereas it was ZF-2209 and LX-24 in 2023. The "W-W-W" biplot showed that DY-519 and JG-18 were the optimal varieties in 2022, and ZF-2208 and ZF-2210 were optimal in 2023. The "mean vs. stability" biplot indicated that JG-18, DY-605, and DY-213 (in 2022) and ZF-2208, LX-24, and ZF-2209 (in 2023) were the optimal varieties. Additionally, both the discrimination and representative biplot and the ranking biplot reflected that BinChuan and ShiDian (in 2022) and GengMa and YongSheng (in 2023) were the ideal test environments. In conclusion, DY-519, DY-605, ZF-2208, and LX-24 hybrids could be used for variety promotion. Moreover, BinChuan, ShiDian, GengMa, and YongSheng were the ideal test environments for selecting varieties. Therefore, the AMMI model and GGE biplot can be used to complement each other for a comprehensive evaluation of maize yield. In this way, excellent maize hybrids with high yield and stability can be selected, which could promote the selection and popularization of varieties and shorten the breeding process. [ABSTRACT FROM AUTHOR]

The multi‐environmental trials aid breeders in selecting the best genotypes for specific or general adaptability to different environments before commercial release. This study aimed to assess the stability of 13 new soybean pure lines, along with two controls, in terms of seed yield and important agronomic traits. The assessment was based on a completely randomized block design with three replications across four areas during 2020–2022. Various adaptability methods, including parametric, AMMI, GGE biplot, PCA, and SIIG were employed. The mixed analysis showed that the effects of environment, genotype, and genotype–environment (GE) interaction were significant for most studied traits. The AMMI showed the highest portion of environment (65.89%) in soybean seed yield. Based on all stability parameters, lines 2 and 5 were selected for their average seed yields of 3349 and 3142 kg ha−1, respectively. Additionally, lines 6 and 5 showed the most stability, yielding higher than the average, which were 2992 and 3142 kg ha−1, respectively, according to GGE biplot results. Furthermore, lines 2, 5, and 8 were identified as the ideal genotypes concerning seed yield and other agronomic traits, with high SIIG parameters and yields exceeding the average. Finally, the soybean line 5 was deemed the most suitable due to its higher yield, stability, and early maturity (128‐day growth period). Therefore, line 5 is considered appropriate for its high stability and earliness in various regions of Iran. [ABSTRACT FROM AUTHOR]

Background: Pigeonpea is second most important pulse crop of India after chickpea and it is necessary to identify its high yielding and stable genotypes to feed the increasing population of country. Methods: The present study was laid down in a randomized block design with three replications during kharif season of 2016-2019 at GBPUAT, Pantnagar using twenty genotypes of pigeonpea with an aim to identify the high yielding and stable genotypes. The hierarchical cluster analysis (HCA) based on mean seed yield and Average of Sum of Ranks (ASR) of all measures (Parametric and nonparametric) was used in present study. Result: The pooled ANOVA revealed the presence of significant differences among genotypes, environments and G x E interaction effects. The results of hierarchical cluster analysis indicated the genotypes PA 622 (yield=1774.85 kg/ha, ASR=2.00), PA 620 (yield= 1579.92 kg/ha, ASR=2.18), UPAS 120 (yield=1268.57 kg/ha, ASR=2.87), PA 626 (yield=1571.40 kg/ha, ASR=5.56), PUSA 992 (yield= 1331.17 kg/ha, ASR=5.68) and PA 628 (yield= 1271.50 kg/ha, ASR=7.06) as most stable and high yielding and hence these genotypes can be recommended for pigeonpea improvement programmes. [ABSTRACT FROM AUTHOR]

Genotypes VL907, HS562, HPW484 were ranked as topped three in comparison to the other during the evaluation of nine wheat genotypes at major locations of the north hills zone of the country under rain fed conditions. The least values of AMMI stability measure (ASV) had expressed the desirability of HPW484, HS562, VL2041 genotypes whereas the genotypes HS562, HPW484, VL2041 had been identified by least values of Modified Ammi Stability Value (MASV). The minimum value of simultaneous selection index measure based on the MASV (ssiMASV) had selected HS562, HPW484, VL2041 wheat genotypes while values of ssiWAASB measure found the suitability of HPW484, HS562, HS691 wheat genotypes. The composite non parametric measure NPi(2) had favoured the VL892, HS562 genotypes and values of NPi(3) measure had settled for VL892, HS562 genotypes while VL892, HPW349 wheat genotypes had been pointed by the last composite measure NPi(4). The Ward’s method of Hierarchical Clustering had placed the VL907 genotype in a separate group as compared to others. The shorter rays of measures IPC2, IPC5, IPC3, SD had reflected the less contribution of the joint effects of genotypes and measures in the biplot analysis. Non parametric composite measure NPi(1) had expressed tight direct relation with Si¹, Si³, Si4,Si 5, Si6, Si7 values. The values of IPC6 & IPC4 had maintained the direct association with BLUP based analytic measures HMGV, RPGV, HMPRVG*Meanb, GAI, Meanb, RPGV*Meanb values. Moreover the values of CV measure had clustered with Si², Si³, Si4, Si5, Si7 measures of this study. [ABSTRACT FROM AUTHOR]

Alfalfa (Medicago sativa L.) is used to support livestock. A stability study was carried out over three years. The stability indices for yield and main quality characteristics such as plant height, number of nodes, the yield of green mass and dry matter, crude protein and fiber (%), and ash (%), were examined. Statistical analysis revealed significant differences that indicated the presence of high genotype–year interactions. Heritability was higher in the case of qualitative traits than quantitative traits. The most intriguing correlation was between green mass yield and crude protein content because positive correlations may lead to indirect and simultaneous selection. According to the statistical biplot models AMMI and GGE, the best genotypes for almost all traits to use, regardless of the environment and cultivation type, were the G8 (Population 2) followed by cultivar G3 (Yliki). Despite the high index values shown by the parameter number of nodes, the latter and yield showed low heritability. [ABSTRACT FROM AUTHOR]

Context. Micronutrient enrichment of pearl millet (Pennisetum glaucum (L.) R.Br.), an important food source in arid and semi-arid Asia and Africa, can be achieved by using stable genotypes with high iron and zinc content in breeding programs. Aims. We aimed to identify stable expression of high grain iron and zinc content in pearl millet lines across environments. Methods. In total, 29 genotypes comprising 25 recombinant inbred lines (RILs), two parental lines and two checks were grown and examined from 2014 to 2016 in diverse environments. Best performing genotypes were identified through genotype + genotype × environment interaction (GGE) biplot and additive main-effects and multiplicative interaction (AMMI) model analysis. Key results. Analysis of variance showed highly significant (P < 0.01) variations. The GGE biplot accounted for 87.26% (principal component 1, PC1) and 9.64% (PC2) of variation for iron, and 87.04% (PC1) and 6.35% (PC2) for zinc. On the basis of Gollob's F validation test, three interaction PCs were significant for both traits. After 1000 validations, the real root-mean-square predictive difference was computed for model diagnosis. The GGE biplot indicated two winning RILs (G4, G11) across environments, whereas AMMI model analysis determined 10 RILs for iron (G12, G23, G24, G7, G15, G13, G25, G11, G4, G22) for seven for zinc (G14, G15, G4, G7, G11, G4, G26) as best performers. The most stable RILs across environments were G12 for iron and G14 for zinc. Conclusions. High iron and zinc lines with consistent performance across environments were identified and can be used in the development of biofortified hybrids. Implications. The findings suggest that AMMI and GGE, as powerful and straightforward techniques, may be useful in selecting better performing genotypes. [ABSTRACT FROM AUTHOR]

Field pea (Pisum sativum L.) is a key cool-season food legume grown in Ethiopia, particularly in the Southeastern Arsi Zone. Although there is potential for field pea production in the area, adopting new and improved varieties is challenging because local farmers frequently prioritize cereal crops over field peas. To tackle this issue, a study was conducted to identify and promote high-yielding, improved field pea varieties suitable for the Southeastern farming community and similar agro-ecologies. The study focused on assessing the relationship between genotype and environment as well as the stability pattern of 14 advanced field pea genotypes. The genotypes were evaluated in eight environments in two consecutive cropping seasons (2014–2015) in southeast Ethiopia. The study utilized a randomized complete block design consisting of four replications. Various parametric stability analyses were used, including joint regression, additive main effect and multiplicative interaction (AMMI), and additive main effect and multiplicative interaction stability value (ASV). The grain yield was significantly influenced by genotype, location, and their interactions. The average grain yield ranged from 2809.5 kg ha−1 to 3509.1 kg ha−1. Eberhart's stability analysis identified stable genotypes: G3, G4, and G8, while G14 was unstable. According to additive main effect and multiplicative interaction stability value (ASV) and yield stability index (YSI), genotypes G12 and G13 had very low ASV values, whereas genotype G4 had very low ASV and YSI values. The AMMI and GGE biplot graphs showed that the principal component axes (PC1) and (PC2) accounted for 64.1 % and 61.36 % of the total variation, respectively. The results indicated that genotypes responded differently to environmental conditions and that the environment also influenced genotype performance. Genotypes G4 and G3 consistently performed well and exhibited remarkable stability, making them excellent cultivars for improving field pea productivity.

This dataset offers a comprehensive analysis of rice grain yield and quality traits over a period of three years, spanning 27 different environments in Bangladesh. The focus is on evaluating the performance of various genotypes (G), environments (E), and their interactions (GEI). The study's goal is to identify a stable and adaptive rice genotype that not only exhibits high-quality traits but also has a low glycemic index. The combined ANOVA showed significant effects of G, E, and GEI on grain yield (p ≤ 0.001). Genotype BRC266-5-1-1-1 achieved the highest average yield at 7.62 t/ha, while BRRI dhan28 had the lowest yield at 6.87 t/ha. In the Y2E4 environment, genotype BRC266-5-1-1-1 reached the highest yield at 8.86 t/ha. Genetic parameter estimation revealed significant effects of G, E, and GEI (p < 0.05), with GEI accounting for 82.85 % of the variance. The broad-sense heritability was low at 12 %, but the heritability of means was 0.80, indicating high selection accuracy (0.89). AMMI analysis showed significant effects from E (62.62 %) and GEI (16.19 %), with the first two principal components explaining 100 % of the variance. AMMI-based stability analysis identified BRC266-5-1-1-1 as the most stable genotype, with the highest AMMI-based stability parameter (ASTAB) at 2.11 and AMMI stability value (ASV) at 3.04. GGE biplot analysis revealed that BRC266-5-1-1-1 performed best in 14 environments. Quality trait analysis showed that BR16 and BRC266-5-1-1-1 had the highest milling outturn (70.90 %), with BRC266-5-1-1-1 also having the highest head rice yield (66.5 %). BRRIdhan28 had the highest glycemic index value (GIV) at 75.7, followed by BRC266-5-1-1-1 (55.04). These findings emphasize the significance of choosing appropriate genotypes for varietal release and taking environmental factors into account in rice breeding programs.

Pigeonpea [Cajanus cajan (L.) Millspaugh] is a widely grown pulse with high seed protein content that contributes to food and nutritional security in the Indian subcontinent. The majority of pigeonpea varieties cultivated in India are of medium duration (

ABSTRACT Common bean (Phaseolus vulgaris L.) is a grain legume rich in proteins and micronutrients, particularly iron and zinc. In this study, 30 small‐seeded genotypes were planted in five locations in Ethiopia, following an alpha lattice design with three replications, to determine environmental and genotypic influence on the Fe and Zn concentration. Based on their Fe and Zn contents, bean cultivars were evaluated for adaptability and stability using AMMI analysis. The Fe concentrations of raw bean seed varied from 44.4 to 84.4 μg/g within the panel of small‐seeded genotypes, with an average range of variance of 18 μg/g across environments, and its seed Zn concentrations varied from 19.7 to 32.3 μg/g, with an average range of variance of 12.6 μg/g across environments. The averages bean Fe concentration among the small‐seeded genotypes across sites in Ethiopia was 62.2 and 26.1 μg/g for Zn concentrations. Results from the analysis of variance using the AMMI model indicated that genotypes accounted for 20.53% and 9.49% of the total variance in seed Fe and Zn concentrations, respectively. The environment had a greater impact, affecting 60.92% and 81.52% of total sum of squares for Fe and Zn concentrations, respectively. According to the broad‐sense heritability, there appears to be some genetic control over Fe and Zn concentrations. However, the substantial effects of the environment and genotype‐by‐environment interaction on Fe and Zn concentrations in small‐seeded genotypes indicates breeding for higher amounts of trace minerals in new bean varieties could be a challenging task. This means the notion that beans can be biofortified to have higher concentrations of Fe and Zn might not be achievable in Ethiopia. A shift in breeding strategies that focuses on traits to enhance the bioavailability of Fe and Zn from bean is warranted and could be a solution to enhance the delivery of iron from small‐seeded beans produced in Ethiopia.

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.