Multi-environment testing for G×E interactions and identification of high-yielding, stable, medium-duration pigeonpea genotypes employing AMMI, GGE biplot, and YREM analyses.

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 high-yielding 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.

GGE biplot analysis for cane yield and sugar yield in advanced clones of sugarcane (Saccharum sp. complex).

The present investigation was taken up to study the G × E interaction and stability in 14 sugarcane clones during 2020-2021 and 2021-2022 at four different locations namely Pantnagar, Kashipur, Dhanauri (Haridwar), and Dhakrani (Dehradun) for cane yield (CY) and sugar yield (SY) at the 10-month and 12-month stages. The research aimed to identify stable, high-yielding sugarcane clones adaptable to diverse environmental conditions, enhancing productivity and profitability for farmers in Uttarakhand, India. The combined ANOVA revealed significant differences among the clones (22.20% to 29.54% variation), environments (35% to 39.62% variation), and their interactions (19.91% to 24.16% variation) for CY and SY at both stages. To analyze the stability of genotypes and G × E interactions, the GGE biplot method was employed. The first two PCs explained 77.94% for CY, 74.39% for SY at the 10-month stage, and 81.01% for SY at 12-month stage of the total variation of the GGE model. The GGE biplots revealed that for CY, the mega-environment exhibited CoPant 16222 and CoPant 16223 as the winning genotypes. For SY at the 10-month stage, CoPant 17221 and CoPant 16222 were the best clones in two different mega-environments, while at the 12-month stage, the mega-environment showed CoPant 16222 and CoPant 16223 as the winning genotypes. Dehradun (2020) and Kashipur (2020) were identified as the best test environments for selecting widely and specifically adapted genotypes, respectively, for CY and SY at the 10-month as well as 12-month stages. In a nutshell, GGE biplot analysis identified the best-performing sugarcane clones and best test environments in Uttarakhand, India. Clone CoPant 16222 showed high mean performance and stability for cane and sugar yield, making it suitable for recommendation to farmers.

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses.

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.

Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming.

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.

Yield stability among potato varieties suitable for different agroecological regions of Bangladesh.

Multilocation trials are usually performed in breeding and variety evaluation programs to identify stable genotype(s) with similar crop performance in various environments. The present study evaluated the stability of six selected potato varieties (BARI Alu-7, BARI Alu-8, BARI Alu-25, BARI Alu-28, BARI Alu-36, and BARI Alu-41) suitable for multiple locations (Barishal, Bogura, Cumilla, Jamalpur, Jashore, Munshiganj, Mymensingh, and Rajshahi) in Bangladesh from 2014 to 2019. The study considered genotype and environment as treatments, year as replications and used a randomized complete block design (RCBD) with to construct the genotype plus genotype-vs-environment interaction (GGE) model. The joint analysis of variance revealed significant differences among the genotypes and environments (GE). The scores of PC1 (principal component 1) and PC2 (principal component 2) cumulatively explained approximately 63 % of the total variation in GE interactions and were used to construct the GGE biplot. BARI Alu-8 and BARI Alu-28 were the best genotypes, with high average yields and high stability across the locations. Jamalpur and Munshiganj was identified as the desired locations among the tested environments for growing all the genotypes. This study will help potato growers select highly stable high-performance varieties for a particular environment to achieve maximum tuber production.

Multi-location evaluation of field pea in Indian climates: eco-phenological dynamics, crop-environment relationships, and identification of mega-environments.

Characterization of crop-growing environments in relation to crop's genotypic performance is crucial to harness positive genotype-by-environment interactions (GEI) in systematic breeding programs. Given that, the study aimed to delineate the impact of diverse environments on crop phenology and yield traits of dwarf-statured field pea, pinpointing location(s) favoring higher yield and distinctiveness within breeding lines. We tested twelve field pea breeding lines across twenty locations in India, covering Central Zone (CZ), North Western Plain Zone (NWPZ), North Eastern Plain Zone (NEPZ), and Northern Hill Zone (NHZ). Across these locations, maximum and minimum temperatures during flowering (TMAXF, TMINF) and reproductive period (TMAXRP, TMINRP) ranged 18.9-28.3, 3.3-18.0, 15.0-30.8, and 7.9-22.1oC, respectively. Meanwhile, notable variations in phenological and agronomic traits (coefficient of variation) were observed: flowering (31%), days to maturity (21%), reproductive period (18%), grain yield (48%), and 100-seed weight (18%). Combined ANOVA demonstrated an oversized impact of environment (81%) on yield, while genotype and GEI effects were 2% and 14%, respectively. The variables TMINF, TMINRP, and cumulative growing degree-day showed positive correlations with yield, while extended vegetative and maturity durations negatively influenced yield (p < 0.05). Additionally, linear mixed-models and PCA results explained that instability in crop phenology had significant influence on field pea yield. Seed weight was markedly varied within the locations (9.9-20.8 g) and both higher and lower seed weights were associated with lower yields (Optimal = 17.1 g). HA-GGE biplot-based on environment focus-scaling demonstrated three mega-environments and specific locations viz. Kota (CZ), SK Nagar (CZ), Raipur (CZ), Sehore (CZ), and Pantnagar (NWPZ) as the ideal testing-environments with high efficiency in selecting new genotypes with wider adaptability. The study findings highlight distinct impact of environments on crop phenology and agronomic traits of field pea (dwarf-type), hold substantial value in designing efficient field pea (dwarf-type) breeding program at mega-environment scale.

Deciphering genotype-by-environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods.

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.

Identification of adaptable sunflower (Helianthus annuus L.) genotypes using yield performance and multiple-traits index.

Sunflower is the most important oil crop ranked as fourth edible oil in the world. The study was conducted in Northern Ethiopia during 2017-2019 cropping seasons using randomized completely block design with three replications. The objective was to decipher the genotype by environment interaction (GEI) in multi-environment trials (MET) and identify adaptable sunflower genotypes. Combined ANOVA, AMMI ANOVA and Eberhart and Rusell regression were analyzed, and GGE bi-plots, AMMI1 and AMMI2 bi-plots, Principal component Analysis (PCA), multi-trait genotype-ideotype distance index (MGIDI), correlation network plot for sunflower traits were sketched. AMMI stability measures, Best Linear Unbiased Prediction (BLUP) based indexes; parametric and non-parametric statistics were computed using R-statistical software. In the AMMI ANOVA the main effects of the environment (E) (54.18 % SS), genotype (G) (16.9 % SS) and GEI (23.50 % SS) were significant (p < 0.001). The genotypic Likely-hood Ratio Test revealed significant for all traits. The AMMI bi-plot and the GGE bi-plots selected G10 and G2 as the most adaptable genotypes. CV, HMGV, RPGV, HMRPGV, Pi, GAI, KRS, S(3) and S(6) also identified G10 as the most stable genotype. Based on the MGIDI, G10 (MGIDI = 1.45) and G5 (MGIDI = 2.19) are selected and these genotypes are recommended for further cultivation in Tigray.

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.).

Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years' data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations-Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

Stability and performance analysis of storage root yield in a dataset of sweet potato varieties (Ipomoea batatas L.).

The dataset focuses on evaluating the performance of 17 sweet potato varieties (G) released by the Bangladesh Agricultural Research Institute (BARI) in terms of storage root yield and stability across five locations (E) in Bangladesh-Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The result revealed that BARI Mistialu-12 exhibited the highest average storage root yield at 45.35 t/ha, closely followed by BARI Mistialu-16 at 44.64 t/ha. Conversely, BARI Mistialu-1 had the lowest mean yield of 25.99 t/ha. Among the locations, Bogura recorded the highest mean root yield at 37.05 t/ha, while Chattogram exhibited the lowest at 31.27 t/ha. A combined analysis of variance revealed the presence of variability in storage root yield attributed to the genotype-location (environment) interaction (GEI). To delve deeper into this interaction, additive and multiplicative interaction effect models (AMMI) along with a linear mixed model (LMM) were employed for further investigations to confirm the significant contribution of GEI variance to root yield. The LMM results showed genetic variance (%), heritability (%), selection accuracy (%), and GEI correlation coefficients of 52.27, 54, 94, and 30, respectively. The AMMI analysis indicated that the first two principal components accounted for 74.60 % of GEI, with 20.16 % attributed to it. Assessing significant Interaction Principal Component Analyses (IPCAs) through the Weighted Average of Absolute Scores (WAAS) indicated that BARI Mistialu-12 is the most stable genotype, followed by BARI Mistialu-16 and BARI Mistialu-8, all displaying above-average root yield. The mega-environment analysis associated the highest root production of BARI Mistialu-11 and BARI Mistialu-2 with the Jamalpur location, while Gazipur, Bogura, and Jashore were linked with the superior performance of BARI Mistialu-12 and BARI Mistialu-16 genotypes. These findings are crucial for future breeding programs and the rapidly growing sweet potato industry, given the stable high-yield potential across diverse agro-ecological conditions. However, it is imperative to repeat the study to ensure reliable outcomes.

Country-wide, multi-location trials of Green Super Rice lines for yield performance and stability analysis using genetic and stability parameters.

Rice (Oryza sativa L.) is an important member of the family Poaceae and more than half of world population depend for their dietary nutrition on rice. Rice cultivars with higher yield, resilience to stress and wider adaptability are essential to ensure production stability and food security. The fundamental objective of this study was to identify higher-yielding rice genotypes with stable performance and wider adaptability in a rice growing areas of Pakistan. A triplicate RCBD design experiment with 20 Green Super Rice (GSR) advanced lines was conducted at 12 rice growing ecologies in four Provinces of Pakistan. Grain yield stability performance was assessed by using different univariate and multivariate statistics. Analysis of variance revealed significant differences among genotypes, locations, and G x E interaction for mean squares (p < 0.05) of major yield contributing traits. All the studied traits except for number of tillers per plant revealed higher genotypic variance than environmental variance. Broad sense heritability was estimated in the range of 44.36% to 98.60%. Based on ASV, ASI, bi, Wi2, σ2i and WAAS statistics, the genotypes G1, G4, G5, G8, G11 and G12 revealed lowest values for parametric statistics and considered more stable genotypes based on  paddy yield. The additive main effects and multiplicative interaction (AMMI) model revealed significant variation (p < 0.05) for genotypes, non-signification for environment and highly significant for G × E interaction. The variation proportion of PC1 and PC2 from interaction revealed 67.2% variability for paddy yield. Based on 'mean verses stability analysis of GGE biplot', 'Which-won-where' GGE Biplot, 'discriminativeness vs. representativeness' pattern of stability, 'IPCA and WAASB/GY' ratio-based stability Heat-map, and ranking of genotypes, the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 were observed ideal genotypes with yield potential more than 8 tons ha-1. Discriminativeness vs. representativeness' pattern of stability identifies two environments, E5 (D.I Khan, KPK) and E6 (Usta Muhammad, Baluchistan) were best suited for evaluating genotypic yield performance. Based on these findings we have concluded that the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 could be included in the commercial varietal development process and future breeding program.

Genetic potential and inheritance pattern of agronomic traits in faba bean under free and infested Orobanche soil conditions.

BACKGROUND: Orobanche is an obligate parasite on faba bean in the Mediterranean region, causes considerable yield losses. Breeding tolerant faba bean genotypes to Orobanche is pivotal to sustain production and ensuring global food security, particularly considering the challenges posed by population growth. In the present study, seven faba bean lines and four testers were used in a line×tester mating design during 2020-2021 and 2021-2022 growing seasons. The eleven parents and their 28 F1 crosses were evaluated under Orobanche free and naturally infested soils. RESULTS: The results demonstrated considerable variations among the evaluated genotypes, wide diversity among the parental materials, and heterotic effects for all studied agronomic traits under Orobanche-free and infested soils. Orbanche infestation displayed a significant adverse impact on all the studied agronomic traits. The genotypes Line1, Line2, Line3, and Line5 displayed superior performance under Orobanche-infested conditions and recorded the highest values of all studied agronomic traits. Additionally, Line1, Line2, Line3, Line5, and Line7 exhibited desirable significant GCA for most evaluated traits under the two infestation conditions. The obtained crosses displayed significant negative or positive heterosis for studied agronomic characters such as plant height, number of branches per plant, number of pods per plant, number of seeds per plant, and seed weight per plant were observed. Furthermore, specific cross combinations such as Line2×Sakha3, Line3×Nubaria5, Line7 × Nubaria5, Line6×Nubaria1, Line5×Sakha3, Line1×Sakha3, and Line1 × Nubaria5 exhibited superior performance in seed yield and contributing traits under Orobanche-infested conditions. Moreover, these specific crosses showed superior efficacy in reducing dry weight of Orobanche spikes. The results obtained from GGE biplot analysis closely aligned with those from the line×tester procedure, affirming the significance of GGE biplot as a valuable statistical tool for assessing genotype combining ability in line× tester data. Both additive and non-additive gene actions were reported to be predominantly involved in the inheritance of the studied agronomic traits in faba bean. CONCLUSIONS: The detected genetic diversity within the evaluated faba bean genotypes and their developed crosses exhibits substantial potential for improving faba bean productivity under Orobanche-infested conditions. The parental genotypes, Line1, Line2, Line3, Line5, and Line7, were identified as effective and promising combiners. Moreover, the developed crosses Line2×Sakha3, Line3×Nubaria5, Line7×Nubaria5, Line6×Nubaria1, Line5×Sakha3, Line1×Sakha3, and Line1×Nubaria5 could be considered valuable candidates for developing high-yielding and tolerant faba bean genotypes to Orobanche.

Testcross performance and combining ability of early-medium maturing quality protein maize inbred lines in Eastern and Southern Africa.

Limited commercial quality protein maize (QPM) varieties with low grain yield potential are currently grown in Eastern and Southern Africa (ESA). This study was conducted to (i) assess the performance of single-cross QPM hybrids that were developed from elite inbred lines using line-by-tester mating design and (ii) estimate the general (GCA) and specific (SCA) combining ability of the QPM inbred lines for grain yield, agronomic and protein quality traits. One hundred and six testcrosses and four checks were evaluated across six environments in ESA during 2015 and 2016. Significant variations (P ≤ 0.01) were observed among environments, genotypes and genotype by environment interaction (GEI) for most traits evaluated. Hybrids H80 and H104 were the highest-yielding, most desirable, and stable QPM hybrids. Combining ability analysis showed both additive and non-additive gene effects to be important in the inheritance of grain yield. Additive effects were more important for agronomic and protein quality traits. Inbred lines L19 and L20 depicted desirable GCA effects for grain yield. Various other inbred lines with favorable GCA effects for agronomic traits, endosperm modification, and protein quality traits were identified. These inbred lines could be utilized for breeding desirable QPM cultivars. The QPM hybrids identified in this study could be commercialized after on-farm verification to replace the low-yielding QPM hybrids grown in ESA.

Stability analysis and nutritional quality of soybean (Glycine max (L).Merrill.) genotypes for feed in southwestern Ethiopia.

Soybean is a leguminous crop known for its multiple utilizations both as food and feed for humans and livestock. The objectives of the study were to identify high dry matter yielder and stable genotypes across environments in southwestern Ethiopia. The effect of genotype environment (G x E) interaction on dry matter yield of soybean genotypes were evaluated in two cropping seasons (2019-2020) under rain fed condition. Eight pre tested soybean genotypes with two checks were used as treatment in a randomized complete block design with three replications. Collected data were recorded and analyzed using GGE biplot models using R software. The combined analysis of variance showed that dry matter yield of soybean genotypes was significantly affected by genotype, environment and genotype-environment (G x E) interaction. The genotype, environment, and genotype-environment interaction, respectively, accounted for 11.4%, 49.5%, and 38.8% of the observed variation to the dry mater yield. This indicates that dry matter yield was significantly more affected by environments and G × E interaction than genotypes. The GGE biplot analysis revealed that six environments used in the current study were grouped into four mega-environments. The mega-environments were identified for genotype evaluation. The biplot showed that the vertex genotypes were G4, G10, and G9 and considered as optimum performance in their respective mega-environments and more responsive to environmental changes. The biplot also showed that ENV5 (Kersa 2020) was an ideal and the most discriminating and representative environment. Genotype G4 (TGX1990-114FN) was the ideal genotype and overall winner in dry matter yield and stability in the findings. Therefore, genotype G4 (TGX-1990-114FN) is the better option to be used as forage soybean in Ethiopia. Further demonstration of the feeding values of high yielders and stable genotypes on animal performances should be done.

Virulence Spectra of Hungarian Pyrenophora teres f. teres Isolates Collected from Experimental Fields Show Continuous Variation without Specific Isolate × Barley Differential Interactions.

Pyrenophora teres f. teres (Ptt), the causal agent of net form net blotch (NFNB) disease, is an important and widespread pathogen of barley. This study aimed to quantify and characterize the virulence of Ptt isolates collected from experimental fields of barley in Hungary. Infection responses across 20 barley differentials were obtained from seedling assays of 34 Ptt isolates collected from three Hungarian breeding stations between 2008 and 2018. Twenty-eight Ptt pathotypes were identified. Correspondence analysis followed by hierarchical clustering on the principal components and host-by-pathogen GGE biplots suggested a continuous range of virulence and an absence of specific isolate × barley differential interactions. The isolates were classified into four isolate groups (IG) using agglomerative hierarchical clustering. One IG could be distinguished from other IGs based on avirulence/virulence on one to five barley differentials. Several barley differentials expressed strong resistance against multiple Ptt isolates and may be useful in the development of NFNB-resistant barley cultivars in Hungary. Our results emphasize that the previously developed international barley differential set needs to be improved and adapted to the Hungarian Ptt population. This is the first report on the pathogenic variations of Ptt in Hungary.

Genotype by environment interaction, AMMI, GGE biplot, and mega environment analysis of elite Sorghum bicolor (L.) Moench genotypes in humid lowland areas of Ethiopia.

This study aimed to evaluate high-yielding, stable sorghum genotypes and determine the ideal (representative and discriminating) testing environments for genotypes in the humid lowlands of Ethiopia. A total of forty-two sorghum genotypes were used for a field trial conducted in six different environments using a randomized complete block design. Yield stability, Additive main effect, multiplicative interaction (AMMI), and genotype and genotype by environment interaction (GGE) were computed. The AMMI analysis explained 62.85% of the G×E variance. The AMMI1 biplot revealed that (G4; Mok079 and (G16; Ba066) genotypes had higher grain yields. AMMI2 biplot suggested that genotypes (G18; Y0470),(G23;100620), (G29; PML981475), and (G11; ETSC300373-4) show higher sensitivity to environmental changes because of their strong genotype-by-environment interactions. The GGE captured 79.46% of the GGE variance, and the GGE biplot identified genotypes (G4; Mok079), (G10; Sl081) and (G16; Ba066) were the most stable genotypes whereas(G39; ETSC120051-3) was the least stable genotypes. The GGE biplot identified Assosa (AS20) as a suitable environment, whereas PW20 and JM20 were the most discriminating and non-representative environments. The GGE biplot was found to identify three main mega-environments for sorghum growing in the humid lowlands of Ethiopia., both the AMMI and GGE biplots revealed (G4; Mok079) had the highest level of adaptability to all tested environments and was approved by the National Variety Release Committee for release in 2022.

Screening of salt tolerance of maize (Zea mays L.) lines using membership function value and GGE biplot analysis.

Soil salinization is a widely recognized global environmental concern that has a significant impact on the sustainable development of agriculture at a global scale. Maize, a major crop that contributes to the global agricultural economy, is particularly vulnerable to the adverse effects of salt stress, which can hinder its growth and development from germination to the seedling stage. This study aimed to screen highly salt-tolerant maize varieties by using four NaCl concentrations of 0, 60, 120, and 180 mMol/L. Various agronomic traits and physiological and biochemical indices associated with salt tolerance were measured, and salt tolerance was evaluated using principal component analysis, membership function method, and GGE biplot analysis. A total of 41 local maize varieties were assessed based on their D values. The results show that stem thickness, germ length, radicle length, leaf area, germination rate, germination index, salt tolerance index, and seed vigor all decreased as salt concentration increased, while electrical conductivity and salt injury index increased with the concentration of saline solution. Under the stress of 120 mMol/L and 180 mMol/L NaCl, changes in antioxidant enzymes occurred, reflecting the physiological response mechanisms of maize under salt stress. Principal component analysis identified six major components including germination vigor, peroxidase (POD), plant height, embryo length, SPAD chlorophyll and proline (PRO) factors. After calculating the comprehensive index (D value) of each variety's performance in different environments using principal component analysis and the membership function method, a GGE biplot analysis was conducted to identify maize varieties with good salt tolerance stability: Qun Ce 888, You Qi 909, Ping An 1523, Xin Nong 008, Xinyu 66, and Hong Xin 990, as well as varieties with poor salt tolerance: Feng Tian 14, Xi Meng 668, Ji Xing 218, Gan Xin 2818, Hu Xin 712, and Heng Yu 369. Furthermore, it was determined that a 120 mMol/L NaCl concentration was suitable for screening maize varieties during germination and seedling stages. This study further confirmed the reliability of GGE biplot analysis in germplasm selection, expanded the genetic resources of salt-tolerant maize, and provided theoretical references and germplasm utilization for the introduction of maize in saline-alkali areas. These research findings contribute to a better understanding of maize salt tolerance and promote its cultivation in challenging environments.

AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet.

Sugar beet, an important sugar crop, contributes significantly to the world's sugar production. However, genotype-environment interactions (GEI) often affect the quality characteristics of sugar beet. Hence, understanding the effects of GEI on sugar beet quality can aid in identifying high-quality genotypes that can adapt to different environments. Traditional variance analysis can only be used to examine the yield of a variety and not its specific adaptability to specific conditions. Therefore, more comprehensive analytical methods are required to evaluate the characteristics of the variety under specific environments. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype × environment interaction (GGE) biplot models can be employed to comprehensively evaluate different varieties and address the drawbacks associated with a single evaluation method. Moreover, these models also allow us to explore new varieties more objectively and comprehensively. In this study, the adaptability and stability of 16 sugar beet varieties, in terms of yield and sugar content, were evaluated using AMMI and GGE biplot analysis in seven pilot projects undertaken in 2022. In the assessment of a small but significant proportion of the total GEI variance for the two qualitative traits (yield and sugar content), 80.58% of the variance was explained by the cumulative contribution of IPC1, IPC2, and IPC3. AMMI and GGE biplots clearly highlighted that KWS4207 (G3) exhibited high and stable quality. They also demonstrated that the experiments in Jalaid Banner (Inner Mongolia) (E7) were the most representative. Together, the results suggested that the comprehensive application of AMMI and GGE biplot analysis allowed for a more comprehensive, scientific, and effective evaluation of sugar beet varieties across different regions. The findings offer a theoretical basis for sugar beet breeding and could guide the rational design of experiments for testing new varieties of sugar beet.

Screen for low-arsenic-risk rice varieties based on environment-genotype interactions by using GGE analysis.

Arsenic (As) accumulation in rice is a global health concern that has received increased attention in recent years. In this study, 12 rice genotypes were cultivated at four As-contaminated paddy sites in Taiwan. According to the different crop seasons and As levels in the soil, the sites were further divided into 18 environmental conditions. For As in soils, results showed that 67% of the studied environments were likely to represent As contamination. For As in rice, the mean total As concentration in brown rice grains ranged from 0.17 to 0.45 mg kg-1. The analysis of variance for the environment effect indicated that grain As concentration was mainly affected by the environmental conditions, suggesting that there was a remarkable degree of variation across the trial environments. According to the combination of the GGE biplot and cumulative distribution function of order statistics (CDFOS) analysis, five genotypes-TCS17, TCS10, TT30, KH139, and TC192-were regarded as stable, low-risk genotypes because the probability of grain As concentration exceeding the maximum permissible concentration (MPC) was lower for these genotypes across all environmental conditions. Particularly, TCS17 was recommended to be the safest rice genotype. Thus, grain As levels in the selected genotypes were applied to assess the health risk to Taiwanese residents associated with As exposure through rice consumption. Results showed that the upper 75th percentile values of the hazard quotient were all less than unity. This suggested that the health risk associated with consuming the selected rice genotypes was acceptable for most of the residents. The methodology developed here would be applicable to screen for stable, low-As-risk rice genotypes across multiple field environments in other regions or countries.

Identification of High-Yielding Genotypes of Barley in the Warm Regions of Iran.

One of the most important effects of climatic changes is increasing temperatures and expanding water deficit stress in tropical and subtropical regions. As the fourth most important cereal crop, barley (Hordeum vulgare L.) is crucial for food and feed security, as well as for a sustainable agricultural system. The present study investigates 56 promising barley genotypes, along with four local varieties (Norooz, Oxin, Golchin, and Negin) in four locations to identify high-yielding and adapted genotypes in the warm climate of Iran. Genotypes were tested in an alpha lattice design with six blocks, which were repeated three times. Traits measured were the number of days to heading and maturity, plant height, thousand kernels weight, and grain yield. A combined analysis of variance showed the significant effects of genotypes (G), environments (E), and their interaction (GEI) on all measured traits. Application of the additive main-effect and multiplicative interaction (AMMI) model to the grain yield data showed that GEI was divided into three significant components (IPCAs), and each accounted for 50.93%, 30.60%, and 18.47%, respectively. Two selection indices [Smith-Hazel (SH) and multiple trait selection index (MTSI)] identified G18, G24, G29, and G57 as desirable genotypes at the four test locations. Using several BLUP-based indices, such as the harmonic mean of genotypic values (HMGV), the relative performance of genotypic values (RPGV), and the harmonic mean of the relative performance of genotypic values (HMRPGV), genotypes G6, G11, G22, G24, G29, G38, G52, and G57 were identified as superior genotypes. The application of GGE analysis identified G6, G24, G29, G52, and G57 as the high-yielding and most stable genotypes. Considering all statistical models, genotypes G24, G29, and G57 can be used, as they are well-adapted to the test locations in warm regions of Iran.