Additive Main Effects And Multiplicative Interaction Research Articles

Genetic stability analysis of early maturing pigeonpea genotypes using AMMI and WAASB models

In the present study, 27 early maturing pigeonpea genotypes were evaluated over multi-locations for yielding potential and analyzed using analysis of variance (ANOVA), additive main effect and multiplicative interaction (AMMI) model and Weighted Average of Absolute Scores (WAASB) models. ANOVA displayed significant variation among genotypes, environments and genotype-environment interaction. AMMI models further explained the main and interaction effects with PC1 and PC2 covering 81.8% and 18.2% variance, respectively. AMMI biplots and WAASB matrix denoted ICP 14444, ICP 8817 and ICP 11890 to be potential early maturing, high yielding stable lines across the tested locations. Warangal and Kanpur noted to be the best environments with least and highest discriminating ability respectively. ICP 11543, ICP 16309, ICP 6992 were identified as best-fit cultivars for Patancheru, Warangal and Kanpur, respectively.

Genotype by Environment Interaction Using AMMI, GGE Biplot and Multivariate Analysis of Nepalese Aromatic Rice Landraces

Aromatic group of rice comprises fine, medium, long and extra-long grain that has rich in agro-biodiversity, carries historical and cultural significance in Nepalese society. The objective was to identify stable, high-yielding aromatic rice landraces for Nepal Thirty aromatic rice landraces including two released varieties as standard checks were evaluated at three locations during 2020 and 2021. The experiment was laid out in a Randomized Complete Block Design with three replications. The combined analysis of variance indicated differences among genotypes and genotype-environment interactions for grain yield. The additive main effects and multiplicative interaction (AMMI) analysis revealed that genotypic effects for 69%, genotype × environment interaction effects for 28% and environmental effects accounted for 3% of the total sum of squares. Similarly, GGE biplot analysis showed that the principal components (PC1 and PC2) accounted for 53% and 47% of the GGE sum of squares, respectively. Based on AMMI1, AMMI2 biplots, and GGE biplots, the genotypes Samba Masuli Sub-1 and Sugandhit Dhan-1 were identified as the high yielding and stable fine and aromatic rice landraces. Other promising genotypes are Damari Dhan, Tilki, Hiupuri, Kalo Basmati, Gouriya, and Brahmamusi Dhan. Specifically, Samba Masuli Sub-1 demonstrated the highest grain yield performance at the NRRP, Hardinath, while Sunaulo Sugandha and Balamsari Dhan excelled in the Tarahara and Khajura environments, respectively. In PCA analysis, four PCs showed significant eigen value >1.0 contributing more than 84% of total variance. Biplot analysis reflected that 1000 grain weight, panicle length, heading and maturity days, and grain yield showed greater variation among the landraces. The results provide a robust basis for the further evaluation and verification of this findings in advance stage trials. The superior and outstanding landraces with higher yields and aroma are potential future varieties that can be either use directly as varieties after selection or utilize in future breeding programs for the development of new aromatic rice varieties.

Genotype by environment interaction across water regimes in relation to cropping season response of quinoa (Chenopodium quinoa).

Genotype × environment (GxE) interaction effects are one of the major challenges in identifying cultivars with stable performance across agri-environments. In this study we analysed GE interactions to identify quinoa (Chenopodium quinoa) cultivars with high and stable yields under different soil moisture regimes, representing control conditions, waterlogging and drought. Waterlogging and drought treatments were artificially induced using normoxia, a combination of hypoxia-normoxia, and 10% PEG (Polyethylene glycol) under hydroponic growth conditions, respectively. Both waterlogging and drought conditions significantly reduced the plant height (PH), number of leaves (NoL) and number of branches (NoB), stem diameter (SD), leaf area (LA) and dry weight (DW) of quinoa genotypes. The genotype, water regime, and genotype by water regime effects all significantly affected the measured quinoa traits. Based on the additive main effects and multiplicative interaction (AMMI) model for DW, the genotypes G18, Puno, Q4, 2-Want, Puno, Real1 x Ruy937 and Titicaca were found to exhibit tolerance and were stable across water regimes. A second-stage evaluation was conducted to test genotype × environment interaction effects in crop production field trials, selecting two contrasting seasons based on soil moisture conditions involving a diverse set of genotypes (58 varieties in total). Our results demonstrate significant variations in both growth and yield among the quinoa genotypes across the cropping seasons. The GGE analysis for grain yield indicate that field conditions matched to G × E under hydroponic experimental conditions and the cultivars G18, Q1, Q4, NL-3, G28, 42-Test, Atlas and 59-ALC were classified within a range of high productivity. Our findings provide a basis for understanding the mechanisms of wide adaptation, while identifying germplasm that enhances the water stress tolerance of quinoa cultivars at early growth stages.

Unveiling Phenotypic and Environmental Dynamics: Exploring Genetic Stability and Adaptability of Faba Bean Cultivars in Norwegian Climates

ABSTRACTThis study evaluated 22 spring‐type faba bean cultivars in the main areas for cultivation of faba bean in Norway to assess the variation of 14 faba bean traits due to cultivar (G), environment (E), and their interaction (G × E), and to assess their stability across environments by using the additive main effects and multiplicative interaction (AMMI) analysis and coefficient of variation (CV). Significant G, E, and G × E effects were found for most traits, with environment accounting for much of the variance in yield and the growing degree days (GDD) to different developmental stages. Yield was highly correlated with thousand kernel weight (TKW) and GDD to BBCH 89 (maturation). The stability of the cultivars was studied for yield, TKW, and GDD to BBCH 89. Stability analysis using the AMMI stability value, yield stability index, CV, and the average sum of ranks identified Birgit, Stella, Bobas, and Macho as the most stable high‐yielding cultivars across environments, achieving a mean yield of 6–6.4 tons ha−1. Bobas, Macho, Stella, and Yukon had the most stable TKW (612–699 g) and Bobas, Capri, Trumpet, and Vertigo were the most stable regarding GDD to BBCH 89 (1257°C days, with a base temperature of 5°C). These stable cultivars can be utilized in breeding programs to achieve high and stable faba bean yield in the main growing areas of Norway and other Nordic‐Baltic countries.

Screening and Site Adaptability Evaluation of Qi-Nan Clones (Aquilaria sinensis) in Southern China

In recent years, plantations of Aquilaria sinensis in China have been dominated by Qi-nan, yet there remains limited research on the growth evaluation and breeding of these clones. In this study, a multi-point joint variance analysis, an additive main effect and multiplicative interaction (AMMI) model, a weighted average of absolute scores (WAASB) stability index, and a genotype main effect plus a genotype-by-environment interaction (GGE) biplot were used to comprehensively analyze the yield, stability, and suitable environment of 25 3-year-old Qi-Nan clones from five sites in southern China. The results showed that all the growth traits exhibited significant differences in the clones, test sites, and interactions between the clones and test sites. The phenotypic variation coefficient (PCV) and genetic variation coefficient (GCV) of the clones’ growth traits at the different sites ranged from 16.56% to 32.09% and 5.24% to 27.06%, respectively, showing moderate variation. The medium–high repeatability (R) of tree height and ground diameter ranged from 0.50 to 0.96 and 0.69 to 0.98, respectively. Among the clones, Clones G04, G05, G10, G11 and G13 showed good growth performance and could be good candidates for breeding. Environmental effects were found to be the primary source of variation, with temperature and light primarily affecting growth, while rainfall influenced survival and preservation rates. Yangjiang (YJ) was found to be the most suitable experimental site for screening high-yield and stable clones across the different sites, whereas the tree height and ground diameter at the Chengmai (CM) site were significantly higher than at the other sites, and the Pingxiang (PX) and Zhangzhou (ZZ) sites showed poor growth performance. The findings suggest that Qi-nan clones are suitable for planting in southern China. There were also abundant genetic variations in germplasm resources for the Qi-nan clones. The five selected clones could be suitable for extensive planting. Therefore, large-scale testing is necessary for determining genetic improvements in Qi-nan clones, which will be conducive to the precise localization of their promotion areas.

Genetic and environmental influences on fatty acid composition in different fenugreek genotypes

Fenugreek (Trigonella foenum-graecum L.) plays a crucial role in both the food and pharmaceutical industries due to its rich nutritional profile and medicinal properties, such as regulating blood sugar and reducing inflammation. This study offers a comprehensive analysis of the fatty acid composition in fenugreek seeds, revealing significant variability influenced by genetic and environmental factors. The analysis showed that linoleic acid ranged from 32.16 % to 45.96 %, linolenic acid from 17.3 % to 31.21 %, oleic acid from 11.40 % to 20.32 %, stearic acid from 3.78 % to 6.76 %, and palmitic acid from 8.74 % to 16.44 %. Both genotype and environmental conditions had a profound impact on these profiles, with notable variations arising from differences in water availability. The Israel and Kermanshah (Iran) genotypes recorded the highest linoleic acid levels under irrigated conditions, while the Kayseri (Turkey) genotype exhibited the highest linolenic and stearic acid values across both irrigated and non-irrigated environments. Additionally, the Ahvaz (Iran) and Ukraine genotypes excelled in oleic and palmitic acid concentrations. Principal component analysis (PCA) and Additive Main Effects and Multiplicative Interaction (AMMI) biplots further highlighted the complex genotype-environment interactions, providing essential insights for breeding fenugreek varieties optimized for diverse agro-climatic conditions.

Identification of high-yielding and stable cultivars of wheat under different sowing dates: Comparison of AMMI and GGE-biplot analyses

Evaluating the adaptability and yield stability of wheat varieties across different sowing dates is crucial for the success of breeding programs. In this study, we used Additive Main Effects and Multiplicative Interactions (AMMI) and Genotype × Genotype–Environment (GGE) biplot analyses to evaluate the performance of 13 wheat varieties across eight sowing dates. The main objective was to compare varieties and sowing dates to identify the most stable varieties for cultivation. Both AMMI and GGE biplot analyses showed significant effects of genotype, environment and genotype-environment (GE) interaction on grain yield. The interaction patterns identified by AMMI and GGE-biplots categorized Mihan, Oroom and Pishgam as the most stable, high yielding and early maturing wheat varieties. Furthermore, while the GGE biplot method proved to be more efficient than the AMMI model for stability analysis, our results indicate considerable overlap between the results of these two approaches. These results provide a valuable basis for optimizing genetic improvement strategies in wheat breeding programs.

Evaluating genetic diversity and seed composition stability within Pan‐African Soybean Variety Trials

AbstractGiven high animal protein costs, protein deficiency is a prevalent form of malnutrition in sub‐Saharan Africa (SSA). Soybean [Glycine max (L.) Merr.] can provide a cheaper high‐quality protein source and fortify lysine‐limited cereal‐based diets. Breeding soybean for seed composition in SSA requires understanding genotype by environment interactions (G × E). African breeding programs submit cultivars for evaluation in the Pan‐African Soybean Variety Trials (PATs), providing the opportunity to examine G × E across diverse environments. With PAT data, we conducted additive main effects and multiplicative interaction (AMMI) and genotype plus genotype‐by‐environment (GGE) biplot analyses on seed protein and oil content of 17 cultivars grown with two replications in nine environments across Zimbabwe. Across environments, protein ranged from 322.8 to 445.1 g kg−1 and oil ranged from 164.8 to 242.7 g kg−1. For protein AMMI analysis, MAKWACHA performed best for both stability and a combination of stability plus content. For oil AMMI analysis, SC SPIKE performed best for stability and KALEYA performed best for a combination of stability plus content. GGE biplot analyses identified three different mega‐environments for protein and oil, with SC EXPT2, KALEYA, and SC EXPT1 having highest protein content and TGX 2002‐9FM, LUKANGA, and SC EXPT3 having highest oil content. We also evaluated genetic diversity of 19 PAT cultivars through phylogenetic analyses with 1059 USDA Germplasm Resources Information Network (GRIN) diversity panel accessions. We recommend stable and adaptable PAT cultivars to breeders and producers while highlighting genetically distinct accessions with valuable traits as a resource for breeding programs.

Evaluation of grain yields of selected bread wheat (triticum aestivum l.) Varieties in changing climatic conditions using different analytical models

In the present study were determined the variety or varieties with high grain yield and stability despite changing climatic conditions, and also to include these varieties as genetic material in parent lists in breeding programs. The additive main effect and multiplicative interaction (AMMI) analysis results the effects of cultivars (C), environments (E) and cultivar x environment interaction (CEI) were found to be important over grain yield at p≤0.01 level. Also, showed that grain yield performance of bread wheats were greatly affected firstly by the environment (70.42%), secondly from the interaction (7.29%), the least by variety (6.79%). The polygon graph obtained by genotype, genotype x environment (GGE) biplot analysis showed that two separate mega environments were formed, Nurkent variety was the best for first mega-environment, while Pehlivan variety for the second mega environment. GGE biplot determined that the ideal environment for bread wheat was environment 9 (E9) and the ideal genotype was Nurkent. Due to the visual presentation of AMMI and GGE biplot graphs, analysis results showed that these two models can be used to visually identify the best genotypes. Bangladesh J. Bot. 53(3): 545-554, 2024 (September)

Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria

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 < 0.001) differences were detected among genotypes, environments, and GEI for grain yield, which ranged between 979.8 kg ha−1 and 3645 kg ha−1 with a mean of 2324 kg ha−1. To assess the stability of genotypes, analyses were conducted using the general linear method, GGE, and the Additive Main Effect and Multiplicative Interaction (AMMI) approach, as well as WAAS and ASV rank indices. In the GGE biplot model, the first two principal components accounted for 67.4 % of the total variation, while in the AMMI model, the first two Interaction Principal Component Axes (IPCA1 and IPCA2) explained 73.20 % and 11.40 % of the variation attributed to genotype by environment interaction, respectively. GGE biplot identified G10 and G16 as the most stable and productive genotypes, while WAASB index revealed G16, G10, G9, G4 and G2 as the most adaptive, stable and productive genotypes across locations, and ASV identified G9, G13, G4, G14 and G10 as the most stable and productive.Consequently, genotypes G2, G4, G9, G10 and G16 displayed outstanding and stable grain yield performance across the test locations and are, therefore, recommended for release as new soybean varieties suitable for cultivation in the respective mega environment where they performed best. More importantly, the two genotypes are recommended for recycling as sources of high-yield and yield stability genes, and as parental lines for high-yield and stable performance for future breeding and genomic selection.

Evolution, identification, evaluation, and characterization of a stable salinity tolerant sugarcane variety CoG 7

From the fluff generated during 2005, after the preliminary experiments (2005–2007), a promising clone G2005047 has been identified. It showed moderate resistance to red rot (3.6 on a 9-scale scoring system), less susceptibility to shoot borer (13.25%) and internode borers (25.35%), and resistance to woolly aphid (0%). In the Advanced Yield Trials (2008–2011), it showed advantages over check for cane yield (CY) (11.79%), commercial cane sugar percent (CCSP) (0.35%), and sugar yield (SY) (20.33%). To ascertain its large-scale cultivation suitability, it has experimented under adaptive research trials (2012–2014) at farmers’ fields. It exhibited 18.04%, 1.27%, and 19.55% supremacy over the check Co 86032 for CY, CCSP, and SY respectively. The stability of G2005047 under salinity was ascertained through a multi-environment-based experiment (2015–2017). AMMI (Additive Main-effects and Multiplicative Interactions) and GGE (Genotype × Genotype-Environment interaction) biplots were utilized. ANOVA revealed that the genotypic variation exerted the most significant effect followed by genotype × environment interaction and environment. G2005047 had the highest mean values for yield and quality traits with minimal ASV (AMMI stability value) (2.38:CY; 0.57: CCSP; & 0.58:SY) indicating its good-yielding ability and stability. AMMI I, AMMI II, and GGE biplots confirmed the stability of G2005047. In the jaggery quality assessment trials (2018 and 2019), it yielded 37.1% increased jaggery over the check. Also, the clone G2005047, exhibited moderate resistance to red rot disease, less susceptibility to shoot borer (13.25%) and internode borer (25.35%), and resistance against sugarcane woolly aphid (SWA). Due to supremacy for yield, quality, better performance under salinized situations, and tolerance to disease and pests, the clone G2005047 was released as a variety CoG 7 in 2022.

AMMI and GGE biplot analysis for barley genotype yield performance and stability under multi environment condition in southern Ethiopia

AbstractBarley (Hordeum vulgare L.) is a major grain crop farmed in Ethiopia throughout the long rainy season (Meher) and the short rainy season (Belg) of the year. Barley genotypes were subjected to multi‐environment experiments in six different settings to identify stable genotypes and estimate the impact of genotype × environment interaction (GEI) on grain production. In each area, the field experiment was conducted from mid‐July to January during the primary cropping season of 2021. Three replications of a randomized complete block design were used to set up the trials. According to the additive main effects and multiplicative interaction (AMMI) study, genotype (18.19%), GEI (22.98%), and environment (58.83%) all had an impact on the major treatment sum of squares. The more variance attributed to the environments is a sign of environmental diversity. Given that the two interaction principal component analysis (IPCAs) accounted for 76.94% of the interaction sum of squares, they were sufficient for cross‐validation of the grain yield variance explained by GEI. In contrast to the GGE biplot approaches, which indicated genotypes G12, G3, and G9 as stable and high‐yielding genotypes throughout the environments, the AMMI stability value identified genotypes G3, G12, and G9 as high yielding with stable performance across environments. In general, the GGE biplot and AMMI analysis models demonstrated that genotypes G12, G3, and G9 were stable and yielded well, making G3 acceptable for cultivation in a wider range of environments and G12 and G9 suitable for release.

Analysis of genotype-by-environment interaction effect in barley genotypes using AMMI and GGE biplot methods

Genotype-by-environment interaction (GEI) analysis play a key role in any breeding program involving the development of new varieties for cultivation across various environments or in a specific region. The additive main effects and multiplicative interaction (AMMI) method and the GGE biplot are the two main statistical tools that have emerged to analyze GEI in multi-environment trials (METs). The main goal of the present study was to identify the best-performing and stable barley genotypes for the warm regions of Iran. For this purpose, 18 new advanced barley genotypes were investigated in five warm locations in Iran during two cropping seasons (2021–2023). In all experiments, test genotypes were evaluated in a randomized complete block design (RCBD) with three replications. Based on results, grain yield was significantly dependent on environments (E), genotypes (G), and GEI. The GEI effect was further divided into three principal component axes (IPCAs). The AMMI method identified genotypes G3, G9, G10, and G14 as ideal genotypes due to their low IPCA scores and high performances. In the GGE biplot analysis, the initial two PCAs accounted for 49.36 % of the total variation of grain yield, including both G and GEI effects. Based on averaged two-year data, genotypes G3, G4, G10, and G14 showed particular adaptability in the Zabol and Moghan regions. Moreover, the ranking of test environments showed good discriminatory and representative abilities for the Zabol and Moghan regions, so these environments constituted a mega-environment in Iran's warm climate. The genotype ranking indicated G3, G10 and G14 genotypes as the superior genotypes with the highest grain yield and stability in different test environments. Moreover, these results were confirmed by the results obtained by WAASB and WAASBY biplots. In conclusion, genotypes G3, G10 and G14 can be suggested for commercial usage and cultivation in various regions in Iran's warm climate.

Grain yield performance of spring maize under different agroecological zones

Maize is an important cereal crop which ranks second in production after rice in Nepal with an increasing demand for livestock and poultry ration but declining in production. This experiment was conducted to screen top- and stable yielding maize hybrids in various agroecological zones of Nepal during the spring season. In this study, nine maize genotypes were evaluated across six environments in randomized complete block design with three replications. The additive main effect and multiplicative interaction (AMMI) ANOVA revealed that environment, genotype, and their interaction had a substantial effect on the grain yield and all five principal components (PCs) were significantly different (P < 0.0001). AMMI stability value revealed that genotypes Rampur composite and Kanchan 101 were the most stable genotypes in all environments. The specific adaptation of genotype as explained by Which-won-where model suggest that Godavari in environment E2, Rajkumar in E4 and E5, and Bisco gold 941 in E1, E3 and E6 were the winning genotypes. Furthermore, the mean-versus-stability model revealed that genotype Kanchan 101 had above average yield with greater stability. In addition, biplot analysis revealed that 78.32% of variation is explained by PC1 and 11.41% by PC2 of the interaction effect. The genotype ranking based on revealed that genotype Kanchan 101 was close to the ideal line and Sano ghogha was at the greatest distance. Conclusively, AMMI and genotype and genotype by environment interaction (GGE) model explicates that genotype Kanchan 101 has both the high yield and stability across all agroecological zones. In future research on multi-year trial with emphasis other agronomic traits to assess the stability and priorities for the development of package of practices for maximizing the grain yield is recommended.

Genotype and environmental interactions determine the root digging time and essential oil production in vetiver (Chrysopogon zizanioides (L.) Roberty)

Thirty different vetiver genotypes were grown for evaluation in the experiments at CSIR- Central Institute for Medicinal and Aromatic Plants (CSIR-CIMAP), Lucknow, U.P. 226015, India, to assess the impact of genotype × environment interactions (GEI) on essential oil yield and chemical composition in vetiver lines grown in north India, as well as identifying appropriate genotypes and representative root digging times throughout the year for essential oil production. The interplay of genotype and environment is a significant constraint in crop development. The additive main effects and multiplicative interaction (AMMI) and the analysis of variance (ANOVA) revealed significant effects for genotype (G), environment (E), and genotype × environment (GE) (p < 0.01). The primary root transverse section confirmed the changes in cortical sclerenchyma thickness and variety observed in the dug-out roots from different vetiver genotypes after six and twelve months. The quantity and number of sclerenchyma rows in the peripheral cortical region vary throughout vetiver genotypes. Following root digging, one component (Khusol content) had 40 %, while Khusilal content had 20 % for genotype VTR-23. However, after twelve months, Khusol content had 19 %, whereas Khusilal content had 41 %. The shift in essential oil makeup was apparent. The level of essential oil did not alter considerably. Based on the GGE biplot analysis, VTR-1, 11, and 14 were identified as the most stable genotypes with high essential yields; therefore, it is recommended to use these genotypes in India's enormous agriculture.

Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains

Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP.

Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model.

Genotype-environment interaction consists of the different response of individual genotypes resulting from changing environmental conditions. Its significance is a phenomenon that makes the breeding process very difficult. On the one hand, the breeder expects stable genotypes, i.e., yielding similarly regardless of environmental conditions. On the other hand, selecting the best genotypes for each region is one of the key challenges for breeders and farmers. The aim of this study was to evaluate genotype-by-environment interaction for grain yield in new maize hybrids developed by Plant Breeding Smolice Co. Ltd., utilizing the additive main effects and multiplicative interaction (AMMI) model. The investigation involved 69 maize (Zea mays L.) hybrids, tested across five locations in a randomized complete block design with three replications. Grain yield varied from 8.76 t ha-1 (SMH_16417 in Smolice) to 16.89 t ha-1 (SMH_16043 in Płaczkowo), with a mean yield of 13.16 t ha-1. AMMI analysis identified significant effects of genotype, environment, and their interaction on grain yield. Analysis of variance indicated that 25.12% of the total variation in grain yield was due to environment factor, 35.20% to genotypic differences, and 21.18% to genotype by environmental interactions. Hybrids SMH_1706 and SMH_1707 are recommended for further breeding programs due to their high stability and superior average grain yield.

Response of field pea (Pisum sativum L.) genotypes for grain yield in a multi-environment trial in Southeastern Ethiopia

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.

Evaluation of the stability of quantitative traits of winter oilseed rape (Brassica napus L.) by AMMI analysis

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.

Genotype by environment interactions and stability for grain yield and other agronomic traits in selected sorghum genotypes in Ethiopia

AbstractEnvironmental changes pose major impacts on the performance of crop genotypes with important implications for crop improvement strategies. Hence, breeders pay attention to the effects of genotype by environment interaction (GEI) to mine genetic resources and select adapted genotypes. Twenty sorghum genotypes selected from a large collection of Ethiopian sorghum landraces and two improved varieties were evaluated using a randomized complete block design with three replications at eight locations representing different environmental conditions in Ethiopia. The study aimed at assessing GEI and identifying stable and high‐yielding genotypes of sorghum for grain yield and major agronomic traits. Analysis of variance and additive main effect and multiplicative interaction (AMMI) revealed highly significant (p ≤ 0.001) variance due to genotypes, environments, and GEI among all traits except for days to maturity. Plant height, days to maturity, panicle width, panicle weight, and grain yield were highly affected by environment and GEI, while days to flowering, panicle length, and 1000‐grain weight were mainly affected by genotypic variations. The data also suggest the importance of considering GEI in screening for high‐yielding and stable sorghum genotypes across environments. Among testing sites, Chawaka, Gute, and Uke were ideal environments for grain yield and Asosa was the most discriminative environment. Three genotypes (ETSL100808, Merera, and ETSL100474) were superior and stable across test environments for grain yield and related traits. Overall, based on mean grain yield and disease reaction, AMMI, GGE (genotype and genotype by environment interaction) biplot, and regression models, ETSL100808 was the most stable, high‐yielding, and disease‐tolerant sorghum genotype, suggesting its potential both in breeding program, as donor of traits, and for direct release as a variety.