Stable Genotypes Research Articles

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.

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.

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.

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ) and for TCH ( ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

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.

Assessing the yield and nutrient potential of horse gram mutants (Macrotyloma uniflorum Lam. Verdc.) an underutilized legume through a multi-environment-based experiment

The agronomic stability and nutritional importance of 30 (Test genotypes: 29 + Check: 1 = 30) promising horse gram mutants were evaluated in this multi-environment-based experiment (MEE). Attempts were made to (i) identify stable mutants for agronomic traits through AMMI and GGE biplot models, (ii) quantify nutritional traits, (iii) understand the linkage between yield and nutritional traits, and (iv) estimate physical (PP) and cooking properties (CP) of selected genotypes to fix their food-chain usability. The ANOVA of the pooled data exhibited significant differences among environments (E), genotypes (G), and GxE interaction. The combined AMMI and GGE results helped to identify a few good-yielding and stable genotypes (GYSM) (G1, G25, G3, and G27). The yield advantages of these GYSMs over the parent PAIYUR 2 are 42.99%, 34.63%, 28.68%, and 30.59% respectively. The nutrient profiling of mutants revealed (i) a significant coefficient of variation for macronutrients (fat: 29.98%; fibre: 20.72%, and protein: 5.01%), (ii) a good range of variation for micronutrients, and (iii) helped to identify macro (MaNSM) and micro nutrient-specific mutants (MiNSM). The relationship analysis between yield and nutrient traits ascertained that yield had (i) positivity with protein (r2 = 0.69) and negativity for micronutrients except for Mn (r2 = 0.63), Cu (r2 = 0.46), and B (r2 = 0.01) in GYSM, (ii) positivity with protein and fibre in MaNSM, and (iii) negativity with micronutrients in MiNSM. Of the GYSM, G1 and G25 offer scope for commercial exploitation, and their PP and CP analyses revealed that G1 can be used for pastry and baked product preparation while G25 for weaning foods. Cooking time exhibited positivity with seed size parameters and negativity with water absorption capacity (r2 = − 0.53). An LC–MS–MS-based amino acid (AA) fractionation study showed the effect of induced mutagenesis on the contents of amino acids and also revealed the significance of horse gram for its lysine and methionine contents.

Detection of superior rice genotypes and yield stability using AMMI and MTSI models

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.

Identification of High Erucic Acid Brassica carinata Genotypes through Multi-Trait Stability Index

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha−1 seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phenological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously.

Evaluation of Seed Yield Stability of Lentil Genotypes by Linear Mixed‐Effects Models and Multitrait Stability Index

ABSTRACTTo evaluate performance stability for 13 lentil genotypes, experiments were performed at three locations, Ardebil, Zanjan, and Maragheh, during three growing seasons (2011–2013). Environment, genotype, and interaction of genotype × environment were significantly effective on 100‐seed weight, plant height, number of days to flowering, number of days to maturity, rate of seed filling, and single seed weight. Nominal grain yield biplot identified stability of 2, 12, 1, 3, and 6 genotypes with regard to grain yield. According to biplot analysis, Genotypes 1, 3, and 10 not only produced the highest grain yield but also showed the greatest stability in yield production. Genotypes 1, 6, 7, and 13 were identified as highly stable and productive based on grain yield, mean weight stability index, and weighted average of absolute scores of best linear unbiased predictions (WAASB). Genotypes 1 and 7 were best genotypes according to multitrait stability index (MTSI). Harmonic mean of the relative performance of genotypic predicted value (HMRPGV) recognized Genotypes 6, 1, 5, and 3 as highly stable, productive, and adaptable genotypes. In general, Genotype 1 indicated highest grain yield and desirable agronomic traits compared to other genotypes tested, suggesting this genotype as new cultivar.

A comprehensive phenotypic and genotypic evaluation of Spanish groundnuts from diverse crosses to identify superior and stable donors for fresh seed dormancy

Breeding high yielding groundnut cultivars with 2–3 weeks of fresh seed dormancy, particularly in Spanish-type cultivars, enhances the sustainability of agriculture in groundnuts. In this context, we conducted a comprehensive phenotypic and genotypic evaluation of advanced breeding lines developed in the genetic background of Spanish types. By employing multi-phenotyping and marker data, we identified PBS 15044, 16004, 16013, 16015, 16016, 16017, 16020, 16021, 16026, 16031, 16035, 16037, 16038, 16039, 16041, and 16042 with 2–3 weeks dormancy (> 90%).The various parametric and non-parametric estimates identified the stable fresh dormant genotypes with one or more superior economic trait. PBS 16021, 15044, 16038, and 16039 identified with high hundred pod weight (HPW) were also reported having high intensity of dormancy (> 90% for up to 3 weeks); PBS 15044, 16016, PBS 16038 and PBS 16039 with high hundred kernel weight (HKW) also reported with up to 3 weeks fresh seed dormancy; and PBS 16013, 16031, and 16038 with up to 3 weeks fresh seed dormancy had high shelling percentage (SP). They can be used to develop lines with the desired level of dormancy, and high yields, by designing appropriate breeding strategies.

Multi-model approach for optimizing cold-wave resilient maize selection: unveiling genotype-by-environment interaction and predicting yield stability

Cold waves both significantly reduce yield & damage crops as well; unforeseeable nature of cold waves makes it challenging for farmers to manage risk. Thus, we aim to select maize hybrids that thrive under cold stress (both escaping early cold-waves and tolerating cold snaps); pinpoint stable, high-yielding hybrids ideal for regions prone to cold stress. In this investigation 2 years winter trial in Nepal was appraised on diverse maize hybrids for cold wave tolerant, stable across four stations with a Randomized Complete Block Design & 3 replications at each station. Likewise, this research employed 4 statistical methods both fixed effect and linear mixed model: genotype-environment interactions (AMMI), visually analyzes genotype performance and stability across environments (GGE) breeding values of genotypes for selection (BLUP), multiple traits for selection (MTSI). This scholarship revealed significant (P < 0.001) impacts of genotype, environment, and their interaction (GEI) on yield. This GEI, accounting for 100% of yield variance, was mainly captured by 3 principal components, with the first explaining 49%. Notably, mixed-effects models and biplots identified superior hybrids exhibiting both high average yields and consistent performance. GGE biplot analysis unveiled high-yielding and adaptable: GK3157, NK6607, RMH1899 Super, GK3254, RMH666, Shan 111, DKC9149, and Sweety-1. Further, BLUP and WAASBYY analyses delineated the superior performers and stabilized hybrids for yield, with DKC9141, Uttam 121, NK6607, MM2929, RMH-666, GK 3254, and GK3157, and RMH-1899 super candidates for both high yield and stability. In Nepalgunj, Delta 3333, MM2122, and Shaan 111 excelled in both yield and stability, while Rampur favored Rampur Hybrid 6 and MM2424 for stability. Parwanipur and Tarahara shared similar winners for stability and yield, including MM2122, Shaan 111, and Delta 3333 in Parwanipur, and NK7884, MM2424, and Delta 2222 in Tarahara. Based on Multi genotype ideotype distance (MGDI), 9 hybrids were selected for yield and stability, including MM 2033, NK 6607, Sweety 1 so on; exhibited escape to cold waves whilst GK3254, TMMH-846, and MM-9442, were chosen for cold waves adapted hybrids. Moreover, by identifying cold-tolerant maize hybrids, this study has potential to mitigate risks for farmers (economic burden, crop failure) and bolster food security.

Combining AMMI and BLUP analysis to select high‐yielding soybean genotypes in Benin

AbstractThirty soybean [Glycine max (L.) Merr.] genotypes, along with three checks, were evaluated over three seasons across five communes in Benin. The experiments were laid out in an alpha lattice design with three replicates. Additive multiplicative mean interaction (AMMI) and best linear unbiased predictor (BLUP) analysis were combined to assess differential agronomic performance and yield stability among genotypes. There was significant variation (p &lt; 0.001) between genotypes for all traits, with highly significant environmental and genotype × environment interaction (GEI) effects on soybean grain yield (p &lt; 0.001). The likelihood ratio test indicated that both genotype and interaction effects were highly significant (p &lt; 0.001). The low R2 (0.21) for GEI reflected the presence of high residual variation in the GEI component, in contrast to the AMMI analysis of variance, which explained a high proportion of the GEI through the first two interaction principal component axes (52%). The very high value of the predictive accuracy (0.89) confirmed the model's reliability in selecting superior genotypes. The low (0.33) genotypic correlation between environments indicated that it was difficult to select superior genotypes for each environment. Based on the superiority index (weighted average absolute scores from BLUP for yield) of BLUP, simultaneous selection led to the identification of Jenguma 2.67 ± 0.06 t ha−1 as the most stable and productive genotype across environments, followed by Favour 2.34 ± 0.08 t ha−1, and Afayak 2.46 ± 0.08 t ha−1. The agronomic performance of soybean in this study suggested great potential for diversifying cotton‐based cropping systems in Benin, thereby improving their sustainability. The effect of these soybean genotypes on the productivity of intercrop combinations and sequences of cash crops, such as cotton, is yet to be investigated.

Evaluation of sugarcane genotypes (Saccharum sp. hybrid) for multi-trait stability analysis across diverse environments

The current study evaluated a total of 21 sugarcane genotypes across 27 diverse environments, including nine locations over three cropping seasons, to identify the superior and stable sugarcane genotypes. The pooled ANOVA results showed substantial genotype-environment interactions among the 21 genotypes under study. The genotype Co 11015 exhibited the highest mean value for CCS yield, CCS percentage, and sucrose percentage. With the help of GGE analysis and biplots, both stable genotypes as well as mega environments were identified. The following genotypes such as Co 13014, Co 11015, Co 13018 and Co 14016 emerged as the high-mean performers and exhibited stability across diverse environments. The outcomes from ‘which-won-where’ patterns suggested the existence of different mega environments which are classified as ME1, ME2, and ME3. When the genotypes were ranked, it revealed that Co 14016 is closer to the ideal genotype, followed by Co 13014, Co 11015, and Co 13018 as the most desirable genotypes. Similarly, the environments E8, E25, and E26 were identified as the most desirable ones, based on GGE biplot environment rankings. The Multi-Trait Stability Index (MTSI) values highlighted the following genotypes such as Co 13018, Co 13003, and Co 11015 as stable ones with high performance across the traits and environments. The MGIDI (Multi-trait Genotype Ideotype Distance Index) values inferred that Co 11015, Co 15007, and Co 13018 are the superior genotypes. Consequently, Co 11015 was released for commercial cultivation based on its stability and superior performance for biomass yield and quality traits across the locations and environments.

Evaluation of Yield Stability and Adaptability of Oat Genotypes (Avena sativa L.)

This study’s main objectives were (i) to determine the stability of oat genotypes for the grain yield (t ha-1), (ii) to investigate the relationship of stability parameters with the grain yield in the analyzed set of genotypes, and (iii) to identify the high-yielding, stable, and adaptive oat genotypes for breeding purposes. The study was conducted during the 2015-22 period and involved fourteen winter oat genotypes maintained in the Bulgarian Seed Gene Bank. The trial was laid out in a randomized block design of variants in three replications and an experimental plot size of 10 m2. Sixteen grain yield stability parameters were determined. The AMMI stability value (ASV), the yield stability index (YSI), and the genotype selection index (GSI) were also calculated. A year and genotype x year interaction had an almost equally dominant effect on the grain yield per hectare. The Spearman’s rank correlation coefficients indicated that grain yield had significant positive associations with Thennarasu’s non-parametric statistics—the NP(2), NP(3), and NP(4), Kang’s rank-sum (KR), and the YSI. The yield stability parameters estimated the G11, G12, G13, and the G1 genotypes as the most stable. The ASV identified the G14, G8, and the G12 as the most stable genotypes, while the YSI detected the G14, G12, and G11, respectively. The GSI classified the G14, G12, G8, and the G11 as genotypes with the broadest adaptability to adverse climatic conditions. Thus, they could serve as a source material in winter oat breeding programs.

Deciphering genotype-by-environment interaction of grass pea genotypes under rain-fed conditions and emphasizing the role of monthly rainfall

Rainfed regions have inconsistent spatial and temporal rainfall. So, these regions could face water deficiency during critical stages of crop growth. In this regard, multi-environment trials could play a key role in introducing stable genotypes with good performance across several rainfed regions. Grass pea, as a potential forage crop, is a resilient plant that could grow in unsuitable circumstances. In this study, agro-morphological attributes of 16 grass pea genotypes were examined in four semi-warm rain-fed regions during the years 2018–2021. The MLM analysis of variance showed a significant genotype-by-environment interaction (GEI) for dry yield, seed yield, days to maturity, days to flowering, and plant height of grass pea. The PLS (partial least squares) regression revealed that rainfall in the grass pea establishment stage (October and November) is meaningful. For grass pea cultivation, monthly rainfall during plant growth is important, especially in May, with an aim for seed yield. Regarding dry yield, G5, G10, G11, G12, G13, and G15 were selected as good performers and stable genotypes using DY × WAASB biplots, while SY × WAASB biplot manifested G2, G3, G12, and G13 as superior genotypes with stable seed yield. Considering equal weights for yield as well as the WAASB stability index (50/50), G13 was selected as the best one. Among test environments, E2 and E11 played a prominent role in distinguishing the above genotypes from other ones. In this study, MTSI (multi-trait stability index) analysis was applied to select a stable genotype, considering all measured agro-morphological traits simultaneously. Henceforth, the G5 and G15 grass pea genotypes were discerningly chosen due to their commendable performance in the WAASBY plot. In this context, G13 did not emerge as the winner based on MTSI; however, it exhibited an MTSI value in close proximity to the outer boundary of the circle. Consequently, upon comprehensive consideration of all traits, it is deduced that G5, G13, and G15 can be appraised as promising superior genotypes with stability across diverse environmental conditions.

Assessing onion genotypes stability and potential in diverse Indian environments

Assessing onion genotypes stability and potential in diverse Indian environments

Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators

The selection of highly productive genotypes with stable performance across environments is a major challenge of plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing the superiority and/or stability of genotype performance across environments. However, these metrics are traditionally estimated using phenotypic values only and are not well suited to an unbalanced design in which genotypes are not observed in all environments. The objective of this research was to propose and evaluate new estimators of the following GE metrics: Ecovalence, Environmental Variance, Finlay–Wilkinson regression coefficient, and Lin–Binns superiority measure. Drawing from a multi-environment genomic prediction model, we derived the best linear unbiased prediction for each GE metric. These derivations included both a squared expectation and a variance term. To assess the effectiveness of our new estimators, we conducted simulations that varied in traits and environment parameters. In our results, new estimators consistently outperformed traditional phenotype-based estimators in terms of accuracy. By incorporating a variance term into our new estimators, in addition to the squared expectation term, we were able to improve the precision of our estimates, particularly for Ecovalence in situations where heritability was low and/or sparseness was high. All methods are implemented in a new R-package: GEmetrics. These genomic-based estimators enable estimating GE metrics in unbalanced designs and predicting GE metrics for new genotypes, which should help improve the selection efficiency of high-performance and stable genotypes across environments.

Stability Assessment of Groundnut ABLs Using AMMI and GGE Biplot Analysis for Yield Performance under Multi-environments

The current study focused on Identification of stable line among the eight groundnut ABLs and two checks were assessed in three different environments in Karnataka during the 2020-2021 Kharif and Rabi seasons with randomized complete block design (RCBD) with three replications. The analysis of variance revealed significant differences (p≤0.01) among the genotypes, environments, and the genotype by environment interaction (G×E) for kernel yield. The AMMI analysis also showed highly significant differences (p≤0.01) for varieties, environments, and their interaction on kernel yield. The IPCA1 and IPCA2 components explained 72.72% and 25.00% of the total G×E sum of squares, respectively. The variations in kernel yield were attributed to the environment (16.44%), ABLs (81.87%), and ABLs by environment interaction (1.68%). ABLs T65, T77, T81, and T82 were identified as stable across all three environments based on ASV and SI for kernel yield per plant. These stable lines can be used as parents in breeding programs. The AMMI model and GGE biplots were effective tools for evaluating the adaptability and stability of groundnut genotypes in diverse environments.

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.

Multi-trait stability index for identification of stable green gram (Vigna radiata (L.) Wilczek) genotypes with MYMV resistance

Multi-environment trials (MET) are crucial for selecting genotypes that are well-suited to different environmental conditions. Incorporating multiple traits in the analysis can provide more reliable recommendations for selecting genotypes with desirable traits, including resistance to the Mungbean Yellow Mosaic Virus (MYMV) and high yield potential. The use of a Multi-Trait Stability Index (MTSI) is a good approach for analyzing the stability of genotypes across multiple traits under MYMV stress. In the present investigation, the performance of thirteen green gram genotypes were evaluated for traits such as yield, plant height, number of branches per plant, and resistance to MYMV. The main objective of the study is to identify highly productive and stable mung bean genotypes resistant to MYMV. MTSI can be calculated by combining information on the performance of genotypes across multiple traits and environmental conditions to provide a single index that indicates the overall stability of genotypes across traits and environments. The results helped to identify two green gram genotypes (Yadadri and JNG-18) that were high-yielding with stable resistance to MYMV stress across multiple environmental conditions. This can provide useful information to breeders for the development of suitable genotypes against MYMV in the affected areas.