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In response to the growing population and increasing demand for cattle products, enhancing sorghum forage yield is essential for ensuring food security. This study aimed to identify stable genotypes with high forage yields and key yield traits for sorghum breeding programs. Ninety-five forage sorghum lines were evaluated under five distinct climatic conditions over two years (2020–2021), revealing significant genotype × environment interaction (GEI) effects for 14 agronomic traits. Two BLUP-based mixed model stability methods, weight average absolute score based on BLUP (WAASB) and the multi-trait stability index (MTSI), were employed for stability analysis. Three genotypes, G90 (424B), G80 (382B) and G3 (349B) were identified stable and high yielding for forage yield based on WAASB based methods. The MTSI, a novel simultaneous selection index, effectively selected genotypes based on multiple agro-morphological traits, except for the leaf-to-stem ratio. Genotypes G81, G90, G80, and G89 were identified as desirable based on the MTSI. The strength and weakness plot is highlighted as a valuable graphical tool for identifying and selecting genotypes based on trait strengths and weaknesses. Among these, G90 (424B) and G80 (382B) stood out as superior, excelling in both forage yield and early maturity, as determined by WAASB based methods and MTSI method. These genotypes warrant further comprehensive investigation across diverse environments and show significant potential for future breeding programs. [ABSTRACT FROM AUTHOR]

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

Potatoes (Solanum tuberosum L.) are an important plant crop, whose yield may vary significantly depending on pedo-climatic conditions and genotype. Therefore, the analysis of the genotype × environment interaction (GEI) is mandatory for the setup of high-yielding and stable potato genotypes. This research evaluated the tuber yield (t ha−1) and yield characteristic of nine potato cultivars over 3 years and 4 organic farms in Poland by additive main effects and multiplicative interactions (AMMIs) and genotype plus genotype environment interaction (GGE) biplot analyses. The results of these analyses indicated significant differentiation of tuber yield among genotypes in individual environments. It was found that the environment (E, where E = L (localization) × Y (year)), genotype (G) and GEI, but not replication, significantly affected tuber yield. The AMMI analysis showed that the environment factor explained the most considerable part of tuber yield variations (52.3%), while the GEI and G factors explained a much lower part of the variations. The AMMI and GGE analyses identified five cvs.: Twister (46.4 t ha−1), Alouette (35.8 t ha−1), Kokra (34.8 t ha−1), Levante (33.1 t ha−1), and Gardena (30.4 t ha−1), as leading cultivars in the studied organic farms due to their high productivity coupled with yield stability. The statistical measure Kang (YSi) showed that these cvs. can be considered as adaptable to a wide range of organic environments. In the case of morphological traits of tubers (tuber shape and depth of tuber eyes), the most important factor influencing both these traits was genotype (G). Influence of other factors, like localization (L), year (Y), and all interactions (double and triple), were much less significant or insignificant. In case of taste and non-darkening of tuber flesh, the main effects which significantly affected the values of these traits were genotype (G) and localization (L). We observed that cooking type can vary depending on the year (Y) and the localization (L). [ABSTRACT FROM AUTHOR]

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

Genotype by environment interaction (GEI) is a complex problem that complicates the barley selection and breeding process. The knowledge of the relationship between cereal phenology and climatic data is important for understanding GEI and the physiological pathways responsible for the interaction effect. The grain yield of twenty winter barley genotypes in six environments was observed. Factors influencing the variability were analyzed using a linear mixed model. The partial least squares regression (PLSR) model was applied to determine the most relevant environmental variables in certain stages of development that explained GEI effects. Biplot with environmental variables explained 43.7% of the GEI. The barley was generally the most sensitive to the environmental conditions (relative humidity, maximum temperature and its variation, sun hours, and precipitation) during the anthesis and filling stage (May) which caused GEI. Temperature variables did not show significance only in the vegetative phase. Different genotypes responded differently to environmental factors. Genotypes NS-525, NS-589, and J-103 were highlighted as widely adaptable, and Zaječar was a suitable and reliable location for yield testing. The GEI information presented in this paper can be useful in traditional plant breeding and future breeding programs through molecular research of crop developmental genes and examination of physiological processes in two-row barley. [ABSTRACT FROM AUTHOR]

• The performance of the hybrids was primarily affected by the environment, planting dates, and their interactions. • The magnitudes of the GEI sum of squares were more than five times that of the hybrid sum of squares for the grain yield. • The stability of hybrids did not show the same trend across environments among the planting dates. • AMMI, Pi, GGE, YSI, and ASV analysis methods effectively identified stable and high-yielding hybrids. • Dealing with GEI is a valuable strategy to optimize resources in maize breeding programs. Maize is one of the most important field crops for feed, food, and raw material in South Africa. It is the main staple food for the majority of the people impoverished households in South Africa. Maize is susceptible to drought, and erratic rainfall and extreme climate variation are major maize production constraints in South Africa. This study aimed to determine the influence of planting dates on the grain yield stability of six maize genotypes in five environments for three (2019/20 to 2021/22) consecutive seasons. The additive main effect and multiplicative interaction (AMMI) model indicated that the environment contributed 59.20 % and 47.76 % of the total variation in grain yield at planting dates one (PD1) and two (PD2), respectively. The magnitudes of the genotype x environment interaction sum of squares were 5.20 for PD1 and 12.30 for PD2 times that of the genotype (genotype) sum of squares for the grain yield (GEI). The first principal component accounted for 53.14 % (PD1) and 47.76 % (PD2) of the total variation in the dataset. The AMMI genotype superiority measure (Pi), genotype and genotype by environment interaction (GGE), yield stability index, and AMMI stability value analyses identified genotypes DKC75–65BR and DKC78–45BRGEN as stable and high yielding across environments and planting dates. Genotype DKC71–44B was the least stable at both planting dates. Bothaville (E7) for PD1 and Coligny 2021/22(E13) for PD2 had high yield potential and were favorable environments, whereas Potchefstroom 2019/20 and 2021/22 were the least favorable environments for PD1 and PD2, respectively. [ABSTRACT FROM AUTHOR]

Maize is the staple food crop for millions of people in sub-Saharan Africa. Iron (Fe) and zinc (Zn) deficiency is a significant health risk that mainly affects low-income populations who rely solely on maize-based diets. This problem can be alleviated by developing micronutrient-rich maize grain. The aim of this study was to determine the adaptation and performance of hybrids for Fe and Zn concentration and grain yield under low soil nitrogen (N) and optimal conditions. Eighteen hybrids derived from lines and testers with low, medium, and high Fe and Zn concentration were grown during the summer rainy seasons of 2017 and 2018 at three locations under low and optimal N conditions. There were significant genotype and environment effects for grain yield, and Fe and Zn concentration, but the genotype by environment interaction effects were the largest, accounting for between 36% and 56% of variation under low N conditions. Low N levels significantly reduced grain yield, and Fe and Zn concentration. Hybrids G1, G2, G4, G7, G10, G11, and G16 were relatively stable, with relatively high mean Fe and Zn concentrations, and low additive main effects and multiplicative interaction (AMMI) stability values and iron stability index (FSI) and zinc stability index (ZSI) under low N conditions. These genotypes can be considered for production under low N stress conditions. Two environments (E4 and E3) were identified for good discriminatory power for genotype performance in terms of Fe and Zn content, respectively. Stable and high-yielding genotypes with high Fe and Zn concentration can be used as biofortified hybrids, which can contribute to a sustainable solution to malnutrition in the region, especially under low N conditions. [ABSTRACT FROM AUTHOR]

Wheat is one of the products with the widest cultivation area and adaptation ability in the world. Although the heritability of the variety is important in grain yield, the environment and genotype x environment interaction is significantly effective. This study was carried out in 6 different environments. According to the Additive Main Effect and Multiplication Interaction (AMMI) model; genotype, environment and genotype x environment interaction were found to be effective on grain yield, respectively, by 9.40%, 56.79% and 16.28%. It has been determined that, there is a positive relationship between grain yield and the number of spike per square meter and the number of grain in each spike. In the study, 3 mega environments were formed. G19 in the first mega environment, G13 in the second mega environment and G21, G22 in the third mega environment came to the fore. G9, G13 and G19 were seen as hopeful lines in the grain yield. Also, E5 was the ideal environment and, G19 had the highest adaptability and grain yield. It has been concluded that G19 may be a candidate for variety because genotypes that are stable in different environmental conditions are preferred by the producers. [ABSTRACT FROM AUTHOR]

The expected gains in the productivity and sustainability of cotton cultivars across environmental spectra may face many challenges. To address this issue, a set of 572 cotton accessions, including 284 F1 hybrids along with their parental lines and checks, were evaluated and compared across multiple test environments. Popular biometrical tools viz; best linear unbiased prediction (BLUP), additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype × environment interaction effects (GGE) were utilized for reliable estimation graphical depiction of GEI effects. The first two interaction coordinates as principal components in BLUP, AMMI, and GGE biplots explained the maximum proportion of GEI regarding seed cotton yield. The contrasting environments did not cluster together, thus revealing their varying influences on the genotypes. A set of 44 F1 hybrids featuring higher stability, 46 with higher adaptability, and 5 F1 hybrids exhibited higher stability, adaptability, and highest seed cotton yield across the investigated environments simultaneously. F1 hybrids of cotton were concluded as more stable and adaptable regarding seed cotton yield across environments. Accordingly, switching from line to hybrid breeding could enable a cotton breeding program to address the broader issue of growing this crop in a wide range of target environments. [ABSTRACT FROM AUTHOR]

Multilocation testing plays a vital role in the release of new high-yielding cultivars of stevia (Stevia rebaudiana B.) in Indonesia. In stevia, the leaf yield potential demonstrates an important characteristic for superior genotype selection. The study sought to identify the effects of genotype by environment interactions (GEI) on stevia yield and select the genotypes with stable yield resulting from radiation and hybridization through AMMI and GGE biplot analyses under three growing environments. The experiments took place in three locations, namely, Bandung, Sumedang, and Garut, West Java, Indonesia, consisting of a randomized complete block design (RCBD), with three replications. The combined analysis of variance (ANOVA) attained the genotype by environment interactions measurements. Calculating yield stability used the additive main effects and multiplicative interaction (AMMI), AMMI stability value (ASV), genotype stability index (GSI), and genotype plus genotype by environments (GGE) biplot measurements. The results revealed significant effects of environments (92.38%), followed by GEI (5.20%), and genotype effects (2.43%) of the total variation on stevia yield. The stevia genotypes viz., G11, G27, G2, and G5 gave a higher and more stable yield based on the AMMI-1 biplot. Based on the GGE biplot and the genotypes? sustainable performances, the stevia genotypes, i.e., G27, G2, G11, G20, and G26, gained selection as stable. The three selected stevia genotypes displayed the highest yields and proved stable in three environments viz., G2 (Tamangwangu EMS mutant number A), G11 (Bogor mutant with gamma ray radiation 5 number C), and G27 (a hybrid from Garut × Bogor-3). These promising genotypes exhibit the potential for further development into new superior stevia genotypes. [ABSTRACT FROM AUTHOR]

The performance of genotypes under diverse environments can be used to determine their adaptability and stability. However, information on the performance of coffee genotypes in various environmental conditions is limited. Thus, this study's objectives were to estimate genotype by environment interaction (GEI), evaluate the mean performance and stability of 16 fruity flavored coffee genotypes in eight specialty coffee growing environments in western Ethiopia, and assess the magnitude of correlations among different stability parameters. The experiment was laid out in a randomized complete block design (RCBD) with two replications. For coffee yield, data were recorded and a combined analysis of variance and stability analysis were performed. Additive main effect and multiplicative interaction (AMMI) analysis revealed that genotypes, environments, and GEI showed highly significant differences (P <.0.01) for coffee bean yield. AMMI analysis also revealed that 73.2% of the GEI sum of squares for coffee bean yield was accounted for by the first three interaction principal component axes (IPCA). The standard check variety G16 (Menesibu), G3(W54/99), and G10 (W107/99) gave the highest average yields of 1537, 1458 and 1375 kg of clean coffee per hectare across environments, respectively. Despite no genotypes consistently performing well across environments due to the high GEI, G1 (W13/99) and G5 (W54/99) were relatively stable. Therefore, these were recommended as useful genetic resources for breeding of high-yielding genotypes. However, since all the genotypes gave a mean yield below the standard check variety, additional genotypes should be tested in more environments to develop stable and high-yielding coffee varieties. [ABSTRACT FROM AUTHOR]

The demand by industries for large-seeded peanuts is increasing in Thailand and Southeast Asia. New large-seeded peanut lines were recently developed in Thailand to respond to the demand. In this study, a multilocation yield trial was performed to identify the best genotype(s) in Thailand's central region and investigate the genotype-environment interaction (GEI) on peanut production. Twelve promising largeseeded peanut lines and two check varieties (KU50 and KK6) were planted at 12 different planting locations during the dry and rainy seasons of 2018 and the dry season of 2019. This study found significant yield potential variability in the promising lines of peanuts evaluated at different planting locations. A combined analysis of variance presented that the environment and genotypes had a considerable impact (p < 0.001) on the pod and seed yield. The GEI showed a high impact (p < 0.01) on pod yield and an effect (p < 0.05) on seed yield. The environment presented the most significant influence on pod and seed yield variations, followed by genetics and GEI. The total variation in seed yield was 64.22%, composed of PC1 and PC2 values at 45.71% and 18.51%, respectively. The GGE biplot analysis of the yield potentials at each location indicated that KUP12BS029-1-1-3 was the ideal genotype, with a high yield potential and most stability at multilocations, followed by KUP12BS030-3-4-1 and KUP12BS030-1-4-3. These promising lines will be released as new peanut varieties in central Thailand and are recommended as parental lines in breeding programs for large-seeded and yield potential in Thailand and Southeast Asia. [ABSTRACT FROM AUTHOR]

Analysis of genotype-by-environment interaction (GEI) is critical in the local potato breeding and selection programme to obtain information on the performance of the genotypes for yield adaptability and stability. The objective of this study was to assess the marketable tuber yield of 18 advanced potato clones compared to the commercial variety Spunta at four locations (Bigara, Réduit, St. Antoine and Rivière du Poste), representative of four major soil types in Mauritius. They were analysed for adaptability and stability using the additive main effects and multiplicative interaction (AMMI) model and genotype main effect and genotype x environment interaction (GGE) biplot. Five clones gave significantly the highest marketable tuber yield in terms of overall ranking with yield increase of 47.4% to 59.6% over the control variety Spunta. AMMI analysis of variance detected significantly (P < 0.001) higher proportion of variation in marketable tuber yield due to environment (42%); followed by genotype x environment interaction (21%) which justified multi-locational testing. AMMI1 biplot demarcated clones 142/161/2 and 142/161/5 as high yielding and most stable while AMMI 2 biplot identified the winning genotypes for a specific environment. Thus, clones142/161/4 and 161/142/16 had specific adaptation to Bigara, 29/5/2 and 21/5/3 were adapted to St. Antoine, 21/5/10 to Rivière du Poste whereas 29/5/3 was adapted to Réduit. The GGE biplots identified clones 142/161/2 and 142/161/5 as the two most desirable genotypes close to the “ideal genotype”. The “which- won- where” view of the GGE biplot further pointed to the presence of two mega-environments, which corresponded to the sub-humid irrigated/humid environments (Réduit, St. Antoine and Rivière du Poste) and the high altitude super-humid environment (Bigara). These results showed that in future both AMMI and GGE methods can be integrated in the local potato breeding programme to select superior genotypes through multi-year and multi-locational yield evaluation. [ABSTRACT FROM AUTHOR]

Increasing the maize production capacity to ensure food security is still the primary goal of global maize planting. The purpose of this study was to evaluate genotypes with high yield and stability in summer maize hybrids grown in the Huanghuaihai region of China using additive main effects and multiplicative interaction (AMMI) analysis and best linear unbiased prediction (BLUP) technique. A total of 18 summer maize hybrids with one check hybrid were used for this study using a randomized complete block design (RCBD) with three replicates at 74 locations during two consecutive years (2018–2019). A three-way analysis of variance (ANOVA) and an AMMI analysis showed that genotype (G), environment (E), year (Y) and their interactions were highly significant (p < 0.001) except G × E × Y for all evaluated traits viz., grain yield (GY), ear length (EL), hundred seed weight (HSW) and E × Y for hundred seed weight. The first seven interaction principal components (IPCs) were highly significant and explained 81.74% of the genotype by environment interaction (GEI). By comparing different models, the best linear unbiased prediction (BLUP) was considered the best model for data analysis in this study. The combination of AMMI model and BLUP technology to use the WAASB (weighted average of absolute scores from the singular value decomposition of the matrix of BLUP for GEI effects generated by linear mixed model) index was considered promising for similar research in the future. Genotypes H321 and Y23 had high yield and good stability, and could be used as new potential genetic resources for improving and stabilizing grain yield in maize breeding practices in the Huanghuaihai region of China. Genotypes H9, H168, Q218, Y303 and L5 had narrow adaptability and only apply to specific areas. The check genotype Z958 had good adaptability in most environments due to its good stability, but it also needs the potential to increase grain yield. Significant positive correlations were also found between the tested agronomic traits. [ABSTRACT FROM AUTHOR]

The identification of high-yielding and stable genotypes for cultivation across differential production regions is among the key breeding objectives in cowpea improvement programs. This study was aimed to determine genotype-by-environment interaction (GEI) for grain yield to select high-yielding and stable cowpea genotypes for production in South Africa and identical agro-ecologies, and for cultivar development. Fifty cowpea genotypes were tested for grain yield across seven environments of South Africa using a 10 × 5 alpha lattice design replicated three times, during the 2019/2020 and 2020/2021 planting seasons. Grain yield data were subjected to analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype-by-environment interaction (GGE) biplot analyses. ANOVA and AMMI showed significant genotype, environment and GEI effects. High grain yield was recorded for genotypes G35 (0.47 t ha−1), G1 (0.45 t ha−1) and G47 (0.43 t ha−1) across test environments. AMMI stability values (ASV); identified Acc-Cowp44 as the most stable genotype across all sites, recording the lowest ASV of 0.03. The comparison view of GGE biplot revealed Acc-Cowp29, Acc-Cowp38 and Acc-Cowp5 as ideal genotypes, possessing high grain yield of 0.19, 0.47 and 0.36 t ha−1, respectively. The identified genotypes are recommended for production and inclusion in subsequent breeding activities. [ABSTRACT FROM AUTHOR]

Background: The genetic yield potential of groundnut (Arachis hypogaea L.) has been continuously challenged by several diseases including early leaf spot (ELS). Methods: In the current study, we evaluated groundnut mini core collections under artificial and natural disease epiphytotics in six environments to identify stable elite sources for ELS resistance and pod yield. Mixed model analysis was done to adequately capture the variance component as a result of genotype (G), environment (E) and G x E interaction (GEI). Result: Highly significant (p <0.001) effects for G and GEI on ELS and pod weight were observed. The parametric and non-parametric stability models ranked the genotype differently for their stability to ELS. The GGE biplot identified ICG 1519 as a stable genotype for the ELS resistance. For pod weight, ICG 8896 and ICG 7897 were consistently stable from all the stability models including the GGE biplot. ICG 9449 and ICG 4540 were identified as stable genotypes for both ELS and pod weight. These elite sources of ELS resistance identified in the current study will be useful in the development and deployment of groundnut varieties with resistance to ELS and high pod yielding potentials. [ABSTRACT FROM AUTHOR]

Determining the amount of genotype by environment interaction (GEI) is an important step in identifying high yielding and stable genotypes for cultivar development. Keeping in view the significance of genotype and environment, a multi environment study was conducted to evaluate the response of genotypes to different cropping systems and environments. The performance of vegetative growth traits of the 40 exotic bread wheat lines was assessed across seven different environments during 2015-16 and 2016-17 growing seasons. The trials were conducted using RCB design at four locations for two years; Research Farm, of the University of Agriculture, Peshawar; Agriculture Research Station, Buner; Agriculture Research Station, Baffa, Mansehra; and Barani Agriculture Research Station, Jarma, Kohat (one year only). Data were recorded on leaf area, plant height, biological yield and straw yield. Highest plant height was recorded for G02, G31 and G11. The exotic lines included in the study generally had more plant height in Peshawar as compared to other locations. The highest maximum mean for leaf area was observed for G17 and CSA while minimum was observed for G03 followed by G33 and G25. Among the seven environments, genotypes on average produced larger flag leaves having maximum leaf area in Peshawar while lowest was observed in Mansehra. For straw yield of the 40 genotypes across seven environments, the highest straw yield of 12547 kg ha-1 was produced at Mansehra in 2016-17 and the lowest yield of 2440 kg ha-1 was recorded at Buner in 2017-18. Overall, the genotypes included in the study had more biological yield in Mansehra as compared to other locations. Line G06 and G17 produced maximum biological yield. The lines with desirable characteristics can be used in hybridization programs to combine these characteristics in single line, or to transfer them to other high yielding varieties. [ABSTRACT FROM AUTHOR]

A critical comparison of stability models is essential to give an insight to breeders for developing relatively stable wheat cultivars. A multi-environment trial (MET) was conducted to assess the pattern of genotype by environment interaction (GEI) effects on yield using multiple stability models viz. Additive Main effect and Multiplicative Interaction (AMMI), GGE biplot analysis and stability parameters. Eighty-one wheat genotypes were evaluated during three consecutive years (2013/14, 2014/15 and 2015/16) across nine environments (sites x year combination) in Khyber Pakhtunkhwa, Pakistan. Graphical stability approaches such as AMMI and GGE provided almost similar results for identifying the high-yielding and stable wheat genotypes. The AMMI analysis identified G-58 and G-79, whereas GGE biplot identidified G-79 as the most stable and high yielding genotype. Numerical stability parameters like Eberhart and Russell's model and Francis coefficient of variation (CV) declared G-79 as top-ranked genotype while Shukla stability value (σi²) and Wrick' s ecovalence (Wi) identified G-80 (check cultivar Janbaz), G-52 and G-79 as leading wheat genotypes based on grain yield. Spearman's rank correlation revealed significant positive correlations of AMMI stability value (ASV) with CV, σi² with Wi, and Wi with CV indicating that these parameters could invariably be used for identifying stable wheat genotypes depending upon the nature of the experiment, breeding material, and the complexity of data. Different stability models identified G-79 as high yielding and stable genotype and thus could be recommended for commercialization in the province of Khyber Pakhtunkhwa, Pakistan. Furthermore, stability parameters can supplement the use of AMMI and GGE biplot analysis to get more credible and reliable scrutiny of wheat genotypes in METs. [ABSTRACT FROM AUTHOR]

Genotype by environment (GE) interaction complicates selection process of desirable genotypes. Therefore, newly developed germplasm or genotypes needs to be evaluated in multi-environment trials (METs) to know yield potential and adaptability. This experiment was designed to interpret GE interaction using 55 F5:8 bread wheat recombinant inbred lines (RILs) with five checks. Experimental material was planted using alpha lattice design in two replicates at Peshawar (E1and E5), Kohat (E2 and E6), Sarai Naurang (E3 and E7) and Dera Ismail Khan (E4 and E8) (Khyber Pakhtunkhwa) during 2014/15 and 2015/16. Locations in each year were considered as independent environments. Pooled ANOVA revealed significant interaction due to GE for days to heading, days to maturity, grain filling duration, grain growth rate, grains spike-1, 1000-grain weight and grain yield. Averaged over eight environments, wheat RIL's 44, 55, and 25 were desirable for days to heading, days to maturity, grain filling duration, grain growth rate, grains spike-1 and grain yield. Mean grain yield ranged from 1613 to 2471 kg ha-1 across environments. Among the tested environments, Sarai Naurang (E3 and E7) was identified as highly productive environment. The RIL25 had superior performance for various traits. Wheat RIL25 produced maximum grain yield in E2 (3123 kg ha-1), E3 (3665 kg ha-1) and E7 (3256 kg ha-1), thus emerged as promising line with wider adaptation. Conversely, RIL24 had higher grain yield in E1 (2800 kg ha-1); RIL1 in E4 (2884 kg ha-1); RIL58 in E5 (2370 kg ha-1); RIL7 in E6 (3200 kg ha-1) and RIL53 in E8 (2339 kg ha-1) suggesting their specific adaptability to respective environments. Grain yield was significantly correlated with days to heading, grain growth rate and grains spike-1. Keeping in view the significance of GE interaction, it would be worthwhile to carryout stability analysis for identifying high yielding RILs with suitable stability. [ABSTRACT FROM AUTHOR]

A set of 286 recombinant inbred lines (RILs) along with the parents and a popular wheat variety in India were grown for two consecutive years at three locations belonging to the two major wheat growing zones of India and evaluated for four grain quality traits. Rare recombinants with high trait value appeared for protein content (PC), thousand-kernel weight (TKW), sedimentation value (SV), and kernel hardness (KH). The magnitude of environmental effects was more pronounced than genotypic effects and genotype-environment interaction (GEI). The cumulative contribution of environment and GEI components to the total variance was highest in the expression of PC followed by TKW, SV, and KH. The top five percent (14 RILs) of genotypes with high trait value were subjected to Eberhart and Russell (1966) (ER), genotype and genotype-environment (GGE) and additive main effects and multiplicative interaction (AMMI) stability models. Five RILs were identified as stable in all the three stability models. RIL61 with 38.8%, RIL101 with 8.9%, RIL226 with 26.1% superiority over check variety were the most stable genotypes in all the three stability models for PC, TKW and KH, respectively. RIL113 was found to be stable genotype in ER and GGE models, whereas, RIL231 was the most stable genotype in AMMI and GGE models in the expression of SV. These common stable genotypes with high trait value identified through ER, AMMI and GGE models could be potential donors in active breeding programs to develop high yielding wheat varieties with improved PC, TKW, SV and KH. [ABSTRACT FROM AUTHOR]

The development of stable and adaptable new cultivars are based only on positive results obtained from the interaction between the genotype and the environment. Therefore, the study aimed to test the stability and general adaptability of promising barley lines in terms of grain yield and traits in multi-environments. For this purpose, twelve barley genotypes were used in the study. The trials were carried out with four replications in a random design at seven environments in years 2012-13 and 2013- 14. The superior and stable genotypes were identified with GGE biplot and AMMI (Additive main effects and multiplicative interaction) models. The AMMI analysis showed that the major treatment sum of squares was affected by environments (80.6%), GE (14.0%) and genotypes (5.4%), respectively. On the other hand, the first two principal component axes (PCA 1 and PCA 2) contributed to the complete interaction with 88.1%, whereas, PCA 3 and PCA 4 axes only with 12.0%. The GGE biplot indicated that G4 is adaptable for all environments, while Altikat, G2 and G3 showed specific adaptation to E1, E3 and E5, G6, G7 and G8 to E6, respectively. According to both techniques, G2, G3, G6, G7, G8 and Altikat were the best genotypes with high yield, whereas G4 was the best with high yield, and stable and general adaptation. The results of biplot indicated that G4 (ARUPO /K8755//MORA/3/CERISE/SHYRI//ALEL I/4/CANELA/5/HART-BAR) was recommended for release and it was released as HEVSEL in 2017. On the other hand; G7 and G6 were protected as genetic material to use as parent in breeding programs for yield stability and quality respectively. [ABSTRACT FROM AUTHOR]

The objective was to select productive and stable E. grandis families across contrasting sites in Brazil and Uruguay. Survival and growth of 130 open pollinated families were evaluated three years after planting. Survival ranged from 28 to 89% and mean annual increment from 20.5 to 48.4 m3ha- y-1. Low productivity in one site was attributed to unusually low rainfall and in the other, because of Cylindrocladium leaf disease. Heritability among families and the correlation family by environment were intermediate. Genetic pairwise correlations ranged from 0.03 to 0.81 across sites. The lowest genetic correlation between sites was observed where Cylindrocladium leaf disease occurs. Selection of top families across sites allowed selecting the best ones in each site. However, the effect of genotype-environment interactions was observed where climatic conditions are not adequate to the E. grandis, favoring the occurrence of Cylindrocladium disease. [ABSTRACT FROM AUTHOR]

Knowledge of genotype by environment interaction (GEI) is essential for identifying environment-specific and widely adapted genotypes. Multi-environment trials were conducted to evaluate the magnitude of genotype (G), environment (E) and GEI effects on yield and yield components of wheat. Eightyone wheat genotypes were evaluated under nine environments within Khyber Pakhtunkhwa, Pakistan during 2013/14, 2014/15 and 2015/16 cropping seasons. Combined analyses of variance for G and GEI were significant for almost all traits. Significant GEI suggested that the performance of genotypes was not consistent across environments, revealing strong influence of environments. Generally, GEI explained major portion of total variation and thus had larger effect than G and E on the expression of phenotype for all traits. Average over nine environments, genotype G-79 exhibited maximum values for tillers m-2 (181 tillers); grains spike-1 (71 grains); grain yield (4862 kg ha-1) and thus appeared as leading genotype for yield and production traits. Similarly, G-79 had higher grain yield in all test environments except E-03 i.e. E-01 (4537 kg ha-1); E-02 (4840 kg ha-1); E-04 (5035 kg ha-1); E-05 (4976 kg ha-1); E-06 (4797 kg ha-1); E-07 (5024 kg ha-1); E-08 (4767 kg ha-1); E-09 (4886 kg ha-1) signifying it as high yielding and widely adaptable genotype. However, G-08 with higher grain yield in E-01 (5272 kg ha-1) and E-07 (4840 kg ha-1), G-37 in E-04 (5106 kg ha-1) and E-08 (4906 kg ha-1), G-41 in E-05 (5034 kg ha-1) and E-06 (5272 kg ha-1), G-9 in E-02 (4975 kg ha-1) and G-49 in E-03 (5271 kg ha-1), established their specific adaptability in respective environments. Among environments, E-01, E-02, E-03, E-06 and E-07 were identified as highly productive environments in terms of grain yield. Correlation analysis revealed significant positive association of grain yield with tillers m-2 (rg = 0.72**), grains spike-1 (rg = 0.41**) and 1000-grain weight (rg = 0.30**). Based on mean yield performance, G-79 was found as high yielding genotype and thus could be recommended for commercialization in Khyber Pakhtunkhwa. [ABSTRACT FROM AUTHOR]

Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana) production has become stagnant over the years and one of the possible ways to increase the production can be spread of widely adaptable high yielding cultivars. Five national finger millet cultivars were grown in randomized complete block design at ICARVivekananda Institute of Hill Agriculture for six consecutive years to evaluate the grain yield stability. The grain yield data were subjected to AMMI and GGE biplot techniques for assessing the stability and patterns of GE interaction in finger millet National cultivars. The combined ANOVA showed that finger millet grain yield was significantly affected by environment, which explained 54.67% of the total treatment (G+E+GE) variation, whereas the G and GEI accounted for 10.38% and 34.96%, respectively. The partitioning of GEI sum of squares using AMMI analysis indicated that the first two PCAs were highly significant. The first IPCA axis (IPCA1) accounted for 50.3% of the G×E interaction sum of squares. The second IPCA axis accounted for 38.2% of the interaction sum of squares. Both represented a total of 88.5% variation. AMMI 1 biplot indicated the general adaptation of genotype HR 374 across the environments, whereas the other genotypes showed specific adaptation to one or other environments. GGE-biplot graphical analysis further confirmed the results and revealed that HR 374 as an ideal genotype in terms of high yield and stability followed by RAU 8 as desirable genotype. In our research, both of AMMI and biplot models were successful in assessing the performance of genotypes and the selection of best genotype was identical in both of them. [ABSTRACT FROM AUTHOR]

The prevalence of Iron (Fe) defficiency in Indonesia is around 31 - 63.5 %. High Fe content rice lines had been developed to overcome the problem. This study was aimed to explore the effect of genotype (G) and genotype × environment inter-action (GEI) on yield of 21 high Fe content rice genotypes under 5 irrigated field environments. The research was conducted at DS 2011 in 2 locations and DS 2012 in 3 locations following randomized complete block design with three replications in each location. Combined analysis of variance showed genotype x environment inter-action at 1% probability level, where G and GEI captured totally 88.8% of total variability. There were two Mega-environments constructed, i.e. Mega-E1 that contained environments of trials in dry season 2011 (E4 and E5) with the winner of G12 (BP9474C-1-1-B) and Mega-E2 that contained environments of trials in dry season 2012 (E1, E2, and E3) with the winner of G3 (A69-1). E1 (Subang, DS 2012), E2 (Karawang, DS 2012), and E3 (Indramaru, DS 2012) had good discriminativeness and representasiveness for yield trait of high Fe content rice lines. Mean performance and stability of genotypes indicated that G3 (A69-1; average 6.72 t ha-1) was highly stable with high yield. [ABSTRACT FROM AUTHOR]

Grass pea (Lathyrus sativus L.) is a crop used for both animal and human consumption. Recently, ICARDA has developed new grass pea lines with the objectives of improving its yield potential and adaptability. The objective of this study was to investigate grain and forage yields stability of several ICARDA released grass pea lines by using additive main effects and multiplicative interactions (AMMI) and site regression genotype plus genotype-by-environment interaction (SREG GGE) model. Fourteen grass pea advanced lines were evaluated at three research stations located in semi-warm regions in Iran, during 2005-08 crop seasons. The combined ANOVA showed that forage and grain yields were significantly affected by environment (E), genotype (G) and interactions of G×E. The partitioning of the sum of squares indicated that E accounted for 85% and 91% of the total (G+E+GEI) variation for forage and grain yields, respectively; whereas, G and GEI accounted for 4 and 11% for forage yield and 2 and 7% for grain yields, respectively. Using AMMI and GGE biplots, the lines identified as stable and high grain yielding were Sel.474, Sel.669, and Sel.686, while for forage yield the lines were Sel.474, Sel.678 and Sel.669. Kermanshah and Gachsaran were selected as favorable test environments for grass pea grain and forage yield ME trials, respectively. [ABSTRACT FROM AUTHOR]

Genotype×environment interactions (GEIs) can affect breeding programs because they often complicate the evaluation and selection of superior genotypes. This drawback can be reduced by gaining insights into GEI processes and genotype adaptation. The objectives of this research were to evaluate: (1) the yield stability of promising wheat lines across locations and (2) the relationship among the test environments for selecting superior lines within the cold climate mega-environments of Iran. A total of 35 wheat promising lines were grown at 7 locations during the 2008-2009 cropping season. Combined analysis of variance showed that the environment (E) accounted for 75.7% of the model sum of squares. The magnitude of the GEI sum of squares was about three times larger than that for genotypes. To determine the effects of GEI on yields, the data were subjected to the additive main effects and multiplicative interaction (AMMI) and genotype+(genotype×environment) interaction (GGE) biplot analysis. The AMMI1 model was found to explain up to 88% of the main and interaction effects. According to the AMMI1 and GGE biplots, the lines G5 and G4 were found to produce high and stable yields across environments. There were three mega-environments (Euromieh and Ardebil as mega-environment I, Mashhad, Arak, Hamedan and Jolgerokh as mega-environment II, and Karaj as mega-environment III) according to the site regression genotype (SREG) GGE model. Application of AMMI and GGE biplots facilitated visual comparison and identification of superior genotypes for each target set of environments. [ABSTRACT FROM AUTHOR]

Genotype-by-environment interactions (GxEs) in naturally selected traits have been extensively studied, but the impact of GxEs on sexual selection has only recently begun to receive attention. Here, we review recent models and consider how GxEs might affect the evolution of sexual traits through influencing sexual signal reliability and also how GxEs may influence variation in sexually selected traits and the process of reproductive isolation. We then assess the current empirical literature on GxEs in sexual selection and conclude by highlighting areas that need additional work. Research on GxEs and sexual selection is an important new area of study for the discipline, which has largely focused on relatively simple mate choice/competition scenarios to date. Investigators now need to apply this knowledge to more complex, but realistic, situations, to more fully explore the evolution of sexual traits, and in this review we suggest potentially useful directions for future research. [ABSTRACT FROM AUTHOR]

Abstract: Genotype-by-environment interactions (GEI) have been identified as an important component of the genotypic variation for grain and stover yield traits of rabi sorghum varieties and hybrids in India. It has been argued that obtaining an understanding of the causes of these GEI is an important step to identify the scope for genetic improvement of grain and stover yield by conventional breeding. Pattern analysis was used to investigate the regional structure of the GEI for the four traits grain yield, stover yield, days-to-flower, and plant height, measured on the varieties and hybrids tested over 10 years in the All India Coordinated Sorghum Improvement Program (AICSIP). There was evidence that regional differences accounted for part of the GEI for grain yield and days-to-flower but not for stover yield and plant height. Cluster analysis was used to group the locations included in the ACSIP experiments. The five-group level was chosen to examine the causes of GEI among the location groups. Hypotheses were proposed for the observed regional grouping of trials for grain yield. One group of trials consisted of predominantly irrigated trials, the other four groups differed in the timing and intensity of drought stress imposed on the entries. This retrospective analysis provides a basis for testing the hypothesised contributions of environmental variation in water availability to regional GEI for grain and stover yield. If these hypotheses are substantiated, the current multi-environment testing strategy used for the AICSIP trials could be modified to ensure adequate sampling of the five regional groups identified by the retrospective pattern analysis. [Copyright &y& Elsevier]

Abstract: Grain and stover yield are key traits for the improvement of rabi sorghum varieties and hybrids in India. Large genotype-by-environment interactions (GEI) were identified for both grain and stover yield based on 10 years’ data available from the All India Coordinated Sorghum Improvement Program (AICSIP) experiments conducted from 1986/87 to 1996/97. From the estimates of the genotypic, GEI and residual components of variance for the four traits, a multi-environment testing strategy based on 15–20 locations, 2–3 years, and two replicates per trial would be required to achieve estimates of repeatability of approximately 0.70 for grain yield, 0.45 for stover yield, 0.70 for flowering time, and 0.85 for plant height. The results of these experiments indicate that over the 10-year period there was little genetic progress demonstrated for both yield traits and greater grain yields were associated with earlier flowering and greater stover yields were associated with greater plant height. Selection for greater grain and stover yield, while holding flowering time and plant height constant will be difficult. Unless the physiological trade-off between grain and stover yield and the causes of the large GEI for both traits can be better understood, conventional selection on phenotypic performance for grain and stover yield in the AICSIP trials is likely to continue to be slow. [Copyright &y& Elsevier]

Different seed priming treatments are widely used in order to improve the nutritional status of wheat, as well as to improve its grain yield and yield- related traits. The present study aimed to evaluate the impact of seed priming with zinc oxide nanoparticles (ZnO NPs) on the yield related traits, such as, field emergence, plant height, spike length and grain yield per plant of four winter wheat genotypes (Triticum aestivum L.) during two vegetation seasons of 2018/2019 and 2019/2020. The seeds of each wheat genotypes were primed with different concentrations of ZnO NPs (0 mg L−1, 10 mg L−1, 100 mg L−1 and 1000 mg L−1) for 48 h in a dark box by continuous aeration and were sown in soil pots with 60–70% moisture content until full maturity. The additive main effects and multiplicative interaction (AMMI) models were used to study the genotype environment effects. The results indicated that the plants response to ZnO nanoparticles significantly increased all of the observed traits of the wheat, while its maximum rates reduced the traits of the wheat. The AMMI analysis revealed the very complex nature of the variation observed in the trial and showed the significant effect of the G×E interaction, in which the first main component was significant for all components. [ABSTRACT FROM AUTHOR]