Additive Main Effects And Multiplicative Interaction Stability Research Articles

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-Locational Evaluation of Forage-Suited Selected Sudan Pearl Millet [Pennisetum glaucum (L.) R. Br.] Accessions Identified High-Yielding and Stable Genotypes in Irrigated, Arid Environments

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a subtropical grain and forage crop. It is privileged with several desirable forage attributes. Nevertheless, research on pearl millet is limited, especially as a forage crop, in developing countries. Therefore, the objectives of this study were to investigate the field performance and stability of pearl millet genotypes for forage yield across seven environments. The study was conducted in seven environments (combination of locations and seasons) during the 2016/2017–2018/2019 seasons. Twenty-five pearl millet genotypes, selected based on forage yield from a core collection of 200 accessions, were arranged in an alpha lattice design with three replications. The parameters measured were fresh forage yield, days to flowering, plant height, number of culms m−2, leaf-to-stem ratio, and stem girth. The combined analysis revealed that environments, genotypes, and their interaction had significant effects on all traits studied except the genotypic effect on stem girth. Across the seven environments, four genotypes (G14, G01, G12, and G22) outyielded the check genotype in fresh matter yield by 20.7, 16.5, 11.0 and 9.8%, respectively. The additive main effects and multiplicative interaction (AMMI) analysis showed that the genotype, environment, and their interaction were highly significant (p ≤ 0.001) for fresh matter yield. The results of AMMI stability values (ASVs) and the genotype selection index (GSI) combined with the AMMI estimate-based selection showed that genotypes G14, G22 and G01 were the most stable and adapted genotypes and were superior to the check genotype. These results indicate that forage pearl millet varieties could be developed directly through evaluating the wealth of available collections or indirectly through hybridization in crop breeding programs.

Genotype by year interaction for selected traits in sweet maize (Zea maize L.) hybrids using AMMI model

This study investigated genotype × environment interactions for the stability of expression of four productivity traits (cobs yield, cobs I class trade share, lend of cobs and fulfilment of cobs) of sweet maize hybrids (Zea mays L.). The additive main effects and multiplicative interaction (AMMI) model was employed to assess genotype × environment interaction. AMMI stability value was used to evaluate both stability and genotype. The genotype selection index was calculated for each hybrid, incorporating both the average trait value and the stability index. Ten sweet maize hybrids were evaluated: Golda, GSS 1453, GSS 3071, GSS 5829, GSS 8529, Overland, Noa, Shinerock, Sindon, and Tessa. Trials were ran conducted over four vegetative seasons at a single location in the Wielkopolska region using replicated field experiments. The AMMI model revealed significant genotypic and environmental effects for all analyzed traits. Based on their superior stability and favorable average trait values, both the Golda cultivar and the GSS 3071 hybrid are recommended for further breeding program inclusion.

Interacción genotipo por ambiente de Solanum quitoense (Lam.) usando el modelo AMMI

The basis for genetic improvement for the cultivation of lulo (Solanum quitoense Lam.) in Colombia is limited. Research related to genotype-by-environment interaction (GEI) and stability in agronomic traits is scarce. To identify outstanding genotypes, GEI and the stability of fruit weight (FW), ascorbic acid (AA), and yield of 10 half-sib families (HSF) were evaluated. At the locations of San Pedro de Cartago, Arboleda, Tangua and La Union of the department of Nariño (Colombia), four trials were established under the randomised complete block design with four repetitions. To analyse GEI, the additive main effects and multiplicative interaction (AMMI) model and some AMMI stability parameters were used. In yield, the HSF7 and the control presented specific adaptation for Tangua, with 12.82 and 13.41 t ha-1, respectively and FW greater than 100 g. In Arboleda, HSF29 obtained the highest yield (16.14 t ha-1) with an FW of 100.53 g. HSF4, HSF28 and HSF49 reached yields above 9.0 t ha-1 and a FW greater than 100 g; therefore, they are recommended for any of the environments evaluated given their stability. HSF25 in AA and HSF29 in yield presented specific adaptation in Arboleda. Stable families across environments and with good yield were HSF4, HSF28, and HSF49, HSF4 and HSF28 showed higher values in two of the three traits, and HSF49 was outstanding in all three variables. These families can be used in plant breeding programmes as parents or distributed to farmers as improved varieties.

Genotype by Environment (G × E) Interaction and Yield Stability of Chickpea (Cicer arietinum L.) Varieties Across Agroecological Regions of Ethiopia

ABSTRACTGenotype by environment (G × E) interaction obstructs breeding by persuading variations in genotype performance. The aim of the present study was to determine the stability and yield performance of Desi and Kabuli chickpea varieties at different agroecological regions of Ethiopia, using different stability parameters. The experiment was laid out in a randomized complete block design (RCBD) with three replications. The additive main effect and multiplicative interaction (AMMI) analysis of variance (ANOVA) indicated highly significant differences (p ≤ 0.01) for environments, genotypes, and importantly G × E interaction. AMMI and GGE biplot, AMMI's stability value (ASV) indicate that the Desi chickpea variety Teketay with mean yield of 2225.6 kg/ha (highest) and the variety Dimtu (1603.9 kg/ha) followed by Natoli with mean yield of 2004.9 kg/ha were found to be stable and adaptable to all environments. Similarly, from the Kabuli chickpea varieties, the variety Koka with mean grain yield of 2257.1 kg/ha (highest) and the variety Ejere with mean yield of 1997.6 kg/ha followed by Shasho (1798.59 kg/ha) were found to be stable and adaptable to all environments and should be promoted for production in chickpea‐growing areas of Ethiopia. In conclusion, identification of stable improved varieties for the different agroecological regions can assist the producers such as the farmers for the effective chickpea production. This leads to sustainable self‐sufficiency of food at the household and country level.

Estimating phenotypic stability for relevant yield and quality traits in French bean (Phaseolus vulgaris L.) using AMMI analysis

The presence of strong G x E (genotype by environment interaction) is a major hurdle for selecting superior genotypes when genotypes are placed into new and unfamiliar production systems. Genotype or cultivar (s) with high yield potential and having less adaptability and stability to particular environment is never a suitable choice for a breeder and farmer particularly. Purposefully, four successive seasons were chosen to enumerate the phenotypic stability of 27 French bean genotypes for yield and quality traits by involving modern statistical tools like AMMI (Additive Main Effect and Multiplicative Interaction), GGE [G + (G x E)] and cluster analysis. AMMI analysis of variance witnessed magnitude of G, E and G x E was 81.94%, 11.58% and 6.48% of the total variation respectively. The IPCA I (Interaction Principal Component Axes) was contributed with 55.44%, 73.60%, 71.81%, 81.69% and 72.16% G x E variations of days to 50% flowering, pod length, number of pods per plant, average pod weight and pod yield respectively. For qualitative traits i.e., protein content (mg/100g FW), total soluble solids (%) and total phenol content (mg GAE/g FW) the involvement of IPCA I to total genotype by environment interaction variations were 89.55%, 96.07% and 66.52% respectively. The AMMI biplot revealed French bean genotypes viz., IC632961, Arka Sukomal, IIHR-PV-29, IIHR-PV-30 having low AMMI stability value and higher mean value for relevant yield and quality traits in both late kharif and rabi as two mega-environments. Multivariate analysis demonstrated significant higher contribution of pod yield associated traits towards total variations and positive correlation between them. The 27 French bean genotypes formed five groups as per Euclidean distance and the clustering revealed the nature of diversity of French bean genotypes viz., IC 632961, IIHR-B-PV-24, Arka Sukomal, Arka Arjun, Ayoka and Phalguni in response to changing environments and can be utilized in future breeding programme. The study revealed pole type French bean genotypes viz., IC 632961, Arka Sukomal and bush type French bean genotypes viz., IIHR-B-PV-29, IIHR-B-PV-30 could be promising for utilization in future breeding programmes for the concerned traits.

Selection of Durum Wheat and SSR Markers for Organic Farming in Central Italy Using AMMI Analysis

Durum wheat is one of the main crops in the Mediterranean region, which is characterized as the hotspot of climate change, with large year-to-year weather fluctuations. Although chemical input reduction in agriculture is strongly demanded, as well as healthy food, there is still a lack of stable and high-yielding crop varieties specifically adapted for organic conditions. This study evaluates the performance of fifteen durum wheat varieties in terms of suitability for organic farming in central Italy and assesses the impact of the genotype–environment interaction (GEI) on productive and quality traits. Variety performance was evaluated in field experiments over four successive seasons. In addition, a genotypic diversity analysis of 38 microsatellites associated with traits important for organic farming was performed. The AMMI (additive main effects and multiplicative interaction) stability analysis revealed that the best and most stable genotype regarding quality traits, such as thousand-kernel weight, protein content, and test weight was the ancient variety, Senatore Cappelli. The most stable and high yield was determined for the Fuego, Iride, and Mv-Pelsodur genotypes. Moreover, SSR markers that could be used for plant breeding, targeting organic farming systems based on molecular markers and GEI results, were identified.

Analysis of stability for nut yield and ancillary traits in cashew (Anacardium occidentale L.)

Cashew is cultivated in varied agro-ecological regions of India and yield levels vary with regions. Therefore, to identify stable genotype for yield, 18 genotypes were tested in four environments for nut yield and ancillary traits during 2008 to 2018 in randomized block design with two replications. The data of 6th annual harvest and cumulative nut yield of six years was analyzed employing additive main effect and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) methods. Analysis of variance for 6th annual harvest indicated significant differences (p < 0.01) for eight traits. Environments varied significantly (p < 0.01) for seven traits. Genotype by environment (G × E) interactions were significant (p < 0.01) for all traits. Analysis of variance for cumulative yield revealed significant variations between genotypes, environments, G x E interactions. Interaction principal component analysis (IPCA) 1 (84.39%) and IPCA 2 (10.27%) together captured 95% of variability. Genotypes, environments and G × E interaction were accounted for 16.18%, 4.50% and 77.22% respectively of total variation. The environment Pilicode discriminated better while Vridhachalam was representative. BPP-8 and Vengulra-7 were the winning genotypes in Bhubaneswar while Kanaka and Priyanka in Pilicode, Vengurla-4 in Jhargram and UN-50 in Vridhachalam. Therefore, promoting cultivation of these winning genotypes in the corresponding environments is highly recommended to enhance cashew nut production. As per ASV (AMMI stability value,) K-22-1 was stable genotype followed by Bhubaneswar-1. As per YSI (yield stability index), Bhubaneswar-1 was stable and high yielding followed by K-22-1 and BPP-8. Thus stable genotypes identified in this study viz., K-22-1 and Bhuvaneswar-1 are recommended for cultivation in west and east regions of India which have most cashew growing areas for increasing the cashew nut production.

Cultivar variability and stability of vegetable-type soybean for seed yield and pod shattering

Vegetable-type soybean, also known as “edamame”, was introduced to South Africa in 2009 to serve as an alternative high-protein cash crop for production by small-scale growers. Since their introduction, cultivars have been evaluated and morphologically characterised to identify suitable cultivars for production in specific environments and also to gather data for future breeding programmes. The objective of this study was to investigate genotype by environment interaction and cultivar stability through additive main effects and multiplicative interaction (AMMI) as well as genotype plus genotype by environment biplot analysis across seven environments and two growing seasons in 15 vegetable-type soybean cultivars for seed yield and pod shattering. Based on AMMI stability values and genotype stability index scores, cultivars AGS418, AGS457 and AGS458 were the most desirable in terms of stability and performance for both seed yield and pod shattering resistance and could be considered for commercial release. Of all vegetable-type soybean cultivars studied, cultivar AGS354 had the best pod shattering resistance and it showed specific adaptation to the Winterton, and Mariannhill October and Mariannhill November planting dates mega-environment in terms of seed yield. This cultivar should be considered for production in these environments. Ladysmith was identified as the most suitable environment for selecting and breeding cultivars with wider adaptability.

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

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

Simultaneous evaluation of yield and stability of sugar beet (Beta vulgaris L.) varieties under Egyptian conditions using AMMI and GGE biplot approaches

An experiment was conducted on stability analysis of seven diverse monogram sugar beet (Beta vulgaris L.) varieties for root and sugar yields and their attributes grown under three harvesting dates after sowing during 2021-22 and 2022-23. The data generated from eighteen environments representing the combinations among harvesting dates, locations, and seasons were subjected to additive main effects and multiplicative interaction (AMMI) genotype main effect and genotype x environment interaction (GGE Biplot) analysis. Results obtained from AMMI combined analysis of variance showed that the main effects of sugar beet varieties, environments, and their interaction were highly significant for all studied traits. It is observed that sugar beet plants grown in the Nubaria region and harvested after 210 days, gave the best results for most root and sugar quality traits. The analysis further revealed that Volna and Klara varieties were the elite ones regarding root and sugar yields, and juice quality parameters as an overall mean across the environments. AMMI stability analysis indicated the variety Vangelis was broadly or narrowly stable under different environments and reflected somewhat good performance for most studied traits, while Klara and Volna were considered very promising as above stable ones using GGE Biplot graphs. The findings indicated that GGE Biplot graphs are more accurate and more informative as compared to AMMI stability analysis.

The influence of planting window on yield stability of maize genotypes in semi-arid areas

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.

Genotype x Environment Interaction and Oil yield Stability of Linseed (Linum usitatissimum L.) Genotypes in North, Central and Southeast Ethiopia

Linseed (Linum usitatissimum L.) is one of the most prominent industrial oilseed crops cultivated for both seed and fiber. Lack of stable genotypes across the linseed production area is one of the problems. Thirteen linseed genotypes were planted in randomized complete block design with three replications at six linseed major growing agro-ecologies of North, central and Southeastern Ethiopia (Werabe, D\Markos, Welkite, Holeta, Kulumsa and Adiet) in 2021/2022 cropping season. With the objectives of determining the effects of GEI, on oil yield of linseed and identifying better performing and well adapted linseed genotypes than the control variety, and to prepare for registration and release of selected high oil yielding genotypes in the different linseed agro-environment conditions of Ethiopia. The oil yield subjected to the combined analysis of variance showed a highly significant (p&lt;0.01) effect of genotype, location, and genotype x location interactions (GLI). Similarly the combined AMMI ANOVA for oil yield revealed that there were highly significant differences among genotypes, locations and genotype by location interactions and accounted 22.11%, 31.40% and 46.49% of the total variations respectively. The highest percentages of environmental variations are an indication that environment is the major factor that influences the yield performance of linseed oil yield in Ethiopia. In addition, the first two IPCAs were significant and accounted for 80.77% of the total interactions sum squares. Six stability measures viz Eberhart and Russell analysis (bi and S2di), Additive Main Effect and Multiplicative Interaction (AMMI) model, AMMI Stability Value (ASV),Yield Stability Index (YSI),Genotype Main Effect and Genotype by Environment Interaction Effect (GGE) bi plot analysis Model were used to evaluate the stable genotypes across the testing locations. Genotypes 10097(G2), 10103 (G3) and 239716 (G7) were more stable by Eberhart and Russell analysis and AMMI Stability Value. Genotypes 10103 (G3) and 208360 (G8) were more stable by Yield Stability Index. Genotypes 208360 (G8) and 10103 (G3) were selected as better genotypes that appeared in the five and four locations by AMMI analysis, respectively. According to one-year data, the six locations are grouped into one mega environment for linseed production with one winning genotype and genotype 208360 (G8) was an ideal genotype, while location A diet was an ideal environment by GGE analysis. Genotypes 208360 (G8), 234005 (G4)and 10103 (G3) are the three of the best performing genotypes than the other genotypes and control variety (Berene) in oil yield across locations. Therefore, those three highest oil yielder genotypes have a potential to be registered in Ethiopia. However, this trail need to be repeated for one more season, and or three of the best performing genotypes will be verified along with the check on farmers' fields for release.

AMMI Analysis of Genotype × Environment Interaction on Sugar Beet (Beta vulgaris L.) Yield, Sugar Content and Production in Romania

The overall yield and sugar content of sugar beet (Beta vulgaris L.) were determined by the genotype and its interaction with the environment. This study aimed to analyze the interaction of 23 genotypes with different environmental conditions during two growing seasons. To estimate the variance of genotypes, environment, and genotype function of the environment, the R 3.5.1 software package was used. In addition, the multivariate stability method was used to explain the G (genotype) × E (environment) interaction based on the GGE (Genotype plus Genotype-by-Environment) and AMMI (additive main effects and multiplicative interaction) biplots. The AMMI ASV (AMMI stability value) and biplot analysis revealed that only two genotypes (G10 and G11) showed higher values for yield and sugar content and production compared to the other genotypes. The AMMI ASV analysis also showed that the environment significantly influenced the sugar beet yield, sugar content, and sugar production, which were the descriptors for production in this study.

Grain yield stability of soybean (Glycine Max (L.) Merrill) for different stability models across diverse environments of Ethiopia

AbstractFor grain yield stability analysis, genotype by environment interactions are crucial in properly identifying and discriminating between varieties and locations. Hence, this experiment was conducted with the objectives to evaluate the stability of soybean using additive main effects and multiplicative interaction (AMMI), multi‐trait stability index (MTSI), weighted average absolute scores biplot (WAASB), Eberhart and Russell regression model, and genotype plus genotype by environment interaction (GGE) biplot analysis for grain yield of soybean genotypes and identified stable genotypes in the different soybean agroecologies of Ethiopia. Twenty‐four soybean genotypes were planted at six soybean environments with RCBD in three replications in the 2015/2016 cropping season. Stability measures, namely, AMMI, AMMI stability value, and GGE biplot analysis were used to identify the high‐yielding and stable genotypes across the testing environments. AMMI‐1 biplot showed Pawe as the ideal environment; Bako as a favorable environment; Asosa an average environment; and the rest namely, Dimtu, Jimma, and Metu as unfavorable environments. On the other hand, AMMI‐2 biplot analysis certain genotypes like Prichard, Spry, Delsoy 4710, and Croton 3.9 were identified as stable genotypes. Bako and Metu were identified as the most discriminating environments. Mega environments and the best yielding soybean genotypes on each mega environment were revealed by the GGE biplot analysis model. For other multivariate statistics used for this study, MTSI, WAASB, and regression models, stable and superior varieties for grain yield were revealed. Through the MTSI, the four genotypes, namely, Liu yue mang, SCS‐1, Clarck‐63k, and AFGAT, were found to be stable and superior over the rest tested genotypes. Overall, the genotypes SCS‐1 and AGS‐7‐1 were stable across soybean growing environments and are recommended for mega environment production.

Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan

Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments ranged from 3522 kg/ha in G201 to 4132 kg/ha in G217. Results of additive main effect and multiplicative interaction (AMMI) analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly affected grain yield. From the total sum of squares due to treatments (G + E + GEI), E attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%) and G (1.3%). Based on the first four AMMI selections for grain yield in the 24 environments, genotypes G217, G219, G211, and G213 were selected in 23, 12, 11, and 9 environments, respectively. The genotype and genotype × environment biplot (GGE) biplot polygon view showed that the environments were separated into three mega-environments. The winning genotypes in these mega-environments were G217, G214, and G204. Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values (ASV), whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index (GSI), indicating their better stability and adaptability to the test environments. The results indicated that genotypes G217, G219, G211, G213, and G220 combined both high grain yield and stability/adaptability under dry but irrigated and heat-prone environments. An in-depth analysis of the superior genotypes could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability under the changing climate.

Comprehensive Stability Analysis of Wheat Genotypes through Multi-Environmental Trials

In rainfed areas, due to variable environmental factors, improving the yield stability of the introduced cultivars along with increasing yield should be considered. The main aim of this study is to obtain high-yield wheat genotypes that are stable and adaptable to cold climatic conditions in Iran. For this purpose, 25 wheat genotypes were evaluated in a randomized complete blocks design with three replications during three cropping seasons (2013-2016) under supplementary irrigation and rainfed conditions. PBSTAT-GE software was used for genotype × environment interaction (GEI) analysis and comprehensive sustainability analysis. The results showed that G5, G14, G16 and G18 genotypes had good stability and general adaptation based on parametric and non-parametric stability statistics. Combined analysis of variance based on the Additive Main Effect and Multiplicative Interaction (AMMI) model showed that GEI is significant in the term of grain yield. Also, the ratios between the sum of squares G, GE and IPC1 showed that the AMMI is suitable for data analysis. GGE biplot analysis identified five mega-environments (MEs), in which ME I including E1, E2, E3, E4, E5, E6, and G7, G5, G14, G13, G16, G18, G20 being the superior ME I genotypes. According to AMMI and GGE biplote stability methods, lines G20, G18, G13, G16, G14 and Saein cultivar (G5) can be considered as desirable genetic resources in wheat production programs under variable environments in Iran, due to having the appropriate combination of yield and stability.

Evaluation of Indonesian Butterfly Pea (Clitoria ternatea L.) Using Stability Analysis and Sustainability Index

Yield and yield attributes are important components in genotypic evaluation. The butterfly pea is a native plant of Indonesia, and it is considered an underutilized crop. The goals of this study were to evaluate genotypes using environment (year) interactions (GEIs) with yield and yield attributes, and evaluate butterfly pea genotypes based on stability measurements and sustainability index (SI). The study was conducted at the Ciparanje Experimental Field, Faculty of Agriculture, Universitas Padjadjaran using 35 butterfly pea genotypes in a randomized complete block design with two replications. The field trial was conducted over three years (2018–2020). The results showed that the yield and yield attributes were influenced by GEIs. Additive main effects and multiplicative interaction (AMMI) selected 11 stable genotypes (31.43%); genotype plus genotype by environment interaction (GGE) biplot, AMMI stability value (ASV), and genotype stability index (GSI), each selected six genotypes (17.14%) that were stable and high-yielding, and SI selected 18 genotypes (51.43%) that were stable and high-yielding. There were three genotypes identified by all measurements, namely G2, G14, and G16. These three genotypes can be selected as the superior genotypes of the butterfly pea for flower production, and can be used as material for crosses in plant-breeding prog.

Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress

Additive main effects and multiplicative interaction (AMMI), as well as various AMMI-derived statistics, Genotype, and Genotype × Environment Interaction (GGE) models were employed on 24 sugarcane genotypes planted during two seasons (2017–18, 2018–19) under different induced salinity stress environments using saline water irrigation (iw) viz., E1 (Normal iw during crop season 2017–18), E2 (Normal iw during crop season 2018–19), E3 (4 dsm−1 ECiw during crop season 2017–18), E4 (4 dsm−1 ECiw during crop season 2018–19), E5 (8 dsm−1 ECiw during crop season 2017–18), E6 (8 dsm−1 ECiw during crop season 2018–19), E7 (12 dsm−1 ECiw during crop season 2017–18) and E8 (12 dsm−1 ECiw during crop season 2018–19) to assess the genotype by the environment interaction for the cane yield, commercial cane sugar (CCS) yield, number of millable cane (NMC), single cane weight (SCW), and pol % in juice. Individual and interactive effects of the genotype and environment for all the traits were significant. In the expression of total variability, the environmental contribution was higher for the cane yield (66.98%), CCS yield (67.60%), NMC (65.78%), and SCW (43.27%), whereas genotypic contribution was higher in the expression of pol% (82.48%). As per AMMI Stability Value (ASV), G14 (Co 13033), G23 (Co 15026), G7 (Co 05009), G17 (Co 13036), and G2 (Co 15025) were the most stable genotypes for the cane yield. Whereas as per GSI (genotype selection index), genotypes G24 (Co 15027), G21 (Co 15023), G23 (Co 15026), and G17 (Co 13036) were found most stable. The Sustainability Index (SI) of the cane yield (CY) and its contributing and CY-based computed traits were low for most of the genotypes, which indicates the negative impact of increased levels of irrigation-induced salinity in the expression of these traits. In the mean vs stability biplot analysis, G21 (Co 15023), G24 (Co 15027), G16 (Co 13036), G6 (Co 0238), and G20 (Co 14036) were found to be highly productive and stable genotypes for the cane yield. The superior and stable performance of early maturing notified varieties G21 (Co 15023) and G6 (Co 0238) for CY and CCS yield indicates that they will help the farmers to obtain sustainable income in saline soil conditions.

AMMI analysis for grain yield in bread wheat recombinant inbred lines

Identification of high yielding relatively stable genotypes is an integral objective of plant breeding programs. Testing of genotypes across environments is required to determine yield stability of genotypes. The specific objective of the current study was to analyze genotype by environment interaction (GEI) for grain yield of 44 bread wheat recombinant inbred lines (RILs) and six check cultivars using additive main effect and multiplicative interaction (AMMI) model. Experiments were planted using alpha lattice design with two replicates in Peshawar (E-1 and E-3), Hangu (E-2 and E-4) and Kohat (E-5) Khyber Pakhtunkhwa province, Pakistan. Analysis of variance revealed significant differences among RILs for all traits while interactions due to genotype by environment were significant for all traits except days to emergence and 1000-grain weight. Significant GEI justified environment-specific as well as AMMI analysis to identify RILs with specific and wider adaptation. The AMMI analysis revealed that the first interaction principal component analysis (IPCA 1) captured 64% of GEI sum of squares while the second interaction principal component analysis (IPCA 2) explained 25.8% of the interaction sum of square. The AMMI biplot identified G30 as high yielding genotype followed by G19 and G49, whereas low yielding RILs were G13, G8 and G7. Similarly G30, being close to IPCA1 axis, was the most stable RIL with wider adaptability followed by G31 and G25. Based on AMMI stability value (ASV), RILs G18 (2.15), G5 (2.78), G27 (3.72), G44 (4.31), G25 (4.43), G42 (4.57), G43 (5.78), G11 (5.82), G1 (7.66) and G29 (7.81) were recognized in the given order of relative stability. Stability analysis identified G49 (Wafaq ? Ghaznavi-98-3) as high yielding stable genotype among RILs which can be commercialized after fulfilling procedural requirements.