Additive Main Effects And Multiplicative Interaction Model Research Articles

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.

An assessment of the impact of traditional rice cooking practice and eating habits on arsenic and iron transfer into the food chain of smallholders of Indo-Gangetic plain of South-Asia: Using AMMI and Monte-Carlo simulation model

The current study was designed to investigate the consequences of rice cooking and soaking of cooked rice (CR) with or without arsenic (As) contaminated water on As and Fe (iron) transfer to the human body along with associated health risk assessment using additive main-effects and multiplicative interaction (AMMI) and Monte Carlo Simulation model. In comparison to raw rice, As content in cooked rice (CR) and soaked cooked rice (SCR) enhanced significantly (at p < 0.05 level), regardless of rice cultivars and locations (at p < 0.05 level) due to the use of As-rich water for cooking and soaking purposes. Whereas As content in CR and SCR was reduced significantly due to the use of As-free water for cooking and soaking purposes. The use of As-free water (AFW) also enhanced the Fe content in CR. The overnight soaking of rice invariably enhanced the Fe content despite the use of As-contaminated water in SCR however, comparatively in lesser amount than As-free rice. In the studied area, due to consumption of As-rich CR and SCR children are more vulnerable to health hazards than adults. Consumption of SCR (prepared with AFW) could be an effective method to minimize As transmission and Fe enrichment among consumers.

Elucidating genotype × environment interactions for grain iron and zinc content in a subset of pearl millet (Pennisetum glaucum) recombinant inbred lines

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 &lt; 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.

Genotype × environment interactions of organic winter naked barley for agronomic, disease, and grain quality traits

AbstractThe modeling of genotype × environment interactions (GEI) is important to understand how new crops perform in different environments and management systems. Naked barley (Hordeum vulgare L.) is a type of barley where the hull threshes freely from the grain and can be used for multiple end uses, including food, malt, and animal feed. We examined the performance of a winter naked barley trial grown in organic conditions across three regions and 3 years in northern latitude regions of the United States. We recorded yield, test weight, heading date, plant height, threshability, winter survival, barley stripe rust (Puccinia striiformis f. sp. hordei Westend), barley scald (Rhynchosporium commune Zaffarano, McDonald and Linde sp. nov), and spot blotch (Cochliobolus sativus, anamorph Bipolaris sorokiniana (S. Ito &amp; Kurib.). Barley stripe rust, plant height, heading date, preharvest sprouting, and threshability had high heritability and low GEI. Grain yield, test weight, scald, and winter survival had high GEI and were analyzed using the additive main effect and multiplicative interaction (AMMI) model, genotype plus GEI biplot (GGE), and Finlay–Wilkinson (FW) stability analysis. The environmental patterns explained by the AMMI and GGE models indicated that selection for high GEI traits should occur within target environments and across multiple years within the target environments. Environmental groups were separated by Oregon as the first group and Wisconsin and New York as the second group.

Stability analysis of groundnut (Arachis hypogaea L.) genotypes using AMMI and GGE biplot models and ideal genotype selection indicator

A study on stability analysis was carried out on groundnut (Arachis hypogaea L.) genotypes grown at multilocation over two cropseasons. Based on additive main effects and multiplicative interaction (AMMI) analysis, the first two IPCAs explained 91.93% of the GEIvariation (74.84 and 17.09% for IPCA1 and IPCA2, respectively). Based on AMMI 2, the best genotype for environments RA1 and RA2 was 201. The genotypes, ICG192 and ICG130 were found suitable for environments TA1 and TA2, respectively. ICG178 was better adapted to environment MA2, whereas ICG140 and the control NC2 were the best genotypes for environment RA2. In the GGE biplot, PC1 and PC2 explained 81.22 and 13.33% of the total GGE variance. Based on the ideal genotype selection index (IGSI), the genotypes, ICG115, ICG201, and ICG178 were stable and can be used in the breeding programs to develop new varieties.

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p &amp;lt; 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques.

Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant's capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.

Strategies for identifying stable lentil cultivars (Lens culinaris Medik) for combating hidden hunger, malnourishment, and climate variability.

Iron and zinc malnutrition is a global humanitarian concern that mostly affects newborns, children, and women in low- and middle-income countries where plant-based diets are regularly consumed. This kind of malnutrition has the potential to result in a number of immediate and long-term implications, including stunted growth, an elevated risk of infectious diseases, and poor development, all of which may ultimately cause children to not develop to the fullest extent possible. A determination of the contributions from genotype, environment, and genotype by environment interactions is necessary for the production of nutrient-dense lentil varieties that offer greater availability of iron and zinc with a high level of trait stability. Understanding the genotype and environmental parameters that affect G x E (Genotype x Environment) interactions is essential for plant breeding. We used GGE(Genotype, Genotype x Environment interactions) and AMMI (Additive Main effects and Multiplicative Interaction) models to study genetic stability and GE(Genotype x Environment interactions) for grain Fe, Zn, Al, and anti-nutritional factors like phytic acid content in sixteen commercially produced lentil cultivars over several different six geographical locations across India. Significant genetic variability was evident in the Fe and Zn levels of different genotypes of lentils. The amounts of grain iron, zinc, and phytic acid varied from 114.10 to 49.90 mg/kg, 74.62 to 21.90 mg/kg, and 0.76 to 2.84 g/100g (dw) respectively. The environment and GE (Genotype x Environment interactions) had an impact on the concentration of grain Fe, Zn, and phytic acid (PA). Heritability estimations ranged from low to high (53.18% to 99.48%). The study indicated strong correlation between the contents of Fe and Zn, a strategy for simultaneously increasing Fe and Zn in lentils may be recommended. In addition, our research revealed that the stable and ideal lentil varieties L4076 (Pusa Shivalik) for Fe concentration and L4717 (Pusa Ageti) for Zn content, which have lower phytic acid contents, will not only play an essential role as stable donors in the lentil bio-fortification but will also enable the expansion of the growing area of bio-fortified crops for the security of health and nutrition.

A comparative study on single and multiple trait selections of equatorial grown maize hybrids

Maize (Zea mays L.) production in tropical equatorial regions faces significant challenges due to agroclimatic and soil fertility variability, necessitating the evaluation of maize hybrid adaptability and phenotypic stability across diverse agroecosystems. This study compares the effectiveness of the additive main effects and multiplicative interaction (AMMI) and multi-trait genotype-ideotype distance (MGIDI) models for identifying superior maize hybrids well-suited to the equatorial climate. Fifteen genotypes, including 13 hybrid candidates and two popular commercial varieties (BISI 2 and NASA 29), were analyzed in 10 distinct environments in Indonesia over three consecutive years (2018–2020). The ANOVA method used in the AMMI model analyzed variance into three major components, with PCA analysis indicating that environments (E), genotypes (G), and their interaction (G × E) had a highly significant effect on yield (p &amp;lt; 0.001). Two hybrids, HM04 (CI301032/G102612) and HM02 (CI272022/G102612), displayed high adaptability and stability across various environments, with significantly higher yields than the grand mean by AMMI analysis. Additionally, HM10 (MAL03/CLYN231) and HM09 (G102612/CLYN231) were narrowly adapted to the ME-1 and ME-2 mega-environments, indicating they are best suited for these specific environments. Similar to AMMI, the MGIDI model suggested HM04 (MGIDI index = 1.74) and HM02 (MGIDI index = 1.76) as the two highest-performing hybrids, determined by their yield and nine other traits. Using the multiple trait combination index as a tool to assess the performance of these hybrids enabled researchers to determine the most effective traits for each genotype. The two models are recommended and may be integrated for comprehensive data interaction analysis, which simplifies the process of delineating genotypes with the environment and enables stakeholders to select desired traits while considering their strengths and weaknesses.

Selection of High-Yielding and Stable Genotypes of Barley for the Cold Climate in Iran.

The interaction between genotypes and environments plays an important role in selecting superior genotypes for target locations. The main objectives of the present study were to analyze the effect of the genotype-by-environment interaction (GEI) and identify superior, newly developed, and promising barley genotypes for cold regions in Iran. For these purposes, a set of genotypes obtained from breeding programs for cold climates in Iran, along with two reference genotypes, were investigated at eight research stations (Tabriz, Ardabil, Arak, Miandoab, Mashhad, Jolge Rokh, Karaj, and Hamadan) during two consecutive growing seasons (2019-2020 and 2020-2021). The results of the freezing test (LT50) showed that most of the tested genotypes had significant cold tolerance at the seedling stage. Based on the additive main effect and multiplicative interaction (AMMI) analysis, environment (E) and GEI effects explained 49.44% and 16.55% of the total variation in grain yield, respectively. Using AMMI1 and AMMI2 models, G2 and G20 were found to be superior genotypes in terms of grain yield and stability. Moreover, AMMI-based stability parameters considered the G20 genotype to be the ideal genotype. A two-plot analysis of the genotype-by-environment interaction (GGE) biplot showed that the 16 experimental environments were grouped into 2 mega-environments. Of the test environments, ARK1 and KAJ2 had the highest discriminating power and representativeness ability, and these were identified as ideal environments for testing advanced genotypes for yield and stability performance during early barley breeding practices in cold areas in Iran. In conclusion, both AMMI and GGE biplot models identified several superior genotypes, among which G20, with a high average yield relative to the overall average yield and the lowest IPC1 score, was found to have high yield stability and is recommended for inclusion in breeding programs for cold climates in Iran.

Development of stable blackgram [Vigna mungo (L.) Hepper] genotypes by deciphering genotype × environment interaction using Eberhart-Russell and AMMI models

The genotype and environment (G × E) interaction is a key area of research for creating stable cultivars as it has a major impact on crop yield performance. In this study, we examined the stability and adaptability of the seed yields of elite blackgram genotypes in four Agricultural Research Centers of PJTSAU consisting of diverse environments in Telangana, India during Rabi 2019-20 using Eberhart-Russell and Additive Main effects and Multiplicative Interaction (AMMI) models. A combined analysis of variance showed significant differences between the varieties and the interactions between the varieties and locations for seed yield. Both Eberhart and Russell and AMMI1 model analyses of G×E interaction identified G1 (LBG-752) as the stable variety with respect to yielding ability suitable for cultivation under diverse conditions. Environments A, B, and C were found to be ideal environments for genotypes G7, G8, and G6, respectively, based on AMMI 2. The selected elite varieties based on different stability analyses could be used for further exploitation for cultivar release. Keywords: G × E interaction, Adaptability, AMMI l, Eberhart &amp; Russell, Seed yield, Vigna mungo.

STABILITY IN YIELD PERFORMANCE EVALUATION OF SELECTED COTTON (Gossypium hirsutum) GENOTYPES IN VARIABLE AGRO-ENVIRONMENTS

Yield constancy is a crucial characteristic for a variety to become popular among growers. To study this aspect, the present trial was carried out at seven locations in the cotton belt of Punjab during 2020-21. Twenty-five upland cotton strains from different breeding stations were tested along with the standard variety CIM-602. The main objective was to choose super-yielding plus stable strains. Maximum variability due to environments (65%) followed by GEI (22.8%) was observed. The first two interaction principal components (IPC) were squeezed with 72.3% of cumulative variability due to GEI. The analysis of additive main effects and multiplicative interaction (AMMI) diagnosed AMMI5 as an appropriate model. Strain CKC-5 gave maximum mean yield (1812kg ha-1) and winner in all AMMI models. Test sites were split into two mega environments (ME). ENT7 (CRS Faisalabad) site was bearing the highest mean seed cotton yield of (2532kg ha-1) with the biggest (52.18) IPC1 score. The correlation between sites and IPC1 scores was (0.68) as recorded by AMMI analysis. AMMI1 ranks depicted that (PCI2) CIM-875 bears yield advantage of (29.09%) at ENT6 (Vehari) site over (trial winner strain CKC-5) due to micro adaptations. Genotype Selection Index (GSI) discriminated strain BS-J5 as yielder cum stable one with the least GSI value. Approval of this strain for general cultivation from the respective forum may boost cotton production in the province.

Additive Main Effect and Multiplicative Interaction Analysis for Grain Yield of Early Maturing Sorghum [Sorghum Bicolor (L.) Moench] Varieties in Drought Prone Areas of Temben, North Ethiopia.

Shortage of widely adapted and high yielding variety is one of the major bottlenecks for production and productivity of sorghum in dry lowlands of Tigray region, Northern Ethiopia. A field experiment was conducted during the main seasons of 2018 and 2019 at four environments using randomized complete block design with three replications to evaluate the performance of ten early maturing sorghum varieties for grain yield using AMMI (Additive Main Effects and Multiplicative Interaction) model. The combined analysis of variance revealed highly significant (P≤0.01) genotype (G), environment (E) and genotype × environment interaction (GEI). The significant genotype by environment interaction effects were further partitioned in to two significant interaction principal components by using AMMI model. The AMMI analysis of variance showed that the genotype, environment and interaction sum squares contributed 41.55 %, 28.67 % and 29.78 % to the treatment sum squares for grain yield respectively. In addition, the first two IPCAs and interaction residual were significant. The first two IPCAs accounted for a total of 82.20 % of the interaction sum square. The results revealed that the observed yield variation among genotypes were due to genetic potential of genotypes and interaction rather than location differences. The highest yield was obtained from Melkam (3276 kg ha-1), while the lowest was from Grana-1 (2094 kg ha-1) and the average grain yield of genotypes was 2462 kg ha-1. Therefore, Melkam is selected as the best stable variety with consistent yielding performance across the testing environments in dry lowland areas of Temben and similar agro-ecologies in Tigray region, Northern Ethiopia.

Dissection of genotype‐by‐environment interaction and simultaneous selection for grain yield and stability in faba bean (Vicia faba L.)

AbstractIncreasing faba bean production is indispensable to supply the growing demand for plant‐based protein on the global scale. A thorough understanding of genotype (G) × environment (E) interaction (GEI) patterns is critical to developing high‐yielding varieties with wider adaptation. Thirteen faba bean genotypes were evaluated in 15 environments during 2019–2020 in western Canada to estimate their yield stability using different stability statistics. The combined analysis of variance and additive main effects and multiplicative interaction (AMMI) analysis revealed that G, E, and GEI effects were highly significant (p &lt; 0.001), indicating differential responses of the genotypes across the environments, enabling the stability analysis. The result of the model comparison found the best linear unbiased prediction (BLUP) to outperform AMMI models. The BLUP‐based biplot of the weighted average of absolute scores (WAASB) stability and mean grain yield (GY) identified AO 1155 (Navi), 1089‐1‐2, 1310‐5, DL Tesoro, and 1239‐1 as high‐yielding and stable genotypes. The correlation analysis revealed that most of the stability parameters had a strong association with GY and with each other, indicating that they should be used in combination with one another to select genotypes with high yield. Overall, the WAASB superiority index and the average sum of ranks of all stability statistics identified the same genotypes in terms of high‐yielding and stability, and genotype AO 1155 is considered the most stable and highest yielding among the tested genotypes. Genotypes with stable yields across environments would be beneficial for faba bean genetic improvement programs globally.

Use of the reversible jump Markov chain Monte Carlo algorithm to select multiplicative terms in the AMMI-Bayesian model.

The model selection stage has become a central theme in applying the additive main effects and multiplicative interaction (AMMI) model to determine the optimal number of bilinear components to be retained to describe the genotype-by-environment interaction (GEI). In the Bayesian context, this problem has been addressed by using information criteria and the Bayes factor. However, these procedures are computationally intensive, making their application unfeasible when the model's parametric space is large. A Bayesian analysis of the AMMI model was conducted using the Reversible Jump algorithm (RJMCMC) to determine the number of multiplicative terms needed to explain the GEI pattern. Three a priori distributions were assigned for the singular value scale parameter under different justifications, namely: i) the insufficient reason principle (uniform); ii) the invariance principle (Jeffreys' prior) and iii) the maximum entropy principle. Simulated and real data were used to exemplify the method. An evaluation of the predictive ability of models for simulated data was conducted and indicated that the AMMI analysis, in general, was robust, and models adjusted by the Reversible Jump method were superior to those in which sampling was performed only by the Gibbs sampler. In addition, the RJMCMC showed greater feasibility since the selection and estimation of parameters are carried out concurrently in the same sampling algorithm, being more attractive in terms of computational time. The use of the maximum entropy principle makes the analysis more flexible, avoiding the use of procedures for correcting prior degrees of freedom and obtaining improper posterior marginal distributions.

Genotype-environment Interaction for Yield and Yield Associated Traits in Chilli

The present study was conducted on six chilli genotypes across 4 environments in randomized complete block design with three replications during 2019-20 to evaluate the performance of chilli genotypes with higher yield and stability. The AMMI (Additive main effect and Multiplicative Interaction) model was used to estimate the genotype-environment interaction. Genotypes and environments were significantly varied for all the traits, which revealed the presence of genetic variability in the materials under study. The total sum of squares for genotype (G), environment (E) and G × E interactions ranged from 27.44 to 80.49%, 1.26 to 45.09% and 14.60 to 62.67% interactions respectively. The genotypes account large proportion (&gt; 50%) of total variation for Number of fruits per plant and fruit length, which means that genotype, was more important factor for these traits. GGE biplot methodology was used for graphical display of yield data after subjecting the genotypic means of each environment to GGE biplot. Principle components Analysis (PC1 and PC2) were used to depict the stability and superiority performance of the genotypes for yield and yield related traits. the chilli genotypes Co631(G6) and Co632 (G5) were stable and had comparatively less fluctuation for yield and yield related trait across environments which was found promising for recommendation and release for wider adapted variety. Cross over GEI across environment and among genotypes showed that the most general adopted environment with high mean performance, Magura has the best condition for growing chilli.

Genotype and environment interaction and stability of grain yield and oil content of rapeseed cultivars.

Investigating the interaction of genotype and environment in multi-environment experiments (MET) is one of the reliable techniques to demonstrate the most stable and compatible cultivars. The main contribution of this study is to evaluate the stability and compatibility of rapeseed cultivars using additive main effects and multiplicative interaction (AMMI) and genotype plus genotype environment interaction (GGE) bi-plot methods for grain yield and oil content. For this purpose, an experiment in a randomized complete block design (RCBD) with three replications was conducted for 10 rapeseed cultivars across 10 environments (five regions in 2 years). Hence, the proposed technique can be used to identify the superior cultivars corresponding to the multivariant properties including yield and oil content. To do so, a case-study analysis was conducted over rapeseed, while more than 96% of the data variance for grain yield and more than 94% of the data variance for oil content were explained based on the AMMI model. According to the AMMI model, it was observed that the "Zarfam" and "Licord" genotypes were introduced as favorable genotypes for grain yield and oil content, respectively. "Karaj1" and "Sanandaj1" were selected as the superior environments for yield trait, "Kashmar2" for oil content, and "Licord" and "Kashmar2" were identified as the superior genotypes and environment for oil content, respectively. Graphical GGE bi-plot illustrated that "Hyola401," "Okapi," and "Sarigol" for grain yield and "Option500" and "Sunday" for oil content were identified as stable and high-yield genotypes. "Sanandaj1" for grain yield and "Karaj2" for oil content were identified as environments with high differentiation and screening power.

Multivariate analyses of Ethiopian durum wheat revealed stable and high yielding genotypes.

Improving crop adaptation and stability across diverse and changing environmental conditions is essential to increasing grain yield per unit area. In turn, this contributes to meeting the increasing global food demand. Nevertheless, a number of factors challenge the efficiency of crop improvement programs, of which genotype-by-environment interaction (GEI) is one of the major factors. This study aimed to evaluate the performance and phenotypic stability of 385 Ethiopian durum wheat landraces and 35 cultivars; assess the pattern of genotype by environment interaction (GEI) effect, and identify stable and high-yielding landraces or cultivars using the additive main effect and multiplicative interaction (AMMI) and genotype main effect plus genotype by environment interaction biplot (GGE-biplot). The experiment was laid out in an alpha lattice design with two replications at five test sites (Akaki, Chefe Donsa, Holeta, Kulumsa, and Sinana). The combined analysis of variance revealed highly significant effects (P ≤ 0.01) of environments (E), genotype (G), and GEI on a phenotypic variation of traits evaluated, including grain yield. For all traits, the amount of phenotypic variance and GEI explained by the GGE biplot was higher than in AMMI2, but both exhibited significant effects of E and GEI on the genotypes. The AMMI model identified G169, G420, G413, G139, G415, G416, G417, and G418 as stable genotypes across testing sites. Whereas, the GGE biplot identified G169, G420, G415, G139, G106, G412, G413, and G417 as both high-yielding and stable across test sites. Hence, genotypes identified as stable and high yielding in the present study could be used in a durum wheat breeding program aimed at identifying genes and molecular markers associated with the crop’s productivity traits as well as developing stable and high-yielding cultivars for use in East Africa and beyond.

The AMMI model application to analyze the genotype–environmental interaction of spring wheat grain yield for the breeding program purposes

Changing climatic conditions and the emphasis on the cultivation of genetically stable and resilient varieties as well as efficiently managing water and mineral resources require the commencement of appropriate research already at the stage of plant breeding. For this purpose, breeders must have the necessary tools not only in the form of an experimental network, but also statistical tools that enable the correct interpretation of the obtained results. In the presented research, the additive main effects and multiplicative interaction (AMMI) model, supplemented with cluster analysis, was used to assess the stability and yielding level of 26 spring wheat genotypes, in six locations. The main reason for the yield variability in studied genotypes was environmental factor (89%). In spite of differential conditions in the experimental network locations, the studied environments, which had a similar effect on the genotypes, in the growing season of 2021, were grouped. The AMMI stability value (ASV), yield stability index (YSI) and genotype selection index (GSI) coefficients were used to evaluate the studied genotypes. Based on the analyses, the following genotypes were selected for further breeding work: STH 21-03, STH 21-09 and KOH 18279, as stable and widely adapted.

Genotype-environment interaction of crocin in Gardenia jasminoides by AMMI and GGE biplot analysis.

To investigate the effect of genotype-environment interaction (GEI) on Gardenia jasminoides crocin contents, this study combined the additive main effects and multiplicative interactions (AMMI) model and genotype × environment interaction (GGE) biplot method to investigate the adaptation and stability of 11 Gardenia jasminoides genotypes at three experimental sites in Jiangxi Province with replications. The AMMI analysis showed that genotype, environment, and the GEI had extremely significant effects on Gardenia jasminoides crocin contents (p < .001). The GEI was the main factor causing the content variation, followed by genotype. The AMMI model and biplot analysis showed that the HC20 genotype had high and stable crocin contents. The GGE biplot analysis showed that Fengcheng and Gongqingcheng were optimal test sites for the selection of Gardenia jasminoides genotypes based on their crocin contents; additionally, the HC20 genotype was a suitable dominant genotype to promote cultivation in the test areas, and the GY8 genotype could be widely planted in the Gongqingcheng area. Therefore, the AMMI and GGE biplot genotype evaluation results were basically consistent. Comprehensive and effective evaluation of genotype and regional test sites can provide a theoretical basis for the breeding and development of Gardenia jasminoides clones with high and stable crocin contents and the selection of test sites.