Effect Of Genotype By Environment Interaction Research Articles

Genetic gains in forage sorghum for adaptive traits for non - conventional area through multi-trait-based stability selection methods.

In the Asian tropics, unpredictable weather increases the risk of abiotic stresses in sorghum areas, making it harder to meet predicted demand. Genotype-by environment interaction (GEI) and the lack of an effective multi-trait-based selection approach make it challenging to breed climateresilient forage sorghum that adapts to nonconventional areas. The present investigation carried out to estimate genetic parameters, inter trait associations, genetic gain under selection (SGs) of 95 diverse forage sorghum genotypes. Fourteen forage yield and other secondary traits were evaluated at five different growing seasons at two locations. Negative and positive genetic gains under selection were estimated across different growing seasons including Kharif, Rabi and Summer in the year 2020 and 2021. The GEI effects were significant (P < 0.001) for all the studied traits. The multi trait based stability indices have been said to assist breeders in ensuring sustained progress in primary traits likeforage yield without sacrificing genetic advancement in secondary traits. Fourteen genotypes were selected through each evaluation methods including genotype - ideotype distance index (MGIDI), multi-trait stability index (MTSI), multi-trait stability and mean performance (MTMPS) and multi-trait index based on factor analysis and genotype-ideotype distance (FAIBLUP Index), assuming 15% selection intensity. According to MGIDI, the selected genotypes exhibited desired positive genetic gains for dry forage yield per plant, inter-nodal length, green forage yield per plant, and plant height and negative genetic gains for days to 50% flowering. The strength and weakness plot is a potential graphical tool as portrayed by MGIDI, to identify and develop desirable genotype for particular environment. Two genotypes, G36 (302B) and G89 (348B) were found to be common across all four evaluation methods based on all the studied traits. Multi-trait stability evaluation approaches are reliable and accessible for selecting multiple traits under varied testing environments with low multicollinearity issues. These tools proved effective in enhancing selection strategies and optimising breeding schemes for the development of climate-resilient forage sorghum genotypes. The aforementioned genotypes were found to be the most reliable, high-yielding, and earlymaturing and could be suggested for variety and hybrid development and ideotype breeding programmes to ensure the food and nutritional security.

Interpreting the Interaction of Genotype with Environmental Factors in Barley Using Partial Least Squares Regression Model

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.

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.

AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions.

The two most common styles to analyze genotype-by-environment interaction (GEI) and estimate genotypes are additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot. Therefore, the aim of this study was to find the winning genotype(s) under three locations, as well as to investigate the nature and extent of GEI effects on Bambara groundnut production. The experiment was carried out in the fields of three environments with 15 Bambara groundnut accessions using the randomized complete block design (RCBD) with three replications each in Ibadan, Osun, and Odeda. Yield per plant, fresh seed weight, total number of pods per plant, hundred seed weight, length of seeds, and width of seeds were estimated. According to the combined analysis of variance over environments, genotypes and GEI both had a significant (p < 0.001) impact on Bambara groundnut (BGN) yield. This result revealed that BGN accessions performed differently in the three locations. A two-dimensional GGE biplot was generated using the first two principal component analyses for the pattern of the interaction components with the genotype and GEI. The first two principal component analyses (PCAs) for yield per plant accounted for 59.9% in PCA1 and 40.1% in PCA2. The genotypes that performed best in each environment based on the "which-won-where" polygon were G8, G3, G2, G11, G6, and G4. They were also the vertex genotypes for each environment. Based on the ranking of genotypes, the ideal genotypes were G2 and G6 for YPP, G1 and G5 for FPW, G15 and G13 for TNPP, G3 and GG7 for HSW, G7 and G12 for LOS, and G10 and G7 for WOS. G8 was recorded as the top most-yielding genotype. G8, G4, G7, and G13 were high yielding and the most stable across the environments; G11, G14, and G9 were unstable, but they yielded above-average performance; G14, G12, G15, and G1 were unstable and yielded poorly, as their performances were below average. Bowen was the most discriminating and representative environment and is classified as the superior environment. Based on the performance of accessions in each region, we recommend TVSU 455 (G8) and TVSU 458 (G3) in Bowen, TVSU 455 (G8) and TVSU 939 (G6) and TVSU 454 (G1) in Ibadan, and TVSU 158 (G2) and TVSU 2096 (G10) in Odeda. The variety that performed best in the three environments was TVSU 455 (G8). They could also be used as parental lines in breeding programs.

GENOTYPE BY ENVIRONMENT INTERACTION AND STABILITY ANALYSIS OF ETHIOPIAN COMMERCIAL DURUM WHEAT (Triticum turgidum L.) CULTIVARS IN SOUTHERN ETHIOPIA

&lt;p class="abstrakinggris"&gt;The use of improved varieties with wider adaptability and stability is necessary to maximize the productivity of durum wheat. However, due to genotype by environment interaction (GEI) effect, superior genotypes in one environment could be inferior in other environments. Hence, a multi-environmental trial (MET) was commenced to determine the magnitude of GEI effect and identify stable durum wheat genotypes across environments. The MET was conducted using nine durum wheat cultivars in randomized completely block design with three replications across four contrasting locations in 2020 crop seasons. The result of combined analysis of variance showed the presence of significant differences among the main effects; environments, genotypes, and GEI effects for grain yield. The additive main effects and multiplicative interaction (AMMI) combined ANOVA for main effects of environment, genotype, and GEI revealed highly significant differences among cultivars with 66.2%, 26.4%, and 7.3% share of sum square variation, respectively, of the total variation. AMMI and genotype plus genotype by environment (GGE) Bi-plot analysis identified the cultivars Fetan, Denbi, and Mangudo as high yielders and adaptive to the favourable locations. AMMI stability value and yield stability index identified Mangudo as the most stable and adaptive cultivar across locations. The AMMI Bi-plot analysis showed that the first two interaction principal component analysis (IPCAs) captured 90.45% of the total interaction sum of squares (ISS), where IPCA1 took 55.61% and IPCA2 accounted for 34.84% of GEI effects. This study identified Mangudo as the most stable cultivar with acceptable yield while Fetan was the top yielder genotype across locations.&lt;/p&gt;

Crop phenotype prediction using biclustering to explain genotype-by-environment interactions.

Phenotypic variation in plants is attributed to genotype (G), environment (E), and genotype-by-environment interaction (GEI). Although the main effects of G and E are typically larger and easier to model, the GEI interaction effects are important and a critical factor when considering such issues as to why some genotypes perform consistently well across a range of environments. In plant breeding, a major challenge is limited information, including a single genotype is tested in only a small subset of all possible test environments. The two-way table of phenotype responses will therefore commonly contain missing data. In this paper, we propose a new model of GEI effects that only requires an input of a two-way table of phenotype observations, with genotypes as rows and environments as columns that do not assume the completeness of data. Our analysis can deal with this scenario as it utilizes a novel biclustering algorithm that can handle missing values, resulting in an output of homogeneous cells with no interactions between G and E. In other words, we identify subsets of genotypes and environments where phenotype can be modeled simply. Based on this, we fit no-interaction models to predict phenotypes of a given crop and draw insights into how a particular cultivar will perform in the unused test environments. Our new methodology is validated on data from different plant species and phenotypes and shows superior performance compared to well-studied statistical approaches.

Evaluating the adaptability and stability of common peanut varieties (Arachis hypogaea L.) in Northern Mozambique using the AMMI Bayesian model

This study evaluated the stability and adaptability of common peanut cultivars (Arachis hypogaea L.) in three locations across northern Mozambique over four years, using the additive main effects and multiplicative interaction model (AMMI) under a Bayesian approach. The multi-environmental data consisted of 20 genotypes evaluated in three locations. We analyzed grain yield in tons per hectare in a complete randomized block design for each location. The results indicated that genotypes with higher marginal yield contribute to the genotype by environment interaction (GEI) and thus are not largely recommended for the entire target environment. The Namapa (NMP) location showed consistent behavior and did not contribute to the GEI effect, and in this sense, G6 and G7 would be the best indications for this location. Moreover, genotypes considered stable, with emphasis on the G20 genotype, did not have a good average yield. Mapupulo (MPPL) and Nampula (NLP) had a significant contribution to GEI, and the best genotypes for these locations were G7 and G3, respectively. In this sense, the results of the analysis specified that using genotypes in specific environments would be the best strategy to decrease the effect of GEI and increase peanut productivity in the environments considered

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction.

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.

Influence of genotype and environment on grain yield among cowpea (Vigna unguiculata (L.) Walp) genotypes under dry land farming system

ABSTRACT 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.

Genotype by Environment Interaction Analysis for Grain Yield and Yield Components of Summer Maize Hybrids across the Huanghuaihai Region in China

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 &lt; 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.

Genotype by environment interaction and yield stability of cowpea (Vigna unguiculata (L.) Walp.) genotypes in moisture limited areas of Southern Ethiopia

Genotype by environment interaction (GEI) markedly influences the success of breeding strategies in a versatile crop such as cowpea (Vigna unguiculata (L.) Walp.). Twenty cowpea genotypes were tested in a randomized complete block design with three replications at Gofa, Kucha, and Humbo in Meher seasons of 2016 and 2017 (E1 to E6) and Belg seasons of 2017 and 2018 (E7 to E12) to quantify and evaluate the effects of genotypes, environments and their interactions for grain yield of cowpea genotypes and to identify stable and/or high-yielding genotypes. The environment, genotype, and GEI effects were highly significant (p < 0.001), with the contribution of 42.3%, 23.0%, and 34.7%, respectively to the TSS. Additive main effect and multiplicative interaction (AMMI), genotype main effects plus genotype-environment interaction (GGE), ASV (AMMI stability value), and Genotype stability index (GSI) were used to identify stable genotypes. The GGE-biplot model showed that the twelve environments used for the study clustered under three mega-environments. Our results showed that IT96D-604(G12), IT-89KD (G16), IT93K-293-2-2 (G14), 93K-619-1(G13), IT97K-569-9(G20), and IT99K-1060(G15) scored the highest grain yield (1.67, 1.62, 1.55, 1.51, 1.51, and 1.45 t ha−1), respectively, over environments. AMMI and GGE biplots analyses identified G16 (IT-89KD) and G14 (IT93K-293-2-2) as stable and high-yielding genotypes across environments and can be further tested in variety verification and later on released as varieties and can also be used for different breeding purposes in all cowpea growing areas in southern Ethiopia. The four high-yielding genotypes IT96D-604, 93K-619-1, IT97K-569-9, and IT99K-1060 could be recommended to be included in breeding or variety verification trials for release. Moreover, our results denoted the effectiveness of AMMI and GGE biplot techniques for selecting stable genotypes, high yielding, and responsive.

Genotype-by-environment interaction and stability analyses of grain yield in pigeonpea [Cajanus cajan (L.) Millspaugh

ABSTRACT Genotype-by-environment interaction (GEI) analysis is fundamental in crop improvement programmes to guide selection and for recommendation of high performing and stable genotypes for breeding or production. The objectives of this study were to quantify the GEI effects and determine grain yield stability among diverse pigeonpea genotypes to select candidate lines for breeding in Malawi. Eighty-one pigeonpea genotypes were tested under six environments using a 9 × 9 alpha-lattice design with two replications. Data collected were subjected to additive main effects and multiplicative interaction (AMMI) analysis and genotype plus genotype-by-environment interaction (GGE) biplot analysis. Genotype, environment, and genotype × environment interaction (GEI) accounted for 16.4, 33.5, and 49.6%, respectively, of the total variation for grain yield. The test environments were delineated into three mega-environments, based on site and seasonal variability. The AMMI and GGE analyses indicated that the top-yielding and stable genotypes across the test environments were MWPLR 24, ICEAP 01155, MWPLR 14, TZA 5582, and MWPLR 4. The selected genotypes are recommended as parental lines for grain yield improvement in Malawi or similar agro-ecologies.

Modelling of genotype by environment interaction to improve the recommendation of sugarcane cultivars for the state of Goiás, Brazil

Abstract The objective of this work was to identify environmental factors with significant effects on the genotype by environment interaction (GEI) of sugarcane, and to generate thematic maps yield adaptability of genotypes for the state of Goiás, Brazil, through the integrated use of factorial regression models and the geographic information system (GIS). The study was based on the yield of recoverable sugar (YRS) from cultivar field trials carried out in nine locations. Fourteen environmental factors were used, out of which 11 were divided into 10 crop growth phases, totaling 113 environmental covariates (ECs). The selection of ECs was done by successive simple linear regressions, and the respective genotypic sensitivity coefficients were used to generate adaptability maps. Approximately 57% of the GEI effects were related to the covariates longitude, average temperature at crop germination phase, and maximum temperature at the beginning of the phase of greatest growth. For YRS, the RB034128 and RB034021 clones show specific yield adaptations, and the RB034045 cultivar can share the growing area with the RB867515 check cultivar.

Adaptability and stability analyses of plants using random regression models.

The evaluation of cultivars using multi-environment trials (MET) is an important step in plant breeding programs. One of the objectives of these evaluations is to understand the genotype by environment interaction (GEI). A method of determining the effect of GEI on the performance of cultivars is based on studies of adaptability and stability. Initial studies were based on linear regression; however, these methodologies have limitations, mainly in trials with genetic or statistical unbalanced, heterogeneity of residual variances, and genetic covariance. An alternative would be the use of random regression models (RRM), in which the behavior of the genotypes is characterized as a reaction norm using longitudinal data or repeated measurements and information regarding a covariance function. The objective of this work was the application of RRM in the study of the behavior of common bean cultivars using a MET, based on Legendre polynomials and genotype-ideotype distances. We used a set of 13 trials, which were classified as unfavorable or favorable environments. The results revealed that RRM enables the prediction of the genotypic values of cultivars in environments where they were not evaluated with high accuracy values, thereby circumventing the unbalanced of the experiments. From these values, it was possible to measure the genotypic adaptability according to ideotypes, according to their reaction norms. In addition, the stability of the cultivars can be interpreted as variation in the behavior of the ideotype. The use of ideotypes based on real data allowed a better comparison of the performance of cultivars across environments. The use of RRM in plant breeding is a good alternative to understand the behavior of cultivars in a MET, especially when we want to quantify the adaptability and stability of genotypes.

Multi-environmental evaluation of maize hybrids developed from tropical and temperate lines

Agronomic performance of maize is subject to genotype-by-environment interaction (GEI), necessitating multi-environment evaluation to identify superior genotypes. The objectives of this study were to evaluate the adaptation range of hybrids developed from crossing tropical with temperate maize lines for improving yield stability and select hybrids with broad and specific adaptation in five sites across South Africa and Zimbabwe. One hundred and seventeen genotypes were evaluated using a 9 × 13 alpha lattice design with two replications. The additive main effects and multiplicative interaction (AMMI) analysis showed that effects of genotypes (G), environments (E) and their interaction (GEI) on grain yield were significant (p ≤ 0.001). The G and GEI effects accounted for 36.67% of the total variation. Hybrids 18C2542 (mean grain yield 6.12 t/ha), SC727 (7.05 t/ha), SC410 (5.16 t/ha), and 18C2572 (7.55 t/ha) were identified as superior for Potchefstroom, Panmure, Rattray Arnold Research Stations and the AU-DRS mega-environment, respectively. Hybrids 18C2550 (mean grain yield 7.24 t/ha), 18C2578 (7.42 t/ha), 18C2538 (7.12 t/ha) and 18C2546 (7.39 t/ha) exhibited high grain yield and broad stability.t The Africa University (AU) site in Zimbabwe and Dundee Research Station (DRS) in South Africa were clustered in one mega-environment, indicating that they were correlated. The study reveals that hybrids generated from temperate × tropical inbred lines could be useful for replacing currently used poor performing commercial hybrids. The hybrids 18C2545, 18C2577, 18C2578, 18C2546, 18C2550, 18C2579, 18C2535 and 18C2572 exhibited superiority over commercial hybrids and were recommended for further yield tests and cultivar replacement.

Genotype-by-environment interaction analysis of nutritional composition in newly-developed sweetpotato clones

Development of sweetpotato genotypes with enhanced nutritional composition is key to improve food and nutrition security and for industrial applications. The objective of this study was to determine the effect of genotype-by-environment interaction (GEI) on nutritional composition of sweetpotato clones to identify and select promising clones for large-scale production. Root dry matter content (RDMC), protein (Prot), β-carotene (β-car), iron (Fe), Zinc (Zn), starch (Star), sucrose (Suc), fructose (Fruc) and glucose (Gluc) contents were evaluated among 24 newly developed sweetpotato clones and one check variety across six diverse environments in southern Ethiopia using a 5 × 5 simple lattice design. Data was subjected to additive main effects and multiplicative interaction (AMMI) to estimate genotype (G), environment (E) and GEI effects. According to Gollob’s F-test and FR-test, four interactions principal component axis (IPCA) were significant (p ≤ 0.001) in the AMMI model for most traits, except glucose content where three and two IPCA’s were identified as significant by both tests. As a result, AMMI-4 was diagnosed for all traits, except glucose content where AMMI-2 was recommended. AMMI revealed significant (p ≤ 0.001) G, E and GEI effects on the studied nutritional traits. G explained 54 and 93 % of total variation for DRMC and β-car, respectively indicating these traits were less influenced by GEI effects. Larger E effects were observed for Prot (65 %), Fe (55 %) and Zn (64 %) while larger GEI effects were observed for Star (58 %), Fruc (77 %), Suc (50 %) and Gluc (56 %). Some of the newly-developed clones such as G5 (Ukrewe × Ogansagan-5), G6 (Resisto × Ejumula-7), G12 (Resisto × Temesgen-12), G13 (Resisto × Temesgen-14) and G22 (Ejumula × PIPI-19) with high RDMC (> 30 %), Prot (> 7 %), β-car (> 12 mg 100 g−1), Fe (> 2 mg 100 g−1), Zn (> 1 mg 100 g−1), Star (> 60 %), Fruc (> 4 %), Suc (>10 %) and Gluc (> 5 %) contents were selected for further evaluations for large-scale production, industrial use and and use in breeding programmes.

Genotype-by-environment interaction of grain yield among candidate dryland wheat genotypes

Genotype-by-environment interaction (GEI) analysis is an essential step to identify superior and stable yielding genotypes and suitable test environments for cultivar development. The objective of the present study was to examine GEI of grain yield among candidate dryland wheat genotypes and to identify promising genotypes for breeding. Seventeen genetically diverse wheat genotypes were evaluated at two sites during the 2015 (high-yielding environment) and 2016 (low-yielding environment) growing seasons in the Limpopo province, South Africa. Grain yield data were subjected to the additive main effect and multiplicative interaction (AMMI) and genotype and genotype-by-environment (GGE) biplot analyses. AMMI analysis of variance revealed highly significant (p ≤ 0.001) differences among genotypes (G), environments (E) and GEI effects, suggesting differential genotypic response across test environments. Large E (90.5%), and low G (2.96%) and GEI (4.1%) effects were observed on grain yield response. The study identified genotypes G312, G344 and G717 as high yielding and stable, and these were recommended as useful genetic resources for breeding of high-yielding genotypes under South African conditions or similar environments.

Genotype-by-environment interaction and yield stability of sunflower hybrids across production environments in South Africa

Genotype-by-environment interaction (GEI) is typical in multi-environment trials, which complicates the identification of superior genotypes, making selection and recommendation of new sunflower genotypes for different environments challenging and expensive. The objectives of this study were to investigate the GEI, identify the most discriminating and representative environments, and determine the performance and yield stability of 15 sunflower cultivars across diverse environments in South Africa. A randomised complete block design with three replicates was used. Seed yield data were subjected to combined analysis of variance, and a genotype main effects and genotype environment interaction (GGE) bi-plot method. The results showed that environment, genotype and genotype-by-environment interaction significantly affected seed yield. The environment effect was a predominant source of variation and accounted for 52.95% of the total sum of squares, whereas genotypic and GEI effects contributed 2.73% and 20.98%, respectively. The GGE bi-plot explained 60.67% of the total variation relative to genotype, GEI and showed that G9, G14, G12, G4 and G13 were high yielding, stable and might be considered as desirable genotypes. In terms of location, Senekal (E14) was identified to be the most discriminating and representative environment to evaluate sunflower genotypes.

Genotype-by-environment interaction of newly-developed sweet potato genotypes for storage root yield, yield-related traits and resistance to sweet potato virus disease

Genotype-by-environment interaction analysis is key for selection and cultivar release, and to identify suitable production and test environments. The objective of this study was to determine the magnitude of genotype-by-environment interaction (GEI) for storage root yield, yield-related traits and sweet potato virus disease (SPVD) resistance among candidate sweet potato genotypes in Tanzania. Twenty-three newly bred clones and three check varieties were evaluated across six diverse environments using a randomized complete block design with three replications. The Additive Main Effect and Multiplicative Interaction (AMMI) and genotype and genotype-by-environment (GGE) biplot analyses were used to determine GEI of genotypes. Genotype, environment and GEI effects were highly significant (P ≤ 0.01) for the assessed traits. Further, AMMI analysis of variance revealed highly significant (P ≤ 0.001) differences among genotypes, environments and G × E interaction effects for all the studied traits. Both AMMI and GGE biplot analyses identified the following promising genotypes: G2 (Resisto × Ukerewe), G3 (Ukerewe × Ex-Msimbu-1), G4 (03-03 x SPKBH008), G12 (Ukerewe × SPKBH008) and G18 (Resisto × Simama) with high yields, high dry matter content and SPVD resistance across all test environments. The candidate genotypes are recommended for further stability tests and release in Tanzania or similar environments.

Revealing GE Interactions from Trial Data without Replications

Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statistically challenging to discover the GE interaction effects because many published data were just entry means under each environment rather than repeated field plot data. In this study, we propose a new methodology, which can be used to impute replicated trial data sets to reveal GE interactions from the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new imputation method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data. The results from the imputed data were highly consistent with those from the original data set, indicating that imputed data from the method we proposed in this study can be used to reveal information including GE interaction effects harbored in the original data. Therefore, this study could pave a way to detect the GE interactions and other related information from historical crop trial reports when replications were not available.