Genotype-environment Interaction Effects Research Articles

Variance and covariance components of agronomic and quality traits assessed in tetraploid potato and their implications on practical breeding

Potato is a versatile food crop and major component of human nutrition worldwide. Model calculations and computer simulations can be used to optimize the resource allocation in potato breeding programs but require quantitative genetic parameters. The objectives of our study are to (i) estimate quantitative genetic parameters of the most important phenotypic traits in potato breeding programs, (ii) compare the importance of inter- vs. intra-population variance, (iii) quantify genotypic and phenotypic covariances among phenotypic traits, and (iv) examine the effect of a preselection in the single hills stage on variance and covariance components in later stages of the breeding program. Our study was based on a total of 1066 clones from three breeding programs which were evaluated in a non-orthogonal way in 15 environments for a total of 26 phenotypic traits. The examined traits showed an overall high to medium heritability, and variance analysis revealed trait-specific differences in the influence of the genotypic, environmental, and genotype-environment interaction effect. Accounting for heterogeneity in the residual variances between the 15 environments led to a significant improvement of the variance parameter estimation. The result of our study suggested that the first selection step at the single hills stage did not negatively impact the genetic variability of the target traits implying that the traits assessed in the earlier stages were not correlated with the traits influencing market success. Our results can be used as base for further simulation studies and, thus, help to optimize the resource allocation in breeding programs.

Mitigating Genotype–Environment Interaction Effects in a Genetic Improvement Program for Liptopenaeus vannamei

Mitigating Genotype–Environment Interaction Effects in a Genetic Improvement Program for Liptopenaeus vannamei

Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery

AbstractThis study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late‐season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispectral relationship matrix (M‐matrix), and vegetation indices (VIs) were considered. GP involved the genomic relationship matrix (G), extended to multi‐kernel predictors by incorporating environmental and genotype–environment interaction effects, complemented with multispectral reflectance data. Two different models including PLSR (partial least square regression) and Bayesian genomic best linear unbiased prediction regressor were applied. The phenological stage of spectral data collection impacted the trait prediction accuracy correlating with the relationship between multispectral data and measured traits. Higher correlations resulted in higher PP prediction accuracy. The results revealed that spectral bands and M‐matrix outperformed VIs by 10%–40% across different timepoints and all timepoints together for grain yield (GY) prediction. The single‐kernel GP model (G) outperformed PP by 28% (using Bayesian) and 29% (using PLSR). The integration of multi‐kernel GP models with spectral data improved GY prediction by up to 4%. In terms of days to maturity (DM) prediction, phenomic methods excelled, surpassing the single‐kernel GP (G: r = 0.63) model by 11% (Bayesian). In conclusion, this study underscores the effectiveness of phenomics prediction for traits like DM and its potential to enhance predictions for complex traits such as GY while highlighting the importance of correlation between measured traits and spectral data, kernel combinations, and model selection for prediction accuracy.

Identification of high-yielding and stable cultivars of wheat under different sowing dates: Comparison of AMMI and GGE-biplot analyses

Evaluating the adaptability and yield stability of wheat varieties across different sowing dates is crucial for the success of breeding programs. In this study, we used Additive Main Effects and Multiplicative Interactions (AMMI) and Genotype × Genotype–Environment (GGE) biplot analyses to evaluate the performance of 13 wheat varieties across eight sowing dates. The main objective was to compare varieties and sowing dates to identify the most stable varieties for cultivation. Both AMMI and GGE biplot analyses showed significant effects of genotype, environment and genotype-environment (GE) interaction on grain yield. The interaction patterns identified by AMMI and GGE-biplots categorized Mihan, Oroom and Pishgam as the most stable, high yielding and early maturing wheat varieties. Furthermore, while the GGE biplot method proved to be more efficient than the AMMI model for stability analysis, our results indicate considerable overlap between the results of these two approaches. These results provide a valuable basis for optimizing genetic improvement strategies in wheat breeding programs.

Influence of different environmental challenges on the expression of reproductive traits in Holstein cattle in Southern Brazil.

The aim of this study was to assess the impact of genotype-environment interaction (GEI) on the manifestation of traits such as age at first calving (AFC), age at first service (AFS), and calving interval (CI) through the application of the reaction norm model in Holstein cattle raised in Paraná state, Brazil. Utilizing data from the milk testing service of the Paraná Association of Holstein Cattle Breeders (APCBRH), this study analyzed records from 179,492 animals undergoing their first, second, and third lactations from the years 2012 to 2022. These animals were part of 513 herds spread across 72 municipalities in Paraná. The environmental gradient was established by normalizing contemporary group solutions, derived from the animal model, with the 305-day-corrected milk yield serving as the dependent variable. Subsequently, reaction norms were determined utilizing a Random Regression Model. Spearman's correlation was then applied to compare the estimates of breeding values across different environmental gradients for the studied traits. The highest EG (+ 4) indicates the least challenging environments, where animals experience better environmental conditions. Conversely, lower EG (-4) values represent the most challenging environments, where animals endure worse conditions. The only trait that exhibited a moderate heritability magnitude was AFC (0.23) in the least challenging environmental condition. The other traits were classified as having low heritability magnitudes regardless of the evaluated environmental gradient. While minimal evidence was found for the influence of GEI on CI, a clear GEI effect was observed for AFC and AFS across all environmental gradients examined. A reversal in genotype ranking occurred under extreme environmental conditions. The findings suggest that the best-performing genotype under one environmental gradient may not necessarily excel under another.

Application of AMMI and GGE biplot for genotype by environment interaction and yield stability analysis in potato genotypes grown in Dawuro zone, Ethiopia

The performance of most crop genotypes is greatly influenced by the interactions between genotype and environment. Providing information on well-adapted and high-yielding potato genotypes in a given environment is paramount for small-holder farmers to enhance their productivity. The study aimed to understand GEI effect on tuber yield, and identify and select widely or specifically adapted high-yielding potato genotypes for production. Eleven genotypes were evaluated using a randomized complete block design with three replications. The results of the AMMI analysis of variance showed that tuber yield significantly (p ≤ 0.05) influenced genotype-environment interaction. This reveals that genotypes exhibit varying mean performance in tuber yield across different environments. The sum squared results showed that genotype (62.40 %) and environment (26.73 %) were the main contributors to tuber yield variation, while the genotype-environment interaction effect (10.87 %) contributed least to then tuber yield total variation. The AMMI, GGE biplot, and GSI analysis revealed Gudanie and Gorebella as superior genotypes in tuber yield, demonstrating high mean performance across tested environments. Thus, Gudanie and Gorebella have been chosen as the most broadly adaptable genotypes for production in all potato-growing agroecologies in the Dawuro zone. The GGE and AMMI biplot provided the genotype-by-environment interaction association among environments and genotypes.

Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia

Abstract The performance of most crop genotypes varies significantly depending on their genotype–environment interactions. Providing information on high-yielding and well-adapted common bean genotypes in a given environment is crucial for small-holder farmers to boost their productivity. A field trial was conducted at the Gendo and Wara locations over two consecutive seasons to understand genotype–environment interactions on grain yield and to identify and select common bean genotypes that were better adapted and had high yield potential for production. Ten genotypes were evaluated for their performance using a randomized complete block design with three replications. The AMMI and GGE models were used to identify superior genotypes regarding their mean grain yield performance. The AMMI analysis of variance results showed that grain yield was significantly (p ≤ 0.05) influenced by genotype–environment interactions. This shows that genotypes varied in grain yield mean performance across environments, indicating the possibility of selecting suitable genotypes with specific and broad adaptability. The sum of squares factor indicated that grain yield variation was contributed by the genotype (61.0%), followed by the environment (29.80%) and the genotype–environment interaction effect (9.20%) of the total variation. The AMMI and GGE biplot analysis results showed that SER-119 and SER-125 were identified as the best genotypes regarding their mean grain yield performance. Additionally, the genotype selection index analysis result indicated that SER-119 and SER-125 were stable and consistently exhibited high mean performance in all evaluated settings. Therefore, SER-119 and SER-125 are prime candidates for production in various agroecologies suitable for common bean cultivation in Dawuro zones. The use of tools like GGE biplots and AMMI analysis helped visualize the genotype–environment interactions, aiding in the selection of appropriate genotypes for both specific and wide adaptability.

Unraveling genotype-by-environment interaction in maize using cutting edge statistical tools: Innovative empirical selection for increased yield stability

By prioritizing high yields in ideal conditions, traditional breeding might miss out that genotypes with superior yield and stability. Thus,this study seeks to identify stable genotypes that offer more predictable and reliable yields; under both stress and resourceful environments, leading to not only greater food security but financial stability as well. Incorporating both fixed (AMMI&GGI) and random effects models (BLUP & MTSI), not only leveraged theoretical but also empirical comparisons-weakness of each model is covered by all 4 models-to select elites hybrids Maize. Furthermore, a multi-environment trial assessed 41 hybrids across 4 research stations in 2 winter seasons using RCB design. Likewise, compiled cutting-edge statistical methods for GEI analyses from the “metan” package (R-Software), selected hybrids with consistent performance with meticulous analysis. AMMI(Additive Main effects and Multiplicative Interaction)analysis revealed that of significant (p ≤ 0.05) effects of genotype, environment, & genotype-environment interaction (GEI), explaining yield variance with interaction effects were captured by 3 interaction principal components (IPCs). Moreover,GEI graphics of AMMI biplots elucidates four hybrids(MRM-4062, Super-22, PAC-744, and Gen-4118) with high yield stability and above-average performance. Additionally, eight hybrids (4118, 4558, 5454, NMH1258, PAC 746, PAC 745 Gold, and SUPER 9090) exhibited superior yield, regardless of their stability. Nepalgunj exhibited the lowest GEI value. Conversely, Rampur displayed an above-average GEI, indicating a strong ability to modulate varieties performance. Besides this, GGE biplot revealed two potential mega-environments based on yield similarities. Nine high-yielding hybrids (SUPPER 6768, SUPPER 9396, PAC750, MBS5622, PAC745 Gold, MRM4065, NMH8392, Gen-4118, and Gen-4558) were identified as vertex hybrids, signifying their superior performance across wide environments. Even supposing, mixed effects model, both the best linear unbiased prediction (BLUP) weighted with average absolute score standardized by yields (WAASBY) models, Gen-4118 ranked highest in terms of average yield, followed by NMH-8392, TMH2858, and others, and Multi-Trait Stability Index (MTSI) identified SUPPER-9090 as the most stable and high-yielding genotype, followed by NMH4040, Super-22, Gen-4118, MBS-1144, and NMH1255. In a nutshell, Gen-4118, Super-9090, MRM-4062, NMH1258, NMH-8392, TMH2858, PAC 746, PAC 745 Gold emerge as the highest-yielding and most consistently high-performinghybrids, offering promise for commercial farming across diverse environments. Clearly,this study has potential to mitigate risks for farmers (economic burden, crop failure) and bolster food security/climate resilience. What's more, it paves the way for future research on cold tolerance genes for even greater crop improvement.

Genome-wide association study and development of molecular markers for yield and quality traits in peanut (Arachis hypogaea L.)

BackgroundThis study aims to decipher the genetic basis governing yield components and quality attributes of peanuts, a critical aspect for advancing molecular breeding techniques. Integrating genotype re-sequencing and phenotypic evaluations of seven yield components and two grain quality traits across four distinct environments allowed for the execution of a genome-wide association study (GWAS).ResultsThe nine phenotypic traits were all continuous and followed a normal distribution. The broad heritability ranged from 88.09 to 98.08%, and the genotype-environment interaction effects were all significant. There was a highly significant negative correlation between protein content (PC) and oil content (OC). The 10× genome re-sequencing of 199 peanut accessions yielded a total of 631,988 high-quality single nucleotide polymorphisms (SNPs), with 374 significant SNP loci identified in association with the nine traits of interest. Notably, 66 of these pertinent SNPs were detected in multiple environments, and 48 of them were linked to multiple traits of interest. Five loci situated on chromosome 16 (Chr16) exhibited pleiotropic effects on yield traits, accounting for 17.64–32.61% of the observed phenotypic variation. Two loci on Chr08 were found to be strongly associated with protein and oil contents, accounting for 12.86% and 14.06% of their respective phenotypic variations, respectively. Linkage disequilibrium (LD) block analysis of these seven loci unraveled five nonsynonymous variants, leading to the identification of one yield-related candidate gene and two quality-related candidate genes. The correlation between phenotypic variation and SNP loci in these candidate genes was validated by Kompetitive allele-specific PCR (KASP) marker analysis.ConclusionsOverall, molecular markers were developed for genetic loci associated with yield and quality traits through a GWAS investigation of 199 peanut accessions across four distinct environments. These molecular tools can aid in the development of desirable peanut germplasm with an equilibrium of yield and quality through marker-assisted breeding.

Deciphering genotype-by-environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods.

The multi-environmental trials aid breeders in selecting the best genotypes for specific or general adaptability to different environments before commercial release. This study aimed to assess the stability of 13 new soybean pure lines, along with two controls, in terms of seed yield and important agronomic traits. The assessment was based on a completely randomized block design with three replications across four areas during 2020-2022. Various adaptability methods, including parametric, AMMI, GGE biplot, PCA, and SIIG were employed. The mixed analysis showed that the effects of environment, genotype, and genotype-environment (GE) interaction were significant for most studied traits. The AMMI showed the highest portion of environment (65.89%) in soybean seed yield. Based on all stability parameters, lines 2 and 5 were selected for their average seed yields of 3349 and 3142 kg ha-1, respectively. Additionally, lines 6 and 5 showed the most stability, yielding higher than the average, which were 2992 and 3142 kg ha-1, respectively, according to GGE biplot results. Furthermore, lines 2, 5, and 8 were identified as the ideal genotypes concerning seed yield and other agronomic traits, with high SIIG parameters and yields exceeding the average. Finally, the soybean line 5 was deemed the most suitable due to its higher yield, stability, and early maturity (128-day growth period). Therefore, line 5 is considered appropriate for its high stability and earliness in various regions of Iran.

Genotype × Environment Interaction: A Comparison between Joint Regression Analysis and Weighted Biplot Models

This work examines the obstacles presented by genotype-environment interaction (GEI) in plant breeding and the significance of accurate analysis in selecting superior genotypes. Although current models, such as the JRA and GGE biplot models, have their limitations, especially when dealing with multi-environment data with a single trait and several environments, our approach addresses the challenges of GEI in plant breeding. We introduce new models and conduct a comprehensive comparison with the existing ones. The inclusion of mega-environment analysis and the evaluation of individual test environments within each mega-environment adds depth to this study, aiming to provide a more nuanced understanding of the causes and effects of GEI. We intend to validate and test the proposed models on real-world datasets to assess their effectiveness and practical applicability in the field of plant breeding. Additionally, communicating the benefits and potential limitations of your proposed models will contribute to the broader understanding and adoption of improved methods for analyzing GEI in plant breeding. We conclude that joint use of the JRA and GGE Biplot models has proven effective in exploring genotype × environment interaction, particularly for multi-environment data (MET). JRA model provides a most robust and reliable representation of patterns in the data related to genotypes and environments.

Agronomic performance and yield stability of extra-early maturing maize hybrids in multiple environments in the Sahel

Frequent occurrence of drought, heat, low soil fertility and Striga infestation are the main stress factors reducing maize yield in the Sahel. Adoption of stable multiple stress tolerant maize cultivars in the region is crucial for achieving food security. However, selection of a stable high yielding cultivar is complicated by genotype × environment interaction (GEI) due to differential responses to growing conditions. Eleven extra-early maturing multiple-stress tolerant maize hybrids and two checks arranged in a randomized complete block design was evaluated across nine locations for two years in Mali and Niger. The objectives of this study were to identify (i) stable and high-yielding maize hybrids, and (ii) suitable test locations for selecting promising extra-early maize hybrids. GGE biplot was used for graphical analysis. Significant genotype, location and GEI effects were detected for grain yield and number of ears per plant. EEWQH-13 produced the highest grain yield (3860 kg ha−1) while EEYQH-1 had the poorest yield (2663 kg ha−1) with trial mean of 3395 kg ha−1 for all hybrids. GGE biplot explained 69.6 % of the total variation in grain yield among the hybrids. The polygon view identified EEWQH-13 as the best hybrid across six of the nine test locations. EEPVAH-58 was identified as the most stable high yielding hybrid across the nine test locations followed by EEWQH-16 and EEWQH-13. The nine locations were clustered under two mega-environments (ME1, ME2). Among the nine test locations, Tara and Aderaoua clustered in ME1 were the most suitable ones for selecting promising extra-early maize hybrids for wider adaptation. The three hybrids, EEPVAH-58, EEWQH-16, and EEWQH-13, identified in this study could be recommended for on-farm evaluation to confirm the consistency of their yield performance for possible release and commercialization in Mali and Niger.

Genetic analysis of genotype-specific parameters in the DSSTA-CROPGRO-soybean phenology simulation model via a multi-GWAS method

ContextGenotype-specific parameters (GSP) are the fundamental elements for phenotypic prediction in the DSSAT-CROPGRO soybean phenology simulation model (DCS-PSM). Yet, it is unclear about the quantitative relationship between GSP and genomics, which limits the crop models used for breeding research. ObjectiveExploring the genetic composition of GSP related to four soybean phenological traits in DCS-PSM for providing a basis for whether GSP are genetic coefficient. Highlighting the breeding value of DCS-PSM from a genetic perspective and strengthening its genetic understanding, which could support a further crop model-assisted molecular breeding. MethodsWe conducted a multi-genome-wide association study (GWAS) method by intrgrating the single-locus with multi-locus GWAS models for GSP associated quantitative trait nucleotides (QTN) detection. And the heritability, functional annotation of QTN and genotype-environment interaction (GEI) effect were further analyzed. Results(i). All GSP had high broad-sense heritability, ranging form 88.20–98.80%. (ii). Multi-locus GWAS significantly increased the QTN detection efficiency, the GSP associated QTN detected by multi-GWAS method contributed 22.11–50.25% phenotypic variation explained (PVE) of GSP. Besides, the GSP associated QTN accounted for a high PVE of four phenological traits, ranging from 54.25% to 74.75%. (iii). Most QTN overlapped with the function-known genes which regulate soybean phenology through photoperiod-temperature pathways. (iv). GSP associated QTN could reveal the GEI effect and contributed 1.45–6.27% PVE of soybean phenology. ConclusionsThis study indicated that the genetic factor was the primary source of GSP’s variation, and the multi-GWAS method could be an alternative way for the genetic analysis of GSP. Phenology-related GSP had genetic potential and could be regarded as genetic coefficients and breeding targets.

Multi-trait selection in multi-environments for performance and stability in cassava genotypes.

Genotype-environment interaction (GEI) presents challenges when aiming to select optimal cassava genotypes, often due to biased genetic estimates. Various strategies have been proposed to address the need for simultaneous improvements in multiple traits, while accounting for performance and yield stability. Among these methods are mean performance and stability (MPS) and the multi-trait mean performance and stability index (MTMPS), both utilizing linear mixed models. This study's objective was to assess genetic variation and GEI effects on fresh root yield (FRY), along with three primary and three secondary traits. A comprehensive evaluation of 22 genotypes was conducted using a randomized complete block design with three replicates across 47 distinct environments (year x location) in Brazil. The broad-sense heritability () averaged 0.37 for primary traits and 0.44 for secondary traits, with plot-based heritability ( consistently exceeding 0.90 for all traits. The high extent of GEI variance () demonstrates the GEI effect on the expression of these traits. The dominant analytic factor ) accounted for over 85% of the total variance, and the communality (ɧ) surpassed 87% for all traits. These values collectively suggest a substantial capacity for genetic variance explanation. In Cluster 1, composed of remarkably productive and stable genotypes for primary traits, genotypes BRS Novo Horizonte and BR11-34-69 emerged as prime candidates for FRY enhancement, while BRS Novo Horizonte and BR12-107-002 were indicated for optimizing dry matter content. Moreover, MTMPS, employing a selection intensity of 30%, identified seven genotypes distinguished by heightened stability. This selection encompassed innovative genotypes chosen based on regression variance index (, , and RMSE) considerations for multiple traits. In essence, incorporating methodologies that account for stability and productive performance can significantly bolster the credibility of recommendations for novel cassava cultivars.

New protocol for rapid cassava multiplication in field conditions: a perspective on speed breeding.

Despite the economic and social importance, high-yielding cassava cultivars are only released after extensive research, mainly due to the low multiplication rate. This study aimed to assess the impact of using smaller-sized seed cuttings treated with agrochemicals (8MP) compared to the conventional planting size (16 cm) on genetic parameters, agronomic performance, and the ranking of cassava clones based on yield and growth attributes. The evaluation was carried out in clonal evaluation trial (CET), preliminary yield trial (PYT), and uniform yield trials (UYT). Additionally, a new selection scheme for cassava breeding programs was proposed. A total of 169 clones were evaluated, including 154 improved clones at different stages of selection and 15 local varieties used as checks. Field trials were conducted using both sizes of propagative material (8MP and 16 cm) in each phase of the breeding program. The data were analyzed using mixed models, considering the random effects of genotype and genotype-environment interaction (G×E) to determine variances and heritabilities. Bland-Altman concordance and correlation analysis of selection indices were employed to examine the consistency in the ranking of cassava clones using different seed cutting sizes. The distribution of variance components, heritabilities, means, and range of the 8MP and 16 cm trials in different phases of the cassava breeding program exhibited remarkable similarity, thereby enabling a comparative assessment of similar genetic effects. With a selection intensity of 30%, the concordance in clone ranking was 0.41, 0.57, and 0.85 in CET, PYT, and UYT trials, respectively, when comparing the selection based on 8MP and 16 cm trials. It is worth noting that the ranking of the top 15% remained largely unchanged. Based on the findings, proposed changes in the cassava selection scheme involve increasing the number of trials starting from the CET phase, early incorporation of G×E interaction, elimination of the PYT trial, reduction of the breeding cycle from 5 to 3 years, and a decrease in the time required for variety development from 11 to 9 years. These modifications are expected to lead to cost reduction and enhance the effectiveness of cassava breeding programs.

Genotype×Environment Interaction and Stability Analysis Using GGE Biplot for Grain Yield of Bread Wheat (Triticum aestivum) Genotypes under Low Moisture Stress Areas of Ethiopia

A multi-location Experiment was conducted using 28 genotypes and two checks during the main cropping seasons of 2017 and 2018 at Kulumsa, Dhera, Melkasa, Asasa, Ilala, and Geregera in moisture-stress areas of Ethiopia to evaluate the genotype-Environment interaction effect and grain yield stability of bread wheat genotypes. The experiment was arranged in alpha lattice design replicated three times. The stability analysis was carried out for grain yield using R software. ANOVA showed a highly significant difference among genotypes and genotype×environmental interaction for traits; days to heading, days to maturity, plant height, grain yield, 1000 kernel weight. The average mean yield of the genotypes across the environments was 4.5 with the range of 3.5–5.3 t ha-1. The genotypes ETBW 9578, ETBW 9565, ETBW 9570 and ETBW 9571 were top-yielding, ideal, and stable across the ten environments. However ETBW 9589 and Ogolcho were the low-yielding genotypes. The “which-won-where” graph categorizes the genotypes into four groups. The locations within the same group were closely correlated and share the same winning(vertex) genotype. E7 was ideal that has both discriminating abilities of the genotypes and representative of the other test environments. ETBW 9578 gave 37.7% and 39.1% yield advantage over the standard check Kingbird and local check Ogolcho, respectively. As a result the genotype ETBW 9578 was released in 2020 and designated by the variety name “Dursa”. Thus, it was concluded that the Dursa variety could be used as a stable variety for cultivation.

Grain γ-oryzanol and its constituent compounds show high genetic variability, diversity and significant site x genotype interactions in rice

Among the dietary antioxidants that counteract oxidative stress to avert the incidence of non-communicable diseases (NCDs), g-oryzanol, a unique antioxidant in rice shows potential health benefits. Although rice germplasm exhibits significant variation for g-oryzanol content, much less is studied on the influence of environment (E) and genotype-environment interaction (GEI) on the g-oryzanol content in rice bran. Evaluated under three sites, in a combined analysis of variance, a set of 18 genotypes showed significant effect of environment, genotype, and GEI on the g-oryzanol and its constituent compounds. A large proportion of total phenotypic variance was found due to genotype, indicating high heritability of the trait. GEI is the second major contributor to the phenotypic variance of g-oryzanol and its components, while the contribution of environment was found to be the least. CSR 23 was the superior genotype for g-oryzanol content and stability based on AMMI, GGE biplot and WAASB stability models analyzed. Besides CSR 23, genotypes such as DHMAS-70G-164-29, Chittimutyalu and HUR-200-57-1 were identified as stable and high g-oryzanol producing lines. These superior, stable lines can be used as potential donors for improving g-oryzanol content in rice. The present study assumes importance as the first report of GEI for total g-oryzanol and its five components.

Studying the Effect of Genotype-Environment Interaction on the Quantitative and Qualitative production Potential of Different Sugar Beet Cultivars (Beta vulgaris L.)

Studying the Effect of Genotype-Environment Interaction on the Quantitative and Qualitative production Potential of Different Sugar Beet Cultivars (Beta vulgaris L.)

A comprehensive investigation of Genotype-Environment interaction effects on seed cotton yield contributing traits in Gossypium hirsutum L. Using multivariate analysis and artificial neural network

The prediction of seed cotton yield is a critical aspect of cotton breeding. In the present study, an artificial neural network (ANN) and a multiple linear regression (MLR) model were used to predict seed cotton yield based on experimental data obtained from quantitative traits measured under different environmental conditions, including number of bolls per plant (NB), boll weight (BW), 100 seed weight (SI), number of sympodia per plant (NS), lint index (LI), internode length (IL), and seed cotton yield per plant (SCY). The experimental data underwent ANOVA and correlation analysis across different environments. The selected features were utilized for ANN and MLR modeling. The results demonstrated that the ANN model provided precise predictions of SCY, with a root mean square error (RMSE) of 6.63 g/plant and a determination coefficient (R2) of 0.888, which outperformed the MLR model, which showed an RMSE of 8.613 g/plant and an R2 of 0.816. Sensitivity analysis revealed that the number of bolls per plant had the most significant impact on yield estimates, while 100 seed weight had the least impact, as determined by both ANN and MLR models. Furthermore, the ANN model was less influenced by environmental factors than the MLR model, as indicated by R2 values. Overall, this study provides a comprehensive analysis of genotype-environment interaction effects on seed cotton yield and contributes to the development of effective cotton breeding strategies.

Effect of genotype-environment interaction on some important quality parameters of common wheat (Triticum aestivum L.)

Abstract. In ecological field experiment involving common wheat varieties, several quality parameters were analyzed, which express different aspects of grain quality. Objective of the study was to establish in detail the main relations about the influence of the conditions (location and year) and their interaction with the genotype on each of the parameters, separately. The accepted hypothesis was that the growing conditions have a different strength and direction of effect on each parameter, which should ultimately be reflected in a unique way on the performance of each variety of the studied group. In four locations, which represent a sample of the main grain-producing regions of the country, twenty-four varieties of common winter wheat were investigated. Five indexes of grain quality were analyzed as follows: Sedimentation index (Zeleny); Deformation energy (W); P/L alveograph configuration ratio (P/L); Swelling index (G); Dough stability time (Dstab). All possible aspects of the interaction of genotype (GEN), environment (ENV) as well as the interaction between them (GEN*ENV) were investigated. Statistical approaches and methods that are specialized for this purpose were used. Each of the three main factors – “location”, “year” and “genotype” influenced the variation of the group of varieties through the changes of the conditions, independently and in combination with each other. In this combination of effects expressed as (GEN*ENV) the most essential role was the “location”. The established significant interaction caused an adequate (linear) and inadequate (non-linear) change of the varieties, relative to those of the conditions. For the majority of parameters, this change was mostly linear (PC1=70%), with the exception of the P/L (alveograph configuration ratio), where both effects had parity (PC1≈PC2-4). The environments in the locations during individual seasons had high degree of repeatability (H2=0.75 – 0.94), which allows a high degree of prediction of the values of each single parameter. All parameters were affected to varying degrees by the studied factors and the interaction between them. The influence of the conditions was relatively the strongest on the Dough stability time index (Dstab), and the Swelling index (G) was most closely related to the genetic predisposition of the variety. The effects of the interaction of the genotype with the environments (GEN*ENV) made up about 20-30% of the total variation of three of the parameters, for Deformation energy index (W) the effect reached 40%, and for the Dough stability time index (Dstab) it was only about 13%. The environments during the seasons were the cause of a dynamic change of the correlations between the yield and some of the parameters, in some of the locations studied. Probably, this was directly dependent on the specific combination between the levels of extraction and the level of a given parameter.