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SUMMARY: Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant‐environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9–48%), year (4–52%), and genotype‐by‐year interactions (14–36%) to varying extents throughout development. Genome‐wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits. Significance Statement: Unmanned aerial vehicles were used to collect high temporal resolution morphological data of maize plants and model growth curves of genetically diverse inbred lines. These growth curves provide a more complete understanding of how plants differentially interact with the environment. Dissecting the genetic basis of growth curves also provides a deeper understanding of the genetic basis of complex polygenic traits. [ABSTRACT FROM AUTHOR]

Background: The present investigation was carried out with eight chickpea genotypes under six environments to study the stability performance of the genotypes for grain yield and other yield related traits to select the best stable high yield genotypes for the suitable environments. Methods: The experiment was performed using a randomized complete block design (RCBD) with three replications and all the statistical analysis was done by using the software R-studio 4.1.0 with the help of package Metan 1.15.0. Result: The genotypes ICCV14102 (1220.57 kg/ha), ICCV 15115 (1125.40 kg/ha) and ICCV14108 (1110.01 kg/ha) have their mean grain yield above the grand mean yield (1054.48 kg/ha) whereas five genotypes have yield lower than the grand mean yield. Based on the yield stability index and WAAS ranking, the most stable genotypes were, ICCV 14102 (YSI = 4) and ICCV 15115 (YSI = 8) across the environment and can be suggested for release of these genotypes after critical evaluation. [ABSTRACT FROM AUTHOR]

Hand harvesting of lentils is laborious, time-consuming, and costly. Hence, there is a need to develop a mechanical harvesting system to improve the profitability of lentil crop. Lodging resistance is an important trait to be considered in developing lentil varieties suitable for mechanical harvesting. In the present study, genetic variability for lodging resistance and other agro-morphological and phenological traits was assessed in 50 lentil genotypes in three environments under Moroccan semi-arid conditions. Genotype and genotype-by-environment interaction had significant effects on lodging and other traits. High heritability values obtained for lodging indicate that expected genetic gain for lodging resistance can be achieved through breeding. The linear correlation and path analysis showed the importance of stem diameter, pods per peduncle, seedling vigour, and hundred-seed weight in the selection for lodging resistance; however, no trait showed a strong and consistent direct effect on lodging. Among all assessed genotypes, G11, G18, G27, G17, G44, and G41 were identified as promising. These genotypes could be exploited to develop lentil varieties with improved standing ability for mechanical harvesting. The mechanical harvesting was successfully achieved for the genotype G18 during two growing seasons with low seed losses. This advanced line successfully passed the DUS (Distinctness, Uniformity and Stability) and VCU (Value for Cultivation and Use: Agronomic and Technological Value) registration tests and has been registered in Morocco as new variety. The release of lentil varieties suitable for mechanical harvesting can help increase the production and profitability of lentil in Morocco and other regions, thereby contributing to food security and agricultural sustainability. [ABSTRACT FROM AUTHOR]

Abstract Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study’s objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone’s reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone’s reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study's objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone's reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone's reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations. [ABSTRACT FROM AUTHOR]

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

BACKGROUND: Yam is a major staple food that provides both energy (starch) and bioactive compounds. However, there is a lack of knowledge on its cooking quality. We have determined the cooking quality of five varieties of water yam (among those most appreciated by consumers), when they were steamed or boiled. The yams were grown in two contrasting locations with regard to pedoclimatic conditions. RESULTS: Ratio of length to mean circumference of the tuber, difficulty to peel, cooking time, color attributes, hardness of steamed pulp, and dry matter (DM) and starch contents significantly varied among varieties. Cooking time and hardness of the cooked pulp, either steamed or boiled, were higher for tubers grown at the drier location, with vertisols, than at the rainy one, with a ferralitic soil. The raw pulp was richer in starch at the rainy location. We found no correlation between either textural properties or DM and the cooking time. A slight (r = 0.44) but significant correlation was recorded between the DM of the raw pulp and the hardness of the steamed product. CONCLUSION: We propose a comprehensive multicriteria approach for determination of yam cooking quality, textural properties, color attributes and chemical composition, along with varietal and environmental influences. This approach takes into consideration the complexity of food quality, allows a better understanding of its determinants and provides a basis for useful guidelines for breeders. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Bananas and plantains are important food sources for many people in the world. Their high starch content places them among the highest energy providers. This study aimed to determine the effects of altitude on banana starch properties in Cameroon. A dessert banana, a cooking banana, a plantain cultivar, and a plantain‐like hybrid were grown at low and high altitudes (respectively at 80 m and 1300 m above sea level). RESULTS: Starch analyses showed an increase in moisture and pH values against a drop in total titratable acidity and dry matter content with respect to altitude. Amylose content, as well as water absorption capacity, oil absorption capacity and syneresis of high‐altitude plantain and plantain‐like hybrid, were significantly higher. Starch digestibility was low and ranged between 13.4% and 37.9% after 2 h of incubation. High‐altitude plantain starches contained more amylose and were more resistant to enzymatic hydrolysis. CONCLUSION: Starches from CARBAP K74 and Kelong mekintu, grown at high altitude, showed good water and oil absorption capacities, low digestibility, and high resistance. The adequate properties of these banana starches predispose them for use as thickeners and gelling agents as well as ingredients for the formulation of low‐calorie foods. This study highlights the importance of altitude when discussing banana and plantain starch properties. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Simple Summary: Growth traits have been widely studied as economically important traits in the beef cattle industry. However, traditional studies often miss how these traits change under different environmental conditions, only focusing on single genetic changes that affect traits directly. In our study, we analyzed how genetics and environment interact to affect growth in beef cattle, considering four growth traits and two environmental factors. This analysis uncovered several genetic markers for growth traits that are not usually evident in standard studies, showing that some genes have effects that can be obliterated by environmental conditions Further testing showed whether these genetic markers are grouped in specific genes or functional pathways, helping us understand how genetics can influence growth under different environmental conditions. By uncovering novel genetic loci, genes, and candidate biological mechanisms associated with growth traits, our study provides valuable information for selection prediction and breeding decisions in the beef cattle industry. Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator's effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10−6, indicating that these SNPs, which do not show main effects in traditional genome-wide association studies (GWAS), may have non-additive effects across genotypes but are obliterated by environmental means. The gene-based analysis using MAGMA identified three genes that overlapped with the GEWIS results exhibiting G × E, namely SMAD2, PALMD, and MECOM. Further, the results of functional exploration in gene-set analysis revealed the bio-mechanisms of how cattle growth responds to environmental changes, such as mitotic or cytokinesis, fatty acid β-oxidation, neurotransmitter activity, gap junction, and keratan sulfate degradation. This study not only reveals novel genetic loci and underlying mechanisms influencing growth traits but also transforms our understanding of environmental adaptation in beef cattle, thereby paving the way for more targeted and efficient breeding strategies. [ABSTRACT FROM AUTHOR]

Drought stress is one of the major environmental stresses that dramatically reduces agricultural production around the world. Barley (Hordeum vulgare L.) plays an important role in both food and feed security. The objective of this study was to identify the superior drought-tolerant genotypes using grain yield and several yield-based indices of tolerance and susceptibility by applying various multivariate selection models. To achieve this objective, a set of promising new barley genotypes was evaluated in three drought-prone regions of Iran (Mashhad, Karaj, and Hamadan) during two consecutive growing seasons (2019–2020 and 2020–2021). The results of additive main effect and multiplicative interaction (AMMI) analysis showed significant effects for genotypes (G), environments (E), and their interaction (G × E). Based on the AMMI model, G3, G7, G9, and G13 were identified as the four highest-ranked genotypes in terms of grain yield. Based on the Smith-Hazel, factor analysis and genotype-ideotype distance index (FAI), and genotype–ideotype distance index (MGIDI) selection models, genotypes G4 and G13 showed the greatest tolerance to drought stress conditions in the three regions. Moreover, the most significant selection gain was found for the stress tolerance index, yield index, and grain yield under drought stress conditions (Ys). The results of the genotype (G) + genotype × environment (GGE) biplot analysis coincided with those obtained, in which the G4 and G13 genotypes showed specific adaptability in drought environments. In addition, among the environments tested, the Karaj region was selected as an ideal target environment with significant discriminatory power and representative ability. In conclusion, the collective analysis using the AMMI, GGE biplot, and multi-index selection models identified genotypes G4 and G13 as superior genotypes. Consequently, these genotypes may be candidates for commercial introduction. [ABSTRACT FROM AUTHOR]

Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous environments (e.g., sex, disease-affection status, or presence/absence of an exposure) and the other in response to continuous environments (e.g., physical activity, nutritional measurements, or continuous socioeconomic measures). Here we develop a novel model to jointly account for dichotomous and continuous environments. We develop the model in terms of a joint genotype-by-sex (for the dichotomous environment) and genotype-by-social determinants of health (SDoH; for the continuous environment). Using this model, we show how a depression variable, as measured by the Beck Depression Inventory-II survey instrument, is not only underlain by genetic effects (as has been reported elsewhere) but is also significantly determined by joint G×Sex and G×SDoH interaction effects. This model has numerous applications leading to potentially transformative research on the genetic and environmental determinants underlying complex diseases. [ABSTRACT FROM AUTHOR]

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene–Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene–environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB. [ABSTRACT FROM AUTHOR]

Soybean ( Glycine max (L.) Merrill.) yielding potential depends on environmental conditions (precipitation, temperature, soil). The aim of the work was to evaluate stability of yielding (and other traits) of three soybean cultivars (Abelina, SG Anser, Merlin) grown under the climatic conditions of central-eastern Poland. The studied material was obtain in a field experiment conducted at Łączka (52°15' N, 21°95' E) during the growing seasons of 2017–2019. Trait stability was determined based on Shukla’s genotype stability variance and Wricke’s ecovalence describing the genotype-by-environment interaction. For all the examined parameters, there were found significant differences between successive growing seasons, cultivars, and cultivars within study years. The greatest influence of environmental conditions (years) was determined for plant height (64%) and first pod height (54.2%). Stability parameters indicated that cv. Abelina was the most stable in terms of yielding, 1000 seed weight, seed number per pod and average seed number per pod, cv. SG Anser being the least stable in this respect

Dispersal affects evolutionary processes by changing population size and genetic composition, influencing the viability and persistence of populations. Investigating which mechanisms underlie variation in dispersal phenotypes and whether populations harbour adaptive potential for dispersal is crucial to understanding the eco‐evolutionary dynamics of this important trait. Here, we investigate the genetic architecture of dispersal among successfully recruited individuals in an insular metapopulation of house sparrows. We use an extensive long‐term individual‐based ecological data set and high‐density single‐nucleotide polymorphism (SNP) genotypes for over 2500 individuals. We conducted a genome‐wide association study (GWAS), and found a relationship between dispersal probability and a SNP located near genes known to regulate circadian rhythm, glycogenesis and exercise performance, among other functions. However, this SNP only explained 3.8% of variance, suggesting that dispersal is a polygenic trait. We then used an animal model to estimate heritable genetic variation (σA2), which composes 10% of the total variation in dispersal probability. Finally, we investigated differences in σA2 across populations occupying ecologically relevant habitat types (farm vs. non‐farm) using a genetic groups animal model. We found different adaptive potentials across habitats, with higher mean breeding value, σA2, and heritability for the habitat presenting lower dispersal rates, suggesting also different roles of environmental variation. Our results suggest a complex genetic architecture of dispersal and demonstrate that adaptive potential may be environment dependent in key eco‐evolutionary traits. The eco‐evolutionary implications of such environment dependence and consequent spatial variation are likely to become ever more important with the increased fragmentation and loss of suitable habitats for many natural populations. [ABSTRACT FROM AUTHOR]

While the response of Arabidopsis thaliana to drought, herbivory or fungal infection has been well‐examined, the consequences of exposure to a series of such (a)biotic stresses are not well studied. This work reports on the genetic mechanisms underlying the Arabidopsis response to single osmotic stress, and to combinatorial stress, either fungal infection using Botrytis cinerea or herbivory using Pieris rapae caterpillars followed by an osmotic stress treatment. Several small‐effect genetic loci associated with rosette dry weight (DW), rosette water content (WC), and the projected rosette leaf area in response to combinatorial stress were mapped using univariate and multi‐environment genome‐wide association approaches. A single‐nucleotide polymorphism (SNP) associated with DROUGHT‐INDUCED 19 (DI19) was identified by both approaches, supporting its potential involvement in the response to combinatorial stress. Several SNPs were found to be in linkage disequilibrium with known stress‐responsive genes such as PEROXIDASE 34 (PRX34), BASIC LEUCINE ZIPPER 25 (bZIP25), RESISTANCE METHYLATED GENE 1 (RMG1) and WHITE RUST RESISTANCE 4 (WRR4). An antagonistic effect between biotic and osmotic stress was found for prx34 and arf4 mutants, which suggests PRX34 and ARF4 play an important role in the response to the combinatorial stress. [ABSTRACT FROM AUTHOR]

The contribution of genetic adaptation and plasticity to intraspecific phenotypic variability remains insufficiently studied in long‐lived plants, as well as the relevance of neutral versus adaptive processes determining such divergence. We examined the importance of phylogeographic structure and climate in modulating genetic and plastic changes and their interdependence in fitness‐related traits of a widespread Mediterranean conifer (Pinus pinaster). Four marker‐based, previously defined neutral classifications along with two ad hoc climate‐based categorizations of 123 range‐wide populations were analyzed for their capacity to summarize genetic and plastic effects of height growth and survival (age 20) in 15 common gardens. The plasticity of tree height and differential survival were interpreted through mixed modeling accounting for heteroscedasticity in the genotype‐by‐environment dataset. The analysis revealed a slight superiority of phylogeographic classifications over climate categorizations on the explanation of genetic and plastic effects, which suggests that neutral processes can be at least as important as isolation by climate as a driving factor of evolutionary divergence in a prevalent pine. The best phylogeographic classification involved eight geographically discrete genetic groups, which explained 92% (height) and 52% (survival) of phenotypic variability, including between‐group mean differentiation and differential expression across trials. For height growth, there was high predictability of plastic group responses described by different reaction norm slopes, which were unrelated to between‐group mean differentiation. The latter differences (amounting to ca. 40% among groups) dominated intraspecific performance across trials. Local adaptation was evident for genetic groups tested in their native environments in terms of tree height and, especially, survival. This finding was supported by QST > FST estimates. Additionally, our range‐wide evaluation did not support a general adaptive syndrome by which less reactive groups to ameliorated conditions would be associated with high survival and low growth. In fact, a lack of relationship between mean group differentiation, indicative of genetic adaptation, and predictable group plasticity for height growth suggests different evolutionary trajectories of these mechanisms of phenotypic divergence. Altogether, the existence of predictable adaptive‐trait phenotypic variation for the species, involving both genetic differentiation and plastic effects, should facilitate integrating genomics and environment into decision‐making tools to assist forests in coping with climate change. [ABSTRACT FROM AUTHOR]

The genetic progress achieved by animal breeding programs may be affected by genotype-by-climate interactions (GxC). This systematic literature review assesses the scientific evidence for GxC on multiple traits of cattle, pigs, and poultry. Two search engines (Scopus, Web of Science) were queried for original peer-reviewed scientific (English full-text) studies. We included (1) observational studies and designed experiments considering dairy or beef cattle, swine, chicken or turkeys, where (2) at least one production, fertility, or health trait was tested for GxC, (3) the existence of GxC was tested directly based on temperature, relative humidity, or climatic indices for heat or cold stress, and (4) genetic effects within a breed or line were investigated. The search resulted in 46 eligible studies, with a (low) risk that the requirement of full-text English studies may have resulted in some work not included in this review. Our review shows an increase in GxC studies from 2015 onwards. Cattle was the most studied species (n = 36; 78.3%), whereas eligible studies on turkeys were lacking. Climatic parameters used in the studies ranged from well-known parameters (e.g., temperature, temperature-humidity index) to more complex indices combining temperature, relative humidity, wind speed and solar radiation. All observational studies (n = 40; 87.0%) used weather station data. In total, 75 traits were studied, which were predominantly production traits regardless of species. Studies on fertility and health traits have been emerging from 2010 onwards, but their numbers still lag far behind those for production traits. Genotype-by-climate interaction was confirmed in 54.0% of the study outcomes. This systematic review shows that little is known about the role of GxC in health and fertility traits in cattle, and for all traits in pigs and poultry in general. As current evidence shows that genotype-by-climate interaction is common across species and traits, we suggest to collect detailed climatic data and use them to assess the presence of GxC in indoor and outdoor production systems, as well. [ABSTRACT FROM AUTHOR]

Parenting behaviors are among the most robust predictors of youth resilience to adversity. Critically, however, very few studies examining these effects have been genetically-informed, and none have considered parenting as an etiologic moderator of resilience. What's more, despite the multidimensionality of resilience, extant etiologic literature has largely focused on a single domain. The current study sought to fill these respective gaps in the literature by examining whether and how parental nurturance shapes the etiology of academic, social, and psychological resilience, respectively. We employed a unique sample of twins (N = 426 pairs; ages 6–11) exposed to moderate-to-severe levels of environmental adversity (i.e., family poverty, neighborhood poverty, community violence) from the Twin Study of Behavioral and Emotional Development in Children. As expected, parental nurturance was positively correlated with all forms of resilience. Extended univariate genotype-by-environment interaction models revealed that parental nurturance significantly moderated genetic influences on all three domains of resilience (academic resilience A1= -0.53, psychological resilience A1= -1.22, social resilience A1= -0.63; all p <.05), such that as parental nurturance increased, genetic influences on youth resilience decreased. Put another way, children experiencing high levels of parental nurturance were more resilient to disadvantage, regardless of their genetic predisposition towards resilience. In the absence of nurturing parenting, however, genetic influences played an outsized role in the origins of resilience. Such findings indicate that parental nurturance may serve as a malleable protective factor that increases youth resilience regardless of genetic influences. [ABSTRACT FROM AUTHOR]

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.

Abstract Background The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement. Methods The study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times. Results The pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression ( S d i 2 ), the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance ( S ( 2 ) i ), indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones. Conclusions Therefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.

Abstract Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.

Dissection of genotype-by-environment interaction (GEI) effects has a key role in identifying stable high-yielding genotypes across various environments before commercial release. This study sought to assess the GEI effect and to identify superior barley genotypes using 32 parametric and non-parametric stability statistics. Eighteen new promising genotypes along with one improved cultivar (as check) were evaluated across ten environments during the 2019–2021 cropping seasons in the warm climate of Iran. The AMMI analysis of variance indicated that genotype, environment, and GEI effects were significant for grain yield. Furthermore, partitioning of the GEI effect showed that the first six interaction principal components (IPCA1–IPCA6) were highly significant. Multivariate analyses classified all measured statistics into five groups based on the dynamic and static concepts of stability. Among the stability statistics, HMGV, RPGV, HMRPGV, CV, NP(2), NP(3), NP(4), KR, S(3), and S(6) showed a dynamic concept of stability. Based on all methods, genotypes G2, G3, G9, G11, G14, and G17 were identified as stable high-yielding genotypes. In general, based on all of the used approaches, G9 and G11 were identified as the best genotypes for cultivation in the warm regions of Iran; hence, these genotypes can be considered for commercial release. [ABSTRACT FROM AUTHOR]

Simple Summary: Lamb fattening on pasture needs to address consumer demands for the extensive production of animal products with high animal welfare standards and also reduce management load. Therefore, breeding programs should focus on adequate growth and fattening performance in pasture-based production environments. In practice, progeny testing to evaluate fattening performance is commonly performed indoors. This does not represent the actual production environment, which is on pasture in many farms. In this simulation study, different scenarios with varying progeny testing schemes for fattening traits were simulated. The results revealed that a higher number of available phenotypes generally increased the accuracy of breeding value estimation. The highest genetic gain was generated in the scenario with both station and field progeny testing. As the simulation design is highly flexible, the methodology used is applicable in other sheep populations after input data modification. Translating these results into practice could improve the conservation of traditional pasture-based sheep farming in terms of environmental and social factors and increase the income of sheep breeders by improving lamb growth. Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain. [ABSTRACT FROM AUTHOR]

Summary: Plants can rapidly mitigate the effects of suboptimal growth environments by phenotypic plasticity of fitness‐traits. While genetic variation for phenotypic plasticity offers the means for breeding climate‐resilient crop lines, accurate genomic prediction models for plasticity of fitness‐related traits are still lacking.Here, we employed condition‐ and accession‐specific metabolic models for 67 Arabidopsis thaliana accessions to dissect and predict plasticity of rosette growth to changes in nitrogen availability.We showed that specific reactions in photorespiration, linking carbon and nitrogen metabolism, as well as key pathways of central carbon metabolism exhibited substantial genetic variation for flux plasticity. We also demonstrated that, in comparison with a genomic prediction model for fresh weight (FW), genomic prediction of growth plasticity improves the predictability of FW under low nitrogen by 58.9% and by additional 15.4% when further integrating data on plasticity of metabolic fluxes.Therefore, the combination of metabolic and statistical modeling provides a stepping stone in understanding the molecular mechanisms and improving the predictability of plasticity for fitness‐related traits. [ABSTRACT FROM AUTHOR]

Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator’s effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10−6, indicating that these SNPs, which do not show main effects in traditional genome-wide association studies (GWAS), may have non-additive effects across genotypes but are obliterated by environmental means. The gene-based analysis using MAGMA identified three genes that overlapped with the GEWIS results exhibiting G × E, namely SMAD2, PALMD, and MECOM. Further, the results of functional exploration in gene-set analysis revealed the bio-mechanisms of how cattle growth responds to environmental changes, such as mitotic or cytokinesis, fatty acid β-oxidation, neurotransmitter activity, gap junction, and keratan sulfate degradation. This study not only reveals novel genetic loci and underlying mechanisms influencing growth traits but also transforms our understanding of environmental adaptation in beef cattle, thereby paving the way for more targeted and efficient breeding strategies.

The genetic progress achieved by animal breeding programs may be affected by genotype-by-climate interactions (GxC). This systematic literature review assesses the scientific evidence for GxC on multiple traits of cattle, pigs, and poultry. Two search engines (Scopus, Web of Science) were queried for original peer-reviewed scientific (English full-text) studies. We included (1) observational studies and designed experiments considering dairy or beef cattle, swine, chicken or turkeys, where (2) at least one production, fertility, or health trait was tested for GxC, (3) the existence of GxC was tested directly based on temperature, relative humidity, or climatic indices for heat or cold stress, and (4) genetic effects within a breed or line were investigated. The search resulted in 46 eligible studies, with a (low) risk that the requirement of full-text English studies may have resulted in some work not included in this review. Our review shows an increase in GxC studies from 2015 onwards. Cattle was the most studied species (n = 36; 78.3%), whereas eligible studies on turkeys were lacking. Climatic parameters used in the studies ranged from well-known parameters (e.g., temperature, temperature-humidity index) to more complex indices combining temperature, relative humidity, wind speed and solar radiation. All observational studies (n = 40; 87.0%) used weather station data. In total, 75 traits were studied, which were predominantly production traits regardless of species. Studies on fertility and health traits have been emerging from 2010 onwards, but their numbers still lag far behind those for production traits. Genotype-by-climate interaction was confirmed in 54.0% of the study outcomes. This systematic review shows that little is known about the role of GxC in health and fertility traits in cattle, and for all traits in pigs and poultry in general. As current evidence shows that genotype-by-climate interaction is common across species and traits, we suggest to collect detailed climatic data and use them to assess the presence of GxC in indoor and outdoor production systems, as well.

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program. [ABSTRACT FROM AUTHOR]

Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the lowyielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multienvironment trial through genomic prediction. [ABSTRACT FROM AUTHOR]

Background: The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement. Methods: The study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times. Results: The pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression (Sdi2), the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance (S(2)i), indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones. Conclusions: Therefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max (L.) Merrill.) yielding potential depends on environmental conditions (precipitation, temperature, soil). The aim of the work was to evaluate stability of yielding (and other traits) of three soybean cultivars (Abelina, SG Anser, Merlin) grown under the climatic conditions of central-eastern Poland. The studied material was obtain in a field experiment conducted at Łączka (52°15' N, 21°95' E) during the growing seasons of 2017-2019. Trait stability was determined based on Shukla's genotype stability variance and Wricke's ecovalence describing the genotype-by-environment interaction. For all the examined parameters, there were found significant differences between successive growing seasons, cultivars, and cultivars within study years. The greatest influence of environmental conditions (years) was determined for plant height (64%) and first pod height (54.2%). Stability parameters indicated that cv. Abelina was the most stable in terms of yielding, 1000 seed weight, seed number per pod and average seed number per pod, cv. SG Anser being the least stable in this respect. [ABSTRACT FROM AUTHOR]

In Cambodia, rice is predominantly produced in areas with rainfed lowland conditions where photoperiod-sensitive varieties are cultivated. A number of varieties have been released for rainfed lowland areas, and the rice grain yield has reportedly increased by approximately 12% over the past ten years. Moreover, great fluctuations in yield performance have been observed across different soil types of the rainfed ecosystems of Cambodia. Therefore, the present study aimed to analyze the grain yield and stability among ten popular varieties that were released for rainfed lowland ecosystems across the four different soil types in Cambodia in two years. The grain yield varied 566 g m−2 as the highest in clay soil and about 220 g m−2 as the lowest in sandy soil. A combined ANOVA revealed significant differences for the main effect of genotype, environment, and genotype-by-environment interaction (GEI) for all yield-related traits and grain yield per square meter. The principal component test results showed that the heterogeneity of grain yield was mainly attributable to the effect of environment, followed by the effect of genotype. In fertile conditions, a higher percentage of filled grains was supported by higher leaf N until the late stage with a wider flag leaf. In conditions of moderate fertility, larger numbers of panicles were supported by a higher percentage of productive culms with higher leaf N until the late stage. In conditions of poor fertility, a higher percentage of filled grains was supported by higher leaf N until the late stage, which is considered to be important for higher grain yield. The variety Phka Rumduol showed these preferable traits and produced higher yields in fertile to poor natural soil fertility conditions with moderate variation. This variety is considered to be more desirable and ideal due to its stability and higher grain yield. The other varieties, namely, Phka Mealdei, Phka Rumdeng, and CAR4, were identified as above-average yielders. Therefore, those varieties potentially may be recommended for cultivation in rainfed lowland rice ecosystems in Cambodia due to their high yields. CAR4 showed moderate variation at the same level as Phka Rumduol. From the point of stability, Phka Rumduol and CAR4 can be expected to excel. [ABSTRACT FROM AUTHOR]

Due to increasing farmers' attention to sorghum (Sorghum bicolor (L.) Moench) cultivation in arid and semi-arid regions, it is necessary to introduce new, suitable sorghum cultivars. The present study aimed to evaluate the yield stability of grain sorghum genotypes via AMMI and GGE biplot analyses to identify high-yielding and best adapted genotypes for release in Iran's warm-temperate regions. Seven promising grain sorghum lines (KGS15, KGS19, KGS23, KGS25, KGS27, KGS32, and KGS36), along with three commercial cultivars (Kimiya, Payam, and Sepideh), were studied in seven regions of Iran (Karaj, Isfahan, Gorgan, Birjand, Shiraz, Zabol, and Hamedan) during the 2019 and 2020 growing seasons. AMMI analysis showed that genotypes G7, G3, and G6 had the lowest AMMI stability values (ASV). GGE biplot analysis showed that genotype G6 had the highest grain yield (GY) in most environments, whereas genotype G1 was specifically adapted to the Hamedan province. According to the average-environment coordination of the GGE biplot, genotype G6 was the most desirable genotype, significantly better than the second and third best genotypes G4 and G3 (G4 was missed by AMMI). This study also indicated that the GGE biplot method was more effective than the AMMI method in analyzing genotype by environment and identifying superior genotypes. Overall, G6 can be introduced as the superior-most genotype for cultivation in Iran's warm-temperate regions. [ABSTRACT FROM AUTHOR]

Domestication and agricultural intensification dramatically altered maize and its cultivation environment. Changes in maize genetics (G) and environmental (E) conditions increased productivity under high-synthetic-input conditions. However, novel selective pressures on the rhizobiome may have incurred undesirable tradeoffs in organic agroecosystems, where plants obtain nutrients via microbially mediated processes including mineralization of organic matter. Using twelve maize genotypes representing an evolutionary transect (teosintes, landraces, inbred parents of modern elite germplasm, and modern hybrids) and two agricultural soils with contrasting long-term management, we integrated analyses of rhizobiome community structure, potential microbe-microbe interactions, and N-cycling functional genes to better understand the impacts of maize evolution and soil management legacy on rhizobiome recruitment. We show complex shifts in rhizobiome communities during directed evolution of maize (defined as the transition from teosinte to modern hybrids), with a larger effect of domestication (teosinte to landraces) than modern breeding (inbreds to hybrids) on rhizobiome structure and greater impacts of modern breeding on potential microbe-microbe interactions. Rhizobiome structure was significantly correlated with plant nutrient composition. Furthermore, plant biomass and nutrient content were affected by G x E interactions in which teosinte and landrace genotypes had better relative performance in the organic legacy soil than inbred and modern genotypes. The abundance of six N-cycling genes of relevance for plant nutrition and N loss pathways did not significantly differ between teosinte and modern rhizospheres in either soil management legacy. These results provide insight into the potential for improving maize adaptation to organic systems and contribute to interdisciplinary efforts toward developing resource-efficient, biologically based agroecosystems.

Abstract Fusarium head blight (FHB) is one of the major biotic constraints to wheat due to its direct detrimental effects on yield quality and quantity. To manage the disease, the deployment of resistant genotypes is ideal in terms of effectiveness, eco-friendliness, and sustainability of production. The study was conducted to determine the responses of different wheat genotypes to FHB, and to identify suitable and stable wheat genotype(s) regarding the FHB resistance and yield performance. A field study was carried out using eleven bread wheat genotypes in seven locations in southern Ethiopia during the 2019 main cropping season. A randomized complete block design with three-time replicates was applied in this study. The results showed that the lowest mean FHB severity (11.33%) and highest mean yield (4.54 t/ha) were recorded at Bonke. Conversely, the highest mean FHB severity (83.38%) and the lowest mean yield (0.94 t/ha) were observed at North Ari. It was also showed that maximum mean FHB severity (49.25%) and minimum mean yield (2.95 t/ha) were recorded on the genotype Hidase under crosswise assessment. Across locations, a minimum mean FHB severity (17.54, 18.83, and 21.31%) and maximum mean yield (3.92, 3.96, and 3.93 t/ha) were noted from the Shorima, Bondena, and Wane genotypes, respectively. GGE biplot analysis and various comparison tests for FHB severity revealed a higher percentage of variation concerning FHB resistance reactions due to the environment (47% as an interactive element), followed by genotype by environment interaction (21%). AMMI analysis revealed genotype, environment, and genotype by environment interaction had a total variation of 7.10, 58.20, and 17.90% for yield performance, respectively. The inconsistency between genotype responses to FHB and yield performance demonstrated that the environmental component was responsible for significant variability in FHB reaction, yield performance, and the dominance of cross-over interaction. However, the greatest level of resistance to FHB was comparatively found in the genotypes Shorima, Bondena, Wane, and Huluka across locations. Considering both FHB resistance response and yield stability, in most environments, Shorima, Bondena, Wane, and Huluka genotypes were suggested for consideration of cultivation where they are well-performed under the pressure of FHB. North Ari and Hulbareg were acknowledged as more discriminating environments than the others for test genotypes against FHB. Bonke and Chencha were considered ideal environments for selecting superior genotypes with good yield performance.

Abstract Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype‐by‐environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype‐specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype‐specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site‐specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar‐specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.

Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.

Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones.

Abstract Background Genotypic information produced from single nucleotide polymorphism (SNP) arrays has routinely been used to identify genomic regions associated with complex traits in beef and dairy cattle. Herein, we assembled a dataset consisting of 15,815 Red Angus beef cattle distributed across the continental U.S. and a union set of 836,118 imputed SNPs to conduct genome-wide association analyses (GWAA) for growth traits using univariate linear mixed models (LMM); including birth weight, weaning weight, and yearling weight. Genomic relationship matrix heritability estimates were produced for all growth traits, and genotype-by-environment (GxE) interactions were investigated. Results Moderate to high heritabilities with small standard errors were estimated for birth weight (0.51 ± 0.01), weaning weight (0.25 ± 0.01), and yearling weight (0.42 ± 0.01). GWAA revealed 12 pleiotropic QTL (BTA6, BTA14, BTA20) influencing Red Angus birth weight, weaning weight, and yearling weight which met a nominal significance threshold (P ≤ 1e-05) for polygenic traits using 836K imputed SNPs. Moreover, positional candidate genes associated with Red Angus growth traits in this study (i.e., LCORL, LOC782905, NCAPG, HERC6, FAM184B, SLIT2, MMRN1, KCNIP4, CCSER1, GRID2, ARRDC3, PLAG1, IMPAD1, NSMAF, PENK, LOC112449660, MOS, SH3PXD2B, STC2, CPEB4) were also previously associated with feed efficiency, growth, and carcass traits in beef cattle. Collectively, 14 significant GxE interactions were also detected, but were less consistent among the investigated traits at a nominal significance threshold (P ≤ 1e-05); with one pleiotropic GxE interaction detected on BTA28 (24 Mb) for Red Angus weaning weight and yearling weight. Conclusions Sixteen well-supported QTL regions detected from the GWAA and GxE GWAA for growth traits (birth weight, weaning weight, yearling weight) in U.S. Red Angus cattle were found to be pleiotropic. Twelve of these pleiotropic QTL were also identified in previous studies focusing on feed efficiency and growth traits in multiple beef breeds and/or their composites. In agreement with other beef cattle GxE studies our results implicate the role of vasodilation, metabolism, and the nervous system in the genetic sensitivity to environmental stress.

As one of the keys to healthy performance, robustness of farm animals is gaining importance, and with this comes increasing interest in genetic dissection of genotype-by-environment interactions (G×E). Changes in gene expression are among the most sensitive responses conveying adaptation to environmental stimuli. Environmentally responsive regulatory variation thus likely plays a central role in G×E. In the present study, we set out to detect action of environmentally responsive cis-regulatory variation by the analysis of condition-dependent allele specific expression (cd-ASE) in porcine immune cells. For this, we harnessed mRNA-sequencing data of peripheral blood mononuclear cells (PBMCs) stimulated in vitro with lipopolysaccharide, dexamethasone, or their combination. These treatments mimic common challenges such as bacterial infection or stress, and induce vast transcriptome changes. About two thirds of the examined loci showed significant ASE in at least one treatment, and out of those about ten percent exhibited cd-ASE. Most of the ASE variants were not yet reported in the PigGTEx Atlas. Genes showing cd-ASE were enriched in cytokine signaling in immune system and include several key candidates for animal health. In contrast, genes showing no ASE featured cellcycle related functions. We confirmed LPS-dependent ASE for one of the top candidates, SOD2, which ranks among the major response genes in LPSstimulated monocytes. The results of the present study demonstrate the potential of in vitro cell models coupled with cd-ASE analysis for the investigation of G×E in farm animals. The identified loci may benefit efforts to unravel the genetic basis of robustness and improvement of health and welfare in pigs. [ABSTRACT FROM AUTHOR]

Soybean is a crop in high demand, in particular as a crucial source of plant protein. As a short-day plant, soybean is sensitive to the latitude of the growing site. Consequently, varieties that are well adapted to higher latitudes are required to expand the cultivation. In this study, we employed 50 soybean genotypes to perform a multi-location trial at seven locations across Germany in 2021. Two environmental target regions were determined following the latitude of the locations. Adaptation and trait stability of seed yield and protein content across all locations were evaluated using Genotype plus Genotype-by-Environment (GGE) biplots and Shukla's stability variance. We found a moderate level of crossing-over type genotype-by-location interaction across all locations. Within the environmental target regions, the genotype-by-location interaction could be minimised. Despite the positive correlation (R = 0.59) of seed yield between the environmental target regions and the same best-performing genotype, the genotype rankings differed in part substantially. In conclusion, we found that soybean can be grown at a wide range of latitudes across Germany. However, the performance of genotypes differed between the northern and southern locations, with an 18.8% higher mean yield in the south. This in combination with the observed rank changes of high-performing genotypes between both environmental target regions suggests that selection targeted towards environments in northern Germany could improve soybean breeding for those higher latitude regions. [ABSTRACT FROM AUTHOR]

The soybean yield is a complex quantitative trait that is significantly influenced by environmental factors. G × E interaction (GEI), which derives the performance of soybean genotypes differentially in various environmental conditions, is one of the main obstacles to increasing the net production. The primary goal of this study is to identify the outperforming genotypes in different latitudes, which can then be used in future breeding programs. A total of 96 soybean genotypes were examined in two different ecological regions: Faisalabad and Tando Jam in Pakistan. The evaluation of genotypes in different environmental conditions showed a substantial amount of genetic diversity for grain yield. We identified 13 environment-specific genotypes showing their maximum grain yield in each environment. Genotype G69 was found to be an ideal genotype with higher grain yield than other genotypes tested in this study and is broadly adapted for environments E1 and E2 and also included in topyielding genotypes in E3, E4, and E5. G92 is another genotype that is broadly adapted in E1, E3, and E4. In the case of environments, E3 is suggested to be a more ideal environment as it is plotted near the concentric circle and is very informative for the selection of genotypes with high yield. Despite the presence of GEI, advances in DNA technology provided very useful tools to investigate the insight of advanced genotypes. Association mapping is a useful method for swiftly and efficiently investigating the genetic basis of significant plant traits. A total of 26 marker-trait associations were found for six agronomic traits in five environments, with the highest significance (p-value = 2.48 × 10-08) for plant height and the lowest significance (1.03 × 10-03) for hundred-grain weight. Soybean genotypes identified in the present study could be a valuable source for future breeding programs as they are adaptable to a wide range of environments. Genetic selection of genotypes with the best yields can be used for gross grain production in a wide range of climatic conditions, and it would give an essential reference in terms of soybean variety selection. [ABSTRACT FROM AUTHOR]

Genotype-by-environment (GEI) analysis guides the recommendation of best-performing crop genotypes and production environments. The objective of this study was to determine the extent of GEI on seed yield in tepary bean for genotype recommendation and cultivation in drought-prone environments. Forty-five genetically diverse tepary bean genotypes were evaluated under non-stressed and drought-stressed conditions for two seasons using a 9 × 5 alpha lattice design with three replications in four testing environments. Data were collected on seed yield (SY) and days to physiological maturity (DTM) and computed using a combined analysis of variance, the additive main effect and multiplicative interaction (AMMI), the best linear unbiased predictors (BLUPs), the yield stability index (YSI), the weighted average of absolute scores (WAASB) index, the multi-trait stability index (MTSI), and a superiority measure. AMMI analysis revealed a significant (p < 0.001) GEI, accounting for 13.82% of the total variation. Genotype performance was variable across the test environments, allowing the selection of best-suited candidates for the target production environment. The environment accounted for a substantial yield variation of 52.62%. The first and second interaction principal component axes accounted for 94.8 and 4.7% of the total variation in the AMMI-2 model, respectively, of surmountable variation due to GEI. The AMMI 2 model family was sufficient to guide the selection of high-yielding and stable genotypes. Based on best linear unbiased predictors (BLUPs), yield stability index (YSI), superiority measure (Pi), and broad adaptation, the following tepary bean genotypes were identified as high-yielding and suited for drought-prone environments: G40138, G40148, G40140, G40135, and G40158. The selected tepary bean genotypes are recommended for cultivation and breeding in Malawi or other related agroecologies. [ABSTRACT FROM AUTHOR]

Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones. [ABSTRACT FROM AUTHOR]

The costs and benefits of being social vary with environmental conditions, so individuals must weigh the balance between these trade‐offs in response to changes in the environment. Temperature is a salient environmental factor that may play a key role in altering the costs and benefits of sociality through its effects on food availability, predator abundance, and other ecological parameters. In ectotherms, changes in temperature also have direct effects on physiological traits linked to social behaviour, such as metabolic rate and locomotor performance. In light of climate change, it is therefore important to understand the potential effects of temperature on sociality. Here, we took the advantage of a 'natural experiment' of threespine sticklebacks from contrasting thermal environments in Iceland: geothermally warmed water bodies (warm habitats) and adjacent ambient‐temperature water bodies (cold habitats) that were either linked (sympatric) or physically distinct (allopatric). We first measured the sociability of wild‐caught adult fish from warm and cold habitats after acclimation to a low and a high temperature. At both acclimation temperatures, fish from the allopatric warm habitat were less social than those from the allopatric cold habitat, whereas fish from sympatric warm and cold habitats showed no differences in sociability. To determine whether differences in sociability between thermal habitats in the allopatric population were heritable, we used a common garden breeding design where individuals from the warm and the cold habitat were reared at a low or high temperature for two generations. We found that sociability was indeed heritable but also influenced by rearing temperature, suggesting that thermal conditions during early life can play an important role in influencing social behaviour in adulthood. By providing the first evidence for a causal effect of rearing temperature on social behaviour, our study provides novel insights into how a warming world may influence sociality in animal populations. [ABSTRACT FROM AUTHOR]

Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain.

As one of the keys to healthy performance, robustness of farm animals is gaining importance, and with this comes increasing interest in genetic dissection of genotype-by-environment interactions (G×E). Changes in gene expression are among the most sensitive responses conveying adaptation to environmental stimuli. Environmentally responsive regulatory variation thus likely plays a central role in G×E. In the present study, we set out to detect action of environmentally responsive cis-regulatory variation by the analysis of condition-dependent allele specific expression (cd-ASE) in porcine immune cells. For this, we harnessed mRNA-sequencing data of peripheral blood mononuclear cells (PBMCs) stimulated in vitro with lipopolysaccharide, dexamethasone, or their combination. These treatments mimic common challenges such as bacterial infection or stress, and induce vast transcriptome changes. About two thirds of the examined loci showed significant ASE in at least one treatment, and out of those about ten percent exhibited cd-ASE. Most of the ASE variants were not yet reported in the PigGTEx Atlas. Genes showing cd-ASE were enriched in cytokine signaling in immune system and include several key candidates for animal health. In contrast, genes showing no ASE featured cell-cycle related functions. We confirmed LPS-dependent ASE for one of the top candidates, SOD2, which ranks among the major response genes in LPS-stimulated monocytes. The results of the present study demonstrate the potential of in vitro cell models coupled with cd-ASE analysis for the investigation of G×E in farm animals. The identified loci may benefit efforts to unravel the genetic basis of robustness and improvement of health and welfare in pigs.

The soybean yield is a complex quantitative trait that is significantly influenced by environmental factors. G × E interaction (GEI), which derives the performance of soybean genotypes differentially in various environmental conditions, is one of the main obstacles to increasing the net production. The primary goal of this study is to identify the outperforming genotypes in different latitudes, which can then be used in future breeding programs. A total of 96 soybean genotypes were examined in two different ecological regions: Faisalabad and Tando Jam in Pakistan. The evaluation of genotypes in different environmental conditions showed a substantial amount of genetic diversity for grain yield. We identified 13 environment-specific genotypes showing their maximum grain yield in each environment. Genotype G69 was found to be an ideal genotype with higher grain yield than other genotypes tested in this study and is broadly adapted for environments E1 and E2 and also included in top-yielding genotypes in E3, E4, and E5. G92 is another genotype that is broadly adapted in E1, E3, and E4. In the case of environments, E3 is suggested to be a more ideal environment as it is plotted near the concentric circle and is very informative for the selection of genotypes with high yield. Despite the presence of GEI, advances in DNA technology provided very useful tools to investigate the insight of advanced genotypes. Association mapping is a useful method for swiftly and efficiently investigating the genetic basis of significant plant traits. A total of 26 marker–trait associations were found for six agronomic traits in five environments, with the highest significance (p-value = 2.48 × 10–08) for plant height and the lowest significance (1.03 × 10–03) for hundred-grain weight. Soybean genotypes identified in the present study could be a valuable source for future breeding programs as they are adaptable to a wide range of environments. Genetic selection of genotypes with the best yields can be used for gross grain production in a wide range of climatic conditions, and it would give an essential reference in terms of soybean variety selection.

Fusarium head blight (FHB) is one of the major biotic constraints to wheat due to its direct detrimental effects on yield quality and quantity. To manage the disease, the deployment of resistant genotypes is ideal in terms of effectiveness, eco-friendliness, and sustainability of production. The study was conducted to determine the responses of different wheat genotypes to FHB, and to identify suitable and stable wheat genotype(s) regarding the FHB resistance and yield performance. A field study was carried out using eleven bread wheat genotypes in seven locations in southern Ethiopia during the 2019 main cropping season. A randomized complete block design with three-time replicates was applied in this study. The results showed that the lowest mean FHB severity (11.33%) and highest mean yield (4.54 t/ha) were recorded at Bonke. Conversely, the highest mean FHB severity (83.38%) and the lowest mean yield (0.94 t/ha) were observed at North Ari. It was also showed that maximum mean FHB severity (49.25%) and minimum mean yield (2.95 t/ha) were recorded on the genotype Hidase under crosswise assessment. Across locations, a minimum mean FHB severity (17.54, 18.83, and 21.31%) and maximum mean yield (3.92, 3.96, and 3.93 t/ha) were noted from the Shorima, Bondena, and Wane genotypes, respectively. GGE biplot analysis and various comparison tests for FHB severity revealed a higher percentage of variation concerning FHB resistance reactions due to the environment (47% as an interactive element), followed by genotype by environment interaction (21%). AMMI analysis revealed genotype, environment, and genotype by environment interaction had a total variation of 7.10, 58.20, and 17.90% for yield performance, respectively. The inconsistency between genotype responses to FHB and yield performance demonstrated that the environmental component was responsible for significant variability in FHB reaction, yield performance, and the dominance of cross-over interaction. However, the greatest level of resistance to FHB was comparatively found in the genotypes Shorima, Bondena, Wane, and Huluka across locations. Considering both FHB resistance response and yield stability, in most environments, Shorima, Bondena, Wane, and Huluka genotypes were suggested for consideration of cultivation where they are well-performed under the pressure of FHB. North Ari and Hulbareg were acknowledged as more discriminating environments than the others for test genotypes against FHB. Bonke and Chencha were considered ideal environments for selecting superior genotypes with good yield performance. [ABSTRACT FROM AUTHOR]

Deciphering the genetic basis of complex traits like nitrogen use efficiency (NUE) requires understanding the genotype-by-environment (G × E) interaction and linking physiological functions and agronomic traits to DNA markers. Multi-environment experiments were conducted in different environments representing low and high nitrogen levels combined with rain-fed and irrigation production conditions at three different locations in Ethiopia: Debre-Tabor, Injibara and Koga, in 2013 and 2015. The objectives of the study were to determine the G × E interaction and stability of genotypes for NUE of potato and to identify markers associated with NUE and NUE-related agronomic and physiological traits in potato under these diverse environments. Data were analyzed using GenStat, and genotype plus the genotype and environment (GGE) biplot model; the marker-trait associations were discovered using the R-software package GWASpoly. The analysis of variance that included location and production system had estimates for genotype variance (σ2g) that were low compared with the estimates for environment variance (σ2e) and genotype-by-environment interaction variance (σ2ge) for most measured traits at both N levels. The GGE analysis identified two mega-environments that coincided with the two production systems. The high N level environments both at Debre-Tabor and Injibara, and the low N environment at Koga, respectively, were the most suitable environments for discriminating the potato cultivars and being representative test environments for NUE evaluation in the rain-fed mega-environment and irrigation mega-environment. A total of 77 marker trait associations were identified for NUE and NUE-related agronomic and physiological traits. Multi-trait genomic regions that harbored significant marker-trait associations for NUE and NUE-related traits were found on chromosomes III, V and VI. The effect of production season was greater than the effect of N levels on QTL × environment interaction for most NUE-related traits. [ABSTRACT FROM AUTHOR]