Genotype Environment Research Articles

Performance, adaptability, and multi‐trait stability of rice varieties (Oryza sativa L.) in rainfed conditions

AbstractRainfed rice (Oryza sativa L.) cultivation is crucial for meeting food demand in regions like Bangladesh, where irrigation resources are scarce. A study was conducted in five rainfed districts during the 2022 Aman season. The objective was to assess grain yield performance, adaptation patterns, and identify a stable rice variety using multiple trait evaluations. The results showed that Binadhan‐17 produced the highest grain yield, reaching 5.58 t/ha, with its maximum yield recorded in Gopalganj at 6.12 t/ha. Heritability estimates from a linear mixed model were high for plant height (0.93) and grain yield (0.65), with genotypic variance contributing 92.70% and 64.84% to these traits, respectively. A negative correlation (r = −0.43) indicated that shorter varieties tend to perform better for grain yield. Stability analysis using weighted average absolute scores and best linear unbiased prediction identified Binadhan‐17 as the most stable for grain yield, while the genotype–genotype environment biplot confirmed its adaptability across all locations. Factor analysis of the multi‐trait stability index showed stability across traits, with selection gains from 1.49% to 6.99%. Binadhan‐22 was identified as the most stable for average performance and multi‐trait stability. Given its high yield and consistent performance across environments, Binadhan‐17 is recommended for large‐scale cultivation, while Binadhan‐22 offers a reliable, stable alternative across various traits.

Genotypic Differences in Agro-Physiological, Yield and Yield-Related Traits Responses to Saline Field Environment for Rice Genotypes through Line x Tester Analysis

Genotypic Differences in Agro-Physiological, Yield and Yield-Related Traits Responses to Saline Field Environment for Rice Genotypes through Line x Tester Analysis

Maladaptation in cereal crop landraces following a soot-producing climate catastrophe.

Aerosol-producing global catastrophes such as nuclear war, super-volcano eruption, or asteroid strike, although rare, pose a serious threat to human survival. Light-absorbing aerosols would sharply reduce temperature and solar radiation reaching the earth's surface, decreasing crop productivity including for locally adapted traditional crop varieties, i.e. landraces. Here, we test post-catastrophic climate impacts on barley, maize, rice, and sorghum, four crops with extensive landrace cultivation, under a range of nuclear war scenarios that differ in the amount of soot injected into the climate model. We used a crop growth model to estimate gradients of environmental stressors that drive local adaptation. We then fit genotype environment associations using high density genomic markers with gradient forest offset (GF offset) methods and predicted maladaptation through time. As a validation, we found that our GF models successfully predicted local adaptation of maize landraces in multiple common gardens across Mexico. We found strong concordance between GF offset and disruptions in climate, and landraces were predicted to be the most maladapted across space and time where soot-induced climate change was the greatest. We further used our GF models to identify landrace varieties best matched to specific post-catastrophic conditions, indicating potential substitutions for agricultural resilience. We found the best landrace genotype was often far away or in another nation, though countries with more climatic diversity had better within-country substitutions. Our results highlight that a soot-producing catastrophe would result in the global maladaptation of landraces and suggest that current landrace adaptive diversity is insufficient for agricultural resilience in the case of the scenarios with the greatest change to climate.

Stability of anthocyanin, carotenoid and root yield of 10 sweet potato genotypes

ABSTRACT Sweet potato roots are rich in phytochemicals. The objective of this study was to evaluate the stability of number of storage roots, storage root dry weight, harvest index and anthocyanin and carotenoid contents of 10 sweet potato genotypes. Ten sweet potato genotypes were evaluated in a randomised complete block design with three replications at three locations in Surin province, including Rajamangala University of Technology Isan (RMUTI), Chomphra district and Sanom district during November 2022 to March 2023. Results indicated that the environment significantly influenced most traits, except for carotenoid content. Genotypes and genotype–environment interactions were significant for all traits. Number of storage roots was highest in genotypes found in RMUTI. Storage root yield and anthocyanin content was highest in genotypes found in Chomphra district, while harvest index and carotenoid content were highest in genotypes found in Sanom district. SR 2022/03 had the highest storage root yield across locations and in Chomphra district. KS 2022/07 had the highest harvest index and anthocyanin content across locations. SR 2022/10 had the highest anthocyanin content at RMUTI and also had the highest carotenoid content across locations. KS 2022/07 had the most stable storage root yield. SR 2022/01 had the most stable anthocyanin and carotenoid contents. Information on the levels and variability of phytochemicals in sweet potato enables breeders to select sweet potato genotypes with high and stable phytochemicals for sweet potato breeding.

Application of univariate, multivariate, and mixed models to the stability analysis of Ethiopian tetraploid wheat cultivars under irrigation condition

AbstractThe testing of durum wheat (Triticum turgidum subsp. durum) varieties in different irrigated environments is critical for determining the stability of their performance and adaptation. In this study, 12 popular and newly developed durum wheat varieties were examined in eight irrigated locations with the purpose of investigating genotype–environment interaction and their effect on Ethiopian tetraploid wheat yield stability across diverse environments. The field experiment has two replications with a randomized complete block design. Multivariate (analysis of variance, additive main effect and multiplicative interaction [AMMI], and genetics, genetics × environment [GGE] biplot) and univariate (bi [regression coefficient], S2d [deviation from regression], σi2 [Shukla's stability variance], Wi2 [Wricke's ecovalence], YSi [yield stability statistic], and CVi [coefficient of variance]) analysis methods were used to identify stable varieties. The AMMI analysis showed that the genetic × environmental interaction was highly significant (p > 0.01), while the genotype and environment variation were not significant. The first two principal component axes (IPCA1 and IPCA2) were highly significant (p > 0.01) and contributed 79% of the total GE interaction. Univariate statistical models indicate that Bulala is a stable, high‐yielding variety that can adapt to various environments. GGE biplot analysis revealed that the eight test environments were clustered into three mega‐environments, ranked Bulala as the most stable variety across diverse environments. The results of the combined mean analysis, multivariate and univariate models revealed that Bulala is a high yielder (3.46 tons ha−1) and stable variety across all test environments, while Mukiye variety has a high yield (3.43 tons ha−1) but is not as stable or adaptive to multiple locations. As a result, Bulala was recommended for further demonstration and popularization in test locations and places with similar agroecologies under irrigation.

Assessing genotype-by-environment interactions for maydis leaf blight disease in maize (Zea mays L.) germplasm and identification of stable donors for breeding resistant hybrid varieties

Abstract This study investigates the impact of environmental factors and genotype-by-environment interactions (GEI) on the expression of maydis leaf blight (MLB) resistance in a diverse maize germplasm comprising 359 genotypes. Extensive field trials were conducted, involving artificial inoculations and disease scoring across two locations over two years. Using genotype and genotype–environment (GGE) biplot analysis based on the site regression model (SREG), we identified stable MLB-resistant 10 donors with consistent genotypic responses. These inbred lines, which consistently exhibited disease scores of ⩽3 across locations, are recommended as potential parents for breeding MLB-resistant varieties. Furthermore, the identification of a non-crossover interaction and high correlations among testing locations allowed us to define a single mega-environment for the initial screening of MLB resistance in a large set of maize germplasm. This study suggests that initial screenings can be efficiently conducted in one representative location, with validation of resistant lines at multiple sites during advanced breeding stages. This approach optimizes the use of land, labour and resources in MLB resistance testing.

Integrating genomics with crop modelling to predict maize yield and component traits: Towards the next generation of crop models

Conventional breeding of ideotypes for target environments is quite challenging because of the genotype by environment interaction and the nature of the genetic complexity for economic traits. Simulation of the adaptive capacity of existing and new germplasms using crop model and genetic information can efficiently assist in determining the potential of well-adapted genotypes for target environments. This study aimed to design a marker-based model by detecting associated markers for target traits associated with model input parameters and incorporating the genetic effects into the CERES-Maize model. To achieve this goal, a two-year trial with 282 maize genotypes across five locations in Northern China was conducted for phenotypic and genotypic data collection. The marker effects on target traits were integrated with crop model to develop a marker-based model. The performance of the integrated model was tested using four independent sub-datasets, (i) observed genotypes grown in observed environments; (ii) observed genotypes phenotyped in new environments; (iii) new genotypes in characterized environments; and (iv) new genotypes in new environments. The model simulated the anthesis date, kernel number, kernel weight and yield reasonably well across 282 genotypes. The marker-based prediction performance of simpler morphological traits, such anthesis date and kernel number were generally improved compared to highly complex quantitative traits, such as kernel weight and yield. The performance of the model was affected by new genotypes or new environments depending on the types of traits being simulated. Maker-based simulation of maize yield and its component traits across five locations and 37 years in Northern China was used as a case study to demonstrate the model applications for studying genotype–environment interactions. The biplot revealed the top yielding genotypes and most ideal environment by comparing yield performance and stability of 282 genotypes in five phenotyping sites under both water-limited and well-water conditions. Breeding programs could further exploit marker-based modelling to predict adaptation in diverse environmental and management conditions for new genotypes before they are globally distributed for multilocation yield testing.

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

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

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

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

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

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

Testing source elevation versus genotype as predictors of sugar pine performance in a post‐fire restoration planting

AbstractClimate change is motivating a reassessment of how seeds are selected for reforestation, as rapid environmental change can lead to local maladaptation in trees. Genetic association studies and past seed source climate both have the potential to help identify appropriate planting stock, but these techniques have not been compared and tested as part of an operational planting program. In this study, we combined an analysis of single nucleotide polymorphisms (SNPs) associated with environmental gradients in sugar pine (Pinus lambertiana) with an analysis of post‐fire seedling survival and growth in a restoration experiment. Our genotype–environment association (GEA) tests of 92 individuals from varying climates within CA revealed 829 SNPs (out of 300,604) with significant association with climate gradients, especially April snowpack. Of these, 323 either had annotations that suggested potential functional importance or were identified by two different methods. We then built Bayesian models of survival and growth for all seedlings in a separate post‐fire planting experiment, to test the relative predictive ability of source elevation (a common proxy for source climate) versus the proportion of seedling alleles expected to be locally advantageous based on GEA. Across three sites within the King Fire scar in Eldorado National Forest in 2017, 2018, and 2019, 1774 seedlings were planted. Of these, 206 had enough green needles in 2020 to allow sample collection, and 161 were successfully genotyped. We found that source elevation was generally better at predicting seedling performance than genotype indices, perhaps because of the limited scope of the association analysis. Seed sources from 500 to 1800 ft (152.4–548.6 m) lower in elevation, and one seed zone further south generally performed as well or better than local seed sources. This result, and those of similar previous studies, suggest that “climate matching” using past climate information for existing seed sourcing units is a reasonable starting point for finding seedlings suited to already‐altered planting site climate conditions. However, further tests with more extensive genomic and performance data may improve the utility of genotype information for seed selection.

Progeny Selection and Genetic Diversity in a Pinus taeda Clonal Seed Orchard

The present article describes the development of an improved Pinus taeda clonal seed orchard adapted to the edaphoclimatic conditions of Uruguay. Initially, 2068 hectares distributed in nine companies were prospected, and 124 plus trees were identified based on growth, straightness, and health traits. These trees were clonally propagated via grafting to establish a clonal seed orchard. For the genetic evaluation of the orchard, two progeny tests were carried out in the Rivera and Paysandú municipalities. Quantitative genetic analyses allowed us to identify a simple genotype–environment interaction and an expected genetic gain for volumes of 17%, 13%, and 8% for selection intensities of 12%, 25%, and 50%, respectively. Moreover, the genetic diversity of the 124 clones of the orchard was assessed using 10 microsatellite markers. The fingerprinting profiles allowed us to identify a total of 224 alleles. The polymorphism information content of the different markers was in the range of 0.594 to 0.895. The combined probability of identity and probability of identity among siblings had a discrimination power of 8.26 × 10–14 and 5.91 × 10–5, respectively. Analysis of the genetic structure demonstrated that the seed orchard population was not structured by the supplier.

Genotype and environmental interactions determine the root digging time and essential oil production in vetiver (Chrysopogon zizanioides (L.) Roberty)

Thirty different vetiver genotypes were grown for evaluation in the experiments at CSIR- Central Institute for Medicinal and Aromatic Plants (CSIR-CIMAP), Lucknow, U.P. 226015, India, to assess the impact of genotype × environment interactions (GEI) on essential oil yield and chemical composition in vetiver lines grown in north India, as well as identifying appropriate genotypes and representative root digging times throughout the year for essential oil production. The interplay of genotype and environment is a significant constraint in crop development. The additive main effects and multiplicative interaction (AMMI) and the analysis of variance (ANOVA) revealed significant effects for genotype (G), environment (E), and genotype × environment (GE) (p < 0.01). The primary root transverse section confirmed the changes in cortical sclerenchyma thickness and variety observed in the dug-out roots from different vetiver genotypes after six and twelve months. The quantity and number of sclerenchyma rows in the peripheral cortical region vary throughout vetiver genotypes. Following root digging, one component (Khusol content) had 40 %, while Khusilal content had 20 % for genotype VTR-23. However, after twelve months, Khusol content had 19 %, whereas Khusilal content had 41 %. The shift in essential oil makeup was apparent. The level of essential oil did not alter considerably. Based on the GGE biplot analysis, VTR-1, 11, and 14 were identified as the most stable genotypes with high essential yields; therefore, it is recommended to use these genotypes in India's enormous agriculture.

Multivariate Analysis Techniques and Tolerance Indices for Detecting Bread Wheat Genotypes of Drought Tolerance

Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (σgen:env2) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes.

Geographic variation in developmental plasticity among populations of the canyon treefrog in response to temperature and pond‐drying

AbstractUnderstanding how species respond to environmental changes, particularly in the context of climate change, is crucial for biodiversity conservation. This study focuses on the plastic responses of canyon tree frog (Dryophytes arenicolor) larvae to variations in temperature and pond‐drying, examining potential consequences of climate change. Frog larvae serve as an excellent model due to their high responsiveness to environmental cues during development. We analysed the impact of temperature and pond‐drying on morphological and life‐history traits, via a common garden experiment with individuals from three distinct populations with different ecological conditions. The experiments revealed significant differences in responses among populations, indicating geographic variation in plasticity. Pond‐drying treatments led to reduced survival and reduction of morphological traits and growth, challenging the assumption that tadpoles have adaptive responses to drying conditions. In contrast, temperature treatments showed variable effects, with elevated temperatures generally favouring growth rates, reducing metamorphosis time, and having population‐specific morphological shifts. We emphasize the importance of considering both morphological and life‐history traits, as well as geographic variation, in assessing species' vulnerability to climate change. Furthermore, the integration of environmental standardized plasticity index (ESPI) and relative distances plasticity index (RDPI) in amphibian developmental plasticity will allow to quantify and compare plastic responses among populations and even other amphibian species in which these metrics are obtained in the future. Our results underscore the complexity of phenotypic plasticity, revealing genotype–environment interactions. These findings contribute valuable insights into the potential adaptability of D. arenicolor populations to ongoing climate changes, highlighting the need for comprehensive inter‐population studies for a more nuanced understanding of species' responses to environmental change, and suggest that certain populations may be more vulnerable to extinction or better equipped to handle climate change based on their ability to adapt to environmental change.

Genetic analysis of popping quality traits and development of superior quality popcorn hybrids for a sustainable popcorn breeding program in India.

Popcorn is the most popular specialty maize and it makes a significant contribution to the Indian and global economies. Despite perfect exploration of heterosis in field corn, progress in popcorn breeding remains constrained due to its narrow genetic base, leading to a significant dependence on imports. In this study, 15 landrace- and population-derived inbreds from temperate and tropical germplasm were crossed with five testers, which are the parents of superior popcorn hybrids, in a line × tester mating design. Significant variation was observed in popping quality and agronomic traits among crosses evaluated across three locations representing diverse maize-based agro-climatic zones in India. Additive genetic variance governed the traits related to popping quality, whereas dominance variance was responsible for the agronomic traits. In addition to significant heterosis specific to certain traits, we identified promising crosses that exhibited superior performance in both popping quality and grain yield (GY). The genotype + genotype × environment (GGE) biplot methodology identified PMI-PC-104 and PMI-PC-101 as the best discriminating testers for popping quality traits and Dpcl-15-90 for GY. Lines PMI-PC-205, PMI-PC-207, and PMI-PC-209 were the best general combiners for popping quality traits and GY. The heterotic groups identified based on GGE-biplots and the magnitude, direction and stability of combining ability effects would serve in the development of competitive popcorn hybrids for a sustainable popcorn market. Using the additive nature of popping quality traits and the dominant nature of GY, recurrent intrapopulation selection can be employed to derive desirable popping quality inbreds with high GY for genetic enhancement. Desirable popping quality alleles from novel germplasm can thus be combined with high-yielding domestic elite inbreds to establish a sustainable popcorn breeding program. © 2024 Society of Chemical Industry.

Genotype by environment interaction and stability analysis for harvest date in sugar beet cultivars

This research assessed the quantitative and qualitative reactions of commercially grown sugar beets to four different harvest dates and their yield stability. The study followed a split-plot design within a randomized complete block design over 3 years. The main plot involved 10 sugar beet cultivars, while the subplot involved four harvest dates: August 13 (HD1), September 7 (HD2), October 3 (HD3), and November 12 (HD4). The study found that environmental conditions, genotypes, and harvest dates significantly affected various traits of sugar beet. Yearly environmental variations and their interactions with genotypes and harvest dates had substantial impacts on all measured traits at the 1% probability level. Additive main effect and multiplicative interaction analysis based on white sugar yield indicated that genotype and environment's additive effects, as well as the genotype–environment interaction, were significant at 1% probability level. Shokoufa and Arya, which exhibit high white sugar yield (WSY) and low first interaction principal component (IPC1) values, are identified as desirable due to their stability across different environments. Among the harvest dates in different years, the fourth and third dates showed a higher yield than the total average. Perfekta and Ekbatan exhibited high specific adaptability. According to the multi-trait stability index, Arta, Arya and Sina were recognized as stable and superior across all measured traits.

Eggs on a plate: Population‐dependent effects of humic substance rich water on perch egg size and hatching success

Abstract The ability of fish to sustain environmental perturbations during fragile early‐life stages can be of vital importance for populations and species facing rapid global environmental change. One of the potential challenges for aquatic organisms, including teleost fish, is rising concentration of dissolved organic carbon (DOC) in freshwater environments, known as brownification, which causes a cascade of changes including a decreased visibility and pH. Conversely, humic substances are increasingly recognised for their potential antimicrobial and immunostimulant properties. The questions remain whether exposure to humic waters high in DOC during the development causes significant perturbation, and to what extent genetic and parental factors may maintain optimal development in such a possibly challenging environment. To address these questions, we generated pure and hybrid (inter‐population) crosses of Eurasian perch (Perca fluviatilis) originating from clear‐water and humic lakes. Fertilised eggs from each cross were exposed to natural humic and clear‐water lake water. Intra‐chorionic space diameter (embryo diameter) and hatching success were recorded. Perch embryos successfully developed even in a small volume of water (5 mL) and majority hatched (70%) by 13 day post‐fertilisation. Embryo diameter was influenced by the interaction between cross type and treatment where embryos with maternal clear‐water origin were smaller than embryos of humic water origin in both clear‐water and humic water treatments. The probability of hatching was not related to female size, but rather to embryo diameter at the eyed stage and the interaction between cross type and treatment, resulting in pure clear‐water crosses hatching earlier and at higher numbers. Our study shows that effects of humic water are not uniformally negative in perch and discloses population‐dependent responses to humic water exposure during development. Our experimental design also illustrates the utility of multi‐well plates for conducting complex high‐throughput experiments beyond traditional model species and opens new possibilities for testing perch genotype–environment associations in early‐life traits. Taken together, results of this study add to the growing evidence that humic substances may have complex effects on fish during early life, motivating further investigations on their role in shaping natural phenotypes and potential use in the applied sector.

Model‐based multi‐genotype characterization of drought stress Target Population of Environments for the common bean in East Africa

AbstractEfforts toward the development of common bean varieties that can perform better under the drought conditions in Eastern Africa are constrained by significant genotype–environment–management (G × E × M) interactions. To address this, an attempt was made previously to assess drought stress for Eastern Africa using the Target Population of Environments (TPE) approach, albeit by using a single cultivar. Here, we extend earlier TPE work with a larger sample of representative genotypes from East Africa including commercial and breeding lines. By using a larger genotype pool, we are able to (1) assess the extent to which TPEs identified vary across genotypes (i.e., generality of the TPEs) and (2) identify best‐performing genotypes for the various environments identified. To perform the analyses, we used the CROPGRO‐DRYBEAN cropping system model available in the Decision Support System for Agrotechnology Transfer (DSSAT) v4.7 calibrated and evaluated from field trials in Africa and Colombia with 10 genotypes. Our findings indicate that despite some level of genotype‐related variation, the dominant drought stress patterns, and their priorities—drought stress‐free conditions remain the dominant TPE, followed by terminal drought stress—remain unaltered. Moreover, we find that it is possible to reduce the extent and intensity of drought stress in various TPEs by adequately targeting genotypes from the pool considered herein. Importantly, the genotype characteristics for stress‐free, extreme terminal drought and moderate terminal drought stress are significantly different, confirming a differentiated breeding strategy is required between the TPEs. The findings from this study are useful for the modernization of the bean breeding programs in Africa.

Genotype × environment interaction for seed amylopectin content in waxy sorghum [Sorghum bicolor (L.) Moench

Waxy sorghum flour can be put to multiple industrial uses. Thirteen waxy and three non-waxy sorghum genotypes were grown across four environments to assess relative genotype (G), environment (E) and genotype × environment (GEI) effects on amylopectin, grain yield and other morphological traits. The amylopectin content (APT) among the high amylopectin sorghum (HAS) lines across the locations ranged from 96.7 (HAS 9) to 99.1 (HAS 3, HAS 5, HAS 6 and HAS 12), while among the checks, it ranged from 78.1 to 81.8%. The G, E, GEI were significant for all the target traits. The genetic component explained more than 90% of the total variance for APT and the environment component for grain yield (GY). The APT and GY correlated significantly and negatively (r = -0.55; p &lt;0.05). The HAS lines produced smaller seeds in size than checks. The HAS 4 with red pericarp and HAS 5 with cream pericarp showed high GY and APT and HAS 4 is suitable for wider adaptation. The HAS lines yielded more at location Hagari, whereas HAS 6 (cream pericarp) and HAS 12 (red pericarp) were specifically adapted.