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Heat and drought are major stresses that significantly reduce seed yield of the common bean (Phaseolus vulgaris L.). In turn, this affects the profitability of the crop in climatic-vulnerable tropical arid regions, which happen to be the poorest and in most need of legume proteins. Therefore, it is imperative to broaden the sources of heat and drought resistance in the common bean by examining closely related species from warmer and drier environments (i.e., Tepary bean, P. acutifolius A. Gray), while harnessing such variation, typically polygenic, throughout advanced interspecific crossing schemes. As part of this study, interspecific congruity backcrosses for high temperature and drought tolerance conditions were characterized across four localities in coastal Colombia. Genotypes with high values of CO2 assimilation (>24 µmol CO2 m−2 s−1), promising yield scores (>19 g/plant), and high seed mineral content (Fe > 100 mg/kg) were identified at the warmest locality, Motilonia. At the driest locality, Caribia, one intercrossed genotype (i.e., 85) and the P. acutifolius G40001 control exhibited sufficient yield for commercial production (17.76 g/plant and 12.76 g/plant, respectively). Meanwhile, at southernmost Turipaná and Carmen de Bolívar localities, two clusters of genotypes exhibited high mean yield scores with 33.31 g/plant and 17.89 g/plant, respectively, and one genotype had an increased Fe content (109.7 mg/kg). Overall, a multi-environment AMMI analysis revealed that genotypes 13, 27, 82, and 84 were environmentally stable with higher yield scores compared to the Tepary control G40001. Ultimately, this study allows us to conclude that advanced common bean × Tepary bean interspecific congruity backcrosses are capable of pyramiding sufficient polygenic tolerance responses for the extreme weather conditions of coastal Colombia, which are likely to worsen due to climate change. Furthermore, some particular recombination events (i.e., genotype 68) show that there may be potential to couple breeding for heat and drought tolerance with Fe mineral biofortification, despite a prevalent trade-off, as a way to fight malnutrition of marginalized communities in tropical regions.

Capsicum spp. support diverse fresh and processing value chains, yet integrated assessments of phenotypic stability and genome-enabled prediction remain limited. In this study, 32 representative accessions, selected from a panel of 235 genotyped entries from the Colombian Capsicum germplasm collection, were evaluated across three contrasting environments to characterize physicochemical traits (texture, pH, soluble solids, color) and biochemical attributes (total carotenoids, capsaicin, dihydrocapsaicin, phenolics, antioxidant capacity). Variance partitioning and AMMI models quantified the contributions of genotype (G), environment (E), and G × E interactions (GEIs). Significant effects were detected for most traits. The AMMI analysis identified stable genotypes across locations for pH, moisture, firmness, and cohesiveness. In contrast, color attributes, total carotenoids, and phenolic compounds showed greater environmental responsiveness. Texture-related and solid content traits showed broad adaptability and high phenotypic stability, making them reliable targets for selection under variable production conditions. For the genomic component, we analyzed 235 accessions genotyped with 68,481 high-quality SNPs obtained through GBS. These data were used to estimate genomic heritability and prediction accuracy with Bayesian and semi-parametric models. Among them, BayesC showed the best performance. Prediction accuracy reached r = 0.94 within the training environment and ranged from r = 0.64 to 0.73 when tested across contrasting environments. Genomic heritability was highest for pH (h2 = 0.48) and pungency-related traits, including capsaicin (h2 = 0.39) and dihydrocapsaicin (h2 = 0.48), indicating strong additive genetic control. Finally, by integrating AMMI-based stability analysis and BayesC genomic prediction, we identified genotypes exhibiting both high performance and environmental robustness. This combined selection approach provides a comprehensive framework for genomic-assisted breeding to enhance fruit quality, carotenoid content, and pungency stability in Capsicum spp. under heterogeneous environments.

Assessing interspecific adaptive genetic variation across environmental gradients offers insight into the scale of habitat-dependent heritable heterotic effects, which may ultimately enable pre-breeding for abiotic stress tolerance and novel climates. However, environmentally dependent allelic effects are often bypassed by intra-specific single-locality genome-wide associations studies (GWAS). Therefore, in order to bridge this gap, this study aimed at coupling an advanced panel of drought/heat susceptible common bean (Phaseolus vulgaris L.) × tolerant tepary bean (P. acutifolius A. Gray) interspecific lines with last-generation multi-environment GWAS algorithms to identify novel sources of heat and drought tolerance to the humid and dry subregions of the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme weather. A total of 87 advanced lines with interspecific ancestries were genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Five yield traits were recorded for each genotype and inputted in modern GWAS algorithms (i.e., FarmCPU and BLINK) to identify the putative associated loci across four localities in coastal Colombia. Best-fit models revealed 47 significant quantitative trait nucleotides (QTNs) distributed in all 11 common bean chromosomes. A total of 90 flanking candidate genes were identified using 1-kb genomic windows centered in each associated SNP marker. Pathway-enriched analyses were done using the mapped output of the GWAS for each yield trait. Some genes were directly linked to the drought tolerance response; morphological, physiological, and metabolic regulation; signal transduction; and fatty acid and phospholipid metabolism. We conclude that habitat-dependent interspecific polygenic effects are likely sufficient to boost common bean adaptation to the severe climate in coastal Colombia via introgression breeding. Environmental-dependent polygenic adaptation may be due to contrasting levels of selection and the deleterious load across localities. This work offers putative associated loci for marker-assisted and genomic selection targeting the common bean’s neo-tropical lowland adaptation to drought and heat.

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation.

Grafting induces precocity and maintains clonal integrity in fruit tree crops. However, the complex rootstock × scion interaction often precludes understanding how the tree phenotype is shaped, limiting the potential to select optimum rootstocks. Therefore, it is necessary to assess (1) how seedling progenies inherit trait variation from elite ‘plus trees’, and (2) whether such family superiority may be transferred after grafting to the clonal scion. To bridge this gap, we quantified additive genetic parameters (i.e., narrow sense heritability— h 2 , and genetic-estimated breeding values—GEBVs) across landraces, “criollo”, “plus trees” of the super-food fruit tree crop avocado ( Persea americana Mill.), and their open-pollinated (OP) half-sib seedling families. Specifically, we used a genomic best linear unbiased prediction ( G -BLUP) model to merge phenotypic characterization of 17 morpho-agronomic traits with genetic screening of 13 highly polymorphic SSR markers in a diverse panel of 104 avocado “criollo” “plus trees.” Estimated additive genetic parameters were validated at a 5-year-old common garden trial (i.e., provenance test), in which 22 OP half-sib seedlings from 82 elite “plus trees” served as rootstocks for the cv. Hass clone. Heritability ( h 2 ) scores in the “criollo” “plus trees” ranged from 0.28 to 0.51. The highest h 2 values were observed for ribbed petiole and adaxial veins with 0.47 (CI 95%0.2–0.8) and 0.51 (CI 0.2–0.8), respectively. The h 2 scores for the agronomic traits ranged from 0.34 (CI 0.2–0.6) to 0.39 (CI 0.2–0.6) for seed weight, fruit weight, and total volume, respectively. When inspecting yield variation across 5-year-old grafted avocado cv. Hass trees with elite OP half-sib seedling rootstocks, the traits total number of fruits and fruits’ weight, respectively, exhibited h 2 scores of 0.36 (± 0.23) and 0.11 (± 0.09). Our results indicate that elite “criollo” “plus trees” may serve as promissory donors of seedling rootstocks for avocado cv. Hass orchards due to the inheritance of their outstanding trait values. This reinforces the feasibility to leverage natural variation from “plus trees” via OP half-sib seedling rootstock families. By jointly estimating half-sib family effects and rootstock-mediated heritability, this study promises boosting seedling rootstock breeding programs, while better discerning the consequences of grafting in fruit tree crops.

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation.

Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent ‘big data’ developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these ‘big data’ approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

Extreme climate events—such as heatwaves, floods, and droughts—are increasingly affecting ecosystems, with the global average temperature projected to rise by up to 3 °C (IPCC, 2023) due to anthropogenic greenhouse gas emissions. These changes pose critical challenges to food security, as evidenced by 733 million people facing hunger in 2024. In response, crop modeling considering different climate change scenarios has become a valuable tool to guide the development of climate-resilient agricultural strategies. Despite its nutritional importance and capacity to thrive across diverse environments, Ipomoea batatas (sweet potato) remains understudied in terms of potential spatial distribution forecasting, particularly in regions of high agrobiodiversity such as northwestern South America. Therefore, in this study we modeled the projected distribution of wild and landrace sweet potato genepools in the northern Andes under four future timeframes using seven machine learning algorithms. Our results predicted a 50% reduction in the climatically suitable range for the wild genepool by 2081, coupled with an average altitudinal shift from 1537 to 2216 m above sea level (a.s.l.). For landraces, a 36% reduction was projected by 2080, with a shift from 62 to 1995 m a.s.l. By the end of the century, suitable zones for both wild and cultivated genepools are expected to converge in high-altitude regions such as the Colombian Massif, with additional remnants of wild populations near the mountain range of Farallones de Cali. This modeling approach provides essential insights into the spatial dynamics of I. batatas under climate change, highlighting the need for ex situ conservation planning in vulnerable regions as well as assisted migration to more suitable areas. Future research should integrate edaphic and biotic interaction data to better approach the realized niche of the species and understand potential responses under a niche conservatism assumption, as well as genomic data to account for the species’ intrinsic adaptative potential, overall informing conservation, germplasm mobilization, and pre-breeding strategies that may ultimately secure the role of sweet potato in resilient food systems.

Cowpea is one of the most popular dry-land legumes cultivated for food and forage in arid and semi-arid areas. Genetic diversity for global germplasm can be organized into core collections providing optimum resources to serve breeding requirements. Here, we present diversity analysis and genome-wide association study (GWAS) results for part of the cowpea core collection of the United States Department of Agriculture (USDA) along with breeding line controls. Included in the analysis were a total of 373 accessions analyzed with 6880 Single Nucleotide Polymorphism (SNP) markers from Genotyping by Sequencing (GBS). Population structure differentiated accessions into two groups irrespective of geographical origin and formed three clusters based on taxa upon phylogenetic analysis. A total of 56 SNPs were significantly associated to nine traits including pod length (25 Quantitative Trait Nucleotides, QTNs), seed anti-oxidant content (7 QTNs), dry pod color (7 QTNs), plant maturity (5 QTNs), flower color (5 QTNs), seed weight (4 QTNs), tolerance to low phosphate (1 QTN), growth habit (1 QTN), and response to rock phosphate (1 QTN) using Bayesian-information, Linkage-disequilibrium Iteratively Nested Keyway (BLINK), and Fixed and random model Circulating Probability Unification (FarmCPU) association models. Key genes related to all significant SNPs were identified based on annotations of the cowpea reference genome, including a flavonoid gene controlling flower color (Vigun08g040200.1), a root nodulation regulator for tolerance to low phosphate (Vigun11g168000.1), and numerous genes involved in signaling, biosynthesis, metabolite transport, and abiotic stress. Our results highlight the importance of maintaining public phenotyping databases at USDA and strengthening collaborations for data collection in cowpea to maximize research impacts.

Seed uniformity and stability testing, and multiplication, are key steps in the seed supply chain of the common bean (Phaseolus vulgaris L.) and other crops. Optimizing agronomical practices in these phases can ultimately ensure seed quality and availability, and germplasm prospective utilization. However, farmers have rarely standardized seed testing and propagation protocols in local common bean landraces conserved in situ. An example of this is the Liborino variety (var.), a promising yellow Andean common bean known for its presumably high digestibility and adaptation to the local conditions of the Cauca river canyon (northwest Andes of Colombia), but likely experiencing genetic erosion after decades of suboptimal propagation. Therefore, this work intended to evaluate and select locally adapted genotypes of common bean var. Liborino for commercial use, to be later multiplied, evaluated by participatory breeding, and eventually shared with farmers. Specifically, we evaluated 44 accessions of var. Liborino common bean in six adaption and yield field trials in the Cauca river canyon at 1100 and 1400 m a.s.l, and in AGROSAVIA’s “La Selva” research station at 2100 m a.s.l. In parallel, we carried out standardized seed multiplication of a Liborino genotype using best practices to guarantee uniformity and stability. From the 44 accessions, nine were well adapted to the tested local conditions. Four of these accessions exhibited a bush type growth habit, while the remaining five were climbers. The trials revealed maximum average extrapolated yields of up to 1169.4 ± 228.4 kg ha−1 for the bush types (G8152) and up to 1720.0 ± 588.4 kg ha−1 for the climbers (G51018), both at 2100 m a.s.l. Three climbing accessions matched farmers’ expectations for seed coat color and shape, according to a participatory selection exercise. Uniform and stable seed of the selected genotype was delivered in 2022 to 39 farmers, ~6.5 kg of seeds per farmer. Our results will allow implementing bean genetic improvement pipelines, promoting var. Liborino commercialization, and boosting the economic and sustainable development of the rural communities in the Cauca river canyon. Seed uniformity testing and multiplication pipelines must be extended to other bean landraces conserved in situ.