Advanced Breeding Research Articles

한국 수산 양식 생산-에너지-경영 정보 상관관계 분석

The aquaculture industry in South Korea has developed advanced breeding technologies and high-quality farming techniques, contributing to the steady growth of aquaculture production since 2013. However, domestic aquaculture operations still rely on traditional farm environments, with farm managers making decisions based on experiential judgment. Despite the industry's growth, aquaculture operators face challenges in predicting market trends due to the biological complexities of the aquaculture environment and fluctuating fish prices driven by market demand. While recent research has primarily focused on the volatility of fish prices themselves, there is a lack of studies exploring other factors influencing these price fluctuations. This paper serves as a foundational study for sustainable aquaculture management, aiming to identify factors that impact fish prices through data analysis. By collecting and analyzing data on production, energy, and management within Korea's aquaculture industry, this research seeks to provide aquaculture managers with insights that will support efficient decision-making in their operations.

A multi-faceted approach involving laboratory assay, glasshouse, and field experiments in identifying stem rot (incited by Sclerotium rolfsii Sacc.) resistance in advanced breeding lines of groundnut

A multi-faceted approach involving laboratory assay, glasshouse, and field experiments in identifying stem rot (incited by Sclerotium rolfsii Sacc.) resistance in advanced breeding lines of groundnut

Analysis of Genetic Variability, Trait Relationships, and Yield Determinants in Soybean

Aim: This study was conducted to enhance soybean yield by evaluating genetic variability, heritability, and trait correlations among 52 advanced breeding lines. Study Design: A randomized complete block design with three replications was used to assess key traits, including plant height, primary branches, nodes per plant, pods per plant, seeds per pod, flowering duration, maturity duration, and yield per plant. Place and Duration of Study: Trials were carried out during the Kharif season of 2023 at the N.E. Borlaug Crop Research Centre, GBPUAT, Pantnagar, Uttarakhand. Methodology and Results: High genetic coefficients of variation and heritability for pods per plant and yield per plant indicate their strong genetic control and potential for selection, which was carried out in 52 genotypes. Correlation analysis identified pods per plant as the most influential trait associated with yield, making it a critical selection criterion. Path analysis further highlighted its direct positive impact on yield, while seeds per pod and days to maturity also contributed positively. Complex indirect effects of traits like nodes per plant and days to flowering emphasize the need for a balanced selection approach. Conclusion: These findings provide a comprehensive framework for optimizing selection strategies in soybean breeding programs, contributing to the development of high-yielding varieties.

Advance breeding approaches for quality enhancement in apiaceae family

Advance breeding approaches for quality enhancement in apiaceae family

Breeding potential of red- and white-skinned onion (Allium cepa L.) genotypes for economic traits using multivariate analysis

ABSTRACT The improvement of onion (Allium cepa L.) depends on the characterization and evaluation of the germplasm to find superior genotypes with desired horticultural qualities and utilization of diversity in future breeding. The study aimed to evaluate advanced breeding lines (consisting of both red- and white-skinned types) through genetic component analyses and genetic divergence using multivariate analysis to identify genotypes that can be utilized in breeding for table and dehydration purposes, respectively. Genotypes ‘(EM-AVT-2) REC-21-48,’ ‘(RIET) RVA-21-01,’ and ‘(EM-AVT-2) REC-21-46’ were found to be high yielders. Higher estimates of the genotypic coefficient of variation, heritability and genetic advance for the percentage of the double bulb, total soluble solid, and reducing sugar contents indicate additive gene action, and selection based on these parameters would be more reliable. The plant height, polar diameter, equatorial diameter, and neck thickness emerged as important selection indices. Principal components such as the plant height, number of leaves per plant, percentage of bolting and double bulb had values of >1, accounting for 81.73% of the total variation. Based on mean values and multivariate analysis, onion genotypes ‘(EM-AVT-2) REC-21-46’ (red-skinned), ‘(EM-AVT-2) REC-21-52’ (red-skinned), ‘(WIET) WVA-21-47’ (white-skinned), ‘(WAVT-2) WVC-21-74’ (white-skinned), ‘(WIET) WVA-21-39’ (white-skinned) and ‘(WHTSS-AVT-2) WTC-21-84’ (white-skinned) could be utilized as potential genitors in future breeding for table and dehydration purpose.

Genetic Variability Studies in Dolichos Bean (Lablab purpureus L.) for Yield and Yield Traits

Aims: To study the genetic variability by phenotypic coefficient of variation, genotypic coefficient of variation, heritability and genetic advance as percent of mean in advanced breeding lines of Dolichos bean. Study Design: Augmented RCBD with six blocks. Place and Duration of Study: Department of Genetics and Plant Breeding, College of Agriculture, Shivamogga, during Late Kharif 2023. Methodology: The experimental material consisted of 64 advanced breeding lines of Dolichos bean including four checks viz., Hebbal avere-1, Hebbal avere-3, Hebbal avere-4 and Arka Jaya. Results: Analysis of variance revealed that significant variation was observed among the genotypes for all the characters studied. A high genotypic coefficient of variation (GCV) was observed for pods per plant (48.10%) followed by pod yield per plant, clusters per plant, pods per cluster, plant height and pod width, which indicates better scope for improvement of characters. A high phenotypic coefficient of variation (PCV) was observed for pod yield per plant (50%) followed by pods per plant, clusters per plant, pods per cluster, plant height and pod width. High heritability (>60%) was observed for all the traits studied. High heritability combined with high genetic advance as percent of mean was found for pods per plant (95.49%) followed by pod yield per plant, clusters per plant, pods per cluster, pod width and plant height which indicates that improvement of these charateristics through selection can be possible due to additive gene action. Conclusion: Yield can be increased by selecting the number of pods per plant, pod yield per plant, seed yield per plant number of cluster per pod, number of pods per plant and plant height.

Genetic improvement in buffaloes for enhancing global food security

The global food crisis is worsening, with around 282 million people experiencing acute food insecurity in 2023, largely due to extreme weather events and inflation. This crisis is particularly severe in the Asia-Pacific region, where the rising cost of healthy food has left 232.8 million people unable to afford a nutritious diet. In India, home to approximately 200 million malnourished people, buffaloes have become essential in the fight against hunger, contributing around 45% to the nation’s milk production and significantly to meat production. Buffalo meat and milk, known for their high nutritional value, are crucial in addressing food insecurity. Additionally, buffalo farming is linked to sustainable practices, such as efficient feed use and reduced greenhouse gas emissions. Over the past fifty years, India’s efforts to improve buffalo genetics have led to significant increases in productivity. Despite challenges like smallholder farming constraints, there are opportunities to expand buffalo meat exports and value-added dairy products. Adopting precision dairy farming and advanced breeding programs could transform India’s dairy industry, enhancing its global competitiveness and sustainability.

Can nanotechnology and genomics innovations trigger agricultural revolution and sustainable development?

At the dawn of new millennium, policy makers and researchers focused on sustainable agricultural growth, aiming for food security and enhanced food quality. Several emerging scientific innovations hold the promise to meet the future challenges. Nanotechnology presents a promising avenue to tackle the diverse challenges in agriculture. By leveraging nanomaterials, including nano fertilizers, pesticides, and sensors, it provides targeted delivery methods, enhancing efficacy in both crop production and protection. This integration of nanotechnology with agriculture introduces innovations like disease diagnostics, improved nutrient uptake in plants, and advanced delivery systems for agrochemicals. These precision-based approaches not only optimize resource utilization but also reduce environmental impact, aligning well with sustainability objectives. Concurrently, genetic innovations, including genome editing and advanced breeding techniques, enable the development of crops with improved yield, resilience, and nutritional content. The emergence of precision gene-editing technologies, exemplified by CRISPR/Cas9, can transform the realm of genetic modification and enabled precise manipulation of plant genomes while avoiding the incorporation of external DNAs. Integration of nanotechnology and genetic innovations in agriculture presents a transformative approach. Leveraging nanoparticles for targeted genetic modifications, nanosensors for early plant health monitoring, and precision nanomaterials for controlled delivery of inputs offers a sustainable pathway towards enhanced crop productivity, resource efficiency, and food safety throughout the agricultural lifecycle. This comprehensive review outlines the pivotal role of nanotechnology in precision agriculture, emphasizing soil health improvement, stress resilience against biotic and abiotic factors, environmental sustainability, and genetic engineering.

Association mapping and genomic prediction for processing and end-use quality traits in wheat (Triticum aestivum L.).

End-use and processing traits in wheat (Triticum aestivum L.) are crucial for varietal development but are often evaluated only in the advanced stages of the breeding program due to the amount of grain needed and the labor-intensive phenotyping assays. Advances in genomic resources have provided new tools to address the selection for these complex traits earlier in the breeding process. We used association mapping to identify key variants underlying various end-use quality traits and evaluate the usefulness of genomic prediction for these traits in hard red spring wheat from the Northern United States. A panel of 383 advanced breeding lines and cultivars representing the diversity of the University of Minnesota wheat breeding program was genotyped using the Illumina 90K single nucleotide polymorphism array and evaluated in multilocation trials using standard assessments of end-use quality. Sixty-three associations for grain or flour characteristics, mixograph, farinograph, and baking traits were identified. The majority of these associations were mapped in the vicinity of glutenin/gliadin or other known loci. In addition, a putative novel multi-trait association was identified on chromosome 6AL, and candidate gene analysis revealed eight genes of interest. Further, genomic prediction had a high predictive ability (PA) for mixograph and farinograph traits, with PA up to 0.62 and 0.50 in cross-validation and forward prediction, respectively. The deployment of 46 markers from GWAS to predict dough-rheology traits yielded low to moderate PA for various traits. The results of this study suggest that genomic prediction for end-use traits in early generations can be effective for mixograph and farinograph assays but not baking assays.

Breeding and biotechnology approaches to enhance the nutritional quality of rapeseed byproducts for sustainable alternative protein sources- a critical review.

Global protein consumption is increasing exponentially, which requires efficient identification of potential, healthy, and simple protein sources to fulfil the demands. The existing sources of animal proteins are high in fat and low in fiber composition, which might cause serious health risks when consumed regularly. Moreover, protein production from animal sources can negatively affect the environment, as it often requires more energy and natural resources and contributes to greenhouse gas emissions. Thus, finding alternative plant-based protein sources becomes indispensable. Rapeseed is an important oilseed crop and the world's third leading oil source. Rapeseed byproducts, such as seed cakes or meals, are considered the best alternative protein source after soybean owing to their promising protein profile (30%-60% crude protein) to supplement dietary requirements. After oil extraction, these rapeseed byproducts can be utilized as food for human consumption and animal feed. However, anti-nutritional factors (ANFs) like glucosinolates, phytic acid, tannins, and sinapines make them unsuitable for direct consumption. Techniques like microbial fermentation, advanced breeding, and genome editing can improve protein quality, reduce ANFs in rapeseed byproducts, and facilitate their usage in the food and feed industry. This review summarizes these approaches and offers the best bio-nutrition breakthroughs to develop nutrient-rich rapeseed byproducts as plant-based protein sources.

Genetic Studies on Variability, Heritability and Genetic Gain of Yield and Yield Related Traits in Foxtail Millets (Setaria italic L.)

The present work comprised of 40 foxtail millet advanced breeding lines obtained from small millet scheme, MARS, Dharwad. These lines with checks viz., DHFT-109-3 and HMT-100-1 were evaluated in replicated yield trial at millet scheme, MARS, Dharwad, during rabi 2020-21 for assessing genetic variability, heritability, and genetic gain among 17 traits. Wide range of variation provides an ample scope for selection of superior and desired advanced breeding lines by the plant breeders for further improvement of these characters. The phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV) for all the traits under study. In the present study foxtail millet advanced breeding lines exhibited moderate PCV and GCV for the most important economic traits viz., panicle length (18.01 and 15.85 % 15.85% and18.01%), panicle girth (15.25% and 15.45%), productive tiller per plant (18.44% and 19.40%) and straw yield per hectare (19.92% and 21.39%). The high phenotypic coefficient of variation and genotypic coefficient of variation were observed for characters viz., specific leaf area (21.08% and 22.44%), specific leaf weight (20.52% and 26.72%), grain yield per plant (9.32% and 10.99 %), grain yield per plot (8.85 % and 10.49%) and straw yield per ha (19.92 and 21.39). Most of the traits viz., plant height(79 %), flag leaf length(98%), flag leaf width ( 80%), number of productive tillers per plant (90%), panicle length(98%), panicle girth (97 %), SPAD at 50 per cent flowering (92%), test weight (63%), specific leaf area (88%), specific leaf weight (87%), grain yield per plant(71%) and straw yield per hectare (87%) showed the high estimates of broad sense heritability (h2 b) indicating the reliability of the estimates for variation between advanced breeding lines and effectiveness of selection.

Association of Seedling Vigour and Salinity Tolerance in Field Pea.

Soil salinity results in reduced productivity in field peas, making soil salinity tolerance a critical breeding objective. In this study, four pot experiments were carried out in semi-controlled environments over four consecutive years to assess the contribution of seedling vigour to salinity tolerance at the seedling stage. Split-plot designs were used to assess the effect of salt stress (sodium chloride solution at 16 dSm-1) and control conditions. Extensive sets of advanced breeding lines were used in 2018-2020 to assess growth differences in relation to the treatment, with elemental analysis used on a subset of 15 lines in 2021. A salt tolerance index (STI) was defined as a proportion of shoot biomass under salt stress (DWstress) relative to the shoot dry weight under control (DWctrl). Visual scores of salt stress were recorded on a 1-10 scale (1 = tolerant, 10 = susceptible) from salt stress treatments. The consistent positive and significant correlations (p < 0.01) between shoot DWctrl and DWstress indicated that vigorous genotypes maintained higher shoot DWstress. Both the shoot DWctrl and shoot DWstress had negative and significant (p < 0.01) correlations with visual scores of salt stress. Shoot DWstress showed strong positive correlations with STI (p < 0.01). Both the shoot DWctrl and Shoot DWstress had negative correlations (p < 0.01) with shoot Na+ whereas shoot DWstress had a positive correlation (p < 0.05) with root Na+ concentration. The results indicated that seedling vigour (measured as shoot DWctrl) contributed to salinity tolerance by maintaining improved shoot DWstress, limiting Na+ deposition in shoot and enduring less tissue damage in field pea seedlings. Additional field evaluations are required to establish the correlations of tolerance at seedling stage with yield under saline conditions. The insights obtained from this study may assist field pea breeders in identifying salt-tolerant parent plants, offspring, and breeding lines during the initial growth phases.

Dwarfism mechanism in Malus clonal rootstocks.

The dwarfing mechanism in apple clonal rootstocks is driven by complex interactions between anatomical, hormonal, genetic, and phenolic factors, offering potential for advanced genetic manipulation to optimize tree size and enhance orchard productivity. The widespread adoption of dwarfing rootstocks is pivotal to modern commercial apple (Malus × domestica Borkh) orchards due to their ability to control tree size, shorten the juvenile period, and enhance reproductive growth and overall productivity. The underlying mechanisms of rootstock-induced dwarfism are multifaceted and involve interactions between phenotypic, anatomical, genetic, and phytohormonal factors. This review consolidates current understanding, highlighting the importance of auxin (IAA), cytokinins (CKs), gibberellins (GAs), and abscisic acid (ABA) in mediating growth suppression through impaired transport and hormone signaling. The phenotypic impacts, including reduced root growth, shorter sylleptic shoots, and higher floral bud densities, are discussed alongside genetic loci such as Dw1, Dw2, and Dw3, and the influence of key genes/TFs like MdWRKY9, RGL, and PIN. Anatomically, dwarf rootstocks exhibit a higher bark-to-wood ratio and restricted hydraulic conductivity, which contribute to reduced scion vigour. Furthermore, the accumulation of phenolic compounds in the graft union of dwarfing rootstocks further modulates the growth inhibition. These insights lay the groundwork for advanced molecular breeding strategies, incorporating gene-editing technologies to improve dwarf rootstock development, providing avenues for enhanced orchard management and apple productivity.

Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

Comprehensive analysis and selection of high oleic peanut varieties in China: A study on agronomic, yield, and quality traits

Peanut (Arachis hypogaea L.) is an important oil crop. Oleic acid is a major factor that determines the quality of peanuts. Therefore, the high oleic and high oleic to linoleic acid ratio are the target traits in an advanced peanut breeding program. This study provided an extensive evaluation of the genetic and physical characteristics as well as disease resistance of 220 high oleic peanut varieties in China. Notably, these varieties clustered into five major categories based on their traits. A majority of these varieties have been bred using interspecific hybridization or selected from mutants of self-crossed parents, with the main parent varieties being Kaixuan 016 and CTWE. Analysis of disease resistance showed that most high oleic peanut varieties could resist two or three diseases. However, those varieties with resistance to multiple diseases were relatively scarce. Moreover, some high oleic peanut varieties showed no disease resistance or inadequate testing. The results further indicate that the genetic basis for high oleic peanut breeding is insufficient, highlighting the need for its further development. Importantly, our findings lay a critical foundation for future high oleic peanut breeding and promote better understanding of the genetic and trait diversity offered by these varieties.

Assessment of Genetic Variability in Groundnut (Arachis hypogaea L.)

The present study was carried out with thirty advanced breeding lines of groundnut for estimation of genetic variability, heritability and genetic advance. Analysis of variance revealed highly significant differences among genotypes for all the studied characters indicating that there is substantial variability among the genotypes for these traits. High estimates of GCV and PCV was observed for pod yield per plant, kernel yield per plant, dry haulm yield per plant and number of mature pods per plant. High heritability coupled with high genetic advance as per cent of mean were recorded for number of mature pods per plant, pod yield per plant, kernel yield per plant, dry haulm yield per plant, number of immature pods per plant, plant height, harvest index and hundred kernel weight indicating the preponderance of additive gene action in expression of these traits and hence simple phenotypic selection would be effective for improvement of these characters.

Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning

AbstractFusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels.

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.

Characterization of Dynamin-related proteins (DRP) in bread wheat: TaDRP1D-B as regulator of biotic and abiotic stresses

Bread wheat (Triticum aestivum) is a vital global staple food, providing 30 % of the world's caloric intake and nutritional needs. It was domesticated over 10,000 years ago and adapted to various biotic and abiotic stresses, crucial for maintaining food security. Modern research highlights the interconnected signalling pathways for both biotic and abiotic stresses, that help wheat cope with these biotic and abiotic stresses. Identification of regulatory proteins is essential for advanced wheat breeding. In the current study, 32 DRP genes in wheat were identified that are evenly distributed on all the chromosomes with the presence of conserved dynamin-related domain. PPI analysis reveals that the TaDRP2-like genes interact with each other. Gene ontology analysis indicating the significant involvement of DRP genes in various processes including GTPase activity, binding, microtubule binding, and various cells including membrane (GO:0,016,020), cytoplasm (GO:0,005,737), microtubule (GO:0,005,874). Cis-element prediction reveals the enrichment of total 2006 elements including CAAT-box (390), TATA-box (327), MYB (131), and ABRE (93). Transcriptome and qRT-PCR analyses showed that TaDRP1-like, TaDRP2-like, and TaDRP3-like genes are highly expressed in roots, stems, leaves, and spikes, with lower expression in grains. Notably, TaDRP1D-B emerged as a potential candidate for enhancing resistance to powdery mildew, rust, drought, and heat stress. Furthermore, the interaction compatibility of TaDRP1D-B with PPA2 further confirms the potential role in regulating plant disease response. This research provides a foundation for developing strategies to enhance wheat resilience, directly contributing to global food security.

High-protein soybean lines with stable seed protein content under heat and drought stresses

A significant factor determining the value of soybean is protein content, in addition to its importance as an oilseed. Increasing the seed protein content in soybean will make it a more valuable food and feedstock and enhance its importance in the international market. Due to this reason, developing high-protein soybean varieties is gaining increased industrial interest. To ensure profitable production, the stability of the high-protein trait needs to be verified under environmental stresses. The objectives of this study were to test whether the high-protein soybean genotypes can maintain their high seed protein content under heat and drought stresses imposed during seed filling and to assess the changes in their oil composition under stress conditions. The changes in seed protein and oil contents and oil composition of the high-protein lines NLM09–77, N14-7017, and N16-9924 under heat and drought stresses were assessed and compared against those of a recently released high-yielding, high-protein variety Benning HP; elite cultivars NC-Dunphy, NC-Raleigh, and NC-Dilday; low-protein breeding lines N11–9228 and N09-2505; heat-tolerant line DS25-1; and heat-susceptible line DT97-4290. We found that the high-protein genotypes NLM09–77, N16–9924, N14-7017, and Benning HP maintained their high seed protein contents under heat and drought stresses (46–55 %). Among them, NLM09–77 and N14-7017 were unique with no apparent negative relationship between the seed protein and oil contents. In terms of oil composition, N14-7017, NLM09-77, and Benning HP were similar to the elite cultivars NC-Dunphy and NC-Dilday. Additionally, the high-protein genotypes N14-7017, NLM09-77, and Benning HP did not exhibit any yield drag (2993–3150 kg ha−1vs 3289–3368 kg ha−1 for the elite cultivars). Taken together, our study demonstrates the usefulness of the advanced breeding lines NLM09–77 and N14-7017 and cultivar Benning HP for developing new varieties with enhanced nutritional value under climate change-associated stresses.