Search Results (280)

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. Full article

Background/Objectives: Global warming and rising CO₂ concentrations pose significant challenges to plant systems. Amid these pressures, this study contributes to understanding how tropical species respond by simultaneously evaluating reproductive and genetic traits. It specifically investigates the effects of maternal exposure to warming and elevated CO₂ on progeny physiology, genetic diversity, and population structure in Stylosanthes capitata, a resilient forage legume native to Brazil. Methods: Maternal plants were cultivated under controlled treatments, including ambient conditions (control), elevated CO₂ at 600 ppm (eCO₂), elevated temperature at +2 °C (eTE), and their combined exposure (eTEeCO₂), within a Trop-T-FACE field facility (Temperature Free-Air Controlled Enhancement and Free-Air Carbon Dioxide Enrichment). Seed traits (seeds per inflorescence, hundred-seed mass, abortion, non-viable seeds, coat color, germination at 32, 40, 71 weeks) and abnormal seedling rates were quantified. Genetic diversity metrics included the average (A) and effective (Ae) number of alleles, observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (Fis). Population structure was assessed using Principal Coordinates Analysis (PCoA), Analysis of Molecular Variance (AMOVA), number of migrants per generation (Nm), and genetic differentiation index (Fst). Two- and three-way Analysis of Variance (ANOVA) were used to evaluate factor effects. Results: Compared to control conditions, warming increased seeds per inflorescence (+46%), reduced abortion (−42.9%), non-viable seeds (−57%), and altered coat color. The germination speed index (GSI +23.5%) and germination rate (Gr +11%) improved with warming; combined treatments decreased germination time (GT −9.6%). Storage preserved germination traits, with warming enhancing performance over time and reducing abnormal seedlings (−54.5%). Conversely, elevated CO₂ shortened GSI in late stages, impairing germination efficiency. Warming reduced Ae (−35%), He (−20%), and raised Fis (maternal 0.50, progeny 0.58), consistent with the species’ mixed mating system; A and Ho were unaffected. Allele frequency shifts suggested selective pressure under eTE. Warming induced slight structure in PCoA, and AMOVA detected 1% (maternal) and 9% (progeny) variation. Fst = 0.06 and Nm = 3.8 imply environmental influence without isolation. Conclusions: Warming significantly shapes seed quality, reproductive success, and genetic diversity in S. capitata. Improved reproduction and germination suggest adaptive advantages, but higher inbreeding and reduced diversity may constrain long-term resilience. The findings underscore the need for genetic monitoring and broader genetic bases in cultivars confronting environmental stressors. Full article

Cowpea (Vigna unguiculata [L.] Walp) is a vital food security crop in sub-Saharan Africa, including Eswatini. The productivity of the crop is low (<600 kg/ha) in the country due to a lack of improved, locally adapted, and farmer-preferred varieties with biotic and abiotic stress tolerance. The objective of the study was to assess the agronomic performance of newly developed elite cowpea mutants to select best-yielding and adapted pure lines for production and genetic improvement in Eswatini. A total of 30 cowpea genotypes, including 24 newly developed advanced mutant lines, their 3 founder parents and 3 local checks, were profiled for major agronomic traits in two selected sites (Lowveld Experiment and Malkerns Research Stations) using a 6 × 5 alpha lattice design with three replications. A combined analysis of variance revealed that the genotype x location interaction effects were significant (p < 0.05) for germination percentage (DG %), days to flowering (DTF), days to maturity (DMT), number of pods per plant (NPP), pod length (PDL), number of seeds per pod (NSP), hundred seed weight (HSW), and grain yield (GYD). Elite mutant genotypes, including NKL9P7, BRR4P11, SHR9P5, and NKL9P7-2 exhibited higher grain yields at 3158.8 kg/ha, 2651.6 kg/ha, 2627.5 kg/ha, and 2255.8 kg/ha in that order. The highest-yielding mutant, NKL9P7, produced 70%, 61%, and 54% more grain yield than the check varieties Mtilane, Black Eye, and Accession 792, respectively. Furthermore, the selected genotypes displayed promising yield components such as better PDL (varying from 13.1 to 26.3 cm), NPP (15.9 to 26.8), and NSP (9.8 to 16.2). Grain yield had significant positive correlations (p < 0.05) with DG %, NSP, and NPP. The principal component analysis (PCA) revealed that 81.5% of the total genotypic variation was attributable to the assessed quantitative traits. Principal component (PC) 1 accounted for 48.6%, while PC 2 and PC 3 contributed 18.9% and 14% of the overall variation, respectively. Key traits correlated with PC1 were NPP with a loading score of 0.91, NSP (0.83), PDL (0.73), GYD (0.68), HSW (0.58), DMT (−0.60), and DTF (−0.43) in a desirable direction. In conclusion, genotypes NKL9P7, BRR4P11, SHR9P5, NKL9P7-2, Bira, SHR3P4, and SHR2P7 were identified as complementary parents with relatively best yields and local adaptation, making them ideal selections for direct production or breeding. The following traits, NPP, NSP, PDL, GYD, and HSW, offered unique opportunities for genotype selection in the cowpea breeding program in Eswatini. Full article

Durum wheat, the world’s second most cultivated wheat species, is a staple crop, critical for global food security, including in Ethiopia where it serves as a center of diversity. However, climate change and genetic erosion threaten its genetic resources, necessitating genomic studies to support conservation and breeding efforts. This study characterized genome-wide diversity, population structure (STRUCTURE, principal coordinate analysis (PCoA), neighbor-joining trees, analysis of molecular variance (AMOVA)), and selection signatures (F_ST, Hardy–Weinberg deviations) in Ethiopian durum wheat by analyzing 376 genotypes (148 accessions) using an Illumina Infinium 25K single nucleotide polymorphism (SNP) array. A set of 7842 high-quality SNPs enabled the assessments, comparing landraces with cultivars and breeding populations. Results revealed moderate genetic diversity (mean polymorphism information content (PIC) = 0.17; gene diversity = 0.20) and identified 26 loci under selection, associated with key traits like grain yield, stress tolerance, and disease resistance. AMOVA revealed 80.1% variation among accessions, with no significant differentiation by altitude, region, or spike density. Landraces formed distinct clusters, harboring unique alleles, while admixture suggested gene flow via informal seed exchange. The findings highlight Ethiopia’s rich durum wheat diversity, emphasizing landraces as reservoirs of adaptive alleles for breeding. This study provides genomic insights to guide conservation and the development of climate-resilient cultivars, supporting sustainable wheat production globally. Full article

Under the scenario of global climate warming, meteorological risks affecting sunflower cultivation in Xinjiang’s 10th Division were investigated by developing a meteorological-growth coupling model. Field experiments were conducted at three representative stations (A1–A3) during 2023–2024 to assess temperature and precipitation impacts on yield and quality traits among sunflower cultivars with varying maturation periods. The main findings were: (1) Early-maturing cultivar B1 (RH3146) exhibited superior adaptation at low-temperature station A1, achieving 12% higher plant height and an 18% yield increase compared to regional averages. (2) At thermally variable station A2 (daily average temperature fluctuation ± 8 °C, precipitation CV = 25%), the late-maturing cultivar B3 showed enhanced stress resilience, achieving 35.6% grain crude fat content (15% greater than mid-maturing B2) along with 8–10% increases in seed setting rate and 100-grain weight. These improvements were potentially due to optimized photoassimilated allocation and activation of stress-responsive genes. (3) At station A3, characterized by high thermal-humidity variability (CV > 15%) during grain filling, B3 experienced a 15-day delay in maturation and a 3% reduction in ripeness. Two principal mitigation strategies are recommended: preferential selection of early-to-mid maturing cultivars in regions with thermal-humidity CV > 10%, improving yield stability by 23%, and optimization of sowing schedules based on accumulated temperature-precipitation modeling, reducing meteorological losses by 15%. These evidence-based recommendations provide critical insights for climate-resilient cultivar selection and precision agricultural management in meteorologically vulnerable agroecosystems. Full article

The BAHD acyltransferase family plays a critical role in plant secondary metabolism by catalyzing acyl transfer reactions that are essential for synthesizing metabolites involved in environmental adaptation. However, systematic investigation of this superfamily in Brassica napus has not been reported. In this study, 158 BnaBAHD genes were identified by comprehensive analyses of evolutionary relationships, motif structures, chromosomal distribution, gene collinearity, and selection pressures, and these genes were phylogenetically classified into five clades harboring conserved catalytic domains (HXXXD and DFGWG). Transient overexpression combined with metabolomic profiling demonstrated that two homologous seed-specific Clade V members, BnaBAHD040 and BnaBAHD120, which exhibited elevated expression during late seed development, significantly enhanced the accumulation of acylated metabolites contributing to biotic/abiotic stress resistance. This study provides the first experimental validation of the catalytic functions of BAHD enzymes in B. napus, establishing a theoretical foundation for leveraging this gene family in genetic improvement to develop novel rapeseed cultivars with enhanced stress tolerance and yield. Full article

Lentil (Lens culinaris Medikus) is a critical food crop offering high protein and essential micronutrients. However, its productivity and nutritional quality are increasingly threatened by climate change. In this study, 36 lentil genotypes were evaluated across two Moroccan locations under normal, heat stress, and combined heat and drought stresses. Significant effects of genotype, environment, and their interactions were observed on seed yield, seed size, cooking time, and nutritional quality. Heat and drought stresses caused substantial reductions in seed yield (up to 40% under combined stress), protein content, iron, and zinc concentration, and increased phytic acid levels, which negatively impacted iron and zinc bioavailability. Cooking time significantly decreased under stress conditions, with up to 54% reduction under combined heat and drought stresses at Annoceur research station. Correlation analysis revealed complex trade-offs among yield, nutritional quality, and cooking traits under stress conditions. Principal component analysis and GGE biplot analyses identified genotypes with superior yield, micronutrient concentration, and cooking time stability across environments. Genotypes such as G32, G3, and G36 combined high iron and zinc levels; G13 and G30 showed low phytic acid, while G 15 exhibited the shortest cooking time. These genotypes also demonstrated adaptability across the tested environment. This study highlights the potential of selecting climate-resilient, nutrient-dense lentil genotypes to support breeding efforts aimed at improving food security in the face of global climate variability. These genotypes can be suggested as elite climate-resilient parental lines to support breeders in enhancing lentil yield, nutritional quality, and stability under multiple stress conditions. Full article

Heracleum sosnowskyi Manden. (Sosnowsky’s hogweed), originally introduced to Central and Eastern Europe as a fodder crop, has become a highly invasive species due to its ecological adaptability, high reproductive capacity, and efficient seed dispersal. Despite its negative impact on native flora and its health risks to humans and animals, the species also represents a valuable source of biologically active compounds. In this study, we demonstrate that the leaves of H. sosnowskyi contain substantial amounts of furanocoumarins—phototoxic compounds with notable therapeutic potential, particularly as natural photosensitizers in anticancer therapies. To extract furanocoumarins from H. sosnowskyi, microwave-assisted extraction (MAE) was employed, with optimization of key parameters including extraction solvent (hexane), temperature (70 °C), extraction time (10 min), and solvent-to-solid ratio (20:1). Four major compounds—angelicin (2.3 mg/g), psoralen (0.15 mg/g), methoxsalen (0.76 mg/g), and bergapten (3.14 mg/g)—were identified and quantified using gas chromatography–mass spectrometry and gas chromatography with flame ionization detection. To purify the extract and selectively isolate the target compounds, a solid-phase extraction method was developed using a Strata Eco-Screen sorbent and stepwise elution with a hexane–acetone mixture. As a result, pure angelicin, pure methoxsalen, and various mixtures of the furanocoumarins were obtained. These findings highlight the potential of H. sosnowskyi as a sustainable source of furanocoumarins for pharmaceutical applications. Full article

Maize seed variety classification has become essential in agriculture, driven by advancements in non-destructive sensing and machine learning techniques. This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Spectral data were acquired by hyperspectral imaging technology from five maize varieties and processed using Savitzky–Golay (SG) smoothing, along with standard normal variate (SNV) preprocessing. To enhance feature selection, the competitive adaptive reweighted sampling (CARS) algorithm was applied to reduce redundant information, identifying 100 key wavelengths from an initial set of 774. This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. These results showed that the CNN-LSTM model excelled in extracting complex spectral features and offering strong generalization and classification capabilities. Therefore, the model proposed in this study served as an effective tool for maize variety identification. Full article

Vernalization genes (Vrn) play a key role in plant adaptation to various geographic locations and their allelic diversity can have fundamental importance for breeding programs. In the current study, 340 barley genotypes were studied, including germplasm accessions and advanced breeding lines. For phenotype evaluation in South-Eastern Kazakhstan, the transition of barley plants from vegetative to reproductive stages was estimated in field trials with spring- and winter-sown seeds. For molecular analysis, 10 previously described molecular markers were studied in three barley vernalization loci: Vrn-H1, Vrn-H2 and Vrn-H3. The comparison between molecular results and phenotypes for plant development confirmed 211 spring genotypes, 56 winter and 28 facultative. Vrn-H1 haplotypes 1A and recessive allele vrn-H3 were in the majority. Best spring and winter high-yielding advanced breeding lines were identified. Based on Vrn allele combination, a breeding line 76/13-4 with facultative type development showed superior results in both winter and spring sowings, presenting a new prospective barley cultivar that can be grown equally either in spring or winter sowing conditions. The presented results can be used for barley marker-assisted selection predicting crosses with favourable combinations of Vrn alleles for prospective breeding line development. Full article

Straw row grouping is vital in conservation tillage for precision seeding, and accurate centerline extraction of the seedling bands enhances agricultural spraying efficiency. However, the traditional single-threshold Otsu segmentation struggles with adaptability and accuracy under complex field conditions. To overcome these issues, this study proposes an adaptive multi-threshold Otsu algorithm optimized by a Simulated Annealing-Enhanced Differential Evolution–Whale Optimization Algorithm (SADE-WOA). The method avoids premature convergence and improves population diversity by embedding the crossover mechanism of Differential Evolution (DE) into the Whale Optimization Algorithm (WOA) and introducing a vector disturbance strategy. It adaptively selects thresholds based on straw-covered image features. Combined with least-squares fitting, it suppresses noise and improves centerline continuity. The experimental results show that SADE-WOA accurately separates soil regions while preserving straw texture, achieving higher between-class variance and significantly faster convergence than the other tested algorithms. It runs at just one-tenth of the time of the Grey Wolf Optimizer and one-ninth of that of DE and requires only one-sixth to one-seventh of the time needed by DE-GWO. During centerline fitting, the mean yaw angle error (MEA) ranged from 0.34° to 0.67°, remaining well within the 5° tolerance required for agricultural navigation. The root-mean-square error (RMSE) fell between 0.37° and 0.73°, while the mean relative error (MRE) stayed below 0.2%, effectively reducing the influence of noise and improving both accuracy and robustness. Full article

This research assessed 42 lentil genotypes developed by ICARDA along with a local variety over three growing seasons (2019–2022) in Southeastern Türkiye. Phenological, morphological, and yield attributes were determined to observe earliness, yield stability, and adaptation properties. Genotype G3771 showed outstanding performance in grain yield (2579 kg ha⁻¹), 1000-seed weight (54.9 g), and harvest index (37.3%), although it had lower stability under more severe drought conditions. Early-maturing genotypes like G3744, G3715, and G3716 consistently flowered and matured sooner, making them better suited for escaping terminal drought stress areas. The highest yields were recorded during the 2019–2020 season, which experienced favorable rainfall and soil nutrient levels, while the lowest yields occurred due to changing climatic conditions in the 2020–2021 season, highlighting the crop’s sensitivity to climate. Principal component analysis, hierarchical clustering, the Modified Multi-Trait Stability Index (MTSI), and the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) aided in effective genotype classification. Although G3771 was the most productive, genotypes G3687, G3715, and G3689 proved to be the most stable and early maturing based on MGIDI scores. Strong relationships between grain yield, biological yield, and seed size identified these as key selection criteria. This study underscores the value of multi-trait selection tools like MGIDI and MTSI in consistently pinpointing lentil genotypes that balance earliness, productivity, and adaptability, laying a strong foundation for developing climate-resilient varieties suited to semi-arid climates. Full article

Oat is a highly nutritious cereal crop, and the moisture content of its seeds plays a vital role in cultivation management, storage preservation, and quality control. To enable efficient and non-destructive prediction of this key quality parameter, this study presents a modeling framework integrating hyperspectral imaging (HSI) technology with a dual-optimization machine learning strategy. Seven spectral preprocessing techniques—standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD), and combinations such as SNV + FD, SNV + SD, and SNV + MSC—were systematically evaluated. Among them, SNV combined with FD was identified as the optimal preprocessing scheme, effectively enhancing spectral feature expression. To further refine the predictive model, three feature selection methods—successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA)—were assessed. PCA exhibited superior performance in information compression and modeling stability. Subsequently, a dual-optimized neural network model, termed Bayes-ASFSSA-BP, was developed by incorporating Bayesian optimization and the Adaptive Spiral Flight Sparrow Search Algorithm (ASFSSA). Bayesian optimization was used for global tuning of network structural parameters, while ASFSSA was applied to fine-tune the initial weights and thresholds, improving convergence efficiency and predictive accuracy. The proposed Bayes-ASFSSA-BP model achieved determination coefficients (R²) of 0.982 and 0.963, and root mean square errors (RMSEs) of 0.173 and 0.188 on the training and test sets, respectively. The corresponding mean absolute error (MAE) on the test set was 0.170, indicating excellent average prediction accuracy. These results significantly outperformed benchmark models such as SSA-BP, ASFSSA-BP, and Bayes-BP. Compared to the conventional BP model, the proposed approach increased the test R² by 0.046 and reduced the RMSE by 0.157. Moreover, the model produced the narrowest 95% confidence intervals for test set performance (Rp²: [0.961, 0.971]; RMSE: [0.185, 0.193]), demonstrating outstanding robustness and generalization capability. Although the model incurred a slightly higher computational cost (480.9 s), the accuracy gain was deemed worthwhile. In conclusion, the proposed Bayes-ASFSSA-BP framework shows strong potential for accurate and stable non-destructive prediction of oat seed moisture content. This work provides a practical and efficient solution for quality assessment in agricultural products and highlights the promise of integrating Bayesian optimization with ASFSSA in modeling high-dimensional spectral data. Full article

In plants, alternative splicing (AS) is a crucial post-transcriptional regulatory mechanism that generates diverse mature transcripts from precursor mRNA, with the resulting functional proteins regulating a wide range of plant life activities. The regulation of AS is intricate and complex, playing pivotal roles in controlling plant biological processes like seed germination, flowering time control, growth, and development, as well as responses to abiotic and biotic stresses. The regulation of AS is a multilayered and intricately coordinated network system, primarily involving two core components: cis-regulatory elements and trans-acting factors on pre-mRNA. The precise execution of AS relies on the splicing factors by recognizing cis-elements to modulate splice site selection. Regulated by their own sequence variation, environmental cues, and identification of different spliceosomes, functional genes enable AS to achieve precise spatiotemporal regulation, thereby allowing plants to dynamically respond to developmental signals and environmental challenges. Here, we provide a comprehensive overview of AS patterns, functional genes, and splicing factors undergoing AS and its regulatory mechanisms during different processes, highlighting how AS-mediated gene regulation contributes to plant development and stress response, and offering potential strategies for improving plant adaptation by manipulation of AS-regulated genes. Full article

Appropriate deep sowing holds significant potential in enhancing wheat production, particularly in dry and low-rainfall regions. Henan Province is a major winter wheat-producing area in China; evaluating the adaptability of wheat varieties to deep sowing through scientific methods is crucial to improve wheat production. This study investigates 26 wheat cultivars in Henan. By assessing key traits of seeds and seedlings at various sowing depths, we analyzed the effects of sowing depth on seed germination and seedlings. A comprehensive index for deep sowing tolerance was established using principal component analysis (PCA) and the membership function method, followed by the classification of the varieties according to their tolerance to deep sowing. The results indicated that, with increased sowing depth, seedling emergence time, coleoptile length, and coleoptile internode length increased, while seedling emergence rate, seedling height, leaf area, and shoot dry weight per unit area decreased. Based on PCA and membership function values, the 26 wheat varieties were classified into three categories: deep sowing tolerant, moderately tolerant, and intolerant, comprising 3, 19, and 4 varieties. This study provides valuable insights for optimizing wheat variety selection and improving sowing practices in Henan Province, offering both theoretical and practical contributions to local wheat production. Full article