Centro Internacional de Mejoramiento de Maíz Y Trigo

Spot blotch (SB), caused by Bipolaris sorokiniana, is a major yield-limiting disease of wheat (Triticum aestivum L.) in the warm, humid agroclimatic zones of South Asia. The development of resistant cultivars through molecular approaches offers a sustainable strategy for managing this disease. This study aimed to identify resistant genotypes and associated single-nucleotide polymorphism markers in a panel of 187 spring bread wheat lines via field-based phenotyping and genome-wide association study (GWAS). The panel was genotyped via the DArTSeq genotyping-by-sequencing platform and evaluated for SB resistance under artificially inoculated field conditions across two crop seasons (2019-2020 and 2020-2021) at Agua Fria, Mexico. Significant phenotypic variation was observed, with genotypes BGD54, IND56, and BGD55 showing high levels of resistance, indicating their potential as resistance donors. GWAS identified multiple marker‒trait associations (MTAs) linked to SB resistance, with seven stable MTAs consistently detected across years and models, located on chromosomes 3D, 5A, 3B, and 1B, and explaining 3.86%-18.17% phenotypic variance. Most of these MTAs colocalized with previously reported genomic regions for SB resistance. In silico analysis revealed candidate genes within these regions encoding potassium transporters, zinc finger proteins, glutathione S-transferases, FBD domain proteins, leucine-rich repeats, protein kinases, dirigent proteins, and cytochrome P450 enzymes. The identified stable MTAs and associated candidate genes offer valuable resources for marker-assisted selection and functional validation in wheat breeding programs targeting SB resistance.

The emergence of antimicrobial resistance necessitates the exploration of novel therapeutic agents from natural sources. This study investigated the antimicrobial properties of Clausena anisata fruit ethanol extract (CAFE) and its isolated compounds against 11 bacterial and three fungal strains. The dried fruits were extracted with ethanol using a Soxhlet apparatus. The extract was partitioned and subjected to chromatography to isolate two compounds, which were characterized as stigmasteryl 3-palmitate (C-01) and phellopterin (C-02) using NMR spectroscopy and LC-MS analysis. CAFÉ demonstrated notable inhibition zones in agar well diffusion assays, with the strongest activity against Klebsiella pneumoniae (14.67 ± 2.08 mm) and Staphylococcus saprophyticus (13.67 ± 0.58 mm). Microbroth dilution assays revealed MIC values ranging from 0.0781 to 1.2500 mg/mL for CAFÉ and 0.0781 to 1.2500 mg/mL for stigmasteryl 3-palmitate and phellopterin, respectively. CAFE demonstrated bactericidal activity (MLC/MIC ≤ 4) against Pseudomonas aeruginosa, Proteus mirabilis, Salmonella typhimurium, K. pneumoniae, Streptococcus sanguis, S. saprophyticus, and Candida glabrata, while showing bacteriostatic activity (MLC/MIC > 4) against Candida albicans. Stigmasteryl 3-palmitate exhibited bactericidal activity against P. mirabilis, S. typhimurium, K. pneumoniae, and S. sanguis, with bacteriostatic effects against the other organisms. Phellopterin demonstrated primarily bacteriostatic activity except against S. saprophyticus. Both compounds showed potent fungicidal activity against Candida species. These findings highlight the therapeutic potential of C. anisata fruit and its constituents against typhoid fever, hospital-acquired pneumonia, and invasive candidiasis. This is the first report on the antibacterial and antifungal activities of C. anisata fruit, stigmasteryl 3-palmitate, and phellopterin.

Genomic selection (GS) holds great promise for accelerating breeding progress in plants, and the advancement of across-population GS is essential to realize its full potential. However, conventional across-population GS heavily relies on precisely aligned markers across dense genotypes, whereas the feasibility of using flexible low-density markers remains underexplored. This study developed GS-Impute, a residual convolutional denoising autoencoder-based neural network framework that enables accurate genotype imputation for low-density across-population GS. A key breakthrough of GS-Impute is an automatic matching algorithm that resolves the persistent challenge of targeted training in the presence of both sporadic and systematic missing data. Additionally, GS-Impute incorporates a data augmentation strategy and several advanced techniques to enhance imputation accuracy, including residual blocks, dynamic learning-rate optimization, and layer normalization. Comprehensive evaluations across rice and maize breeding populations demonstrated that GS-Impute outperforms the latest versions of established benchmark tools, including Beagle5.4, Minimac4, and STICI. Importantly, the results indicate that GS-Impute makes across-population GS feasible with low-density markers, establishing a resource-efficient strategy with the potential to transform genomic breeding programs.

Crop disease presents significant threats to global food security and agricultural sustainability. Traditional monitoring methods, reliant on visual inspections and laboratory analyses, are labor intensive and unsuitable for large-scale implementation. Hyperspectral remote sensing has emerged as a promising tool for operational crop disease monitoring. Here, we provide a broad review, starting with a hyperspectral-based description of observable symptoms of common crop disease and then examining hyperspectral features, including spectral and textural features, pigment light absorption, solar-induced chlorophyll fluorescence (SIF), temporal information, and auxiliary data. We also analyze the algorithms used for disease detection, including traditional statistical methods, machine learning (ML)-based methods, and physically based methods. The review highlights the effectiveness of these methods in distinguishing various stressors, detecting early disease, assessing crop resistance, and monitoring large-scale disease. Additionally, we present two case studies of uncrewed aerial vehicle (UAV)-based hyperspectral imaging for maize leaf spot monitoring. Based on a quantitative literature review, we summarize current research trends. Future research should emphasize integrating physical models with deep learning (DL), ensuring the sensitivity and robustness of spectral features and promoting international data sharing.

Post-harvest physiological deterioration (PPD) is a significant challenge in the storage of cassava roots. The shelf life of cassava roots ranges from 24 to 72 hours, which reduces their economic value. PPD is characterized by blue/black to brown vascular streaking and the release of reactive oxygen species. Breeding efforts aimed at improving PPD resistance are hindered by limited information on the genetic diversity of different shelf-life types and cassava's natural heterozygosity. This study aimed to identify cassava varieties with delayed PPD. Twenty varieties were assessed and evaluated changes in metabolite content and gene expression during storage. Cassava varieties were planted at Kenyatta University's Plant Transformation Laboratory. After maturity, After maturity visual scoring and imaged based scoring to assess PPD at 0, 3, 5, 8, and 11 days post-harvest. Spectrophotometry was utilized to measure the levels of phenolic compounds, flavonoids, carotenoids, and anthocyanins in the stored roots. Additionally, expression of PPD related genes was monitored PAL, APX-2, APX-3, and AP using RT-PCR in both tolerant and susceptible varieties. The study's results demonstrated significant differences in PPD responses among the cultivars. Notably, the varieties NASE18 and KCA0013 exhibited a delayed onset of PPD symptoms, whereas TZ130 and Tajirika were more susceptible. Furthermore, the phenolic and flavonoid content were higher in KCA0013 and NASE18, while the carotenoid levels reached their peak in MM96/7151. Interestingly, the anthocyanin content significantly increased in the susceptible variety TZ130. The PPD-tolerant varieties showed elevated levels of PAL, AP, and APX-3 expression, while the expression of APX-2 was higher in the PPD-susceptible varieties. This study identifies promising cassava genotypes for breeding by highlighting the distinct biochemical and molecular profiles associated with PPD tolerance.

Abstract Sustainable intensification among smallholder farming in the Indo-Gangetic Plains of South Asia aims to enhance productivity while reducing negative environmental impacts. Early wheat sowing is promoted to increase yields and may have the co-benefit of reducing nutrient pollution, as it minimizes physiological stress and thus maximizes nutrient uptake. However, evidence of these co-benefits is sparse in ‘real-world’ conditions. We address this gap by analyzing farm survey data collected from Eastern Uttar Pradesh and Bihar states in Eastern India. Using an instrumental variable method, our results indicate that early wheat sowing (i.e., before the third week of November) improves nitrogen, phosphorus, and potassium use efficiencies by 5–8% and increases productivity by 6–8%. However, these benefits are heterogeneous, with larger farms and those applying higher doses of fertilizer being the primary contributor to environmental pollution. Our findings suggest targeted policy interventions to optimize the yield and environmental benefits of early wheat sowing in the Eastern Uttar Pradesh and Bihar states within the Eastern Indo-Gangetic Plains.

Although rice is the primary staple crop in Nepal, its yields have not increased sufficiently to keep pace with population growth and food security needs. The key factors contributing to this challenge is inadequate agricultural extension services that meets farmers’ demand and the low adoption rate of technologies that could enhance rice productivity. This study evaluates the effects of sequential agricultural training and real-time digital extension services on the technology adoption and rice yield in Nepal’s Western Terai, where evidence on the effectiveness of such combined intervention remains limited. Using data from a stratified random sample of 1,396 households, including treatment, control-in, and control-out groups, we assess the impact of these interventions on technology adoption and rice yield. We apply quasi-experimental methods including treatment effects model (IPWRA), seemingly unrelated regression, and instrumental variable approaches. Our findings consistently indicate that smallholder farmers who receive agricultural training combined with digital extension services have 11.4–15.7 percentage points higher adoption rate of split urea application in three doses at the recommended stages of plant growth, resulting in a 35–56% increase in rice yield compared to the reference group. These results suggest that the combined effect of agricultural training and digital extension services significantly outperforms agricultural training alone in both technology adoption and yield improvement, which is one of the key contributions of this study. The findings highlight the potential of bundled approach to enhance rice productivity and strengthen food security. However, limited Internet access in farming communities and difficulties in interpreting digital advisories may constrain the broader adoption of these agricultural practices.