Rajasekaran Ravikesavanand

Fall armyworm (FAW) is a major constraint in maize production and cause considerable yield loss. Although chemical control methods are being widely used for management of FAW, there are increasing concerns regarding issues like pesticide resistance and ecological imbalance. Exploiting host plant resistance represents the most eco-friendly and cost-effective approach. Growing resistant varieties are one of the ecofriendly approach in FAW management. A prime phase in the line of research is identification of resistant sources and underlying mechanisms contributing towards resistance. Present study integrates field screening, ovipositional preference assay, volatile profiling, and olfactory behavioural assays to characterize resistance in maize genotypes against FAW. Initial screening trials across two seasons revealed a dominance of susceptible genotypes, with DMRE 63 exhibiting resistance and CML 122 as moderate resistance. Leaf damage was highest at 14 days after infestation which corresponds to late larval feeding stage. Ovipositional preference study revealed DMRE 63 had lowest egg deposition under choice and no-choice conditions, indicating antixenosis. Induction of resistance is clearly evidenced through volatile profiling, showing distinct variation among genotypes in healthy, infested plants. Resistant genotype DMRE 63 produced biologically active aldehydes and inducible terpenoid–ketone compounds, whereas susceptible genotype CML 170 was dominated by structural hydrocarbons. Olfactometer assays confirmed behavioural avoidance of infested plants in resistant, moderately resistant, susceptible genotypes. Principal component analysis explained 96.7% variability, linking resistance to behavioural deterrence and reduced oviposition which are components of antixenosis mechanism. This study highlights the need to integrate field screening with chemical ecology ensuring sustainable pest management.

Sweet corn ( Zea mays L.) is a widely consumed vegetable crop; however, most commercial cultivars contain low levels of provitamin A carotenoids and anthocyanins, limiting their nutritional value. In this study, marker‐assisted backcross breeding was employed to simultaneously enhance beta‐carotene content, anthocyanin accumulation, and sweetness by introgressing the favourable crtRB1 allele, the recessive sh2 allele, and purple aleurone pigmentation into the elite sweet corn inbred DBT15 using the purple maize landrace Chujak Purple from the North‐Eastern Himalayan Region (NEHR) as the donor parent. Foreground selection for crtRB1 , phenotypic selection for purple kernel pigmentation and shrunken endosperm, and background selection using 102 polymorphic SSR markers facilitated the recovery of six stable BC ₂ F ₄ purple sweet corn introgression lines. Based on kernel pigmentation and anthocyanin composition, the lines were grouped into dark greyish‐purple and greyish‐purple types. At 24 days after pollination, the improved lines exhibited total soluble solids of 15.56–16.3°Brix, total sugars of 16.8%–17.57% and beta‐carotene concentrations of 7.25–8.20 μg/g, comparable to the recurrent parent DBT15 and substantially higher than the donor parent. High‐performance liquid chromatography identified cyanidin‐3‐glucoside, pelargonidin‐3‐glucoside and peonidin‐3‐glucoside as the predominant anthocyanins and evaluated across environments. These results demonstrate the successful development of biofortified purple super‐sweet corn combining enhanced provitamin A, elevated anthocyanin content and desirable sweetness, providing valuable germplasm for developing nutritionally superior sweet corn cultivars.

Maize (Zea mays L.), a key staple crop, supports human consumption, animal nutrition and diverse industrial uses. High-density planting (HDP) is an important strategy to improve crop yield per unit land area. Leaf angle (LA) is a key factor in determining maize plant architecture, photosynthetic efficiency and suitability for HDP. An urgent need exists to elucidate the genetic basis of these complex, polygenic traits, such as LA, to address the remaining information gaps in optimising canopy architecture. This review offers comprehensive insights into the molecular basis of regulatory networks involved in LA morphogenesis. It revisits advances in mutant and quantitative genetics research, identifying quantitative trait locus (QTL) across all 10 chromosomes and candidate genes through diverse studies employing genome-wide association studies (GWAS), genotyping by sequencing and linkage analysis. Leaf angle regulation is mediated by phytohormones and their crosstalk, as well as agronomic and environmental factors. Future progress depends on integrating advanced phenomics tools, such as 3D LiDAR, UAV-based hyperspectral imaging and deep learning, with CRISPR/Cas9-assisted breeding to precisely tune LA without undesirable pleiotropy. A standardised, ideotype-driven framework targeting upper-erect and lower-horizontal leaves will enable the development of climate-resilient, high-yielding maize varieties optimised for dense canopies.

Ninety-four CIMMYT maize (Zea mays L.) hybrids, along with 6 commercial checks, were evaluated at Kovilpatti, Tamil Nadu, India, during the rabi 2024 and kharif 2025 seasons using an alpha lattice design with two replications to identify major traits associated with grain yield. Significant variability was observed among the hybrids for grain yield and its related traits. Grain yield showed positive association with ear height, number of plants per plot, ear length and ear girth, indicating that ear related traits play a critical role in yield improvement. Path coefficient analysis revealed that number of plants per plot, ear girth and ear length exerted the highest positive direct effects on grain yield, suggesting that these traits can be effectively used as selection criteria in maize breeding programs. Phenotypic correlations followed similartrends but with relatively lower magnitudes, reflecting the influence of environmental factors on trait expression. In contrast, best linear unbiased predictors (BLUP) based correlations were more conservative and provided stable estimates across environments. However, the major trait associations remained consistent across seasons. Hierarchical cluster analysis grouped the hybrids into three distinct clusters, demonstrating substantial variability for grain yield and related traits. Principal component analysis (PCA) further confirmed that ear-related and plant architectural traits contributed significantly to the total phenotypic variation. Overall, integrating BLUP based correlation, path coefficient and multivariate analyses enhances selection efficiency and accelerates genetic gain in multi-environment maize breedingprograms.