Fostering a culture of continuous improvement through regular monitoring of genetic trends in breeding pipelines is essential to improve efficiency and increase accountability. This is the first global study to estimate genetic trends across the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding pipelines in eastern and southern Africa (ESA), South Asia, and Latin America over the past decade. Data from a total of 4152 advanced breeding trials and 34,813 entries, conducted at 1331 locations in 28 countries globally, were used for this study. Genetic trends for grain yield reached up to 138 kg ha⁻¹ yr⁻¹ in ESA, 118 kg ha⁻¹ yr⁻¹ South Asia and 143 kg ha⁻¹ yr⁻¹ in Latin America. Genetic trend was, in part, related to the extent of deployment of new breeding tools in each pipeline, strength of an extensive phenotyping network, and funding stability. Over the past decade, CIMMYT’s breeding pipelines have significantly evolved, incorporating new tools/technologies to increase selection accuracy and intensity, while reducing cycle time. The first pipeline, Eastern Africa Product Profile 1a (EA-PP1a), to implement marker-assisted forward-breeding for resistance to key diseases, coupled with rapid-cycle genomic selection for drought, recorded a genetic trend of 2.46% per year highlighting the potential for deploying new tools/technologies to increase genetic gain.

The Fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith), is a highly destructive lepidopteran pest known for its extensive feeding on maize (Zea mays L.) and other crops, resulting in a substantial reduction in crop yields. Understanding the metabolic response of maize to FAW infestation is essential for effective pest management and crop protection. Metabolomics, a powerful analytical tool, provides insights into the dynamic changes in maize’s metabolic profile in response to FAW infestation. This review synthesizes recent advancements in metabolomics research focused on elucidating maize’s metabolic responses to FAW and other lepidopteran pests. It discusses the methodologies used in metabolomics studies and highlights significant findings related to the identification of specific metabolites involved in FAW defense mechanisms. Additionally, it explores the roles of various metabolites, including phytohormones, secondary metabolites, and signaling molecules, in mediating plant–FAW interactions. The review also examines potential applications of metabolomics data in developing innovative strategies for integrated pest management and breeding maize cultivars resistant to FAW by identifying key metabolites and associated metabolic pathways involved in plant–FAW interactions. To ensure global food security and maximize the potential of using metabolomics in enhancing maize resistance to FAW infestation, further research integrating metabolomics with other omics techniques and field studies is necessary.

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.