Within-plant variability in pod maturity and preharvest sprouting in peanut (Arachis hypogaea L.): Physiological drivers, agronomic impacts, and integrated mitigation strategies

Peanut ( Arachis hypogaea L.) exhibits an unusually asynchronous reproductive cycle, in which flowering, peg penetration, pod development, and seed filling occur over an extended period. This results in the simultaneous presence of immature and preharvest sprouted (PHS) pods on the same plant a dual challenge that undermines yield, compromises seed quality, and complicates postharvest management. Immature pods reduce harvest efficiency, while PHS diminishes flavor, uniformity, and storage stability. Both genetic and environmental determinants ranging from temporal variation in peg initiation and hormonal gradients to microenvironmental heterogeneity and differential seed dormancy shape this variability. However, despite advances in pod biology, systematic field-based quantification of intra-plant temporal variation, genotype × environment interactions, and localized microclimatic influences remains limited. This review aims to synthesize current understanding of within-plant variability in pod maturation and PHS in peanut, to elucidate critical knowledge gaps at physiological and field scales, and to evaluate emerging strategies for mitigation. Particular emphasis is given for underexplored interface between physiological mechanisms and field-scale dynamics. Emerging innovations including hyperspectral imaging, soil and canopy moisture sensing, and molecular markers offer promising avenues for precise monitoring of pod maturity and early detection of PHS risk. Integrating these tools with targeted breeding strategies for synchronous flowering, enhanced dormancy, and late-season stress resilience, alongside adaptive agronomic practices such as optimized sowing, irrigation scheduling, nutrient management, and harvest timing, could substantially reduce yield and quality losses. Future progress will depend on bridging molecular insights with predictive models that capture mixed maturity and sprouting risk under variable environments.