Physiological readiness for harvest in machine-harvested cotton: Linking canopy regulation, maturity synchrony, diagnostic thresholds, and lint yield and quality realization

Xinjiang has developed one of the world’s most advanced and productive machine-harvested cotton systems under arid irrigated conditions, yet the central challenge is no longer yield formation alone but the reliable conversion of pre-harvest productivity into clean, harvestable lint with stable fiber quality. This critical review introduces physiological readiness for harvest as a product-oriented concept linking canopy regulation, maturity synchrony, pre-harvest crop status, and final lint yield and quality realization in machine-harvested cotton. We synthesize evidence on how plant density, chemical regulation and topping, irrigation, nitrogen management, fertigation, and harvest-aid application shape readiness through canopy architecture, boll maturity distribution, carbon–nitrogen coordination, root–shoot interactions, source–sink allocation, and defoliation responsiveness. The evidence indicates that high biomass, high boll density, or persistent canopy greenness does not necessarily ensure high harvestable productivity when lower-canopy shading, late vegetative persistence, maturity heterogeneity, or premature harvest-aid application reduces lint yield retention or fiber quality. Because the available studies differ in cultivar, ecological region, management intensity, and harvest-aid practice, this review does not propose universal numerical thresholds. Instead, it identifies candidate indicators, threshold dimensions, and locally calibratable diagnostic variables, including boll opening percentage, boll age structure, canopy greenness decline, late regrowth, harvest-aid responsiveness, lint yield retention, harvest cleanliness, and fiber-quality risk. We argue that harvest aids mainly amplify existing readiness rather than compensate for inadequate whole-season regulation. Readiness-based diagnosis, regional threshold calibration, and ground-validated digital sensing of maturity heterogeneity and spray-window suitability should improve machine-harvested cotton management in Xinjiang and other mechanized cotton regions.