Mechanical grain harvesting (MGH) reduces post-harvest losses and improves operational efficiency in maize, but its adoption is limited mainly by grain breakage arising from excessive grain moisture content (GMC) at harvest. To assess the potential and underlying mechanisms of nitrogen (N) fertilizer management in modulating maize grain moisture, a three-year field study (2023–2025) was conducted in the irrigated oasis zone of Northwest China. In 2023–2024, a single-factor experiment on hybrid DK159 tested four N fertilizer application rates (0, 300, 375 and 450 kg/ha); in 2025, the design was expanded to two hybrids (DK159 and XY1225), two planting densities (7.5 × 10⁴ and 11.25 × 10⁴ plants/ha) and an 18-level N gradient (0~765 kg/ha). Elevated N advanced silking by 2~3 d but delayed physiological maturity (PM) by 6~7 d, a ‘promote-early but delay-late’ phenological effect in 2023–2024. The effect of N on GMC was small and hybrid- and density-dependent: under high density, GMC of XY1225 increased by approximately 0.34% per 100 kg N, whereas DK159 responded minimally. N did not consistently alter post-PM grain dehydration rate (GDR), and leaf area index (LAI) at PM showed no significant linear relationship with GDR. Although some N–GMC associations were statistically significant, they were small, partly confounded by N-induced phenological delay, and associative rather than mechanistic, because key canopy physiological variables (microclimate, transpiration, stomatal conductance) were not measured. Interactive hybrid × density effects derive solely from the 2025 dataset and require cautious generalization. We conclude that N management has limited, context-dependent capacity to lower GMC at harvest toward MGH-suitable levels and that PM-measured LAI is not a primary structural determinant of grain dehydration; breeding hybrids with intrinsically rapid dehydration should therefore be prioritized over N management as a tool for lowering GMC.

High kernel moisture content at harvest severely restricts mechanical kernel harvesting in China, necessitating an in-depth understanding of the dynamic coordination between kernel filling and dehydration, alongside robust and practical prediction models. Here, we systematically quantified the temporal coordination between kernel filling and dehydration rates, and developed a thermal-time-based LogisticPower model ($M={\displaystyle \frac{90}{1+{\left(\frac{T}{a}\right)}^b}}$) using two independent field trials (multi-ecological network from 2023 to 2025 and multi-sowing date from 2024 to 2025) conducted in the Hexi Corridor, a representative sub-region of the Northwest irrigated maize area in China. Our results showed that kernel dehydration rates at Pre-PM (1.35∼1.85%/day) and Post-PM (0.30∼0.75%/day) were primarily determined by genotype, though modulated by precipitation as an environmental covariate rather than a model input. While a simple positive correlation existed between filling and dehydration rates (r = 0.312), partial correlation controlling for filling duration revealed a negative association (r = −0.218), indicating that kernel filling duration may confound the simple correlation; a formal mediation analysis was not performed, so this reflects a statistical association rather than an established causal mediating effect. Some cultivars showed the co-occurrence of relatively rapid filling and rapid dehydration under the tested conditions, but this association requires further validation before being treated as a breeding target. The LogisticPower model exhibited excellent predictive performance across environments (CCC: 0.921∼0.961; RMSE: 3.45∼4.58%; R²: 0.932∼0.958). The average kernel moisture content at physiological maturity in this sub-region was 33.5%. Critically, the thermal time from sowing to 25% kernel moisture content was validated as a relatively stable index with high cross-environmental robustness under well-watered and well-fertilized conditions for cultivar screening, providing crucial support for breeding high-yielding cultivars adapted to mechanical kernel harvesting.

To investigate the adoption behavior and decision-making mechanism of mechanized ear harvesting for seed corn, this study analyzed 786 household survey data from the Hexi Corridor using binary logistic regression, marginal effect analysis, interaction effect tests, and mediation models. The results revealed that the overall adoption rate of mechanized ear harvesting stands at 31.8%, with significant variations across regions and farm sizes. Adoption was found to follow a sequential decision chain: ‘technical feasibility → economic feasibility → comparative benefit assessment.’ Parental line lodging resistance (OR = 3.48) and field contiguity (OR = 3.01) were shown to positively influence adoption indirectly, mediated by perceived harvest loss reduction and perceived machinery efficiency enhancement. A 1% increase in kernel breakage rate is associated with a 2.7 percentage-point reduction in adoption probability. Enterprise-organized mechanical services were identified as the strongest adoption driver (OR = 6.19) and were found to function as a ‘scale equalizer’, significantly reducing the adoption advantage of larger farms (interaction coefficient B = −0.043, p = 0.024). Additionally, a pronounced scale-threshold effect is identified: adoption rates are observed to rise sharply beyond 3.33 hm², contradicting linear scale-adoption assumptions. These findings highlight the critical roles of inclusive enterprise services, a minimum efficient scale, and the sequential decision process. Coordinated innovation across breeding, equipment engineering, and extension systems is required for sustainable mechanization.