Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.

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.

Water and nitrogen availability jointly influence dryland wheat yield and grain protein formation; the ability of crop models to simulate the synergistic effects of these two factors depends on the accuracy of key parameters. However, the large number of model parameters and their complex interactions make parameter screening and calibration challenging. In this study, the model was calibrated using annual yield records for dryland wheat in Anding District, Dingxi City, and Gansu Province, from 2015 to 2019, along with field observation data (grain protein content) from Anjiagou Village, Fengxiang Town, and Anding District, collected during the same period. The model was then validated using independent data from two growing seasons (2020–2021) in the same study area. Based on the Agricultural Production sIMulator Next-Generation (APSIM NG) and using water–nitrogen response as the foundation, a multi-objective parameter calibration framework was developed with yield and grain protein content as calibration targets. The extended Fourier amplitude test (EFAST) was used to screen key model parameters, and the performance of the DREAM–zs algorithm, the Nelder–Mead simplex algorithm, and the multi-objective rime optimization algorithm (MORIME) in calibrating these parameters was compared and analyzed. All three algorithms reduced the simulation error in yield; for grain protein content, the Nelder–Mead and MORIME algorithms reduced the simulation error, while DREAM-zs did not. The simulation accuracy of the Nelder–Mead and MORIME algorithms was generally similar, but MORIME yielded a set of Pareto solutions that characterized the trade-off between the simulation accuracy of yield and grain protein content; therefore, its parameter solutions were selected for independent validation. The validation results showed that the RMSE for yield decreased from 248.45 kg hm⁻² to 213.90 kg hm⁻², and the NRMSE decreased from 12.60% to 10.85%; for grain protein content, the RMSE decreased from 0.280% to 0.250%, and the NRMSE decreased from 1.86% to 1.66%. Furthermore, the incorporation of parallel computing reduced MORIME’s runtime by 82.99%. The study demonstrates that MORIME with parallel computing can provide methodological support for the multi-objective, efficient optimization of crop model parameters.

Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of “time-series perception–dynamic simulation–feature identification–early prediction”, providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.

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.