Organic fertilizers have been widely applied in Chinese agriculture. Soil aggregate composition and pore size distribution indicate the effects of fertilizer practices on soil quality. While the effects of the long-term application of organic fertilizers on total soil pore space have been reported, few studies have examined the distribution of connected pore spaces (of critical importance for solute transport in the soil). Soil physical and chemical properties influence plant growth and soil processes. We compared the effects of organic fertilizer application (10 years, 5 years, and no application) on soil connectivity pore structures and physicochemical properties within the root zone (20–40 cm depth) around date palms through CT scanning or core samples. Specifically, when the number of years of organic fertilizer application increased, the proportion of 2–0.25 mm particle size aggregates in the soil increased, and soil connectivity pore structure became more complex and stable, with higher connectivity, pore numbers, and numbers of pore throats. The connectivity of particles of the 0–300 μm size class of pores increased and the proportion of the 500–1000 μm class of pore throats increased. Soil organic matter and enzyme activity were significantly related to soil pore structure characteristics. In conclusion, the application of organic fertilizer improved soil pore structures, and the effects became more pronounced with the increased duration of application. These results provide theoretical and practical support for the application of organic fertilizer to improve soil structures in arid areas, and the findings have significant implications for sustainable agriculture in arid and semi-arid regions.

Leaf nitrogen concentration (LNC) exhibits pronounced vertical heterogeneity across canopy layers, which affects the accuracy of nitrogen diagnosis derived from UAV-based remote sensing imagery. To address the differential contributions of leaf nitrogen from distinct canopy strata and the limitations associated with single-source features, this study proposes an integrated framework that combines cumulative LNC indicators across canopy layers with multi-source feature sets (vegetation indices and texture features). Centered on three core technical innovations—(1) incorporating canopy-layer aggregation logic into LNC modeling, (2) integrating spectral and structural information through CNN-based feature fusion, and (3) combining deep feature extraction with gradient boosting regression to improve robustness under multi-stage conditions—the framework systematically evaluates three machine learning algorithms: Random Forest (RF), a Convolutional Neural Network–Extreme Gradient Boosting hybrid model (CNN_XGBoost), and K-Nearest Neighbor (KNN) for cotton LNC estimation across multiple growth stages. The results demonstrate that cumulative canopy-layer nitrogen indicators more effectively represent overall plant nitrogen status than single-layer measurements. The integration of multi-source features further enhances model performance. Under both single-variable inputs and combined VI–TF feature sets, the CNN_XGBoost model consistently outperforms the other models in calibration accuracy and stability across all growth stages. Its optimal performance occurs during the cotton flowering and boll stage, achieving a calibration R² of 0.921. Overall, the proposed framework substantially improves the estimation accuracy of cotton LNC and provides both a theoretical foundation and technical support for precision nitrogen management and sustainable agricultural development.