Biochar (BC) demonstrates considerable potential for reducing nitrogen emissions. However, it frequently exhibits a limited capacity for the adsorption of NO₃⁻-N, thereby reducing its effectiveness in mitigating N₂O emissions. Nano-biochar (NBC) is attracting attention due to its higher surface energy, but there is a lack of information on enhancing NO₃⁻-N adsorption and reducing N₂O emissions. Accordingly, this study conducted batch adsorption experiments for NO₃⁻-N and simulated N₂O emissions experiments. The NO₃⁻-N adsorption experiments included two treatments: bulk BC and NBC; the N₂O emissions experiments involved three treatments: a no-biochar control, BC, and NBC. N₂O emissions experiments were incorporated into the soil at mass ratios of 0.3%, 0.6%, 1%, and 3%. The results demonstrate that NBC exhibits nearly twice the NO₃⁻-N adsorption capacity compared to bulk biochar (BC), with adsorption behavior best described by a physical adsorption model. The enhanced adsorption performance was primarily attributed to NBC’s significantly increased specific surface area, pore volume, abundance of surface acidic functional groups, and higher aromaticity, which collectively strengthened multiple sorption mechanisms, including physical adsorption, electrostatic interactions, π–π interactions, and apparent ion exchange. In addition, NBC application (0.3–3%) reduced cumulative N₂O emissions by 11.60–54.77%, outperforming BC (9.16–32.65%). NBC treatments also increased soil NH₄⁺-N and NO₃⁻-N concentrations by 2.4–8.2% and 7.3–59.0%, respectively, indicating improved inorganic N retention. Overall, NBC demonstrated superior efficacy over bulk BC in mitigating N₂O emissions and conserving soil nitrogen, highlighting its promise as a sustainable amendment for integrated nutrient management and greenhouse gas reduction in soil.

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

Soil erosion and the consequent loss of nutrients have consistently been significant factors contributing to land degradation and nonpoint source pollution. While runoff serves as the primary carrier for nutrient loss, the hydraulic processes governing the mechanisms of nutrient loss remain not entirely clear. This paper aims to investigate the impacts of rainfall intensity and the slope gradient on hydraulic parameters, soil loss rates, and ammonia nitrogen loss rates, with the objective of determining the optimal hydraulic parameters for more accurate predictions of soil erosion and nutrient loss rates. A series of simulated rainfall experiments with three rainfall intensities (25, 50, and 75 mm min⁻¹) and four slope gradients (8.7%, 17.6%, 26.8%, and 36.4%) were conducted on a 5 m × 10 m slope. The results indicated that the flow velocity, shear stress, stream power, unit stream power, and unit energy all increased with the increase in slope gradient or rainfall intensity. The water depth decreased with an increase in the slope gradient but increased with an increase in the rainfall intensity. Laminar flow occurred in all experiments (Reynolds number < 500). Only the overland flow under a 25 mm h⁻¹ rainfall intensity and 8.7% slope gradient was subcritical flow (Froude number < 1). Hydraulic parameters, the soil loss rate, and ammonia nitrogen loss rate could be all expressed as the product of rainfall intensity and slope power function, with R² ranging from 0.949 to 0.997. The average soil loss rate and process soil erosion rate could both be fitted using the power function of hydraulic parameters, with the optimal fitting parameter being stream power (R² = 0.980 and 0.909). The average ammonia nitrogen loss rate exhibited a linear relationship with the hydraulic parameters, and the optimal fitting parameter was also stream power (R² = 0.933). However, there were relatively low correlations between hydrodynamic parameters and the ammonia nitrogen loss rate (R² = 0.450–0587). Our results contribute to a deeper understanding of the hydraulic processes involved in nutrient loss.

Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat