Soil moisture, as an important variable affecting water–heat exchange between land and atmosphere, is an important feedback to climate change. Soil moisture is of great concern in Northern China, where arable land is extensive, but water resources are distributed unevenly and extremely sensitive to climate change. Using measured soil moisture data collected by the China Meteorological Administration from 164 stations during 1980–2021, we explored the drivers of soil moisture variation by analyzing its spatiotemporal variability using linear regression, partial correlation analysis, and geostatistical methods. The results indicated that (1) soil moisture increased from northwest to southeast in Northern China, with the lowest soil moisture in the IM; (2) the overall trend of soil moisture in most regions decreased, especially in the arid northwest and northeast China. However, soil moisture in some regions began to increase gradually in recent years, such as in northwestern Xinjiang and the central-eastern part of IM; and (3) soil moisture in the whole region was negatively correlated with temperature and sunshine duration and positively correlated with precipitation and relative humidity. The results of the study can provide valuable guidance for timely agricultural irrigation and the adjustment of cropping structures, thereby ensuring agricultural production and food security.

The subtropical region of East China is characterized by abundant water and temperature resources conducive to crop cultivation, and large areas of lowland have been widely used for agricultural planting. The objectives of the study were to explore feasible methods of greenhouse gas (GHG) reduction for rice–wheat rotation systems and to explain the mechanism underlying the effect of drainage on GHG reduction. Shallow ditch (SD) and deep ditch (DD) treatments in the wheat season were set up for drainage to control the paddy soil water content, with conventional non-ditching as the control group (CG). CH₄ and N₂O emission fluxes were continuously measured, and related soil physical and chemical properties were also measured in this study. The results showed that CH₄ emissions from paddy soil accounted for most of the global warming potential (GWP) in the rice–wheat rotation system. Drainage led to a significant reduction in cumulative soil CH₄ emissions during the rice and wheat seasons; however, the overall cumulative N₂O flux increased significantly. The total GWP produced by SD and DD in the three years was reduced by 58.21% and 54.87%, respectively. The GHG emission intensity (GHGI) of SD and DD declined by 60.13% and 56.40%, respectively. The CH₄ emission flux was significantly positively correlated with the 5 cm ground temperature but negatively correlated with the soil redox potential (soil Eh). The drainage decreased the soil water and soil organic matter contents and increased soil pH, which were the mechanisms that reduced the CH₄ emissions. The drainage increased the soil nitrogen content, which is the main reason for regulating N₂O. The findings indicate that SD and DD not only ensured a stable increase in production but also effectively reduced GHG emissions, and we recommend SD treatment for agricultural production.

Scientific management and environmental regulation of facility strawberries depends on the level of accurate prediction and forecasting of low temperature freezes in plastic greenhouses during winter and spring strawberry cultivation. Accurate identification of potential factors affecting layer-by-layer minimum temperatures in plastic greenhouses and selection of optimal forecasting methods are important for safe strawberry production. However, the identification of important drivers of minimum temperatures in plastic greenhouses and the prediction of potential drivers of use are still unclear. In this study, we used Classification and Regression Tree (CART) to identify the importance of the potential factors affecting the minimum temperatures at different depths and different heights of plastic greenhouses. Random forest (RF), back-propagation (BP), and multiple linear regression (MLR) were used to establish the minimum temperature prediction models for plastic greenhouses at different depths and heights, respectively. The results showed that T_smin10, T_smin25, T_amin150, T_amin320, and T_amin150 were the most important variables explaining the changes in minimum temperatures at heights T_smin25, T_smin10, T_smin2, T_amin150, and T_amin320 respectively. RF, BP performed much better than MLR, as it showed much lower error indices (AE and RMSE) and higher R² than MLR. The superiority of RF and BP in predicting minimum temperatures is related to their ability to deal with non-linear and hierarchical relationships between minimum temperatures and predictors. The low-temperature frost protection and fine management of strawberries in the Changfeng area can be related to the prediction method of minimum temperature in plastic greenhouses constructed in this study.