Yunhua Zhang

Efficient allocation of climatic resources is essential for improving the sustainability and productivity of cropping systems under changing climate conditions. This study aimed to evaluate whether diversified grain–forage rotations could optimize inter-annual growth period configurations and improve annual thermal and radiation resource use in the North China Plain (NCP). A field experiment was conducted from 2023 to 2025 to compare five cropping systems: wheat–maize (W-M), wheat–silage maize (W-SM), wheat–sweet sorghum (W-SS), ryegrass–silage maize (R-SM), and ryegrass–sweet sorghum (R-SS). Growth period characteristics, annual thermal and radiation resource allocation, dry biomass yield, crude protein yield, resource use efficiency, and economic performance were evaluated. The results showed that alternative cropping systems extended annual resource use duration and altered seasonal resource allocation compared with W-M. R-SS exhibited the longest annual growth duration and the most balanced seasonal distribution of thermal and radiation resources, whereas R-SM achieved the highest annual biomass yield. All alternative systems improved thermal and radiation resource use efficiencies compared with W-M, with increases particularly evident during the summer–autumn season. Moreover, diversified systems reduced cultivation costs and improved economic returns. Overall, optimizing inter-annual growth period configurations might provide an effective strategy for enhancing the resource use efficiency, productivity, and economic sustainability of grain–forage cropping systems in the NCP and other regions with similar agro-meteorology in the world.

Optimizing tillage and fertilization practices is of vital importance for enhancing soil carbon retention, improving soil quality and increasing crop productivity in the intensive wheat (Triticum aestivum L.)–maize (Zea mays L.) double cropping system (WM). However, the combined effects of subsoiling (ST) and liquid manure (LM) application on yield sustainability and the dynamic changes in labile organic carbon (LOC) fractions (LOCs) remain insufficiently quantified in WM in the North China Plain (NCP). A two-year field experiment evaluated the responses of grain yields, the sustainable yield index (SYI), soil organic carbon (SOC), LOCs, C pool management indexes (CPMIs), and the soil quality index (SQI) to both patterns of tillage [conventional shallow rotary tillage (RT) and ST] and fertilization [conventional fertilization (CF), LM broadcast (LMB), and LM injection (LMI)] in WM in the NCP. Compared with RT, ST significantly enhanced crop grain yields (3.5~4.1%) and the annual SYI (4.1%) (p < 0.05). The contents of SOC, total labile OC (TLOC), high LOC (HLOC), and medium LOC (MLOC) and the values of SQI were higher in soil layers at both 0–20 cm and 20–40 cm under ST than those under RT. Compared with CF, LMI significantly enhanced grain yields (5.8~6.1%) and the annual SYI (5.4%). LMI significantly increased the contents of SOC, TLOC, HLOC, and MLOC and the SQI values in both soil layers relative to CF, while no significant difference was observed for grain yields, the annual SYI, and the SQI between LMB and CF. The higher contents of SOC and LOC led to an increase in the values of CPMIs based on TLOC (TCPMI), HLOC (HCPMI), and MLOC (MCPMI). The combination of both ST and LMI enhanced SOC retention through the increase in recalcitrant organic carbon (ROC) content and the transformation process of LOCs. It was obvious that HLOC and MLOC affected SOC, HCPMI, and MCPMI in the soil layers at both 0–20 cm and 20–40 cm, and thus can be regarded as sensitive indicators reflecting the dynamic changes in SOC and soil quality. Therefore, the combination of subsoiling and liquid manure injection can promote labile OC transformation, SOC retention, soil quality, and yield sustainability, providing an effective management strategy for the achievement of sustained agricultural production in the NCP or other regions with similar conditions.

Understanding the spatial correlation network of inter-provincial wheat production is vital for ensuring food security and achieving sustainable agricultural development in China. However, the spatial correlation characteristics of wheat production and their determinants remain unclear. In this study, an improved gravity model was used to calculate the spatial correlation of inter-provincial wheat production in China based on available panel data from 2000 to 2020. The spatial-temporal evolution characteristics and the driving factors of the spatial correlation network of inter-provincial wheat production in China were analyzed using social network analysis (SNA) and a quadratic assignment procedure (QAP). The findings indicated that (1) the spatial correlation of inter-provincial wheat production first increased and then decreased. The network density increased from its lowest value (0.2598) in 2000 to its maximum value (0.2782) in 2016 and then continued to fluctuate. (2) The spatial correlation network of inter-provincial wheat production presented a “core-periphery” distribution pattern for the major wheat-producing areas (such as Jiangsu, Anhui, and Hubei) and non- major wheat-producing areas (such as Jilin, Qinghai, Guangxi, and Beijing), and the roles of the blocks in the network varied with time and space. (3) The implementation of grain-related policies (such as the abolition of agricultural taxes, the implementation of industry nurturing agriculture, and the minimum grain purchase price policy) positively affected the development of the spatial correlation network of wheat production. Since the implementation of the minimum purchase price policy for wheat in 2006, the network density reached its maximum value (0.2782), the network efficiency reached its minimum value (0.5985), and the stability of the network structure greatly improved. (4) The interactions between the internal natural conditions and the external socioeconomic factors promoted the construction of a spatial correlation network for wheat production. The differences in geographical adjacency, land resources, temperature, and sunlight hours were all significant at the 1% level, highlighting the substantial impact of these factors on the spatial correlation intensity of wheat production in China. This study provides a reference for the development of cooperative cross-regional wheat production and the formulation of distinct policies for the production of wheat and other grains.

Suitability evaluation of tea cultivation is very important for improving the yield and quality of tea, which can avoid blind expansion and achieve sustainable development; however, to date, relevant research at town and village scales is lacking. This study selected Xinming Township in Huangshan City, Anhui Province, as the study area, which is the main production area of Taiping Houkui Tea—one of the ten most famous teas in China. We proposed a machine learning-based tea cultivation suitability evaluation model by comparing logistic regression (LR), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), random forest (RF), Gaussian Naïve Bayes (GNB), and multilayer perceptron (MLP) to calculate the weight accuracy of the evaluation factors. We then selected 12 factors, including climate, soil, terrain, and ecological economy factors, using the RF with the highest accuracy to calculate the evaluation factor weights and obtained the suitability evaluation results. The results show that the highly suitable area, moderately suitable area, generally suitable area, and unsuitable area land categories for tea cultivation were 14.13%, 27.25%, 32.46%, and 26.16%, respectively. Combined with field research, the highly suitable areas were mainly distributed in northwest Xinming Town, which is in line with the distribution of tea cultivation at the Xinming township level. The results provide a scientific reference to support land allocation decisions for tea cultivation and sustainable green agricultural development at the town and village scales.