In the context of tightening resource and environmental constraints, quantitative measurement and influencing factors of cultivated land use eco-efficiency (CLUE) have become hot topics in current academic research. Existing studies primarily focus on the influence of natural, social, and economic factors on CLUE but ignore exploring the impact mechanism and effect of environmental policies on CLUE. Therefore, this study aims to explore the impact of environmental regulations on CLUE. To achieve this objective, a super-efficiency slack-based measure (super-SBM) model is used to calculate the CLUE for 31 provinces (municipalities and autonomous regions) in China from 2000 to 2009. Additionally, the intermediary effect model and the threshold effect model are used to empirically investigate the transmission mechanism and nonlinear characteristics between environmental regulation and the CLUE. The results show that: (1) the temporal dynamics of CLUE exhibit a pattern of initial fluctuating decline followed by gradual growth in China as a whole and across its eastern, central, and western regions. (2) Environmental regulation has a significant negative impact on CLUE, and the effect exhibits a nonlinear characteristic of decreasing marginal effects. (3) Agricultural technological innovation and agricultural industrial structure play a mediating role between environmental regulation and CLUE, reducing the negative impact of environmental regulation on CLUE. This study provides some implications for formulating scientifically sound environmental policies to optimize land use and enhance resource utilization efficiency.

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.

The Middle Route Project of China’s South-to-North Water Diversion Project (SNWDP) is a national-level water source protection zone and the ecological safety of its water quality and surrounding soil is of great significance. In this study, heavy metals in the surface water and topsoil in the core water source area were quantitatively analyzed using a geographic information system (GIS) and geostatistical techniques combined with environmental pollution and ecological risk assessment models to determine their environmental contamination levels, ecological risk levels, and spatial distribution patterns. Cd was identified as an essential factor responsible for the overall slight heavy metal pollution in the topsoil layer. Heavy metal contamination in surface water was primarily driven by alert-level concentrations of Hg and was consistently distributed in areas with high concentrations of Hg in the topsoil. Applying the potential ecological risk index (RI) revealed two key results. First, surface water showed no ecological risk. The concentrations of heavy metals in surface water met the goals set by relevant authorities in China. Second, overall, the topsoil was at low ecological risk, with a spatial pattern primarily influenced by Cd and Hg. Some heavy metals might have similar pollution sources and originate from human activities such as industrial activities, mining and smelting, and pesticide and chemical fertilizer applications. The study is important for improving the soil and water ecology in the reservoir area and ensuring the northward diversion of high-quality water. In addition, it provides a sound basis for making decisions about local heavy-metal remediation and treatment projects.

Biopesticides are perceived as a feasible alternative to chemical pesticides, providing an effective approach to pest management while mitigating the undesirable effects of chemical pesticide overuse. Yet, due to the distinctive attributes of the two types of pesticides, farmers often adopt a combination of both. This study aimed to probe the interdependent relationship underlying the mixed use of chemical pesticides and biopesticides by farmers in the rice–crayfish integrated system (RCIS) in Hubei province, China. We also sought to identify influencing factors and associated consequences of this practice. Given that the adoption of chemical pesticides and biopesticides by farmers is not mutually exclusive, a multivariable probit model was utilized to estimate simultaneous pesticide applications. Survey data from 736 RCIS farmers revealed that 10.50% of the sample farmers employ a combination of chemical pesticides and biopesticides. A substitution relationship was observed between the adoption of chemical pesticides and biopesticides by farmers, whereas no significant complementary relationship was found in the application of the two types of pesticides. The primary motivation for the mixed use of pesticides by farmers is to achieve superior pest control efficacy. Notably, when integrating the use of pesticides, farmers pay less attention to factors such as resource endowments and multiple production objectives. Further analysis indicated that farmers display significant concern for pesticide attributes including insecticidal efficacy, toxicity, insecticidal spectrum, and validity period. The mixed use of pesticides has resulted in a decreased frequency of pesticide application but has escalated the cost. While the mixed use of pesticides elevated the selling price of rice, no significant improvement was observed in yield and output value. The insights garnered from this study offer strategic implications for policymakers on how to enhance the judicious adoption of pesticides by farmers.