Using the Life Cycle Assessment (LCA) method, with 1 ton of food waste as the functional unit, the LCA system boundary was constructed, covering the entire process of raw material acquisition, collection and transportation, pretreatment, mesophilic anaerobic fermentation, and digestate treatment. Combined with measured data and the East Asia regional adapted inventory of the Ecoinvent3.0 database, the CML 2001 evaluation system was adopted to quantify 7 types of environmental impact categories: abiotic depletion potential (ADP), global warming potential (GWP), acidification potential (AP), human toxicity potential (HTP), photochemical ozone creation potential (POCP), eutrophication potential (EP), and aerosol quality potential (AQP). The environmental impacts of the digestate treatment mode and 6 optimized processes of a large-scale food waste biogas project in Jinhua City, Zhejiang Province were systematically evaluated. The results show that the comprehensive environmental impact potential value of the "ammonia stripping + biochar + membrane separation" mode is -1.835×10^-11, which is 6.93% lower than that of the sewage treatment benchmark mode (-1.716×10^-11). This mode enhances NH₄⁺-N recovery (the recovered product is ammonium sulfate) through pH 10.5-11.0 regulation, and the effluent NH₄⁺-N removal rate reaches 92.32%. Membrane separation technology combined with biochar adsorption achieves soluble chemical oxygen demand (SCOD) and total phosphorus (TP) removal rates of 71.51% and 91.82%, respectively, and has the best inhibitory effect on EP (-6.977×10^-12). Compared with the sewage treatment mode (unit energy consumption 5.553×10⁵ kJ/t), it saves energy by 406.80% (unit energy consumption 3.63×10⁵ kJ/t). Both the "ammonia stripping + biochar + membrane separation" and "ammonia stripping + microalgae" modes reduce the concentration of NH₄⁺-N in biogas slurry compared with the sewage treatment mode through pH regulation, and their comprehensive potential values are better than the sewage treatment mode, confirming that NH₄⁺-N recovery is the core path to control eutrophication. Sensitivity analysis shows that the increase in collection and transportation distance will aggravate the deterioration of GWP and AP indicators, while increasing the proportion of biogas power generation substitution to 100% can reduce EP and AP by 30%～50%. Therefore, economic and environmental benefits can be improved through measures such as path optimization, electric energy substitution for vehicles, and biogas self-power generation. The research results provide a systematic decision-making framework based on LCA for the environmental management of anaerobic fermentation projects, and have important reference value for achieving the construction goal of "Zero-Waste City".

In the context of precision agriculture, recent years have witnessed a remarkable increase in the abundance of remote sensing image resources, accompanied by a significant boost in computing power. This progress has led to more refined land cover classification maps through semantic segmentation of remote sensing images, which is of great significance for farmland ecosystem analysis. In the agricultural domain, analyzing the distribution characteristics of farmland and roads,key components of the rural infrastructure network, enables accurate determination of straw yield and the distribution data of farmland roads. This information is crucial for formulating scientific and reasonable collection, storage, and transportation route plans, which is essential for efficient agricultural resource management.In existing research, within the scope of agricultural informatics, the application of deep-learning models to extract farmland road information encounters challenges such as high complexity. In this study, leveraging semantic segmentation technology, a core technique in remote sensing data processing for agriculture, research on the extraction of farmland road information from remote sensing images is carried out. Based on the land cover classification extracted from the images, research on optimizing the location selection of straw collection and storage stations is conducted, which is an important part of agricultural waste management.Through ablation experiments, it is demonstrated that both the asymmetric fusion non - local block (AFNB) and the Structure of the Dual Attention Module contribute positively to the segmentation effect. Compared with the original network model, the integrated results after combining these two components lead to increases in IoU, Acc-road, and mIoU by 5.2, 7.78, and 2.73 percentage points, respectively. By utilizing class activation graph analysis, this model significantly enhances the accuracy and efficiency of farmland road information extraction, a fundamental task in agricultural remote sensing. Its superiority is validated across multiple datasets, with achieving a minimum mIoU of 68.98%.To verify the generalization of the improved DlinkNet model in other rural regions within the agricultural landscape, Gaoyi County in Hebei Province is taken as an example. The farmland road extraction task is completed, and an in - depth analysis is performed based on the extraction results. Through research on optimizing the location selection of straw collection and storage stations, the straw yield and theoretical available amount of winter wheat, key parameters in agricultural production assessment, are calculated. Based on the World Cover dataset, this study optimized the site selection of straw collection and storage stations using the K-means clustering algorithm. By defining straw resource distribution density, residential areas, rivers, lakes, and other environmental factors, the optimal locations for village-level straw collection stations were determined. The results demonstrate that the selected sites meet the requirements of environmental protection, straw resource availability, and transportation accessibility, laying the foundation for subsequent research on straw transportation route optimization. This contributes to the establishment of a comprehensive optimization system for straw collection, storage, and transportation.

This study aims to promote the full-value utilization of corn stover. Data sources were selected as the China Knowledge Infrastructure database and the Web of Science. Visualization software was used to draw knowledge graphs in the field of corn stover use. The current status of research and trends were summarized to analyze the key generic technologies of corn stover use. Two stages were divided in the utilization of corn stoves: 1990-2007 and 2008-2022. In the latter stage, the research system of corn stover use was gradually formed using fertilizer and feed that was supplemented by fuel. A literature review found that the research on corn stover use roughly experienced three phases of hot spot migration, each of which lasted about 10 years. The research was focused mainly on stover returning during the period 1990-2000; The focus of the research was straw returning to the field, straw energy, and feed use during the period 2001-2010; The utilization of corn stover was diversified to gradually form during the period 2011-2022. The utilization of corn stover was achieved mainly in the fields of stover returning and biomass energy production. According to the knowledge graph, the hot directions of corn stover use included biomass energy production, land improvement and protection, animal husbandry development, and feed production. Finally, the knowledge engineering and induction show that there were still some bottlenecks in the technology of corn stover use, such as delayed collection and transportation, low quality of returning, immature technology of stover feed, high cost, and low added value of energy technology. Driven by the important national demand for the utilization of agricultural waste resources, 13 key generic technologies were summarized for the full-value utilization of corn stover, including intelligent control and management of agricultural machinery and equipment, digital agriculture, high-quality field-returning, field-returning under the local conditions, sealed storage, high-yield cellulose strain of screening, selection, and breeding of efficient microbial strains for lignin degradation, strain matching and enzyme application, high-efficiency biomass energy conversion, stover cellulose to ethanol, lignocellulosic sugar, stover cultivation substrate preparation, and bio-based material manufacturing.