Analysis-based Research on Sustainable Development of Corn Planting Ecosystems in Sichuan Province

yuejuan Yang^1*yutao Ran¹yue Zhang¹hongliang Ji¹zhengqi Yin²jiahui Liu³

• ¹潍坊学院, 山东省 / 潍坊市 / 奎文区, China
• ²Sichuan University, Chengdu, Sichuan Province, China
• ³Shandong Normal University, Jinan, China

As a vital grain production hub in southwestern China, Sichuan province's corn planting industry is critical for maintaining regional food security. Through an evaluation system comprising 18 indicators, this study applied an emergy analysis to assess the sustainability of Sichuan's corn planting ecosystems in 2022, encompassing natural resource emergy, industrial support energy, and purchased renewable resources. The results were as follows: (1) In terms of emergy input and output, Chengdu city had the highest emergy input density at 10.59×10 11 sej•m -2 , which was nearly double the value observed in the Garzê Tibetan Autonomous Prefecture (5.36×10 11 sej•m -2 ). In contrast, the Aba Tibetan and Qiang Autonomous Prefecture exhibited the lowest emergy output density, reaching only 40% of the levels achieved by Dazhou city. (2) In terms of investment benefits, Chengdu city maintained the highest emergy investment ratio (4.13), and demonstrated the lowest net emergy yield ratio (1.17). By contrast, Ziyang city achieved the highest net emergy yield ratio (2.07), indicating significant regional imbalances in resource input and output efficiency. (3) In terms of management and environmental impact, we found significant regional variations. Suining city demonstrated the highest scale management degree at 59.8, while mountainous regions averaged only 21.64, reflecting obvious topographical differences. Moreover, 67% of the regions had an environmental loading ratio below 1, while high-level areas showed an average environmental loading ratio of 1.71, indicating significant ecological pressure.Moving weighted trend surface analysis indicated a northeast-to-southwest declining pattern in the environmental sustainability index (R²=0.63, P<0.05), demonstrating the combined influence of natural resource availability and human energy inputs.Using partial least squares discriminant analysis, the study area was categorized into four distinct groups. The analysis identified three key factors driving regional differences: net emergy yield ratio, emergy output density, and scale management degree. These findings provide a scientific basis for formulating regionally differentiated agricultural development strategies and optimizing resource allocation.