生态学杂志2026，Vol.45Issue(1)：328-337,10.DOI:10.13292/j.1000-4890.202601.010
基于不确定性理论的区域农田生态系统健康评估:以河南省为例
Uncertainty-enabled regional agroecosystem health assessment:A case study of Henan Province
摘要
Abstract
Assessing the health of regional farmland ecosystems from the perspective of uncertainty can improve the scientific basis and accuracy of agricultural economic decision-making.Based on the concept of ecosystem health,we constructed an evaluation index system of farmland ecosystem health considering four dimensions:carrying ca-pacity,productivity,competitiveness,and innovation.Combined with Monte Carlo random simulation,we assessed the influence of weight-setting uncertainty on health evaluation results and revealed the key role of weight changes in the 10-year evaluation.The results showed that grain yield per unit area increased significantly(by 32.9％-56.0％)in Henan Province from 2011 to 2020,but farmland ecosystem health level varied markedly among regions.The Central(Zhengzhou),Southern(Nanyang),and Western(Luoyang)regions were mainly at the sub-healthy level due to high pressure on cultivated land resources,with membership probabilities of 46.3％,59.3％,and 67.4％,respectively.In contrast,the Eastern(Kaifeng)and Northern(Anyang)regions experienced significant improve-ments,driven by increased grain yield and enhanced ecosystem productivity.However,insufficient innovation limit-ed the overall health,which remained mainly sub-healthy,with membership probabilities ranging from 39.8％to 45.2％.Based on uncertainty simulation and spatiotemporal analysis,this study accurately identified the trends and main influencing factors of regional farmland ecosystem health levels,and provides theoretical support and data ref-erence for improving the scientific basis and accuracy of agricultural economic decision-making.关键词
农业经济/评估系统/Monte Carlo模拟/时空特征/河南省Key words
agricultural economy/evaluation index system/Monte Carlo simulation/spatial characteristics/Henan Province引用本文复制引用
张田,王美娥,杨阳,陈卫平,张瑶..基于不确定性理论的区域农田生态系统健康评估:以河南省为例[J].生态学杂志,2026,45(1):328-337,10.基金项目
国家自然科学基金项目(41977146)和中国科学院战略性先导科技专项(XDA28020103)资助. （41977146）
