Evaluating regional agricultural water resources system resilience and analyzing driving factors in the Hongxinglong Administration

Abstract

A Projection Pursuit Classification (PPC) model optimized by the Cat Swarm Optimization algorithm (CSO-PPC) was proposed to evaluate system resilience in the Hongxinglong Administration of Heilongjiang Province, China. The driving forces behind resilience were analyzed using Principal Component Analysis (PCA). CSO-PPC was used to evaluate resilience for the 12 farms in the Hongxinglong Administration, and PCA was applied to select the key factors driving their resilience. Results showed that the key factors were per capita water, unit area grain yield, application of fertilizer per unit cultivated area and the proportion of cultivated land, which were closely related to human production and planting area. Overall water resources system resilience had improved by 2011 compared to 2005. Specifically, water resources system resilience grades for the 12 farms were divided into five levels from inferior to superior, i.e. I to V. After six years of development, the resilience of eight farms had improved. Farm Youyi and Farm 853 were upgraded from inferior level II to the best level V. However, according to the data, four farms still had low resilience that had not improved in recent years. Further results showed that the driving forces decreased from 1998 to 2003 and increased from 2003 to 2011. HIGHLIGHTS A total of 17 indicators were screened out to evaluate the regional agricultural water resources system.; A Projection Pursuit Classification model optimized by the Cat Swarm algorithm was proposed to evaluate the resilience of a regional agricultural water resources system.; Principal Component Analysis was applied to select the key driving factors to analyze the driving force of water resources system resilience.;

Keywords

• agricultural water resources system
• cso-ppc
• driving force
• hongxinglong administration
• resilience