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The rapid progress of urbanization and rural revitalization in developing countries has led to dramatic changes to the rural ecological environment. Assessing the rural ecosystem health (REH) is a crucial foundation for promoting sustainable development in rural areas. This study, focusing on rural areas around the Qinling Mountains in Shaanxi Province, establishes an evaluation system based on appropriate evaluation indicators for assessing the composite ecosystem. This evaluation system comprises four rural ecosystem subsystems: resource, environment, society, and economy. By employing a comprehensive indicator evaluation model and remote sensing image data, this study examines the health status of rural ecosystems in the 40 counties and districts across the study area, as well as their spatial differentiation characteristics, using ArcGIS (10.8) spatial analysis. The REH scores of these areas range from 0.6856 to 0.8818, with a fluctuating downward trend from north to south. This suggests that the rural ecosystems around the Qinling Mountains in Shaanxi Province are relatively healthy, with the northern area being notably healthier than the southern area. Spatial Gini coefficient analysis reveals a much smaller coefficient for the overall ecosystem compared to the subsystems in the study area, indicating that the distribution of health levels is dispersed and not concentrated. After establishing REH grades and quantity metrics, the 40 counties and districts are categorized into 13 types, followed by an analysis of the influencing factors for each type. Recommendations and management strategies are then proposed to enhance the health of rural ecosystems. [ABSTRACT FROM AUTHOR]

The frequent occurrence of meteorological disasters in China has caused huge losses to agriculture. Risk assessment serves as a bridge from disaster crisis management to disaster risk management. Therefore, it is necessary to carry out a refined comprehensive risk assessment of meteorological disasters in typical areas. However, several limitations remain in the disaster loss risk research, such as too coarse resolution and too single risk indicator. Additionally, less research has examined geographical information on risk clustering and barycenter migration, as well as temporal information on the sustainability of trends. Consequently, it is significant to unearth the geographical and temporal information on disaster loss and identify the refined spatial and temporal evolution pattern of crop risk. For this reason, we evaluated the risk of corn production in Gansu Province. First, based on maize yield data, a risk evaluation index system was constructed using the characteristics of variation trends, fluctuations, and extreme values of disaster losses. Then, the spatial distribution patterns and temporal evolution characteristics of maize production risks on a county scale in Gansu Province were determined using spatial analysis and climate diagnosis technology. The results show that there is a large interdecadal fluctuation in risk. In the 1980s, 1990s, 2000s, and 2010s, the average yield reduction rates of maize in Gansu Province were −11.8%, −12.6%, −8.7%, and −8.5%, and the proportions of counties with severe yield reduction were 34.8%, 44.4%, 20.8%, and 9.7%, respectively. Second, most counties belong to medium-low or low-risk areas for maize production. High-risk counties are primarily located in eastern and southern Gansu, whereas low-risk counties are mostly found along the Hexi Corridor. Third, most risk indicators exhibit some geographical aggregation. The Jiuquan region falls within the low-low-risk aggregation zone. In contrast, the Qingyang region is a high-high aggregation zone with a gradual expansion trend. Four, each risk indicator's geographical barycenter migrates over a complicated path, but the direction and distance vary considerably. The comprehensive risk migrates along the south-northwest-southeast trajectory, albeit at a shorter distance. Five, the proportion of counties with a medium, medium-severe, severe, and total yield reduction tended to decline. In addition, the annual precipitation is significantly or very significantly correlated with most risk indicators and the comprehensive risk level. The results can guide agricultural production processes at all levels, as well as government disaster prevention. [ABSTRACT FROM AUTHOR]