Production–living–ecological land (PLEL) is one of the research focuses of land planning and regional sustainable development in China. This paper builds a three-level classification system of PLEL based on the identification of the main land use functions (LUFs). Taking 215 typical towns in the hilly area of Sichuan Province, Southwest China as samples, the quantitative, spatial, and functional characteristics and impact factors of PLEL were studied. The results showed that (1) production land holds a dominant role in the hilly area of Sichuan Province, and production land (PL), living land (LL) and ecological land (EL) account for 66.06%, 7.60%, and 26.34% of the area, respectively. The area of agricultural production land is the largest; forestland and rural living land rank second and third. (2) The spatial patterns of PLEL in different regions of hilly area have differences. The proportion of PL gradually decreases from north to south, while the proportion of EL gradually increases from north to south, and the difference in LL is not obvious. The EL is mainly distributed in the upper and middle parts of hills, and the PL and LL are mainly distributed in the foot slopes and valleys. (3) The main functions of PLEL in the hilly area of Sichuan are production and ecology. The production function is mainly for agricultural and forestry products, and the living function is mainly for cultural leisure and residential functions. There are little differences among the ecological sub-functions. (4) There is a strong correlation between PLEL and natural–social–economic factors in the hilly area of Sichuan. Natural conditions such as latitude, relative height, and surface roughness have significant impacts on PL and EL. Social and economic factors such as population density, location and total industrial output value have a significant impact on LL. The results of this study provide valuable implications for the spatial planning and sustainable development in the Sichuan Basin and upstream of the Yangtze River.

The purpose of this study was to establish a spatial multi-scale integrated assessment framework for critical areas of ecosystem service supply and demand, in order to provide theoretical support for regional ecological protection planning and refined management. Taking the typical hilly area of the upper reaches of the Yangtze River in China as an example, based on the assessment matrix of land use and ecosystem services, we used the method of spatial heterogeneity assessment and self-organizing feature mapping (SOFM) to explore the identification and regionalization of critical areas of ecosystem services at regional and small scales. The results show that there was spatial heterogeneity and scale effects of ecosystem services under the two scales. The small-scale supply–demand pattern was greatly affected by microtopography and land use patterns, and the importance of ecosystem services was as follows: forest area in the upper part of the mountain > orchard and dry area in the middle and lower part of the mountain > valley farming area > flat town and farming area. The regional-scale supply–demand pattern was greatly affected by landscape structure, location conditions and social economy, and the importance of ecosystem services was as follows: south > west > north > central. The SOFM network quantitatively identified four types of ecological areas with clear dominant functions at regional and small scales. The balance between supply and demand in the studied ecosystem service areas was I < II < III < IV, in which IV was the critical area in terms of supply and I was the critical area in terms of demand. This assessment framework can improve the spatial accuracy and objectivity of the quantification and mapping of ecosystem services, and provide new ideas for multi-scale identification and expression of ecosystem services.

Achieving the dual goals of ensuring food security and environmental security is a global challenge in sustainable nutrient management. However, previous studies have neglected the spatialization of nutrient management. Here, we proposed a new approach to assess and optimize fertilizer rates by combining data-driven forecasting and machine learning methods to address spatially optimal allocation of nutrient resources. We found that the contribution of fertilizer application to crop yields in Southwest China decreased by 1–3% from 2009 to 2019. Crop nutrients were widely unbalanced, with obvious nitrogen excess, local phosphorus deficiency, and general potassium deficiency, while plains and riverbanks were hotspots of nutrient excess. Multiobjective optimization reduced the nitrogen fertilizer rate by 18% (11×10⁴ t) with a slight increase in the crop yield, whereas the ratio of nitrogen, phosphorus, and potassium fertilizers was optimally adjusted from 1:0.38:0.33 to 1:0.51:0.42, conforming with national expectations.