The negative impacts of conventional agriculture and the imperative to adopt conservation tillage garnered significant attention. However, the effects of conservation tillage on soil enzyme activities still lack comprehensive cognition. Here, we collected 14,308 pairwise observations from 369 publications worldwide to systematically evaluate the effects of different conservation tillage practices (reduced tillage (T), reduced tillage with straw return (TS), reduced tillage with straw mulch return (TSO), no-tillage (NT), no-tillage with straw return (NTS), and no-tillage with straw mulch return (NTSO)) on the activities of 35 enzymes in soil. The results showed that: (1) the effect of conservation tillage on soil enzyme activity varied by enzyme type, except for peroxidase (-12.34%), which showed an overall significant positive effect (10.28-89.76%); (2) the NTS and TS demonstrated strong potential to improve soil enzyme activities by increasing a wide variety of soil enzyme activities (12-15) and efficacy (9.76-75.56%) than other conservation tillage (8.60-68.68%); (3) in addition, the effect of conservation tillage on soil enzyme activity was regulated by soil depth, crop type, years of conservation tillage, climate (mean annual precipitation and temperature), and soil physicochemical properties (e.g., pH, bulk density, electrical conductivity, organic matter, ammonium nitrogen, total phosphorus, available phosphorus, total potassium, available potassium, etc.). Overall, our quantitative analysis clearly suggests that conservation tillage is an effective measure for improving soil enzyme activity on global croplands, where combination of reduced tillage or no-till with straw return are considered to have great potential and promise. The results contribute to better comprehend the effects of conservation tillage on soil activity and provide a valuable insight for agricultural management.

Watershed landscape ecological security and ecosystem service functions are the material basis and environmental guarantee for promoting socioeconomic development. Analyzing the spatiotemporal characteristics of landscape ecological risks (LERs) and ecosystem services (ESs) and exploring the coupling coordination relationship between the two are of great significance for promoting the construction of ecological civilization and achieving sustainable development in the watershed. With the Min River Basin as the study area, the landscape ecological risk assessment, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), and Carnegie Ames–Stanford Approach (CASA) models were used to evaluate the LERs and ESs based on the shared socioeconomic pathways (SSPs), and the patch-generating land use simulation (PLUS) model was used to predict the land use distribution of the Min River Basin in 2030. On this basis, the coupling coordination degree model was used to explore the coupling coordination relationship between the LERs and ESs. The results show that, from 2000 to 2020, the LER of the Min River Basin gradually decreased, and the overall spatial distribution pattern was “high in the north and low in the south”. The ES of the Min River Basin initially decreased and then increased, showing a spatial distribution pattern of “low in the south and high in the north”. Among the SSPs in 2030, the LER is the largest under the SSP3 scenario and the smallest under the SSP4 scenario. The ES improvement is the most significant under the SSP1 scenario and the lowest under the SSP3 scenario. From 2000 to 2030, the coupling coordination degree of the Min River Basin first decreased and then increased, showing a spatial distribution pattern of “high in the south and low in the north”. Among the five SSPs, the coupling coordination degree was the highest under SSP1. The spatial distribution of urban area is the main driving factor affecting the coupling coordination relationship between the LER and ES, and the development of social and economy is the beginning of landscape pattern optimization.

Soil erosion (SE) is one of the most serious disasters in the world, which directly damage the productivity of the land and affect human well-being. How to effectively mitigate soil erosion is a challenge faced by all countries in the world. In this study, soil erosion was quantitatively assessed base on the RULSE model in an ecologically fragile area [Xiushui watershed (XSW)], and the effects of three major categories of factors (land use/cover change, landscape fragmentation and climate) on soil erosion were investigated using correlation analysis and structural equation model. The results indicated that there was no continuous increase or decrease trend on the SE of XSW with impact of rainfall, the mean values of SE were 2205.27 t/ha, 3414.25 t/ha and 3319.44 t/ha from 2000 to 2020 and the hot areas of SE were mainly distributed around the Xiushui river channel, respectively. The expansion of urbanization (the area of impervious increased from 113.12 to 252.57 km²) aggravated landscape fragmentation, and the landscape fragmented area had some overlap with the hot zone of SE. Additionally, the LUCC factor dominated by NDVI, landscape fragmentation factor and climate factor dominated by rainfall had a directly driving effect on SE, where the path coefficient of landscape fragmentation was 0.61 (P < 0.01), respectively. We also found that except increasing forest area, improving forest quality (NDVI, canopy closure, structure) deserved emphasized in SE management, and the effect of landscape fragmentation on SE also should not be ignored. Moreover, soil erosion assessment at large scales over long time periods tends to underestimate the driving force of rainfall on SE, and it is a great challenge to evaluate the effect of extreme rainfall on soil erosion at short time scales in a downscale manner. This research provides insights for ecological sustainable management and soil erosion protection policies.

Assessing the influence of landscape pattern changes on ecosystem service value (ESV) is critical for developing land-use polies and increasing ecosystem services. The data sources include remote-sensing image data and statistical yearbooks from 2000, 2010, and 2020. This study employs the patch-generating land-use simulation model, landscape pattern index, and ecological service value estimation to analyse the changes in landscape patterns and ESV in Fujian coastal areas over the last 20 years. The landscape pattern and ESV in the future (2050) are then simulated under the low-carbon scenario (LCS), with the natural development scenario (NDS) serving as a comparison. The results show that: (1) the most noticeable changes from 2000 to 2020 are the reduced cultivated land area and the rapid expansion of construction land area. By 2050, construction land will account for 7.67% of the total land area under LCS, whereas NDS will account for 9.45%, and changes in the landscape pattern indices all indicate there will be greater variety and fragmentation of the landscape, with the NDS being more serious than the LCS; (2) From 2000–2020, the total ESV value showed a decreasing trend. In 2050, the ESV under the LCS will be 122.387 billion yuan, which is higher than the 121.434 billion yuan under the NDS. Regulating services contribute the most to the total ESV, followed by support services; and (3) In the past 20 years, except for a slight increase in water area, the ESV of other landscapes has decreased, with a net decrease of 3.134 billion yuan in total. The R² fitting between the area change of cultivated and construction land and the total ESV reached 0.9898 and 0.9843, respectively. The correlations between ESV and landscape indices indicate that landscape pattern changes significantly impact ESV. Simulating ESV in LCS can provide guidance for optimising landscape patterns, promoting the benign operation of the regional ecosystem, and achieving sustainable ecological development.

Urban vegetation plays a very important role in regulating urban climate and improving the urban environment. There is an urgent need to construct an effective index to quickly detect urban vegetation quality changes. In this study, a synthetic vegetation quality index (VQI) was proposed using a holistic approach based on the quality of vegetation itself and the spatial relationship with its surroundings, composed of four selected variables: normalized difference vegetation index (NDVI), patch aggregation index (AI), patch density (PD), and percentage of landscape (PLAND). Principal component analysis (PCA) was employed to calculate weights for each variable due to its objectivity. Then, taking Fuzhou City, southeast China as the case study, the scale effects of the VQI under different moving window sizes (500 m, 1 km, 2 km, …, 5 km) and the spatiotemporal changes were explored. The results showed that a VQI with a window size of 3 km had the highest correlations with all the selected indicators. Meanwhile, the representativeness and the effectiveness of the VQI were validated by the percentage eigenvalues of PC1, as well as Pearson correlation analysis and bivariate spatial autocorrelation analysis. We also revealed that the proposed VQI had the greatest explanatory power for land surface temperature (LST) among all the factors in both studied years (2000 and 2016), with the VQI’s interpretation of LST being 0–44% better than any individual indicator except for AI in 2000. Additionally, our work revealed that the location of vegetation has a great impact on the urban thermal environment. The VQI can assess urban vegetation quality effectively and quickly.