Natural capital investments in China undermined by reclamation for cropland

Climate-adaptive crop distribution can feed food demand, improve water scarcity, and reduce greenhouse gas emissions

Optimizing crop distribution stands as a pivotal approach to climate change adaption, enhancing crop production sustainability, and has been recognized for its immense potential in ensuring food security while minimizing environmental impacts. Here, we developed a climate-adaptive framework to optimize the distribution of staple crops (i.e., wheat, maize, and rice) to meet the multi-dimensional needs of crop production in China. The framework considers the feasibility of the multiple cropping systems (harvesting more than once on a cropland a year) and adopts a multi-dimensional approach, incorporating goals related to crop production, water consumption, and greenhouse gas (GHG) emissions. By optimizing, the total irrigated area of three crops would decrease by 7.7 % accompanied by a substantial 69.8 % increase in rain-fed areas compared to the baseline in 2010. This optimized strategy resulted in a notable 10.0 % reduction in total GHG emissions and a 13.1 % decrease in irrigation water consumption while maintaining consistent crop production levels. In 2030, maintaining the existing crop distribution and relying solely on yield growth would lead to a significant maize production shortfall of 27.0 %, highlighting a looming challenge. To address this concern, strategic adjustments were made by reducing irrigated areas for wheat, rice, and maize by 2.3 %, 12.8 %, and 6.1 %, respectively, while simultaneously augmenting rain-fed areas for wheat and maize by 120.2 % and 55.9 %, respectively. These modifications ensure that production demands for all three crops are met, while yielding a 6.9 % reduction in GHG emissions and a 15.1 % reduction in irrigation water consumption. This optimization strategy offers a promising solution to alleviate severe water scarcity issues and secure a sustainable agricultural future, effectively adapting to evolving crop production demands in China.

Widening global variability in grassland biomass since the 1980s

Global change is associated with variable shifts in the annual production of aboveground plant biomass, suggesting localized sensitivities with unclear causal origins. Combining remotely sensed normalized difference vegetation index data since the 1980s with contemporary field data from 84 grasslands on 6 continents, we show a widening divergence in site-level biomass ranging from +51% to -34% globally. Biomass generally increased in warmer, wetter and species-rich sites with longer growing seasons and declined in species-poor arid areas. Phenological changes were widespread, revealing substantive transitions in grassland seasonal cycling. Grazing, nitrogen deposition and plant invasion were prevalent in some regions but did not predict overall trends. Grasslands are undergoing sizable changes in production, with implications for food security, biodiversity and carbon storage especially in arid regions where declines are accelerating.

The impact of large-scale ecological restoration projects on trade-offs/synergies and clusters of ecosystem services

Understanding the relationships between ecosystem services (ES) and the factors driving their changes over long periods and multiple scales is key for landscape managers in decision-making. However, the widespread implementation of restoration programs has led to significant ES changes, with trade-offs across space and time that have been little explored empirically, making it challenging to provide effective experience for managers. We quantified changes and interactions among five ES across various stages of the Grain-to-Green Program in the eastern Loess Plateau, examining these dynamics at threefold spatial scales. We observed notable increases in soil retention and Net Ecosystem Production but declines in habitat quality and Landscape aesthetics under afforestation. Over time, and with more integrated restoration strategies, synergies between ES pairs weakened, and non-correlations (even trade-offs) increased. To avoid unnecessary trade-offs, we recommend incorporating socio-ecological factors driving ES changes and ES bundles, informed by empirical experience, into proactive spatial planning and environmental management strategies for multi-ES objectives. The temporal lags and spatial trade-offs highlighted by this study offer crucial insights for large-scale restoration programs worldwide.

Sensitivity of gross primary production to precipitation and the driving factors in China's agricultural ecosystems

Recent climate warming has significantly affected the sensitivity of Gross Primary Productivity (GPP) to precipitation within China's agricultural ecosystems. Nonetheless, the spatial and temporal nonlinear evolution patterns of GPP-precipitation sensitivity under climate change, as well as the underlying drivers and long-term trends of this sensitivity, are not well understood. This study employs correlation analysis to quantify the sensitivity between GPP and precipitation in China's agricultural ecosystems, and utilizes nonlinear detection algorithms to examine the long-term changes in this sensitivity. Advanced machine learning techniques and frameworks are subsequently applied to analyze the driving factors of GPP-precipitation sensitivity in China's agricultural ecosystems. The findings reveal that approximately 49.00 % of the analyzed pixels exhibit a significant positive correlation between GPP and precipitation. Nonlinear change analysis indicates spatial heterogeneity in GPP-precipitation sensitivity across China's agricultural ecosystems, with patterns showing initial increases followed by decreases accounting for 25.12 %, and patterns of initial decreases followed by increases at 13.27 %. Machine learning analysis identifies temperature, soil moisture, and crop water footprint as the primary factors influencing GPP-precipitation sensitivity in agricultural ecosystems. This study is the first to introduce crop water footprint as a significant factor in the analysis of GPP-precipitation sensitivity. It not only offers new insights into the temporal nonlinear changes and driving factors of GPP-precipitation sensitivity but also underscores the importance of enhancing agricultural water efficiency to maintain agricultural ecosystem health and ensure food security under climate change.

Challenges for sustainable development goal of land degradation neutrality in drylands: Evidence from the Northern Slope of the Tianshan Mountains, China

The SDG 15.3.1 target of Land Degradation Neutrality (LDN) only has 15 years from conception (in 2015) to realization (in 2030). Therefore, investigating the effectiveness and challenges of LDN has become a priority, especially in drylands, where fragile ecosystems intersect with multiple disturbances. In this study, solutions are proposed and validated based on the challenges of LDN. We chose the Northern Slope of the Tianshan Mountains as a case study and set baselines in 2005 and 2010. The region and degree of land change (including degraded, stable, and improved) were depicted at the pixel scale (100 × 100 m), and LDN realization was assessed at the regional scale (including administrative districts and 5000 × 5000 m grids). The results showed a significant disparity between the two baselines. The number of areas that realized the LDN target was rare, regardless of the scale of the administrative districts or grids. Chord plots, Spearman's correlation, and curve estimation were employed to reveal the relationship between LDN and seven natural or socioeconomic factors. We found that substantial degradation was closely related to the expansion of unused, urban, and mining land and reduction in water, glaciers, and forests. Further evidence suggests that agricultural development both positively and negatively affects LDN, whereas urbanization and mining activities are undesirable for LDN. Notably, the adverse effects of glacier melting require additional attention. Therefore, we consider the easy-to-achieve and hard-to-achieve baselines as the mandatory and desirable targets of LDN, respectively, and focus further efforts in three aspects: preventing agricultural exploitation from occupying ecological resources, defining reasonable zones for urbanization and mining, and reducing greenhouse gas emissions to mitigate warming. Overall, this study is expected to be a beneficial addition to existing LDN theoretical systems and serve as a case validation of the challenges of LDN in drylands.

Dynamics of ecosystem services and nonlinear responses to increased anthropogenic pressure

Escalating global human activities elicit diverse ecosystem service responses, yet understanding remains limited. This study establishes a framework to clarify these responses, focusing on the Yangtze River Economic Belt in China. Analyzing 2000-2020 data, it calculates ecosystem service economic value and human footprint index. It introduces the ecosystem services response index and comprehensive responsiveness index to assess response characteristics and intensity to anthropogenic pressures. Results show a fluctuating decline in ecosystem services and an increase in anthropogenic pressures. There is a nonlinear relationship: ecosystem services decline with rising pressures, following a U-shaped trend. Notably, nonurban agglomerations experience more significant ecosystem service evolution than urban agglomerations due to differing environmental conditions. This highlights regional disparities in human activity impacts on ecosystems, crucial for planning.

Evaluation of vegetation restoration effectiveness along the Yangtze River shoreline and its response to land use changes

Assessing the effectiveness of vegetation restoration along the Yangtze River shoreline and exploring its relationship with land use changes are imperative for providing recommendations for sustainable management and environmental protection. However, the impact of vegetation restoration post-implementation of the Yangtze River Conservation Project remains uncertain. In this study, utilizing Sentinel-2 satellite imagery and Dynamic World land use data from pre- (2016) and post- (2022) Yangtze River Conservation Project periods, pixel-based binary models, transition matrices, and geographically weighted regression models were employed to analyze the status and evolution of vegetation coverage along the Yangtze River shoreline. The results indicated that there had been an increase in the area covered by high and high-medium vegetation levels. The proportion of vegetation cover shifting to better was 4201.87 km2 (35.68%). Hotspots of vegetation coverage improvement were predominantly located along the Yangtze River. Moreover, areas witnessing enhanced vegetation coverage experienced notable land use changes, notably the conversion of water to crops (126.93 km2, 22.79%), trees to crops (59.93 km2, 10.76%), and crops to built area (59.93 km2, 10.76%). Notably, the conversion between crops and built area emerged as a significant factor influencing vegetation coverage improvement, with average regression coefficients of 0.68 and 0.50, respectively. These outcomes underscore the significance of this study in guiding ecological environmental protection and sustainable management along the Yangtze River shoreline.

Projecting the response of ecological risk to land use/land cover change in ecologically fragile regions

Anthropogenic activities have dramatically altered land use/land cover (LULC), leading to ecosystem service (ES) degradation and further ecological risks. Ecological risks are particularly serious in ecologically fragile regions because trade-offs between economic development and ecological protection are prominent. Thus, ways in which to assess the response of ecological risks to LULC change under each development scenario in ecologically fragile regions remain challenging. In this study, future LUCC and its impact on ESs under four development scenarios in 2040 in western Jilin Province were predicted using a patch-generating land use simulation model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Ecological risk was assessed based on future LUCC possibilities, and potential ES degradation and potential drivers of ecological risks were explored using a geographic detector. The results showed that the cropland development scenario (CDS) would experience large-scale urbanization and cropland expansion. Carbon storage (CS), habitat quality (HQ), and water purification (WP) degraded the most under the CDS, and grain yield (GY) and water yield (WY) degraded the most under the ecological protection scenario (EPS). The LUCC probability under the CDS (14.37 %) was the highest, while the LUCC probability under the comprehensive development scenario (CPDS) (8.68 %) was the lowest. The risk of WP degradation was greatest under the CDS, but the risk of soil retention (SR) degradation was greatest under the natural development scenario (NDS), EPS, and CPDS. Ecological risk coverage was the largest (98.04 %), and ecological risks were the highest (0.21) under the CDS, while those under the EPS were the opposite. Distance to roads and population density had a higher impact on ecological risks than other drivers. Further attention should be given to the ecological networks and pattern establishment in urbanized regions. This study will contribute to risk prevention and sustainable urban and agricultural development.