Food security has emerged as a crucial strategy for China. However, its spatial association and spillover effect in China have not been thoroughly investigated. This study innovatively integrates spatial association network, revised gravity model and food security index to construct spatial network of food security to reveal its spatial association characteristics and spillover effects in the agrifood system from 1995 to 2019. Results show that the combination of a high clustering coefficient and moderate average path length in the spatial network indicates clear 'small world' characteristics emerging prominently after 2015. This suggests a strong modular structure in spatial associations of food security, characterized by efficient local connectivity and enhanced coordination capabilities. Additionally, four modules show significant spillover effects, highlighting the interdependent nature of provincial regions. This integrative approach effectively addresses critical gaps in systematically understanding spatial complexities in the agrifood system and helps support the holistic principle of 'whole' while clarifying the provinces' responsibility in achieving national food security.

China is the world's leading apple producer and vital to global fruit supply and food security. Apple production depends on yield and planting area, both strongly influenced by climate. Regional climate variations cause differing cultivation suitability, exacerbating production disparities. Although climate models can project impacts on apples, the lack of high-resolution distribution data limits reliable large-scale assessment. Mapping apple orchards is more challenging than field crops due to complex growing conditions. To tackle this challenge, we developed the first 30-m national-scale apple orchard distribution map of China (AOMC) for 2019-2021, covering 96% of China's total apple orchard area. This dataset is generated based on an apple-phenology-driven algorithm framework, which integrates apple phenology and multi-source geographical information with the time-weighted dynamic time warping algorithm. Validations revealed that the dataset accurately represented orchard locations (Overall accuracy: 87%) and correlated strongly with municipal-level statistics (R²: 0.892-0.925). The AOMC supports the monitoring of spatiotemporal changes in China's apple production patterns, serves as critical input for crop models, and advances the sustainable development and climate resilience of apple industry.

Global food security faces severe threats that are critically impeding progress toward the 2030 Agenda for sustainable development. Food security is not only directly linked to SDG 2 (Zero Hunger) but also fundamentally intertwined with the achievement of numerous other sustainable development goals. Through the assessment of four key food security indicators, we have identified a concerning reversal in global food security trends since 2015. This reversal has been driven by intensifying pressures from agricultural pollution, food waste, supply chain disruptions, and the adverse impacts of climate change. To address these challenges, we propose a comprehensive strategy and introduce an innovative framework: the global six-dimensional (availability, access, utilization, stability, agency, sustainability) food security early-warning and decision-support system. This framework integrates multi-source data and advanced tools to enhance the resilience and adaptability of food systems, providing new solutions to support the achievement of global sustainability goals.

Terrestrial vegetation plays a crucial role in regulating earth system processes. Spatial synchrony, a fundamental feature of vegetation dynamics, quantifies the degree to which vegetation growth fluctuates coherently across space and can provide important information on environmental stress and ecosystem stability. Despite recognition of spatiotemporal variability of growth synchrony among species and regions, the detailed global patterns and temporal dynamics remain poorly understood. This study integrated satellite-based vegetation index (1982–2022) with complex network analysis to systematically quantify global spatiotemporal patterns of vegetation growth synchrony. Macro-scale network metrics revealed that global vegetation growth synchrony transitioned to a more modular, less cohesive, and more fragmented structure from 1982 to 2001 to 2002–2021. Synchrony hubs shifted from the mid-latitudes of the Northern Hemisphere to the Indian subcontinent and southern China. Spatial autocorrelation analysis identified two hotspot regions, central and northeastern North America (CNENA) and southeastern China (SECN), which exhibited substantial changes in network degree fields. CNENA functioned as a major hub in 1982–2001, with strong internal connectivity and prominent teleconnections to central Eurasia, both of which declined substantially by 2002–2021. SECN emerged as a key hub in the later period, characterized by intensified internal cohesion and strengthened teleconnections, particularly with the Indian subcontinent. We discuss potential drivers underlying these divergent trajectories, suggesting that shifting climate teleconnections, regional climate change, and large-scale human interventions may play major roles. Our findings provide a global assessment of vegetation growth synchrony dynamics, offering new insights for ecosystem monitoring and adaptive management amid increasing climate change and human activities.

Previous attempts to quantify tree density have often underestimated the numbers of trees in mountainous regions with complex terrain. We surveyed trees with a diameter at breast height (DBH) of >= 10 cm across 1,926 plots. By utilizing recursive feature elimination (RFE), we identified six key variables for our meta-learner in the stacking process, including the soil silt content, soil clay content, elevation, Normalized Difference Vegetation Index (NDVI), precipitation in the wettest month, and precipitation in the coldest quarter, all of which were found to influence tree density. We developed a stacking ensemble learning algorithm, which ultimately generated a tree density map with a spatial resolution of 30 m for the mountainous regions of Northeast China. The estimated tree count is approximately 27.497 billion. Compared to global tree density datasets, our approach increased R2 to 0.454, while root mean square error (RMSE) and bias improved by 47.90 % and 74.52 %, respectively. This approach can increase the accuracy of local tree density simulations, which is crucial for the precise modeling of the forest carbon sequestration potential, the development of targeted forest conservation strategies, and the implementation of effective carbon management practices.

Africa faces significant challenges in food security, which are compounded by rapid population growth. The situation is expected to worsen without effective interventions. One of the key obstacles to mitigating food insecurity is the lack of reliable high-resolution data, particularly regarding harvested area datasets, which directly reflect the agricultural situation on the continent. To tackle this challenge, we have developed the African Harvested Area Dataset (AHAD), which covers 22 major crops across the continent at a resolution of 5 arcmin (approximately 10 km) for the years 2000, 2010, and 2020. The dataset is built upon 8 well-used global gridded harvested area datasets, through verifying, merging, calibrating, and confining with available information, including point-specific crop distribution, accurate cropland map data, subnational statistics, and cropping intensity data. In addition to the primary datasets, we also provide data quality assessments for each step of the process. The AHAD could provide geospatial and temporal patterns of harvested area, offering potential for advancing agricultural practices and enhancing food security across Africa.

Forests are critical components of the global carbon cycle and thus influence climate change. Climate, in turn, strongly affects forest growth. Projecting the growth–climate relationship to unobserved regions or to past and future periods is crucial for climate change mitigation and adaptation. Tree‐ring data, quantifying changes in wood biomass—a key component of forest productivity—provide valuable insights into this relationship. We investigate the generalizability and application domains of three climate‐based modelling approaches for projecting tree growth. These include a time‐fitted model using climate over time at specific sites, a space‐fitted model using climate across regions and a spatiotemporal (ST)‐fitted model integrating both dimensions. All models were developed using Random Forest to predict the growth of Picea mariana under varying climate conditions. Key findings include (1) Both time‐ and ST‐fitted models performed well for temporal projections at a given observation site ( R 2 = 0.69, 0.68), while the space‐fitted model performed poorly ( R 2 ≈ 0). (2) Both space‐ and ST‐fitted models performed well for spatial projections based on multiple sites, each with partial observations ( R 2 = 0.81, 0.80), while the time‐fitted model performed poorly ( R 2 ≈ 0). (3) Only the ST‐fitted model generated acceptable projections in completely unobserved regions ( R 2 = 0.21). (4) Model performance declined progressively from training to internal validation and subsequently to external validation. Synthesis : Our results emphasize that integrating spatiotemporal information improves model generalizability and tree growth projection accuracy under climate change, especially for unobserved regions. The study also highlights the necessity of independent external validation in model evaluation. These findings offer actionable guidance for identifying reliable modelling approaches for tree growth projection, thereby informing forest management strategies for climate change mitigation and adaptation.

A bibliometric analysis of food security publications in Africa reveals that research hotspots primarily focus on climate change, impacts, Sub-Saharan Africa, and agriculture. Despite there are large amount publications with first author coming from institutions located in Africa, average citation of these publications except those from Kenya are generally lower than that from the rest of the world, particularly Europe and USA. It is notable that publications may not reflect exactly the actual crop plantation realities in Africa. There is a significant mismatch between the harvested area and publication output for local commonly cultivated crops, such as sorghum, millet, cassava, groundnut, and cowpea. These crops together occupy 37.0

Boreal forests, which serve as major terrestrial carbon sinks, are experiencing rapid warming across much of their range. Spatial synchrony in tree growth is crucial for the stability and persistence of these forests. Despite its importance, the geographic patterns and drivers of tree growth synchrony in boreal forests remain underexplored. This study aims to address these gaps by investigating growth synchrony of white spruce ( Picea glauca ), a widespread boreal species of significant ecological and economic value. Using tree-ring data from 187 sites, we quantified growth synchrony with the synchronous growth change coefficient, a non-parametric index capturing consistency in year-to-year variations. We then analyzed its spatial pattern and drivers using complex network analysis and multiple regression on distance matrices (MRM). We found that white spruce growth synchrony follows a clear biogeographical pattern, decreasing from northwest to southeast. The relationship between growth synchrony and geographic distance was non-linear, deviating from the typical distance-decay pattern described by Tobler’s First Law of Geography. Specifically, synchrony increased as geographic distance decreased at shorter distances, but reversed at longer distances, where more distant sites showed relatively stronger synchrony. MRM analysis showed that climate factors explained 55% of the variance in growth synchrony, with geographic proximity contributing minimally after accounting for climate (increasing to 56%). These results suggest that synchronization of climate, particularly temperature, was the primary driver of spatial synchrony in white spruce growth, while spatial proximity-related mechanisms played a limited role. Given that high synchrony can reduce population stability, we recommend prioritizing management efforts that promote asynchronous growth, especially in regions exhibiting strong synchrony (e.g. northern Northwest Territories and Yukon). These findings provide new insights into boreal forest dynamics and inform adaptive management and conservation strategies in the face of ongoing climate change.

The growing global meat consumption has serious consequences on human health, the environment and ultimately impacts global food security. Therefore, identifying the drivers of meat consumption and predicting its evolution is necessary. We compared four machine learning methods in modelling meat consumption, leading to the selection of a random forest-based model to detect main drivers for global meat consumption. Our results show that per capita meat consumption is mainly driven by socioeconomic factors, such as national GDP and urbanization. However, the strength of these drivers declined between 1990 and 2018. Pork, beef, and poultry consumption are mainly driven by socioeconomic factors, whereas mutton consumption appears driven by other factors such as the per capita agricultural land. In this work, the model-agnostic interpretability method is introduced to measure the marginal effect of each driver on meat consumption. We found that there may be insufficient evidence to support the inverted U-shaped relationship between per capita GDP and meat consumption, which is reported in previous studies. Our analysis may provide avenues for predicting meat consumption at the national scale.

A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China’s county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods’ accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops.

Agriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China's carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insufficient to characterize regional heterogeneity. Here, we selected the county-level administrative district as the basic geographical unit and then generated a county-level dataset on the intensity of carbon emissions from crop farming in China during 2000-2019, using random forest regression with multi-source data. This dataset can be used to delineate spatio-temporal changes in carbon emissions from crop farming in China, providing an important basis for decision makers and researchers to design agricultural carbon reduction strategies in China.

With increasing concentrations of atmospheric greenhouse gases, the interaction between climate change and agriculture is receiving widespread attention as part of food security and sustainable human development. However, a comprehensive summary of knowledge in the field of climate change and agriculture from a scientometric perspective is still lacking. Here, we selected 25,872 papers related to climate change and agriculture from the Web of Science Core Collection database for the period 1985 to 2023 and used VOSviewer software to reveal the research status and trends. The main results were as follows: (1) the number of papers in this field showed a rapidly increasing trend after 2007, with a clear interdisciplinary characteristic; (2) The United States was the most influential country in this field with 6819 papers and 363,109 citations. China had the second highest number of papers (3722 papers), but the Chinese Academy of Sciences was the most influential institution with the most papers. On an author level, Pete Smith was the most influential; (3) All keywords were divided into four different research topics, such as the impact of climate change on agriculture, climate change mitigation and adaptation in agriculture, and crop growth in response to climate change. Among them, some keywords related to climate change adaptation were the most recent topics of interest in this field. These findings provide effective scientific references for relevant scientists and practitioners to better conduct future theoretical and practical research on climate change and agriculture.

Global climate action is urgent, with forest carbon stock critical for mitigating climate change, yet vulnerable to its impacts. However, the long-term dynamics of climate-driven forest carbon stock have not fully been expressed. Here, we introduce the Forest Carbon Stock Accumulated by Single Tree growth (FAST) framework and constructed a counterfactual scenario to isolate and quantify the climate-driven changes in forest total carbon stock for 1901-2022. Results show that breakpoints in climate-driven forest carbon stock occurred post1970 are observed over 62% of the study areas, with Europe experiencing the latest, followed by Asia, and North America the earliest. Furthermore, we observe a prevailing increasing trend in climate-driven forest carbon stock, especially in post-breakpoints period (from 53% to 68%), indicating that climate changes have alleviated constraints on forest carbon storage capacity in most areas. FAST can be utilized for historical, current and future forest carbon stock estimation, providing scientific support for sustainable forest management decisions.

BACKGROUND:Maize, wheat, rice and soybean production are intimately linked to food security. Identifying the key factors affecting crop yields and determining the countries where increased irrigation and nitrogen application most effectively enhance yields are essential steps towards achieving sustainable development goals and ensuring food security. Identifying these areas is crucially dependent on yield gaps. However, the lack of comparability between different regions in current regional-scale yield gap studies stems from varied methodologies. Moreover, global yield gap research, relying on statistical models and regression methods, tends to neglect the crop growth process. In this study, we used a random forest model, based on statistical and meteorological data, to pinpoint the key factors influencing crop yields. Subsequently, using unified yield data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), derived from crop models simulations, we applied the yield gap method to calculate the potential yield increase for four crops across countries, under conditions of full irrigation and nitrogen application. RESULTS:Our research finds that nitrogen application is the main factor affecting yields globally, while irrigation plays a crucial role in the major producing countries. The countries with high potential for yield increases are located at the border between Africa and Eurasia. The global average yield of the four major crops increased 13.7-29.8% under full irrigation, 2.9-39.1% under full nitrogen application and 29.4-97.8% under both conditions. CONCLUSION:This study provides crucial insights into global crop yield changes and their determinants, which are highly important for global sustainable agriculture and food security efforts. © 2024 Society of Chemical Industry.

Global climate action is urgent, with forest carbon stock critical for mitigating climate change, yet vulnerable to its impacts. However, the long-term dynamics of climate-driven forest carbon stock has not fully been expressed. Here, we introduce the Forest carbon stock Accumulated by Single Tree growth (FAST) framework and constructed a counterfactual scenario to isolate and quantify the impacts of major climatic drivers on forest carbon stock for 1901-2022. Results show that most breakpoints in climate-driven forest carbon stock occurred post-1970, with Europe experiencing the latest, followed by Asia, and North America the earliest. Furthermore, we observe a prevailing increasing trend in climate-driven forest carbon stock, especially in post-breakpoints period (from 53% to 68%), indicating that climate changes have alleviated climatic constraints on forest carbon stocks in most areas. FAST can be utilized for historical, current and future forest carbon stock estimation, providing scientific support for sustainable forest management decisions.

Food supply shock is defined as a drastic shortage in food supply, which would likely threaten the achievement of Sustainable Development Goals 2: zero hunger. Traditionally, highly-connected global food supply system was deemed to help overcome shortages easily in response to food supply shock. However, recent studies suggested that overconnected trade networks potentially increase exposure to external shocks and amplify shocks. Here, we develop an empirical–statistical method to quantitatively and meticulously measure the diversity of international food supply chain. Our results show that boosting a country’s food supply chain diversity will increase the resistance of the country to food shocks. The global diversity of food supply chain increased gradually during 1986–2021; correspondingly, the intensity of food shocks decreased, the recovery speed after a shock increased. The food supply chain diversity in high-income countries is significantly higher than that in other countries, although it has improved greatly in the least developed regions, like Africa and Middle East. International emergencies and geopolitical events like the Russia–Ukraine conflict could potentially threaten global food security and impact low-income countries the most. Our study provides a reference for measuring resilience of national food system, thus helping managers or policymakers mitigate the risk of food supply shocks.

The agricultural production space, as where and how much each agricultural product grows, plays a vital role in meeting the increasing and diverse food demands. Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types, using methods such as remote sensing or statistical metrological analysis. In this study, we characterize the agricultural production space (APS) by bipartite network connecting agricultural products and provinces, to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China. The results show that core products are cereal, pork, melon, and pome fruit; meanwhile the milk, grape, and fiber crop show an upward trend in centrality, which is in line with diet structure changes in China over the past decades. The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable. Additionally, identified provincial communities closely resemble China’s agricultural natural zones. Furthermore, the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production. Despite the superior production capabilities of southern provinces, recent years have witnessed a notable decline, warranting special attentions. The findings provide a comprehensive perspective for understanding the complex relationship of agricultural products’ relatedness, production capabilities and production patterns, which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

Given the unprecedented rates of climate change, population growth, and natural resources consumption, global food security has emerged as a critical issue in the twenty-first century. Over the past few decades, the African continent has been plagued by serious food insecurity issues, resulting from poverty and mismanagement of resources. Recent studies have suggested that a comprehensive assessment of food security in Africa and an investigation into the factors that affect the food self-sufficiency ratio (SSR) are increasingly essential. Estimating variations of SSR throughout Africa together with examining its affecting mechanisms is thought to be resolving ongoing food crisis and achieving sustainable food security in Africa. Here, we have assessed spatio-temporal evolutions of SSR in Africa and its associated drivers based on the Granger causality method. SSR in Africa showed an overall downward trend from 1961 to 2018, mainly aggregating in Northern and Southern regions. Among 44 African countries studied, Eastern African countries had relatively higher SSRs, while Northern and Southern regions had lower SSRs. Between 1970 and 2018, Granger causality analysis revealed that about 52%, 34%, and 34% of African countries were affected by GDP, food production, and import quantity, respectively. Additionally, the influence of climatic factors (temperature and precipitation) on SSR was detected in about 27% of countries, while their direct impact on food production was found in around 45% of the regions. Implementing viable solutions such as improving food production, facilitating international trades and cooperation, and promoting sustainable economic development can largely contribute to safeguarding food security in Africa and elsewhere facing similar challenges.