Search Results (931)

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

Given the significant shifts in rural labor mobility patterns and their continuous influence on the transformation of the land factor market, it is crucial to understand the relationship between labor factor prices and land factor prices. This understanding is essential to keep land factor prices within a reasonable range. This study establishes a theoretical framework to investigate how migrant workers’ return shapes land price formation mechanisms. Using 2023 micro-level survey data from eight counties in Jiangsu Province, China, this study empirically examines how migrant workers’ return affects land transfer prices and its underlying mechanisms through OLS regression and instrumental variable approaches. The findings show that under the current pattern of labor mobility, the outflow factor alone is no longer sufficient to exert substantial downward pressure on land transfer prices. Instead, the localized return of labor has emerged as a key driver behind the rise in land transfer prices. This upward mechanism is primarily realized through the following pathways. First, factor substitution effect: this effect lowers labor prices and increases the relative marginal output value of land factors. Second, supply–demand effect: migrant workers’ return simultaneously increases land demand and reduces supply, intensifying market shortages and driving up transfer prices. Lastly, the results demonstrate that enhancing the stability of land tenure security or increasing local non-agricultural employment opportunities can mitigate the effect of rising land transfer prices caused by the migrant workers’ return. According to the study’s findings, stabilizing land factor prices depends on full non-agricultural employment for migrant workers. This underscores the significance of policies that encourage employment for returning rural labor. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

Reliable remote sensing-based Land Use and Land Cover (LULC) information is crucial for assessing Earth’s surface activities. Brazil’s agricultural dynamics, including year-round cropping, multiple cropping, and regional climate variability, make LULC monitoring a highly challenging task. The country has thirteen remote sensing-based LULC products specifically tailored for this purpose. However, the differences and the results of these products have not yet been synthesized to provide coherent guidance in assessing their spatio-temporal agricultural dynamics and identifying promising approaches and issues that affect LULC analysis. This review represents the first comprehensive assessment of the advantages, challenges, and limitations, highlighting the main issues when dealing with contrasting LULC maps. These challenges include incompatibility, a lack of updates, non-systematic classification ontologies, and insufficient data to monitor Brazilian LULC information. The consequences include impacts on intercropping estimation, diminished representation or misrepresentation of croplands; temporal discontinuity; an insufficient number of classes for subannual cropping evaluation; and reduced compatibility, comparability, and spectral separability. The study provides insights into the use of these products as primary input data for remote sensing-based applications. Moreover, it provides prospects for enhancing existing mapping efforts or developing new national-level initiatives to represent the spatio-temporal variation of Brazilian agriculture. Full article

In this work, we examine the correlation between financial inclusion and the Environmental, Social, and Governance (ESG) factors of sustainable development with the assistance of an exhaustive panel dataset of 103 emerging and developing economies spanning 2011 to 2022. The “Account Age” variable, standing for financial inclusion, is the share of adults owning accounts with formal financial institutions or with the providers of mobile money services, inclusive of both conventional and digital entry points. Methodologically, the article follows an econometric approach with panel data regressions, supplemented by Two-Stage Least Squares (2SLS) with instrumental variables in order to control endogeneity biases. ESG-specific instruments like climate resilience indicators and digital penetration measures are utilized for the purpose of robustness. As a companion approach, the paper follows machine learning techniques, applying a set of algorithms either for regression or for clustering for the purpose of detecting non-linearities and discerning ESG-inclusion typologies for the sample of countries. Results reflect that financial inclusion is, in the Environmental pillar, significantly associated with contemporary sustainability activity such as consumption of green energy, extent of protected area, and value added by agriculture, while reliance on traditional agriculture, measured by land use and value added by agriculture, decreases inclusion. For the Social pillar, expenditure on education, internet, sanitation, and gender equity are prominent inclusion facilitators, while engagement with the informal labor market exhibits a suppressing function. For the Governance pillar, anti-corruption activity and patent filing activity are inclusive, while diminishing regulatory quality, possibly by way of digital governance gaps, has a negative correlation. Policy implications are substantial: the research suggests that development dividends from a multi-dimensional approach can be had through enhancing financial inclusion. Policies that intersect financial access with upgrading the environment, social expenditure, and institutional reconstitution can simultaneously support sustainability targets. These are the most applicable lessons for the policy-makers and development professionals concerned with the attainment of the SDGs, specifically over the regions of the Global South, where the trinity of climate resilience, social fairness, and institutional renovation most significantly manifests. Full article

Non-Native Tree Species (NNTs) play crucial roles in global and European forests. However, in the Czech Republic, NNTs represent a tiny fraction of the forested areas due to limited research on their potential use. The country is actively afforesting abandoned agricultural lands; NNTs which are already tested and certified could enhance the country’s forestry system. This study aimed to evaluate the initial growth of Castanea sativa, Platanus acerifolia, and Corylus colurna under three soil treatments on abandoned agricultural soil, evaluate the survival and mortality of the tree species, and further compare the soil dynamics among the three ecosystems to describe the initial state and short-term changes in the soil environment. The research plot was set in the Doubek area, 20 km East of Prague. Moreover, soil-improving materials, Humac (1.0 t·ha⁻¹) and Alginite (1.5 t·ha⁻¹), were established on the side of the control plot at the afforested part. The heights of plantations of tree species were measured from 2020 to 2024. Furthermore, 47 soil samples were collected at varying depths from three ecosystems (afforested soil, arable land, and old forest) in 2022. A single-factor ANOVA was run, followed by a post hoc test. The result shows that the Control-C plot (Castanea Sativa + Platanus acerifolia + Corylus colurna + agricultural soil without amendment) had the highest total growth (mean annual increment in the year 2024) for Castanea sativa (KS = 40.90 ± a21.61) and Corylus colurna (LS = 55.62 ± 59.68); Alginite-A (Castanea Sativa + Platanus acerifolia + Corylus colurna + Alginite) did best for Platanus acerifolia (PT = 39.85 ± 31.52); and Humac-B (Castanea Sativa + Platanus acerifolia + Corylus colurna + Humac) had the lowest growth. Soil dynamics among the three ecosystems showed that the old forest (plot two) significantly differs from arable soil (plot one), Humac and Platanus on afforested land (plot three), Platanus and Alginite on afforested land (plot four), and Platanus without amendment (plot five) in horizon three (the subsoil or horizon B) and in horizon four (the parent material horizon or horizon C). Results document the minor response of plantations to soil-improving matters at relatively rich sites, good growth of plantations, and initial changes in the soil characteristics in the control C plot. We recommend both sparing old forests and the afforestation of abandoned agricultural soils using a control treatment for improved tree growth and sustained soil quality. Further studies on the species’ invasiveness are needed to understand them better. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Changing climatic conditions are significant determinants of agricultural productivity. Rio Grande do Sul is the southernmost state and the second-largest agricultural producer in Brazil. The suitability of its land for farming can be used as a proxy for agricultural and economic success, making it a pertinent case for exploring the consequences of climate change on major crop production. The latest available climate and environmental data was used to develop an agricultural Suitability Index (SI) that quantifies the suitability of land for rice, tobacco, soybean, and corn production in 2020 (present), 2050 (near-future), and 2100 (far-future) under moderate (SSP245) and extreme (SSP585) climate scenarios. SI scores for each municipality of Rio Grande do Sul consider inputs from a three-layer framework (climatic, non-climatic, and current production) to provide critical insight into potential shifts in agricultural productivity. While terrestrial suitability for crop growth varies both spatially and temporally, widespread decreases in suitability for all four crops are expected across the state under both scenarios. Soybean is expected to be the least affected crop, and rice is the most affected crop, tied to shifting patterns in precipitation, which significantly determines suitability. Local and state governments, agribusinesses, and family producers will have to adapt to environmental challenges to ensure the provision of food, labor, and economic security. Full article

The Lijiang River Basin (LRB) is a karst ecosystem that presents unique challenges for agricultural land planning. Evaluating cultivated land suitability based on natural factors is critical for ensuring food security in this region. This study was based on the cultivated land distribution data of the LRB in the China Land-Use and Land-Cover Chang dataset, selecting 22 restriction factors across five dimensions: climate, topography, soil, hydrology, and social conditions, and the suitability of cultivated land (paddy fields and drylands) in the LRB was evaluated using the MaxEnt model to further identify the main restricting factors affecting the spatial distribution. The research showed that (1) For paddy fields, high-suitability areas covered 2875.05 km², medium-suitability 1670.58 km², low-suitability 3187.25 km², and non-suitable 9368.46 km². The main restriction factors were distance to villages, slope, surface gravel content, soil thickness, soil pH, and total phosphorus content. (2) For drylands, high-suitability areas covered 3282.3 km², medium-suitability 2260.93 km², low-suitability 4536.27 km², and non-suitable 6836.85 km². The main restriction factors were soil thickness, distance to roads, surface gravel content, elevation, soil pH, and soil texture. This research can provide a scientific basis for the layout of food security and planning agricultural land use in the LRB. Full article

In order to evaluate spatial pollution patterns of the Shaying River and assess human health risk, thirty-three sampling points were established in different reaches of the upper, middle, and lower reaches of the Shaying River. According to the difference in human activities and land use types, the sampling points were artificially divided into three areas: mountainous area, urban area, and agricultural area. Water samples and sediments were collected at each sampling site, and the physicochemical parameters of the water at each site were measured simultaneously. The nutrient content of water samples and the heavy metal content of sediments were measured in the laboratory. The water pollution status of the Shaying River, as well as the status of heavy metal pollution and its associated risk to human health, were assessed and analyzed using the Water Quality Index (WQI) method, principal component analysis (PCA) method, potential ecological risk index method, and health risk assessment method, respectively. The results of the Water Quality Index indicated that the water quality of the Shaying River was moderate, with the reaches in the urban area being more polluted, the agricultural area being the second most polluted, and the mountainous area being in better condition. The results of the principal component analysis showed that soluble ions, organic matter, and nutrients were the main factors contributing to water pollution in the Shaying River, and there was significant variability in the factors contributing to water pollution in different regions, with human activities being the main cause of this variation. The results of a potential ecological risk assessment of heavy metals in sediments showed that heavy metal pollution in the water bodies of the Shaying River was serious and had significant spatial variability. Mountain reaches were the most polluted, followed by agricultural reaches, and urban reaches were the least polluted. The results of the health risk assessment showed that non-carcinogenic risks of heavy metals in different reaches of the Shaying River were within acceptable limits, while carcinogenic risks in agricultural areas exceeded thresholds. Among them, agricultural areas had the highest health risk, with Cr being the most carcinogenic heavy metal and Pb and Cr being the most non-carcinogenic heavy metals. The assessment also found that children’s carcinogenic risk was 8.4 times higher than adult males and 7.3 times higher than adult females. This study involves the typical diverse areas where the Shaying River passes, in order to provide data support and a theoretical basis for environmental protection of the Shaying River Basin. Full article

Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we propose DAENet, a novel deep learning framework designed for accurate cropland extraction from high-resolution GaoFen-1 (GF-1) satellite imagery. DAENet employs a novel Geometric-Optimized and Boundary-Restrained (GOBR) Block, which combines channel attention, multi-scale spatial attention, and boundary supervision mechanisms to effectively mitigate challenges arising from disjointed cropland parcels, topography-cast shadows, and indistinct edges. We conducted comparative experiments using 8 mainstream semantic segmentation models. The results demonstrate that DAENet achieves superior performance, with an Intersection over Union (IoU) of 0.9636, representing a 4% improvement over the best-performing baseline, and an F1-score of 0.9811, marking a 2% increase. Ablation analysis further validated the indispensable contribution of GOBR modules in improving segmentation precision. Using our approach, we successfully extracted 25,556.98 hectares of cropland within the study area, encompassing a total of 67,850 individual blocks. Additionally, the proposed method exhibits robust generalization across varying spatial resolutions, underscoring its effectiveness as a high-accuracy solution for agricultural monitoring and sustainable land management in complex terrain. Full article

This study examines the impact of economic policy uncertainty (EPU) on agricultural land conversion. Using a newspaper-based index of EPU and a comprehensive panel dataset covering 270 prefecture-level cities in China, we estimate a city fixed effects model to explore this relationship. Our results indicate that a one-standard-deviation increase in EPU leads to a 22.2% increase in the conversion of agricultural land to urban residential, commercial, and industrial uses. This finding suggests that the surge in EPU triggered by the global financial crisis accounts for approximately 45% of the increase in agricultural land conversion. The adverse effect on agricultural land preservation mainly stems from intensified fiscal pressures and heightened demands on local governments to meet economic growth targets. To address potential endogeneity concerns, we employ the one-period lagged U.S. EPU index and its temporal variations as an instrument for China’s EPU, leveraging cross-country spillover effects. Our instrumental variable estimates confirm the validity of the land conversion effect and its underlying mechanisms. Furthermore, we find that the effects of EPU are particularly pronounced in cities located in non-eastern China and those that depend heavily on fixed asset investment for local economic development. Finally, our analysis of potential policy interventions to mitigate EPU-induced agricultural land loss suggests that strengthening market-oriented reforms and reducing province-level quotas on agricultural land conversion can effectively offset the impact of rising EPU. Full article

Rural settlements in agro-pastoral ecotones reflect the complex interplay between natural constraints and human land use, particularly in ecologically sensitive and climatically transitional regions. This study investigated the agro-pastoral ecotone of eastern Inner Mongolia, a representative region characterized by environmental heterogeneity and competing land use functions. Landscape pattern indices, ordinary least squares (OLS) regression, and geographically weighted regression (GWR) were employed to analyze settlement morphology and its environmental determinants. The results reveal a distinct east–west spatial gradient: settlements are larger and more concentrated in low-elevation plains with favorable hydrothermal conditions, whereas those in mountainous and pastoral areas are smaller, sparser, and more fragmented. OLS regression revealed a strong positive correlation between arable land and settlement density (r > 0.8), whereas elevation and slope were significantly negatively correlated. GWR results further highlight spatial non-stationarity in the influence of key environmental factors. Average annual temperature generally shows a positive influence on settlement density, particularly in the central and eastern agricultural areas. In contrast, forest cover is predominantly negative, especially in the Greater Khingan Mountains. Proximity to water resources consistently enhances settlement density, although the magnitude of this effect varies across regions. Based on spatial characteristics and land use structure, rural settlements were categorized into four types: alpine pastoral, agro-pastoral transitional, river valley agricultural, and forest ecological. This study provides empirical evidence that natural factors (topography, climate, and hydrology) and land use variables (farmland, pasture, and woodland) collectively shape rural settlement patterns in transitional landscapes. The findings offer methodological and practical insights for targeted land management and sustainable rural development in agro-pastoral regions under ecological and socioeconomic pressures. Full article

Heavy metal pollution is a growing public health concern owing to rising environmental pollution throughout the world. The situation is more vulnerable in Bangladesh; therefore, this study assessed contamination levels in different land use categories such as rural, local market, industrial, research, and coastal areas, as well as the related health risks for farmers in Bangladesh. A total of 45 soil samples were considered from three depths (0–5 cm, 5–10 cm, and 10–15 cm) across five different areas, with three replications per depth, following the monsoon season. Samples were prepared using a diacid mixture, and heavy metals (Cu, Ni, Mn, Cr, Zn, Pb) were investigated using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Health risks were evaluated using standard assessment models. The results showed that coastal agricultural soils had the highest heavy metal concentrations (except Pb), while rural areas had the lowest (except Cu and Ni), with no clear depth-based pattern. Two contamination sources were identified: component 1 (Cu, Ni, Mn, Cr, Zn) and component 2 (Pb, Zn), indicating mixed and anthropogenic sources, respectively. The Pollution Load Index (PLI) was highest in coastal areas and lowest in rural areas. The average daily intake of metals followed the order of inhalation > dermal > ingestion, with inhalation being the primary exposure route. The highest cumulative cancer risk (CCR) was observed in coastal agricultural soils (5.82 × 10⁻⁹), while rural soils had the lowest CCR (8.24 × 10⁻¹⁰), highlighting significant regional differences in health risks. Full article

The mounting global population and the challenges posed by climate change underline the need for sustainable food production systems. This review synthesizes evidence for a dual-track bioeconomy, green (terrestrial plants and insects) and blue (aquatic algae), as complementary pathways toward sustainable nutrition. A comprehensive review of the extant literature, technical reports, and policy documents published between 2015 and 2025 was conducted, with a particular focus on environmental, nutritional, and techno-economic metrics. In addition, precision agriculture datasets, gene-editing breakthroughs, and circular biorefinery case studies were extracted and compared. As demonstrated in this study, the use of green resources, such as legumes, oilseeds, and edible insects, results in a significant reduction in greenhouse gas emissions, land use, and water footprints compared with conventional livestock production. In addition, these alternative protein sources offer substantial benefits in terms of bioactive lipids. Blue resources, centered on micro- and macroalgae, furnish additional proteins, long-chain polyunsaturated fatty acids, and antioxidant pigments and sequester carbon on non-arable or wastewater substrates. The transition to bio-based resources is facilitated by technological innovations, such as gene editing and advanced extraction methods, which promote the efficient valorization of agricultural residues. In conclusion, the study strongly suggests that policy support be expedited and that research into bioeconomy technologies be increased to ensure the sustainable meeting of future food demands. Full article