Search Results (1,975)

Agroecology refers to the greening of agrosystems with the mobilization of ecosystem services in order to limit exogenous inputs, enhance biodiversity and moderate the exploitation of natural resources. Agroecological practices offer pathways for transformation and transition not only of agricultural systems but of entire food systems. Through its objectives, agroecology aims at both sustainable land management and the strengthening of the livelihoods of producers and rural people and thus contributes to the fight against desertification. Currently, there is little scientific literature on the characteristics of agroecology in the Maghreb region. Several studies provide important information but they do not allow drawing up a global panorama of agroecology in the region. The proposed article highlights general characteristics of agroecology in North Africa from a review of 88 sustainable agriculture projects, which it analyzes, through an inventory of agroecological practices supported by these projects, from the frameworks of the High-Level Panel of Experts on Food Security and Nutrition, 2019, on the principles of agroecology and the transition levels approach developed by Gliessman and fellows since 2007. The results show (i) differences in the observed practices depending on the agrosystems and (ii) predominant common practices across these diverse agrosystems; (iii) significant evolution in these practices over time. The majority of the agroecological innovations identified are at the plot and farm scales, with the exception of those found in oasis and mountain agrosystems, where practices integrate the scales of the territory and value chains in a more complete way. Full article

Small-scale open-pit, non-artisanal mining of low-value ores is an understudied practice despite its widespread occurrence and potential impact on freshwater resources due to mining-induced land-use/cover changes (LUCCs). This research investigates the long-term impacts of andesite mining in Pasuruan, Indonesia, on the Umbulan Spring’s water discharge within its watershed. System Dynamics (SD) modeling captures the systemic and systematic impact of mining-induced LUCCs on discharge volumes and groundwater recharge. Agricultural and reservoir-based land reclamation scenarios then reveal post-mining temporal dynamics. The no-mining scenario sees the spring’s discharge consistently decrease until an inflection point in 2032. With mining expansion, reductions accelerate by ~1.44 million tons over two decades, or 65.31 thousand tons annually. LUCCs also decrease groundwater recharge by ~2.48 million tons via increased surface runoff. Proposed post-mining land interventions over reclaimed mining areas influence water volumes differently. Reservoirs on reclaimed land lead to ~822.14 million extra tons of discharge, 2.75 times higher than the agricultural scenario. Moreover, reservoirs can restore original recharge levels by 2039, while agriculture only reduces the mining impact by 28.64% on average. These findings reveal that small-scale non-artisanal andesite mining can disrupt regional hydrology despite modest operating scales. Thus, evidence-based guidelines are needed for permitting such mines based on environmental risk and site water budgets. Policy options include discharge or aquifer recharge caps tailored to small-scale andesite mines. The varied outputs of rehabilitation scenarios also highlight evaluating combined land and water management interventions. With agriculture alone proving insufficient, optimized mixes of revegetation and water harvesting require further exploration. Full article

The accurate prediction of soil organic carbon (SOC) is important for agriculture and land management. Methods using remote sensing data are helpful for estimating SOC in bare soils. To overcome the challenge of predicting SOC under vegetation cover, this study extracted spectral, radar, and topographic variables from multi-temporal optical satellite images (high-resolution PlanetScope and medium-resolution Sentinel-2), synthetic aperture radar satellite images (Sentinel-1), and digital elevation model, respectively, to estimate SOC content in arable soils in the Wuling Mountain region of Southwest China. These variables were modeled at four different spatial resolutions (3 m, 20 m, 30 m, and 80 m) using the eXtreme Gradient Boosting algorithm. The results showed that modeling resolution, the combination of multi-source remote sensing data, and temporal phases all influenced SOC prediction performance. The models generally yielded better results at a medium (20 m) modeling resolution than at fine (3 m) and coarse (80 m) resolutions. The combination of PlanetScope, Sentinel-2, and topography factors gave satisfactory predictions for dry land ($R^2$ = 0.673, MAE = 0.107%, RMSE = 0.135%). The addition of Sentinel-1 indicators gave the best predictions for paddy field ($R^2$ = 0.699, MAE = 0.114%, RMSE = 0.148%). The values of $R^2$ of the optimal models for paddy field and dry land improved by 36.0% and 33.4%, respectively, compared to that for the entire study area. The optical images in winter played a dominant role in the prediction of SOC for both paddy field and dry land. This study offers valuable insights into effectively modeling soil properties under vegetation cover at various scales using multi-source and multi-temporal remote sensing data. Full article

The diversity of land use and consolidation is fundamental to ensuring sustainable development. However, the impact of diverse land uses and consolidation on the well-known shallow accumulation pattern of soil nitrogen (N) remains unclear. This existence of this knowledge gap severely constrains the sustainable production of newly created farmland. Therefore, the objective of this study was to investigate the effects of land use and gully land transformation on the vertical distribution of soil N in agricultural and nature catchments. Methodologically, soil nitrate (NO₃⁻), ammonium (NH₄⁺) and total nitrogen (TN) were measured to a depth of 100 cm in the hillslope forestland, grassland and gully cropland areas of the treated (gully landform reshaping) and untreated (natural gully) catchments on the Chinese Loess Plateau (CLP). The results indicated that soil N in the hillslope forestland and grassland exhibited a shallow accumulation pattern, while the vertical distribution of soil N in the gully cropland areas displayed a homogeneous, random or deep accumulation pattern. In the hillslope areas, vegetable cover was the dominant factor controlling N variation in the topsoil. In contrast, in the gully areas, the interaction of landform transformation and hydrology was the primary factor influencing the distribution of soil N. In the treated catchment, soil NO₃⁻ exhibited a significant deep accumulation pattern in the newly created farmland through gully landform reshaping. In the untreated catchment, soil NH₄⁺ showed a significant deep accumulation pattern in the undisturbed natural gully. This study provides valuable insights into how land use and gully landform transformation affect the soil N profile. This information is crucial for the sustainable development and scientific management of valley agriculture at the catchment scale. Full article

Considering the importance of coastal wetlands as key land resources and the ecological degradation caused by large-scale and multi-stage reclamation, as well as the significant synergistic and superimposed effects of reclamation on wetland degradation in temporal and spatial dimensions, it is vital to conduct in-depth research on the impact mechanisms and cumulative effects of reclamation on wetland degradation. However, the existing methods for evaluating these cumulative effects still have some shortcomings in characterizing the spatiotemporal scale. Consequently, it is urgent to introduce or develop a cumulative effect evaluation method based on remote sensing. Taking the Jiangsu coastal wetland as a typical case study area, the present study constructed a cumulative effect evaluation method based on calculus theory combined with landscape succession modeling and statistical analysis. This method was then used to quantitatively analyze the impacts and cumulative effects of reclamation on wetland degradation in the Jiangsu coastal region from 1980 to 2024. The results show that degradation of the Jiangsu coastal wetlands over the last 45 years covered 2931.54 km², accounting for 46.92% of the area in 1980. This degradation primarily reflects a shift from natural wetland to constructed wetland. In addition, the reclaimed area of 2119.61 km² is mainly used for aquaculture and agricultural cultivation. The reclamation rate of Jiangsu showed insignificant fluctuations and significant spatial differences. The reclamation rate of the north counties and cities presented a downward trend, while that of the south counties and cities presented an upward trend. Reclamation has a significant impact on wetland degradation, with a contribution rate of 50.62%. The cumulative effect in the study area reached its maximum value in 2015, except for Nantong City. This study provides a new perspective for quantitatively analyzing the impacts and cumulative effects of coastal wetland reclamation and provides guidance for the effective management and sustainable utilization of coastal wetland resources. Full article

Accurate evapotranspiration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region. Full article

Based on the principle of water supply and demand flow and the natural flow of water, this paper analyzes the flow direction and discharge of water resources in the study area. In order to provide scientific and systematic implementation suggestions for regional water resource protection management and ecological compensation, a SWAT (Soil and Water Assessment Tool) model was constructed to quantify the water resource supply of the upper Hanjiang River basin at three spatial scales: pixel, sub-basin, and administrative unit. The water demand at the three spatial scales was calculated using the LUCC (Land Use and Land Coverage) and water consumption index. The supply and benefit zones under different spatial and temporal scales were obtained. Simultaneously, this study uncovered the spatiotemporal dynamics inherent in water resource supply and demand, alongside elucidating the spatial extent and flow attributes of water supply. The ecological compensation scheme of water resource supply–demand was preliminarily determined. The findings indicate an initial increase followed by a decrease in both the water supply and demand in the upper reaches of the Han River, accompanied by spatial disparities in the water supply distribution. The direction of the water supply generally flows from branch to main stream. The final ecological compensation scheme should be combined with natural conditions and economic development to determine a reasonable financial compensation system. Full article

Livestock grazing is a major land use in the Great Barrier Reef Catchment Area (GBRCA). Heightened grazing density coupled with inadequate land management leads to accelerated soil erosion and increased sediment loads being transported downstream. Ultimately, these increased sediment loads impact the water quality of the Great Barrier Reef (GBR) lagoon. Ground cover mapping has been adopted to monitor and assess the land condition in the GBRCA. However, accurate prediction of ground cover remains a vital knowledge gap to inform proactive approaches for improving land conditions. Herein, we explored two deep learning-based spatio-temporal prediction models, including convolutional LSTM (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN), to predict future ground cover. The two models were evaluated on different spatial scales, ranging from a small site (i.e., <5 km²) to the entire GBRCA, with different quantities of training data. Following comparisons against 25% withheld testing data, we found the following: (1) both ConvLSTM and PredRNN accurately predicted the next-season ground cover for not only a single site but also the entire GBRCA. They achieved this with a Mean Absolute Error (MAE) under 5% and a Structural Similarity Index Measure (SSIM) exceeding 0.65; (2) PredRNN superseded ConvLSTM by providing more accurate next-season predictions with better training efficiency; (3) The accuracy of PredRNN varies seasonally and spatially, with lower accuracy observed for low ground cover, which is underestimated. The models assessed in this study can serve as an early-alert tool to produce high-accuracy and high-resolution ground cover prediction one season earlier than observation for the entire GBRCA, which enables local authorities and grazing property owners to take preventive measures to improve land conditions. This study also offers a new perspective on the future utilization of predictive spatio-temporal models, particularly over large spatial scales and across varying environmental sites. Full article

The decline in primary natural forests worldwide has intensified research on the effects of forest transformation on soil carbon (C), nitrogen (N), and phosphorus (P) cycles and stocks. However, the extent to which soil C, N, and P stocks and stoichiometry are affected by forest conversion remains unclear. Here, we examined the effects of forest transformation on soil nutrient storage capacity and stoichiometric characteristics in native broadleaf forests (BFs), plantation forests (PFs), tea gardens (TGs), cultivated lands (CLs), and urban artificial green spaces (GSs) at a county scale in subtropical China. The results showed that the other forest types exhibited significantly reduced soil C and N contents and stocks but increased soil P content and stock compared to BFs. The soil C:N:P stoichiometric ratios for BFs and the converted PFs, TGs, GSs, and CLs were sequentially decreased as follows: 444.8:24.2:1, 95.0:10.0:1, 30.2:3.9:1, 23.1:3.7:1, and 19.4:1.9:1, respectively. Within the altitude (AL) span of 180 to 1200 m surveyed, the AL decided the type of forest conversion and significantly influenced the stock levels and stoichiometric ratios of soil C, N, and P. The results of this study highlight the importance of the ecological management of TGs and the optimization of soil P production in CLs, TGs, and GSs. Full article

Karst ecosystems have become complex social–ecological systems (SESs) as a result of the interventions of large-scale ecological restoration programs, and the ecosystem services (ESs) that provide regional well-being can, to some extent, be described as social–ecological system services (S–ESs). Understanding the relationships among multiple S–ESs and exploring their drivers are essential for effective ecological management in karst areas, especially in regions differently affected by ecological engineering programs. Taking South China Karst (SCK) as a study area, we first identified two regions as comparative boundaries, namely significant engineering impact regions (SEERs) and non-significant ecological engineering impact regions (NEERs). Then we used ES assessment models, Spearman correlation, and optimal parameter geographic detector to identify the supply capacity, trade-offs/synergies, and their drivers of six types of S–ESs in SEERs and NEERs. The findings included: (1) SEERs were predominantly concentrated in the central and southern SCK regions, accounting for 33.98% of the total SCK area, with the most concentrated distribution observed in Guizhou and Guangxi. (2) Within the entire SCK, six S–ESs maintained a relatively stable spatial distribution pattern over time, with the most pronounced increase in soil conservation and a slight decrease in water retention, and the S–ES hotspots were more concentrated within the SEERs. (3) Most S–ES pairs within SEERs were optimized synergistically, with lower trade-off intensity and higher synergy intensity compared to NEERs. (4) S–ES pairs were affected by the interactions between the natural and socio-economic factors, with land use changes playing a crucial role, and natural factors were difficult to predict but cannot be ignored. Based on the results, we propose different SES sustainable development suggestions, with a view to providing theoretical support for the optimization of SES functions and the consolidating of integrated ecological construction. Full article

The Chinese government has undertaken a significant forest restoration project, leading to a notable increase in forested areas. Despite this achievement, there is uncertainty regarding its impact on wildlife protection. To assess this, we utilized high-resolution remote sensing data to gather information on land use, bioclimatic conditions, geography, and human activity. This information was used to model and analyze changes in suitable habitats for Chinese endemic brown-eared pheasants over the past 30 years to determine the effects of the forest restoration project on wildlife habitats. Our analysis revealed that although the suitable habitat area for the brown-eared pheasant has expanded, the increased forested area did not influence their distribution. Our study also found that increasing elevation and decreasing grassland area in landscape patches promoted the distribution of brown-eared pheasants. Furthermore, the annual variation of the min temperature of coldest month and annual precipitation is an important factor affecting the suitable habitat distribution of brown-eared pheasants. Research showed that the suitable habitat of brown-eared pheasant is seriously fragmented, and the connectivity between habitats should be strengthened in the future. Based on our findings, we believe that existing forest restoration project policies cannot effectively protect wildlife due to neglecting key environmental factors at the landscape scale. Therefore, we recommend developing refined land use management policies at the landscape level to guide future ecological protection and biodiversity conservation. These findings significantly affect policy and future research on wildlife protection and forest restoration. Full article

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality. Full article

The expansion of cities and land use changes have led to the emergence of peri-urban areas representing a transition between fully urbanized and agricultural regions in Southeast Asia. Peri-urban communities provide essential ecosystem services but are vulnerable to climate-related disruptions and socioeconomic challenges. Utilizing their unique characteristics, peri-urban communities can contribute to sustainable development and resilience. This study assesses the potential of peri-urban areas to meet future challenges for sustainable development in a changing world, focusing on the local pandan farming community of Pathum Thani, approximately 53 km north of Bangkok, using the Community-Based Resilience Analysis (CoBRA) approach. A formally established group of peri-urban farming households identified COVID-19, water quality, and solid waste as their primary disruptive challenges. The community identified economic stability and resources (land ownership, financial security, and government support), community and social support (collaborative community, and healthcare facilities), an environmental dimension (sufficient food and clean water), and an information dimension (news and knowledge update) as key community resilience characteristics, which highlight their comprehensive approach to hazard resilience. The study concludes that the community was moderately resilient to hazards and COVID-19 was the primary disrupting event over the past 10 years. To address future challenges in peri-urban agriculture, it is suggested to focus on enhancing economic diversification, strengthening social networks and support systems, implementing sustainable land management practices, and promoting access to timely and accurate information. Additionally, investing in infrastructure for water management and waste recycling, supporting small-scale farming initiatives, and fostering collaboration between farmers and local authorities can contribute to building resilience in peri-urban agricultural communities. Full article

In central Sakha (Yakutia) Republic, groundwater icings, primarily formed by intrapermafrost water, are less prone to contamination and serve as a stable freshwater resource. The periodic growth of icings threatens infrastructure such as roads, railways, and bridges in permafrost areas. Therefore, research in this field has become urgently necessary. This study aims to analyze the impacts of various factors on the scale of icing formation using Landsat satellite data, Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, Global Land Data Assimilation System (GLDAS) data, and field observation results. The results showed that the surface area of icings in the study area showed an overall increasing trend from 2002 to 2022, with an average growth rate of 0.06 km²/year. Suprapermafrost water and intrapermafrost water are the main sources of icings in the study area. The total Groundwater Storage Anomaly (GWSA) values from October to April showed a strong correlation with the maximum icing areas. Icings fed by suprapermafrost water were influenced by precipitation in early autumn, while those fed by intrapermafrost water were more affected by talik size and distribution. Climate warming contributed to the degradation of the continuous permafrost covering an area of 166 km² to discontinuous permafrost, releasing additional groundwater. This may also be one of the reasons for the observed increasing trend in icing areas. This study can provide valuable insights into water resource management and infrastructure construction in permafrost regions. Full article

Arthropods play a crucial role in ecological processes and agricultural productivity. Soil physicochemical properties, indicators of soil health, are closely linked to arthropod communities. Gravel-sand mulching, commonly employed in arid farming, initially enhances water retention and temperature regulation but may contribute to land degradation with prolonged use. This study investigated how varying tillage durations affected soil properties and arthropod diversity under gravel-sand mulching. The analysis employed multiple comparison methods, covariance analysis (ANCOVA), non-metric multidimensional scaling (NMDS), and redundancy analysis (RDA). The results indicated that while soil fertility was better preserved in cultivated fields compared to in the desert grassland, arthropod diversity significantly decreased with longer cultivation periods. A total of 1099 arthropods from 79 species were sampled, by Barber trap. The highest diversity was observed in native grassland (NG), with 305 arthropods from 39 species, while tillage 21 years (GPS-21Y) exhibited the lowest diversity, with only 103 arthropods from 6 species. Dominant species included the carnivores Labidura japonica and Cataglyphis aenes. The analysis revealed low similarity in arthropod communities between GPS-21Y and other fields and high similarity in soil physicochemical properties between NG and the transition zone (STZ). RDA showed available potassium (APP) was negatively correlated with arthropod species diversity and concentration, total Nitrogen (TN) was positively correlated with arthropod species diversity but negatively correlated with species concentration, total phosphorus (TP) was negatively correlated with arthropod species diversity and concentration. This study provides insights into the relationship between maintaining soil fertility and supporting arthropod diversity in grassland agriculture. While soil fertility and arthropod diversity were correlated, continuous cropping practices negatively impacted arthropod diversity, offering valuable information for pest management and sustainable agricultural practices. Full article