Effective Land Management Research Articles

Unveiling the nexus of scientific plans and real-world outcomes: A comparative analysis of land use optimization and agent-based simulation results in a natural landscape

Sustainable land management requires balancing the socio-economic needs of local communities with land conservation. Effective land management programs hinge on how well plans align with the demands and actions of land users in natural landscapes. This study aimed to create optimized land use plans for a Jahan-Nama protected area in Iran as the initial step, followed by simulating stakeholder plans and conducting a comparative analysis. A combination of compromise programming and a simulated annealing algorithm, alongside an agent-based model (ABM) to simulate stakeholder preferences, was employed for land use optimization. The study compared the outcomes of eight optimized scenarios that considered both socio-economic needs (social, economic) and environmental sustainability (environmental), along with stakeholder preferences reflected in multi-objective scenarios with varying priorities and a single-use livestock grazing scenario. Findings revealed that the grazing scenario (single-use) offered the highest employment rate (201 individuals) within the study area, but had the lowest economic efficiency (profitability ratio: 13.38). Additionally, the grazing scenario exhibited the highest water consumption (42,200 L/day) and the highest erosion rate compared to the other scenarios. Conversely, the beekeeping and ecotourism scenario emerged as the most economically efficient (profitability ratio: 46.31) with the lowest water consumption (23,000 L/day). Among the scenarios examined, the highest level of agreement was observed for the simulated sites chosen by beekeepers and the beekeeping and ecotourism (BTP) scenario (Kappa = 0.176). Conversely, the economic scenario (EC) had the lowest agreement with the beekeepers' selected sites (Kappa = 0.135). In terms of ecotourism, the results indicated the highest agreement between the tourists' sites and the ecotourism and recreational scenario (Kappa = 0.332 in single-agent mode and 0.405 in multi-agent mode). On the other hand, the livestock grazing scenario had the lowest level of agreement with the tourists' selected sites (Kappa = 0.282 in single-agent mode and 0.362 in multi-agent mode).

The Use of Sentinel-1 C-Band Dual Polarimetric and SAR Polarimetry Method for Peatland Analysis in South Kalimantan Indonesia

Abstract Peatlands are a crucial factor in assessing environmental quality due to their capacity to store carbon in their biomass. Tropical peatlands, which account for 11 percent of the world’s total peatland area, are significant terrestrial carbon reservoirs, holding up to 19 percent of the global carbon. Besides carbon storage, peatlands offer numerous ecological benefits to local communities. However, Indonesia’s peat swamp forests are experiencing unprecedented rates of deforestation, drainage, and alteration. Therefore, sustainable management of peatlands through monitoring is essential. Accurate data and information on peatlands are needed for effective land management and use while maintaining hydrological functions. Synthetic Aperture Radar (SAR) is a highly efficient active remote sensing technology compared to optical remote sensing. The Polarimetric SAR method is expected to enhance the effectiveness and efficiency of peatland identification. According to the official website pantaugambut.id, Kalimantan Island has the second-largest peatland area. This research aims to identify peatlands in South Kalimantan, Indonesia, using Sentinel-1 SAR imagery. The study will evaluate the potential of the Polarimetric SAR technique with Sentinel-1 dual polarization for peatland identification. Expected outcomes include identifying peatlands and analyzing their spatial distribution. The research is intended to promote rapid and accurate peatland mapping and serve as a foundational step for global carbon monitoring. The analysis revealed that the unsupervised classification identified 26,376 hectares as peatland, achieving an overall accuracy of 85.7%, precision of 87.5%, recall of 82.4%, and a kappa value of 0.7135.

Crop Rotation of Sainfoin on the Longzhong Loess Plateau Has a Positive Effect on Enhancing Soil Carbon Sequestration Potential

The impact of various crop rotation systems on the potential for soil carbon sequestration and stoichiometric characteristics is not yet fully understood, which poses challenges for effective land management and utilization. This study selected three typical crop rotation methods in the Longzhong Loess Plateau: maize–alfalfa rotation (MA), maize–sainfoin rotation (MS), and maize–wheat rotation (MW). Soil physical and chemical indices were measured, and the soil carbon density and soil stoichiometry were calculated and analyzed. The results show that the soil C/N of the surface soils was low across the rotation methods, indicating a rapid rate of organic matter decomposition and mineralization, which may hinder soil nutrient accumulation. The soil N/P was found to be lower than the national average of 8.0, indicating that nitrogen is a limited nutrient in the soil under the three crop rotation systems in this region. The soil total nitrogen content can be increased by rotation with leguminous forage. Sainfoin rotation can enhance the soil total carbon and organic carbon content, thereby improving the soil’s carbon sequestration potential. The research findings provide a theoretical foundation for the selection of appropriate rotation methods and the maintenance of the stability of agricultural ecosystems in semi-arid regions.

Spatial distribution and transport characteristics of debris flow sediment using high resolution UAV images in the Ohya debris flow fan

Debris flows are significant geomorphic processes that can pose hazards to human settlements and infrastructure, making the understanding of their sediment transfer characteristics and impacts on the surrounding landscape crucial for effective land management and hazard mitigation strategies. This study investigates the distribution patterns of sandstone, vegetation, and shale classes within the Ohya debris flow fan, aiming to gain insights into the sediment characteristics, grain size variations, and vegetation cover trends along the flow path. Using a combination of field surveys and image processing we analyzed sediment characteristics and grain size variations a three study zones: upstream, transfer, and downstream. Statistical parameters such as mean, mode, skewness, and kurtosis were employed to quantify the grain size distribution and identify variations across the study zones. Results reveal distinct spatial variations in the distribution of sandstone, vegetation, and shale classes along the flow path. We observed a decrease in sandstone and vegetation cover from the upstream to the downstream sections, while the area covered by the shale class exhibited an increasing trend in the downstream direction. These findings suggest that debris flows selectively remove smaller particles downstream. The distribution of particle sizes within the sandstone class also exhibited variations, with a higher proportion of smaller pebbles downstream due to more effective removal of finer particles in the upstream zone. This study highlights the complex interplay between debris flow dynamics, sediment transport, and vegetation colonization. The findings contribute to the scientific knowledge of debris flow processes and can be used to inform land management decisions and mitigate the hazards associated with debris flows in similar environments.

How does land use affect soil quality and biological fertility in the arid ecosystem of Kutch, India?

Arid regions cover a large part of the Earth's surface and are at risk of increased agricultural activity and expected shifts in climate. Unfortunately, there is still a lack of comprehensive knowledge about the impact of land useon soil quality in these soils. Using a state factor approach, we studied the effects of different land use types on soil physico-chemical and biological characteristics in the Bhuj region of Kutch, Gujarat (India). Our analysis identified six key land uses: barren land (BL), natural forest (NF), grazing land (GL), and cultivated land under monocropping (MC), intercropping (IC), and crop rotation (CR). Our findings demonstrated significantly higher levels of soil organic carbon (SOC), calcium carbonate (CaCO3), plant-available nutrients (nitrogen, potassium, sodium, calcium, and magnesium), and enzyme activities in NF and GL (P < 0.05) than cultivated land and BL. For instance, SOC content in NF and GL exceeded that in cultivated land by 130% and 73.33%, respectively. Conversely, soil pH and plant-available phosphorus were higher in cultivated land. A strong correlation was observed between SOC and soil enzymes (P < 0.05), highlighting the importance of preserving SOC for optimal soil biological health in arid regions. Our study provides crucial baseline data on various soil quality indicators for an arid region, informing the development of landscape-scale models and guiding effective land management strategies.

Urban Land Management System in Ethiopia: an Empirical Study of Policy Implementation

This study aims to assess urban land management in Ethiopia from a policy perspective. A mixed approach with a concurrent nested strategy was employed. A total of 353 questionnaires were collected from civil servants and clients of urban land management systems, 8 focus group discussions (FGDs) with civil servants, and 24 interviews with experts, middle and top managers were conducted. The data were analysed and presented using logistic and multiple regressions. The results indicated that urban land management in Ethiopia continues to employ outdated and traditional systems. The main reasons for the unsatisfactory state of urban land management in the country are lack of commitment, lack of human resources, political influence, maladministration, instability of rules, etc. These factors are hampering the effective management of urban land in Ethiopia. Hence, people are not satisfied with the service of urban land management. Thus, the regional governments must reconsider the policy, employ an automated system, and work to address the identified problems.

Heavy metal(loid)s pollution in soils of a typical agricultural and rural area: Source apportionment and derived risk quantification.

In light of the pervasive contamination of soils with heavy metals (HMs) on a global scope, the precise identification and assessment of sources and areas of contamination are crucial for the effective management and utilization of land. This study combines several models to quantitatively apportion possible sources of HMs and environmental risks in agricultural and rural areas of Langfang City, Hebei Province. The findings indicate that the accumulation of HMs is influenced by human activities, with varying degrees of accumulation observed. The Positive Matrix Factorization (PMF) model identified five sources, with the order of magnitude of their contributions to the soil HMs content being as follows: coal combustion source (24.9%), industrial activities source (23.1%), agricultural activities source (19.2%), natural source (16.6%), and traffic emissions source (16.3%). The ecological risks present in the study area are not significant; however, the human health risks exceed the acceptable limits. The primary sources of pollution that pose a risk to human health are industrial, followed by natural source. As, Cr and Ni were responsible for the non-carcinogenic risk, while Cr was the dominant factor in the carcinogenic effect. The risk distribution map indicates that the areas exhibiting the highest risk are situated in the east-central region of the study area. This study had identified the priority control factors (including HMs, sources of pollution and high-risk areas) for the use and management of the land.

Machine Learning Enhances Soil Aggregate Stability Mapping for Effective Land Management in a Semi-Arid Region

Soil aggregate stability (SAS) is needed to evaluate the soil’s resistance to degradation and erosion, especially in semi-arid regions. Traditional laboratory methods for assessing SAS are labor-intensive and costly, limiting timely and cost-effective monitoring. Thus, we developed cost-efficient wall-to-wall spatial prediction maps for two fundamental SAS proxies [mean weight diameter (MWD) and geometric mean diameter (GMD)], across a 5000-hectare area in Southwest Iran. Machine learning algorithms coupled with environmental and soil covariates were used. Our results showed that topographic covariates were the most influential covariates in predicting these SAS proxies. Overall, our SAS maps are valuable tools for sustainable soil and natural resource management, enabling decision-making for addressing potential soil degradation and promoting sustainable land use in semi-arid regions.

Salinity drives niche differentiation of soil bacteria and archaea in Hetao Plain, China

Soil salinization is a critical environmental issue that limits plant productivity and disrupts ecosystem functions. As important indicators of soil environment, soil microbes play essential roles in driving nutrient cycling and sustaining ecosystem services. Therefore, understanding how microbial communities and their functional potentials respond to varying levels of soil salinization across different land use types is crucial for the restoration and management of salt-affected ecosystems. In this study, we randomly selected 63 sites across the Hetao Plain, covering an area of ∼2500 km2. Our results showed that both salinity- and fertility-related soil parameters were significantly correlated with bacterial and archaeal diversities, with soil salinity emerging as the stronger predictor of prokaryotic diversity. Intriguingly, bacterial and archaeal communities were tightly interlinked but displayed opposite trends in response to environmental factors, indicating a clear microbial niche differentiation driven by soil salinity. Moreover, the generalist functions of bacteria and archaea (e.g., chemoheterotrophy) exhibited contrasting responses to environmental parameters, while their specialist functions (e.g., nitrification) responded consistently. These findings highlight the pivotal role of soil salinity in shaping the niche differentiation of bacterial and archaeal communities in saline soils, providing insights to guide salinity-centered restoration strategies for effective marginal land management.

Quantification of the Effect of Soil and Biophysical Parameters on Water Balance Modelling Using SWAT+ in Forested Catchments

ABSTRACTAccurate simulation of water balance components is crucial for effective water and land management practices. The performance of process‐based hydrological models relies on the accurate determination of input variables. The objective of this study is to quantify the magnitude of the effect of soil properties (depth and texture) and biophysical parameters on water balance simulation for a forested catchment using the Soil and Water Assessment Tool (SWAT+). Simulations were carried out for a baseline scenario using the default soil inputs, followed by extending the soil profile depth up to 15 m under three different rainfall scenarios. Sensitivity analysis of model outputs was performed using the SENSitivity ANalysis (SENSAN) programme of the Parameter ESTimation (PEST) suite, coupled with SWAT+. The results showed that increasing soil profile depth to 15 m led to around 50% increase in water yield, and around 20% reduction in percolation with slight variations across the three rainfall scenarios. Evapotranspiration rates were slightly increased in deeper soil profiles. The sensitivity of evapotranspiration, surface runoff, and percolation to LAI‐related biophysical parameters was pronounced, highlighting the need to include such parameters in SWAT+ model calibration. The water uptake from deeper soil layers by deep roots, even in rocky substrates, as documented in the literature, is not adequately captured by the SWAT+ model. Our work showed that in general, developing local soil databases with detailed information on deeper layers is needed, to improve the accuracy and reliability of hydrological models in predicting water fluxes, thereby supporting informed water resources management decisions.

Understanding the effects of livestock grazing on dryland plant communities within the context of abiotic variability

AbstractDrylands, which cover more than 40% of the Earth's terrestrial surface, face rising agricultural demand and the influence of climate change. Understanding how livestock grazing pressure and local climate affect these environments is pivotal for informed land management. We studied big sagebrush plant communities in southwestern Wyoming over grazing gradients created by artificial livestock watering points. To explore the role of abiotic factors in shaping plant community response to grazing, we assessed the response of plant functional groups to grazing while accounting for soil texture variability across a precipitation gradient. Our models estimated that sagebrush cover responded positively to grazing intensity, with a 6% increase in cover with intensity when comparing the heaviest grazing treatment with the lowest grazing intensity. Perennial bunchgrass cover, the primary forage component, had a small negative response, a 3% decrease in cover from the lowest grazing intensity to the highest. Grazing intensity had no effect on bunchgrass density, perennial forbs, or rhizomatous grasses. Compared with abiotic factors, grazing intensity had a small effect on perennial bunchgrass and bare ground. We found that precipitation explained a 13% increase in perennial bunchgrass cover and a 34% decrease in bare ground cover compared with a 6% increase with increasing intensity across our grazing gradients. Sand content also had a larger effect on perennial bunchgrass cover and density than grazing. Increased sand content was associated with increased bunchgrass cover and density, supporting the inverse texture hypothesis. Our results show that while livestock grazing impacts sagebrush plant communities, its effect is small when compared with the effects of climate and soil. Our study contributes to a growing body of research emphasizing the need to contextualize plant community responses to grazing within specific climatic and edaphic conditions, which will promote effective land management in dryland ecosystems.

Estimation and assessment of water erosion in the Peixe Angical basin, Brazil

Water erosion causes the displacement of soil particles from higher to lower elevations, and this process intensifies when land use and vegetation cover change, such as through the conversion of forests into pastures or agricultural fields. Identifying priority areas for soil and water conservation practices is essential for promoting sustainable agriculture. Equally important is identifying the most influential factors driving erosion, as understanding these can guide effective land management strategies. Machine learning techniques, such as Random Forest, are valuable tools for analyzing large datasets and assessing the importance of variables. The primary aim of this study was to estimate soil losses due to land-use changes in the Peixe Angical Reservoir drainage basin using the Revised Universal Soil Loss Equation (RUSLE) within a Geographic Information System (GIS) framework, and to identify priority areas for soil conservation. Additionally, the study aimed to evaluate the contribution and importance of the RUSLE model factors (R, K, LS, and C) to soil loss using the Random Forest regression algorithm. Soil losses were computed for the chronological scenarios (1990, 2000, 2010, and 2017), using rasters with 90 m resolution to calculate the product of the R, K, LS, and C factors, along with the P factor. These soil losses were classified into erosion risk categories, ranging from very low (0–2.5 Mg ha−1 yr−1) to extremely high (greater than 100 Mg ha−1 yr−1). Soil losses in the basin increased over time. The Random Forest algorithm was applied to evaluate the importance of each factor. Rainfall erosivity was found to vary spatially, ranging from 7047.64 MJ mm ha−1 h−1 yr−1 to 11,348.5 MJ mm ha−1 h−1 yr−1, while the LS factor exhibited values ranging from near 0 to over 20. Litholic Neosol (Entisol) was the predominant soil type in the drainage basin. In terms of land use, forests accounted for the largest portion of the basin: 55.60% in 1990, 51.31% in 2000, 48.88% in 2010, and 48.21% in 2017. The C factor, which reflects vegetation cover, was the most significant contributor to soil loss, accounting for 44.8% in 1990, 43.5% in 2000, 44.2% in 2010, and 44.4% in 2017, followed by the K factor (soil erodibility). These assessment techniques can be utilized in guiding conservation planning, thereby supporting sustainable land use practices.

Integrated Assessment and Agricultural Planning in Selenium-Rich Hilly Soils: A Case Study on Land Use, Heavy Metal Contamination, and Nutrient Element Distribution

The strategic development of selenium-enriched soil cultivation is essential for effective agricultural land management. This research explores the sustainable utilization of selenium-rich soils in Qiongzhong County, Hainan Island. An extensive evaluation was conducted on 7266 surface soil samples, assessing the selenium concentrations, nutrient levels, heavy metals, pH values, and soil organic matter (SOM). In addition, analyses of 70 samples for organochlorine compounds and PAHs were performed. The results indicated average selenium content of 0.46 mg/kg, with 55.2% of the samples surpassing the selenium enrichment threshold of 0.4 mg/kg. According to the GB15618-2018 standards, 127 samples (1.75%) showed medium or high chromium-associated risks. No contamination from organochlorine compounds or PAHs was found, including the 16 priority-controlled PAHs with an average concentration of 30.3 µg/kg, confirming the soil’s high quality. The correlation and factor analysis identified surface enrichment as the main factor influencing selenium accumulation, presenting minimal environmental risks. Consequently, three categories of selenium-rich soil were defined: selenium-rich, pollution-free, and high-nutrient selenium-rich soil. Recommendations based on the soil characteristics and existing agricultural practices were made for the cultivation of selenium-rich vegetables, rubber, and nuts. This study lays a foundation for the sustainable management of selenium-rich soils, providing insights for further research and decision-making to optimize these resources, thus promoting environmental protection and agricultural sustainability.

Assessing the effects of soil and water conservation in gully land consolidation using the modified SWAT-Terrace model

To alleviate the competition for cropland between ecological restoration and agricultural production, a Gully Land Consolidation (GLC) project has been widely executed in the Chinese Loess Plateau. This project involves mechanically excavating slopes and filling gullies to expand cropland area. Therefore, in the context of climatic and topographical changes, an assessment of the impact of GLC as a key land use measure of water and sediment in space and time is urgently needed. However, due to scale effects, there is a lack of relevant assessments for the GLC. This study utilized the Soil and Water Assessment Tool (SWAT) and SWAT-Terrace model to analyze the temporal-spatial changes of water and sediment in the Yanhe watershed during 1985–1998 (R1) and 1999–2017 (R2). Additionally, it assessed the effectiveness of GLC in erosion control across different sub-basin clusters. The results showed that (1) at the temporal scale, the runoff and sediment decreased by 30.09 % and 72.73 % respectively, during R2 compared to R1; (2) at the spatial scale, 56 % and 71 % of the sub-basins located in the central and northern parts of the watershed showed a reduction in water and sediment yield, respectively, during R2 compared to R1, while the remaining sub-basins located in the southern part showed an increasing trend in water and sediment yield; and (3) the GLC can reduce 1.17 %-1.93 % of water yield and 0.94 %-1.91 % of sediment yield in the three clusters of sub-basins. The assessment of the soil and water conservation effects of the GLC is beneficial to policymakers in designing effective land management plans and the extension of the project to other regions.

Mapping anthropogenic impacts on natural resources in the northeastern Sahara region: A case study on the past 26 years over the Ziban region (Algeria)

The objective of this research was to map and analyze anthropogenic impacts on natural resources in the Ziban region of Algeria using remote sensing imagery from 1995 to 2021. Medium-resolution satellite images from Landsat TM and OLI were utilized, with image processing techniques such as colorful compositions and supervised classification to map land cover. Seven land use and land cover (LULC) classes were identified, distinguishing human-derived categories (palm, agriculture, urban, and greenhouses) from natural surfaces (nebka, natural vegetation, and bare soil). The results indicated significant increases in human-driven elements like palm, urban areas, agriculture, greenhouses, and nebka, and notable decreases in natural components like vegetation and bare soil. Specifically, the rate of change (Tc) showed increases of 0.9% for palm, 1.14% for urban, 1.55% for agriculture, 1.91% for greenhouses, and 9.37% for nebka, and decreases of -3.68% for vegetation and -11.19% for bare soil. These findings highlight the impact of agricultural policies, population growth, and natural and human-induced conditions on natural resources. Palm areas remained largely unchanged due to state policies, whereas agriculture saw significant conversions to bare soil and palm. Vegetation declined substantially due to adverse climatic conditions and agricultural expansion. Nebka and urban areas had moderate to high conversion rates, and bare soil saw notable changes due to sand movement and urban/agricultural development. The study highlights the role of remote sensing and land cover analysis in managing natural resources sustainably, considering factors like agricultural policies and population growth. It emphasizes developing data-driven strategies for effective land use and management.

Flowering plant communities mediate the effects of habitat composition and configuration on wild pollinator communities

Abstract There is strong evidence that landscape‐scale factors such as habitat diversity, composition and configuration are important drivers of declines in pollinators and pollination services. However, context and species‐specific responses make it challenging to draw general conclusions about the most important components of landscapes that support diverse and abundant pollinator communities. In this study, we took a functional‐traits approach to community assembly and tested the hypothesis that landscape properties act most strongly on pollinators indirectly, through their influence on flowering plant communities. Using plant and pollinator data from 96 landscapes in Britain, we tested the associations between plant and pollinator communities and local environmental factors, such as habitat cover and configuration, using path analysis based on Mantel and partial Mantel statistics. When all pollinators were considered, we found that the environmental factors had stronger links to the composition of flowering plant communities than to the composition of pollinator communities. Further, the flowering plant community was strongly linked to the pollinator community suggesting a mediating role between land use and pollinators. When separating the pollinator community into taxonomic groups, we found the same result for hoverflies, but wild bees were linked to both environmental factors and flowering plants. We further explored these links with structural equation models using the response‐effect trait framework as a guiding principle. We found strong evidence that land‐use composition and configuration influence the trait distribution and functional diversity of the pollinator community via plant community composition. These findings suggest that the indirect effect of land use on pollinators via flowering plants should be considered in informing the design of pollinator friendly landscapes and in future research of the effects of land use and management on wild pollinators. Read the free Plain Language Summary for this article on the Journal blog.

Cadastral-to-Agricultural: A Study on the Feasibility of Using Cadastral Parcels for Agricultural Land Parcel Delineation

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F1-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land.

GIS-Based Land Use and Land Cover Change Assessment Around Assosa District, Upper Blue Nile Basin, Ethiopia

Land, with its intricate blend of geological, topographical, hydrological, and ecological elements, reflects the interplay of natural processes and human activities. In Ethiopia, traditional agricultural practices often lack systematic planning based on land suitability and physical characteristics, hindering agricultural productivity and economic progress. Understanding shifts in land use and cover is crucial for comprehending broader global transformations. Early studies have unveiled significant alterations in land use and cover between 2009 and 2013. Our investigation extends from 2013 to 2023, employing Landsat 8 imagery at three-year intervals (2013, 2018, and 2023) to analyze five key land cover categories: forests, farmland, built-up areas, barren land, and water bodies. Notably, farmland and built-up areas witnessed substantial expansion over these periods, juxtaposed with a significant decline in forest cover. Forests decreased by 27% from 2013 to 2018, with an additional 7% decrease from 2018 to 2023. Conversely, farmland expanded by 19% from 2013 to 2018 and by 2% from 2018 to 2023, while built-up areas saw respective increases of 4% and 10% over the same periods. These trends underscore potential environmental challenges if unaddressed. Effective land management strategies are crucial to mitigate adverse impacts on ecosystems, agricultural productivity, and overall socio-economic stability. By integrating scientific insights with sustainable practices, we can navigate the intricate dynamics of land use, paving the way for a resilient and prosperous future.

Assessing sustainability in smallholder vegetable farms in Benin Republic: A matrix approach

This study aims to assess the level of sustainability in vegetable-based agrifood production systems in Benin and to propose actions to enhance sustainability, food safety, and year-round production in the vegetable production systems. Semi-structured interviews were conducted with 200 vegetable farmers in contrasting agroecological areas (with areas of extensive production of staples and intensive production of vegetables), using the “Indicateur de Durabilité des Exploitations Agricoles” (IDEA) framework (an on-farm sustainability index). Most of the surveyed vegetable farmers produced a wide range of crops, including leafy vegetables (amaranth, African eggplant, and African basil) and peppers, grown by more than 50% of the farmers. The average scores achieved by the vegetable farms regarding three dimensions of sustainability—ecological, social, and economic—were 35, 41, and 63, respectively, out of a maximum score of 100. All three sustainability dimensions of the vegetable farms were, on average, at a low level and improvements were needed for them to reach an acceptable standard. The vegetable farms located in the south of Benin had, on average, a higher sustainability score than those in the north: around 50% of vegetable farms in the south had a medium score, while the sustainability level of almost 75% of vegetable farms in the north was low. Interventions seeking to improve the sustainability of vegetable farms in Benin should focus on the promotion and adoption of eco-responsible practices that improve on-farm biodiversity, water conservation, and the effective allocation and management of land and labor, to mitigate the environmental impacts of vegetable production.

Spatio-temporal evolution of water erosion in the western Songnen Plain: Analysis of its response to land use dynamics and climate change

Preventing water erosion is crucial for maintaining ecosystems and ensuring food security, necessitating a comprehensive understanding of the spatial and temporal patterns of water erosion and its underlying drivers. In the context of global warming, analyzing the impacts of land use dynamics and climate change on water erosion contributes to effective land management and sustainability of both industry and agriculture. This study aims to analyze the spatial distribution of water erosion in the western Songnen Plain from 1990 to 2020 using the Revised Universal Soil Loss Equation (RUSLE), with a focus on assessing the impacts of land use and climate on water erosion. The results revealed a 7.1 % increase in the area experiencing water erosion above light levels in the western Songnen Plain. The hotspots for water erosion were located in the southeast and northwest of the study area. The rapid expansion of farmland and land salinization, leading to reduced vegetation cover and soil property deterioration, were the main causes of intensified water erosion in the region before 2000. Although water erosion was slightly alleviated after 2000, the further expansion of farmland, the worsened water erosion intensity in alkaline land and frequent extreme weather still posed serious threats to water erosion in the study area. Water erosion was positively correlated with temperature and dry/wet alternation events, including frequency, duration, and severity. In addition, land use type was the main factor influencing the heterogeneous distribution of water erosion in the western Songnen Plain, whose interaction with dry/wet alternation events had the strongest explanatory power. Therefore, this study calls for the implementation of soil and water conservation measures to mitigate the impacts of land cultivation, salinization, and climate change on water erosion.