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Soil salinity is one of the significant environmental issues that can reduce crop growth and productivity, ultimately leading to land degradation. Therefore, accurate monitoring and mapping of soil salinity are essential for implementing effective measures to combat increasing salinity. This study aims to estimate the spatial distribution of soil salinity using machine learning methods in Huludao City, located in northeastern China. By meticulously collecting data, soil salinity was measured in 310 soil samples. Subsequently, environmental parameters were calculated using remote sensing data. In the next step, soil salinity was modeled using machine learning methods, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Additionally, to estimate uncertainty, the lower limit (5%) and upper limit (95%) prediction intervals were used. The results indicated that accurate maps for predicting soil salinity could be obtained using machine learning methods. By comparing the methods employed, it was determined that the RF model is the most accurate approach for estimating soil salinity (RMSE=0.03, AIC=-919, BIS=-891, and R2=0.84). Furthermore, the results from the prediction interval coverage probability (PICP) index, utilizing the uncertainty maps, demonstrated the high predictive accuracy of the methods employed in this study. Moreover, it was revealed that the environmental parameters, including NDVI, GNDVI, standh, and BI, are the main controllers of the spatial patterns of soil salinity in the study area. However, there remains a need to explore more precise methods for estimating soil salinity and identifying salinity patterns, as soil salinity has intensified with increased human activities, necessitating more detailed investigations. [ABSTRACT FROM AUTHOR]

Increasing global land degradation and human population necessitate more agricultural production; therefore, plant cultivation may expand to nutrient-limited areas to meet growing food demands. As an environmentally safe application, plant growth-promoting rhizobacteria have a high potential to promote plant growth under stress conditions. The present study examined the impacts of single and co-inoculations of two high- and two low-biofilm- and exopolysaccharide (EPS)-producing Bacillus (B) and Azotobacter (A) strains on wheat growth in nutrient-limited soil. High- and low-biofilm-/EPS-producing strains were designated as + ve and − ve, respectively. Among the treatments applied, high-biofilm- and EPS-producing B (−) A (+) and B (+) A (−) treatments significantly enhanced shoot length by 20.4–24.5%, shoot fresh weight by 22.2–27.4%, root length by 31.4–37.1%, root fresh weight by 31.9–34.8%, and root dry weight by 27.2–31.4% over the non-inoculated control. Such two treatments also increased the uptake of nutrients (Ca, K, Mg, Fe, Cu, Mn, and Zn) by the roots more than almost all the other applications. A higher nutrient uptake enhanced the leaf chlorophyll contents and decreased antioxidant enzymes (catalase and peroxidase) activities in wheat seedlings exposed to the respective two treatments. The synergy between co-inoculated bacterial strains and their high-biofilm-/EPS-producing potential in B (−) A (+) and B (+) A (−) treatments appear to have a better role in enhanced wheat growth under nutrient stress by improving plant nutrient availability. The consortium of high-biofilm- and EPS-producing rhizobacteria may be used as potential bioinoculants to increase plant growth in nutrient-limited barren soils. [ABSTRACT FROM AUTHOR]

Gully erosion is one of the most severe forms of land degradation and poses a serious threat to regional food security, biodiversity, and human survival. However, there are few methods for the quantitative evaluation of gully activity, and the relationships between gully activity and influencing factors require further in-depth study. This study takes the Sunshui River Basin, as a case study. Based on field investigation, unmanned aerial vehicle (UAV) photography and remote sensing images, 71 typical gullies were identified. The vegetation coverage (VC), slope and main-branch gully ratio (MBGR) were used as evaluation indicators, and the gully activity was calculated using the fuzzy mathematics membership degree and then evaluated quantitatively. The factors influencing different active gullies were also analyzed. The results showed that (1) the fuzzy comprehensive evaluation method can be used to identify gully activity. Different levels of gully activity were defined based on the gully activity index. The active indices of stable gullies ranged from 0–0.25, those of semiactive gullies ranged from 0.25–0.75, and those of active gullies ranged from 0.75–1. (2) The activity indices of the 71 gullies ranged from 0.054 to 0.999, with an average value of 0.656. There are 31 active gullies, and 31 semiactive gullies. A total of 87.32% of the gullies in the study area were in the early or middle stage of gully development. Gully erosion was intense, which is consistent with the serious reality of soil erosion. (3) Gully activity was affected by multiple factors. It was significantly positively correlated with topographic relief (TR) (r = 0.64, P<0.01) and surface curvature (SC) (r = 0.51, P<0.01), while it was significantly negatively correlated with land use type (LUT) (r = -0.5, P<0.01). Surface roughness (SR) (r = 0.2, P<0.01) was positively correlated with gully activity; but not significantly. There was no significant correlation between aspect (As) and gully activity. The results of this study are helpful for quantitatively determining the level of gully activity and understanding the development process and mechanism controlling gullies, providing a reference for research on related regions and geomorphologic information. [ABSTRACT FROM AUTHOR]

Land degradation is a major problem in Ethiopia, as it contributes to climate change by releasing greenhouse gases (GHG) and slowing carbon sequestration rates. The objective of this review was to assess the role of conservation agriculture (CA) in climate change adaptation and mitigation in Ethiopia. The critical review method processes for identifying and synthesizing peer-reviewed research and review articles, reports, proceedings, and book chapters were followed, with materials obtained from relevant search engines. The findings of the various reports revealed that minimum tillage assists in soil moisture conservation when compared to conventional tillage. Conservation tillage maintains crop residue, reduces soil temperature significantly, and increases nutrient buildup in the surface soil layer, all of which lead to higher crop growth and production thus help as adaptation to climate change. Furthermore, agriculture and other land uses significantly contribute to greenhouse gas emissions; nevertheless, conservation agricultural methods improve soil organic carbon (SOC), soil aggregation, and carbon in aggregate, as well as soil health that contribute to climate changing mitigation. Several studies found that soil health indicators such as soil aggregation, soil organic carbon storage, soil enzymes, and microbial biomass improved under conservation tillage practices, potentially improving the carbon-nitrogen cycle, soil stability, and overall crop productivity. In terms of climate adaptation and mitigation, CA is one of the non-substitutable choices for reducing greenhouse gas emissions. Crop diversity, increased nitrogen consumption efficiency, crop rotation, improved soil carbon sequestration methods; crop residue retention, minimum soil disturbance, manure incorporation, and integrated farming systems are all important factors in minimizing GHG emissions. Moreover, factors impeding CA adoption include a lack of appropriate equipment and machinery, weed control methods, the use of crop residue for fuel wood and animal feed, a lack of awareness about the benefits of CA on soil health and sustainability, and a lack of government technical and financial support for smallholder farmers. Adoption and scaling up of CA practices are critical for ensuring a sustainable development goal and resilient future. Thus, relevant stakeholders should consider the aforementioned considerations while promoting the technology on a large scale through integration with enhanced technology. [ABSTRACT FROM AUTHOR]

As land use intensifies globally, it increasingly exerts pressure on protected areas. Despite open, nonforested landscapes comprising up to 40% of protected areas globally, assessments have predominately focused on forests, overlooking the major pressures on rangelands from livestock overgrazing and land conversion. Across the southern Caucasus, a biodiversity hotspot extending over 5 countries, we conducted a broadscale assessment of the extent to which protected areas mitigate land‐use pressure on rangelands in them. Using satellite‐based indicators of rangeland vegetation greenness from 1988 to 2019, we assessed the effectiveness of 52 protected areas. This period encompassed the collapse of the Soviet Union, economic crises, armed conflicts, and a major expansion of the protected area network. We applied matching statistics combined with fixed‐effects panel regressions to quantify the effectiveness of protected areas in curbing degradation as indicated by green vegetation loss. Protected areas were, overall, largely ineffective. Green vegetation loss was higher inside than outside protected areas in most countries, except for Georgia and Turkey. Multiple‐use protected areas (IUCN categories IV–VI) were even more ineffective in reducing vegetation loss than strictly protected areas (I & II), highlighting the need for better aligning conservation and development targets in these areas. Mapping >10,000 livestock corrals from satellite images showed that protected areas with a relatively high density of livestock corrals had markedly high green vegetation loss. Ineffectiveness appeared driven by livestock overgrazing. Our key finding was that protected areas did not curb rangeland degradation in the Caucasus. This situation is likely emblematic of many regions worldwide, which highlights the need to incorporate degradation and nonforest ecosystems into effectiveness assessments. [ABSTRACT FROM AUTHOR]

Introduction: The research purpose is to scientifically substantiate an integrated approach to solving the problem of land degradation, based on the idea of land degradation neutrality (LDN), taking into account ecosystem services when planning land use to maximize the conservation of natural capital. The methodological basis of the research is the provisions and principles of the concepts of sustainable development, achieving LDN, and ecosystem services, as well as the research results revealing various aspects of land use, particularly their degradation. Methods: The following research methods are used in the paper: dialectical – to determine the cause-and-effect conditions of land degradation; analysis – to highlight the current state of land use in Ukraine and the factors that have led to land degradation; synthesis – for global trends towards achieving LDN; deduction – to explore the possibility of introducing global experience in achieving LDN in Ukraine; structural-functional analysis – to substantiate the feasibility of introducing an ecosystem approach to land-use planning to achieve LDN. Results: As a result of the research, the current land degradation state in Ukraine has been analyzed, and ways of achieving LDN in land-use planning through the prism of an ecosystem approach have been substantiated. Based on statistical data, the current and potential levels of arability of the territory of Ukraine have been calculated by natural-climatic zones, and the areas of eroded arable lands in Ukraine have been determined by the erodibility factor (low-eroded, mediumeroded,and highly-eroded). Discussion: For the first time, a structural-logical scheme has been developed for organizational-economic support for the effective use of degraded and low-productive agricultural lands in the context of implementing the idea of LDN, which is a tool for rational allocation and use of degraded lands. This scheme can serve as a basis for the development of land-use planning strategies for territorial communities, for institutions, and organizations competent in the field of land management. [ABSTRACT FROM AUTHOR]

The growing severity of farmland abandonment in rural areas has sparked concerns about the lack of people willing to cultivate the land. This study attempts to find the endogenous factors of farmland land abandonment among Chinese farmers and to curb land degradation to stabilize food security. Based on the China Labor-force Dynamics Survey (2016–2018), this paper explores the effect of famine experience on farmland abandonment using logit and DID models. We find that famine experience does increase the probability and extent of farmland abandonment, and undergo a series of robustness tests. We also find that the famine stigma effect increases the likelihood and extent of farmland abandonment by increasing risk aversion and reducing social trust and investment in agriculture. The growth of urbanization in China worsens the effects of famine, whereas local communal production services remove the effects of famine. The overall findings of this study provide useful insights for the government to protect land quality and food security and to carefully consider the role played by communities. [ABSTRACT FROM AUTHOR]

Sandy soils contain around 70% sand in their composition, making them highly fragile and susceptible to land degradation. Practices such as no-tillage cultivation, the use of bioinoculants, and the application of organic amendments can restore the organic matter in these soils, ensuring sustainable production. In this context, this work aimed to study the microbiological aspects of two sandy soil areas (Brazilian Northeast and South) under contrasting climatic conditions (tropical and temperate). With this purpose, prokaryotic communities were evaluated, and the plant growth-promoting potential of isolated bacteria was assessed by rice inoculation in sandy soil. Despite the high sand content in both soils, soil from the NE was related to the highest phosphorous, calcium, potassium, copper, sodium, zinc, magnesium, and manganese contents, organic matter percentage, and pH. The Shannon diversity index indicated that prokaryotic communities in NE were more diverse than in SU, and PCA revealed that microbial composition exhibited distinct patterns. The rice inoculation experiments were executed to verify if the bacterial isolates displayed a similar growth promotion potential when inoculated in sandy soil areas subjected to different climatic conditions. When all PGP characteristics evaluated were pooled in a PCA, a similar pattern was observed for SU and NE. Burkholderia sp. SU94 was related to highest PGP characteristics evaluated. Paraburkholderia sp. NE32 showed similar results to those of the non-inoculated control. This similar effect of rice growth in the Northeast and South of Brazil suggests that isolate SU94 adapts to different environmental conditions. [ABSTRACT FROM AUTHOR]

The growing of mining sector is generally accompanied by socio‐economic and environmental consequences. Most previous work has either analyzed economic, social, or environmental effects without considering all three dimensions of sustainable development at once. Similarly, the perceptions of the populations living near mining companies are not widely considered in these studies. The objective of this paper is to analyze the populations' perception of the effects of industrial gold mining on environment (noise, air pollution, and land degradation), social (prostitution, banditry, and alcoholism), and household income in Burkina Faso. A probit model is used on a sample of 251 households living near four industrial mining sites. The results show that mining activity has contributed to improve the per capita income of the households surrounding certain industrial mining sites. However, it has contributed to a decline in the quality of life of the households due to noise and air pollution and social problems such as banditry. The government must protect mining areas from banditry and support households with local economic infrastructure to avoid a drop in their income after the mines close. [ABSTRACT FROM AUTHOR]

Agricultural innovation is a top priority on the global sustainable development agenda to solve food insecurity, land degradation, climate-change issues, poverty, and unemployment. This paper aims to identify systemic problems and socio-technical transition factors that hinder the development, dissemination, and utilization of improved maize varieties in Ethiopia. Primary data were gathered through structured and semi-structured interview schedules, and then analyzed thematically. Results show that systemic failures such as the absence of key actors, institutional problems, lack of interaction between actors, and inadequate infrastructure are the major hindrances to the systems functioning. Three functions, including entrepreneurship, knowledge development, and creating legitimacy, were found to be weaker than others in the case of improved maize varieties released by Pioneer Hi-Bred Seeds Ethiopia Plc. However, these functions were stronger in the case of improved maize seed varieties released by public research centres. Knowledge dissemination and resource mobilization were the stronger functions of Pioneer Hi-Bred Seeds Ethiopia Plc., which were found to be weak in the public research centres. The result also shows that policies and governance affected the sustainable development of improved maize varieties released by Pioneer Hi-Bred Seeds Ethiopia Plc. Promoting knowledge development and resource mobilization within networks of policymakers, the public sector, and local and foreign private sectors, and a regulation on breeders' rights to encourage the involvement of private companies in the sector are recommended to enable the innovation system to function sustainably. [ABSTRACT FROM AUTHOR]

[Display omitted] • Land use change profoundly impacts the migration of heavy metals (HMs) in the soil. • The comprehensive pollution index was in VGF>CVL>WSL>WDL. • Soil microbial and enzymatic activities did not directly affect HMs concentration. The accumulation of heavy metals (HMs) in soil has become a pressing global concern due to their persistent and non-degradable nature, resulting in the accumulation of toxic levels in the environment. In recent years, human activities have significantly altered land use patterns, leading to a marked increase in soil HM levels and subsequent land degradation. This research focused on four distinct land use patterns (vegetable fields (VGF), woodland (WDL), cultivated land (CVL), and wasteland (WSL)) commonly found in the Taojia River Basin, Hunan, China, which is facing severe HM pollution. Soil samples were collected from three different depths (0–10 cm, 10–20 cm, and 20–30 cm) to investigate HM distribution and intensity (cadmium (Cd), lead (Pb), copper (Cu), nickel (Ni), and zinc (Zn)), comprehensive soil pollution index, and their relationship with soil microbial and enzymatic activities. The results revealed that the Cd content exceeded the national standard for soil environmental quality, whereas concentrations of other HMs did not exceed the national standard across the four land use patterns. Most soil HMs were concentrated in the surface layer, with Cu, Pb, and Zn concentrations gradually decreasing with increasing soil depth. Conversely, Ni and Cd concentrations tended to increase with greater soil depth. There were significant positive correlations among HMs, indicating homogeneity among them. The pollution index across the HMs ranked as Cd > Ni > Zn > Cu and Pb, with land uses following the order of VGF (2.70) > CVL (2.12) > WSL (1.72) > WDL (1.63). Soil microbial and enzymatic activity was not the primary factor influencing HM concentration. The specific impacts of land use changes on soil HMs depend on management practices, local conditions, and the distribution characteristics of the HMs involved. [ABSTRACT FROM AUTHOR]

Using descriptive and inferential techniques together with simplified metrics derived from the ecological discipline, we offer a long-term investigation of the Environmental Sensitive Area Index (ESAI) as a proxy of land degradation vulnerability in Italy. This assessment was specifically carried out on a decadal scale from 1960 to 2020 at the province (NUTS-3 sensu Eurostat) level and benefited from a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic ('what ... if') approach. The spatial distribution of the ESAI was investigated at each observation year (1960, 1970, 1980, 1990, 2000, 2010, 2020, 2030) calculating descriptive statistics (central tendency, variability, and distribution shape), deviation from normality, and the increase (or decrease) in diversification in the index scores. Based on nearly 300 thousand observations all over Italy, provinces were considered representative spatial units because they include a relatively broad number of ESAI measures. Assuming a large sample size as a pre-requisite for the stable distribution of the most relevant moments of any statistical distribution—because of the convergence law underlying the central limit theorem—we found that the ESAI scores have increased significantly over time in both central values (i.e., means or medians) and variability across the central tendency (i.e., coefficient of variation). Additionally, ecological metrics reflecting diversification trends in the vulnerability scores delineated a latent shift toward a less diversified (statistical) distribution with a concentration of the observed values toward the highest ESAI scores—possibly reflecting a net increase in the level of soil degradation, at least in some areas. Multiple exploratory techniques (namely, a Principal Component Analysis and a two-way hierarchical clustering) were run on the two-way (data) matrix including distributional metrics (by columns) and temporal observations (by rows). The empirical findings of these techniques delineate the consolidation of worse predisposing conditions to soil degradation in recent times, as reflected in a sudden increase in the ESAI scores—both average and maximum values. These trends underline latent environmental dynamics leading to an early desertification risk, thus representing a valid predictive tool both in the present conditions and in future scenarios. A comprehensive scrutiny of past, present, and future trends in the ESAI scores using mixed (parametric and non-parametric) statistical tools proved to be an original contribution to the study of soil degradation in advanced economies. [ABSTRACT FROM AUTHOR]

Desertification poses a significant threat to dry and semi-arid regions worldwide, including Northeast Iran. This study investigates the impact of future climate and land-use changes on desertification in this region. Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). To enhance the model's reliability, an ensemble model was employed. Future climate and land-use scenarios were projected using the CNRM-CM6 model and Markov chain analysis, respectively. Results indicate that the RF and SVM models performed best in mapping current desertification patterns. The ensemble model highlights a 2% increase in decertified areas by 2040, primarily in the northwestern regions. The study underscores the importance of land-use change and climate change in driving desertification and emphasizes the need for sustainable land management practices and climate change adaptation strategies to mitigate future impacts. [ABSTRACT FROM AUTHOR]

Land degradation (LD) is the decline in a land's functional capacity and productive potential, which includes various anthropogenic and natural drivers. This study focuses on three primary manifestations of LD including soil erosion, landslides, and rockfalls, which are the most prevalent in the Shaqlawa district. A set of 22 LD conditioning factors, encompassing curvature, lithology, aspect, river density, soil type, lineament density, river distance, elevation, road distance, length slope (LS), land use land cover (LULC), stream power index (SPI), valley depth, profile curvature, slope, solar radiation, road density, lineament distance, rainfall, topographic wetness index (TWI), plan curvature, and normalized difference vegetation index (NDVI), were integrated into the analysis. Variance inflation factors (VIF) and tolerance (TOL) values from linear regression indicate that most LD factors have acceptable levels of multicollinearity. The Information Gain Ratio (IGR) identified key variables TWI, NDVI, and lithology—as pivotal factors for predicting LD. Additionally, the study evaluated degradation factors using various machine learning (ML) algorithms, including random forest (RF), Naive Bayes, logistic regression, rotation forest, forest penalized attributes (FPA), and Fisher's Linear discriminant analysis (FLDA). This facilitated categorizing the study area into five susceptibility categories. The FLDA model categorized the highest area under very high degradation risk at 26.72%, emphasizing the varied insights each algorithm brought to characterizing the degradation risk. Additionally, the receiver operating characteristic curves (ROC) were employed for model validation, identifying RF as the most successful model in the training dataset with an area under the curve (AUC) of 0.882, while FLDA outperformed in the testing dataset with an AUC of 0.883. The identified LD-prone areas will help land-use planners and emergency management officials apply effective mitigation strategies for similar terrains. [ABSTRACT FROM AUTHOR]

Soil erosion is both the cause and effect of land degradation. Land use/land cover conversion that changes the inherent landscape structure of watersheds leads to soil loss increase. Pantabangan-Carranglan Watershed (PCW) as a major source of irrigation, electricity, biodiversity, livelihood, and other ecosystem services, thus, it is imperative to spatially and temporally estimate the soil erosion within its boundary to assist and guide decision-makers in planning conservation and management of the watershed. Using the Revised Universal Soil Loss Equation (RUSLE) model, remotely sensed data, soil analysis, and geographical information system, the soil erosion rate in PCW was estimated. Results showed that there is increasing soil erosion in PCW over time. In 2010 soil erosion rate was estimated to be 134 tons·ha-1·yr-1 which increased to 141 tons·ha-1·yr-1 and 154 tons·ha-1·yr-1 in 2015 and 2020, respectively. Considering the average soil erosion rate and land cover types in PCW, annual crop and open/barren land cover types have the highest average soil erosion rate through time with moderate and catastrophic erosion levels, respectively. [ABSTRACT FROM AUTHOR]

The analysis of early studies and methods of land degradation assessment demonstrated that the existing assessment procedure does not reflect the real situation. The existing methods do not consider ecological aspects as loss of social-recreational and protective-restoration functions. The existing methods of assessing damage from the mining and processing enterprise require improvement with mandatory consideration of the environmental component. The object of the study is the land in mining and industrial regions, where minerals are extracted underground and industrial masses are formed on the surface. The main types of land use have been systematized, which makes it possible to apply an estimated aggregate approach. The loss of a number of functions of economic turnover is a classification sign of land degradation assessment. A three-criteria approach to the assessment of land degradation is proposed. This approach considers the impact of loss of social-recreational functions, loss of protective and restorative functions, and the impact of the main mining type of activity. [ABSTRACT FROM AUTHOR]

Malawi, like the majority of African countries, is experiencing land degradation as a result of climate change and unsustainable farming methods. Land degradation is a worldwide problem, but Africa is the worst affected, with 75% of arable land already degraded. Malawi is primarily an agricultural country, with 11 million people practicing small-scale subsistence farming. Given the hilly terrain, forests, and tough pastures, only one-third of the county is considered cultivable. However, agriculture accounts for 30% of Malawi's GDP, exporting 80% of its agricultural produce. Small-scale farmers are essential producers, farming 5.3 million hectares of land and accounting for over 70% of Malawi's food consumption. Unfortunately, as climate change and economic demands increase, so does land degradation. Even though indigenous knowledge has been used for generations to adapt to changing environments, there is an unmistakable distinction between scientific and indigenous knowledge, with scientific being seen as far superior. Several studies have revealed that depending entirely on one knowledge system is insufficient for effectively addressing environmental concerns. Thus, there is a need for more comparisons that allow for a complementary application of the two knowledge systems rather than their contradiction. In this work, we identify indigenous indicators of land degradation and land management practices utilized by Malawian farmers, and we investigate how indigenous and scientific knowledge systems might be combined to adapt and address the issue of land degradation swiftly. To do this, 100 semi-structured interviews were conducted with farmers in five villages. Atlas-ti software was used to do thematic content analysis on the dataset. Our findings show farmers' coping techniques are still based on IK and motivated by experiential learning from earlier climatic crises. Though most farmers know scientific land management practices, current adaption strategies rely on expensive resources and are inappropriate for indigenous farmers. The study investigates various ways scientific and indigenous knowledge could be integrated to enhance climate change adaptation against land degradation. These encompass developing indigenous indicator baselines, including new measures for measuring soil fertility, and more effective and energy-efficient irrigation technologies. [ABSTRACT FROM AUTHOR]

Evaluating vegetation restoration sustainability is crucial to avoid conflicts between human water demand and ecosystem consumption, especially with the surge in leaf area index (LAI) due to revegetation projects in China. However, current methods for assessing vegetation sustainability are still limited. Here, we developed a sustainability index for vegetation systems (vegetation sustainability index, VSI) from water demand and supply aspects based on reliability, resilience, and vulnerability in arid and semi‐arid areas. VSI was built upon a vegetation overplanting index (dLAI) which is the difference between the maximum LAI supported by precipitation (LAIp) and the observed LAI (LAIobs). A case study in the mountainous area of the Haihe River basin reveals gradually declining VSI after 2000. Forests are the primary vegetation type in areas with VSI < 0.5, indicating decreased sustainability due to overplanting. The framework of VSI can be a useful tool for planning and implementing vegetation restoration strategies in arid and semi‐arid regions. Plain Language Summary: Vegetation restoration stands out as a highly effective ecological engineering solution for overcoming land degradation and climate change mitigation with widespread implementation. However, overplanting can threaten the sustainability of vegetation systems, leading to increased evaporative water consumption, decreased catchment water yield and soil moisture in arid and semi‐arid regions. Evaluating the sustainability of vegetation restoration is vital for managing conflicts between human water demand and ecosystem needs in the context of increasing vegetation coverage resulting from revegetation efforts in China. Previous studies suggested using numerous variables to evaluate the sustainability, making it difficult to present or explain clearly. This study addresses the limitations of current methods for assessing vegetation sustainability by developing a novel sustainability index for vegetation systems (VSI) in arid and semi‐arid regions. The VSI framework incorporates aspects of water demand and supply based on reliability, resilience, and vulnerability. A case study in the mountainous Haihe River basin indicates a gradual decline in VSI after 2000, which may be attributed to the overplanting. The VSI framework offers a valuable tool for guiding vegetation restoration strategies in arid and semi‐arid regions, improving the management of water resources and ecosystem sustainability. Key Points: A sustainability index for vegetation systems based on water demand and supply aspects was developedWater demand and supply were represented by the observed leaf area index (LAI) and maximum LAI supported by precipitationDecreased sustainability was found due to overplanting in forests [ABSTRACT FROM AUTHOR]

The increasing demand for water resources has resulted in environmental degradation and conflicting interests among diverse users. This research aimed to scrutinize the causes and repercussions of land use exploitation and degradation within the Samambaia River Basin (SRB), focusing on potential environmental impacts linked to the management of irrigated agriculture. The study encompassed the entire SRB. Adopting climatological data from 1981 to 2010, distinct dry and wet seasons in the SRB were delineated. A precise slope map has been produced based on an elevation dataset, while vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been computed. Indices such as the Woody Vegetation Biomass Index (WVBI), Vegetation Vulnerability Index (VVI), and Land Vulnerability Index (LVI) were derived from the NDVI. The dataset underwent descriptive statistical analysis, incorporating adjustment and validation factors. The root mean square error (RMSE) values for the daily rainfall data were notably low (7.0711 and 7.9223 mm). The years 2015, 2016, and 2018 experienced heightened rainfall during the dry season, while 2015 and 2018 recorded significant rainfall in the wet season. NDWI ranged from − 0.41 to 0.24, with positive values indicating the presence of water bodies in the SRB. During the dry season, regions with denser vegetation are largely associated to areas with central pivot irrigation. The WVBI revealed intensified vegetation in the wet seasons of 2014 and 2019. The LVI during the dry season varied from moderate to very low, with limited high-risk areas predominantly along water body boundaries. [ABSTRACT FROM AUTHOR]

Land degradation poses a significant global challenge, adversely affecting soil quality and diminishing the productivity of arable land, which, in turn, impacts food production and ecosystem services. This degradation manifests in various forms, including soil compaction, salinity, nutrient depletion, loss of biodiversity, and contamination, ultimately rendering soil unproductive and exacerbating climate change. Given the challenges arising from conventional farming practices and climate change, there is an urgent necessity to develop agricultural systems that not only enhance agronomic efficiency but also improve environmental performance. This study focuses on addressing food security in Africa by investigating the role of land restoration and biodiversity in sustainable food systems through a comprehensive bibliometric analysis for documents between 2010 to 2023, identifying 64 relevant documents. The analysis reveals insights into the most-cited documents and thematic evolution, underscoring the significant contributions from Ethiopia and advocating for collaborative efforts among African nations. Key themes identified include "climate change", "food security", "biodiversity conservation", and "agroecosystems", all highlighting the importance of sustainable agricultural practices. The study anticipates a continued prioritization of agroecosystems, smart agriculture, and biodiversity conservation to tackle food security challenges in the face of climate change. Collaboration, investment in research, and practical initiatives emerge as essential components for achieving sustainable food security and mitigating climate change impacts in Africa. This research provides valuable insights into the current landscape and future trends regarding the contributions of land restoration and biodiversity to food systems in Africa, emphasizing the critical need for concerted efforts to address these pressing issues. [ABSTRACT FROM AUTHOR]

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil–vegetation–atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS's performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly. [ABSTRACT FROM AUTHOR]

Climate change has brought about credible changes in arid landscapes which have resulted in further challenges for pastoralists who require good quality rangeland resources to sustain their livestock. This study assessed local level coping and adaptivemanagement strategies by pastoralists in Namibia and South Africa using a capital assets approach, and also explored non-climate related barriers that increase their vulnerability to climate change. A case study approach was used, which included focus group discussions and semi-structured interviews. The main findings of the research indicate that pastoralists have been exposed to increased temperature extremes and droughts of varying duration over time which reduced the quality and quantity of forage and resulted in losses of income and animal health. The 46 different coping and adaptation strategies used by pastoralists included financial (n =15), natural (n = 15), human (n =12), physical (n = 2) and social (n = 2) capital. Of these, 61% of the strategies were common between the two countries. Strategies included income diversification, supplementary feeding and livestock mobility. Pastoralists had to overcome a hierarchy of barriers to implement their coping and adaptation strategies, which were largely related to human capital in Namibia and to natural capital in South Africa. These barriers included a lack of access to grazing lands, land degradation and the loss of traditional knowledge to manage livestock and rangeland resources. Policy development should take these barriers into account while building on the foundation of existing adaptation strategies to reduce the vulnerability to climate change of pastoralists in dryland regions. [ABSTRACT FROM AUTHOR]

Aim: Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species? Location: Global. Time period: 1990–2017. Major taxa studied: Trees. Methods: We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated. Results: Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large‐scale land degradation. Main conclusions: By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large‐scale management and conservation practices. [ABSTRACT FROM AUTHOR]

Simple Summary: This study analyzed the effect of forest plantations on the taxonomic and functional diversity of dung beetles in comparison to natural forests in southern Ecuador. The results indicated a greater abundance of generalist species in the plantations, likely due to their high level of adaptation to disturbances and their presence in nearby forests. This could be attributed to the proximity of these areas to urban zones, which may influence the dung beetle community, as their primary food source depends on mammals. However, this study highlights the lack of information on this topic in the Neotropics, particularly in Ecuador, suggesting that further research is needed to compare and validate these findings. This study sheds light on the complex relationship between land use, biodiversity, and the functional traits of dung beetles in Ecuador. The results indicate that the richness and abundance of dung beetles vary across different land uses and regions, with forests generally having a positive impact, while eucalyptus and pine plantations have a negative effect in certain areas. Specific indicator species, such as Homocopris buckleyi for forest areas and Onthophagus curvicornis for eucalyptus plantations, were identified. This study also found that functional diversity analysis, based on morphological traits, revealed that certain traits, such as biomass, pronotum width, head width, and elytra length, were significant contributors to differences in dung beetle communities across various land uses and regions. This study highlights the potential conservation value of certain modified habitats and emphasizes the importance of considering both taxonomic and functional diversity when assessing the impact of land use on the ecosystem services provided by dung beetles. It underscores the potential value of plantations as refuges for dung beetle communities and the need for long-term assessments to better understand biodiversity changes over time. [ABSTRACT FROM AUTHOR]

Semi-arid agroecosystems are crucial for food security and ecosystem services, but land degradation threatens their sustainability. This study assessed land degradation neutrality (LDN) in a semi-arid agroecosystem of Matabeleland North Province, Zimbabwe, leveraging trends.earth within QGIS and data from the European Space Agency Climate Change Initiative (1992–2020). We analysed land use/land cover (LULC), soil organic carbon (SOC), and land productivity to identify areas of degradation and stability. Additionally, we simulated 2050 land-use/land-cover maps using a cellular automata model within a GIS framework assuming a business-as-usual scenario. The model considered the 2015 LULC map as a baseline along with environmental variables such as the digital elevation model and slope. While historical trends (1992–2020) showed a decrease in bare areas (-71%) and an increase in settlements (+ 163%), cellular automata modeling predicted a concerning future trajectory with further expansion of bare land (+ 238%) and settlements (+ 72%) by 2050, alongside a decline in water bodies (-23%) and forests (-3.5%). Notably, around 26.5% of the land exhibited degradation, often linked to low SOC levels in croplands, while 59.55% remained stable over the study period. However, caution is necessary as increases in greenness may not always reflect positive restoration. Land cover transitions, particularly the conversion of forests to grasslands and settlements, emerged as potential drivers of degradation, likely leading to ecosystem service loss, habitat fragmentation, and potentially exacerbating the impacts of climate change. These findings highlight the urgent need for targeted restoration and land management strategies focused on improving SOC levels in croplands and conserving vital forest and grassland ecosystems to achieve LDN in this semi-arid region. Further research is needed to quantify specific degradation drivers, assess the effectiveness of intervention strategies, and explore the socio-economic dimensions of land degradation and LDN efforts. [ABSTRACT FROM AUTHOR]

From land degradation and desertification to cyclones and tropical storms, and so on, the repercussions of global change have become increasingly severe in recent years. Such environmental impacts require continuous assessment and monitoring. Thus, to study and analyse these impacts, a variety of time-series forecasting approaches have been developed, including statistical ones [i.e. Moving Average (MA), Auto-Regressive Integrated Moving Average (ARIMA), etc.], and machine-learning approaches such as Recurrent Neural Networks (RNN), Long-Short-Term Memory Network (LSTM) and Convolutional Neural Networks (CNN). In this study, accuracy of the most used forecasting approaches (i.e. MA, LSTM and Conv-LSTM) has been quantified, and three impacts (i.e. climate regions, land cover and satellite sensors) have been brought to light. Firstly, eight Mediterranean regions were selected (i.e. two hot arid regions, two cold arid regions, two regions with temperate hot summers and two regions with temperate warm summers) based on Köppen climate classification. Secondly, 654 hyperspectral images retrieved from three different satellites (i.e. Sentinel-2, Landsat-8 and MODIS) from 2016 to 2022 were computed in order to predict 29 vegetation biophysical indices (i.e. NDVI, GNDVI, EVI, CVI, etc.). Accordingly, more than 18,000 images were computed, resulting in 696 time series forecasted using the aforementioned approaches. Finally, 2088 forecasted time series have been determined, and their accuracy has been compared. As a result, Landsat-8 and Sentinel-2 images had the highest forecasting accuracy in the three approaches, reaching 86% of the computed indices being over 50% accurately predicted in LSTM model, while MODIS data had the highest forecasting accuracy only in MA model, with a percentage of 72% of the computed indices being over 50% accurately predicted. Furthermore, we have identified that region climate impacts vegetation forecasting accuracy. For instance, arid regions showed low accuracies across all models, while temperate regions showed higher accuracies in the Mediterranean region. [ABSTRACT FROM AUTHOR]

Black soil is a precious soil resource, yet it is severely affected by gully erosion, which is one of the most serious manifestations of land degradation. The determination of the location and shape of gullies is crucial for the work of gully erosion control. Traditional field measurement methods consume a large amount of human resources, so it is of great significance to use artificial intelligence techniques to automatically extract gullies from satellite remote sensing images. This study obtained the gully distribution map of the southwestern region of the Dahe Bay Farm in Inner Mongolia through field investigation and measurement and created a gully remote sensing dataset. We designed a multi-scale content structure feature extraction network to analyze remote sensing images and achieve automatic gully extraction. The multi-layer information obtained through the resnet34 network is input into the multi-scale structure extraction module and the multi-scale content extraction module designed by us, respectively, obtained richer intrinsic information about the image. We designed a structure content fusion network to further fuse structural features and content features and improve the depth of the model's understanding of the image. Finally, we designed a muti-scale feature fusion module to further fuse low-level and high-level information, enhance the comprehensive understanding of the model, and improve the ability to extract gullies. The experimental results show that the multi-scale content structure feature extraction network can effectively avoid the interference of complex backgrounds in satellite remote sensing images. Compared with the classic semantic segmentation models, DeepLabV3+, PSPNet, and UNet, our model achieved the best results in several evaluation metrics, the F1 score, recall rate, and intersection over union (IoU), with an F1 score of 0.745, a recall of 0.777, and an IoU of 0.586. These results proved that our method is a highly automated and reliable method for extracting gullies from satellite remote sensing images, which simplifies the process of gully extraction and provides us with an accurate guide to locate the location of gullies, analyze the shape of gullies, and then provide accurate guidance for gully management. [ABSTRACT FROM AUTHOR]

Land degradation is a global environmental problem that affects the livelihoods of communities. Understanding the extent and causative factors of this phenomenon is important to design the cause-targeted strategies to cope with this problem. This study aimed to assess the effects of biophysical and socioeconomic factors on the occurrence of land productivity degradation at the territorial level in Eastern DR Congo, using the Kalehe territory as a case study. The land productivity dynamics were determined based on the trend analysis of Landsat-based NDVI annual composites over the 1987-2020 period. Additionally, a binary logistic regression model was developed to identify the determinant factors and to map the susceptibility of this degradation at the territorial level. The results revealed that 31.25% (1,303.175 km2) of the territory had experienced a decrease in land productivity, with 10.8% (453.83 km2) characterized by a significant decrease at the confidence level of 90%, representing the hotspots area of land productivity degradation where conservation actions should be prioritized. This decrease in land productivity is location-specific as it depends on the land cover types and health zones. It is likely to occur at high altitudes, in areas close to artisanal mining sites, rivers, and administrative centers. These results imply that policy interventions to cope with the problem of land productivity degradation should be developed following the heterogeneity of the landscape, and sustainable land management practices that take into account both the biophysical and socio-economic drivers of land degradation should be implemented in this region to achieve the land degradation neutrality by 2030. [ABSTRACT FROM AUTHOR]

The escalating trend of land degradation poses a significant challenge, especially in sloping agricultural terrains, driven by the increasing global demand for food and the limited availability of flat arable land. In response to these challenges, farmers are compelled to shift their focus towards cultivating sloping terrains. This research aimed to employ a comprehensive methodology that integrates onsite field surveys, meticulous laboratory soil analyses, and geospatial data for mapping soil erodibility. The parameters under scrutiny encompass various crucial aspects, including soil texture (ranging from coarse sand to very fine sand, silt, and clay), soil structure, organic matter content, and permeability. The meticulous examination of these factors serves as the foundation for calculating soil erodibility, utilizing the well-established Wischmeir and Smith formula developed in 1978. The research findings present a nuanced understanding of soil erodibility in the study location, revealing a spectrum spanning low to very high erodibility. Specific units, such as Unit 1, Unit 2, Unit 3, Unit 7, Unit 9, Unit 10, Unit 13, and Unit 16, exhibit very low to low erodibility. In contrast, Unit 4, Unit 6, Unit 14, and Unit 15 showcase moderate erodibility, while units like Unit 5, Unit 8, Unit 11, Unit 12, Unit 17, and Unit 18 are characterized by moderately high to very high erodibility. These insightful results shed light on the diverse erodibility levels within the studied locations and provide valuable guidance for formulating sustainable land management practices. [ABSTRACT FROM AUTHOR]

Agricultural systems have a long history of responding to various economic and socio-political forces, including supply and demand, political preferences and power inequalities. Our current food system continues to respond to contemporary forces with a call for sustainable transformations in the face of increased pressure and competition over resources, severe consequences of climatic change, widespread degradation of land and water resources, and the accelerating loss of biodiversity. Both agroforestry and agroecology have evolved as approaches to agricultural management that focus on the application of ecological principles to achieve sustainable agriculture. Agroecological principles are designed to engage with the entire agrifood system, for instance identifying broad participation and involving a diversity of actors and knowledge systems. Agroecological approaches to production have significant historical and contemporary links to agroforestry approaches, but not all agroecology involves trees and not all agroforestry is in line with agroecological principles. Drawing on (1) a literature review, (2) case studies on the agroforestry and agroecology nexus presented at the 5th World Congress on Agroforestry (WCA), and (3) audience responses to statements presented at the Congress, we explore the two main ways that agroforestry and agroecology come together: agroforestry that encompasses agroecological principles and agroecological transitions that involve trees. We review the status of agroecology, the functions specific to agroforestry that can enhance the achievement of agroecological outcomes, the tensions between simple agroforestry systems and agroecology, and the larger potential of multidimensional sustainability of agroforestry with the inclusion of agroecological principles. We also present the level of agreement on four key statements about the agroforestry-agroecology nexus enumerated at the WCA. It is clear that some features of agroforestry operationalize agroecological principles that aim to transition away from monocultures and the use of environmentally disruptive agrochemicals, and toward strengthening biodiversity and resilience. Yet, much remains to be done to enhance agroecological principles more fully in framing agroforestry practices and to incorporate trees within agroecological practices. Pathways to strengthen the nexus of agroforestry and agroecology are proposed, which focus on the role of trees in multifunctionality and resilience and using agroecological principles related to knowledge sharing and equity to enrich agroforestry practices. [ABSTRACT FROM AUTHOR]

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes. [ABSTRACT FROM AUTHOR]

Soil-based emissions from land conversions are often overlooked in climate planning. The objectives of this study were to use quantitative data on soil-based greenhouse gas (GHG) emissions for the state of Georgia (GA) (USA) to examine context-specific (temporal, biophysical, economic, and social) climate planning and legal options to deal with these emissions. Currently, 30% of the land in GA has experienced anthropogenic land degradation (LD) primarily due to agriculture (64%). All seven soil orders were subject to various degrees of anthropogenic LD. Increases in overall LD between 2001 and 2021 indicate a lack of land degradation neutrality (LDN) in GA. Besides agricultural LD, there was also LD caused by increased development through urbanization, with 15,197.1 km2 developed, causing midpoint losses of 1.2 × 1011 kg of total soil carbon (TSC) with a corresponding midpoint social cost from carbon dioxide (CO2) emissions (SC-CO2) of USD $20.4B (where B = billion = 109, $ = U.S. dollars (USD)). Most developments occurred in the Metro Atlanta and Coastal Economic Development Regions, which indicates reverse climate change adaptation (RCCA). Soil consumption from developments is an important issue because it limits future soil or forest carbon (C) sequestration potential in these areas. Soil-based emissions should be included in GA's carbon footprint. Understanding the geospatial and temporal context of land conversion decisions, as well as the social and economic costs, could be used to create incentives for land management that limit soil-based GHG emissions in a local context with implications for relevant United Nations (UN) initiatives. [ABSTRACT FROM AUTHOR]

Soil erosion is a global issue—with gully erosion recognized as one of the most important forms of land degradation. The purpose of this study is to compare and contrast the outcomes of four machine learning models, Classification and Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), used for mapping susceptibility to soil gully erosion. The controlling factors of gully erosion in the Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation in Google Earth and gully erosion samples (n = 159) were used for modelling and spatial prediction. The XGBoost and RF models achieved identical results for the area under the receiver operating characteristic curve (AUROC = 88.50%), followed by the SVM and CART models, respectively (AUROC = 86.17%; AUROC = 85.11%). In all models analysed, the importance of the main controlling factors predominated among Lineaments, Land Use and Cover, Slope, Elevation and Rainfall, highlighting the need to understand the landscape. The XGBoost model, considering a smaller number of false negatives in spatial prediction, was considered the most appropriate, compared to the Random Forest model. It is noteworthy that the XGBoost model made it possible to validate the hypothesis of the study area, for susceptibility to gully erosion and identifying that 9.47% of the Piraí Drainage Basin is susceptible to gully erosion. Furthermore, replicable methodologies are evidenced by their rapid applicability at different scales. [ABSTRACT FROM AUTHOR]

Forest roads have been recognized as one of the significant contributors to soil erosion processes in forested areas. The construction and maintenance of forest roads can cause severe environmental impacts, including soil erosion, sedimentation, and degradation of aquatic ecosystems. The main objective of the present study is to analyze the impact of forest road networks on soil erosion and sedimentation in the context of the Zagros forestlands, Iran. This study aims to assess the soil erosion and sedimentation on forest roads in four case studies in the Zagros forestlands. This study collected data using field surveys and SEDMODL equations to determine input factors and sedimentation and erosion rates. This study found that roadside erosion is strongly correlated with geological factors, road width, and precipitation factors. The height changes of 144 benchmarks were recorded during one study year (2021–2022) on four study roads, and the measured results of erosion benchmarks indicated an average soil erosion of 3, 2.6, 4.7, and 3.5 mm per year around the Bideleh, Kohian, Nazi, and Tabarak roads, respectively. This study measured soil erosion and sedimentation at three distances (5, 15, and 25 m) from the road, and found a significant difference in the height changes of the benchmarks at varying distances from the study roads. A hot-spot analysis was conducted using GIS 10.8, and the results indicated that a significant portion of the studied forest roads had very high erosion production and hot spots. The results of the hot-spot analysis indicated that 30.8%, 22.6%, 39.8%, and 14.5% of the study forest roads, Nazi, Tabarak, Bideleh, and Kohian roads, respectively, are identified as areas with very high erosion production and hot spots. These results highlight the need for effective management strategies to minimize the impact of erosion on road infrastructure and the surrounding environment. Overall, this study provides important insights into the soil erosion and sedimentation on forest roads, and the findings presented here can be used to inform future road construction and maintenance. [ABSTRACT FROM AUTHOR]

This review summarises the available evidence on the prospects for using microalgae or their extracts to support crop production. The evidence is limited but suggests technological promise in several distinct ways, namely, higher core productivity, enhanced resilience to biotic and abiotic stresses, and better-quality produce. The different efficacy pathways of these microalgal technologies were examined to assess their scope to help address key farmer priorities. Their scope to help farmers face climate change and land degradation was a particular focus, given the magnitude of these threats. These microalgal technologies are framed in terms of their pertinence to farmer priorities due to the centrality of farmers to food systems. Notably, farmers' technology adoption decisions are key to food system outcomes. The findings reported suggest that these crop support technologies could potentially deliver major benefits to farmers, consumers, and the environment. For the moment, however, this emerging literature remains largely neglected. Possible reasons for this are considered, as are potential ways forward. The review focuses particularly on the two most researched and widely available microalgae, the genera Arthrospira and Chlorella, in the interest of highlighting options farmers could adopt rapidly while research on the wider body of microalgae-based crop technologies continues. [ABSTRACT FROM AUTHOR]

Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters' use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, "quadcopters", and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters' use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments. [ABSTRACT FROM AUTHOR]

Coal gangue hill, a significant anthropogenic interference, can cause various forms of land degradation. The promoting effect of coal gangue on soil qualities has also been discovered. However, few studies investigated the soil properties and microbiome of prolonged gangue hills. Here, we investigated soil microbial communities and chemical properties in a vegetated gangue hill and adjacent cropland and wasteland (regarded as gangue‐free lands). We did not find any significant differences in the content of organic matter, total nitrogen, and total phosphorus between the gangue hill and the cropland (Kruskal–Wallis test, p > 0.05). The content of all metals we investigated (Pb, Ni, Sb, Fe, and Al) did not exceed the risk control values (GB 15618‐2018; GB 36600‐2018). The content of Fe and soil electrical conductivity of the gangue hill was significantly higher than the gangue‐free lands (about 2 times and 20 times, respectively), which were also the key factors shaping microbial communities (Mantel's test, p < 0.05). There were no significant differences in the composition of the bacterial communities between the gangue hill and gangue‐free lands. The random‐forest model identified four species belonging to Actinobacteria and Ascomycota as the unique species in the gangue hill. Functional annotation revealed that the significant differences between the two land types were in bacterial nutrition types, fungal saprophytic types, and nitrogen cycling. Our study provided a theoretical foundation for land management and sustainable utilization in abandoned mining areas. [ABSTRACT FROM AUTHOR]

Land restoration and sustainable land management projects have now been proposed for up to a billion hectares globally, responding to continuous land degradation processes. But, a lack of reliable cost estimates hinders implementation, which results in a high perceived risk of restoration investments. This study addresses this gap, using cost data from 243 restoration projects from multiple sources to estimate the cost ranges for different types of restoration more precisely. The analysis shows median restoration costs vary from 185 $US/ha to 3012 $US/ha depending on the restoration measure, implying current global land restoration commitment cost 0.04%–0.27% of annual global GDP to implement. Since the majority of these commitments are made by developing countries, international cost‐sharing mechanisms, through climate or private finance, are required to move from goal setting to implementation. [ABSTRACT FROM AUTHOR]

Soil erosion emerged as a significant land degradation concern, causing serious threat to soil ecosystem services in the Himalayan region. The complex topography of the region poses limitations to the measurement of soil redistribution (erosion, transport, and deposition) rate, necessitated for the effective soil conservation planning. The study investigated soil redistribution processes over a typical complex hillslope of the mid‐Himalayan region using the fallout radionuclide (FRN)—137Cs method and Revised Universal Soil Loss Equation (RUSLE) model. It involved a comparison of 137Cs measured soil redistribution and the RUSLE model estimates, aiming to assess its correspondence over the complex hillslope. Analysis of 137Cs measurements revealed the highest net erosion (−13.2 t ha−1 year−1) at the upper hillslope with a convex shape, while sediment deposition occurred at the lower (36.9 t ha−1 year−1) and valley (32.5 t ha−1 year−1) hillslope positions with a concave shape. The RUSLE model also estimated the highest erosion on the upper hillslope (−12.3 t ha−1 year−1) but the lowest erosion at the lower (−0.88 t ha−1 year−1) and valley (−0.32 t ha−1 year−1) hillslopes, that differed with the 137Cs method. The 137Cs method provided soil redistribution rate (either net erosion or deposition), whereas RUSLE model only showed the gross erosion rate. Thus, the estimate from RUSLE corresponds only to hillslope positions with a convex and straight shapes. The distribution of 137Cs measurements has clearly revealed the influence of slope shape and steepness in governing erosion and deposition processes at the hillslope positions with convex and concave slope shapes, respectively. In addition, terraces effectively trap sediments from upslope areas. Investigation of soil erosion using 137Cs measurement along with the RUSLE model helped to validate erosion and deposition processes over the hillslope positions. The study will help to suggest suitable conservation measures for the various hillslope positions in the mid‐Himalayan region. [ABSTRACT FROM AUTHOR]

Assessing drought impacts is necessary for pursuing sustainable development goals relevant to food security and land degradation. Data availability is a major restriction and remote sensing has been promoted for this purpose. Version 3 of WaPOR has been released in 2023, which provides global coverage of remote sensing–derived water productivity indicators and could allow improved analysis of drought impacts, but validation is still needed. This study explores the utility of remote sensing–derived productivity data from WaPOR as a proxy indicator for agricultural drought impacts. The analysis utilized (1) production surveys, (2) meteorological measurements for drought analysis, and (3) remote sensing–derived gross and net biomass water productivities (GBWP & NBWP) and total biomass production (TBP). All layers were analyzed against the Standardized Precipitation and Standardized Precipitation Evapotranspiration Indices (SPI and SPEI) over drought-vulnerable locations in Irbid and Madaba governorates in Jordan. Strong and significant correlations (R2 0.5–0.8, P < 0.05) were obtained between drought intensities and GBWP and NBWP layers, particularly in the May–Sep periods. These correlations were higher than previously tested remotely sensed indicators for agricultural drought impacts. Water productivity and biomass production averages were lower during drier periods and higher during wet periods, but pairwise testing did not reveal significant differences. There is sufficient evidence that WaPOR data demonstrates behavior that reflects agricultural response to drought, and further assessment in other agroclimatic zones is recommended. This could potentially allow for enhanced evaluation of management strategies, decision support, and policy recommendations for drought mitigation. [ABSTRACT FROM AUTHOR]

In the Sahel region of West Africa, land degradation has raised concerns about the threat of desertification, leading to the establishment of the United Nations Convention to Combat Desertification (UNCCD) in 1994. Over time, the focus has shifted from simply combating desertification to a more comprehensive international program focused on preserving the health of our land by offsetting any damage with restoration efforts by 2030 to sustain ecosystem functions and services. This balancing process—which is in line with the Sustainable Development Goals (SDGs)—is known as Land Degradation Neutrality (LDN). We examine Land Degradation Neutrality (LDN) patterns, namely degradation and rehabilitation processes, by integrating participatory mapping with high-resolution satellite imagery with local stories, observations, historical records, and existing studies. The data elicited an understanding of the processes driving land degradation and adaptation strategies among three distinct ethnic groups of crop and livestock farmers in the village of Yallé in southern Burkina Faso. Some of these people were originally from this region, while others moved from places where the land was already degraded. Participants in the study had diverse experiences and perceptions of land degradation, its drivers, and adaptation strategies, which were influenced by their ethnicity, livelihood activities, and life experiences. These differences highlight the impact of cultural and socioeconomic factors on how people view land degradation, as well as the role of local knowledge in managing the environment. The study emphasizes the necessity of incorporating ethnoecological perspectives into projects focused on Land Degradation Neutrality (LDN) to better understand land degradation and improve land management. This integration can significantly contribute to strengthening global sustainability and community resilience. [ABSTRACT FROM AUTHOR]

The objective of this study was to analyse the potential impact of future climate change on grassland cover in Burkina Faso. MODIS NDVI 250 m time series were used to monitor changes in grassland over 2000–2022. The random forest regression (RFR) model was fit by regressing reference data of grassland cover against current climatic and other environmental predictors to predict the current grassland cover map (2022). Projected climate model data of CMIP6 used under SSP 126 and SSP 585 scenarios were integrated into the fit RFR model to predict future change. The results revealed that grassland areas were largely dominated by non-significant productivity change (55%) during 2000–2022. In this period, grassland area knew more increased productivity (35%) than decrease (10%). Burkina Faso is predicted to face more decreased areas of grassland cover than increase by 2061–2080 under SSP 126 and SSP 585 scenarios. The findings of this study can help to set up appropriate adaptation measures to combat climate change in Burkina Faso. [ABSTRACT FROM AUTHOR]

Land degradation in communal rangelands poses significant challenges to environmental sustainability, agricultural productivity, and livelihoods in southern Africa. This study presents a bibliometric analysis of research trends, key contributors, thematic evolution, and collaborative networks in the field of land degradation in communal rangelands from 1997 to 2024. Utilizing data obtained from the Scopus database, we examined 66 publications to identify patterns in publication output, leading journals, influential articles, and prominent authors and institutions. The analysis demonstrates an overall increase in research output, with a notable surge in publications during the past decade, indicating a growing academic and policy interest in this field. Major themes identified include sustainable land management, restoration strategies, and the impacts of grazing management on ecosystem health. Networks showcasing co-authorship and keyword co-occurrence reveal robust collaborative connections among researchers and a concentrated focus on specific dominant themes. Consequently, these findings propose opportunities for expanding interdisciplinary research and exploring underrepresented areas. This study provides a comprehensive overview of the research landscape, offering insights to steer future studies and inform policy interventions aimed at mitigating land degradation and bolstering the resilience of communal rangelands in southern Africa. [ABSTRACT FROM AUTHOR]

Land degradation is a critical issue in Ethiopia, exacerbating food insecurity by reducing agricultural productivity. Soil and water conservation (SWC) practices are essential to control erosion and increase food production. However, there is a lack of comprehensive evaluations on the impact of Ethiopia's Productive Safety Net Program (PSNP) on SWC practices. This study aimed to assess the contribution of the PSNP to SWC in the Amhara Sayint Woreda. The researchers used a mixed-method approach, combining quantitative and qualitative data. Multistage sampling was used to select households, and data were collected through questionnaires, interviews, focus groups, and observations. The study provided empirical evidence that the PSNP has a positive impact on SWC practices. Key factors influencing SWC participation include age, family size, education, plot size, livestock ownership, credit service, and access to extension services. The results suggest that the PSNP should improve payment for public work participants implementing SWC, undertake institutional reform, and increase public awareness of the benefits of SWC in reversing land degradation and improving food security. This study uniquely contributes to the understanding of how the PSNP influences the varying degrees of participation in SWC practices, filling a critical research gap. The findings can inform policymakers and program managers to enhance the PSNP's effectiveness in promoting sustainable land management and food security in Ethiopia. [ABSTRACT FROM AUTHOR]

This study uses GIS to assess the extent of deforestation, forest degradation, and carbon stock in East Java, Indonesia. The dataset for this study required preprocessing and proper GIS analysis tools to track and quantify land cover changes, specifically deforestation and forest degradation from 1990 to 2022. The results show that East Java has experienced significant deforestation and forest degradation, with a total deforested area of approximately 180,000 hectares and a degraded forest area of around 270,000 hectares. Among the subregions, Malang had the highest deforestation rate, with a total area of 42,605 hectares, while Banyuwangi had the highest forest degradation, with around 64,782 hectares affected. Primary mangrove forest in East Java has decreased from approximately 15,000 hectares in 1990 to around 9,000 hectares in 2022. Furthermore, the study estimated the carbon stock changes over the study period. The total carbon stock in 1990 was around 196 million tonnes, but by 2022 it had decreased to approximately 156 million tonnes. The difference in carbon stock between 1990 and 2022 is approximately 40 million tonnes of carbon. Analyzing carbon stock with remote sensing and GIS will give faster results than the manual approach. These findings highlight the urgent need for effective forest management and conservation measures in East Java to mitigate further environmental degradation and carbon emissions. Integrating GIS-based forest monitoring techniques provides a valuable tool for assessing land cover changes and their impact on carbon stock, enabling informed decision-making for sustainable land use planning and conservation strategies. [ABSTRACT FROM AUTHOR]

Mining activities have emerged as major contributors to environmental damage. This paper offers an analyse of the actual situation of environmental damages caused by mining activities in Europe. Alarmingly, pollution from mining activities in the European Union is increasing. Surface and underground mining have detrimental effects on air and water quality, land degradation, waste disposal, noise pollution, deforestation and loss of biodiversity, endangered species, microbiota, economy, and health. Moreover, the risk of dam destruction because of climate change is increased. Globally this fact ranked Europe in second place in terms of dangerousness. The situation of protected species is critical and their status remains poor. The rehabilitation cost is exorbitant, and enforcement of regulations is lacking. It is evident that, due to economic demand for metals, mining will play a central role in EU's future despite the growing emphasis on green initiatives and transition. Unfortunately, public awareness regarding environmental impact of mining activities is insufficient, and their voices are often disregarded. In order to reduce the negative impact of mining on the environment, economy and public health, it is necessary to take urgent political and technical measures. This paper aims to present a comprehensive overview and offer suggestions for future actions.

Background: The frequency and intensity of extreme weather events are expected to increase as a result of climate change, which will contribute to changes in precipitation and temperature, and therefore will affect food security. Research issues related to increasing the sustainability of the agricultural sector, eliminating climate risks in agriculture are coming to the fore. This is especially relevant in arid regions. Objectives: This study aimed to assess the adaptation strategies of smallholder farmers to climate change in Abela Abaya, Wolaita Zone, Southern Ethiopia. Finding optimal practices adapted to random extreme events is fundamental for sustainable food production. In particular, the study attempted to understand the preferences of smallholder farmers in adaptation strategies to climate change in the study area; the main influencing factors determining the choice of adaptation strategies to climate change by smallholder farmers in the study area. Methods: To investigate this issue, qualitative and quantitative data were used within a descriptive research approach to assess potential adaptation strategies of smallholder farmers to climate change. Data on demographic, socio-economic, institutional, physical and psychological factors for adaptation strategies of smallholder farmers were collected through specially designed and pre-tested questionnaires, interviews and focus group discussions (FGDs). Rural counties for the study were selected using simple random sampling due to the same agroecology. The multivariate probit (MVP) model was used as it is a type of correlated binary response regression model that allows for the simultaneous identification and assessment of the impact of a set of independent factors on each of the possible approaches. Five adaptation methods are identified as dependent variables for the Multi-Variate Probit. These include Soil and Water Conservation (SWC) practices, use of drought-tolerant varieties, adjustment of planting dates, use of agroforestry, and implementation of water harvesting. The following were selected as independent variables for the current study. Results: Most farmers (96.7%) acknowledge the ongoing climate change and are concerned that their agriculture will not suffer under the new conditions. This is justified by the fact that farmers confirm their observations regarding the increase in air temperature, the increase in the frequency of plant diseases and the decrease in precipitation. At the same time, only 36.2% of the surveyed farmers reported that society is aware of the possible risks associated with global warming and is trying to adapt agricultural activities to new climatic conditions. In particular, the current study revealed for the first time that smallholder farmers in Abela Abaya primarily prefer approaches such as soil and water conservation (71.6%), adjustment of planting dates (59.4%), and agroforestry (44.5%). Conclusion: The study identified the most influential factors on the adoption of adapted agricultural practices in a specific region of Abala Abaya (Wolaita Zone, Southern Ethiopia), namely, household heads’ access to education, frequency of extension visits, access to climate information and land slope. Thus, decision makers can design and adopt appropriate programs based on the current results to preserve smallholder farming and maintain food security at the national level.

Land degradation is a global environmental problem that affects the livelihoods of communities. Understanding the extent and causative factors of this phenomenon is important to design the cause-targeted strategies to cope with this problem. This study aimed to assess the effects of biophysical and socio-economic factors on the occurrence of land productivity degradation at the territorial level in Eastern DR Congo, using the Kalehe territory as a case study. The land productivity dynamics were determined based on the trend analysis of Landsat-based NDVI annual composites over the 1987-2020 period. Additionally, a binary logistic regression model was developed to identify the determinant factors and to map the susceptibility of this degradation at the territorial level. The results revealed that 31.25% (1303.175 km2) of the territory had experienced a decrease in land productivity, with 10.8% (453.83 km2) characterized by a significant decrease at the confidence level of 90%, representing the hotspots area of land productivity degradation where conservation actions should be prioritized. This decrease in land productivity is location-specific as it depends on the land cover types and health zones. It is likely to occur at high altitudes, in areas close to artisanal mining sites, rivers, and administrative centers. These results imply that policy interventions to cope with the problem of land productivity degradation should be developed following the heterogeneity of the landscape, and sustainable land management practices that take into account both the biophysical and socio-economic drivers of land degradation should be implemented in this region to achieve the land degradation neutrality by 2030.

The escalating trend of land degradation poses a significant challenge, especially in sloping agricultural terrains, driven by the increasing global demand for food and the limited availability of flat arable land. In response to these challenges, farmers are compelled to shift their focus towards cultivating sloping terrains. This research aimed to employ a comprehensive methodology that integrates on-site field surveys, meticulous laboratory soil analyses, and geospatial data for mapping soil erodibility. The parameters under scrutiny encompass various crucial aspects, including soil texture (ranging from coarse sand to very fine sand, silt, and clay), soil structure, organic matter content, and permeability. The meticulous examination of these factors serves as the foundation for calculating soil erodibility, utilizing the well-established Wischmeir and Smith formula developed in 1978. The research findings present a nuanced understanding of soil erodibility in the study location, revealing a spectrum spanning low to very high erodibility. Specific units, such as Unit 1, Unit 2, Unit 3, Unit 7, Unit 9, Unit 10, Unit 13, and Unit 16, exhibit very low to low erodibility. In contrast, Unit 4, Unit 6, Unit 14, and Unit 15 showcase moderate erodibility, while units like Unit 5, Unit 8, Unit 11, Unit 12, Unit 17, and Unit 18 are characterized by moderately high to very high erodibility. These insightful results shed light on the diverse erodibility levels within the studied locations and provide valuable guidance for formulating sustainable land management practices.