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While agricultural systems are a major pillar in global food security, their productivity is currently threatened by many environmental issues triggered by anthropogenic climate change and human activities, such as land degradation. However, the planetary spatial footprint of land degradation processes on arable lands, which can be considered a major component of global agricultural systems, is still insufficiently well understood. This study analyzes the land degradation footprint on global arable lands, using complex geospatial data on certain major degradation processes, i.e. aridity, soil erosion, vegetation decline, soil salinization and soil organic carbon decline. By applying geostatistical techniques that are representative for identifying the incidence of the five land degradation processes in global arable lands, results showed that aridity is by far the largest singular pressure for these agricultural systems, affecting ~40% of the arable lands' area, which cover approximately 14 million km 2 globally. It was found that soil erosion is another major degradation process, the unilateral impact of which affects ~20% of global arable systems. The results also showed that the two degradation processes simultaneously affect an additional ~7% of global arable lands, which makes this synergy the most common form of multiple pressure of land degradative conditions across the world's arable areas. The absolute statistical data showed that India, the United States, China, Brazil, Argentina, Russia and Australia are the most vulnerable countries in the world to the various pathways of arable land degradation. Also, in terms of percentages, statistical observations showed that African countries are the most heavily affected by arable system degradation. This study's findings can be useful for prioritizing agricultural management actions that can mitigate the negative effects of the two degradation processes or of others that currently affect many arable systems across the planet., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Land degradation is accelerating in the Himalayan ecosystem, resulting in the loss of soil nutrients due to severe erosion. Soil erosion presents a significant environmental challenge, resulting in both on-site and off-site consequences, such as reduced soil productivity and siltation in reservoirs. Soil erodibility (K factor), an inherent soil property, determines the susceptibility of soils to erosion. Sampling across hilly and mountainous terrain pose challenges due to its complex landscape. Despite these challenges, it is essential to study K factor variations in different land use/land cover types to comprehend the threat of erosion. Digital soil mapping offers an opportunity to overcome this limitation by providing spatial predictions of soil properties. The objective of our study is to map the spatial distribution of soil erodibility using the Random Forest (RF) model, a machine learning method based on sampled in situ soil data and environmental covariates. We collected 556 surface soil samples from the mountainous catchment (Tehri dam catchment) using the stratified random sampling approach. The model performed satisfactorily in both training (r 2  = 0.91; RMSE = 0.00185) and testing (r 2  = 0.45; RMSE = 0.00318) phases. Subsequently, we generated a digital map with a resolution of 12.5 m to depict the distribution of the K factor. Our analysis revealed that key environmental variables influencing the prediction of the K factor included geology, mean NDVI, and climatic factors. The average K factor value was estimated at 0.0304 and ranging from 0.0251 to 0.0400 t ha h ha -1  MJ -1  mm -1 . A higher K factor was observed in the barren land (0.0344) primarily located in the higher and trans-Himalayan region of seasonally snow-covered areas. These areas typically feature young soils with weak soil formation and unstable soil aggregates. Subsequently cropland/cultivated soils (0.0307) exhibited higher K factor values due to the breakdown of soil aggregates by ploughing activities and exposing carbon to decomposition. The average K factor value of evergreen (0.0294) and deciduous (0.0295) forests were the lowest compared to other land use/land cover types indicating the role of forests in resisting soil erosion. By assessing and predicting soil erodibility, land planners and farmers can implement erosion control measures to protect soil health, prevent sedimentation in water bodies, and sustain agricultural productivity in the Himalayas., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil erosion poses significant ecological and socioeconomic challenges, driven by factors such as inappropriate land use, extreme rainfall events, deforestation, farming methods, and climate change. This study focuses on the Kozhikode district in Kerala, South India, which has seen increased vulnerability to soil erosion due to its unique geographical characteristics, increase in extreme events, and recent land use trends. The research employs RUSLE (Revised Universal Soil Loss Equation), considering multiple contributing factors such as rainfall erosivity (R), slope length and steepness (LS), cover management (C), conservation practices (P), and soil erodibility (K). The study is unique and novel, since it integrates extensive field data collected from agricultural plots across Kozhikode with the RUSLE model predictions, providing a more accurate and context-specific understanding of soil erosion processes and also suggesting management strategies based on risk priority. The study found that Kozhikode experiences an average annual soil loss of 28.7 tons per hectare. A spatial analysis revealed varying erosion risk levels across the district. 52.0% of the area experiences very slight erosion, 10.31% has slight erosion, 6.18% undergoes moderate erosion, 3.88% is moderately severe, 7.34% is at severe erosion risk, 5.6% has very severe erosion, and 14.65% faces extremely severe erosion. Field data collected from agricultural plots across Kozhikode were compared with RUSLE-predicted values, revealing a low root mean square error, indicating a strong correlation between observed and simulated data. Based on these findings, the district was categorized into low, medium, and high-priority regions, with tailored recommendations proposed for each. Implementing these measures could mitigate erosion, preserve soil fertility, and support the long-term sustainability of natural and agricultural ecosystems in Kozhikode. Given the practical challenges in estimating RUSLE factors in Southern India, where data scarcity is a common issue, this preliminary study underscores the need for expanded, long-term field observations to enhance understanding of soil erosion processes at the watershed level., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Sediment loss and export pose significant global environmental issues, profoundly affecting water quality, soil fertility, and ecosystem stability, particularly in vulnerable mountain ecosystems like the Indian Himalayas. The present study used remote sensing data and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) sediment delivery ratio (SDR) model to analyze spatial-temporal variations in soil loss (SL), sediment export (SE), and sediment retention (SR) capabilities in the South Shimla watershed, Himachal Pradesh, India, from 1993 to 2023. The findings showed significant changes in land use and land cover (LULC): evergreen forest and scrub land decreased sharply by 11.53% and 36.43%, respectively, while agricultural areas and built-up areas increased notably by 71.16% and 215.76%, respectively. Despite a decline of 19.18% in SL and 24.43% in SE, sediment loss and export varied across the study area, highlighting the heterogeneous nature of sediment dynamics. The overall retention capacity increased by 2.59%, with scrub forests playing a critical role in SR, while built-up areas showed the lowest retention. Northern and central sub-watersheds (SWs) experienced a significant decrease in retention capacity (from - 1.92 to - 11.6%), whereas those in the southern and eastern regions saw an increase in SR (from 3.69 to 28.24%). These results underscore the complex interactions between LULC changes, sediment dynamics, and retention services, highlighting the importance of preserving natural ecosystems and informing policy for landscape-based conservation and development planning in the vulnerable Himalayan region., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study explores burnout among nursing students in Bangalore, India, focusing on Exhaustion and Disengagement scores. A cross-sectional design was applied using the Oldenburg Burnout Inventory modified for nursing students, collecting data using a survey that was conducted between October and December 2023. The sample consisted of 237 female nursing students from the Bachelor of Science in Nursing program at Bangalore College of Nursing, South India. The study integrated the t-distributed Stochastic Neighbor Embedding (t-SNE) procedure for data simplification into three t-SNE components, used in a hierarchical clustering analysis, which identified distinct student profiles: "High-Intensity Study Group" and "Altruistic Aspirants". While burnout scores were generally high, students with high study hours ("High-Intensity Study Group") reported greater Exhaustion, with a mean score of 26.78 (SD = 5.26), compared to those in the "Altruistic Aspirants" group, who reported a mean score of 25.00 (SD = 4.48), demonstrating significant differences (p-value = 0.005). Conversely, those motivated by altruism ("Altruistic Aspirants") showed higher Disengagement, with a mean score of 19.78 (SD = 5.08), in contrast to "High-Intensity Study Group", which reported a lower mean of 17.84 (SD = 4.74) (p-value = 0.002). This segmentation suggests that burnout manifests differently depending on the students' academic load and intrinsic motivations. This study underscores the need for targeted interventions that address specific factors characterizing the clusters and provide information for designing future research and interventions. This study was not registered. [ABSTRACT FROM AUTHOR]

Soil erosion caused by water refers to the removal of topsoil by rainfall and runoff. Proper selection of an assessment method is crucial for quantifying the spatial variance of soil erosion. The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are widely used for modelling soil erosion. This study aimed to evaluate the effectiveness of the USLE-based soil erosion modelling in different agroecological regions of India, identify potential issues, and provide suggestions for future applications. The review revealed that little attention has been given to estimate soil erosion in high-priority land degradation regions of India. Additionally, many studies failed to thoroughly verify the authenticity of stated soil loss rates in their research regions either by overestimating or underestimating at least one of the five soil loss parameters. Furthermore, flaws in the application of methods to calculate these parameters leading to erroneous values were identified and suggestions for improvement were made. The USLE-based soil erosion modelling is an effective tool for quantifying soil erosion risk, but researchers should put emphasis on thoroughly verifying the methodologies adopted, unit conversions, and data availability for the estimation of soil loss parameters to improve the accuracy of their final results. This paper provides valuable insights to assist researchers in implementing USLE-based erosion models in diverse agroecological regions in India and elsewhere. However, for effective soil conservation and sustainable agriculture, further research is necessary to develop efficient techniques for using USLE-based soil erosion modelling to achieve a comprehensive understanding of erosion risk across different agroecological regions., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The increasing concentrations of greenhouse gas emissions in the atmosphere along with the increasing temperatures exacerbate the situation, making it one of the major issues for food security. This research aims to explore the effects of climate change on food production in India. More specifically, this study investigates the impact of temperature, rainfall, CO2, methane, and N2O emissions and energy use on food production using a time spanning from 1990 to 2019 and explores the major critical factors impacting food production in India. The study used autoregressive distributed (ARDL) estimates for long-run and short-run cointegration. The empirical results demonstrated that energy use, rainfall, and methane emissions improve food productivity, whereas temperature and N2O emissions undermine food production in the long run and short run. However, the results revealed that CO2 emissions do not affect food production. Additionally, we used the dynamic ordinal least squared approach to verify the robustness of ARDL results. The results also demonstrate that N2O emissions and temperature are critical determinants of food production, indicating that a rise in temperature and N2O emissions significantly reduces food production in India. On the other hand, the findings show that methane emissions and rainfall significantly increase food production with a coefficient of 0.19 and 0.059, respectively. The most important adaptation strategies that should be considered due to climate change are reducing greenhouse gas emissions, expanding renewable energy investment, and adopting new technologies to combat environmental challenges. [ABSTRACT FROM AUTHOR]

Erosion of soil by water coupled with human activities is considered as one of the most serious agents of land degradation, posing severe threat to agricultural productivity, soil health, water quality, and ecological setup. The assessment of soil erosion and recognition of problematic watersheds are pre-requisite for management of erosion hazards. In the present study, Revised Universal Soil Loss Equation (RUSLE) integrated with remote sensing (RS) and geographic information system (GIS) has been used to assess the soil erosion in lower Sutlej River basin of Punjab, India, and prioritize the watersheds for implementation of land and water conservation measures. The total basin area was about 8577 km 2 which was divided into 14 sub-watersheds with the area ranging from 357.8 to 1354 km 2 . The data on rainfall (IMD gridded data), soil characteristics (FAO soil map), topography (ALOS PALSAR DEM) and land use (ESRI land use and land cover map) were prepared in the form of raster layers and overlaid together to determine the average annual soil loss. The results revealed that the average annual soil loss varied from 1.26 to 25 t ha -1 , whereas total soil loss was estimated to be 2,441,639 tonnes. The spatial distribution map of soil erosion showed that about 94.4% and 4.7% of the total area suffered from very slight erosion (0-5 t ha -1  year -1 ) and slight erosion (5-10 t ha -1  year -1 ), respectively, whereas 0.11% (9.38 km 2 ) experienced very severe soil loss (> 25 t ha -1  year -1 ). Based on estimated average annual soil loss of sub-watersheds, WS8 was assigned the highest priority for implementation of soil and water conservation measures (323.5 t ha -1  year -1 ), followed by WS9 (303.8 t ha -1  year -1 ), whereas WS2 was given last priority owing to its lowest value of soil loss (122.02 t ha -1  year -1 ). The present study urges that conservation strategies should be carried out in accordance with the priority ranking of diverse watersheds. These findings can certainly be used to implement soil conservation plans and management practices in order to diminish soil loss in the river basin., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Morphometric analysis of a drainage basin along with its stream network is indispensable for analysing a river basin's prevailing geomorphology, geo-hydrology, and erosional susceptibility. In this study, an enhanced morphometric evaluation method using Google Earth Engine (GEE) and Principal Component Analysis (PCA) was used to assess and prioritize 12 sub-basins of the Godavari River basin (GRB), India, based on their erosional susceptibility. The findings of erosional susceptibility were validated using the Sediment Yield (SY) model. Our findings highlight that the sub-basins of Indravati, Pranhita, and Manjira, i.e. 42.42% (132707.4 km2) of the entire GRB are at high risk of erosional susceptibility, requiring immediate implementation of soil conservation measures. Our findings also highlight that 52.71% of the GRB is in the mature stage of geological evolution, wherein erosional landform-building processes are still ongoing. Overall, GRB has a relatively low drainage density with dendritic drainage patterns, permeable soils, and elongated sub-basins of enhanced erosional susceptibility. Moreover, the results of the SY model exhibited 90% agreement with the results of the enhanced morphometric analysis. This study presents an efficient framework for enhanced morphometric analysis that can assist policymakers in the demarcation of priority zones for erosional susceptibility. [ABSTRACT FROM AUTHOR]

Soil degradation in Odisha poses a significant conservation concern for the local environment. The present research focused on a region in central Odisha State, India, affected by drought conditions. Several models have emerged to assess soil loss, with the Revised Universal Soil Loss Equation (RUSLE) standing out as the most suitable option. The erosion computation process entails utilizing the digital elevation model (DEM), Landsat-9 imagery, and soil data from several sources accessible in different forms and scales. The present analysis took into account various elements, namely, crop management factor (C), practice management factor (P), slope length factor (LS), steepness factor of the slope (S), and rainfall factor (R). Multiplying these factors yielded the average rate of soil erosion. Areas with a high slope length factor, such as those in Kandhamal, Kalahandi, and Nuapada, have a high erosion rate. The study reveals that 57% of the land in the study area experiences very low to moderate soil erosion at a rate of 2–10 tons per hectare per year, while 43% faces moderately to very severe erosion at a rate of 10–25 tons per hectare per year. Erosion hotspots, covering 32,205 square kilometers, are mainly identified in agricultural and forested hilly areas where slopes exceed 10°, such as those in Kandhamal, Kalahandi, and Nuapada, which have a high erosion rate. These districts are especially vulnerable to soil loss and resulting climate action (Sustainable Development Goals-13) because of frequent and severe rainfall, shifting agricultural practices, a thin surface soil covering, natural erosion, and barren hills. The research emphasizes the urgent need for implementing conservation and management measures to protect high-risk areas from further degradation. In conclusion, the study underscores the effectiveness of the RUSLE-GIS model in conducting quantitative and spatial assessments of soil erosion on a river watershed scale. The model is deemed crucial in formulating conservation strategies to address the identified erosion issues in the tropical highlands of the area. [ABSTRACT FROM AUTHOR]

In this study, sub-watersheds of Manjira River basin in India is assessed for necessity of soil conservation studies by incorporating morphometric, biophysical and socioeconomic parameters using ArcGIS and Generalised Watershed Loading Function (GWLF) model. Morphometric parameters include watershed aspect, linear aspect, shape aspect and landscape aspect. The biophysical parameters considered are vegetation change, erosion and sediment loadings. The socioeconomic parameters considered are population density, literacy rate and agriculture worker density. The sub-basins were prioritized applying the Fuzzy Analytical Hierarchy Process (FAHP) into five priority classes, namely, "Very High", "High", "Medium", "Low" and "Very Low", covering approximately 36.4%, 15.5%, 24.4%, 11.39% and 11.28% of the surveyed area. An area of 2027.629 km2 was found to be in the very high risk priority class with respect to soil conservation management to be applied in the respective sub-basins. [ABSTRACT FROM AUTHOR]

Gully erosion is a formidable land degradation process globally. It is omnipresent across India, wherein two of the largest badlands regions in the world also exist. However, despite being affected by widespread gullying, gully erosion research in India has been rather limited, with key aspects of gully formation, morphologies and dynamics remaining unknown. Through this comprehensive systematic review, we synthesise previous studies on gully erosion in India and in turn highlight pivotal knowledge gaps. The review starts with a discussion of the causal factors of gullying in India, which underlines how gully initiation in several regions was an aftermath of deforestation and overgrazing. Contrastingly, the badlands of Central and Western India have mainly developed in response to natural triggers like neotectonics and Holocene climate change. The section on mapping highlights how mapping methodologies have not only been dictated by the availability of imagery and/or means of data processing, but also the mapping purpose—that is, individual gully channels or entire badlands. Although a few studies applying concepts of fractal geometry to characterise badlands' geomorphology are innovative and unique, the most striking research gaps we have identified also pertain to understanding and quantification of gully geomorphology and erosion dynamics in different regions of India. Our review reveals that gullies of peninsular India have been the least studied, followed by those of the Himalayan and Sub‐Himalayan region. Although the literature provides interesting examples of linkages between badlands development and the wider geomorphic evolution of the landscape, better chronological understanding is required to disentangle the relative importance of natural and anthropogenic drivers of landscape change in India's badlands. Large‐scale mapping of gully characteristics, quantification of gully morphologies, gully erosion rates and its share in catchment sediment budget across various physiographic regions or river basins of the country also constitute important areas for future research. [ABSTRACT FROM AUTHOR]

Land degradation has become a key concern worldwide due to changes in meteorological variables and human-caused activities. This study primarily focuses on the rate, impact, and pattern of land degradation in western India. In this instance, we evaluated the effectiveness of land degradation neutrality (LDN) between 2000 and 2020 using an integrative approach based on a PSR (pressure-state-response) model developed by the OECD-UNEP under the UNCCD framework. Here, we mainly used MODIS products (e.g., NDVI, PET, LULC, and NPP). Also, soil organic carbon (SOC) and climatic variables (e.g. precipitation, aridity index and soil moisture) were taken into account. These indicators were analysed using the Google Earth Engine (GEE) code editor platform, and post-processing was done through Q-GIS software. The analysed parameters indicate that the NDVI and NPP values are + 0.20 to + 0.3 and 4.27 × 109 to 7.74 × 109 kg Cm−2, respectively. However, overall precipitation and soil moisture depicted a positive trend, and the aridity index adeptly followed a negative trend. Hence, the land degradation rate has increased in the north-western region besides the Aravalli range and neutrality work in the southwest part of the study area. The overall land degradation trend is negative over the last two decades. Therefore, this study anticipates the policymakers and government bodies to understand about land degradation of western India. [ABSTRACT FROM AUTHOR]

This study provides a comparative evaluation of spatio-temporal distribution of soil erosion in Western (Mago Basin) and Eastern (Dibang Basin) basins of Arunachal Pradesh, India as these two basins are vulnerably exposed to soil erosion due to its topographical characteristics of mountainous steep slope and experiences heavy rainfall. The study was carried out for a ten-years period (2003 to 2014) using RUSLE model which encompasses five important factors contributing to soil erosion. Rainfall erosivity (R factor) map was calculated using Climate Prediction Center gridded precipitation. Soil map and soil samples were used to analyze soil erodibility (K factor) map. Slope length and slope steepness (LS factor) maps were computed from SRTM DEM (30 m resolution). MODIS NDVI images were used to obtain cover management (C factor) map. Landuse Landcover map was used to obtained support practice (P factor) map. Higher value in rainfall erosivity and cover management factor was observed in Mago basin which contributed to higher average annual soil loss of 17.423 t ha−1 y−1 in Mago basin and 5.461 t ha−1 y−1 in Dibang basin, whereas the other three factor values were almost the same. The spatial maps showed 56.65% of Mago basin area and 76.27% of Dibang basin area was under the class of slight erosion, with the remaining areas of moderate to severe erosion risk for both the basins. Temporal average soil erosion in Mago basin varied within moderate to very high erosion classes whereas Dibang basin erosion classes varied from slight to moderate. The temporal trend line showed that the overall soil erosion was increasing at an alarming rate for Mago basin whereas a slight increase in Dibang basin was observed. [ABSTRACT FROM AUTHOR]

Understanding the origins and hotspots of suspended sediments is crucial for safeguarding the biotic communities and for management of water quality in river‐reservoir systems. This study is an attempt to develop a new analytical framework to deconvolve the suspended sediment sources contribution using a novel index (i.e., erosion susceptibility index, ESI). The ESI is computed using sediment fingerprinting information, sediment yield, and the areal coverage of the sediment sources (land use). We have used X‐ray diffraction (XRD) and Partial least square regression (PLSR) based spectral fingerprinting technique to apportion the land use classes as sources of sediments in a representative catchment of Chotanagpur Plateau (CP), India (Konar reservoir catchment). PLSR model on the XRD spectra has performed well with R2 > 0.88 and identified agricultural areas as the major contributor of suspended sediments by delivering 28%–43%, varying seasonally (in the period of 2018–2019). However, based on the ESI values barren lands and human settlements were found to be the most crucial land use classes (with highest ESI value of 1.31 and 1.08 respectively) in terms of sediment generation (22%–29% and 14%–23% respectively) with only 14% and 12% areal coverage. The results have demonstrated that the state of critical conservation urgency of different land uses can be quantified more effectively when combining spectral tracers based knowledge with ESI. Plain Language Summary: It is crucial to identify the areas where soil erosion is most prevalent in order to reduce the amount of sediments carried by runoff into the reservoir. One method used to identify the most significant sedimentary contributions into a watershed is sediment fingerprinting. The purpose of this research is to improve our comprehension of sediment dynamics by using a new spectral fingerprinting technology (i.e., XRD‐PLSR) to classify sediment sources according to land use and further use this knowledge to develop a novel index (Erosion Susceptibility Index) to enhance the understanding on sediment dynamics more critically. The primary result of this research is a categorization of land uses according to their conservation priority for mitigating reservoir sediment inputs. Key Points: Effective X‐ray powder diffraction spectral fingerprinting identified agricultural land use as the primary sediment sourceLinked sediment source apportionment and source weightage through a novel indexErosion Susceptibility Index (ESI) has shown the state of critical conservation urgency is more for barren lands than agricultural areas [ABSTRACT FROM AUTHOR]

Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from 'Semi-arid' towards 'Arid' class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions ('Humid' to 'Sub-humid'). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions. [ABSTRACT FROM AUTHOR]

Currently, agroecosystems sustainability is mainly challenged by unsustainable agricultural practices that lead to land degradation and amplified climate change. About 25% of the World's total land area has deteriorated due to improper agricultural land management, resulting in ~24 billion tonnes of annual soil loss. Globally, soil erosion, mainly caused by high‐frequency tilling, crop residue removal or burning, poor pasture management, and inefficient crop rotations, is responsible for >40% of total land deterioration problems. Conservation agriculture (CA) seeks minimum soil disturbance, permanent soil cover, and crop rotation, which are indispensable for soil fertility restoration, carbon stabilization, increased soil biodiversity, and sustainable food production. In the last decades, the global annual adoption rate of CA was 7 M ha yr−1. CA‐based soil and crop management practices enhanced water productivity, soil organic carbon, crop productivity, and energy use efficiency by 18%–66%, 12%–93%, 3.8%–76.2%, and 8.9%–40.2%, respectively, over conventional practices under diverse agroecosystems in India. Hence, adopting CA can avert/minimize the problem of land degradation and food insecurity by improving crop productivity and decreasing soil erosion, energy use, and carbon emission, besides maintaining soil physicochemical and biological health. Moreover, CA can be a sustainable way to ensure global food demand, achieve sustainable development goals (SDGs), and restore soil health, and multiple agroecosystem services. This study aims to provide in‐depth insights into the role of CA in restoring agroecosystem services, attaining SDGs, and ensuring global food security. [ABSTRACT FROM AUTHOR]

Surface soil loss is the major environmental problem faced by farmers in the agriculture sector regarding productivity. This research paper demonstrates the prone soil loss zones using GIS tools, analytical hierarchy process (AHP), and the revised universal soil loss equation (RUSLE) model conducted to assess soil loss, whereas, hematic layers were created using data gathered from climate conditions, geology maps, remote sensing imageries, field observations, and laboratory analyses. A conceptual methodology was constructed to implement the model across the watershed area in the Ganga basin near the Kaushambi-Prayagraj region. Based on the Land Degradation Vulnerability Index (LDVI), the whole watershed area was categorized into four classes area such as low loss (135163.7 ha), moderate loss (114864.9 ha), high loss (56240.8 ha), and severe loss (25675.5 ha). The AUC of the LDVI (84.3%) and CR value of AHP (0.061) are indicating the high accuracy of the outcome. Whereas, average annual soil loss in (tons/ha/year) the watershed area was found as low soil loss (49.64%), moderate soil loss (27.92%), high soil loss (17.35%), and severe soil loss (5.09%). The qualitative and quantitative soil loss-prone areas would be helpful for proper management strategies to mitigate soil erosion and enhance agricultural productivity in the region. [ABSTRACT FROM AUTHOR]

Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC's accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state (northeast India) which are fragmented and intertwined with the forest ecosystem. The current study applied the digital soil mapping (DSM) methodology, based on the CEC values determined in soil samples obtained from 305 points in the region, which is mountainous and difficult to access. Firstly, digital auxiliary data were obtained from three open-access sources, including indices generated from the time series Landsat 8 OLI satellite, topographic variables derived from a digital elevation model (DEM), and the WorldClim dataset. Furthermore, the CEC values and the auxiliary were used data to model Lasso regression (LR), stochastic gradient boosting (GBM), support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) machine learning (ML) algorithms were systematically compared in the R-Core Environment Program. Model performance were evaluated with the square root mean error (RMSE), determination coefficient (R2), and mean absolute error (MAE) of 10-fold cross-validation (CV). The lowest RMSE was obtained by the RF algorithm with 4.12 cmolc kg−1, while the others were in the following order: SVR (4.27 cmolc kg−1) c kg−1) c kg−1) c kg−1). In particular, WorldClim-based climate covariates such as annual mean temperature (BIO-1), annual precipitation (BIO-12), elevation, and solar radiation were the most important variables in all algorithms. High uncertainty (SD) values have been found in areas with low soil sampling density and this finding is to be considered in future soil surveys. [ABSTRACT FROM AUTHOR]

Sediment export and retention are important ecosystem processes in any landscape causing soil erosion and sediment loading in waterways consequently affecting the health of aquatic habitats downstream. The present study quantifies sediment export and retention in four watersheds, viz., Hivra, Satrapur, Konta, and Jagdalpur in the Godavari River Basin, India, using Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) ecosystem service modelling tool. The results revealed that the sediment export yields ranged from 0.75 (Hivra) to 2.77 t/ha/year (Jagdalpur). The mean deviation between modelled values and observed sediment export yield was − 11.11%, which indicated good prediction by the model. The sediment retention ranged from 16.04 (Hivra) to 101.52 t/ha/year (Konta). Most sediment export and retention occurred on cropland or shrubland land use land cover types in all four watersheds. For decision making on soil conservation, soil loss tolerance limits have been established for these watersheds. For aquatic habitats, sediment concentration is considered more important than the total annual sediment export, since water turbidity is an important determinant of water quality, and the aquatic lives therein. Therefore, the temporal distribution of rainfall and corresponding sediment export becomes important, since these two factors determine the sediment concentration as well as turbidity in the waterbody. In current study, "Precipitation Concentration Index adjusted Sediment Export Yield Index" was developed to account for the effects of the temporal rainfall distribution and its impact on sediment export. The index for four watersheds was quantified (Hivra > Satrapur > Konta > Jagdalpur), which is concordant to the turbidity values reported by respective gauge stations. Thus, the proposed index can efficiently capture the impact of temporal rainfall distribution on sediment export, and consequently its effect on water turbidity. The study revealed the potential of InVEST model to quantify the sediment export and retention in the watersheds studied. Together with the proposed index, it would help the policy makers in making informed decisions for planning conservation strategies for aquatic biodiversity. [ABSTRACT FROM AUTHOR]

Chilika Lake is the largest coastal lagoon on the eastern coast of India. The aerial extension of the lake fluctuates between 1165 km2 to 906 km2. The opening of new inlet into the lake has enriched its biodiversity but also has increased the rate of siltation, which inturn affected the lake's water holding capacity, causing ecological repercussions and impacting its flora and fauna. The RUSLE model integrated with remote sensing and GIS techniques was used to assess the soil loss in watershed of Chilika Lake. The model takes into account various factor viz. Crop/cover management factor (C), Soil erodibility factor (K), slope length (L), Conservation support practice factor (P), Rainfall erosivity factor (R), and steepness factor (S) to estimate potential soil loss. The results of the study reveal that 486.92 km2 i.e., 73.16% area of the Chilika watershed falls under least risk category of soil erosion, 44.17 km2 (6.6%) under moderate category, while, about 11% is at high, 8.8% at very high, and 0.043 at severe risk of soil erosion. The analysis estimates the annual average soil loss rate in the catchment of Chilika Lake to be 32.41 ton/ha/yr. The raster layers developed to study spatial soil loss indicate that the catchment of the lake is under the grip of soil erosion and siltation problems. Therefore, the study recommends immediate action of conservation and management planning processes to save the lake from further degradation. [ABSTRACT FROM AUTHOR]

Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin. [ABSTRACT FROM AUTHOR]

Soil erosion is a severe environmental problem worldwide, especially in tropical regions. The Revised Universal Soil Loss Equation (RUSLE), one of the universally accepted empirical soil erosion models, is quite commonly used in tropical climatic conditions to estimate the magnitude and severity of soil erosion. This study, apart from identifying the role of individual parameters in influencing the results of the RUSLE, also aims at refining the RUSLE results by incorporating the state-of-the-art technique Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) in a GIS environment by utilizing its ability to measure minute surface changes in millimetre levels. Apart from this novel approach of prioritising soil erosion classes using PSInSAR, the eroding surface conditions were also studied using low coherence value (<0.75 in this study). The spatially and temporally averaged annual soil loss and net soil erosion (2015-2019), derived through RUSLE and transport limited sediment delivery (TLSD) approach, respectively, was improved by spatially integrating the PSInSAR velocity map. The integrated methodological framework is demonstrated for a tropical river basin in South India (Muvattupuzha River Basin [MRB]), which shows a mean rate of net soil loss of 6.8 ton/ha/yr, and nearly 8% of the area experiences deposition. Our approach to improve the accuracy of RUSLE-based soil erosion classes using PSInSAR techniques clearly demarcated the areas that call for utmost priority in implementing management practices. The corollary results show that the very severe soil erosion class is characterized by PSI velocity with higher negative values, followed by the successively lower classes. Results strongly suggest that RUSLE output can be improved as well as validated using a velocity map derived from radar data. [ABSTRACT FROM AUTHOR]

Gully erosion is a common type of soil erosion form that results in significant soil loss in a variety of climatic environment. The amount of sediment produced by gully erosion is several times higher in comparison to other forms of erosion. Therefore, an effort has been made to improve gully susceptibility assessment in sub-tropical environment of India, using neural network algorithms. Based on satellite image data, a gully inventory map was created and twenty gully conditioning variables were considered for modelling perspective. Study revealed causative factors like slope, land use and drainage density are most significant for gully occurrences. The result of this study revealed the ‘multi-layer perceptron (MLP)’ algorithm is the most robustness model with the AUC (area under curve) is 0.95 compared to the remaining applied models. The findings may provide an outline for more biophysical planning of gullies and associated planning strategies throughout the study area. [ABSTRACT FROM AUTHOR]

In this study, soil loss by water in the Chambal river basin (CHB) has been estimated using Google Earth Engine and Geographic Information System platforms employing the Revised Universal Soil Loss Equation (RUSLE). Google Earth Engine, a cloud-based platform, has been used for generating land use/land cover map from the voluminous satellite remote sensing data. The observed mean rate of soil erosion in the CHB is 1.17 t/ha/yr. The study reveals that very severe type of soil erosion in the Chambal river basin is found in the gullies/ravines and barren lands with the highest mean rate of soil erosion in the gully areas at 13.44 t/ha/yr. Catchment-wise soil loss estimates suggest that the four catchments namely Kali Sindh, Lower Chambal, Upper Chambal and Parbati are experiencing much higher soil loss in comparison to others; hence these catchments are prioritised for soil conservation efforts. Sensitivity analysis of individual factors and their interaction effect indicates that LS factor is the most influential factor in the study area followed by C factor. Research highlights: Estimates soil loss by water for the first time at basin scale in the Chambal river basin (mean rate 1.17 t/ha/yr) by integrating Google Earth Engine and GIS employing RUSLE. Provides insight into the spatial pattern and status of soil erosion in the study area. Reveals very severe type of soil erosion in the areas of gullies/ravines with the highest mean rate 13.44 t/ha/yr and 5.92 t/ha/yr, respectively. LS factor is the most influential factor in the study area, followed by C factor. Prioritises Kali Sindh, Lower Chambal, Upper Chambal and Parbati catchments for soil conservation efforts based on much higher soil loss. [ABSTRACT FROM AUTHOR]

Background: Phosphorus (P) is among the essential elements for plant growth and one of the main elements of fertilizers. Decreased availability of P may limit agricultural production in the coming years. The magnitude of soil aggregation influences phosphorus access to mineral surfaces. Aim: The present study aims to determine phosphorus adsorption processes affected by soil aggregation under different land‐use systems. Methods: The distribution of soil aggregates was determined in representative soil samples in the district Kupwara of Kashmir Valley in India. To predict the phosphorus fertilizer requirement of a particular soil, we used the Freundlich adsorption equation and Langmuir equation and drew a clear comparison between these two models. Results and discussion: Maximum phosphorus (P) adsorption was recorded at the smallest aggregate size, 0.5–0.1 mm. However, soil aggregates >2.0 mm (the largest category) adsorbed the least amount of P. Our results revealed that increasing the addition of P to the soil decreased the percentage of adsorbed P regardless of aggregate size. The maximum P adsorption of different size aggregates varied between 1869–1924, 1872–1900, 1718–1739, and 1800–1890 mg P kg–1 in irrigated agriculture, forest, orchard and rainfed agriculture soils, respectively. The variation in P adsorption parameters across the different land uses was attributed to their mean weight diameter difference. The maximum bonding energy in the forest resulted in higher P adsorption. Langmuir and Freundlich's adsorption equations were fitted to each soil aggregate size and land‐use system. Conclusion: Our results revealed that for all soil aggregate sizes and land use systems, the Freundlich adsorption equation performs better than the Langmuir equation. [ABSTRACT FROM AUTHOR]

In India's Indo-Gangetic plains, river flows are strongly altered by dams, barrages and water diversions for irrigation, urban supply, hydropower production and flood control. Human demands for freshwater are likely to intensify with climatic and socio-economic changes, exacerbating trade-offs between different sustainable development goals (SDGs) dependent on freshwater (e.g. SDG2, SDG6, SDG7, SDG11 and SDG15). Freshwater ecosystems and endangered aquatic species are not explicitly addressed in the SDGs, but only nested as targets within SDG6 and SDG15. Thus, there is high risk that decisions to advance other SDGs may overlook impacts on them. In this study, we link a water resource systems model and a forecast extinction risk model to analyze how alternative conservation strategies in the regulated Beas River (India) affect the likelihood of survival of the only remaining population of endangered Indus River Dolphins (IRD) in India in the face of climate change-induced impacts on river hydrology and human water demands, explicitly accounting for potential trade-offs between related SDGs. We find that the frequency of low flow released from the main reservoir may increase under some climate change scenarios, significantly affecting the IRD population. The strongest trade-offs exist between the persistence of IRD, urban water supply and hydropower generation. The establishment of ecologically informed reservoir releases combined with IRD population supplementation enhances the probability of survival of the IRD and is compatible with improving the status of relevant SDGs. This will require water managers, conservation scientists, and other stakeholders to continue collaborating to develop holistic water management strategies. [ABSTRACT FROM AUTHOR]

Land degradation as one of the mega drivers of loss of biodiversity and ecosystem services has affected billions globally, resulting in rampant loss of livelihoods and accelerated distress migration. India, one of the land degradation hotspots in Asia, has also been reeling under the impacts of the ongoing climate and pandemic crises, which have seriously imperiled local livelihoods and human well‐being. Boosting livelihood‐based social support programs can be a transformative approach to scale up Nature‐based Solutions to achieve Land Degradation Neutrality and to realize global restoration, climate, and biodiversity conservation goals. In this article, we provide evidence for enhancing landscape restoration success by creating green job opportunities to achieve post‐pandemic recovery. The Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) was initiated as a poverty alleviation program in India and can be transformative approach to achieve global climate and sustainable development goals. We use cases from diverse ecological contexts of India to demonstrate the potential of MGNREGS. To revolutionize the process, we propose developing and mainstreaming national‐level landscape restoration guidelines by using locally relevant knowledge for addressing critical gaps, identifying institutions and their roles, developing local capacity, leveraging technology to facilitate decision‐making, encouraging multi‐stakeholder involvement, and promoting inclusiveness and community participation. [ABSTRACT FROM AUTHOR]

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfall data, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km 2 . In addition to this, a scatterplot between the final outputs was computed and found the moderate (R 2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

High‐resolution multi‐temporal digital elevation model (DEM) are key to accurate mapping of gully erosion volume change studies. Owing to the lack of multi‐temporal DEM at a high spatial resolution, gully development rate, and gully erosion‐fill volume change estimates in the Indian badlands are poorly studied. Our study explored the use of multi‐temporal TerraSAR‐X add‐on for digital elevation measurement (TanDEM‐X) derived elevation models to quantify the erosion volume and gully susceptibility mapping in the Chambal badlands, Central India. The average volume of gully erosion based on the DEM subtraction method in the study area was found to be 135 × 105 m3, and the estimated annual rate of soil erosion was ~284 t hr−1 yr−1. Using machine learning models, we trained these data for gully erosion susceptibilities and volume prediction for a larger study region; and validated the results with independent samples. The accuracy of the model in terms of area under the receiver operating curve (AUC) values has reached 0.85 for training and 0.87 for validation, indicating satisfactory model performance. After validation, the best fit model was implemented onto a testing site (no multi‐temporal DEM available) in order to predict erosion zones and erosion volume estimation. The model predicted that about 40% of the area is highly affected by gully erosion, with the maximum gullying process in the north‐Central and lowest in the southwest parts of the testing area. The research framework presented in this study can be useful in estimating the erosion rate in the badlands of the Chambal Valley and can be used effectively in ravine reclamation projects. [ABSTRACT FROM AUTHOR]

Soil erosion is recognised as a significant problem for agriculture in India. The Desertification and Land Degradation Atlas of India identifies Telangana as one of the most affected states in India due to soil erosion, with about 26% of total geographical area degraded due to soil erosion. The objective of this paper is to find out how farmers perceive soil erosion severity in Telangana, India. The study is based on a survey of 400 households in two sub‐watershed areas. The estimates based on ordered probit model suggest that farmers' perceptions about soil erosion correspond well with the degree of soil erosion expected from site‐specific factors such as the slope of the plot, soil depth, soil texture, road connectivity and irrigation. Further, the farmers' perceptions tally with the plot level conservation measures undertaken. The findings from the study also corroborate a number of similar empirical studies from different parts of the world. Given this finding, it is argued that farmers' expertise is important to assessing soil erosion severity, as they add relevant plot‐level information to assessments made through secondary sources. The study findings further highlight the need to apply participatory approaches when working to reduce soil erosion. [ABSTRACT FROM AUTHOR]

Soil, one of the non-renewable resources on this planet, is being depleted at an alarming rate due to various anthropogenic activities. The effect of soil erosion could range from on-site ecosystem fragmentation to off-site reservoir storage capacity loss. Though soil erosion is caused by various agents such as wind, water, etc., water erosion is the major type. Heavy rainfall during the monsoons and the relatively thin cover of soil over the bedrock aggravates the damage potential of this form of erosion in the state of Kerala, India. Water-induced soil erosion in the Bharathapuzha river basin in Kerala, which is blessed with cultivable land resources and intensive agricultural activities, has been undertaken for quite long and has started impacting agricultural activities, drinking water supply, and reservoir storage capacity in the basin. In this background, this study was taken up to assess the effects of climate and land use–land cover dynamics on soil erosion in the Bharathapuzha river basin over the last three decades. Soil erosion from the river basin was estimated by the Revised Universal Soil Loss Equation (RUSLE), and these estimates were verified with the observed sediment load in the Bharathapuzha river at the Kumbidi river gauging station. The storage capacity loss of some of the reservoirs located in the river basin, estimated by the Integrated Bathymetric Survey (IBS), was compared with the likely storage capacity loss of these reservoirs estimated based on soil erosion from the catchment areas upstream. Soil loss from the river basin for a future period was predicted using projections of future climate under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) from four regional climate data experiments and future land use–land cover. Annual soil loss estimation maps for 2020 and 2035 under RCP4.5 and RCP8.5 reveal the urgent necessity to plan and implement soil conservation measures in the erosion hotspots, especially in the Malampuzha, Anamala, and Attapady sub-basins. [ABSTRACT FROM AUTHOR]

The agricultural land of the whole world is deteriorating due to the loss of top fertile soil reducing agricultural productivity and groundwater availability. Mainly, natural conditions and human manipulations have made soils extremely prone to soil erosion. Therefore, information on soil erosion status is of paramount importance to the policymakers for land conservation planning in a limited time. Spatial information systems like GIS and RS are known for their efficiencies. With that prospect, the GIS-based RUSLE model is used in this study to assess the soil erosion losses from Bhandara regions of Maharashtra, India. The study area comes under Wainganga sub-river basin, a portion of the Godavari River basin. We have prepared the required five potential parameters (R*K*LS*C*P) of RUSLE model on pixel-to-pixel basis. We have prepared the R factor map from monthly rainfall data of Indian Meteorological Department (IMD) and K factor map by digital the soil series map of NBSS & LUP, Govt. of India. We have used the digital elevation model data (DEM) of Cartosat-1 for LS-factor map, Landsat 8 and Sentinel-2A satellite dataset to generate LULC and NDVI map to obtain C and P factors. The results and satellite data were validated using Google Earth Pro and field observations. The results showed significant soil erosion from the river banks and wastelands near water bodies, with the soil loss values ranging between 20 and 40 t ha−1 yr−1. The land under reserved forest was very slight erosion-prone soil with soil loss of < 5 t ha−1 yr−1, but few water bodies in forest land have resulted in more than 40 t ha−1 yr−1. Some steep sloppy land near the riverbank showed soil erosion loss ranging from 44.37 to 241.03 t ha−1 yr−1 due to higher water forces, most minor vegetation, high steep slopes, and sandy loam soils. The spatial techniques used for assessing soil erosion in the study area will be helpful for decision-makers and planners to take appropriate land management measures to counter the soil loss and protect the natural environment. Thus, the GIS-based RUSLE model is a convenient, time-efficient, and cost-effective tool to evaluate and map soil erosion on a pixel-to-pixel basis. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is one of the most important nutrients and its low availability in soil is a limiting factor for the growth and development of plants. In jhum cultivation, there is considerable loss of soil nutrients especially phosphorous (P). Since soil P are an important soil nutrient, the use of native phosphate solubilizing bacteria (PSB) capable of converting the insoluble P to soluble form can serve as biofertilizers to increase the amount of plant-available P. PSB native to jhum and jhum fallow soil were isolated and screened for their phosphatase enzyme and indole compound production. These bacteria are being explored for development as biofertilizers to increase crop production in jhum cultivation as well as faster eco-restoration of jhum fallow. Base on their ability of solubilizing tri-calcium phosphate in Pikovskaya agar medium, a total of 44 PSBs were isolated from the current jhum field and different age fallow. Molecular characterization of 44 bacterial showing phosphate solubilization potential was carried out and a phylogenetic tree was constructed. All isolates showed production of acid phosphatase enzyme and indole-like compounds. Bacterial isolates MZUR22 and MZUR37 were evaluated for their efficacy to alleviate the early growth performance of rice in vitro conditions. The in vitro assessment showed that rice plants treated with PSB isolates showed a positively significant growth performance against other treatments. [ABSTRACT FROM AUTHOR]

Soil erosion induced by physical and anthropogenic activities needs serious concern to deal with. In recent days, watersheds have been facing acute soil loss because of deforestation, poor land use management and unscientific agricultural practices. The present paper highlights the goal to decipher soil erosion-susceptible zones in a rain-fed river basin of India having a complex topography comprising the extension of Chotonagpur plateau and parts of Bengal basin. Fuzzy logic algorithm-based analytical hierarchy process (FAHP) with remote sensing and geographical information system (GIS) were used to execute the objective. The soil erosion-susceptible zone (SESZ) was identified assimilating the 14 geo-environmental, hydro-meteorological and anthropogenic conditioning factors such as slope, drainage density, relative relief, stream power index, topographic wetness index, runoff, soil texture, land use and land cover, rainfall erosivity factor, distance from road, distance from settlement, distance from river, geomorphology and soil bareness index based on which sub-basin prioritization for sustainable soil conservation was made. The soil erosion-susceptible zone was classified into five classes, namely very low soil erosion zone (13.20%), low soil erosion zone (25.75%), moderate soil erosion zone (27.62%), high soil erosion zone (21.90%) and very high soil erosion zone (11.53%). Receiver operating characteristics (ROC) curve was generated where area under the curve (AUC) came as 0.82 and a correspondence analysis between the SESZ and the first three ranked factors was also made which together proves the accuracy of the model. [ABSTRACT FROM AUTHOR]

Water-induced soil erosion is one of the challenging threats in various parts of the world, thus systematic investigations of soil loss risk are more crucial for sustainable agricultural production and water management. The present study was carried out to estimate the average soil loss in the Amravati watershed of Tamil Nadu state in South India using Revised Universal Soil Loss Equation (RUSLE) along with Geographical Information System (GIS) and Remote Sensing techniques. In this study, the rain gauge-based rainfall data were completely substituted with the CHIRPS datasets to compute the R factor, since it gives continuous surface rainfall data rather than location-based measurement. The estimated soil loss in the watershed was found to be in the range of 0–280.2 t/ha−1/yr−1, in which about 64.7% of the watershed under very low erosion risk whilst about 12.9% of the area prone to moderately high-to-very high erosion risk. The maximum soil loss rate was identified in the Degraded forest with a mean loss of 59.51 t/ha−1/yr−1 followed by the Degraded plantation (32.17 t/ha−1/yr−1), Scrubland/Wasteland (17.75 t/ha−1/yr−1), Current fallow (12.08 t/ha−1/yr−1), and Rainfed cropland (10.03 t/ha−1/yr−1). The study shows that the present land use / land cover and the landscape of the watershed have a great influence on soil loss. To check the efficiency of the RUSLE model for the assessment of soil erosion risk, the final derived output was validated with the use of 4k UHD Google Earth images. [ABSTRACT FROM AUTHOR]

Estimates of the soil and nutrient losses are essential for design and planning of soil conservation measures in Indian arid region. This study aims at estimating rainfall-runoff, soil and nutrient losses from different cover crops, and to identify the best cover for checking soil loss. The study is conducted for 2013–2015 in research farm of the Central Arid Zone Research Institute, Regional Station, Bhuj, Gujarat, India. Ten treatments comprising four sole-crop, that is, sorghum (Sorghum bicolor), pearl millet (Pennisetum glaucum), green gram (Vigna radiata), and cluster bean (Cyamopsis tetragonoloba) and four cereal-legume intercropping with two controls (cultivated and unplowed fallows) are undertaken in randomized block design with three replications. Multi-slot divisors are fabricated and installed. Analysis of variance (ANOVA) is performed, and their interrelationships are explored. The highest soil loss is recorded from cultivated fallow (108.03 ± 49.95 kg.ha−1.yr−1) and unplowed fallow (78.95 ± 28.42 kg.ha−1.yr−1). Green gram is found effective in controlling soil loss as sole-crop (event-wise soil loss ~0.54–33.94 kg.ha−1) as well as intercropping with sorghum (event-wise soil loss ~0.60–23.37 kg.ha−1) and pearl millet (event-wise soil loss ~2.45–31.11 kg ha−1). ANOVA revealed significance (p <.05) of runoff-generating rainfall, crop cover, and their sole- and intercropping practices. Values of coefficient of determination (R2) indicated highly correlated (R2 ≥ 0.75) relationships of rainfall-runoff for all treatments, rainfall-soil loss for sole cereals and cultivated fallow, and runoff-soil loss for pearl millet and intercropping with green gram. This study concludes that cereal-legume intercropping diminishes the adverse impact of raindrops on soil erosion and crop production. [ABSTRACT FROM AUTHOR]

Globally, soil erosion transports a significant amount of carbon (C), but the magnitude of erosion‐associated loss of soil organic carbon (SOC) from India is poorly quantified. The present study quantified the C flux induced by accelerated erosion for different states of India where soil erosion is the most critical form of land degradation. The lateral transport of eroded C was estimated for individual Indian states using spatially referenced data for soil erosion rates that were assessed using the universal soil loss equation (USLE) applied to the information about 1649 soil association units and their SOC inventories across the country. The results indicated that soil erosion varies widely across the states. The states lying in the north region (Uttar Pradesh, Uttarakhand, Jammu & Kashmir and Himachal Pradesh) and all states of the north‐eastern Himalayan region, Peninsular plateau (Madhya Pradesh, Chattisgarh and others) and Peninsular India (Orissa, Andhra Pradesh, Tamil Nadu, and Karnataka) showed more soil loss due to water erosion. At the national level in India, erosion transported about 4.87 Pg of soil and 115.36 Tg of C every year, which consequently emits about 34.61 Tg of C to the atmosphere. Among the states in India, Madhya Pradesh had the highest value of erosion‐associated C loss (20.33 Tg C yr−1), followed by Chhattisgarh, Maharashtra, Uttar Pradesh and Andhra Pradesh with losses of 9.98, 9.18, 9.11 and 8.70 Tg C yr−1, respectively. The possibility of C sequestration in various degraded lands was also explored. The results revealed that between 19 and 27 Tg C yr−1 could be sequestered in soils by adopting achievable technological options in erosion‐affected areas of India, offering the potential to reduce approximately 24.5% of the total greenhouse gas emissions from the agricultural soils of India (94 Tg C). Highlights: Eroded carbon is an important factor in biogeochemical carbon (C) cycle in India.A new database on lateral transport of C is created for India.Quantity of C lost due to water erosion in India is 115.36 Tg yr−1.Erosion control will help to reduce C emission by 19–27 Tg C yr−1 from India. [ABSTRACT FROM AUTHOR]

The present study assessed the extent to which the gully erosion susceptibility could be successfully modeled in Mayurakshi river basin as a function of four gully controlling factor clusters (topographical, erosivity, erodibility, and resisting). Gully erosion zones have been identified through two different models (correlation matrix and logistic regression) incorporating 15 conditioning indicators. Both the models have generated quite identical result regarding gully erosion susceptibility. It illustrates that extreme erosion-susceptible zones cover almost 16% of the basin area followed by high susceptibility with almost 28% areal coverage. This susceptibility is dominantly confined within the upper catchment. Older and newer alluvial plains of the lower catchment belong to low susceptibility to relatively safe situation. Models are validated by constructing ROC curve and calculating Kappa statistics. Both the approaches certified the validity of the models. The factor clustering shows that the positively influencing clusters like erosivity factors and erodibility factors are playing a dominant role with, respectively, 76 and 75% correlation with gully erosion susceptibility, whereas the resistant factors achieved only 32% correlation which denotes week significance due to containing sparse vegetation cover having low canopy density as well as poor crop management and conservation practice. [ABSTRACT FROM AUTHOR]

Purpose: Accelerated erosion removes fertile top soil along with nutrients through runoff and sediments, eventually affecting crop productivity and land degradation. However, scanty information is available on soil and nutrient losses under different crop covers in a vertisol of Central India. Thus, a field experiment was conducted for 4 years (2010–2013) to study the effect of different crop cover combinations on soil and nutrient losses through runoff in a vertisol. Materials and methods: Very limited information is available on runoff, soil, and nutrient losses under different vegetative covers in a rainfed vertisol. Thus, the hypothesis of the study was to evaluate if different crop cover combinations would have greater impact on reducing soil and nutrient losses compared to control plots in a vertisol. This experiment consisted of seven treatment combinations of crop covers namely soybean (Glycine max) (CC1), maize (Zea mays) (CC2), pigeon pea (Cajanus cajan) (CC3), soybean (Glycine max) + maize (Zea mays) − 1:1 (CC4), soybean (Glycine ma x)) + pigeon pea (Cajanus cajan) −2:1 (CC5), maize (Zea mays) + pigeon pea (Cajanus cajan) − 1:1 (CC6), and cultivated fallow (CC7). The plot size was 10 × 5 m with 1% slope, and runoff and soil loss were measured using multi-slot devisor. All treatments were arranged in a randomized block design with three replications. Results and discussion: Results demonstrated that the runoff and soil loss were significantly (p < 0.05) higher (289 mm and 3.92 Mg ha−1) under cultivated fallow than those in cropped plots. Among various crop covers, sole pigeon pea (CC3) recorded significantly higher runoff and soil loss (257 mm and 3.16 Mg ha−1) followed by that under sole maize (CC2) (235 mm and 2.85 Mg ha−1) and the intercrops were in the order of maize + pigeon pea (211 mm and 2.47 Mg ha−1) followed by soybean + maize (202 mm and 2.38 Mg ha−1), and soybean + pigeon pea (195 mm and 2.15 Mg ha−1). The lowest runoff and soil loss were recorded under soybean sole crop (194 mm and 2.27 Mg ha−1). The data on nutrient losses indicated that the highest losses of soil organic carbon (SOC) (25.83 kg ha−1), total nitrogen (N), phosphorus (P), and potassium (K) (7.76, 0.96, 32.5 kg ha−1) were recorded in cultivated fallow (CC7) as compared to those from sole and intercrop treatments. However, sole soybean and its intercrops recorded the minimum losses of SOC and total N, P, and K, whereas the maximum losses of nutrients were recorded under pigeon pea (CC3). The system productivity in terms of soybean grain equivalent yield (SGEY) was higher (p < 0.05) from maize + pigeon pea (3358 kg ha−1) followed by that for soybean + pigeon pea (2191 kg ha−1) as compared to sole soybean. Therefore, maize + pigeon pea (1:1) intercropping is the promising option in reducing runoff, soil-nutrient losses, and enhancing crop productivity in the hot sub-humid eco-region. Conclusions: Study results highlight the need for maintenance of suitable vegetative cover as of great significance to diffusing the erosive energy of heavy rains and also safe guarding the soil resource from degradation by water erosion in vertisols. [ABSTRACT FROM AUTHOR]

Land use (LU) dynamics and its relation to the accelerated soil erosion phenomenon in two broad geomorphic divisions of the Panchnoi River basin of Northeast India have studied. The present study was based on the Revised Universal Soil Loss Equation (RUSLE). To measure the impact of the LU dynamics on soil erosion, the basin was divided into two broad geomorphic divisions, i.e., plain zone and hilly zone, and the rate of soil erosion has been estimated separately for both of the geomorphic divisions. It has been found that in the plain zone, LU dynamics significantly accelerated soil erosion-from 0.52 ton/ha/yr in 1990 to 0.94 ton/ha/yr in 2015. Similarly, the vegetation density decreased significantly in the mountainous and hilly zone as the mean Normalized Difference Vegetation Index (NDVI) value changed from 0.45 in 1990 to 0.35 in 2015, which accelerated soil erosion from 12.06 ton/ha/yr to 18.30 ton/ha/yr from 1990 to 2015. The study indicates that soil erosion may give rise to a severe environmental as well as economic problem in the Panchnoi river basin, which may trigger issues related to the soil fertility of the basin area. [ABSTRACT FROM AUTHOR]

Soil erosion is a very common phenomenon encountered at many sloped earthen geotechnical structures. For instance, the surface soil of an inclined landfill cover system undergoes the erosion due to various adverse atmospheric variants. This is one of the major causes for performance failure in the cover system. However, previous researchers have rarely conducted the study for field assessment of soil erosion in high rainfall tropical regions such as northeast India. The literature advocates the utilization of vegetation for erosion management. This study investigated the impact of vegetation growth on soil erosion of a cover surface layer under both natural and controlled artificial rainfall. The soil erosion was monitored by collecting the soil loss due to rainfall. Vegetation growth was evaluated based on photographic image analyses. The study clearly indicates that the vegetation growth can contribute to reduction of soil erosion from the landfill cover surface. [ABSTRACT FROM AUTHOR]

The rainfall erosivity (R-factor in USLE) is the long-term average of the sum of the product of rainfall kinetic energy and its maximum 30-min intensity. Therefore, at most 30-min time intervals pluviograph records are required to calculate R-factor. But, such high-resolution data are scarce in many parts of the world and require lengthy processing period. In this study, R-factor was correlated with daily, monthly and annual rainfall, and its spatial variability in Eastern Ghats Highland of east India was mapped. The result showed that power regression models predicted satisfactorily the daily, monthly and annual R-factor, of which annual R-factor model performed best (model efficiency 0.93). Mean monsoon season R-factor was 15.6 and 10.0 times higher than the pre- and post-monsoon season R-factor, and thus remained highly critical with respect to erosion. Annual R-factor values ranged from 3040 to 10,127 MJ mm ha−1 h−1 year−1, with standard deviation of 1981 MJ mm ha−1 h−1 year−1. Rainfall intensity was positively correlated with erosivity density, and numerical value of rainfall intensity was almost double of the erosivity density value. The combination of rainfall and erosivity density was used to identify flood, erosion and landslide-prone areas. The developed iso-erosivity, erosivity density and risk maps can be opted as a tool for policy makers to take suitable measures against natural hazards in Eastern Ghats Highland of east India and elsewhere with similar rainfall characteristics. [ABSTRACT FROM AUTHOR]

High soil erosion and excessive sediment load are serious problems in several Himalayan river basins. To apply mitigation procedures, precise estimation of soil erosion and sediment yield with associated uncertainties are needed. Here, the revised universal soil loss equation (RUSLE) and the sediment delivery ratio (SDR) equations are used to estimate the spatial pattern of soil erosion (SE) and sediment yield (SY) in the Garra River basin, a small Himalayan tributary of the River Ganga. A methodology is proposed for quantifying and propagating uncertainties in SE, SDR and SY estimates. Expressions for uncertainty propagation are derived by first-order uncertainty analysis, making the method viable even for large river basins. The methodology is applied to investigate the relative importance of different RUSLE factors in estimating the magnitude and uncertainties in SE over two distinct morphoclimatic regimes of the Garra River basin, namely the upper mountainous region and the lower alluvial plains. Our results suggest that average SE in the basin is very high (23±4.7 t ha-1 yr-1) with higher values in the upper mountainous region (92±15.2 t ha-1 yr-1) compared to the lower alluvial plains (19.3±4 t ha-1 yr-1). Furthermore, the topographic steepness (LS) and crop practice (CP) factors exhibit higher uncertainties than other RUSLE factors. The annual average SY is estimated at two locations in the basin - Nanak Sagar Dam (NSD) for the period 1962-2008 and Husepur gauging station (HGS) for 1987-2002. The SY at NSD and HGS are estimated to be 6.9±1.2×105 t yr-1 and 6.7±1.4×106 t yr-1, respectively, and the estimated 90% interval contains the observed values of 6.4×105 t yr-1 and 7.2×106 t yr-1, respectively. The study demonstrated the usefulness of the proposed methodology for quantifying uncertainty in SE and SY estimates at ungauged basins. [ABSTRACT FROM AUTHOR]

The long-term average annual soil loss (A) and sediment yield (SY) in a tropical monsoon- dominated river basin in the southern Western Ghats, India (Muthirapuzha River Basin, MRB; area: 271.75 km²), were predicted by coupling the Revised Universal Soil Loss Equation (RUSLE) and sediment delivery ratio (SDR) models. Moreover, the study also delineated soil erosion risk zones based on the soil erosion potential index (SEPI) using the analytic hierarchy process (AHP) technique. Mean A of the basin is 14.36 t ha-1 year-1, while mean SY is only 3.65 t ha-1 year-1. Although the land use/land cover types with human interference show relatively lower A compared to natural vegetation, their higher SDR values reflect the significance of anthropogenic activities in accelerated soil erosion. The soil erosion risk in the MRB is strongly controlled by slope, land use/land cover and relative relief, compared to geomorphology, drainage density, stream frequency and lineament frequency. [ABSTRACT FROM AUTHOR]

Objective: This article overviews training, clinical services, and research on dual disorders across four countries: France, India, Israel, and Spain. Methods: The current dual disorders systems in each of the four countries were reviewed, with a focus on strengths and limitations of each. Results: In France, psychiatric care occurs within the public health care system and involves little training of medical graduates for managing dual disorders. Special courses and forums for specialists have recently started to meet the growing interest of physicians in learning how to manage dual disorders. The Indian health care system grapples with a large treatment gap for mental disorders, and while some treatment services for dual disorders exist, specific research and training efforts on dual disorders are just beginning. Israel has both public- and private sector services for patients with dual disorders, with specialized inpatient and emergency care for the acutely ill as well as day care and therapeutic communities for long-term management. Interest by researchers is growing, but training and education efforts in dual disorders are, however, minimal. Similar to the other countries, addiction and psychiatry disciplines are governed by separate divisions within the National Health System in Spain. There are some dual disorders services available, but they are limited in scope. While medical professionals clearly recognize the importance of dual disorders, there is no such recognition by the national and regional governing bodies. Conclusions: The common thread in various aspects of dual disorder management in each of these four countries is that there is a lower-than-desirable level of attention to dual disorders in terms of care, policy, research, and training. There are growing opportunities for training and continuing education in dual disorders management. We suggest that nations could learn from each other's experiences on how to address the issue of dual disorders. [ABSTRACT FROM AUTHOR]

The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all other Machine Learning Models (MLMs) and two dataset integration combinations. The merged NBR product exhibited improvements in terms of continuous and categorical statistics at all temporal scales as well as in all climatic zones. Our results indicate that the SPEM2L procedure could be successfully used in any other region or basin that has a poor gauging network or where a single precipitation product performance is ineffective. [ABSTRACT FROM AUTHOR]