A study was conducted in the natural forest and agroforestry land uses in the semi-arid ecosystem in Telangana, India to understand the morphological properties of soils in a profile and their vulnerability towards land degradation. A total of five pedons were morphologically studied, four from natural forest and one from agroforestry from the hill side slopes landform of the study area. Soils from the agroforestry ecosystem possess dark reddish brown, sandy loam A horizons and dark reddish brown, sandy clay loam B horizons with dominant sub angular blocky soil structure. Soils of natural forest ecosystems had a dominant soil texture of sandy clay loam at the surface and sandy clay in the subsurface, indicating the process of clay illuviation. Land degradation vulnerability was assessed using the properties viz. thickness of the surface horizon, surface texture, surface structure, erosion, bulk density, pH and organic carbon, available P and K of the surface as indicators. It was found that P3 (0.89) was very high, P1 (0.72), P2 (0.78), and P4 (0.78) were high, and P5 (0.69) was medium in its land degradation vulnerability status. The very high vulnerability of P3 could be attributed to the very severe erosion, lower pH of 5.35 and medium status of organic carbon (0.71), available P and K. The soil reaction, organic carbon content, texture, structure, and nutrient status could thus be considered as good soil quality indicators to assess the land degradation.

Background and aim Accurate estimation of soil organic carbon (SOC) is essential for assessing soil health, guiding land management, and making informed decisions on climate change mitigation strategies. Most studies on soil carbon rely on the Walkley-Black (WB) method due to its simplicity, but it underestimates SOC, especially in highly weathered tropical soils. Methods This study evaluated the WB correction factor (WBCF) by comparing SOC estimated from WB and the carbon-nitrogen (CN) analyzer method for 563 samples collected across the Western Ghats region of India. Results The results showed that WBCF increased with depth from 1.27 at 0-15 cm to 2.84 at 100-150 cm, indicating that the oxidation efficiency of WB method decreased with depth due to the accumulation of recalcitrant and mineral-bound organic matter in sub-soils. Correlation and segmented regression analysis identified a critical breakpoint at CEC/clay ratio of 0.21, below which soils exhibited high correction factors (WBCF) and low SOC recovery. Similarly, a threshold at 50% clay content marked a shift in recovery behavior. The correction factor calculated as the inverse of the regression slope was 1.62 for soils with low CEC/clay ratio (<0.21) and 1.29 for those with >= 0.21, indicating a strong influence of clay reactivity on SOC recovery. Similarly, soils with high clay content (>= 50%) had a higher correction factor (1.54) compared to soils with <50% clay (1.34), indicating the underestimation of SOC by the WB method in clay-rich soils due to greater organic matter stabilization. Conclusion These findings underscore the need for depth and mineralogy-specific correction factors, as the conventional WB factor substantially underestimates SOC in highly weathered, oxide-rich, low-active tropical soils.

The Soil Quality Index (SQI) is a vital tool for crop-specific land-use planning aimed at sustaining productivity and long-term soil health. This study identified key soil indicators that influence soil quality across two major mango-growing belts in southern Karnataka. Soils were sampled from the Central Mango Belt (Davangere, Chikmagalur, Shivamogga, Chitradurga, Mandya, and Mysuru) and the Southern Mango Belt (Kolar, Tumakuru, Chikkaballapura, Bengaluru Rural, Ramanagara, and Bengaluru Urban), representing diverse agro-climatic conditions. Soils samples were collected from the horizons of 15 representative pedons and analysed for their physico-chemical properties. Principal Component Analysis (PCA) was used to derive a Minimum Data Set (MDS) of key soil indicators, and the Soil Quality Index (SQI) was subsequently computed using additive and weighted approaches with both linear (LSF) and non-linear (NLSF) scoring functions. In SMB, Bangalore North, Hoskote, Chintamani, and Srinivasapura taluks showed high to very high SQI, whereas Mulabaghilu exhibited low SQI (0.38–0.43) due to high sand content and exchangeable sodium. Similarly, in the CMB, Tarikere and Hunsur Taluks showed high to very high SQI, while Nagamangala recorded a low SQI (0.36), constrained by a shallow effective soil depth. Yield–SQI relationships showed that NLSF captured soil functional variability more effectively than LSF, while weighted indexing outperformed the additive approach, particularly in semi-arid red ferruginous soils. The findings highlight the importance of incorporating inherent soil quality into land-use planning for perennial crops. These results offer a robust basis for region-specific, crop-optimized land use planning in comparable agro-climatic environments.

The expansion of rubber monoculture (RM) in tropical regions has led to significant land-use changes, contributing to soil acidification and nutrient depletion. This study evaluates soil nutrient status under three rubber-based land use types (RLUTs) in south-western Karnataka: rubber monoculture (RM), rubber agroforestry (R+AF) and rubber with natural vegetation (R+NV). Results indicated that RM exhibited significantly lower pH, SOC (38 and 71% lower than R+AF and R+NV, respectively) and base cations, but increased soil acidification and exchangeable Al3+ and H+ concentrations (p <0.05). RM exhibited significantly lower available nutrients compared with R+AF and R+NV (p <0.05), with reductions of 48 and 34% for N, 340 and 30% for P, 26 and 243% for Ca, 73 and 142% for Mg, 67 and 208% for Zn, and 57 and 74% for B, respectively. Available P, Zn and B were found deficient in soils, while iron (Fe) and manganese (Mn) were in near toxic concentrations. Soil pH, exchangeable acidity and organic carbon (SOC) are critical in maintaining nutrient availability. SOC was positively correlated with available nutrients, namely N, K, Ca, Fe, Mn, Cu, Zn and B, while soil acidity was negatively correlated with available Ca and Mg content. The study recommends avoiding RM or selecting rubber-based agroforestry systems or naturally managed rubber plantations incorporating legumes, cover crops or medicinal plants to improve nutrient availability.

Heavy rainfall in humid tropical regions (annual average 3911 mm) causes soil erosion and damage through landslides. Using the universal soil loss equation (USLE), we estimated soil loss, sediment yield (SY) and sediment load (SL) in Elamdesam (total area 18750.51 ha) in Idukki district, Kerala, via a soil survey. The slope varied from 0 to over 33%. The USLE's rainfall erosivity R-factor was 1498.69 mm ha-1hr-1yr-1; the soil erodibility factor (K) ranged from 0.41 to 0.69, and the LS factor from 0.092 to 18.37. The cropping management factor (C) was 0.006 for rubber and 0.28 for paddy. The conservation practice factor (P) ranged from 0.40 to 0.65. Estimated soil erosion was low (0-5 t ha-1yr-1) in 22 mapping units (12909.48 ha), very severe (> 40 t ha-1yr-1) in five (4694.43 ha), low (5-10 t ha-1yr-1) in two (743.32 ha), and moderate (10-20 t ha-1yr-1) in two (403.26 ha). SY varied from 0.841 to 23.196 t ha-1yr-1, and SL ranged from 372.58 to 47089.98 t ha-1yr-1. The increase in soil erosion rate can be traced to rainfall severity, land cover (LC) changes, vegetation loss, and Ap horizon erosion. S2hG2, S6iH2, S1hH2, S1fH2g1, and S9hB2 experienced soil erosion rates over 40 t ha-1yr-1, marking them as high-risk zones for erosion. Implementing soil conservation measures to achieve land degradation neutrality (LDN) and prevent further soil loss, energy depletion and economic losses is crucial.

The unprecedented expansion of rubber monoculture in the Western Ghats and south-western peninsular India, through tropical forest conversion, supports economic growth but may degrade soil quality and reduce soil organic carbon (SOC). This study evaluates the impact of three rubber-based land-use types (LUTs)-rubber monoculture (RM), rubber + agroforestry (R + AF) and rubber + native vegetation (R + NV); across plantation ages (< 15, 15-25, > 25 years) on soil properties, soil quality index (SQI) and SOC sequestration to identify sustainable rubber systems with minimal land degradation. Long-term RM significantly increased aluminium saturation (15.23%) over R + NV (0.65%) and R + AF (12.54%), leading to soil acidification and poor root development. R + NV and R + AF showed significantly higher SQI (by 36.1% and 9.33%) than RM, and SQI declined with plantation maturity, regardless of LUT. Surface SOC in RM was 56.3% and 23.5% lower than R + NV and R + AF, respectively, with reduced SOC storage in deeper layers. Cluster analysis confirmed that younger R + AF and R + NV plantations enhanced SQI and SOC sequestration, whereas older RM plantations showed the poorest outcomes. Among the three LUTs, R + NV best improved soil quality and carbon storage, while R + AF ensured higher latex yield and economic returns, highlighting a trade-off between ecological benefits and economic viability.

Climatic and landform diversity left its footprints on Indian Peninsula, offers an opportunity to inspect the impact of climate and related changes on key pedogenic soil qualities. To understand different pedogenic processes, we studied eight pedons from five major agro-climatic zones with varying morphology and physiography associated with similar land use. Depending on morphology and physio-chemical soil variables, soils were classified as Rhodic Kandiustults, Rhodic Kandiustalfs, Kandic Paleustalfs, Rhodic Paleustalfs, Typic Rhodustalfs, Aquic Haplustalfs, Typic Haplustepts at sub group level. Given that horizon differentiation is the basic unit of pedogenesis, numerical hierarchical cluster analysis was used to compare and construct the quantifiable soil qualities within horizons for better comprehension of pedogenic processes. The main pedogenic processes were desilication, clay illuviation, base leaching, ferralitization, rubrifaction, ferrugination and braunification. Principal component analysis (PCA) was used to identify the key soil characteristics that affect the pedogenic processes and they were correlated with elevation and climatic factors. Indicating the impact of dryness and climate change on clay mineralogy, potential evapotranspiration (PET) and length of dry period (LDP) showed a significant negative correlation with clay type and soil exchangeability, leading to an accumulation of clays with more sub-active to semi-active CEC/clay ratio classes in sub-surface soils in semi-arid tropical regions of southern Karnataka. In contrast, the horizon sequence of P4 showed a limited effect of time on profile development, due to cyclical variations in erosion and deposition cycles

Security is vital issues to be communicated for execution of e-healthcare scheme. Till now data hiding has most powerful instrument that can handle previous challenges. Reversible data hiding (RDH) planned to embed secret message in cover image at receiving end, both secret message and original cover image is recovered. Previous RDH methods designed for higher embedding rate. In this work, Deep Neural Rhombus Histogram based Dual Reversible Data Hiding (DNRH-DRDH) is proposed method for addressing both embedding rate as well as visual quality. It utilizes interpolated images reversible data hiding with Block Truncated Rhombus Prediction. Prediction error histogram generated embedded with secret data for obtaining stego images of corresponding interpolated pixels. Lastly, classification output is generated by ReLU activation function in extracted part for secured communication among users. Simulation carried on medical test images. Study reveals of proposed method gives higher embedding rate with superior image quality than existing methods.

Land degradation is a serious problem worldwide due to its severe implications on ecosystem services. Land degradation results from soil degradation that arises from a decline in soil fertility, loss in biodiversity, changes in land use or land cover, and vegetal degradation. There are several mechanisms to assess land degradation susceptibility and vulnerability. Geospatial technologies are those that include remote sensing, Global Positioning System, and Geographical Information System (GIS), which are also used in assessing land degradation, mainly in integration with each other. This study deals with the factors that make a piece of land susceptible to degradation, thus making it vulnerable, and the various geospatial technologies used to assess land degradation susceptibility and vulnerability. Integrated remote sensing and GIS techniques provide effective tools and data sources for the assessment of land and soil degradation by analyzing various influencing parameters at local and regional scales. The multispectral images obtained from LANDSAT- MSS, TM, ETM+, OLI, SPOT, IRS – LISS I - IV, Terra-ASTER, and IKONOS, are used for the assessment of land degradation worldwide. Several methods are used to assess soil degradation.The major methods include (1) Global assessment of human-induced soil degradation; (2) Assessment of the status of human-induced soil degradation; (3) World overview of conservation approaches and technologies; (4) classification approach; (5) indicators approach; and (6) modeling of soil erosion and soil loss. Moreover, the Revised Universal Soil Loss Equation is effectively used for the estimation of soil using GIS software. Overall, the descriptions about several methods and techniques are used to assess land and soil degradation, by estimating soil erosion and soil loss, soil salinity, soil alkalinity, LULC changes, and vegetal (forest) degradation in any parts of hydrological units.

Soil, a living entity of our mother earth in nature, it is the largest pool of organic carbon on the objects of the earth's surface. Hence, soil organic carbon (SOC) pools significantly impact the health of terrestrial ecosystems by changing the characteristics and quality of soil along with the emission of fluxes through heterogenic respiration. Since carbon sequestration may be able to lessen climate change, carbon storage (C-storage) in soils has drawn more interest among researchers. Numerous studies have demonstrated the importance of soil spectroscopy as a critical enabler for soil attributes that promote the use of remote sensing (RS) technology for determining SOC concentration on a global scale. Here, we provide an in-depth analysis of the various RS platforms, techniques, and models, as well as their shortcomings and the need for policy integration in estimating terrestrial SOC, including soil carbon stocks in wetlands and belowground soils, as well as changes in carbon fluxes and aboveground biomass as a source of carbon-based on platforms and data-driven (or machine learning) methods. The necessity for monitoring systems that can track and update SOC status and change estimations at scales ranging from local to global is highlighted by the increased awareness of soil carbon as a crucial component in policy and action on climate change as well as for ecosystem health. In order to assist advanced policies and future demands, we have also looked at the approaches and difficulties for determining how disruptions affect SOC assessment.

Monitoring soil quality index (SQI) and soil organic carbon (SOC) stock status of the Western Ghats (WG) forests in India is crucial for providing vital ecosystem services alongside sustainable forest management practices. However, comprehensive profile data on SQI and SOC stock across different forest types under WG forests are limited. The study evaluated SQI and SOC stock under three forest types, i.e. tropical wet evergreen (TWE), tropical semi-evergreen (TSE), and tropical moist deciduous (TMD) across WG in Karnataka. SQI was assessed using principal component analysis with two indexing approaches and scoring methodologies, with weightage indexing through nonlinear scoring functions (NLSF) showing superiority over other methodologies. TMD forests exhibited the highest SQI, followed by TWE and TSE, while the lowest was observed in Rippon Pet RF (0.36 surface, 0.28 control section), primarily due to limitations in organic carbon and clay content. SOC stock mirrored SQI trends (TMD > TWE > TSE), with the highest values in Kollegal RF (339.3 MG ha−1) and lowest in Rippon Pet RF (102.5 MG ha−1). Although SOC and SQI were established to be ideal indicators for dynamic ecosystem services (ESs), high OC content in surface soils of Poomale NF induces pedogenic acidification and Al toxicities, indicating potential forest soil degradation. Significant correlation with control section SQI and SOC (p < 0.05) emphasises monitoring subsurface soil status to identify soil degradation, sustainable forestry practices, and complex ESs in forest systems.

Knowledge on site-specific soil carbon aids in many research projects related to carbon and environment modeling, soil fertility, health and conservation, and climate change mitigation. The demand for site-specific soil carbon content and its stock is increasing as it contributes to soil fertility and health, the global carbon cycle, and environmental sustainability. Thus necessitating the use of cutting-edge technologies for predicting the soil carbon at higher resolution for greater spatial extent, which is being compensated by state-of-the-art digital soil mapping (DSM). DSM is the spatial prediction of soil properties and the associated uncertainty using environmental covariates. Since the last two decades, the DSM concept has flourished, and currently it is moving toward being more operational than research. Worldwide, the DSM concept has been extensively applied for predicting soil properties, including SOC mapping, due to its significance for soil fertility, quality, and atmospheric carbon content. This chapter envisages the basic concept of DSM, its advantages over conventional soil mapping, covariates used for prediction, models involved in DSM, the application of DSM in soil carbon and stock mapping, and case studies from India with the limitations associated with it.

Sentiment analysis task provides the unique challenge of analyzing a fraction of human emotion from text, Malayalam which is already a computationally complex language due to its high morphological features and agglutination further enhances these challenges. A dataset of 22,449 samples was prepared from surveys and social media that have been classified as positive, negative, or neutral. To study the applicability of the dataset a Machine learning-based sentiment analysis was carried out. TF-IDF was used for word vectorization and a Support Vector Machine (SVM) classifier with a Radial Basis Function (RBF) kernel and a Random Forest classifier was used. With an accuracy of $85.07 \%$, the RF classifier outperformed the SVM model, which only managed $59.9 \%$. The study outlines potential improvement by expanding the dataset to better represent the low-resource language of Malayalam.