Texture Classification Research Articles

Vertical distribution of soil properties and soil organic carbon in community managed forest of Siwalik Hill, Nepal

Abstract. Lamichhane U, Ghimire P. 2022. Vertical distribution of soil properties and soil organic carbon in community managed forest of Siwalik Hill, Nepal. Asian J For 6: 91-96. Nepal's Community Forestry (CF) approach is globally recognized as an innovative and successful approach to forest resource management. Shorea robusta Gaertn. is the dominant tree species of Nepal's forest which covers 19.28% of the total stem volume of the country's forest area. Soil represents a significant terrestrial Carbon (C) pool and can play a significant role in mitigating global warming. Therefore, analyzing soil physicochemical properties and Soil Organic Carbon (SOC) stock is essential because they are key parameters for evaluating land use management systems. The present study was conducted in the Durga Mai Community Forest (DMCF) of Makawanpur District, Nepal, to assess the vertical distribution of soil physicochemical properties and SOC stock. Soil samples within a 1 m profile in five depth layers (0-20 cm, 21-40 cm, 41-60 cm, 61-80cm, and 81-100 cm) were collected and analyzed for Bulk Density (BD), soil texture, soil pH, soil carbon content, Total Nitrogen (TN), Available Phosphorous (AP), Available Potassium (AK) and SOC stock. Sandy loam was the dominant soil textural class reported throughout the study area. Average BD, soil pH, TN, AP, and AK in the S. robusta forest's soil in 1 m soil profile depth were found to be 1.16 ±0.07 gmcm-3, 5.78 ±0.62, 0.14 ±0.04 %, 5.94 ±1.04 kgha-1, and 104.20 ±19.66 kgha-1, respectively. The results estimated 107.57 ±3.06 tha-1 of SOC stock in 1 m soil profile, with 0.93 ±0.21% average SOC content. Further, average TN, AP, and AK were found to be decreased with an increase in soil profile depths. Similarly, soil profile depths significantly affected SOC stock and soil properties like BD, soil pH, TN, AP, and AK.

Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset

Soil provides a key interface between the atmosphere and the lithosphere and plays an important role in food production, ecosystem services, and biodiversity. Recently, demands for applying machine learning (ML) methods to improve the knowledge and understanding of soil behavior have increased. While real-world datasets are inherently imbalanced, ML models overestimate the majority classes and underestimate the minority ones. The aim of this study was to investigate the effects of imbalance in training data on the performance of a random forest model (RF). The original dataset (imbalanced) included 6100 soil texture data from the surface layer of agricultural fields in northern Iran. A synthetic resampling approach using the synthetic minority oversampling technique (SMOTE) was employed to make a balanced dataset from the original data. Bioclimatic and remotely sensed data, distance, and terrain attributes were used as environmental covariates to model and map soil textural classes. Results showed that based on mean minimal depth (MMD), when imbalanced data was used, distance and annual mean precipitation were important, but when balanced data were employed, terrain attributes and remotely sensed data played a key role in predicting soil texture. Balanced data also improved the accuracies from 44% to 59% and 0.30 to 0.52 with regard to the overall accuracy and kappa values, respectively. Similar increasing trends were observed for the recall and F-scores. It is concluded that, in modeling soil texture classes using RF models through a digital soil mapping approach, data should be balanced before modeling.

Multidirectional Gradient Feature With Shape Index for Effective Texture Classification

Recently, local gradient microstructure of image textures has become an important field of texture classification, but it is generally to investigate the multiscale local microstructures of image gradient, and rarely consider the multidirectional and multiscale local microstructure of image gradient. The proposed algorithm first extracts the two-order gradient feature of the image from different orthogonal directions and further constructs the multiple shape index of the image, and then calculates the histogram feature vectors of the shape index on different orthogonal directions and scales, and finally connects all histogram feature vectors on different orthogonal directions and scales to obtain the final matching feature vector of the image. To further enhance the discriminant ability of feature vector generated by multidirectional shape index schemes, the weight of each block of images is also considered. Experiments on two texture databases and one palmprint database have fully confirmed the effective of proposed algorithm.

A database of soil physical properties for the Kansas Mesonet

AbstractThis study developed a comprehensive database of soil physical properties to expand the potential applications of soil moisture observations across the Kansas Mesonet environmental monitoring network. The database comprises 14 site‐ and depth‐specific soil hydraulic properties for 40 stations monitoring soil water content. The measured soil hydraulic properties include sand, clay, and silt contents; bulk density; particle density; total porosity; effective saturation; saturated hydraulic conductivity; and water retention at six matric potentials. The soil database spans eight soil textural classes, with silty clay loam, silt loam, and silty clay soils dominating the fine‐textured soils, and sandy loam and sandy clay loam soils comprising the only coarse‐textured soils. All the measured soil hydraulic properties showed low coefficient of variation (CV ≤10%) within soil textural classes, except for the saturated hydraulic conductivity, which varied from a median of 0.520 ± 308 cm d–1 in the clay soils to 47.1 ± 119 cm d–1 in the sandy loam soils. The uncertainty of the Kansas Mesonet soil water content observations was reduced by at least 29% using laboratory sensor calibration equations instead of the factory default sensor equation. Our soil physical property database offers new prospects to use historical and near real‐time in situ soil water content observations across the Kansas Mesonet for diverse agricultural and hydrological applications, including drought monitoring, wildfire preparedness, and estimating potential drainage and groundwater recharge rates.

Spectra transfer based learning for predicting and classifying soil texture with short-ranged Vis-NIRS sensor

Classifying soil texture is critical to investigate soil processes and functions influencing agronomic decisions and environmental stewardship. Despite the multiple benefits together with accurate estimation potential of visible and near-infrared reflectance spectroscopy (Vis-NIRS) over the traditional method of texture analysis, the prediction accuracy of the Vis-NIRS decreases due to the negative effect of soil moisture content (MC) on soil spectra. This study evaluated the performance of piecewise direct standardization (PDS) and direct standardization (DS) for eliminating the soil MC influence from fresh (non-processed wet) soil spectra to improve the accuracy of soil texture classification using a short-ranged (305–1700 nm) Vis-NIRS (Tec5 Technology, Germany). A set of 96 composite soil samples was collected from 18 fields, and the second set of 294 non-composite samples was collected from 10 of these 18 fields. All soil samples were scanned using a Vis-NIRS sensor after drying, grinding, and sieving, while the non-composite samples were additionally scanned in wet conditions. After analysing the composite samples for percent sand, silt, and clay, partial least squares regression (PLSR) models were calibrated (72 samples in calibration set) and validated (23 samples in validation set). Then, the PLSR model was used to predict the textural fractions for the wet, air-dried, DS and PDS transferred spectra of non-composite soil samples followed by their texture classification. Validation results indicated that the Vis-NIRS sensor predicted sand, silt, and clay fractions with excellent accuracies [coefficient of determination (R2) = 0.88–0.93; residual prediction deviation (RPD) = 2.91–3.74; ratio of the performance to interquartile range (RPIQ) = 2.12–5.02; root means square error (RMSE) = 3.62–8.06 %). Spectra transferred by the PDS and DS improved the texture classification accuracy up to 70 % and 90 %, respectively, while the wet soil spectra misclassified most fields (accuracy = 20 %). Unlike PDS, the DS predicted particles’ distribution seemed approximately similar to laboratory observations. Therefore, it was concluded that transferring the wet Vis-NIRS spectra by the DS algorithm can provide a rapid and accurate prediction of particle fractions and classification of soil texture classes with minimized effort, time, and cost.

Two-dimensional signature of images and texture classification

We introduce a proper notion of two-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a two-dimensional object such as an image. It thus serves as a low-dimensional feature for pattern classification. Here, we implement a simple procedure for texture classification. In this context, we show that a low-dimensional set of features based on signatures produces an excellent accuracy.

Maximum A Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection.

One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum-Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.

Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms

Soil texture classes (STCs) influence the physical, chemical and biological properties of the soil, and accurate spatial predictions of STCs are essential for agro-ecological modeling. The purpose of this study was to assess the capabilities of environmental covariates derived from Landsat 8 OLI Science products and a digital elevation model (DEM), as well as three machine learning methods, to provide an accurate and reliable classification of soil texture classes. Estimation efficiency of soil texture classes was investigated using Decision tree (DT), Random forest (RF), and Support vector machine (SVM) algorithms in an area with Fluvisols and Vertisols as predominant reference soil groups in Northwestern Türkiye (Lake of Manyas). The models were validated using the leave-one-out, cross-validation technique. The best of the three machine learning models for soil texture classification was RF, with an overall accuracy of 0.63 and a kappa index value of 0.14, according to the accuracy evaluation. The RF algorithm generated a map whose findings were more consistent with the real environment using the confusion index (RF:0.30) and abundance index (RF:1.0) values as uncertainty criteria. The most important predictor of soil texture classes was the topographic wetness index (mean decrease accuracy: 3%) for tree-based learning algorithms, followed by other indexes based on satellite data. These findings will contribute to support sustainable soil management techniques in a location with a large degree of topsoil texture diversity.

Characterization of Coffea arabica L. parent plants and physicochemical properties of associated soils, Peru

It is important to carry out the morphological characterization of coffee parent plants and the physicochemical properties of the associated soils in the Amazon region, Peru, in order to achieve germplasm conservation. One hundred coffee mother plants were identified and located in five provinces of the region and evaluated according to morphological descriptors such as stipula shape, young leaf color, leaf shape, leaf apex shape, young shoot color, leaf color, fruit color, fruit shape, mature leaf color, and rust incidence percentage. In the plots where the parent plants were located, soil sampling was carried out to determine the physical and chemical properties. The varieties with the greatest presence in the five provinces were Típica and caturra roja, with the greatest number of specimens reported for the province of Bagua. The predominant stipule shape was triangular (91%), lanceolate leaf shape (60%) and red fruit color (90%). Bongará reported the lowest incidence of yellow rust, as well as the Mundo Novo Rojo variety. Soil pH ranged from acidic to neutral values, low electrical conductivity, high organic matter content, low phosphorus content, high potassium levels and medium cation exchange capacity. The predominant textural class was sandy loam. The physical and chemical characterization of the soils under study show favorable ranges to encourage the best development of coffee cultivation.

How does dolomite application affect the greenhouse gases emissions from the terrestrial environment? – A global synthesis

Global climatic energy balance has been increasingly altered by massive emissions of greenhouse gases (GHGs), such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), leading to a variety of natural disturbances in terrestrial ecosystems. Further, the increasing use of fossil fuels and the looming climate crisis have created an unprecedented urgency for the development of a biobased circular economy. Therefore, production of biofuels from plant biomass is currently seen as a promising source of renewable energy , ensuring sustainable development with minimal carbon footprint. Soil acidification is considered one of the major obstacles to crop production and a significant source of GHGs emissions, especially N2O, because acidification changes the physicochemical and biochemical properties of the soil. Dolomite (DM) is the most widely used countermeasure to neutralize soil acidity to improve crop productivity and control net fluxes of GHGs. Nevertheless, the extent of GHG emissions following the application of DM under different environmental conditions is still unclear. Therefore, in this context, we conducted a meta-analysis using 32 peer-reviewed publications to determine the effects of DM, climate zones, and soil properties on GHGs emissions. The results of the current meta-analysis show that DM application significantly increased CO2 emissions (30.34 %) and CH4 emissions (4.91 %), but reduced N2O emissions by 54.88 %. A significant effect of DM (>10 t ha−1) on CO2, CH4, and N2O emissions was also observed. Increasing soil pH increased CO2 and N2O emissions by 188.34 % and 49.78 %, respectively, while reducing CH4 emissions by 81.94 %. Most importantly, WFPS, soil textural class, soil C:N ratio, and climate zones were identified as key edaphic factors affecting the GHG emissions following the application of DM. Overall, this meta-analysis fills in the gaps regarding the impact of the application of DM on GHGs emissions in different climates, soil properties, and experimental conditions. In summary, a better understanding of the interactions between DM and GHGs flux in the terrestrial ecosystem can be used not only to avert climatic upheaval, but also to better predict these emissions from acidic soils.

Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks

Abstract: Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production. There had been a debate regarding the part of the tree to be used for differentiation, whether it should be leaves, fruits, flowers or bark. Studies have proven that bark is of utmost importance as it will be present despite seasonal variations and provides a characteristic identity to a tree by variations in the structure. In this paper, a deep learning based approach is presented by leveraging the method of computer vision to classify 50 tree species, on the basis of bark texture using the BarkVN-50 dataset. This is the maximum number of trees being considered for bark classification till now. A convolutional neural network(CNN), ResNet101 has been implemented using transfer-learning based technique of fine tuning to maximise the model performance. The model produced an overall accuracy of >94% during the evaluation. The performance validation has been done using K-Fold Cross Validation and by testing on unseen data collected from the Internet, this proved the model's generalization capability for real-world uses.

A Unified Understanding of Diverse Spin Textures of Kramers–Weyl Fermions in Nonmagnetic Chiral Crystals

AbstractChiral crystals, characterized by rotation, screw rotation, and translation symmetries are abundant in nature. The existence of Kramers–Weyl fermions (KWFs) at the time‐reversal‐invariant momenta in nonmagnetic chiral crystals (NCCs) has attracted intense attention due to their unique physical properties beyond conventional Weyl fermions. Although the spin texture, one of the most fundamental physical quantities, is found to be dramatically different for different KWFs in different NCCs, a unified understanding of this puzzling phenomenon is still lacking. In this article, combining k · p theory and first‐principles calculations, k‐linear Hamiltonians for KWFs are constructed and consequently a complete classification for the spin textures of KWFs in all the NCCs is made, which are confirmed by a series of material example calculations. Interestingly, it is found that the spin textures are reversed in NCCs with opposite chirality, leading to reversed Fermi arc surface states. Importantly, this study unveils that the nonlinear optical responses around the KWFs in NCCs can be largely determined by their spin texture classification. This study not only provides a unified understanding of the spin textures of KWFs in all the NCCs, but also suggests a principle to design novel KWF‐related spintronics and nonlinear optics.

Comparative Analysis of the Performance of Complex Texture Clustering Driven by Computational Intelligence Methods Using Multiple Clustering Models.

Traditional texture cluster algorithms are frequently used in engineering; however, despite their widespread application, these algorithms continue to suffer from drawbacks including excessive complexity and limited universality. This study will focus primarily on the analysis of the performance of a number of different texture clustering algorithms. In addition, the performance of traditional texture classification algorithms will be compared in terms of image size, clustering number, running time, and accuracy. Finally, the performance boundaries of various algorithms will be determined in order to determine where future improvements to these algorithms should be concentrated. In the experiment, some traditional clustering algorithms are used as comparative tools for performance analysis. The qualitative and quantitative data both show that there is a significant difference in performance between the different algorithms. It is only possible to achieve better performance by selecting the appropriate algorithm based on the characteristics of the texture image.

Zonguldak ili Nebioğlu yöresi doğal meralarının toprak ve vejetasyon özelliklerinin belirlenmesi

The aim of this study was to determine the ecological characteristics of natural rangelands. For this purpose, four different natural pastures were studied in Nebioğlu region in Zonguldak province. When the studies on the subject were examined, it was determined that, while studies were carried out on pastures in almost every region of our country; such a study has not been conducted in the Nebioğlu region before. This situation increases the original value of the research. Vegetation cover in the study areas were identified and the botanical composition was checked at the family level, including Fabaceae, Poaceae and other genus. Soil sampling was done in pasture areas. A total of 40 soil samples, compiled from 10 randomly taken samples from each study area, were subjected to some physical, chemical and biological analyses. In the collected soil samples, soil texture class, actual pH (H2O), lime content, electrical conductivity, total nitrogen, organic carbon, available phosphorus and available potassium contents were analyzed. According to the results of the study, one way-Anova analysis of variance was performed in the SPSS program in order to understand if the Fabaceae, Poaceae, other genus ratios and soil properties are different among the research areas. Duncan test was applied to determine different groups. According to the results of the study, the canopy coverage of vegetation was calculated as 100% in all pasture areas. The highest rate of legumes was in Hallaç rangeland with 48.7%, grasses rate was in Gölyanı range with 39.8%, other genus rate was 30.8% in Alçak and Hallaç pastures; The lowest rate of legumes was found in Alçak pasture with 38.3%, the rate of grasses in Hallaç with 20.5%, and the rate of other families in Gölyanı range with 20.1%. According to the results of the soil analysis, it was determined that the soils in all pasture areas were in the clayey soil class, slightly alkaline and had low electrical conductivity.

GIS and fuzzy logic integration in land suitability assessment for surface irrigation: the case of Guder watershed, Upper Blue Nile Basin, Ethiopia

Before planning the use of existing land resources for irrigation, it is necessary to determine their availability. The primary goal of this research was to examine the Guder watershed's land resource potential for irrigation development and to create a geo-referenced map of these resources using a geographic information system coupled with fuzzy logic. Irrigation suitability criteria such as slope, land use, proximity to water body, rainfall deficit, soil texture class, soil depth, soil drainage classes, and proximity to road were considered when evaluating prospective irrigable properties. The criteria maps were divided into four suitability classes using a natural break interval range technique. According to the study's findings, 39.8% of the watershed area is Highly suitable, 34.5% is moderately acceptable, 24.5% is marginally suitable, and 1.2% is not suitable for the aforementioned reasons. According to the irrigation suitability study of these characteristics, 70.42% of the slope, 15.57% of the slope, 10.6% of the slope, and 3.95% of the slope are Highly, moderately, and marginally suitable for surface irrigation, respectively. In addition, 15% of the soil in the study Area is suitable for a surface irrigation system. In terms of land cover and use, 75% is highly favorable, whereas 0.3% is not suitable for irrigation development. GIS and remote sensing offer a straightforward and powerful framework for combining spatially complicated field variables for land suitability research. This study demonstrates the effectiveness of the fuzzy logic technique combined with GIS as an effective model for finding prospective irrigable land on a continental scale.

The Use of Crop Residues and High Activity Clays for the Management of Sandy Soils for Rice Production in Sokoto, North-Western Nigeria

A field experiment was carried out to assess the effects of various crop residues (rice husk, rice straw, groundnut husk, and millet husk as well as high activity clay (HAC) on some soil properties, growth and yield parameters of rice (Oryza sativa L). Sixteen treatment combinations, replicated thrice, were applied at the rates of 5 tha-1 and 10 tha-1 each for rice husk, rice straw, millet husk, groundnut husks and high activity clay. Results obtained from the field trial as presented in this paper, showed significant effect of some of the treatments on soil chemical parameters especially organic Carbon, available P and exchangeable K. Similarly, soil physical parameter such as texturally was also significantly influenced by the treatments. The texture of the soil, which was initially loamy sand before the incorporation of the amendments, was altered to new textural classes in all the treatments after the experiment. The treatments had significant effects on all the growth parameters except for plant height at four (4) weeks after transplanting. The treatments also had significant effects on all the yield parameters of the test crop (rice).

Mineralogical, Geochemical Characterization and Physicochemical Properties of Kaolinitic Clays of the Eastern Part of the Douala Sub-Basin, Cameroon, Central Africa

The sedimentary clays of the Douala sub-basin (Cameroon) were studied to determine their mineralogical composition and physicochemical properties to boost their potential suitability as materials for traditional ceramics and eventually modern ceramics. These clayey materials are not widely used locally as building materials and little data are available on these materials in the field of ceramics and they are relatively unknown. Three profiles from 3.9 to 7.4 m thickness were studied on the field in order to determine their mineralogical (X-ray diffraction, infrared), chemical (X-ray fluorescence) and physicochemical (particle size, Atterberg limits, organic matter, cation exchange capacity and hydrogen potential) properties. Globally, ten (10) clay samples were analyzed to highlight the nature and technological properties of these clays. Mineralogically, kaolinite (48.3–69.2 wt.%) and quartz (20.5–41.2 wt.%) were the most abundant minerals in these raw clay materials. They were associated with a very small or moderate quantity of illite, hematite, goethite, feldspar, gibbsite and micas. Geochemically, the clayey materials had high silica (SiO2, 22.21–58.03%) and alumina (Al2O3, 12.84–22.94%) contents, with a significant amount of iron oxides (Fe2O3, 1.07–17.92%). Other oxides (K2O, MgO, TiO2, Na2O, MnO, CaO and P2O5) were in a relatively lower proportion. A high level of alumina content explains the kaolinitic nature of these clayey materials. The results of the granulometric analysis of the clayey materials showed the following distribution: clay (26–99%) followed by silt (1–70%) and sand (0–4%). This corresponds to silty clay soils according to the Belgian textural classification diagram, with high plastic index (63.9%) characteristics. The studied clay materials are good candidates for the production of ceramics and terracotta building. This study is therefore important for any application of this type of clay in various industrial fields.

Fuzzy Color Aura Matrices for Texture Image Segmentation.

Fuzzy gray-level aura matrices have been developed from fuzzy set theory and the aura concept to characterize texture images. They have proven to be powerful descriptors for color texture classification. However, using them for color texture segmentation is difficult because of their high memory and computation requirements. To overcome this problem, we propose to extend fuzzy gray-level aura matrices to fuzzy color aura matrices, which would allow us to apply them to color texture image segmentation. Unlike the marginal approach that requires one fuzzy gray-level aura matrix for each color channel, a single fuzzy color aura matrix is required to locally characterize the interactions between colors of neighboring pixels. Furthermore, all works about fuzzy gray-level aura matrices consider the same neighborhood function for each site. Another contribution of this paper is to define an adaptive neighborhood function based on information about neighboring sites provided by a pre-segmentation method. For this purpose, we propose a modified simple linear iterative clustering algorithm that incorporates a regional feature in order to partition the image into superpixels. All in all, the proposed color texture image segmentation boils down to a superpixel classification using a simple supervised classifier, each superpixel being characterized by a fuzzy color aura matrix. Experimental results on the Prague texture segmentation benchmark show that our method outperforms the classical state-of-the-art supervised segmentation methods and is similar to recent methods based on deep learning.

Laboratory Coking and a New Optical Texture Classification Parameter: Screening of Petroleum Refinery Streams for Premium Needle Coke Production

Laboratory Coking and a New Optical Texture Classification Parameter: Screening of Petroleum Refinery Streams for Premium Needle Coke Production

ASSESSMENT OF SOIL FERTILITY STATUS OF DIFFERENT VILLAGES FROM PALAKKAD DISTRICT, KERALA

An assessment of soil fertility status of different villages from Palakkad district, Kerala carried out 2021-22. The prime objectives of this work were to study the physico-chemical properties of soil at different depths of various sites of Attappady block of Palakkad District, Kerala to determine the availability of macro and micro nutrient on soil of these soil samples and provide Soil Health Card for farmers of the Attappady block, Palakkad District, Kerala. For the assessment 9 sampling locations were selected. Soil samples were collected with depth of 0-15 cm, 15-30 cm and 30-45 cm respectively. The Soil colour (Dry Condition) of soil varied from Brown, Dark brown, Yellowish brown, Yellowish red, Dark greyish brown, very dark brown and Brownish yellow. Soil colour (Wet Condition) of the soil varied Brown, Dark brown, Yellowish red, very dark greyish brown, very dark brown and Brownish yellow colour. Soil textural classes were sandy loam. It clearly indicated that soil has good Water Holding Capacity (54.19 to 61.80 %) and good physical condition, Bulk Density (0.938Mg m-3 to 1.365Mg m-3). Particle Density (2.124 Mg m-3 to 2.866 Mg m-3). Pore Space (50.018 % to 64.321%). The pH of soil is acidic nature (5.517 to 6.687) and the Electrical Conductivity (0.121 to 0.229 dSm-1) was suitable for all crops. Organic carbon ranged from medium to high (0.902 to 1.182 %). These soils have low Nitrogen (127 kg ha-1 to 252 kg ha-1) in all villages. Phosphorus (18 kg ha-1 to 34 kg ha-1) content is high at eight locations and medium at one location. Potassium (131.8 kg ha-1 to 383.9 kg ha-1) is low in one location and high in two locations. Calcium (5.9 cmol (p+) kg-1 to 8.6cmol (p+) kg-1) and Magnesium (1.2 cmol (p+) kg-1 to 9.8 cmol (p+) kg-1) are very sufficient in this soil. Sulphur (17 Mg kg-1 to 30.5 Mg kg-1) is high in five locations. Zinc (0.79 Mg kg-1 to 0.81 Mg kg-1) content is sufficient in this soil. There is an including awareness of the need to pay greater attention in the role of macronutrients enhancement pin the soil for good soil health and proper nutrition of plant so as to attain optimum economic yield and soil is suitable for all major tropical and sub-tropical crops.