Moderate Spatial Dependence Research Articles

Spatial variability of soil organic carbon in Tamil Nadu: Integrating remote sensing and machine learning approaches

Aim: The experiment was carried out to estimate the geographical distribution of soil organic carbon (SOC) in Tamil Nadu, India, using advanced machine learning models. Methodology: High-resolution remote sensing data from Landsat 8, climate indices from TerraClimate, and terrain indices from the Shuttle Radar Topography Mission (SRTM) were integrated to construct reliable SOC estimation models. The models employed in this study included Random Forest (RF), Quantile Neural Network (QNN), Cubist, and Bootstrapped Random Forest (BRF). These models were selected for their ability to capture complex relationships between SOC and environmental covariates. Results: The QNN and Cubist models displayed superior accuracy with mean absolute error (MAE) values of 0.48 and 0.47, respectively. Variogram analysis indicated moderate spatial dependence across all models, with the Cubist model exhibiting the highest partial sill and sill values. Interpretation: These findings demonstrate the effectiveness of combining machine learning with remote sensing for SOC estimation. The results suggest that these models can be valuable tools in improving the accuracy of SOC distribution maps, which are essential for sustainable crop production. By identifying potential SOC sequestration areas, this research contributes to the development of targeted soil management strategies that prioritize soil health and support sustainable agricultural practices. Key words: Carbon sequestration, Digital soil mapping, Estimation models, Environmental covariates, Land management

Spatial interpolators for Delineating management zones to mitigate Mucuna pruriens in sugarcane plantations in the Eastern Amazon

Unmanned Aerial Vehicles (UAVs) have emerged as essential tools in precision agriculture, employing aerial photogrammetry concepts to aid producers in various decision-making processes. This study evauates different spatial interpolators to define management zones in sugarcane fields, aiming to control potential infestations by Mucuna pruriens. We collected images using the EbeeSQ UAV, equipped with a multispectral sensor, and calculated vegetation indices, including NDVI, SAVI, NDRE, and GNDVI. Analysis revealed that GNDVI yielded the most favorable results, with a mean value of 0.304 and a coefficient of variation of 11.747 %. Using regular and random sampling grids, we applied Ordinary Kriging (OK) and Support Vector Machine (SVM) interpolators to assess spatial variability across 13 survey zones. The results indicated a Degree of Spatial Dependence averaging 57.197 % and a Moran Index of 0.609, confirming moderate spatial dependence. Cross-validation showed that OK with random sampling outperformed other methods, achieving a Root Mean Square Error (RMSE) of 0.064 and a coefficient of determination (r2) averaging 0.347. Furthermore, the relationship between the Fuzzy Performance Index (averaging 0.069) and Normalized Classification Entropy (averaging 0.077) enabled the creation of management zone maps. These maps effectively identify distinct classes within the study areas, enhancing decision-making for producers in managing velvet bean weed during critical developmental phases.

High Desertification Susceptibility in Forest Ecosystems Revealed by the Environmental Sensitivity Area Index (ESAI)

This study evaluated the desertification vulnerability of an Anatolian black pine forest in Türkiye using the Environmental Sensitivity Area Index (ESAI). Desertification Risk (DR) and ESAI values were calculated for 90 sampling plots, incorporating key indicators such as vegetation cover, soil depth, rock fragment presence, soil texture, slope gradient, parent material, mean annual precipitation, aridity index, land use intensity, and policy enforcement. These indicators were processed through the Desertification Indicator System for Mediterranean Europe (DIS4ME). Spatial patterns of DR and ESAI were analysed using semivariograms and Kriging-interpolated maps. The mean DR (4.850; range = 2.310–8.090) and ESAI (1.46; range = 1.390–1.580) values indicated significant vulnerability to desertification. DR showed moderate spatial dependence, while ESAI exhibited strong spatial dependence. Ordinary kriging maps revealed critical desertification hotspots within the forest. ESAI values varied with soil organic matter (SOM) content, which was moderately and significantly correlated with ESAI (n = 90, r = −0.58, p < 0.01). These findings provide actionable insights for sustainable land management. Interventions such as improving SOM content through afforestation, enhancing soil conservation practices, and promoting sustainable water use are critical to mitigating desertification and fostering ecosystem resilience. This study identifies high-risk areas and demonstrates how DR and ESAI can guide targeted strategies to restore degraded lands and ensure forest sustainability. This aligns with SDG 15 (Life on Land), which emphasizes the need to combat desertification, restore degraded ecosystems, and promote the sustainable management of forests. Integrating ESAI into regional policy planning highlights its potential as a practical tool for achieving long-term environmental and socioeconomic sustainability in vulnerable forest ecosystems like those in Türkiye.

Spatial distribution pattern of soil organic matter in the wind erosion region of northeastern China based on the cokriging method

Soil wind erosion triggers complex particle migration from the source to downwind regions. During this process, soil organic matter (SOM), primarily adsorbed onto particle surfaces, exhibits distinct spatial differentiation across affected areas. Knowledge about the spatial distribution of SOM and its impact factors in wind erosion regions is essential for a deep understanding of global carbon cycling. In the wind erosion region of northeastern China, a crucial base for livestock and commercial grain production, profound changes in land quality driven by climate change and human activities have intricately altered SOM spatial patterns. However, detailed studies on the spatial variability of surface SOM in this region remain scarce. A total of 374 surface soil samples from this region were analyzed for SOM and soil pH. Combined with spatial data sets including particle-size distribution, soil moisture (SM), air temperature (T), and the normalized difference vegetation index (NDVI), the regional spatial distribution patterns of SOM were revealed and mapped using the ordinary kriging (OK) and cokriging (CK) approaches. The results indicated strong variations of SOM in space, with corresponding coefficients of variation exceeding 35 % across the entire region and each land use. According to the semivariogram analysis, SOM demonstrates moderate spatial dependency in the study region, exhibiting spatially positive correlations with clay content (CLAY), SM, and NDVI, and negative correlations with pH, soil sand content (SAND), and T. Among the various impact factors, incorporating SAND as an auxiliary variable in CK interpolation achieved the greatest improvement in the accuracy of SOM spatial distribution simulation compared to the OK model, increasing the explained variance from 35 % to 75 %. This simulation showed a general downward trend of SOM from the northeast to the southwest of the study region, with 65.24 % of the area displaying SOM ranging from 10-30 g·kg−1. The values of SOM greater than 30 g·kg−1, comprising 19.43 % of the total region, were primarily located in the humid and semi-humid meadow steppes and forests. In contrast, areas with SOM below 10 g·kg−1 accounted for only 15.33 %, mainly situated in the arid desert steppe and the Horqin sandy lands. These findings provide a scientific basis for understanding regional SOM loss and developing efficient measures for sand prevention and control.

Delineation of Management Zones for Site‐Specific Soil Nutrient Management for Sustainable Crop Production

ABSTRACTSoil nutrients deficiencies are one of the major causes of soil degradation in different parts of World, adversely impacting crop production. Delineation of soil nutrients management zones (MZs) is one of the commonly used techniques for evaluating spatial distribution pattern of soil parameters for adoption of site‐specific nutrient management. We, therefore, conducted the present study to understand the spatial distribution pattern of soil nutrients and their associated soil properties, and to delineate soil nutrients MZs in a north‐western Indian Himalayan (NWIH) region. A total of 18,930 representative surface (0–15 cm depth) soil samples were collected and processed. The processed soil sample were analyzed for pH, and electrical conductivity (EC), soil organic carbon (SOC), available N (AN), available P (AP), available potassium (AK), exchangeable Ca (Ex. Ca), exchangeable Mg (Ex. Mg), available S (AS), available Zn (AZn), available Fe (AFe), available Cu (ACu), available Mn (AMn) and available B (AB). The values of studied soil parameters varied widely with coefficient of variation ranging from 11.8% to 156%. Semivariogram analysis revealed stable, exponential and Gaussian best‐fit models for different soil parameters with weak (AP and AB), moderate (rest of soil parameters) and strong (AS) spatial dependence. Varied distribution pattern of soil parameters was visualized from ordinary kriging interpolation. Five soil nutrient management zones (MZs) were identified (using fuzzy performance index and normalized classification entropy values) by employing the techniques of principal component analysis and fuzzy c‐means clustering. Principal components with Eigen value > 1 were considered for further analysis. The soil parameters of identified MZs differed significantly. Thus, the study highlighted the usefulness of MZ delineation technique for site‐specific soil nutrient management in different cultivated areas for sustainable crop production. The developed MZ maps could suitably be used for efficient management of agronomic inputs especially fertilizer nutrients for improved environmental and economic efficiency.

Geo-statistical assessment of spatial variability of soil water-holding capacity for optimal irrigation under semi-arid vertisols in South India

Based on a field survey conducted in Lalgudi block, Tiruchirapalli district, Tamil Nadu, India, details about the crops grown, cropping pattern, land utilization and soil samples were collected from 20 locations under seven different soil textures of semi-arid vertisols. Using the data, efficient geostatistical models have been explored to study the spatial variation of irrigation water requirement of paddy, sugarcane and banana grown in the study area. Geostatistics was applied to identify the best interpolation method to acquire the spatial map of the available water holding capacity. Ordinary kriging and Disjunctive kriging were explored with six models viz., Circular, Spherical, Exponential, Gaussian, Penta-spherical and Sine-Hole effect models and were used for spatial prediction. The cross-validation statistics indicated that the Sine-Hole effect model with disjunctive kriging of available water holding capacity was superior for interpolation with a minimum value of RMSE and moderate spatial dependency. With the available water holding capacity map of Lalgudi block, total available water, readily available water and irrigation interval of paddy, sugarcane and banana were estimated. The irrigation interval of paddy, sugarcane and banana were found to vary between 1-2, 3-5 and 2-4 days respectively within Lalgudi block. The adoption of spatial algorithms for estimating crop water requirements would greatly help irrigation planners and water policymakers create efficient regional plans for precision irrigation under semi-arid vertisols.

Predictive Mapping of Soil Micronutrients Using Geographic Information System for Site Specific Management Interventions in a Semiarid Farm, India

Deficiency of soil micronutrients has an adverse impact on crop productivity in intensive agriculture. Plant availability, spatial pattern and distribution of soil micronutrients such as iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu) content in surface soils were evaluated for an agricultural farm in semiarid region of India. Other soil properties viz. soil pH, electrical conductivity (EC), soil organic carbon (SOC) content and equivalent calcium carbonate (CaCO3) content at the farm were also analysed to depict the soil chemical environment, controlling micronutrient availability. Plant available micronutrient contents within farm soils had very high data variability (coefficient of variation >30%). Soil available micronutrients content were negatively correlated with soil pH and positively correlated with SOC content. As per semivariogram analysis, plant available Fe, Mn, Zn and Cu content within farm soils had moderate spatial dependency as indicated by nugget to sill ratio between 0.30 and 0.50 and had spatial parameter ranges of 404, 801, 954 and 1529 m, respectively. Prediction map of plant available Fe content by inverse distance weightage (IDW) method showed a few patches of iron deficiency (< 4.50 mg kg-1) and a marginal level (4.50 - 9.00 mg kg-1). Spatial distribution map of plant available Zn content through lognormal ordinary kriging method indicated a patch of marginal Zn level (0.60 - 1.20 mg kg-1) within the farm soils. Farm scale spatial variability maps of plant available Mn and Cu content, generated by ordinary kriging method with good accuracy and effectiveness, indicated its adequate level with respect to crop nutrition. The spatial distribution maps of soil available micronutrients content for the farm could be served as reference for its precise and site specific management for intensive crop cultivation, higher productivity and profitability.

PCA and fuzzy clustering-based delineation of soil nutrient (S, B, Zn, Mn, Fe, and Cu) management zones of sub-tropical Northeastern India for precision nutrient management

Understanding the spatial distribution of plant available soil nutrients and influencing soil properties and delineation soil nutrient management zones (MZs) are important for implementing precision nutrient management options (PNMO) in an area to achieve maintainable crop production. We assessed spatial distribution pattern of plant available sulphur (S) (PAS), boron (B) (PAB), zinc (PAZn), manganese (PAMn), iron (PAFe), and copper (PACu), and soil organic carbon (SOC), pH, and electrical conductivity (EC) to delineate soil nutrients MZs in northeastern region of India. A total of 17,471 representative surface (0–15 cm depth) soil samples were collected from the region, processed, and analysed for above-mentioned soil parameters. The values of PAS (0.22–99.2 mg kg−1), PAB (0.01–6.45 mg kg−1), PAZn (0.05–13.9 mg kg−1), PAMn (0.08–158 mg kg−1), PAFe (0.50–472 mg kg−1), PACu (0.01–19.2 mg kg−1), SOC (0.01–5.80%), pH (3.19–7.56) and EC (0.01–1.66 dS m−1) varied widely with coefficient of variation of 15.5–108%. The semivariogram analysis highlighted exponential, Gaussian and stable best fitted models for soil parameters with weak (PACu), moderate (PAB, PAZn, PAFe, SOC, pH, and EC) and strong (PAS, and PAMn) spatial dependence. The ordinary kriging interpolation revealed different distribution patterns of soil parameters. About 14.8, 27.5, and 3.40% area of the region had PAS of ≤15.0 mg kg−1, PAB of ≤0.50 mg kg−1, and PAZn of had ≤0.90 mg kg−1, respectively. About 67.5, and 32.5% area had SOC content >1.00 and < 1.00%, respectively. Soil pH was ≤5.50, and >5.50 to ≤6.50 in 41.7 and 40.3% area of the region, respectively. The techniques of principal component analysis and fuzzy c-mean algorithm clustering produced 6 MZs of the region with different areas and values of soil parameters. The MZs had different levels of deficiency pertaining to PAS, PAB, and PAZn. The produced MZ maps could be used for managing PAS, PAB, PAZn, SOC and soil pH in order to implement PNMO. The study highlighted the usefulness of MZ delineation technique for implementation of PNMO in different cultivated areas for sustainable crop production.

Salinity distribution in agricultural land by geophysical, hydrochemical and geostatistical approaches: a pilot area located in Qelabshowah–Belqas, East Nile Delta region, Egypt

The study area is situated in the Qelabshowah–Belqas region, known for its Quaternary deposits. This research aims to demonstrate the two-dimensional (2D) variation of subsurface layers and salinity distribution using geoelectrical data, hydrochemical analysis, and geostatistical analysis. DC resistivity measurements were taken at fifteen vertical electrical sounding (VES) survey points using a Schlumberger array (AB/2 = 100 m) along three profiles. In addition, an electrical resistivity tomography (ERT) survey was conducted with a dipole–dipole array across one profile. Seven surface water samples were collected in the area. From the 1D and 2D inversion of VES and ERT data sets, three-to-four geoelectric layers were identified, including unconsolidated surface deposits, saturated clayey sand, saturated sand, and a salt-rich layer. The 2D inversion of VES data revealed an ancient salt-rich layer deposited in swampy conditions over a conductive wet sand layer along profile one due to salt mineral infiltration and dissolution. The 2D inversion of ERT data showed accurate lateral geometric accuracy compared to the 2D inversion of VES data, highlighting geological features, such as caves in the second layer and a buried water canal on the ground surface. Surface water samples showed high salinity levels with sodium hazards, indicating an Na–Cl composition. Geoelectric and hydrochemical data sets were geostatistically analyzed using spherical variogram supported ordinary Kriging interpolation. The analysis indicated weak to moderate spatial dependency for true resistivity parameters, while sodium content (SC) and permeability index (PI) showed strong spatial correlation. The 2D spatial distribution resistivity maps based on the 1D inversion of VES data displayed a general decrease in resistivity with depth, likely due to clay minerals or moist soil in the second layer and saline irrigation water infiltration in the third layer. The 2D spatial distribution of SC and PI showed a high concentration zone, posing a potential risk to agricultural crops regardless of soil permeability. It is recommended to use these maps when cultivating plants that can tolerate high sodium levels during the reclamation process.

Spatio-temporal variability of leaf macronutrients in a conilon coffee crop

Studies focusing the understanding of spatio-temporal variability of soil and plant attributes may contribute to the rational use of agricultural inputs, enabling economic and environmental profits. The objective of this work was to determine the spatial and temporal variability of the foliar macronutrients in a Coffea canephora (Conilon coffee) plantation, in two sampling periods (pre-harvest and fruit growth). The study was performed in a Conilon coffee plantation with double spacing of 3.0 x 2.0 x 1.0m (4.000 plat ha-1) under drip irrigation system, in the county of São Mateus, Espírito Santo - Brazil. An irregular mesh with approximately 1.37 ha with 100 points, at a minimum distance of 2 m with each other, was installed. On each sampling point foliar tissue samples were collected in two distinctive periods, during pre-harvest and fruit growth and the levels of foliar macronutrients were determined. Results were submitted to descriptive analysis and geostatistics. A moderate spatial dependence structure was observed and verified for foliar contents of nitrogen, phosphorus, potassium and calcium in both sampling periods.

Integrating GEO-Spatial Techniques for Reflecting Micro-Nutrients and Sulfur Variations and Spatial Distribution to Support Farm Management Practices

To understand the spatial dependency of available sulphur and micronutrients in an experimental farm, 83 soil samples (surface and subsurface) were taken from Agricultural College, Killikulam to characterize the spatial variability of available Sulphur and micronutrients. The geostatistics and geographic information system (GIS) techniques were applied. With the help of geostatistical analyst of ArcGIS software kriged map of different soil parameters were prepared. Available S, Fe, Cu and Zn were fitted with a spherical model and that available Mn was fitted with an exponential model. Soil available Cu had strong spatial dependence with a range of 3.62 km. Available Fe, Zn and Mn had moderate spatial dependence with a range of 9.47 km, 5.61 km and 2.70 km, respectively. Available sulphur had weak spatial dependence with a range of 6.86 km. The spatial distribution of the available sulphur and micronutrients were significantly correlated to the soil formation factors. Agricultural practices such as application of fertilizers and pesticides also had significant effects on the spatial distributions of the available micro-nutrients and sulfur.

Evaluating Spatial Soil Parameters of an Apple Orchard in ‘R’ Software: A Study from Kashmir Valley

Maintaining the ecological balance of the physical and biological environment depends greatly on the spatial variation of soil parameters across a given region and its elucidation using remote sensing and GIS applications. R software was used to apply the geostatistical methods of Ordinary Kriging and Inverse Distance Weightage to the study of the spatial distribution and variability of the soil parameters of an apple orchard in the Valley of Kashmir. Average values for available phosphorus (P), potassium (K), calcium (Ca), available nitrogen (N), and soil organic carbon (OC) were 1.15 percent, 315 percent, 22.5 kg, 221.4 kg, and 250 mg, respectively. Ordinary kriging (OK) and IDW were used to plot the spatial distribution of soil parameters using spherical (pH, OC, and Ca), steradian (EC, N, P, and K), and mean square error (MSE) values. According to the semi-variogram analysis, there was a strong to moderate spatial dependence. The interpolated maps showed various soil distribution patterns for pH (5.4-5.7), EC (0.46-0.56 DSM-1), OC (0.9-1.4 percent), N (296-335) kg ha-1, P (20-25 kg ha-1), K (221-221.8 kg ha-1), and Ca (100-400 mg kg-1) at the regional scale. This study represented a wide range of spatial soil variability. The estimation of MSE for Ordinary Kriging and IDW was used to predict the best model after performing a validation accuracy assessment. The best fit model for interpolation was the standard kriging model with low MSE. The maps of soil spatial distribution created to serve as an effective tool for policymakers and those planning farms.

Spatial heterogeneity of soil organic carbon and soil nutrients and their controlling factors in a small watershed in Northeast China

AbstractUnderstanding the spatial distribution of soil organic carbon (SOC) and soil nutrients is crucial for optimizing land management in watersheds. This study explored the spatial distribution of SOC, total nitrogen (TN) and total phosphorus (TP) in topsoil (0–20 cm) at a hilly small watershed site in Northeast China, and identified the controlling factors. The Gaussian mixture model optimally described SOC and TN with the ratio of nugget to sill variance (C0/[C + C0]) indicating moderate spatial dependence for SOC (63.7%) and TN (59.3%). TP was best modelled by the spherical model and demonstrated weak spatial autocorrelation and strong human influence, with a nugget to sill variance of 0.841. Land use significantly affected SOC, TN and TP contents, with the highest values recorded in woodland, followed by farmland and shrubland. Soil properties including bulk density (BD), gravel content (GC), moisture content (MC), soil porosity (SP) and the mean weight diameter (MWD) of water‐stable aggregates significantly correlated with SOC and TN content, but not with TP. Redundancy analysis revealed that soil properties (BD, GC and SP), topography (slope and aspect) and gully erosion contributed 55.7%, 24.7% and 4.3% of the spatial variation in SOC, TN and TP, respectively. Hence, these are the dominant factors shaping the spatial variation of SOC and soil nutrients at the site. These results can aid the further development of optimized land management strategies.

GIS Based Spatial Prediction of Major Nutrient Status of Soils at Farm Scale for Site Specific Crop Management

ABSTRACT In the current research, Global Positioning System (GPS) based 121 surface soil samples were collected and analyzed in the laboratory for different soil fertility parameters. The results showed that the bulk density (BD) of soils of 1.39 Mg m−3, dark gray and grayish brown, sandy clay loam, neutral to slightly alkaline soil reaction (pH), safe in electrical conductivity (EC), low to medium in organic carbon (OC) and non-calcareous in nature. The nitrogen (N), phosphorus (P), potassium (K) and sulfur (S) were analyzed as low, medium, high and low with NI of 1.26, 2.19, 2.69 and 1.31, respectively. In the Farm, field (F) wise, OC, N, P, K and S content in soil varied from 4.89 (F20) to 7.33 g kg−1(F15), 196.71 (F20) to 264.25 kg ha−1 (F15), 4.10 (F19) to 27.04 kg ha−1 (F16), 292.72 (F2) to 674.80 kg ha−1 (F13) and 3.99 (F18) to 13.75 mg kg−1 (F13), respectively. Post hoc results also revealed that fields and land use types were significantly different. Ordinary Kriging in Geo-statistical tools revealed that the exponential model was best fitted for, N, P, K, OC, √CaCO3 and √K, which exhibited moderate spatial dependence (>25 but < 75%), whereas Gaussian model was used for BD, pH and CaCO3. Correlation ranges(m) for P was 717.57 m and exhibited strong spatial dependency (<25%). The J-Bessel model was used for EC and√EC. The spherical model was used for S which showed 57.95% spatial dependence. The spatial maps could be used as a basis for precision farming.

Spatial modeling of the relative abundance of wading birds in peninsular Florida using citizen science data

AbstractRecent literature has shown citizen science data that include reported counts, such as observations from the eBird database, can be used to model the relative abundance of selected species. The objective of our study was to extend on both the methodology and the range of species considered by previous research on relative abundance modeling; we selected the Floridian peninsula as our geographical area of interest. We confirmed that, much like for other bird species and geographical areas, modeling such data with a classic Poisson generalized linear model was not appropriate due to overdispersion. Moreover, assuming linearity for the effects of all environmental and observer effort covariates on relative abundance was shown to be overly simplistic. These concerns led us to consider a variety of potentially suitable modeling techniques; the quasi‐Poisson generalized additive model displayed the best combination of flexibility, strong predictive performance, and ease of interpretation. Expanding on previous research for eBird data, we chose to explicitly incorporate spatial dependence into the modeling task by performing hierarchical generalized additive modeling with a spatial conditional autoregressive structure for random effects. This practice allows us to account for spatial factors not represented by model covariates, such as species dispersal patterns or colonial breeding habits. After conducting appropriate testing procedures and fitting spatially explicit models, we found that our data contain moderate spatial dependence and that spatially explicit models have overall superior predictive performance to models that do not account for spatial dependence. Upon closer inspection, spatially explicit models perform especially well compared with their nonspatial counterparts in identifying situations with low abundance, with a negligible trade‐off in prediction accuracy for situations with high abundance. We conclude that quasi‐Poisson hierarchical generalized additive models with spatial random effects provide the best representation of the relative abundance of bird populations. Moreover, our spatially explicit models are more realistic based on domain knowledge when regarding the impact of environmental covariates, which is important when considering conservation implications and future projections.

Spatial Variability and Mapping of Selected Soil Physical Properties under Continuous Cultivation

Assessments of Soil physical properties and estimation of their associated variability are essential for making site-specific decisions on soil and crop management This study examined the spatial variability of soil hydro-physical properties and variance structure at Sector F1 of the Jibia Irrigation project Katsina State, Nigeria. Grid sampling technique was used to obtain one hundred and forty-four (144) soil samples from 206 ha of land using Google earth. The grids were drawn using Google earth software at intervals of 150 m x 150 m. Surface soil samples (0 - 20 cm) were collected at grid intersection points. The collected soil samples were air-dried and passed through a 2mm sieve, and analyzed using standard laboratory procedures for physical parameters. The ArcGIS software package 10.3 was used to model the variance structure of Sand, Silt, Clay, Bulk density, Particle density, Percent total porosity and Organic Matter (OM). Results obtained revealed that the coefficient of variation (CV) ranged from 5.724% in particle density to 109% in clay. The Semivariogram showed that the range of spatial dependence varied from 0.342m for (Dry mean weight diameter) to 9.3m (Organic matter) for all measured soil properties. High Spatial dependency ratios were observed for Bulk density, Sand, Silt and clay contents. Particle density exhibited moderate spatial dependency (Nugget to sill ratio 0.25 – 0.75%). Wet Mean weight diameter and organic matter content have a weak spatial dependency. The results indicated that sandy textured soils dominated the greater part of the study area with low to moderate organic matter content. The soils being sandy-dominated has a high infiltration rate and low ability to retain moisture and nutrients were observed as the major characteristics of the soil of the study area.

Spatial variability of dendrometric parameters in a native tree Mabea fistulifera Mart. and its relationship with soil physical properties

Mabea fistulifera Mart. (common name: canudo-de-pito) belongs to the Euphorbiaceae family and is a native tree species in Brazil showing a high potential to recover degraded lands. This study aimed to evaluate the distribution and spatial correlation between the dendrometric parameters of the M. fistulifera plants and the physical attributes of the soil through geostatistics. The study was carried out at the Paulista State University (UNESP), in the city of Selvíria, MS, Brazil, in a typical dystrophic Red Oxisol with a clayey texture. The following properties were analyzed: for soil, penetration resistance, gravimetric moisture, particle density, and, for plants, circumference and diameter at breast height (measured at 130 cm above the ground), tree height, and total volume of the tree. An experiment grid was introduced with 35 sample points spaced 13 m x 13 m. Two soil samples were taken at each point, at 0.00 - 0.10 m and 0.10 - 0.20 m depth. Descriptive data analysis and spatial dependence analysis were carried out through semivariogram adjustments and kriging maps. The dendrometric properties of the species M. fistulifera and the soil gravimetric moisture content showed spatial dependence. The spherical semivariogram model best explained the spatial structure of circumference at breast height, diameter at breast height, tree volume, and soil gravimetric moisture. There was an emphasis on the correlation between the total volume of the tree as a function of the diameter at breast height, showing a moderate spatial dependence. Furthermore, the tree diameter at breast height proved to be a good indicator for determining the total height of the M. fistulifera tree.

Spatial Variability of Soil Erodibility at the Rhirane Catchment Using Geostatistical Analysis

Soil erodibility is one of the most crucial factors used to estimate soil erosion by applying modeling techniques. Soil data from soil maps are commonly used to create maps of soil erodibility for soil conservation planning. This study analyzed the spatial variability of soil erodibility by using a digital elevation model (DTM) and surface soil sample data at the Rhirane catchment (Algeria). A total of 132 soil samples were collected of up to 20 cm in depth. The spatial distributions of the K-value and soil physical properties (permeability, organic matter, and texture) were used to elaborate ordinary Kriging interpolation maps. Results showed that mean values of soil organic matter content were statistically different between Chromic Cambisols (M = 3.4%) vs. Calcic Cambisols (M = 2.2%). The analysis of variance of the organic matter provided a tool for identifying significant differences when comparing means between the soil types. The soil granulometry is mainly composed of silt and fine sand. The soil erodibility showed values varying between 0.012 and 0.077 with an average of 0.034, which was greater in soils with calcic horizons. Statistical evaluation by using Pearson’s correlation revealed positive correlations between erodibility and silt (0.63%), and negative correlations with sand (−0.16%), clay (−0.56%), organic matter (−0.32%), permeability (−0.41%), soil structure (−0.40%), and the soil stability index (−0.26%). The variability analysis of the K-factor showed moderate spatial dependency with the soil erodibility map indicating moderate to highly erodible risk in cropland and sparse grassland land uses. Overall, the study provides scientific support for soil conservation management and appropriate agricultural food practices for food supply.

Spatial distribution and risk associated with shot hole disease in apricot (Prunus armeniaca L.) in Northern Pakistan

Wilsonomyces carpophilus is the major fungus that produces shot hole disease (SHD) in apricots, which results in significant economic losses. This problem can be addressed through the monitoring of the diseases and spotlighting the risks connected with them. This study employed descriptive and geostatistical mapping techniques to track the spatial distribution and risk of SHD in apricot orchards in five apricot-growing valleys. The results showed a spatial distribution of the disease within the study area even varying within the valleys and orchards. A moderate spatial dependence of SHD prevalence (0.524), incidence (0.363), and severity index (0.313) were observed. GIS maps and trend analysis graphs also showed the spatial distribution of the disease from east-west and south-north directions. Climate and cultural practices favor the perpetuation of pathogens and the spread of SHD. This information will help apricot growers regarding disease management and improve sustainable profitability.

A Spatial Distribution of Organic Carbon status in Koppal and Yadgir Taluks of Karnataka, India using GIS and Geostatistics

Abstract Spatial distribution of Soil Organic Carbon (SOC) status is necessary for enhancing crop and soil productivity. In this study, soil samples were collected from Koppal and Yadgir taluks (Northern dry zone and Northeastern dry zone) of Karnataka at 320 m grid interval at 0-15 cm depth and assessed for organic carbon and map was prepared under GIS using Arc GIS 10.4 Geo-wizard Kriging method. The results of the study showed that, SOC was medium in 37 per cent and high in 26 per cent of the total area (1,38,298 ha) in Koppal taluks. Whereas, in Yadgir taluks, SOC status was medium in 38 per cent and high in 31 per cent of the total area (1,71,060 ha). The descriptive statistics were positively skewed with positive kurtosis value. The spatial variability showed a moderate spatial dependence with spherical model in both the taluks. Therefore, the study showed that, most of the soils were medium in SOC status. There is a need of integrated nutrient management with additional emphasis for enhancing SOC for sustainable crop production.