Spatial Variability Of Soil pH Research Articles

Digital Mapping of Soil pH and Electrical Conductivity Using Geostatistics and Machine Learning

This study investigates the spatial variability of soil pH and electrical conductivity (EC) in Suryapet district of Southern Telangana Zone through various digital soil mapping approaches. The 202 surface (0-15cm) soil samples were collected and analysed for pH and EC. The analysed data was further divided into calibration set and validation set in the ratio of 75:25. The geostatistical techniques like Ordinary Kriging, Inverse Distance Weighting (IDW) and Regression Kriging and data mining technique like random forest technique were used to predict the spatial distribution of pH and EC (dSm-1) over the study area. The accuracy of these methods was assessed using validation data set by calculating RMSE, ME and R2 values. The results showed that among all the approaches, random forest (RF) technique performed better with lower RMSE, ME and higher R2 values for spatial prediction of soil pH (RMSE=0.014, ME=0.28 and R2=0.81) and EC (RMSE=0.134, ME=0.022 and R2=0.73). The RF predicted maps show that the pH of soils varied from neutral (6.5-7.5) to slightly alkaline (7.5-8.5) and the soils of Suryapet district were considered as non-saline (EC: 0-2 dSm-1). The findings of the current study shows that among digital soil mapping techniques, random forest model can be an effective tool for assessing spatial variability of soil pH and EC for further studies.

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

AbstractSoil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co‐kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten‐fold cross‐validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring.

Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation

This study aimed to characterize the spatial variability of pH in soils of two farms in the state of Paraná, Brazil, based on two different sampling methods used in precision agriculture, by means of geostatistical analyzes. The first method of sampling the pH grid consisted in the collection of soil samples by the traditional method (1 point / ha). The second method of pH determination was by on-the-go soil sensor (200 points / ha). The spherical model was better suited to most semivariograms, regardless of the sampling method. After adjusting the semivariograms for soil pH determination methods, thematic maps were made using normal kriging. The best spatial distribution of pH was obtained where the attribute was sampled by the on-the-go sensor. The number of pH samples collected and the sampling method influenced the visual representation of pH variability.

Spatial variability in soil pH and land use as the main influential factor in the red beds of the Nanxiong Basin, China.

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for the management of soil nutrients and the prediction of soil pollution. In order to explore the causes of spatial variability in soil pH in red-bed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of soil pH were analyzed by geostatistics and classical statistical methods, and the main factors influencing spatial variability in soil pH are discussed. The coefficient of variation in the red-bed areas of Nanxiong Basin was 17.18%, indicating moderate variability. Geostatistical analysis showed that the spherical model is the optimal theoretical model for explaining variability in soil pH, which is influenced by both structural and random factors. Analysis of the spatial distribution and pattern showed that soil pH is relatively high in the northeast and southwest, and is lower in the northwest. These results indicate that land use patterns and topographic factors are the main and secondary influencing factors, respectively.

Reducing sampling intensity in order to investigate spatial variability of soil pH, organic matter and available phosphorus using co-kriging techniques. A case study of acid soils in Eastern Croatia

ABSTRACTSoil pH, hydrolytic acidity (HA), organic matter (OM) and plant available phosphorus (AP) are factors controlling the environmental-friendly soil management in agroecosystems. These parameters are highly variable in space. The objective of this work is to study spatial variability of pH, HA, OM and AP using several interpolation methods in Eastern Croatia. A total of 1004 (0–30 cm) soil samples were collected, and several univariate and multivariate interpolation performances were tested. The results showed that soils of the study area had high HA and AP, while pH and OM were low. The variogram analysis revealed different spatial structures among studied soil properties and demonstrate a need for variable-rate management. Soil pH and OM had lower spatial variability compared to AP and HA. Ordinary kriging was the most accurate method to estimate the studied variables. The incorporation of auxiliary variables increased the precision of the estimations for HA. Soil AP and OM showed different results for spatial prediction obtained by co-kriging. Overall, the incorporation of pH as auxiliary variable increased the prediction of the models. However, more co-variates should be incorporated in further models, in order to identify with more precision areas that need to be restored.

Characterizing spatial variability of soil properties in alluvial soils of India using geostatistics and geographical information system

ABSTRACTAlluvial soils constitute significant portion of cultivated land in India and it contributes towards food grain production predominantly. The objectives of this study were to assess the spatial variability of soil pH, organic carbon (OC), available (mineralizable) nitrogen (N), available phosphorus (P), available potassium (K) and available zinc (Zn) of alluvial floodplain soils of Kadwa block, Katihar district, Bihar, India. A total of 85 soil samples, representative of the plough layer (0–25 cm depth from surface) were randomly collected from the study area. The values of soil pH, OC, N, P, K and Zn varied from4.4 to 8.4, 0.20% to 1.20%, 141 to 474, 2.2 to 68.2, 107 to 903 kg ha–1 and 0.22 to 1.10 mg kg–1, respectively. The coefficient of variation value was highest for available P (94.3%) and lowest for soil pH (11.3%). Spherical model was found to be the best fit for N, P and Zn contents, while exponential model was the best fit for OC, and Gaussian model was the best-fit model for pH and K. The nugget/sill ratio indicates that except pH and available K all other soil properties were moderately spatially dependent (25–57%). Soil properties exhibited different distribution pattern. It was observed that the use of geostatistical method could accurately generate the spatial variability maps of soil nutrients in alluvial soils.

Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management

Crop productivity under rainfed farming systems in India is low due to poor water and nutrient management. The available small scale information of soil nutrients is inadequate to effectively manage individual farms held by small and marginal farmers. Large scale spatial variability assessment using grid sampling method is a feasible option to identify critical nutrient deficiency zones. The present study was conducted in a part of semi-arid tropical Deccan plateau region, India, to assess the spatial variability of soil pH, organic carbon (OC), soil available nitrogen (N), phosphorus (P), potassium (K) and sulphur (S). A total of 1508 composite samples (0–15cm) were collected by adopting 325×325m grid interval (one sample for 10ha area) and they were analysed for soil fertility parameters. Coefficient of variation (CV) indicated that OC, N, P, K and S were high in heterogeneity (CV>35%). Moreover, pH, P, K and S were non-normally distributed and log transformation produced normalised dataset. The semivariogram parameters (nugget to sill ratio, range and slope) indicated that the spatial distribution of soil properties were inconsistent. The spatial variability of parameters were mapped by ordinary kriging using exponential (pH and OC) and spherical (N, P, K and S) models selected based on root mean square error (RMSE) and r2 values. Multi-nutrient deficiencies were observed in most parts of the study area and N was acutely deficient. Farm level nutrient availability status was derived from spatial variability maps and critical nutrient deficiency zones were identified. Nutrient management recommendations based on soil test results were delivered to farmers for adopting need based variable rate of fertilizer application. The generated maps can serve as an effective tool for farm managers and policy makers in site specific nutrient management.

Spatial variability of soil pH and electrical conductivity in the Jammu district of Jammu and Kashmir

A study was conducted to assess the spatial variability of pH and EC of Jammu district soils and generate their prediction maps. The Jammu district of Jammu and Kashmir covers an area of 2336 sq. km. and falls in the sub-tropical zone of the state. The district has two very distinct regions, the southern irrigated plains and northern rainfed foothills. Soil samples were collected from the entire Jammu district following the stratified random manner. A semi-variance analysis was conducted to estimate nugget, sill and range for quantifying the spatial dependence for both pH and EC. The process of digitization and generation of maps was carried out with ArcGIS 9.2 software following ordinary kriging. Soil pH varied widely across the Jammu district ranging from as low as 4.67 to as high as 9.08 with a mean value of 7.07. The coefficient of variation (CV) was 9.85%. Nugget to sill ratio was 0.396 (39.6%), indicating moderate spatial variability. The range of spatial dependence was 32.3 km. Electrical Conductivity (EC) ranged from 0.021 to 1.523 dS m−1 with a mean value of 0.225 dS m−1. The EC had a high variability with a CV (coefficient of variation) value of 79.94%. It's range of spatial dependence was 32.93 km. EC also exhibited a moderate spatial variability with a nugget to sill ratio of 0.478 (47.8%). Both pH and EC increased towards the southern plains of the district. Topography and associated land use/land cover played a major role in the distribution of pH and EC.

Spatial scaling of soil salinity indices along a temporal coastal reclamation area transect in China using wavelet analysis

ABSTRACTHigh spatial variability of soil salinity in coastal reclamation regions makes it difficult to obtain accurate scale-dependent information. The objectives of this study were to describe the spatial patterns of saline-sodic soil properties (using soil pH, electrical conductivity (EC1:5) and sodium ion content (SIC) as indicators) and to gain knowledge of the scaling relationships between those variables. The soil pH, EC1:5 and SIC data were measured at intervals of 285 m along a 13,965-m temporal transect in a coastal region of China. The spatial variability of soil pH was weak but it was strong for soil EC1:5 and SIC at the measurement scale. There was a significant positive correlation between soil EC1:5 and SIC, while correlations between soil pH and either EC1:5 or SIC were weak and negative. For each saline-sodic soil parameter, the variability changed with the decomposition scales. The high-variance area at the larger scales (≥570 m) occupied less than 10% of the total area in the local wavelet spectrum, which meant that the spatial variations of the salinity indicators were insignificant at these scales. For local wavelet coherency, at a scale of 1500–2800 m and a sampling distance of 0–4500 m, the covariance was statistically significant between any two of the saline-sodic soil parameters.

Mapping the Spatial Variability of Soil Acidity in Zambia

A common strategy for ameliorating soil acidity is the application of agricultural lime. However, this measure is hampered by the lack of high resolution soil maps that can enable lime application according to the spatial variability of soil pH in an area. Therefore, this study was carried out to map soil acidity in South Eastern Zambia. The objective of the study was to apply geostatistical procedures to mapping soil acidity in the country. Ordinary kriging was performed on a set of 119 soil samples collected from the 0–20 cm soil layer whose pH was determined by the electrometric method. The kriging model that was developed was found to be satisfactory with low prediction errors (root mean square error 0.36). Thus, the map produced could be used to draw up strategies for management of soil acidity in the area.

English

Reclamation efforts across Wyoming have been successful in reestablishing vegetative biomass, but restoration of a diverse vegetative community has proven difficult. We hypothesize that the lack of diverse plant species establishment is due, in part, to the homogenization of the soil environment occurring with disturbance and reclamation. Spatial heterogeneity of vegetation and soil characteristics using geospatial statistics has not been widely examined in wildland systems, particularly in relation to post-disturbance recovery of soils and vegetation. The purpose of this project was to quantify the spatial variability of key soil characteristics on a recently installed and reclaimed pipeline and adjacent undisturbed reference site in south central Wyoming. Soils (0-5 cm) were sampled on a grid design at a small spatial scale (10 - 1000 cm). Soils were analyzed for soil moisture, pH, electrical conductivity, and major microbial group abundance. In general, reclaimed soils support more biomass of vegetation and soil microbes than undisturbed soils. We found weak spatial dependence of most soil properties measured, and the response of the property to disturbance and reclamation varied by property. Spatial variability of soil pH, bacteria abundance, and AM fungi abundance appear to be most affected by soil disturbance and reclamation, but further investigation into the spatial patterns of soil and vegetation properties is warranted.

Temporal and Spatial Variability of Soil pH in Patterned-Ground Chronosequences: Jotunheimen, Norway

Glacier foregrounds of Jotunheimen, Norway are characterized by the occurrence of Little Ice Age end moraines, dating to approximately the mid 18th century. Patterned-ground features occurring within chronologically established time units were studied. Young patterned-ground features, forming ~ 10-20 years after deglaciation, are frost active with minimal soil development. With time and distance from the ice margin, frost activity declines within the patterned ground. Frost activity initially decreases at the borders of the features, with the centers later stabilizing. Sorted circles and sorted nets showed significant changes in soil pH across the chronosequences. Temporal thresholds were recognized on two of the chronosequences where patterned-ground features on terrain deglaciated ~ 1930 A.D. show increased acidification rates, suggesting a decline in frost activity and disturbance. Microsite variability was noted on one of the chronosequences with patterned-ground borders being more acidic than that of center positions. All glacier forelands showed a higher soil pH with depth in patterned-ground features. The importance of microsite variability and associated disturbance is stressed and should be incorporated when studying environmental change at traditional landscape scales.

Spatial variability of soil pH and phosphorus in relation to soil run-off following slash-and-burn land clearing in Sumatra, Indonesia

Slash-and-burn land clearing on sloping land may lead to increased soil run-off following disappearance of the protective vegetative cover. In turn, soil run-off and redeposition affects soil fertility and spatial patterns of fertility parameters in a field. This study seeks to clarify the role of spatial patterns of post-burn dead biomass (necromass) in soil run-off and redeposition and their combined effect on spatial patterns in soil pH and resin-extractable P. The study is carried out on a post-productive rubber ( Hevea brasiliensis) agroforest in Sumatra, Indonesia. Soils are classified as Dystric Fluvisols. After slash-and-burn of vegetation, the field was planted with rubber seedlings and rice ( Oryza sativa). For comparison the adjacent rubber agroforest site was sampled. Soil run-off is expressed here as the quantity of downward moving soil that passed the specific location of a flow trap. Existing physical soil run-off barriers and crop performance were scored. Despite serious soil run-off from the steeper upper slopes little soil was actually lost because of the slope form of the field, presence of natural soil run-off barriers, and the planted crop. Spatial variability of soil pH decreased at the expense of small-scale, within-strata, variability mainly because of the patchy distribution of soil run-off barriers. Soil run-off, aggravated by slash-and-burn, did not result in development of a clear soil fertility gradient down slope. In areas of high soil run-off potential, clear burns should be avoided because soil run-off barriers like remnants of slash-and-burn and surface litter maintain the soil and its fertility.

Influence of soil pH on the fractionation of Cr, Cu and Zn in solid phases from a landfill site

The spatial variability of soil pH for engineered Weathered Oxford Clay is described using 35 samples collected from the base of a new cell in an existing landfill. Soil pH variability influences the reactivity of Cr, Cu and Zn in the site. The reactivity of these metals as natural components was determined using a sequential extraction method. The total concentration of Cr, Cu and Zn and the mineralogical composition were also determined. The results showed that due to the presence of a layer rich in pyrite in the base, a natural acidification may occur which can produce a soil pH as low as 2.7. The spatial variability of soil pH in this area has been described with an anisotropical variogram model and the estimation of its values at unsampled locations was carried out using the ordinary kriging algorithm. From the spatial modelling of the soil pH, it was found that the metals in the soil solid phases follow a similar distribution.

MICROSCALE PH SPATIAL DISTRIBUTION IN THE AP HORIZON OF MEXICO SILT LOAM

Conventional pH measurements treat soil pH as a random, independent variable for providing the mean pH of soil samples. This is assumed to represent the unsampled neighborhood. However the measurement will be inadequate if spatially dependent heterogeneity of the soil property exists among the samples. A laboratory study was conducted to determine microscale pH spatial dependence by the semivariogram function. Four soil cores, 13.5 cm in length and 10.5-cm in diameter, were taken from the Ap horizon of a Mexico silt loam (fine montmorillonitic, mesic Udollic Ochraqualfs). Soil pH was measured at 1-cm horizontal and vertical intervals by the pH-sensitive glass microelectrode, and the spatial variability of soil pH within the cores was examined. The pH decreased and the variability increased with depth. The homogeneity among samples was larger near the surface. Semivariograms indicated that semivariances depended on both distance and direction. In all cases, spatial variances in the vertical were larger than in the horizontal. Spatially dependent structures were exhibited by all samples, but only two cores expressed an identical range, and the nugget effect of these cores accounted for a large portion of the sample variance. Extent of pH spatial dependence in the other cores could not be determined because of unbounded variances. This study illustrated that soil heterogeneity and spatial dependence of pH in microscale exist and vary with sampling site and scale. Bulk soil pH measured by conventional procedures does not reveal variability that could have important impacts on plant root and microbial growth.

Field scale variability of cadmium and zinc in soil and barley

CaCl2-extractable soil Cd and Zn contents have been suggested as a measure of bioavailability. To investigate the ability of this measure to reflect spatial patterns of Cd and Zn concentrations in barley (Hordeum vulgare L.) in an arable field, plant and soil samples were taken from a 0.5 ha area sandy soil contaminated with Cd and Zn. Cd and Zn contents in barley and yield were spatially variable. Yield was low, which may have been caused by Zn toxicity or atrazine turnover. For Cd, CaCl2-extractable soil contents explained only 17% of the variation in Cd contents in grain, and for Zn no significant correlation was observed. Nevertheless, surface plots of CaCl2-extractable soil contents and contents of barley grain illustrated their corresponding spatial patterns. Despite the poor linear correlation between CaCl2-extractable soil-Cd and grain-Cd, a stochastic model for long term behaviour of Cd in field soils predicted observed variability in Cd contents of barley grain well from spatial variability of soil pH and organic matter content. The probabilistic model predicted behaviour of Cd in terms of probability, and was more appropriate than the deterministic approach.

Spatial Variability of Soil pH and Organic Matter in Forest Plantations

AbstractThe spatial variability of soil pH and organic matter concentration in 0.4‐ha plots of 45‐yr old monospecific red pine (Pinus resinosa Ait.), Norway spruce [Picea abies (L.) Karst.], and sugar maple (Acer saccharum Marsh.) stands located on acid Inceptisols in south central New York was examined. Soil was sampled every 5 m along east‐west and north‐south transects and also every 30 cm around selected individual trees from next to the base to a distance of 120 cm. A geostatistical approach was used to analyze the components of variability that were systematically changing in space, correlated in space or not spatially related. There was little spatial correlation between samples with respect to pH or organic matter. However, there was systematic change in space in some horizons due to the presence of the different tree species. After 45 yr the surface mineral horizon under spruce was slightly (<0.3 pH unit) more acidic than under maple. There were also differences between stands of the different species in organic matter of the Oe horizons but not of the surface mineral horizons. The greatest variability in pH occurred between samples taken at the base of individual trees compared to samples taken 30 to 120 cm away. This effect occurred in the organic horizons of all three stands and in the surface mineral horizons under spruce and pine and was greatest for the pine. The approach employed in this study was useful in determining the effect of different tree species on soil chemical properties, where there was no plot replication.