The Efficiency of Cokriging Spatial Interpolation to Estimate the Electrical Conductivity of Saturated Paste Extract (ECₑ) Using Soil to Water Ratios

Irrigation water with high electrical conductivity (EC) compromises the sustainability of agricultural soils. Calcium sulfate (CS) or gypsum is commonly used on removal of soluble ions such as sodium (Na), however, large applications of CS can affect soil pH, EC, and nutrient availability to plants. The objective of this study was to investigate the effects of CS and ammonium sulfate (AS) rates on the soil pH, EC, and exchangeable cations in a salt-affected agricultural soil. Samples from the 0-20 cm soil depth layer were collected from an agricultural soil reported to have low potato yield due to high EC irrigation water. Soil was incubated with rates ranging from 0 to 4000 kg ha-1 of CS and 0 to 600 kg ha-1 of nitrogen (N) using AS. The treated soil was incubated for 60 d at 25 ºC and moisture was maintained at 60% of soil field capacity. After incubation, the soil was analyzed for pH, EC, Na, manganese (Mn), and zinc (Zn). Increasing rates of CS resulted in a small decrease in soil pH and a significant linear increase in soil EC, while the application of AS linearly reduced the soil pH and quadratically increased soil EC. The application rate of 200 kg ha-1 of N as AS resulted in a decrease of soil pH from 5.9 to 5.2, while the EC increased from 1.3 to 3.0 dS m-1. Extractable Na increased linearly with the application of AS due to its effect on the soil pH. The soil extractable Mn and Zn were not affected by the application of CS. Applications of AS resulted in a linear increase in soil extractable Mn and Zn concentrations, respectively. Results from this incubation study suggest that the use of large rates of CS for consecutive years may further impair soil conditions for cropping in areas with high EC in the irrigation water.

A correction is proposed for the evaluation of salinity effects on the basis of the electrical conductivity of saturated paste extracts. It is suggested that the dilution of the soil solution during the preparation of the saturated paste must be taken into account. Equations for estimating the electrical conductivity of a soil solution at field capacity are presented. The corrected equations improve the ability to predict the salinity hazard by determining the electrical conductivity of saturated paste extract.

The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables from salt-affected soils in a south Mediterranean region using Sentinel-2 multispectral imagery. In order to realize this goal, a test was carried out using electrical conductivity (EC) data collected in central Tunisia. Soil electrical conductivity and leaf electrical conductivity were measured in an olive orchard over two growing seasons and under three irrigation treatments. Firstly, selected spectral salinity, chlorophyll, water, and vegetation indices were tested over the experimental area to estimate both soil and leaf EC using Sentinel-2 imagery on the Google Earth Engine platform. Subsequently, estimation models of soil and leaf EC were calibrated by employing machine learning (ML) techniques using 12 spectral bands of Sentinel-2 images. The prediction accuracy of the EC estimation was assessed by using k-fold cross-validation and computing statistical metrics. The results of the study revealed that machine learning algorithms, together with multispectral data, could advance the mapping and monitoring of soil and leaf electrical conductivity.

Soil salinity is a global environmental problem and the most widespread land degradation problem that reduces crop yields and agricultural productivity. The characteristic of soil salinity is conventionally measured by the electric conductivity (EC) of soil while remote-sensing techniques have been extensively applied to detect the presence of salts indirectly through the vegetation using crop spectral reflectance. This study aims primarily to investigate whether salt stress the rice can be detected by field reflectance or not, and second, to search the significant bands of vegetation indices that can indicate the relationships between the EC of soil and field hyperspectral reflectance of canopy, grain, and leaf of rice, using the normalized difference spectral index (NDSI). Field investigations on various paddy fields in northeastern Thailand were carried out in late November 2010 during the ripening season just before harvest in an attempt to realize the applications of the field hyperspectral technique for monitoring the spread of saline soils and estimation of the effects of soil salinity on rice plants. Jasmine rice and glutinous rice were two different rice species selected for this study. Rice plant investigations were conducted by collecting data on crop length, panicle length, canopy openness, leaf area index, and digital photographs of plant conditions from each site. The statistical analysis revealed that the changes in soil EC were significantly sensitive to the ripening stages of both jasmine rice and glutinous rice planted on different levels of soil salinity. Among reflectance measurements, canopy reflectance was highly correlated with soil EC. However, the estimated accuracies of the relationship between soil EC and reflectance of glutinous rice were relatively lower than those of jasmine rice.

Abstract. Soil genesis is highly dependent on landforms as they control the erosional processes and the soil physical and chemical properties. The relationship between landform classification and electrical conductivity (EC) of soil and water in the northern part of Meharloo watershed, Fars province, Iran, was investigated using a combination of a geographical information system (GIS) and a fuzzy model. The results of the fuzzy method for water EC showed 36.6 % of the land to be moderately land suitable for agriculture; high, 31.69 %; and very high, 31.65 %. In comparison, the results of the fuzzy method for soil EC showed 24.31 % of the land to be as not suitable for agriculture (low class); moderate, 11.78 %; high, 25.74 %; and very high, 38.16 %. In total, the land suitable for agriculture with low EC is located in the north and northeast of the study area. The relationship between landform and EC shows that EC of water is high for the valley classes, while the EC of soil is high in the upland drainage class. In addition, the lowest EC levels for soil and water are in the plains class.

He, Y., DeSutter, T., Hopkins, D., Jia, X. and Wysocki, D. A. 2013. Predicting ECeof the saturated paste extract from value of EC1:5. Can. J. Soil Sci. 93: 585-594. Many laboratories appraise soil salinity from measurement of electrical conductivity of 1:5 soil to water extract (EC1:5) due to its simplicity. However, the influence of salinity on plant growth is mainly based on electrical conductivity of saturated paste extract (ECe), so it is necessary to convert EC1:5 to ECe in order to assess plant response. The objectives of this research were to develop models relating EC1:5 and ECe under four different 1:5 equilibration methods: (1) shaking, (2) shaking plus centrifuging, (3) stirring, and (4) a United States Department of Agriculture-Natural Resources Conservation Service (2011) equilibration method. One hundred soil samples, which were all derived from glacial parent materials in North Dakota, USA, were selected for this study. Non-transformed, non-transformed separated, ln-transformed, and exponential models were developed between EC1:5 and ECe. Non-transformed, simple linear regression models had obvious segments for all equilibration methods and the residual distributions varied. Therefore, data were separated at EC of 4 dS m-1 and a quadratic curvilinear model was developed for relating EC1:5 and ECe (r2 values ranged from 0.87 to 0.93) when ECe values were less than 4 dS m-1. Although the linear model was significant (P<0.05), soils having ECe greater than 4 dS m-1 had r2 values less than 0.61. Across all soils, the ln-transformed model had r2 values greater than 0.85, which was greater than the non-transformed or exponential models. By comparison of r2, RMSE, and relative percentage difference, the separated curvilinear model that was established when salinity is less than 4 dS m-1, and ln-transformed models were superior at predicting ECe from EC1:5 data compared to non-transformed and exponential models. These results indicate that across all equilibration methods ECe can reliably be predicted from EC1:5 data for soils from this region.

A noncontacting soil resistivity probe, based on electromagnetic induction, was used to measure soil salinity. Good correlations were obtained between the apparent bulk soil conductivity measured by the instrument and the electrical conductivity of saturated paste extracts or the bulk electrical conductivity measured by the Wenner four‐probe method. The electromagnetic induction technique provided less detailed information on salinity changes with depth than the other methods, but was faster and can provide a continuous record of salinity changes along a transect.

ISHS V International Symposium on Irrigation of Horticultural Crops EVALUATION OF A COMBINED SOIL EC AND MOISTURE SENSOR AND ITS USE TO CO-MANAGE SOIL MOISTURE AND VINE NITROGEN IN GRAPEVINES (CV. SHIRAZ) UNDER DEFICIT IRRIGATION

The estuary tides affect groundwater dynamics; these areas are susceptible to waterlogging and salinity issues. A study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that works with solar energy and belong to a commercial farm located in Northern Sudan. To monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources, easy, non-intrusive, and practical procedures are required. The objective of this study was to use remote sensing-determined Sentinel-2 satellite imagery using various soil indices to develop prediction models for the estimation of soil electrical conductivity (EC) and soil calcium carbonate (CaCO3). Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO3. The electrical conductivity of the soil saturation paste extract was represented by average values in soil dataset samples from two fields collected from the topsoil layer (0 to 15 cm) characteristic of the local salinity gradient. The various soil indices, used in this study, were calculated from the Sentinel-2 satellite imagery. The prediction was determined using the root mean square error (RMSE) and cross validation was done using coefficient of determination. The results of regression analysis showed linear relationships with significant correlation between the EC analyzed in laboratory and the salinity index-2 “SI2” (Model-1: R2 = 0.59, p = 0.00019 and root mean square error (RMSE = 1.32%) and the bare soil index “BSI” (Model-2: R2 = 0.63, p = 0.00012 and RMSE = 6.42%). Model-1 demonstrated the best model for predicting soil EC, and validation R2 and RMSE values of 0.48% and 1.32%, respectively. The regression analysis results for soil CaCO3 determination showed linear relationships with data obtained in laboratory and the bare soil index “BSI” (Model- 3: R2 = 0. 45, p = 0.00021 and RMSE = 1.29%) and the bare soil index “BSI” & Normalized difference salinity index “NDSI” (Model-4: R2 = 0.53, p = 0.00015 and RMSE = 1.55%). The validation confirmed the Model-3 results for prediction of soil CaCO3 with R2 and RMSE values of 0.478% and 1.29%, respectively. Future soil monitoring programs might consider the use of remote sensing data for assessing soil salinity and CaCO3 using soil indices results generated from satellite image (i.e., Sentinel-2).

A low agricultural productivity in northeastern Thailand primarily results from saline soils predominately distributed throughout the region. We proposed a remediation technique for the saline soil by using a solar-powered pump and subsurface drainage system (SPSD system) . This study was conducted in Khon Kaen Province, northeastern Thailand. The SPSD system could lower the groundwater levels and subsequently reduce the capillary rise of saline groundwater toward the soil surface. The SPSD system first collects excess groundwater through the drainage pipes connected to a water tank under the ground. Groundwater collected in the tank is automatically discharged out by the solar-powered pump. The electrical conductivity of groundwater (ECw) measured in the area where the SPSD system was installed suggested that lowering of the groundwater levels using the SPSD system could enhance salt leaching from the soil profile by rainfall. The electrical conductivity of soils at the sampling point where the SPSD system was installed was lower than those obtained from the same soil profile at the control sites. Consequently, this study suggests that lowering groundwater levels using the SPSD system may be an effective means for reducing salinity degree of soil and groundwater in northeastern Thailand.

Relationships between carbon dioxide emission and soil properties in salt-affected landscapes

Different methods to estimate electrical conductivity (EC) of saturated soil paste extract (ECe) of 1:2 and 1:5 soil: water suspensions were evaluated. Soil samples were taken from different depths (viz. 0-15, 15-30, 30-45, 45-60, and 60-90 cm) of the wheat field under irrigated saline regimes ranging from 1.5 to 12 dSm-1. The relationship between the electrical conductivity of saturated paste extract (ECe) and soil-water suspension extracts (i.e., EC1:2 and EC1:5) was established beside the development of regression models and conversion factors. Regression models relating ECe with EC1:2, EC1:5 were developed with coefficients of determination (R2) 0.98 and 0.99 respectively. The average conversion factor between ECe~ EC1:2 and ECe~ EC1:5 were 2.7 and 7.7, respectively. Methods standardized in the study can be used to estimate the EC of different saturated soil paste extract ratios, which will be less cumbersome and significantly reduce the estimation time.

Soil properties are an important factor for orchard establishment, precise nutrient management and sustainable production of fruit crops. Therefore, it is important to assess the spatial distribution of fundamental soil properties in well-established orchards. Hence an attempt has been made to assess the extent of soil properties and its spatial distribution in citrus orchards in medium black soils of Madhya Pradesh. The present study was conducted for the assessment of the spatial distribution of physicochemical properties viz. pH, electrical conductivity (EC) and soil organic carbon (SOC) of citrus orchards in medium-deep black soils of India. Results revealed that soil pH ranged from 6.83-8.84 (mean 7.80), soil EC varied from 0.07-0.34dS m-1 (mean 0.18 dS m-1) and soil organic carbon ranged from 0.13-0.89% (mean 0.47%) in 0-20 cm of surface soil layer. Geostatistical analysis showed that the slope of the prediction function of best-fit model (exponential) for soil pH, EC and SOC was 0.31, 0.22 and 0.77, respectively. The corresponding values of root mean square error (RMSE) were 0.35, 0.03, and 0.14. Interpolation of soil properties indicated that 89.2 % area had soil pH between 7.20 to 8.00, 83.4 % area had soil EC between 0.10 to 0.20 dS m-1, while&gt;90 % area had SOC content ranged from 0.25 to 0.75%. Geo-statistical analysis revealed that spatial dependency was moderate for pH and strong spatial dependency was estimated for EC and SOC content. Based on RMSE and slope of prediction function, an exponential model was best-fit model in ordinary kriging for interpolation of measured soil properties.

Introduction: In order to provide a database, it is essential having access to accurate information on soil spatial variation for soil sustainable management such as proper application of fertilizers. Spatial variations in soil properties are common but it is important for understanding these changes, particularly in agricultural lands for careful planning and land management. Materials and Methods: To this end, in winter 1391, 189 undisturbed soil samples (0-30 cm depth) in a regular lattice with a spacing of 500 m were gathered from the surface of Miankangi land, Sistan plain, and their physical and chemical properties were studied. The land area of the region is about 4,500 hectares; the average elevation of studied area is 489.2 meters above sea level with different land uses. Soil texture was measured by the hydrometer methods (11), Also EC and pH (39), calcium carbonate equivalent (37) and the saturation percentage of soils were determined. Kriging, Co-Kriging, Inverse Distance Weighting and Local Polynomial Interpolation techniques were evaluated to produce a soil characteristics map of the study area zoning and to select the best geostatistical methods. Cross-validation techniques and Root Mean Square Error (RMSE) were used. Results and Discussion: Normalized test results showed that all of the soil properties except calcium carbonate and soil clay content had normal distribution. In addition, the results of correlation test showed that the soil saturation percentage was positively correlated with silt content (r=0.43 and p

Due in large measure to the prodigious research efforts of Rhoades and his colleagues at the George E. Brown, Jr., Salinity Laboratory over the past two decades, soil electrical conductivity (EC), measured using electrical resistivity and electromagnetic induction (EM), is among the most useful and easily obtained spatial properties of soil that influences crop productivity. As a result, soil EC has become one of the most frequently used measurements to characterize field variability for application to precision agriculture. The value of spatial measurements of soil EC to precision agriculture is widely acknowledged, but soil EC is still often misunderstood and misinterpreted. To help clarify misconceptions, a general overview of the application of soil EC to precision agriculture is presented. The following areas are discussed with particular emphasis on spatial EC measurements: a brief history of the measurement of soil salinity with EC, the basic theories and principles of the soil EC measurement and what it actually measures, an overview of the measurement of soil salinity with various EC measurement techniques and equipment (specifically, electrical resistivity with the Wenner array and EM), examples of spatial EC surveys and their interpretation, applications and value of spatial measurements of soil EC to precision agriculture, and current and future developments. Precision agriculture is an outgrowth of technological developments, such as the soil EC measurement, which facilitate a spatial understanding of soil–water–plant relationships. The future of precision agriculture rests on the reliability, reproducibility, and understanding of these technologies.