Electrical Conductivity Mapping Research Articles

Groundwater quality mapping based on the Wilcox Classification method for agricultural purposes: Qazvin Plain aquifer case

Water quality is an essential component in managing surface and groundwater resources and for various uses; it is considered a necessary principle in planning. This study aims to map the groundwater quality of the Qazvin Plain aquifer in Iran for agricultural use based on the Wilcox classification method. For this purpose, the parameters of electrical conductivity (EC) and sodium adsorption ratio (SAR) of wells in the years 2015-2018 have been used. For interpolation, the inverse distance weighting (IDW) method with optimal power and Kriging geostatistical techniques were used based on spherical, exponential, and Gaussian semicircle algorithms. Electrical conductivity and SAR maps were drawn in the GIS platform after selecting the best interpolation method due to minor errors. The Wilcox method was used to classify the water quality of the studied wells. In most cases, the IDW method with optimal power was selected as the superior interpolation method. During the study, the results obtained from the water quality maps showed that the level attributed to the "high salinity" water class increased from 25.98 to 36.44, and the level attributed to the "slightly salty" water class decreased from 12.54 to 3.14. Finally, the results showed that the quality of underground water for agricultural purpose in the Qazvin Plain aquifer became more unfavourable during the studied period

Interpolation methods for spatial distribution of groundwater mapping electrical conductivity

This study was carried out to develop a conceptual framework for determining the best interpolation method which mainly is employed to calculate the variability maps of electrical conductivity (EC) in neighboring regions. The considered case study is parts of the Khorasan Razavi province, Iran (including five aquifers Kashmar, Fariman, Doruneh, Sarakhs and Joveyn). In the first step, the empirical variogram (semi-variogram) was computed for the study area. The methods of the variability of a variable with spatial or temporal distance were considered to measure the semi-variogram function. In the next step, the best variogram model (e.g. spherical, exponential or Gaussian) was considered in the Geographic Information System (GIS) environment and f for the Environmental Sciences (GS+) software. By plotting the semi-variogram in GS+ program based on different method as Global Polynomial Interpolation (GPI), Inverse distance weighing (IDW), Radial basis function (RBF), Kriging method, Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), the best variogram model fitted to spatial structure of the EC. Finally, by considering the acceptable range for different parameters which impact on EC and evaluating their impacts by scaling, the best interpolation method has been selected for that area for employing their neighborhood basin. Result indicated that the precipitation located within the range of 140 to 180 mm, RBI has the priority. This process is continued for all 14 parameters and eventually one method gets the most points.

Using electromagnetic induction to inform precision turfgrass management strategies in sand‐capped golf course fairways

AbstractTo meet the turfgrass standards that players expect, golf course superintendents rely on intense irrigation, fertilization, and cultivation programs. However, the overapplication of irrigation water and fertilizer has been shown to have negative effects on water quality. Precision turfgrass management (PTM) is an emerging area of interest as more golf course superintendents are looking to increase input efficiency while simultaneously reducing water and fertilizer input costs, as well as environmental impacts. Our objectives were to (1) use electromagnetic induction (EMI) to determine the spatial variability of apparent electrical conductivity (EC) in sand‐capped fairways and (2) correlate EC to measured soil and turfgrass characteristics to determine the applicability of mapping EC for PTM. Soil samples and EC data were collected in spring 2021 on four sand‐capped fairways from two golf courses (one hybrid bermudagrass and one zoysiagrass) belonging to the same facility in southeast Texas. Apparent EC was found to be positively and significantly correlated with soil volumetric water content (VWC, 0.40 < r > 0.62) and turfgrass normalized difference vegetation index (NDVI; 0.21 < r > 0.46) in three of four fairways, while EC was negatively and significantly correlated with penetration resistance (PR, −0.29 < r > −0.48) in two of four fairways studied. The strengths of these relationships were corroborated by strong visual similarities when comparing spatial maps of EC with those of VWC, NDVI, and PR, indicating that EMI‐based EC data have potential for use in delineating site‐specific management zones for water and fertilizer applications, as well as targeted aeration.

Assessing coastal aquifer vulnerability to seawater intrusion: adaptation of the DCG method and application to the Dradère–Souière coastal aquifer in northern Morocco

Context Seawater intrusion into coastal aquifers represents a major global environmental challenge, leading to salinisation of these water resources. Aims This study aims to protect coastal aquifers from marine salinisation by assessing their vulnerability to seawater intrusion. Methods The DCG method was adapted by considering the weighted sum of the following three critical parameters controlling seawater intrusion: distance from saltwater (D), aquifer hydraulic conductivity (C) and the hydraulic gradient (G). Thereby, the weights assigned to these parameters were objectively determined through sensitivity tests. Key results The DCG map of the Dradère–Souière coastal aquifer shows that vulnerability increases near the sea, with a high to extreme vulnerability zone extending parallel to the coastline up to 5000 m inland. These findings are corroborated by a correlation analysis with the electrical conductivity map. Conclusion This study highlighted the adaptation of the DCG method for assessing the vulnerability of coastal aquifers to seawater intrusion and its successful application to the Dradère–Souière coastal aquifer. Implications The DCG method could be extended to other coastal regions as a strategic tool for protecting against seawater intrusion. Further improvements could be achieved by correlating it with other approaches and integrating it with GIS technologies.

Scenarios for precision nitrogen management in potato: Impact on yield, tuber quality and post-harvest nitrate residues in the soil

Potato belongs to the category of the staple food playing an important role in global food security and nutrition. Because potato plants have a shallow root system and stop nitrogen uptake early in the growing season (between 20 and 60 days after emergence), potato fields are more prone to leaching of nitrate to lower soil layers and the groundwater than deep rooting crops, thus posing a higher risk for polluting the environment with nitrate. On the other hand, lowering fertilizer dosages could result in yield loss and reduced tuber quality. To balance the risk of yield and quality loss against the risk of nitrate leaching, the fertilization dose should be adapted to the local production potential, which can vary considerably within a field. However, there is no consensus on how to adapt the fertilization to this variability. Therefore, the objective of this study was to assess various N fertilization scenarios and their effect on the concentration of residual nitrate, total potato yield and tuber quality. Five potato fields located in Flanders with historical spatial variation in crop productivity were selected as potential candidates for implementing site-specific nitrogen management practice. The fields were divided into management zones based on variation visible in soil maps, maps of electrical conductivity, maps of vegetation indices and soil samples. Nitrogen was applied in three different dosages, including the conventional dose, and two reduced fertilization levels. Based on time series of vegetation indices, cumulative vegetation indices and pair-wise correlations between the vegetation index values obtained from Sentinel-2 satellite imagery (FAPAR, NDVI, FCOVER, LAI) only two fields of five showed temporal stability of the spatial management zones, while in three other fields the zone performance shifted depending on the weather conditions during the growing season. In the fields with stable zones, the lowest N fertilization levels in the areas with lower productivity potential resulted in a reduction of the concentration of leachable nitrates while total yield and potato tuber quality (dry matter and nitrogen content) did not change. For this type of fields with temporally stable zones, it was concluded to recommend reducing the nitrogen dosage in the zones with a lower productivity potential and high mineralization levels to limit nitrate leaching. In the fields where better performing zones change their location in wet and dry years, significant differences between treatments in the total potato yield, dry matter and nitrogen content in the tuber were also often detected. However, application of a low nitrogen dose during the first fertilization in one of the zones in such fields is not recommended as it is not yet known how the zones will behave the coming season, leading to a risk for underfertilization of these zones. Therefore, conventional fertilization remains the recommended practice in case the production potential of the zones depends on the weather during the growing season and is not yet known at the moment of fertilization. However, fertilizer fractions applied later during the growing season may be adapted to the crop development and actual weather conditions.

On-farm experimentation of precision agriculture for differential seed and fertilizer management in semi-arid rainfed zones

IntroductionThis study explores the integration of precision agriculture technologies (PATs) in rainfed cereal production within semi-arid regions.Methodsutilizing the Veris 3100 sensor for apparent soil electrical conductivity (ECa) mapping, differentiated management zones (MZs) were established in experimental plots in Valsalada, NE Spain. Site-specific variable dose technology was applied for seed and fertilizer applications, tailoring inputs to distinct fertility levels within each MZ. Emphasizing nitrogen (N) management, the study evaluated the impact of variable-rate applications on crop growth, yield, nitrogen use efficiency (NUE), and economic returns. For the 2021/2022 and 2022/2023 seasons, seeding rates ranged from 350 to 450 grains/m2, and basal fertilizer dosages varied between high and low levels. Additionally, the total nitrogen units were distributed differently between the two seasons, while maintaining a uniform topdressing fertilizer dose across all treatments.ResultsResults revealed a significant increase in yield in MZ 2 (higher fertility) compared to MZ 1 (lower fertility). NUE demonstrated notable improvement in MZ 2, emphasizing the effectiveness of variable-rate N applications. Economic returns, calculated as partial net income, showed a considerable advantage in MZ 2 over MZ 1, resulting in negative outcomes for low-fertility areas in several of the analyzed scenarios, and highlighting the financial benefits of tailored input management.ConclusionThis research provides quantitative evidence supporting the viability and advantages of adopting PATs in rainfed cereal production. The study contributes valuable insights into optimizing input strategies, enhancing N management, and improving economic returns in semi-arid regions.

Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements

The study of the physical properties of the topside ionosphere is fundamental to investigating the energy balance of the ionosphere and developing accurate models to predict relevant phenomena, which are often at the root of Space Weather effects in the near-Earth environment. One of the most important physical parameters characterising the ionospheric medium is electrical conductivity, which is crucial for the onset and amplification of ionospheric currents and for calculating the power density dissipated by such currents. We characterise, for the first time, electrical conductivity in the direction perpendicular to the geomagnetic field, namely Pedersen and Hall conductivities, in the topside ionosphere at an altitude of about 450 km. For this purpose, we use eight years of in situ simultaneous measurements of electron density, electron temperature and geomagnetic field strength acquired by the Swarm A satellite. We present global statistical maps of perpendicular electrical conductivity and study their variations depending on magnetic latitude and local time, seasons, and solar activity. Our findings indicate that the most prominent features of perpendicular electrical conductivity are located at low latitudes and are probably driven by the complex dynamics of the Equatorial Ionisation Anomaly. At higher latitudes, perpendicular conductivity is a few orders of magnitude lower than that at low latitudes. Nevertheless, conductivity features are modulated by solar activity and seasonal variations at all latitudes.

Gaussian process regression for three-dimensional soil mapping over multiple spatial supports

This study investigates the complexity of spatial soil modelling, particularly focusing on the challenge of variable vertical support in traditional soil data collection. Traditional soil sampling, described in terms of horizons, often fails to accurately pinpoint the specific depths for specific soil properties. This gap is significant, as depth-specific data is crucial for a thorough understanding of soil formation processes and for assessing potential environmental impacts. In digital soil mapping (DSM), the prevalent reliance on standardised depth intervals and mass-preserving spline functions for data resampling results in a modelling approach that tends to disregard depth-related details, thereby introducing potential uncertainties.To address these limitations, this work explores how effectively Gaussian process regression (GPR) can model soil in 3D. This technique comprises two key components: a mean function and a semivariogram-like kernel. Unlike conventional methods that make a single prediction, GPR provides detailed probability distributions. This capability allows for the quantification of prediction uncertainty at various points, offering insights for decision-making and risk assessment purposes. Moreover, GPR has the capability to make volume or block estimates and assess associated uncertainties. Enabling volume-based predictions enriches the range of strategies available for land management.In this research, we employ GPR as a novel 3D soil modelling technique and compare its performance with traditional spline-based methods. The comparison is conducted through a case study on a farm in northern New South Wales, Australia, focusing on the 3D mapping of soil pH and electrical conductivity (EC). Our results demonstrate the ability of GPR to estimate soil properties across various volumes, utilising data from multiple vertical supports, thereby offering a more versatile approach for soil modelling in diverse spatial contexts.

Investigation of the contamination behavior in water and soil of an inactive dump from chemical analysis and geophysical method

In rapidly growing urban areas of developing countries, inappropriate urbanization practices near water bodies lead to significant environmental degradation, including deforestation, soil impermeabilization, and direct waste discharge into rivers and streams. This not only compromises water quality and ecosystem health but also exacerbates flood risks for nearby communities. Dumpsites, common in these regions due to inadequate waste management infrastructure, pose significant environmental threats, including soil and groundwater contamination. Our study employed a frequency-domain electromagnetic survey on an inactive dumpsite adjacent to one of Brazil’s major rivers. This geophysical method provided critical insights for geoenvironmental investigations, facilitating the collection of soil and groundwater samples to understand leachate behavior. We found that the shallower electrical conductivity map aligned with the waste disposal area's boundaries, verified by on-site surveys and excavations. Historical aerial images supported this interpretation and helped explain conductive electrical anomalies in the deeper conductivity map, linked to old irregular housing and waste disposal around the dumpsite. Moreover, the electrical conductivity maps revealed NE-oriented percolation at depth in the study area. Groundwater monitoring wells and soil analyses identified chemical elements exceeding standards set by Brazilian and international environmental agencies in this flow direction. These findings highlight the importance of using investigation techniques with superior spatial resolution compared to traditional environmental monitoring approaches. Electromagnetic methods prove effective in enhancing understanding and management of contamination in dumpsite environments.

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.

Phantom evaluation of electrical conductivity mapping by MRI: Comparison to vector network analyzer measurements and spatial resolution assessment.

To evaluate the performance of various MR electrical properties tomography (MR-EPT) methods at 3 T in terms of absolute quantification and spatial resolution limit for electrical conductivity. Absolute quantification as well as spatial resolution performance were evaluated on homogeneous phantoms and a phantom with holes of different sizes, respectively. Ground-truth conductivities were measured with an open-ended coaxial probe connected to a vector network analyzer (VNA). Four widely used MR-EPT reconstruction methods were investigated: phase-based Helmholtz (PB), phase-based convection-reaction (PB-cr), image-based (IB), and generalized-image-based (GIB). These methods were compared using the same complex images from a 1 mm-isotropic UTE sequence. Alternative transceive phase acquisition sequences were also compared in PB and PB-cr. In large homogeneous phantoms, all methods showed a strong correlation with ground truth conductivities (r > 0.99); however, GIB was the best in terms of accuracy, spatial uniformity, and robustness to boundary artifacts. In the resolution phantom, the normalized root-mean-squared error of all methods grew rapidly (>0.40) when the hole size was below 10 mm, with simplified methods (PB and IB), or below 5 mm, with generalized methods (PB-cr and GIB). VNA measurements are essential to assess the accuracy of MR-EPT. In this study, all tested MR-EPT methods correlated strongly with the VNA measurements. The UTE sequence is recommended for MR-EPT, with the GIB method providing good accuracy for structures down to 5 mm. Structures below 5 mm may still be detected in the conductivity maps, but with significantly lower accuracy.

Fast high-resolution electric properties tomography using three-dimensional quantitative transient-state imaging-based water fraction estimation.

In this study, we aimed to develop a fast and robust high-resolution technique for clinically feasible electrical properties tomography based on water content maps (wEPT) using Quantitative Transient-state Imaging (QTI), a multiparametric transient state-based method that is similar to MR fingerprinting. Compared with the original wEPT implementation based on standard spin-echo acquisition, QTI provides robust electrical properties quantification towards B1 + inhomogeneities and full quantitative relaxometry data. To validate the proposed approach, 3D QTI data of 12 healthy volunteers were acquired on a 1.5 T scanner. QTI-provided T1 maps were used to compute water content maps of the tissues using an empirical relationship based on literature ex-vivo measurements. Assuming that electrical properties are modulated mainly by tissue water content, the water content maps were used to derive electrical conductivity and relative permittivity maps. The proposed technique was compared with a conventional phase-only Helmholtz EPT (HH-EPT) acquisition both within whole white matter, gray matter, and cerebrospinal fluid masks, and within different white and gray matter subregions. In addition, QTI-based wEPT was retrospectively applied to four multiple sclerosis adolescent and adult patients, compared with conventional contrast-weighted imaging in terms of lesion delineation, and quantitatively assessed by measuring the variation of electrical properties in lesions. Resultsobtained with the proposed approach agreed well with theoretical predictions and previous in vivo findings in both white and gray matter. The reconstructed maps showed greater anatomical detail and lower variability compared with standard phase-only HH-EPT. The technique can potentially improve delineation of pathology when compared with conventional contrast-weighted imaging and was able to detect significant variations in lesions with respect to normal-appearing tissues. In conclusion, QTI can reliably measure conductivity and relative permittivity of brain tissues within a short scan time, opening the way to the study of electric properties in clinical settings.

Drip Irrigation Soil-Adapted Sector Design and Optimal Location of Moisture Sensors: A Case Study in a Vineyard Plot

To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management.

Approaches to Consider for Site-Specific Field Mapping

This publication discusses the general concept of site-specific farming, i.e., dividing a farm into several smaller management parcels instead of considering the whole farm as a single unit. This publication provides information on methods available that could be incorporated into the farming system for smart decision-making, for example, varying nutrient applications. The publication also discusses Normalized Difference Vegetative Index (NDVI) maps, Global Positioning System (GPS) units, yield monitors, and electrical conductivity maps.

Zonal Application of Plant Growth Regulator in Cotton to Reduce Variability and Increase Yield in a Highly Variable Field

Variable-rate application has great potential to reduce variability and increase yield by spatially optimizing agricultural inputs. In cotton, plant growth regulators (PGRs) control excessive growth and provide suitable plant height for harvest operations. This study evaluates the effect of variable-rate PGR application compared to constant-rate application to reduce yield spatial variability and increase yield. The variable-rate approach was carried out in 2020 based on zonal applications defined by clustering analysis using soil electrical conductivity, vegetation indexes, and yield maps. Application doses and timings were determined by integrating plant height measurements for the whole field in 2019 and by zone in 2020. To compare the two procedures, cultivar and plant populations were kept constant; fertilization and accumulated rain were similar in both seasons. A reduction in yield spatial variability due to the zonal application was observed, with yield coefficient of variation (CV) decreasing from 18% in 2019 to 12% in 2020. Spatial and temporal analysis of Normalized Difference Vegetation Index satellite images showed higher CV values in 2019 (constant-rate) reaching 30% at the end of the season, whereas in 2020 (variable-rate) CV was constant (approximately 10%). Cotton yield increased from 3.5 to 4.3 t ha-1 between 2019 and 2020, which can be partially attributed to the variable-rate approach. The variable-rate approach based on application zones and plant height measurements was a viable strategy for reducing yield spatial variability and likely increasing yield in a highly variable cotton field.

Hydrogeochemical characterization of the alluvial aquifer of Catania Plain, Sicily (South Italy)

A hydrogeochemical study was carried out on the shallow Catania Plain alluvial aquifer, in eastern Sicily to reconstruct its hydrogeological structure, the meteoric recharge and to assess the influence of human activities on groundwater. To characterize the geochemistry of the shallow aquifer, two sampling campaigns were carried out, August–October 2004 and April–May 2005 in 47 sites distributed throughout the plain. The samples were collected and analyzed for physical–chemical parameters and major ions, as well as stable isotopes (δ18O and δ2H). Alluvial deposits with heterogeneous grain sizes constitute the aquifer. Varying conditions of vertical and horizontal permeability lead to the presence of a multilayered aquifer with different conditions of confinement and partial interconnection among layers. The sampled waters were separated into four groups of different compositions due to the water–rock interaction with the different lithologies present in and around the study area. Maps of electrical conductivity and sulfate show a systematic control of land use, in correspondence with the biggest farms. High sulfate concentration is due to both the natural interaction between local meteoric waters and Etna’s plume and the mixing with groundwater coming from the area where evaporitic rocks of the Gessoso Solfifera formation are present. In addition, anthropogenic contamination cannot be ruled out. A rain gauge network, consisting of 3 sites located at different altitudes, was installed to collect rain waters to determine isotopic data (δ2H and δ18O) and to measure the monthly rainfall amount. Based on the isotopic composition of sampled waters, it has been established that beyond the direct meteoric recharge, the recharging areas are in the North (Mt. Etna) and the South (Hyblean Plateau).

Salinity Properties Retrieval from Sentinel-2 Satellite Data and Machine Learning Algorithms

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.

Integration of Geophysical Survey Data for the Identification of New Archaeological Remains in the Subsoil of the Akrai Greek Site (Sicily, Italy)

For more than a decade now, geophysical prospecting has been considered an integral part of archaeological research; the ability to quickly investigate large areas and locate objects buried in the ground without directly interacting with it is a key feature that makes such surveys essential for identifying and locating, with good accuracy, buried archaeological remains. In this study, two extensive magnetic and electromagnetic surveys were carried out in two different areas of the Akrai archaeological site, where given the distribution of archaeological remains already found at the site, it was likely that additional buried remains were present. The surveys were performed using a proton precession magnetometer and a multifrequency electromagnetic device with a frequency range of 2.5–250 kHz. By processing the data, the vertical magnetic gradient and electrical conductivity maps were obtained. Furthermore, 3D models of electrical conductivity distribution were reconstructed. On comparing the results, it was notable that many anomalies characterized by low vertical gradient values were identified within areas characterized by low electrical conductivity values. These anomalies detected by both surveys can be associated with good probability with buried archaeological remains made up of limestones. In fact, they exhibit shapes and sizes comparable to those of the wall remains already found at the site.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

SALINITY OF SOIL AND IRRIGATION WATER ON RICE PRODUCTIVITY IN THE CANTON OF SAN JACINTO DE YAGUACHI, ECUADOR

The surface of agricultural soils affected with salinity causes degradation and reduction of crop production, negatively impacting the economy of farmers. The objective of this research was to determine the effect of water quality and soil salinity on rice production in the sub-basin of the Yaguachi River, Ecuador. For this purpose, 99 soil samples were georeferenced and analysed over an area of 19 027 ha, as well as water samples representative of the area under study. Soil pH and electrical conductivity (EC) maps were produced and a regression analysis between EC and rice yield was performed on 34 producer plots. The predominant texture of the soils was found to be clayey, with an average pH of 6.73; The 38.4 % of the samples had an EC between 3.8 and 7.1 dS m-1. Rice yield gradually decreased as it was grown in soils with higher salinity and 26.81 % of the yield loss was found to be attributed to the increase in EC. The salinity of the irrigation water and other quality indicators such as the percentage of soluble sodium (Na+) and the sodium adsorption ratio (RAS) classified the water as severely restricted for use. However, given that crop yields did not show as drastic a reduction as expected, it can be said that the rice plant responds to the conditions of the canton of San Jacinto de Yaguachi as moderately tolerant to salinity.