ECa Survey Research Articles

A system for concurrent on-the-go soil apparent electrical conductivity and gamma-ray sensing in micro-irrigated orchards

On-the-go soil apparent electrical conductivity (ECa) sensors are great tools for mapping and monitoring soil properties such as water content, texture, and salinity. ECa maps and surveys are most useful and reliable when obtained in uniformly wet fields. However, soil moisture in micro-irrigated (e.g., drip or micro-sprinklers) orchards is typically non-uniform, with moist soil along tree and irrigation lines, and dry soil between tree rows. We developed a mobile platform and data post-processing algorithm to facilitate geospatial ECa measurements along or near driplines. Gamma-ray (γ-ray) spectrometry is commonly used for clay content and type mapping. Fusion between ECa and γ-ray is often reported to increase the accuracy of field-scale soil maps. However, contrarily to ECa, γ-ray spectrometry is best suited for sensing soils in dry conditions. Micro-irrigated orchards are ideal environments for the combined application of these two sensor technologies. The fusion of topsoil (top 0.5 m) ECa (measured along the driplines) and γ-ray total counts (TC) (measured between the tree rows) data was tested at a 0.4-ha sandy loam citrus orchard in Southern California. Here, we discuss sensor data acquisition, data processing, sensor-directed sampling scheme delineation, and characterization of field-scale soil particle size fraction (0–0.4 m soil profile) spatial variability. Pearson correlation coefficients between sand and silt content with both ECa and TC were significant (p < 0.05). A principal component analysis biplot suggested strong positive relationship with TC and clay content. Backwards stepwise multiple linear regression predicted sand content using TC, elevation, and spatial coordinates as explanatory variables with mean absolute error (MAE) of 3.06 %. Silt content was predicted (MAE=1.55 %) using ECa, elevation, and spatial coordinates. The development of this platform enables better characterization of soil properties in micro-irrigated orchard systems using on-the-go sensing technology.

Relationships among apparent electrical conductivity and plant and terrain data in an agroforestry system in the Ozark Highlands

AbstractMinimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to identify: (i) ECa–forage yield, –tree growth, and –terrain attribute relationships within ECa‐derived soil management zones (SMZs) and (ii) terrain attributes that drive ECa variability within a 20‐year‐old, 4.25‐ha, agroforestry system in the Ozark Highlands of northwest Arkansas. The average of 12 monthly perpendicular (PRP) and horizontal coplanar (HCP) ECa surveys (August 2020 to July 2021) and 14 terrain attributes were obtained. Tree diameter at breast height (DBH) and height (TH) measurements were made in December 2020 and March 2021, respectively, and forage yield samples were collected during Summer 2018 and 2019. Apparent EC‐tree property relationships were generally stronger within the whole site (averaged across tree property and ECa configuration, |r| = 0.38) than within the SMZs (averaged across tree property, ECa configuration, and SMZ, |r| = 0.27). The strength of the SMZs’ terrain‐attribute‐PRP‐ECa relationships were 9% to 205% greater than that for the whole site. In whole‐site, multi‐linear regressions, slope length and steepness factor (10.5%), mid‐slope (9.4%), and valley depth (7.2%) had the greatest influence (i.e., percent of total sum of squares) on PRP ECa variability, whereas valley depth (15.3%), wetness index (11.9%), and mid‐slope (11.2%) had the greatest influence on HCP ECa variability. Results show how ECa relates to plant (i.e., DBH, TH, and forage yield) and terrain data within SMZs in agroforestry systems with varying topography and could be used to precisely manage agroforestry systems.

Definition and Validation of Vineyard Management Zones Based on Soil Apparent Electrical Conductivity and Altimetric Survey

In the current context of increasing costs of production factors, it is essential to optimize the management of available resources, seeking to incorporate technologies that improve knowledge of the variables involved in the agronomic production process. The aim of this study is to define and validate management zones (MZ) in a 3.3 ha vineyard located near Évora, in the South of Portugal. A contact sensor (“Veris 2000 XA”) was used to map soil apparent electrical conductivity (ECa) and a precision altimetric survey of the field was carried out with a global navigation satellite system receiver (GNSS). The results of these surveys were submitted to geostatistical treatments that allowed the definition of three MZ (less, intermediate, and more productive potential). The validation of such MZ was carried out by laboratory analysis of soil samples (texture, pH, organic matter—OM, moisture content, phosphorous, potassium, exchange bases, and cation exchange capacity—CEC), measurements of soil compaction (cone index—CI) with an electronic cone penetrometer, and through indices (Normalized Difference Vegetation Index—NDVI, and Normalized Difference Water Index—NDWI) obtained by remote sensing (RS) using Sentinel-2 satellite images. All these parameters (soil parameters and RS indices) proved the validity of the MZ (of less, intermediate, and more productive potential) defined from the ECa and altimetric survey. This validation attests to the interest of expeditious technological tools for monitoring ECa as a fundamental step in implementing smart agronomic decision-making processes.

Using Apparent Electrical Conductivity to Delineate Field Variation in an Agroforestry System in the Ozark Highlands

Greater adoption and better management of spatially complex, conservation systems such as agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, no work has been conducted using repeated electromagnetic induction (EMI) or apparent electrical conductivity (ECa) surveys in AF systems within the Ozark Highlands of northwest Arkansas. As a result, objectives were to (i) evaluate spatiotemporal ECa variability; (ii) identify ECa-derived soil management zones (SMZs); (iii) establish correlations among ECa survey data and in situ, soil-sensor volumetric water content, sentential site soil-sample EC, and gravimetric water content and pH; and (iv) determine the optimum frequency at which ECa surveys could be conducted to capture temporal changes in field variability. Monthly ECa surveys were conducted between August 2020 and July 2021 at a 4.25 ha AF site in Fayetteville, Arkansas. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged from 1.8 to 18.0 and 3.1 to 25.8 mS m−1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. The largest measured ECa values occurred within the local drainage way or areas of potential groundwater movement, and the smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. The PRP and HCP mean ECa, standard deviation (SD), and coefficient of variation (CV) were unaffected (p &gt; 0.05) by either the weather or growing/non-growing season. K-means clustering delineated three precision SMZs that were reflective of areas with similar ECa and ECa variability. Results from this study provided valuable information regarding the application of ECa surveys to quantify small-scale changes in soil properties and delineate SMZs in highly variable AF systems.

Concurrent variability of soil moisture and apparent electrical conductivity in the proximity of olive trees

Detailed knowledge of soil moisture (θ) is crucial for implementing appropriate soil and crop management decisions and assuring crop productivity and soil functioning. θ measurement is challenging in the stony soils of traditional olive orchards where the operationality of common sensors is often compromised and fine scale variability of soil properties is complex as a result of the presence and specific architecture of the tree root system and canopy. In such environments, non-invasive electromagnetic induction (EMI) sensors constitute an alternative for measuring θ. Here we evaluate this alternative by exploring relationships between θ and apparent electrical conductivity (ECa) and contribute to improved understanding of spatio-temporal θ variability in the proximity of olive trees. Continuously measured θ in a trench on one side of an olive tree was complemented with 47 ECa surveys conducted periodically at the opposite side of the tree during a period of 15 months. θ was measured in the trench at five distances from the trunk (0.6–3.8 m) and at five depths (0.1–0.8 m). ECa was measured at the same distances from the trunk. We observed similar temporal patterns of θ and ECa in response to precipitation and evaporation at the site. The proportionality between spatial means of θ and ECa and the similarity in the relationships between spatial mean and standard deviation for each variable revealed concurrent spatio-temporal patterns. Dependence on distance to the trunk was different for θ and ECa, with an apparent erratic response for the former and a positive relationship for the latter. The slope of the linear θ-ECa models showed power law decay with distance to the trunk as a result of the varying soil and tree properties around the tree. These results pave the way for implementing coupled static-mobile ECa monitoring systems and θ estimation in olive orchards from the plot to the field and farm scales.

Finding Suitable Transect Spacing and Sampling Designs for Accurate Soil ECa Mapping from EM38-MK2

Finding an ideal sampling design is a crucial stage in detailed soil mapping to assure reasonable accuracy of resulting soil property maps. This study aimed to evaluate the influence of sampling designs and sample sizes on the quality of soil apparent electrical conductivity (ECa) maps from an electromagnetic sensor survey. Twenty-six (26) parallel transects were gathered in a 72-ha plot in Southeastern Brazil. Soil ECa measurements using an on-the-go electromagnetic induction sensor were taken every second using sensor vertical orientation. Two approaches were used to reduce the sample size and simulate kriging interpolations of soil ECa. Firstly, the number of transect lines was reduced by increasing the distance between them; thus, 26 transects with 40 m spacing; 13 with 80 m; 7 with 150 m; and 4 with 300 m. Secondly, random point selection and Douglas-Peucker algorithms were used to derive four reduced datasets by removing 25, 50, 75, and 95% of the points from the ECa survey dataset. Soil ECa was interpolated at 5 m output spatial resolution using ordinary kriging and the four datasets from each simulation (a total of twelve datasets). Map uncertainty was assessed by root mean square error and mean error metrics from 400 random samples previously selected for external map validation. Maps were evaluated on their uncertainty and spatial structure of variation. The transect elimination approach showed that maps produced with transect spacing up to 150 m could preserve the spatial structure of ECa variations. Douglas-Peucker results showed lower nugget values than random point simulations for all selected sample densities, except for a 95% point reduction. The soil ECa maps derived from the 75% reduced dataset (by random sampling or Douglas-Peucker) or from 13 transect lines (80 m spacing) showed reasonable accuracy (RMSE of validation circa 0.7) relative to the map interpolated from all survey points (RMSE of 0.5), suggesting that transect spacing of 80 m and reading intervals greater than one second can be used for improving the efficiency of on-the-go soil ECa surveys.

Using low-cost geophysical survey to map soil properties and delineate management zones on grazed permanent pastures

Usually, soils utilised for livestock production have similar high spatial variability as those for agricultural or forest use. As a consequence, it is necessary to determine the spatial patterns of the main soil properties as the first stage to implement site-specific management. However, this has to be performed using an inexpensive technique because the profitability in these types of farm are very low, so owners need a cheap, effective, and reliable method to know which zones have similar production potential. Using soil apparent electrical conductivity (ECa) measurements, obtained with a contact sensor at many locations, as the basis to perform a directed soil sampling, 10 samples were taken at two depths (0–0.25 m and 0.25–0.50 m) in a 2.3 ha field in Evora (southern Portugal). Firstly, relationships between ECa and many soil properties were analysed using regression analysis. Six soil properties (clay, silt, fine sand, soil moisture content, pH, and cation exchange capacity) were significantly correlated with ECa. Consequently, spatial distributions of these variables were visualised using map algebra techniques. Later, a fuzzy clustering algorithm was utilised to delineate management zones, resulting in two subfields to be managed separately. Finally, a principal component analysis was conducted to analyse the influence of the soil properties and elevation on the soil variability. It was determined that elevation and clay were the most important contributing properties. Therefore, these can be regarded as key latent variables in this soil. Results showed that low-cost data based on ECa surveys can be used to implement site-specific management in soils with permanent pastures, such as those in the montado or dehesa ecosystems, in the southwest of the Iberian Peninsula.

Stratified sampling in fruit orchards using cluster-based ancillary information maps: a comparative analysis to improve yield and quality estimates

Estimation of yield or other fruit quality parameter is of great interest to farmers to decide on management actions just before harvesting and, in any case, to anticipate and plan harvesting operations. Making accurate and reliable estimates often requires systematic sampling that, when covering the whole plot, can result in the use of a large number of samples and a significant effort in time and cost for fruit growers. Faced with this whole area sampling strategy, simple random sampling (SRS) using reduced sample sizes is currently a widely used technique despite the less precise estimates that it provides. In this work, different stratified sampling schemes have been tested to estimate yield (kg/tree), fruit firmness (kg/cm2) and the refractometric index (oBaume) in a peach orchard located in Gimenells (Lleida, Catalonia, Spain). In contrast to SRS, the use of ancillary information (NDVI and apparent electrical conductivity, ECa) allowed sampling units or trees to be stratified according to two or three classes (strata) within the plot. The classes or homogeneous stratification zones were delimited by cluster analysis using, either separately or in combination, a multispectral airborne image (NDVI) and a ECa survey map acquired by means of a soil resistivity sensor (Veris 3100). Sampling schemes were then compared in terms of efficiency. In general, stratified sampling showed better results than SRS. Regarding yield estimates, stratified sampling according to two strata of NDVI allowed the sample size to be reduced by 17% compared to the SRS for the same precision. On the other hand, quality parameters may require different stratification strategies concerning the number of strata to be used. While oBaume was better-estimated using also stratified samples based on two strata of NDVI, fruit firmness showed better results when stratifying by three classes or strata of NDVI. In any case, neither the ECa nor the combined use of NDVI + ECa have improved sampling efficiency when used as ancillary maps for stratification.

Assessment of the position accuracy of a single-frequency GPS receiver designed for electromagnetic induction surveys

In precision agriculture (PA), compact and lightweight electromagnetic induction (EMI) sensors have extensively been used to investigate the spatial variability of soil, to evaluate crop performance, and to identify management zones by mapping soil apparent electrical conductivity (ECa), a surrogate for primary and functional soil properties. As reported in the literature, differential global positioning systems (DGPS) with sub-metre to centimetre accuracy have been almost exclusively used to geo-reference these measurements. However, with the ongoing improvements in Global Navigation Satellite System (GNSS) technology, a single state-of-the-art DGPS receiver is likely to be more expensive than the geophysical sensor itself. In addition, survey costs quickly multiply if advanced real time kinematic correction or a base and rover configuration is used. However, the need for centimetre accuracy for surveys supporting PA is questionable as most PA applications are concerned with soil properties at scales above 1 m. The motivation for this study was to assess the position accuracy of a GNSS receiver especially designed for EMI surveys supporting PA applications. Results show that a robust, low-cost and single-frequency receiver is sufficient to geo-reference ECa measurements at the within-field scale. However, ECa data from a field characterized by a high spatial variability of subsurface properties compared to repeated ECa survey maps and remotely sensed leaf area index indicate that a lack of positioning accuracy can constrain the interpretability of such measurements. It is therefore demonstrated how relative and absolute positioning errors can be quantified and corrected. Finally, a summary of practical implications and considerations for the geo-referencing of ECa data using GNSS sensors are presented.

Characterizing Spatial Variability in Soil Water Content for Precision Irrigation Management

Among one of the many challenges in implementing precision irrigation is to obtain an accurate characterization of the soil water content (SWC) across spatially variable fields along the crop growing season. The accuracy of characterizing SWC has been tested primarily on a small-scale and has received little attention from the scientific community at the field scale. Hence, the objective of this study was to assess the characterization of the spatial distribution of soil water content at the field scale by the apparent electrical conductivity (ECa). In evaluating the current aim, ECa survey was compared against repeated measurements of SWC at five depths using neutron probe. Results showed that mean SWC was different across ECa derived management zones, which indicates that on a macro-scale, soil ECa could effectively characterize the mean differences in SWC across management zones. Results also showed that deep ECa (0–150 cm) survey outperformed shallow survey (0–75 cm). Considering other soil properties, such as organic matter content and salt content, further improved the relationship between SWC and ECa.

Simplifying field-scale assessment of spatiotemporal changes of soil salinity

Monitoring soil salinity (ECe) is important for planning and implementing agronomic and irrigation practices. Salinity can be measured through soil sampling directed by geospatial measurements of apparent soil electrical conductivity (ECa). Using data from a long-term (1999–2012) monitoring study at a 32.4-ha saline field located in California, USA, two established field-scale approaches to map and monitor soil salinity using ECa are reviewed: one that relies on a single ECa survey to identify locations that can be repeatedly sampled to infer the frequency distribution of ECe; and another based on repeated ECa surveys that are calibrated, each time, to ECe estimation using ground-truth data from soil samples. The reviewed approaches are very accurate and reliable, but require extensive soil sampling. Subsequently, we propose a novel approach – temporal analysis of covariance (t-ANOCOVA) modeling – that results in accurate spatiotemporal salinity estimations using ECa surveys with a significant reduction in the number of soil samples needed for calibration of ECa to ECe. In this modeling framework, the ECe-ECa relationship is described with a log-transformed linear function. The regression slope indicates the magnitude of the contribution of ECe to ECa and is assumed to remain constant over time, while the intercept represents the secondary factors influencing ECa that are not related to ECe (e.g., soil tillage). Once the t-ANOCOVA slope is established for a field, in subsequent surveys as few as three soil samples are used to estimate a time-specific t-ANOCOVA intercept so that ECa measurements can be converted to ECe estimations. Our results suggest that this approach is reliable at low salinity values (i.e., where common crops can grow). The t-ANOCOVA approach requires further validation before real-world implementations, but represents a significant step towards the use of ECa mobile sensor technology for inexpensive soil salinity monitoring at high temporal resolution.

Mapping soil water dynamics and a moving wetting front by spatiotemporal inversion of electromagnetic induction data

AbstractCharacterization of the spatiotemporal distribution of soil volumetric water content (θ) is fundamental to agriculture, ecology, and earth science. Given the labor intensive and inefficient nature of determining θ, apparent electrical conductivity (ECa) measured by electromagnetic induction has been used as a proxy. A number of previous studies have employed inversion algorithms to convert ECa data to depth‐specific electrical conductivity (σ) which could then be correlated to soil θ and other soil properties. The purpose of this study was to develop a spatiotemporal inversion algorithm which accounts for the temporal continuity of ECa. The algorithm was applied to a case study where time‐lapse ECa was collected on a 350 m transect on seven different days on an alfalfa farm in the USA. Results showed that the approach was able to map the location of moving wetting front along the transect. Results also showed that the spatiotemporal inversion algorithm was more precise (RMSE = 0.0457 cm3/cm3) and less biased (ME = −0.0023 cm3/cm3) as compared with the nonspatiotemporal inversion approach (0.0483 cm3/cm3 and ME = −0.0030 cm3/cm3, respectively). In addition, the spatiotemporal inversion algorithm allows for a reduced set of ECa surveys to be used when non abrupt changes of soil water content occur with time. To apply this spatiotemporal inversion algorithm beyond low induction number condition, full solution of the EM induction phenomena can be studied in the future.

Concurrent temporal stability of the apparent electrical conductivity and soil water content

Knowledge of spatio-temporal soil-water content (SWC) variability in agricultural fields is useful for improving crop management. Spatial patterns of SWC can be characterized using temporal stability analysis of difficult-to-obtain data from high spatial density and temporal frequency. Soil apparent electrical conductivity (ECa) measurements with high spatial density have been widely used to infer the spatial variability of SWC. The objective of this work is to test the hypothesis that temporal stability of ECa can be demonstrated and that relationships between temporal stability characteristics of SWC and ECa can be established. Apparent electrical conductivity and topsoil gravimetric SWC (θ) were periodically measured in an olive orchard in southwest Spain on 6 and 18 occasions, respectively. A temporal stability analysis of ECa elucidated three zones where ECa was close to, consistently substantially smaller than, and substantially larger than the spatial average ECa throughout the study period. Representative locations for θ were found with a chance of 75% within the representative zone for ECa. Yet, the driest locations, with consistently smaller θ than the field average (〈θ〉), could be successfully identified (89%) within the zone with consistently smaller ECa than average. The θ−〈θ〉 relations showed generally a linear behaviour, although a better fit was obtained at the highest θ using either exponential or power law equations at half of the locations. The former provided the best fit within the zone with ECa consistently smaller than average, while the latter performed best in the zone with ECa consistently larger than average. The linear equation provided the best fit within the representative ECa zone. This study demonstrates that temporal stability characteristics of ECa and SWC are linked and that ECa surveys can be used to delimit zones with representative locations for SWC measurement or estimation. Such information is of importance for a range of agricultural applications, i.e. irrigation, crop protection, fertilizer management, and soil and water conservation.

Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping

Mediterranean olive trees traditionally grow under rainfed conditions, on poor soils with steep slopes. Rainfall is mainly concentrated during autumn and winter and is characterized by intense rain pulses, separated by dry periods. The use of electromagnetic induction (EMI) techniques in these olive orchards might be questioned since EMI surveys are generally recommended to be performed under moist soil conditions. A 6.7 ha olive orchard was surveyed for EMI-based apparent electrical conductivity (ECa), both under wet and dry soil conditions. In addition, 48 soil samples were analyzed for soil texture and for soil water content (SWC) under both soil conditions. The relationships between ECa, soil texture and SWC, under both soil conditions were evaluated. Despite the significantly larger ECa values measured during the wet survey as compared to the dry survey, a similar spatial correlation structure was found, indicating temporally stable ECa patterns. Significant correlations (r) were found between both surveys for ECa (r = 0.67) and for SWC (r = 0.63). The correlation between SWC and clay content exceeded 0.60 for both surveys, and the correlation between ECa and clay content was twice as high under wet soil conditions as compared to dry soil. In both situations, the ECa surveys revealed the same patterns of soil texture, indicating that moist soil conditions are not an absolute prerequisite for the use of EMI to map the spatial variability of these soil properties. Nonetheless, measuring the ECa under different moisture conditions can provide additional information about soil moisture dynamics.

Apparent electrical conductivity response to spatially variable vertisol properties

Though much has been done to understand proximally-sensed bulk apparent electrical conductivity (ECa) in agricultural soils, many of the soil properties identified to be mappable using these techniques, such as salinity and clay content, are not expected to drive ECa response in a non-saline Vertisol. In Vertisols, agrillipedoturbation creates meter-scale variability in soil moisture and chemical properties associated with gilgai features, and if developed from calcareous parent material, can exhibit meter and landscape scale variability in inorganic C content. The ability to map inorganic C may be especially useful in a Vertisol due to its strong correlation with shrink-swell potential. The overall goal of this project was to investigate the potential for mapping inorganic C using ECa surveys in a calcareous Vertisol, with the future goal of mapping shrink-swell potential on these landscapes. On a 40- by 50-m field with intact circular gilgai, ECa was mapped under both moist and dry soil conditions. Soil samples were taken for water content, clay content, inorganic C content, salinity, and depth to parent material. Under moist soil conditions, the strongest correlation to ECa was inorganic C content (r=−0.63), followed by water content (r=0.49); however, under dry conditions, only inorganic C content was significant (r=−0.60). In addition, ECa surveys and inorganic C samples were taken for two larger watersheds of 10 and 14ha. Again, inorganic C content was significantly and reliably correlated to ECa for both fields, and the resulting regression slopes and intercepts were not significantly different between watersheds, though the surveys were conducted at different times. Results suggest that ECa can be used to map inorganic C content in Vertisols weathered from calcareous parent materials, allowing for spatial inference of shrink-swell potential which may be useful in distributed hydrology modeling.

A simplified regional-scale electromagnetic induction — Salinity calibration model using ANOCOVA modeling techniques

Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (ECa) is a potential means of characterizing the spatial variability of any soil property that influences ECa including soil salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Multi-field ECa survey data often exhibit abrupt changes in magnitude across field boundaries that complicate the calibration of ECa to soil salinity (i.e., ECe, electrical conductivity of the saturation extract) over large spatial extents. The primary objective of this study is to evaluate three regression techniques for calibrating ECa to ECe over spatial scales ranging from a few thousand to a hundred thousand hectares, where ECa was measured using electromagnetic induction equipment. The regression techniques include analysis of covariance (ANOCOVA), field specific regression (FSR), and common coefficient regression (CCR). An evaluation was made by comparing jack-knifed mean square prediction errors (MSPE) of ECe for two case studies: 2400ha of the Broadview Water District in California's San Joaquin Valley and roughly 100,000ha of the west side of Kittson County in the Red River Valley of Minnesota. The ANOCOVA model outperformed the FSR and CCR regression models on a prediction accuracy basis with the smallest MSPE estimates for depth predictions of soil salinity. The implication of this evaluation is that once ANOCOVA models for each depth are established for a representative set of fields within a regional-scale study area, then the slope coefficients can be used at all future fields, thereby significantly reducing the need for ground-truth soil samples at future fields, which substantially reduces labor and cost. Land resource managers, producers, agriculture consultants, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of regional-scale maps of soil salinity.

Characterizing Compaction Variability with an Electromagnetic Induction Sensor in a Puddled Paddy Rice Field

Paddy rice (Oryza sativa L.) fields are repeatedly puddled at water saturation to loosen the top soil and to form a uniformly compacted plow pan required to reduce losses of water and nutrients. In paddy growing conditions however, the non-invasive detection of compaction variation within the plow pan layer is challenging. This paper evaluates a proximal soil sensing based methodology to identify areas compacted by different intensities of puddling to support precise land preparation of paddy rice fields. Therefore, a 1.6-ha alluvial paddy field of silt loam texture (Aeric Haplaquepts) located in the Brahmaputra floodplain of Bangladesh was selected. Three areas within the field were variably puddled and characterized by soil properties and soil apparent electrical conductivity (ECa). The Floating Soil Sensing System (FLoSSY) employing the EM38 electromagnetic induction sensor was used to record the ECa of the soil for each of the puddling intensities. The effects of plow pan compaction on water percolation losses and paddy yield were assessed. The results show that the variably puddled areas had significant differences in soil bulk density which was linked to the soil penetration resistance. Spatial comparison of ECa data sets showed that the ECa values increased systematically under increased puddling. Sufficient soil compaction could be identified when percolation losses were significantly the lowest and paddy yield was significantly the highest. It was concluded that a non-invasive ECa survey using electromagnetic induction based soil sensing allowed the detection of soil heterogeneity linked to soil compaction caused by puddling, which offers an interesting potential for precision puddling and land preparation in paddy rice fields.

Protocols and Guidelines for Field-scale Measurement of Soil Salinity Distribution with ECa-Directed Soil Sampling

Soil salinity is a spatially complex and dynamic property of soil that influences crop yields when the threshold salinity level is exceeded. The mapping and monitoring of soil salinity is necessary for reclamation, crop selection, and site-specific irrigation management of salt-affected soils in the arid and semi-arid agricultural regions of the world. Because of its spatial and temporal heterogeneity, soil salinity is difficult to map and monitor at field scales. There are various methods for characterizing soil salinity variability, but none of these approaches has been as extensively investigated and is as reliable and cost effective as apparent soil electrical conductivity (ECa) directed soil sampling. Geospatial measurements of ECa are well-suited for characterizing soil salinity spatial distribution because they are reliable, quick, and easy to take with GPS-based mobilized ECa measurement equipment. However, ECa is influenced by a variety of soil properties, which makes the measurement of soil salinity at field scale problematic. It is the goal of this review and analysis paper to provide an overview of the field-scale characterization of soil salinity distribution using ECa-directed soil sampling. Guidelines, special considerations, protocols, and strengths and limitations are presented for characterizing spatial and temporal variation in soil salinity using ECa-directed soil sampling. Original data is presented showing the critical importance of conducting ECa surveys at or near field capacity (≥70% of field capacity). A case study of a 32.4-ha field in San Jacinto, California, is provided as an example to demonstrate the effectiveness of generating soil salinity maps. Land resource managers, farmers, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of field-scale maps of soil salinity.

Estimating Topsoil Water Content of Clay Soils With Data From Time-Lapse Electrical Conductivity Surveys

Spatial estimation of soil-water content (θ) at the field, hillslope, or catchment scale is required in numerous applications. Time-lapse electrical resistivity and apparent electrical conductivity (ECa) surveys are recognized as a useful source of information about both spatial variations in θ and spatial differences in soil properties. The objective of this work was to test the hypothesis that the accuracy of the regression relationships between θ and collocated ECa survey data can be improved for any given time if several time-lapse ECa surveys rather than a single ECa survey are used. Vertisol plots under conventional tillage and direct drilling were surveyed for gravimetric θ (θg) in the top 0.3-m layer at 17 times and for topsoil ECa at 13 times in 2008 through 2010. Both dry and wet periods were covered by the surveys. On four occasions, θg and ECa surveys were done on the same day. Only weak correlations (with R < 0.21) were found between ECa and θg measured on the same day. The accuracy of regression predictions of θg substantially improved when data of several ECa surveys, rather than a single survey, were used. Therefore, the knowledge about the temporal variability in soil properties, as captured by the time-lapse ECa data, can improve the estimation of spatial variability in soil properties affecting soil-water content.

Detecting soil salinity changes in irrigated Vertisols by electrical resistivity prospection during a desalinisation experiment

Increasing soil salinity threatens the sustainability of irrigation schemes in several rice growing areas of the world, particularly in western Africa. Desalinisation is needed to sustain irrigated rice cultivation. However, to keep track of the effectiveness of any desalinisation method, monitoring of soil salt content is required. The objective of this study was to assess the ability of non-destructive electrical resistivity prospection to monitor salinity changes (spatial and temporal) in a Vertisol profile during a desalinisation experiment in Kollo, Niger. Desalinisation was induced by successive ponding and flushing cycles on a 10m×10m square plot. Seven data acquisition campaigns were performed between July 13 and September 24 2009. In each campaign, (i) apparent electrical resistivity measurements were performed with two electrode spacings (10 and 30cm) and were transformed into the inverse apparent electrical conductivity (ECa), (ii) soil samples were extracted from three soil layers (0–10, 10–20, and 20–40cm). Desalinisation was more efficient for the topsoil layer than for the deeper ones. Mean electrical conductivity of 1:5 aqueous extract of soil samples (EC) decreased from initial values of 2.0, 2.8 and 2.9dSm−1 to 1.3, 2.2 and 2.6dSm−1 respectively at 0–10, 10–20 and 20–40cm. The relative decrease in salt stocks was estimated at 35.7, 22.7 and 11.4% of the initial amounts in these respective soil layers. Mean ECa measured at the soil surface also decreased over time, and the highest correlation (R=0.57) was observed between ECa30cm and EC of the upper soil layer (0–10cm). ECa survey allowed the detection of spatial and temporal salinity changes with a high spatial resolution. Relative decrease in ECa over time was an indicator of the desalinisation process. This appears useful to adjust water supply for saline soil reclamation, particularly in regions with high water scarcity.