Directed Soil Sampling Research Articles

Modified ECa – ECe protocols for mapping soil salinity under micro-irrigation

Climate change will increase the frequency and intensity of drought in water-scarce agricultural areas that rely on irrigation. The increased strain on finite water resources for irrigated agriculture will cause a shift from sprinkler and flood irrigation to micro-irrigation. Micro-irrigation results in complex 3-dimensional salinity patterns. Current field-scale apparent soil electrical conductivity (ECa) directed soil sampling protocols and guidelines are inadequate for mapping the complex local-scale 3-dimensional nature of salinity resulting from water applications by micro-irrigation systems (i.e., drip, buried drip, micro sprinklers, bubblers, etc.). A field study was conducted to develop additional ECa-directed soil sampling guidelines to map local- and field-scale variability in salinity under drip-irrigation systems within a commercial nut production orchard (i.e., pistachio orchard) using hard (i.e., salinity or ECe, electrical conductivity of the saturation extract) and soft data (i.e., geospatial ECa measurements), which required an accurate ECa – ECe calibration. The revised ECa-directed soil sampling guidelines for drip irrigation on a mature pistachio orchard indicate that a single soil core should be taken 0.9–1.2 m perpendicular to the drip line within the tree root system, rather than at the drip line, to improve the ECa – ECe calibration. Calibration of ECa to ECe, improved from R2 = 0.25 to R2 = 0.73 for site Flores D01, and from R2 = 0.17 to R2 = 0.72 for site Flores D05. The improved guidelines broaden the scope of application of ECa-directed soil sampling to map field-scale salinity on orchards under drip irrigation. The information presented is of value and benefit to producers, agriculture consultants, irrigation practitioners, cooperative extension specialists, Natural Resources Conservation Service field staff, and soil and water researchers.

An Alternative Approach for Plant Salt Tolerance Studies Using Apparent Soil Electrical Conductivity Directed Soil Sampling

An Alternative Approach for Plant Salt Tolerance Studies Using Apparent Soil Electrical Conductivity Directed Soil Sampling

Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions

Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (ECa)-directed soil sampling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil ECa was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core samples were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between ECa and laboratory-measured electrical conductivity (ECe) was used to predict EC with confidence (R2 between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas.

Field‐scale apparent soil electrical conductivity

AbstractSoils are notoriously spatially heterogeneous and many soil properties are temporally variable. Spatial variability of soil properties has a profound influence on agricultural and environmental processes, such as plant–water–soil interactions, water flow, and solute transport, resulting in within‐field plant yield variation and degradation of soil quality, to mention only a few. Field‐scale mapping of spatial variability and monitoring of temporally dynamic soil properties is necessary for a variety of edaphic activities, such as soil surveys, reclamation, crop selection, sitespecific management, and soil quality assessment. There are various approaches for characterizing soil spatial variability, but none of these 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 the spatial distribution of soil properties because they are reliable, quick, and easy to take with GPS‐based mobilized ECa measurement equipment. Directed soil sampling based on geo‐referenced measurements of ECa is a proven and robust means of characterizing the spatial variability of any soil property that influences ECa, including sol salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. It is the goal of this methodology paper to provide an overview of the characterization of soil spatial variability across multiple scales using ECa–directed soil sampling with a focus on the field scale. Mobile ECa equipment, protocols, guidelines, special considerations, data reliability tests, and strengths and limitations are presented for characterizing spatial and temporal variation in soil properties using ECa–directed soil sampling.

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.

Mapping Soil Spatial Variability with Apparent Soil Electrical Conductivity (ECa) Directed Soil Sampling

Core Ideas Protocols and guidelines are presented for measuring and mapping the spatial variability of soil properties using apparent soil electrical conductivity (ECa). The protocols focus on ECa–directed soil sampling. Mobile ECa equipment, protocols, guidelines, special considerations, data reliability, and strengths and limitations of ECa–directed soil sampling are presented. In this summary, we provide a brief overview of the chapter entitled “Field‐Scale Soil Electrical Conductivity”, recently published in the Methods of Soil Analysis (vol. 1) series.

Soil chemical attributes restricting grain yield in Oxisols under no-tillage system

Abstract: The objective of this work was to identify soil chemical attributes restricting grain yield in Oxisols under the no-tillage system, using directed soil sampling. High, medium, and low yield zones were defined in two agricultural fields using historical yield data of several crops. The yield zones were defined based on the harvest maps of the following crops: corn in 2008/2009, white oat in 2009, and corn in 2012/2013 in field I, with 117.70 ha; and corn in 2009/2010, soybean in 2010/2011, and wheat in 2012 in field II, with 107.30 ha. Soil sampling points were georeferenced in each yield zone, where samples were collected at eight soil depths, spaced 0.05 m apart, totaling 80 variables. Low yields were associated with low cation exchange capacity, low phosphorus and organic matter contents, and high clay content. In both studied fields, the highest organic matter content in the subsurface layers was the main indicator of high yield. Soil sampling considering yield zones is an efficient strategy to identify chemical attributes restricting grain yield and also allows guiding more precise site-specific interventions.

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.

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.

Performance of a Mobile Salinity Monitoring Device Developed for Turfgrass Situations

Spatial salinity mapping with a mobile sensor platform allowing GPS‐labeled data on turfgrass areas would facilitate site‐specific leaching programs for salinity management. A salinity monitoring device (SMD) based on 4‐Wenner array electrical resistivity (ER) electrode configuration was developed for turfgrass sites and tested on three soils at Griffin, GA and a golf course fairway in Naples, FL on two dates where the fairway received saline irrigation water. Using directed soil sampling, the SMD resulted in soil apparent soil electrical conductivity (ECa) vs. laboratory saturated paste extract electrical conductivity (ECe) linear relationships with r2 of 0.59 to 0.87 (p < 0.0002) for 0 to 10 cm and 0 to 20 cm zones at Griffin, GA. For two mapping events varying two‐ to threefold in salinity levels at the golf course fairway, the ECa vs. ECe linear regressions exhibited similar slopes, different intercepts (due to two to threefold difference in background salinity), and r2 of 0.53 to 0.58 (p < 0.002). On another fairway, a detailed spatial salinity map using geographic information systems (GIS) methods was developed using a sampling grid of 2 by 3 m, which was well within the 19 m range determined for spatial autocorrelation of the data. Our data suggest the empirical methods developed for agricultural soils for relating ECa to ECe and for determining average ECa of discrete subsurface zones may differ under turfgrass conditions due to stratification of the surface organic matter layer influencing water holding capacity, soluble salt retention, and averaging ECa within a subsurface zone.

Comparison of Sampling Strategies for Characterizing Spatial Variability with Apparent Soil Electrical Conductivity Directed Soil Sampling

Spatial variability has a profound influence on a variety of landscape-scale agricultural issues including solute transport in the vadose zone, soil quality assessment, and site-specific crop management. Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity [Formula: see text] is a potential means of characterizing the spatial variability of any soil property that influences [Formula: see text] including soil salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Arguably the most significant step in the protocols for characterizing spatial variability with [Formula: see text]-directed soil sampling is the statistical sampling design, which consists of two potential approaches: model- and design-based sampling strategies such as response surface sampling design (RSSD) and stratified random sampling design (SRSD), respectively. The primary objective of this study was to compare model- and design-based sampling strategies to evaluate if one sampling strategy outperformed the other or if both strategies were equal in performance. Using three different model validation tests, the regression equation estimated from the RSSD data produced accurate and unbiased predictions of the natural log salinity levels at the independently chosen SRSD sites. Design optimality scores (i.e., D-, V-, and G-optimality criteria) indicate that the use of the RSSD design should facilitate the estimation of a more accurate regression model, i.e., the RSSD approach should allow for better model discrimination, more precise parameter estimates, and smaller prediction variances. Even though a model-based sampling design, such as RSSD, has been less prevalent in the literature, it is concluded from the comparison that there is no reason to refrain from its use and in fact warrants equal consideration.

Regional‐scale Assessment of Soil Salinity in the Red River Valley Using Multi‐year MODIS EVI and NDVI

The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-10(5) km(2)) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

Soil Scientists Revisit a High-Intensity Soil Survey in Northwest Illinois with Electromagnetic Induction and Traditional Methods

Electromagnetic induction (EMI) and global positioning system (GPS) data were used with geographical information system (GIS) to improve a high intensity soil survey that was completed with traditional methods in northwest Illinois. Apparent conductivity (ECa) maps provided additional layers of information, which improved knowledge of soils and directed soil sampling. The information provided by ECa maps and additional soil sampling led soil scientists to recognize different soils and modify mapping concepts. Within the site, ECa maps assisted the identification and delineation of soil polygons and improved the quality of the high-intensity soil map. White spatial ECa patterns influenced the judgments of soil scientists, ECa maps were not accepted as a substitute for a high-intensity soil map.

Soil salinity assessment using directed soil sampling from a geophysical survey with electromagnetic technology: a case study

Spatial characterization of soil salinity is required for establishing salt control measurements in irrigated agriculture. For that, cost-effective, specific, rapid, and reliable methodologies for determining soil salinity in-situ and processing those data are required. This paper shows the usefulness of an integrated methodology involving a hand-held electromagnetic sensor (Geonics EM38), and the ESAP (Electrical conductivity or salinity, Sampling, Assessment and Prediction) software package to assess, predict and map soil salinity at field scale. The salinity of a 1.74 ha plot of a surface-irrigated field of Navarre, northern Spain, was analyzed by reading the bulk soil electrical conductivity (ECa) with the EM38 sensor at 180 locations. At 20 of those sites, soil core samples were taken at 0.3 m intervals to a depth of 0.9 m, and electrical conductivity of the saturation extract (ECe), saturation percentage (SP) and water content (WC) were measured. Salinity was the dominant factor influencing the EM38 readings. The multiple linear regression (MLR) calibration model predicted ECe from EM38 readings with squar R ranging from 0.38 to 0.90 for the multiple-depth profile. The ESAP software also provided field range average estimates of soil salinity. Eighty-one percent of the field had ECe values above 4 dS mE-1. The obtained salinity map was helpful to display the spatial patterns of soil salinity and identify sources/causes of salt loading.

Development of practical site-specific management methods for reclaiming salt-affected soil

Sustaining irrigated agricultural production systems in semi-arid and arid regions requires consideration of with saline and sodic soil conditions. The spatial variability of these conditions makes soil reclamation an ideal practice in which to apply site-specific management (SSM). We discuss an application of SSM in which preliminary measurements of apparent soil electrical conductivity (EC a), supplemented by EC a-directed soil sampling, are used to construct a GIS map of salinity zones on which site-specific amendment application can be based. Our primary objective was to develop a field-implementable methodology for site-specific soil amendment application. The focus of the program was on cotton production, although the method should be applicable to other crops. A second objective was to establish experiments to test the effectiveness of this program. Evaluating the effectiveness of a sprinkler application for the first irrigation was incorporated as a part of this program in hopes of reducing variability due to poor germination. This study consisted of two commercial fields located in the San Joaquin Valley of California. Based on the consideration that EC a is directly related to electrical conductivity from soil saturation paste extract (EC e) for known saline-sodic conditions, we developed a four-step method for site-specific salinity management in commercial fields. The steps included the following: (1) generation of an EC a map; (2) directed soil sampling for EC e; (3) determination of the estimated amendment requirement as a function of location in the field; and (4) integration of the individual amendment requirements into a practical spatial pattern for amendment application. Cotton yield monitors were utilized to indicate spatial yield variation throughout the replicated plots instead of aggregated yield values. Because of the high levels of variability in these commercial fields and the time required for amendments to have a substantial effect, we can provide only short-term results of these experiments. In both of the experiments no significant differences existed between the irrigation and amendment treatments ( p > 0.05), but increased yield trends supported the benefit of sprinkler application of the first irrigation. A Scheffe test of the cotton yield monitor data indicated significant yield differences between in-plot treatment zones.

Frequency Analysis of Yield for Delineating Yield Response Zones

The yield in any given field or management zone is a product of interaction between many soil properties and production inputs. Therefore, multi-year yield maps may give better insight into determining potential management zones. This research was conducted to develop a methodology to delineate yield response zones by using two-state frequency analysis conducted on yield maps for 3 years on two commercial corn fields near Wiggins, Colorado. A zone was identified by the number of years that yield was equal and greater than the average yield in a given year. Classes producing statistically similar yield were combined resulting in three potential yield zones. Results indicated that the variability of yield over time and space could successfully be assessed at the same time without the drawbacks of averaging data from different years. Frequency analysis of multi-year yield data could be an effective way to establish yield response zones. Seventeen percent of the field #1 consistently produced lower yield than the mean while 43 of the field produced yield over the mean. Corresponding values for field #2 were 6% and 42%.The remainder of the fields produced fluctuating yields between years. These spatially and temporally sound yield response maps could be used to identify the yield-limiting factors in zones where yield is either low or fluctuating. Yield response maps could also be helpful to delineate potential management zones with the help of resource zones such as electrical conductivity and soil maps, along with the directed soil sampling results.

Soil Sampling Techniques for Alabama, USA Grain Fields

Characterizing the spatial variability of nutrients facilitates precision soil sampling. Questions exist regarding the best technique for directed soil sampling based on a priori knowledge of soil and crop patterns. The objective of this study was to evaluate zone delineation techniques for Alabama grain fields to determine which method best minimized the soil test variability. Site one (25.8 ha) and site three (20.0 ha) were located in the Tennessee Valley region, and site two (24.2 ha) was located in the Coastal Plain region of Alabama. Tennessee Valley soils ranged from well drained Rhodic and Typic Paleudults to somewhat poorly drained Aquic Paleudults and Fluventic Dystrudepts. Coastal Plain soils ranged from coarse-loamy Rhodic Kandiudults to loamy Arenic Kandiudults. Soils were sampled by grid soil sampling methods (grid sizes of 0.40 ha and 1 ha) consisting of: (1) twenty composited cores collected randomly throughout each grid (grid-cell sampling) and, (2) six composited cores collected randomly from a ∼3×3m area at the center of each grid (grid-point sampling). Zones were established from (1) an Order 1 Soil Survey, (2) corn (Zea mays L.) yield maps, and (3) airborne remote sensing images. All soil properties were moderately to strongly spatially dependent as per semivariogram analyses. Differences in grid-point and grid-cell soil test values suggested grid-point sampling does not accurately represent grid values. Zones created by soil survey, yield data, and remote sensing images displayed lower coefficient of variations (%CV) for soil test values than overall field values, suggesting these techniques group soil test variability. However, few differences were observed between the three zone delineation techniques. Results suggest directed sampling using zone delineation techniques outlined in this paper would result in more efficient soil sampling for these Alabama grain fields.