Use of Soil Electroconductivity in a Multistage Soil-Sampling Scheme

Tarr, Alison B.; Moore, Kenneth J.; Dixon, Philip M.; Burras, C. Lee; Wiedenhoeft, Mary H.

doi:10.1094/cm-2003-1029-01-rs

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Sampling points were arranged in a triangular grid with inter‐ and intrarow separation distances of 6 m. To obtain data from samples located closer than 6 m, an additional point was sampled within each row at randomly chosen 1‐ or 2‐m separation distances. An earlier study of the pasture site indicated short‐range variation in soil characteristics such as soil P, K, pH, and organic matter (Tarr et al, 2003). As a result, this short‐range variation in soil samples was investigated to obtain a more reliable experimental semivariogram model (Burgess and Webster, 1980; Kravchenko and Bullock, 2002).…”

Section: Methodsmentioning

confidence: 99%

Spectral Reflectance as a Covariate for Estimating Pasture Productivity and Composition

Tarr¹,

Moore

Dixon

2005

Crop Science

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Pasturelands are inherently variable. It is this variability that makes sampling as well as characterizing an entire pasture difficult. Measurement of plant canopy reflectance with a ground-based radiometer offers an indirect, rapid, and noninvasive characterization of pasture productivity and composition. The objectives of this study were (i) to determine the relationships between easily collected canopy reflectance data and pasture biomass and species composition and (ii) to determine if the use of pasture reflectance data as a covariate improved mapping accuracy of biomass, percentage of grass cover, and percentage of legume cover across three sampling schemes in a central Iowa pasture. Reflectance values for wavebands most highly correlated with biomass, percentage of grass cover, and percentage of legume cover were used as covariates. Cokriging was compared with kriging as a method for estimating these parameters for unsampled sites. The use of canopy reflectance as a covariate improved prediction of grass and legume percentage of cover in all three sampling schemes studied. The prediction of above-ground biomass was not as consistent given that improvement with cokriging was observed with only one of the sampling schemes because of the low amount of spatial continuity of biomass values. An overall improvement in root mean square error (RMSE) for predicting values for unsampled sites was observed when cokriging was implemented. Use of rapid and indirect methods for quantifying pasture variability could provide useful and convenient information for more accurate characterization of time consuming parameters, such as pasture composition. Disciplines Agronomy and Crop Sciences | Statistics and Probability CommentsThis is an article from Crop Science 45 (2005): 996, doi:10.2135/cropsci2004.0004. Posted with permission. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.

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Section: Methodsmentioning

confidence: 99%

Spectral Reflectance as a Covariate for Estimating Pasture Productivity and Composition

Tarr¹,

Moore

Dixon

2005

Crop Science

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“…Horney et al [ 92 ] suggested a methodology for salt affected soils with the following steps: (1) building an EC a map; (2) directed sampling for salinity; (3) as a function in the field determination of the estimated improvement requirement and (4) integration into a practical spatial pattern. Tarr et al [ 245 ] used stratification of EC a and terrain attributes to derive a heterogeneous pasture in relatively homogenous sampling zones with fuzzy k-means clustering. The five zones had significant differences in the target variables (i.e., P, K, pH, organic matter and water content).…”

Section: Detecting Soil-related Properties In Non-saline Soils By mentioning

confidence: 99%

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

Heil

Schmidhalter

2017

Sensors

102

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Fast and accurate assessment of within-field variation is essential for detecting field-wide heterogeneity and contributing to improvements in the management of agricultural lands. The goal of this paper is to provide an overview of field scale characterization by electromagnetic induction, firstly with a focus on the applications of EM38 to salinity, soil texture, water content and soil water turnover, soil types and boundaries, nutrients and N-turnover and soil sampling designs. Furthermore, results concerning special applications in agriculture, horticulture and archaeology are included. In addition to these investigations, this survey also presents a wide range of practical methods for use. Secondly, the effectiveness of conductivity readings for a specific target in a specific locality is determined by the intensity at which soil factors influence these values in relationship to the desired information. The interpretation and utility of apparent electrical conductivity (ECa) readings are highly location- and soil-specific, so soil properties influencing the measurement of ECa must be clearly understood. From the various calibration results, it appears that regression constants for the relationships between ECa, electrical conductivity of aqueous soil extracts (ECe), texture, yield, etc., are not necessarily transferable from one region to another. The modelling of ECa, soil properties, climate and yield are important for identifying the location to which specific utilizations of ECa technology (e.g., ECa−texture relationships) can be appropriately applied. In general, the determination of absolute levels of ECa is frequently not possible, but it appears to be quite a robust method to detect relative differences, both spatially and temporally. Often, the use of ECa is restricted to its application as a covariate or the use of the readings in a relative sense rather than as absolute terms.

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“…The advantage of benchmark sampling is that by consistently sampling from the same benchmark sites over time, changes in soil nutrient trends can be monitored [4]. Due to soil variability, different sampling designs attempt to find representative points of a field with the help of different sensory information (e.g., electrical conductivity of the soil) [14,15]. Another sampling scheme acts in multiple stages, where first, a large number of points are generated and then filtered based on some criteria [16].…”

Section: Benchmark Soil Samplingmentioning

confidence: 99%

Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

Kazemi,

Samavati

2023

Agriculture

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Precision agriculture hinges on accurate soil condition data obtained through soil testing across the field, which is a foundational step for subsequent processes. Soil testing is expensive, and reducing the number of samples is an important task. One viable approach is to divide the farm fields into homogenous management zones that require only one soil sample. As a result, these sample points must be the best representatives of the management zones and satisfy some other geospatial conditions, such as accessibility and being away from field borders and other test points. In this paper, we introduce an algorithmic method as a framework for automatically determining locations for test points using a constrained multi-objective optimization model. Our implementation of the proposed algorithmic framework is significantly faster than the conventional GIS process. While the conventional process typically takes several days with the involvement of GIS technicians, our framework takes only 14 s for a 200-hectare field to find optimal benchmark sites. To demonstrate our framework, we use time-varying Sentinel-2 satellite imagery to delineate management zones and a digital elevation model (DEM) to avoid steep regions. We define the objectives for a representative area of a management zone. Then, our algorithm optimizes the objectives using a scalarization method while avoiding constraints. We assess our method by testing it on five fields and showing that it generates optimal results. This method is fast, repeatable, and extendable.

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Use of Soil Electroconductivity in a Multistage Soil-Sampling Scheme

Cited by 8 publications

References 18 publications

Spectral Reflectance as a Covariate for Estimating Pasture Productivity and Composition

Spectral Reflectance as a Covariate for Estimating Pasture Productivity and Composition

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

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