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ARS Home » Southeast Area » Fayetteville, Arkansas » Poultry Production and Product Safety Research » Research » Publications at this Location » Publication #399181

Research Project: Developing Best Management Practices for Poultry Litter to Improve Agronomic Value and Reduce Air, Soil and Water Pollution

Location: Poultry Production and Product Safety Research

Title: Using apparent electrical conductivity to delineate field variation in an agroforestry system in the Ozark Highlands

item YLANGAN, SHANE - University Of Arkansas
item BRYE, KRISTOFOR - University Of Arkansas
item Ashworth, Amanda
item Owens, Phillip
item SMITH, HARRISON - University Of Arkansas
item PONCET, AURELIE - University Of Arkansas

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/11/2022
Publication Date: 11/16/2022
Citation: Ylangan, S., Brye, K.R., Ashworth, A.J., Owens, P.R., Smith, H., Poncet, A.M. 2022. Using apparent electrical conductivity to delineate field variation in an agroforestry system in the Ozark Highlands. Remote Sensing. 14(22). Article 5777.

Interpretive Summary: Soil electrical conductivity (EC) is a metric of the salt content in the soil and is an important indicator of soil health, as it will affect crop yield and quality, plant nutrient availability, as well as the activity of soil microbes that is related to key soil processes. New electromagnetic methods have been developed for characterizing field EC, resulting in very dense soil EC maps. Such maps have applications in precision agriculture, optimized irrigation use, and enhanced soil health practices, although little work has been done to use EC mapping in agroforestry systems to asess within field variability. Researchers set out to 1) develop precision management zones or soil populations that behave similarly, 2) evaluate if EC was linked to soil moisture, pH, and other other soil health indicators; and 3) see how many EC time points would be needed to capture field-level variability in a 20-year agroforestry site. This study found that this 3-ac agroforestry site could be broken into 3 precision management zones to ultimately improve soil health. Further, this study found that monthly mapping was not necessary and that 1 seasonal scan (e.g., Winter, Spring, Summer, and Fall) would provide the same amount of field level information, which would limit soil surface disturbance and save resources. Overall, this study demonstrated the potential versatility, applicability, and ability of a EC survey device to quickly and accurately delineate in-field variability in different landscapes for use in different land uses and precision soil management.

Technical Abstract: Greater adoption and better management of spatially complex, conservation systems like agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, minimal work has been conducted using repeated electromagnetic induction (EMI), apparent electrical conductivity (ECa) surveys in AF systems within regions similar to 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 and ECa and sentential site soil-sample EC, 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 between 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 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 > 0.05) by either 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.