|YLAGAN, SHANE - University Of Arkansas|
|BRYE, KRISTOFOR - University Of Arkansas|
|SMITH, HARRISON - University Of Arkansas|
|PONCET, AURELIE - University Of Arkansas|
|Sauer, Thomas - Tom|
|THOMAS, ANDREW - University Of Missouri|
|PHILIPP, DIRK - University Of Arkansas|
Submitted to: Agrosystems, Geosciences & Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/21/2023
Publication Date: 8/22/2023
Citation: Ylagan, S., Brye, K.R., Ashworth, A.J., Owens, P.R., Smith, H., Poncet, A.M., Sauer, T.J., Thomas, A.L., Philipp, D. 2023. Relationships among apparent electrical conductivity and plant and terrain data in an agroforestry system in the Ozark Highlands. Agrosystems, Geosciences & Environment. 6(3):1-16. https://doi.org/10.1002/agg2.20414.
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 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 assess within field variability. Researchers set out to identify 1) linkages between EC and forage yield, tree growth, and terrain attribute relationships within and ii) terrain attributes that drive EC variability within a 20-year-old agroforestry system in the Ozark Highlands. ECa survey data are correlated with total forage yield and tree growth characteristics and supported the hypothesis that ECa survey data are correlated with terrain attributes. However, results did not support the hypothesis that correlations among ECa survey and tree growth data can be improved with ECa-derived SMZs. Results supported the hypothesis that correlations among ECa survey and total forage yield and terrain attribute data can be improved with ECa-derived SMZs. Results also supported the hypothesis that different terrain attributes would contribute to the ECa variability to different degrees within the AF site and across the SMZs. Results demonstrated that EC can predict plant growth and is linked to terrain conditions. Results of this study provided further support and evidence on the potential versatility, applicability, and usefulness of EC surveys for assessing and contextualizing in-field variability in a variety of ecosystems with different land management systems and can be used for precision nutrient and water management and field planning in agroforestry systems.
Technical Abstract: Minimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to i) evaluate ECa-forage yield, ECa-tree growth, and ECa-terrain attribute relationships within ECa-derived soil management zones (SMZs) and ii) identify 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 that could be used to influence agroforestry system management.