TECHNICAL ARTICLEEstimating Soil Hydraulic Conductivity at the Field Scale With a State-Space ApproachZhang, Xi1; Wendroth, Ole1; Matocha, Christopher1; Zhu, Junfeng2Author Information 1Department of Plant and Soil Sciences, University of Kentucky, Lexington, Kentucky. 2Kentucky Geological Survey, University of Kentucky, Lexington, Kentucky. Address for correspondence: Dr. Xi Zhang, College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331. E-mail: email@example.com Financial Disclosures/Conflicts of Interest: United States Geological Survey/Kentucky Water Resources Research Institute 104-B Student Research Enhancement Program; Water Quality Program SB 271 at the College of Agriculture, Food and Environment at the University of Kentucky; USDA National Institute of Food and Agriculture, Multistate Project KY006093; and Kentucky Small Grain Growers’ Association. Received July 19, 2019. Accepted for publication October 14, 2019. Online date: November 27, 2019 Soil Science: June 2019 - Volume 184 - Issue 3 - p 101-111 doi: 10.1097/SS.0000000000000253 Buy Metrics Abstract A precise description of saturated (Ks) and near-saturated hydraulic conductivity (K−10) and their spatial variability is important for understanding water/solute transport in the vadose zone. However, it is laborious to measure K directly. Alternatively, K could be predicted from easily measurable soil properties using pedotransfer functions (PTFs). Because PTFs ignore the spatial relationships and covariance between soil variables, they often perform unsatisfactorily when field-scale estimations of K are needed. Therefore, the objective of this study was to improve the estimation of K at field scale through consideration of spatial dependences between soil variables. K was measured at 48 locations in a 71 × 71-m grid within a farmland under no-till. An autoregressive state-space approach was used to quantify the spatial relations between K and soil properties and to analyze the spatial variability of K in the field. In comparison, multiple linear regression (MLR) was used to derive PTFs for K estimation. Using various combinations of variables, state-space analysis outperformed PTFs in estimating spatial K distribution across the field. While state-space approach explained 69%, MLR method explained only 6% of the total variation in Ks. For K−10, the best state-space model included silt, clay, and macroporosity and performed almost perfectly (R2 >95%) in characterizing the spatial variability of K−10. In that case, the best MLR-type PTF explained only 60% of the variation. The results indicate that, by considering the spatial relations between soil variables, state-space approach is an effective tool for analyzing the spatial variability of K at field scale. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.