Year of Publication

2017

Degree Name

Master of Science (MS)

Document Type

Master's Thesis

College

Agriculture, Food and Environment

Department

Plant and Soil Sciences

First Advisor

Dr. Brad D. Lee

Abstract

The ability to map soil moisture is becoming more important with changing climates and modeling these effects depends on reliable estimations of hydrologic soil properties under different land managements. This study: 1) tests the application of existing soil hydraulic property estimation methods against in-situ values of six catenas under two covers (forest and grass); 2) validate Random Forest Algorithm (RF) estimates informed from the six catenas on two separate catenas; 3) identify Rapid Carbon Assessment (RaCA) sites within the Shawnee Hills Region that represent different land-uses (Crop, Conservation Reserve Program (CRP), Forest, and Pasture); 4) apply RF learning tree informed from six catenas to RaCA sites in order to estimate soil hydraulic properties; 5) compare landuse affects on said estimations. Existing methods for estimating properties were found not able to mimic in-situ values and RF was found to better mimic field values. A total of 26 RaCA sites were chosen under four different land-uses in the Shawnee Hills. The estimations revealed that the different land-uses had unique soil hydraulic estimation values. Care should be taken when estimating changes in soil hydraulic properties as a function of OM content as some results might be overstated.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2017.048

Included in

Soil Science Commons

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