Graduate Thesis Or Dissertation
 

Merging Field Work with Machine Learning : Exploring Andic Soil Development in the Cascade Range

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  • Andic soils (aka Andisols) have unique properties that are important to society. For soil scientists, the genesis and taxonomy of Andisols is often confused because they can form in both volcanic and non-volcanic material. This dissertation seeks to address such confusion by looking at andic soil development in the Cascade Range through a series of complementary studies. The first study examines the influence of climate on soilscape diversity and soil development where andic properties exist or do not exist. The Cascade Range contains dually defined elevational and latitudinal threshold beyond which cool, moist conditions favor the formation of andic soil properties. The second study examines the effects of climate and parent material on the formation of andic soils. Precipitation, particularly in the form of snow, is crucial for the formation of andic soils, which challenges the status quo view that parent material is the dominant soil-forming factor of this class of soils. The third study examines the ability to predict and extrapolate andic and non-andic soils types with machine learning algorithms. From these three studies a better understanding of the amount, distribution, and processes of andic soil properties is gained. Using this knowledge, predicted climate change threatens over 140,000 hectares of Andisols and their ecological dependents in the Western Central Oregon Cascade Range over the next century.
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