Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was...

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Bibliographic Details
Published in:Soil systems 2021-09, Vol.5 (3), p.41
Main Authors: Kharel, Tulsi P., Ashworth, Amanda J., Owens, Phillip R., Philipp, Dirk, Thomas, Andrew L., Sauer, Thomas J.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Cadmium
Cattle
Chromium
classification and regression tree
Clustering
Feature selection
Forage
Grazing
hierarchical clustering
Landscape
Learning algorithms
Livestock
Livestock production
Machine learning
Metals
Moisture content
multiple linear regression
Optimization
Pasture
Plant production
Productivity
random forest
Regression analysis
silvopasture
Soil fertility
Soil nutrients
Soils
Statistical analysis
Topography
Variables
Variance analysis
Water flow
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Summary:Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels
ISSN:2571-8789
2571-8789
DOI:10.3390/soilsystems5030041