36: Modeling Forage Quantity and Quality Using Machine Learning Models and Remote Sensing Data.

Music City Center 

Efficient monitoring and measuring of forage resources is challenging in livestock production and management. Failure to develop a real-time monitoring tool can result in overgrazed forage resources, ecosystem degradation, decreased animal production, and reduced resiliency to climate change. Utilizing remote sensing data such as satellite imagery provides a cost-effective tool for monitoring forage quality and quantity. This study aims to develop data pipelines to automate the extraction of climate and satellite imagery from Google Earth Engine. Specifically, forage quantity and quality indicators such as Neutral Detergent Fiber, Acid Detergent Fiber, Biomass, and Crude Protein, are predicted using precipitation metrics, seasonal weather metrics, and vegetation Indices. The performance of univariate and multivariate Random Forest, General Additive Model, Least Absolute Shrinkage and Selection Operator model, Autoregressive Integrated Moving Average model, Nonlinear Autoregressive exogenous model, and Multivariate Time Series models are compared. The results show that the non-linear models outperformed the linear models while being computationally efficient.

Livestock

Forage

Machine learning

Remote sensing 

Section on Statistical Learning and Data Science