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

Efficient monitoring and measuring forage resources is one of the major tasks in livestock production and management. Failure to develop a real-time monitoring tool can result in overgrazing of forage resources, ecosystem degradation, decreasing animal production, and reducing the 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, Acid Detergent Lignin, 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.