AI-based framework to predict animal and pen feed intake in feedlot beef cattle

TL;DR

This paper presents an AI framework using novel environmental indices and machine learning to accurately predict feed intake in individual cattle and pens, aiding precision livestock management.

Contribution

Developed a new AI-based framework with environmental indices and machine learning models for accurate feed intake prediction in feedlot cattle.

Findings

XGBoost achieved RMSE of 1.38 kg/day at animal level

EASI-Index effectively predicts feed intake with high accuracy

InComfort-Index correlates well with thermal comfort but less with feed intake

Abstract

Advances in technology are transforming sustainable cattle farming practices, with electronic feeding systems generating big longitudinal datasets on individual animal feed intake, offering the possibility for autonomous precision livestock systems. However, the literature still lacks a methodology that fully leverages these longitudinal big data to accurately predict feed intake accounting for environmental conditions. To fill this gap, we developed an AI-based framework to accurately predict feed intake of individual animals and pen-level aggregation. Data from 19 experiments (>16.5M samples; 2013-2024) conducted at Nancy M. Cummings Research Extension & Education Center (Carmen, ID) feedlot facility and environmental data from AgriMet Network weather stations were used to develop two novel environmental indices: InComfort-Index, based solely on meteorological variables, showed good predictive capability for thermal comfort but had limited ability to predict feed intake; EASI-Index, a hybrid index integrating environmental variables with feed intake behavior, performed well in predicting feed intake but was less effective for thermal comfort. Together with the environmental indices, machine learning models were trained and the best-performing machine learning model (XGBoost) accuracy was RMSE of 1.38 kg/day for animal-level and only 0.14 kg/(day-animal) at pen-level. This approach provides a robust AI-based framework for predicting feed intake in individual animals and pens, with potential applications in precision management of feedlot cattle, through feed waste reduction, resource optimization, and climate-adaptive livestock management.