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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

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Predicting dry matter intake in Pelibuey sheep using machine learning methods.

Enrique Camacho-Perez¹, Cem Tirink², Ricardo Garcia-Herrera³

¹Facultad de Ingeniería. Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes s/n, Mérida, Yucatán, Mexico.

|February 5, 2025

Summary

Machine learning accurately predicts dry matter intake (DMI) in growing Pelibuey sheep. The Multivariate Adaptive Regression Splines (MARS) algorithm demonstrated high reliability, offering a valuable tool for animal nutrition management.

Keywords:

Dry matter intake Hair sheep Machine learning

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Area of Science:

Animal Science
Machine Learning
Agricultural Engineering

Background:

Accurate prediction of Dry Matter Intake (DMI) is crucial for optimizing nutrition in growing livestock.
Pelibuey sheep production is significant in tropical regions, necessitating efficient management strategies.

Purpose of the Study:

To develop and evaluate machine learning models for predicting DMI in growing male Pelibuey sheep.
To compare the predictive performance of Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), and Support Vector Regression (SVR) algorithms.

Main Methods:

Data from 130 Pelibuey sheep (average body weight 23 ± 6 kg) were collected under tropical conditions.
Key variables included diet concentrate (CON), initial and final body weight (IBW, FBW), mean metabolic body weight (MBW), average daily gain (ADG), crude protein (CP), and neutral detergent fiber (NDF).
MARS, CART, and SVR algorithms were employed to build predictive models for DMI.

Main Results:

The MARS algorithm achieved a determination coefficient exceeding 0.90.
All three machine learning methods were utilized to develop predictive algorithms for DMI.
MARS proved to be a highly reliable model for predicting DMI in this sheep breed.

Conclusions:

The MARS algorithm is a robust and reliable tool for predicting Dry Matter Intake in growing Pelibuey sheep.
Machine learning approaches, particularly MARS, offer effective solutions for nutritional management in sheep farming.
This study provides a validated method for enhancing precision feeding strategies in Pelibuey sheep production.