Modern livestock farming under tropical conditions using sensors in grazing systems

Author(s)

Romanzini, Elieder Prates ; Watanabe, Rafael Nakamura ; Boas Fonseca, Natalia Vilas ; Berca, Andressa Scholz ; Brito, Thais Ribeiro ; Bernardes, Priscila Arrigucci ; Munari, Danisio Prado ; Reis, Ricardo Andrade

• Document type: Journal article
• Source: SCIENTIFIC REPORTS; v. 12, n. 1, p. 10-pg., 2022-02-16.

Abstract

The aim of this study was to evaluate a commercial sensor-a three-axis accelerometer-to predict animal behavior with a variety of conditions in tropical grazing systems. The sensor was positioned on the underjaw of young bulls to detect the animals' movements. A total of 22 animals were monitored in a grazing system, during both seasons (wet and dry), with different quality and quantity forage allowance. The machine learning (ML) methods used were random forest (RF), convolutional neural net and linear discriminant analysis; the metrics used to determine the best method were accuracy, Kappa coefficient, and a confusion matrix. After predicting animal behavior using the best ML method, a forecast for animal performance was developed using a mechanistic model: multiple linear regression to correlate intermediate average daily gain (iADG) observed versus iADG predicted. The best ML method yielded accuracy of 0.821 and Kappa coefficient of 0.704, was RF. From the forecast for animal performance, the Pearson correlation was 0.795 and the mean square error was 0.062. Hence, the commercial Ovi-bovi sensor, which is a three-axis accelerometer, can act as a powerful tool for predicting animal behavior in beef cattle production developed under a variety tropical grazing condition. (AU)

• FAPESP's process: 15/16631-5 - Management strategies to reduce environmental impacts of beef cattle production systems
• Grantee: Ricardo Andrade Reis
• Support Opportunities: Research Program on Global Climate Change - Thematic Grants
• FAPESP's process: 18/20753-7 - Genes expression associated with thermoregulation in beef cattle under grazing
• Grantee: Danísio Prado Munari
• Support Opportunities: Regular Research Grants