An Integrated Approach between Computing and Mathematical Modelling for Cattle Welfare in Grazing Systems

R. M. O. Santos, E. F. Saraiva, R. R. Santos


In the last years, the agricultural systems based on Crop-Livestock-Forestry integration
have emerged as a potential solution due to its capacity to maximize land use and reduces the effects of high temperatures on the animals. Within these systems, there exist an interest in technological solutions capable of monitor the animals in real-time. From this monitoring, one of the main interest is to know if an animal is in the sun or in the shade of a tree by using some environmental measures. However, as there is a possibility that the weather is cloudy, real-time monitoring also needs to identify this case. That is, the realtime monitoring also needs to differentiate the shade of a tree from a cloudy weather. The interest in this kind of monitoring is due to the fact that an animal that remains a long time under a shade of a tree provides substantial insights to indicate if this is in thermal stress. This information can be used in decision-making with the goal to reduce the impact of the thermal stress and consequently to provide welfare to the animal and reduces the financial losses. As a solution to identify if an animal is in the sun or in the shade of a tree or if the weather is cloudy, we developed an electronic device, used to capture values of environmental variables, which integrated with a mathematical model predicts the shade state (sun, shade or cloudy) where the animal can be found. We illustrate the performance of the proposed solution in a real data set.


Grazing systems, Thermal stress, Multinomial logistic regression model, Model selection.

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Bayaga, A. Multinomial logistic regression: Usage and application in risk analysis. Journal of Applied Quantitative Methods .5 (2), 288-297, 2010.

Broom, D. M. Animal welfare: concepts and measurement.

Journal of Animal Science, 68 (4)167-4175, 1991.

El-Habil, A. M. An application on multinomial logistic regression. Pakistan Journal of Statistics and Operation Research, 8 (2), 271-291, 2012.

White, J. L. Logistic Regression Model Effectiveness: Proportional Chance Criteria and Proportional Reduction in Error. Journal of Contemporary Research in Education, 2 (1), 4-10, 2017.


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Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)


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