Evolution and Neural Network Prediction of CO2 Emissions in Weaned Piglet Farms

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-04, Vol.22 (8), p.2910
• Main Authors: Rodriguez, Manuel R., Besteiro, Roberto, Ortega, Juan A., Fernandez, Maria D., Arango, Tamara
• Format: Article
• Language: English
• Subjects: Carbon dioxide; Carbon dioxide concentration; CO2 concentration; CO2 emission; Emissions; Greenhouse effect; Hogs; Linear functions; neural network; Neural networks; post weaning cycle; Prediction models; Swine; weaned piglets
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22082910

This paper aims to study the evolution of CO2 concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weight based on setpoint temperature, outdoor temperature, and ventilation flow. The experimental trial was conducted during one transition cycle. Generally, the ventilation flow increased with the reduction in setpoint temperature throughout the cycle, which caused a reduction in CO2 concentration and an increase in emissions. The mean CO2 concentration was 3.12 g m–3. Emissions of CO2 had a mean value of 2.21 mg s−1 per animal, which is equivalent to 0.195 mg s−1 kg−1. A potential function was used to describe the interaction between 10 min values of ventilation flow and CO2 concentrations, whereas a linear function was used to describe the interaction between 10 min values of ventilation flow and CO2 emissions, with r values of 0.82 and 0.85, respectively. Using such equations allowed for simple and direct quantification of emissions. Furthermore, two prediction models for CO2 emissions were developed using two neural networks (for 10 min and 60 min predictions), which reached r values of 0.63 and 0.56. These results are limited mainly by the size of the training period, as well as by the differences between the behavior of the series in the training stage and the testing stage.