Applicability of statistical and machine learning–based regression algorithms in modeling of carbon dioxide emission in experimental pig barns

• Published in: Air quality, atmosphere and health Vol. 15; no. 10; pp. 1899 - 1912
• Main Authors: Basak, Jayanta Kumar, Kim, Na Eun, Shahriar, Shihab Ahmad, Paudel, Bhola, Moon, Byeong Eun, Kim, Hyeon Tae
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2022; Springer Nature B.V
• Subjects: Agricultural industry; Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Barns; Carbon dioxide; Carbon dioxide emissions; Earth and Environmental Science; Emission analysis; Emissions; Environment; Environment models; Environmental Health; Environmental management; Greenhouse gases; Health Promotion and Disease Prevention; Learning algorithms; Livestock; Machine learning; Management systems; Modelling; Performance evaluation; Polynomials; Regression analysis; Regression models; Statistical analysis; Statistics; Body mass; Carbon dioxide; Emission; Model; Model evaluation metrics; Feed intake; Pig
• ISSN: 1873-9318; 1873-9326
• DOI: 10.1007/s11869-022-01225-9

Pig farming is one of the major sources of greenhouse gas (GHG) emissions in the agricultural sector; nevertheless, few studies have been undertaken to directly measure or estimate GHGs, particularly carbon dioxide (CO 2 ) from pig barns. Therefore, the main objective of the present research was to estimate and predict CO 2 emission rate as a function of the mass of pigs and feed consumption. Two identical experiments were carried out in experimental pig barns in 2020 and 2021 to develop and evaluate the performance of CO 2 emission model. The CO 2 emission data (ppm) were collected utilizing Livestock Environment Management Systems (LEMS) and weather sensors, respectively within the pig barns and the outside environment. The models were built using seven statistical and machine learning–based regression algorithms, i.e., linear, multiple linear, polynomial, exponential, ridge, lasso, and elastic net. The findings of the study revealed that among the seven models, the exponential-based regression model performed the best, with a coefficient of determination ( R 2 ) greater than 0.78 in the training stage and 0.75 in the testing stage being suitable to describe the relationship between the feed intake and the rate of CO 2 emission. However, when compared to the other models in the testing stage, the lasso model had the worst performance ( R 2 < 0.65 and RMSE > 20.00 ppm). In conclusion, this study recommends employing an exponential-based regression model by taking feed intake as an input variable in predicting CO 2 for a small number of the experimental dataset.