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Current IssueEditorial BoardOnline First Articles
Indian Journal of
Animal Research
Print ISSN: 0367-6722
Online ISSN: 0976-0555
NASS Rating
6.40
SJR
0.233
CiteScore
0.606

Chief Editor:
M. R. Saseendranath
Kerala Veterinary and Animal Science University, Mannuthy, Thrissur, INDIA
Frequency:Monthly
Indexing:
Science Citation Index Expanded, BIOSIS Preview, ISI Citation Index, Biological Abstracts, Scopus, ...

Research Article

Indian Journal of Animal Research, volume 54 issue 9 (september 2020) : 1165-1170

Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models

Jayanta Kumar Basak, Elanchezhian Arulmozhi, Fawad Khan, Frank Gyan Okyere, Jihoon Park, Deog Hyun Lee, Hyeon Tae Kim
1Department of Bio-systems Engineering, Gyeongsang National University (Institute of Agriculture and Life Science), Jinju 52828, Korea.
Cite article:- Basak Kumar Jayanta, Arulmozhi Elanchezhian, Khan Fawad, Okyere Gyan Frank, Park Jihoon, Lee Hyun Deog, Kim Tae Hyeon (2020). Assessment of the Influence of Environmental Variables on Pig’s Body Temperature using ANN and MLR Models. Indian Journal of Animal Research. 54(9): 1165-1170. doi: 10.18805/ijar.B-1199.

ABSTRACT

An experiment was conducted to find out the most influential factors affecting pig’s body temperature (PBT). For this purpose, eight environmental parameters and three growth related factors were considered as variables. Among these factors, seven environmental parameters, including temperature, CO2, temperature-humidity index inside and outside the pig’s barn and relative humidity inside the barn were taken as input variables for artificial neural networks (ANN) and multiple linear regression (MLR) models due to their good correlation (r ³ 0.5) with PBT. The results showed that ANN and MLR models had the lowest R2 values (0.81 and 0.69, respectively) and the highest RMSE (1.17 and 1.48, respectively) when they were run without temperature-humidity index; however, the maximum R2 (0.90 and 0.75, respectively) and minimum RMSE (0.92 and 1.40, respectively) were found without relative humidity. Based on the results, the temperature-humidity index could represent an important indicator in registering early warning signs of PBT status alternations.   

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