We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Artificial neural networks approach for predicting methionine requirement in broiler chickens.
- Authors
Chanwit Kaewtapee; Charn Khetchaturat; Rattana Nukreaw; Nuttawut Krutthai; Chaiyapoom Bunchasak
- Abstract
The objective of this research was to apply artificial neural networks (ANNs) for predicting the methionine requirement in broiler chickens at day 1-10 (starter period) and day 11-21 (grower period). A total of 28 data was obtained from five hundred and sixty male broiler chicks (Ross 308), which were divided into twenty-eight pens with twenty chickens in each. Body weight was determined at days 10 and 21. A bootstrapping method was used to multiply the observations to overcome the limited data for training. A total of 280 data was obtained and divided into a training set (n = 220) and a testing set (n = 60). The level of TSAA supplementation (%) was used as a variable in the input node, whereas the average daily gain (g) was used as a variable in the output node. The model evaluation was determined by R², mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE). Quadratic regression and ANNs with radial basis function were used to develop the model using Python programing. The results showed that no significant difference (P>0.05) was observed in means between the original data and the bootstrapping data. The ANNs showed greater accuracy of R2 when compared with quadratic regression at the starter (0.7178 vs. 0.7294) and grower (0.8086 vs. 0.8097) periods. For error measurements, ANNs also resulted in lower MAD, MAPE and MSE when compared with quadratic regression at the starter and grower periods. In conclusion, the optimal level of methionine (as total sulphur amino acids) obtained by ANNs was 1.13 and 0.99% for starter and grower periods, respectively. Therefore, ANNs are an alternative method to predict methionine requirements of broiler chickens for improving poultry production.
- Subjects
ARTIFICIAL neural networks; BROILER chickens; SULFUR amino acids; METHIONINE; RADIAL basis functions; MEASUREMENT errors
- Publication
Thai Journal of Veterinary Medicine, 2021, Vol 51, Issue 1, p161
- ISSN
0125-6491
- Publication type
Article
- DOI
10.14456/tjvm.2021.21