Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model
Objective The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood’s model) to the prediction of milk yield during lactat...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Asian-Australasian Association of Animal Production Societies
2021-04-01
|
| Series: | Animal Bioscience |
| Subjects: | |
| Online Access: | http://www.animbiosci.org/upload/pdf/ajas-19-0939.pdf |
| id |
doaj-9f4e1e60f1704ae1ad995a2cf01eb6b9 |
|---|---|
| record_format |
Article |
| spelling |
doaj-9f4e1e60f1704ae1ad995a2cf01eb6b92021-03-11T23:22:32ZengAsian-Australasian Association of Animal Production SocietiesAnimal Bioscience2765-01892765-02352021-04-0134477078210.5713/ajas.19.093924497Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s modelWilhelm Grzesiak0Daniel Zaborski1Iwona Szatkowska2Katarzyna Królaczyk3 Department of Ruminants Science, West Pomeranian University of Technology, 71-270 Szczecin, Poland Department of Ruminants Science, West Pomeranian University of Technology, 71-270 Szczecin, Poland Department of Ruminants Science, West Pomeranian University of Technology, 71-270 Szczecin, Poland Department of Animal Anatomy and Zoology, West Pomeranian University of Technology, 71-466 Szczecin, PolandObjective The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood’s model) to the prediction of milk yield during lactation. Methods The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood’s models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood’s models in the later ones. Conclusion The use of SARIMA was more time-consuming than that of NARX and Wood’s model. The application of the SARIMA, NARX and Wood’s models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.http://www.animbiosci.org/upload/pdf/ajas-19-0939.pdfpredictionheiferlactation curvemilk yieldneural networksstatistical methods |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Wilhelm Grzesiak Daniel Zaborski Iwona Szatkowska Katarzyna Królaczyk |
| spellingShingle |
Wilhelm Grzesiak Daniel Zaborski Iwona Szatkowska Katarzyna Królaczyk Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model Animal Bioscience prediction heifer lactation curve milk yield neural networks statistical methods |
| author_facet |
Wilhelm Grzesiak Daniel Zaborski Iwona Szatkowska Katarzyna Królaczyk |
| author_sort |
Wilhelm Grzesiak |
| title |
Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model |
| title_short |
Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model |
| title_full |
Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model |
| title_fullStr |
Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model |
| title_full_unstemmed |
Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model |
| title_sort |
lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and wood’s model |
| publisher |
Asian-Australasian Association of Animal Production Societies |
| series |
Animal Bioscience |
| issn |
2765-0189 2765-0235 |
| publishDate |
2021-04-01 |
| description |
Objective The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood’s model) to the prediction of milk yield during lactation. Methods The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood’s models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood’s models in the later ones. Conclusion The use of SARIMA was more time-consuming than that of NARX and Wood’s model. The application of the SARIMA, NARX and Wood’s models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time. |
| topic |
prediction heifer lactation curve milk yield neural networks statistical methods |
| url |
http://www.animbiosci.org/upload/pdf/ajas-19-0939.pdf |
| work_keys_str_mv |
AT wilhelmgrzesiak lactationmilkyieldpredictioninprimiparouscowsonafarmusingtheseasonalautoregressiveintegratedmovingaveragemodelnonlinearautoregressiveexogenousartificialneuralnetworksandwoodsmodel AT danielzaborski lactationmilkyieldpredictioninprimiparouscowsonafarmusingtheseasonalautoregressiveintegratedmovingaveragemodelnonlinearautoregressiveexogenousartificialneuralnetworksandwoodsmodel AT iwonaszatkowska lactationmilkyieldpredictioninprimiparouscowsonafarmusingtheseasonalautoregressiveintegratedmovingaveragemodelnonlinearautoregressiveexogenousartificialneuralnetworksandwoodsmodel AT katarzynakrolaczyk lactationmilkyieldpredictioninprimiparouscowsonafarmusingtheseasonalautoregressiveintegratedmovingaveragemodelnonlinearautoregressiveexogenousartificialneuralnetworksandwoodsmodel |
| _version_ |
1724223609698779136 |