Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks
To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regressi...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Journal of King Saud University: Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018364723004706 |
| _version_ | 1797627298149564416 |
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| author | Eunjeong Jeon Sangbuem Cho Seongsoo Hwang Kwanghyun Cho Cedric Gondro Nag-Jin Choi |
| author_facet | Eunjeong Jeon Sangbuem Cho Seongsoo Hwang Kwanghyun Cho Cedric Gondro Nag-Jin Choi |
| author_sort | Eunjeong Jeon |
| collection | DOAJ |
| description | To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regression (MLR), local regression (LOESS), and deep neural networks (DNN). Milk production traits readily available on commercial dairy farms, such as energy-corrected milk (ECM), fat-to-protein ratio, days in milk (DIM), and parity, were used as input variables for BW prediction. The EB was evaluated as the difference between energy intake and energy demand. The DNN model showed the greatest predictive accuracy for BW compared with the LOESS and MLR models. The BW predicted using the DNN model was used to calculate the energy demand. Our results revealed that the day on which the EB status transitioned from negative to positive differed among cows. The cows were assigned to one of the three EB index groups. EB index 1 indicated that the day of EB transition was within DIM ≤ 70. The EB indexes 2 and 3 were 70 < DIM ≤ 140 and 140 < DIM ≤ 305, respectively. EB index 3 had the lowest EB, which is the slowest to transition from a negative to a positive energy balance compared with EB indexes 1 and 2. The highest ECM and feed efficiency were observed for EB index 3. The calving interval was the shortest for EB index 1. EB of individual cows during lactation can be estimated and monitored with moderately high accuracy using EB indexes. |
| first_indexed | 2024-03-11T10:22:17Z |
| format | Article |
| id | doaj.art-008786f7d337444eb8bfffbb59629ca3 |
| institution | Directory Open Access Journal |
| issn | 1018-3647 |
| language | English |
| last_indexed | 2024-03-11T10:22:17Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Science |
| spelling | doaj.art-008786f7d337444eb8bfffbb59629ca32023-11-16T06:05:57ZengElsevierJournal of King Saud University: Science1018-36472024-01-01361103008Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networksEunjeong Jeon0Sangbuem Cho1Seongsoo Hwang2Kwanghyun Cho3Cedric Gondro4Nag-Jin Choi5Dairy Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Republic of Korea; Department of Animal Science, Jeonbuk National University, Jeonju 54896, Republic of KoreaDepartment of Animal Science, Jeonbuk National University, Jeonju 54896, Republic of KoreaAnimal Welfare Research Team, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of KoreaDepartment of Dairy Science, Korea National University of Agriculture and Fisheries, Jeonju 54874, Republic of KoreaDepartment of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI 48824, USADepartment of Animal Science, Jeonbuk National University, Jeonju 54896, Republic of Korea; Corresponding author.To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regression (MLR), local regression (LOESS), and deep neural networks (DNN). Milk production traits readily available on commercial dairy farms, such as energy-corrected milk (ECM), fat-to-protein ratio, days in milk (DIM), and parity, were used as input variables for BW prediction. The EB was evaluated as the difference between energy intake and energy demand. The DNN model showed the greatest predictive accuracy for BW compared with the LOESS and MLR models. The BW predicted using the DNN model was used to calculate the energy demand. Our results revealed that the day on which the EB status transitioned from negative to positive differed among cows. The cows were assigned to one of the three EB index groups. EB index 1 indicated that the day of EB transition was within DIM ≤ 70. The EB indexes 2 and 3 were 70 < DIM ≤ 140 and 140 < DIM ≤ 305, respectively. EB index 3 had the lowest EB, which is the slowest to transition from a negative to a positive energy balance compared with EB indexes 1 and 2. The highest ECM and feed efficiency were observed for EB index 3. The calving interval was the shortest for EB index 1. EB of individual cows during lactation can be estimated and monitored with moderately high accuracy using EB indexes.http://www.sciencedirect.com/science/article/pii/S1018364723004706Body weightDeep neural networksEnergy balanceEnergy corrected milk |
| spellingShingle | Eunjeong Jeon Sangbuem Cho Seongsoo Hwang Kwanghyun Cho Cedric Gondro Nag-Jin Choi Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks Journal of King Saud University: Science Body weight Deep neural networks Energy balance Energy corrected milk |
| title | Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks |
| title_full | Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks |
| title_fullStr | Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks |
| title_full_unstemmed | Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks |
| title_short | Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks |
| title_sort | development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks |
| topic | Body weight Deep neural networks Energy balance Energy corrected milk |
| url | http://www.sciencedirect.com/science/article/pii/S1018364723004706 |
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