Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Training Data
2.2. Features Selection
2.2.1. Filter Selection
2.2.2. Wrapper Selection
2.3. Herd Independent Internal Validation
2.4. External Validation
2.5. Bodyweight Prediction from DHI Dataset
3. Results and Discussion
3.1. Features Selection
3.2. Herd-Independent Internal Validation
3.3. External Validation
3.4. Model Interpretation
3.5. Implementation of BW Predictive Models on DHI Database
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Herd | Origin | Country | Std 1 | Period Coverage | N (n) 2 | |
---|---|---|---|---|---|---|
Min | Max | |||||
h01 | Walloon Breeding Association | Belgium | No | Oct-08 | Dec-08 | 32 (21) |
h02 | Walloon Breeding Association | Belgium | No | May-07 | May-07 | 41 (41) |
h03 | Walloon Agricultural Research Center | Belgium | No | Feb-11 | Sept-12 | 130 (29) |
h03 | Walloon Agricultural Research Center | Belgium | Yes | Jan-15 | Oct-15 | 23 (14) |
h04 | University of Alberta | Canada | No | Jul-14 | Dec-14 | 396 (132) |
h05 | University of Liège | Belgium | No | May-14 | Dec-14 | 155 (47) |
h06 | The Agri-Food and Biosciences Institute | Ireland | Yes | Sept-14 | Dec-14 | 188 (31) |
h07 | Aarhus University | Denmark | Yes | Oct-14 | Jan-15 | 635 (18) |
h08 | Leibniz Institute for Farm Animal Biology | Germany | Yes | May-15 | Jun-16 | 180 (12) |
h09 | University College Dublin | Ireland | Yes | Feb-15 | May-15 | 135 (18) |
Total | 1915 (360) 3 | |||||
h10 | Agriculture Victoria Research | Australia | No | Oct-15 | Dec-17 | 4067 (231) |
Variables 1 | Measure 2 | Training 3 | Validation 4 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
h01 | h02 | h03 | h04 | h05 | h06 | h07 | h08 | h09 | h10 | ||
Total number of records | 31 | 39 | 146 | 377 | 153 | 174 | 630 | 174 | 125 | 4066 | |
Parity | Primiparous | 4 | 14 | 32 | 127 | 46 | 45 | 213 | 12 | 0 | 963 |
Multiparous | 27 | 25 | 114 | 250 | 107 | 129 | 417 | 162 | 125 | 3103 | |
Bodyweight | Mean | 677 | 675 | 624 | 628 | 622 | 614 | 599 | 607 | 656 | 550 |
s.d. | 70 | 73 | 63 | 65 | 75 | 80 | 86 | 60 | 54 | 65 | |
Milk yield | Mean | 22 | 29 | 23 | 20 | 20 | 31 | 38 | 41 | 35 | 26 |
s.d. | 8 | 10 | 8 | 5 | 7 | 11 | 10 | 7 | 7 | 5 | |
DIM | Min | 53 | 15 | 5 | 3 | 1 | 5 | 4 | 5 | 6 | 37 |
Q1 | 101 | 76 | 58 | 59 | 87 | 16 | 18 | 18.3 | 22 | 95 | |
Median | 166 | 144 | 134 | 116 | 170 | 28 | 28 | 29.5 | 31 | 105 | |
Q3 | 210 | 212 | 210 | 181 | 265 | 39 | 40 | 39 | 40 | 116 | |
Max | 280 | 475 | 424 | 312 | 512 | 50 | 50 | 50 | 50 | 161 |
Subset 1 | Nfeat. 2 | SBF Selection | RFE Selection | ||||
---|---|---|---|---|---|---|---|
Subset 3 | Nfeat. 2 | Subset 4 | Nsubsets 5 | max. Nfeat. 6 | RMSESCV 7 | ||
ALL | 800 | ALL_SBF | 379 | ALL_SBF_VIP | 251 | 282 | 47 ± 1.29 |
ALL_SBF_BETA | 251 | 204 | 47 ± 1.37 | ||||
HSO | 280 | HSO_SBF | 159 | HSO_SBF_VIP | 158 | 141 | 47 ± 1.40 |
HSO_SBF_BETA | 158 | 143 | 47 ± 1.45 | ||||
/ | / | HSO_VIP | 231 | 244 | 46 ± 1.30 | ||
HSO_BETA | 231 | 280 | 46 ± 1.40 |
Subset 1 | Nfeatures 2 | Herd Independent Internal Validation | Calibration from Entire Dataset | RMSEv 7 | |||
---|---|---|---|---|---|---|---|
RMSEiv 3 | RMSESCV 4 | Ncomp 5 | RMSESCV 4 | R²scv 6 | |||
(kg) | (kg) | (kg) | (kg) | ||||
ALL_SBF_BETA | 5 | 56 ± 3.16 | 51 ± 1.07 | 2 | 52 ± 1.64 | 0.54 | 101 |
ALL_SBF_VIP | 62 | 53 ± 3.98 | 47 ± 1.13 | 5 | 48 ± 1.54 | 0.61 | 130 |
HSO_BETA | 7 | 55 ± 2.71 | 50 ± 1.46 | 3 | 51 ± 1.79 | 0.56 | 56 |
HSO_VIP | 11 | 52 ± 2.34 | 49 ± 1.22 | 3 | 50 ± 1.63 | 0.58 | 52 |
HSO_SBF_BETA | 8 | 53 ± 3.06 | 49 ± 1.00 | 7 | 50 ± 1.51 | 0.58 | 52 |
HSO_SBF_VIP | 20 | 52 ± 2.38 | 48 ± 1.06 | 7 | 48 ± 1.56 | 0.6 | 116 |
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Tedde, A.; Grelet, C.; Ho, P.N.; Pryce, J.E.; Hailemariam, D.; Wang, Z.; Plastow, G.; Gengler, N.; Brostaux, Y.; Froidmont, E.; et al. Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms. Animals 2021, 11, 1288. https://doi.org/10.3390/ani11051288
Tedde A, Grelet C, Ho PN, Pryce JE, Hailemariam D, Wang Z, Plastow G, Gengler N, Brostaux Y, Froidmont E, et al. Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms. Animals. 2021; 11(5):1288. https://doi.org/10.3390/ani11051288
Chicago/Turabian StyleTedde, Anthony, Clément Grelet, Phuong N. Ho, Jennie E. Pryce, Dagnachew Hailemariam, Zhiquan Wang, Graham Plastow, Nicolas Gengler, Yves Brostaux, Eric Froidmont, and et al. 2021. "Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms" Animals 11, no. 5: 1288. https://doi.org/10.3390/ani11051288
APA StyleTedde, A., Grelet, C., Ho, P. N., Pryce, J. E., Hailemariam, D., Wang, Z., Plastow, G., Gengler, N., Brostaux, Y., Froidmont, E., Dehareng, F., Bertozzi, C., Crowe, M. A., Dufrasne, I., GplusE Consortium Group, & Soyeurt, H. (2021). Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms. Animals, 11(5), 1288. https://doi.org/10.3390/ani11051288