Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows' Dry Matter Intake.
artificial neural network
dairy cows
dimensionality reduction
dry matter intake
feed efficiency
feed intake
machine learning
mid infrared spectra
partial least square
Journal
Animals : an open access journal from MDPI
ISSN: 2076-2615
Titre abrégé: Animals (Basel)
Pays: Switzerland
ID NLM: 101635614
Informations de publication
Date de publication:
04 May 2021
04 May 2021
Historique:
received:
31
03
2021
revised:
30
04
2021
accepted:
01
05
2021
entrez:
2
6
2021
pubmed:
3
6
2021
medline:
3
6
2021
Statut:
epublish
Résumé
We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSE
Identifiants
pubmed: 34064417
pii: ani11051316
doi: 10.3390/ani11051316
pmc: PMC8147833
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : National Fund for the Scientific Research (F.R.S-FNRS)
ID : T.0221.19
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