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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Simple Summary

Dry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values.

Abstract

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 (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.

Details

Title
Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows’ Dry Matter Intake
Author
Tedde, Anthony 1   VIAFID ORCID Logo  ; Grelet, Clément 2   VIAFID ORCID Logo  ; Ho, Phuong N 3 ; Pryce, Jennie E 4 ; Hailemariam, Dagnachew 5 ; Wang, Zhiquan 5 ; Plastow, Graham 5 ; Gengler, Nicolas 6   VIAFID ORCID Logo  ; Froidmont, Eric 2   VIAFID ORCID Logo  ; Dehareng, Frédéric 2   VIAFID ORCID Logo  ; Bertozzi, Carlo 7 ; Crowe, Mark A 8 ; Soyeurt, Hélène 6   VIAFID ORCID Logo  ; Duplessis, Melissa

 AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; [email protected] (N.G.); [email protected] (H.S.); National Funds for Scientific Research, 1000 Brussels, Belgium 
 Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; [email protected] (C.G.); [email protected] (E.F.); [email protected] (F.D.) 
 Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; [email protected] (P.N.H.); [email protected] (J.E.P.) 
 Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; [email protected] (P.N.H.); [email protected] (J.E.P.); School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, VIC 3083, Australia 
 Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; [email protected] (D.H.); [email protected] (Z.W.); [email protected] (G.P.) 
 AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; [email protected] (N.G.); [email protected] (H.S.) 
 Walloon Breeding Association, 5590 Ciney, Belgium; [email protected] 
 UCD School of Veterinary Medicine, University College Dublin, Dublin 4, Ireland; [email protected] 
First page
1316
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20762615
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2531393031
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.