Abstract
Simple SummaryDry 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.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.
Highlights
Dairy cows’ Dry Matter Intake (DMI), which is directly linked to feeding efficiency, is crucial for the economic, environmental, and welfare management of dairy herds to be estimated
Significant differences appeared, especially on milk yield and mid-infrared spectra (MIR) spectral data, which directly affected the predictions of bodyweight, fatty acids, and milk composition
The equations developed showed that the combination of parity, weeks of lactation, milk yield, bodyweight
Summary
Dairy cows’ Dry Matter Intake (DMI), which is directly linked to feeding efficiency, is crucial for the economic, environmental, and welfare management of dairy herds to be estimated. Whether under or overfeeding cows, malnutrition negatively affects animal health [2], reproductive condition [3] and could impact the economic balance of production [4]. Lose bodyweight as an immediate consequence [8] This weight loss should be carefully controlled to avoid health and fertility problems [2,9]. Cows being too fat approaching parturition might undergo a more severe NEB, compromising their reproductive performance [9]. To these ends, routinely monitoring the DMI could provide an earlier warning than monitoring change in bodyweight or other health indicators such as when the body condition score falls below or above a critical threshold. Feed-efficient cows eruct less methane [10] and produce more milk [11], profiting both farm revenue and the environment [12]
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