Affiliations 

  • 1 Department of Animal Nutrition and Management, Swedish University of Agricultural Science, SE-750 07 Uppsala, Sweden
J Dairy Res, 2023 Mar 01.
PMID: 36855229 DOI: 10.1017/S0022029923000171

Abstract

In this research communication we compare three different approaches for developing dry matter intake (DMI) prediction models based on milk mid-infrared spectra (MIRS), using data collected from a research herd over five years. In dairy production, knowledge of individual DMI could be important and useful, but DMI can be difficult and expensive to measure on most commercial farms as cows are commonly group-fed. Instead, this parameter is often estimated based on the age, body weight, stage of lactation and body condition score of the cow. Recently, milk MIRS have also been used as a tool to estimate DMI. There are different methods available to create prediction models from large datasets. The main data used were total DMI calculated as a 3-d average, coupled with milk MIRS data available fortnightly. Data on milk yield and lactation stage parameters were also available for each animal. We compared the performance of three prediction approaches: partial least-squares regression, support vector machine regression and random forest regression. The full milk MIRS alone gave low to moderate prediction accuracy (R2 = 0.07-0.40), regardless of prediction modelling approach. Adding more variables to the model improved R2 and decreased the prediction error. Overall, partial least-squares regression proved to be the best method for predicting DMI from milk MIRS data, while MIRS data together with milk yield and concentrate DMI at 3-30 d in milk provided good prediction accuracy (R2 = 0.52-0.65) regardless of the prediction tool used.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.