Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle
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...
| Main Authors: | , , |
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| Format: | Article |
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Cambridge University Press (CUP)
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/108431/ |
| _version_ | 1848865150221680640 |
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| author | Mohamad Salleh, Suraya Danielsson, Rebecca Kronqvist, Cecilia |
| author_facet | Mohamad Salleh, Suraya Danielsson, Rebecca Kronqvist, Cecilia |
| author_sort | Mohamad Salleh, Suraya |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T14:00:08Z |
| format | Article |
| id | upm-108431 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:00:08Z |
| publishDate | 2023 |
| publisher | Cambridge University Press (CUP) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1084312024-03-22T07:30:00Z http://psasir.upm.edu.my/id/eprint/108431/ Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle Mohamad Salleh, Suraya Danielsson, Rebecca Kronqvist, Cecilia 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. Cambridge University Press (CUP) 2023-03 Article PeerReviewed Mohamad Salleh, Suraya and Danielsson, Rebecca and Kronqvist, Cecilia (2023) Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle. Journal of Dairy Research, 90 (1). 5-8-4. ISSN 0022-0299; ESSN: 1469-7629 https://www.cambridge.org/core/journals/journal-of-dairy-research/article/using-machine-learning-methods-to-predict-dry-matter-intake-from-milk-midinfrared-spectroscopy-data-on-swedish-dairy-cattle/A06673BFE835058C2CA1FDFC975AA58F 10.1017/S0022029923000171 |
| spellingShingle | Mohamad Salleh, Suraya Danielsson, Rebecca Kronqvist, Cecilia Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle |
| title | Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle |
| title_full | Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle |
| title_fullStr | Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle |
| title_full_unstemmed | Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle |
| title_short | Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle |
| title_sort | using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on swedish dairy cattle |
| url | http://psasir.upm.edu.my/id/eprint/108431/ http://psasir.upm.edu.my/id/eprint/108431/ http://psasir.upm.edu.my/id/eprint/108431/ |