S0022029923000171jra 5..8

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 (R = 0.07–0.40), regardless of prediction modelling approach. Adding more variables to the model improved R 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 (R = 0.52–0.65) regardless of the prediction tool used. Dry matter intake (DMI) is an important performance indicator in livestock production and could be used to determine feed efficiency and to optimise feed utilisation. Knowledge of DMI is also crucial to avoid overfeeding, which could lead to undesirable consequences such as metabolic disease in dairy cows, feed inefficiency and associated effects on production economics, as well as problems with calving in overweight animals. However, it is often not possible to measure feed intake at individual level, since dairy cows are often group-fed and since the required equipment is usually not available on farms. In conventional dairy production, milk yield (MY), body weight (BW) and days in milk (DIM) are sometimes used to estimate DMI. However, many modern dairy farms are now participating in milk recording schemes that use mid-infrared spectroscopy (MIRS) analysis of milk samples to measure milk composition (fat, protein and lactose content). This is based on the fact that MIRS on milk samples provides information about the chemical bonds present in the milk, and thus indicates the types of molecules present in the samples. As milk is individually collected, and composition in addition to yield is related to DMI, there may be possibilities to gain information about feed intake from individual cows through milk collection. The usage of MIRS in predicting DMI could be used as a strategy in selecting cows that have high efficiency in the utilisation of feed nutrients in relation to milk production, for breeding purpose. Therefore, it is important to find a robust strategy incorporating parameters and methods that can be used in predicting DMI. Equations using milk MIRS to predict feed intake-related parameters have been developed by several researchers using different methods (Shetty et al., 2017; Wallén et al., 2018; Lahart et al., 2019; Grelet et al., 2020). Partial least square (PLS) regression is commonly used to develop prediction models, as this method is suitable for multivariate data (Soyeurt et al., 2006; Eskildsen et al., 2014; Parrini et al., 2019). Other studies have attempted to use various types of machine learning (ML) algorithms (Ghasemi and Tavakoli, 2013; Contla Hernández et al., 2021; Meza Ramirez et al., 2021). Among these, support vector machine (SVM) and random forest (RF) are widely used supervised learning models that were initially developed to learn the algorithms of non-linear relationship data in correspondence to certain discrete and continuous output. While the PLS method is most widely used in development of prediction models, it is of interest to compare the performance of different approaches and tools. Therefore, the present study compared https://doi.org/10.1017/S0022029923000171 Published online by Cambridge University Press three different approaches (PLS, SVM and RF regression) in terms of their ability to predict DMI in Swedish dairy cattle using milk MIRS data. Materials and methods Data collection and pre-processing Data on milk MIRS and on DMI (forage DMI + concentrate DMI) in the years 2017–2021 were collected from cows in the research herd, containing approximately 240 places for lactating cows, at the Swedish Livestock Research Centre, Lövsta, Sweden. All cows were either Swedish Red or Swedish Holstein breed. All cows were attached to an ID system (DelPro) that logged feed intake and was also linked to the automatic milking system (DeLaval International AB, Tumba, Sweden), which recorded other milking-related parameters (date, time, time since the last milking, yield). The details of the feed intake and milk data records can be found in online Supplementary Text S1. The DMI data were pre-processed before use in developing and validating models for predicting DMI. Total DMI exceeding 40 kg/d was filtered out from the dataset. The common management practice in the research barn is for cows in midto late lactation to be moved to a different part of the barn where forage DMI is not recorded. Therefore, DMI data were available only between 0 and 180 DIM. All cows were also kept partly on pasture during the summer (May–August) and total DMI for the animals was not measured within this period. Daily DMI was averaged over the 3 d immediately preceding the date of MIRS data. Information about DIM was also included in the dataset, so the cows were categorised according to stage of lactation. Predictive model development was performed with all data (3–180 DIM), and with early (3–30 DIM) and mid(30–180 DIM) lactation data separately.