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 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-2 = 0.07-0.40), regardless of prediction modelling approach. Adding more variables to the model improved R-2 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-2 = 0.52-0.65) regardless of the prediction tool used

Gene coexpression network analysis reveals perirenal adipose tissue as an important target of prenatal malnutrition in sheep

We have previously demonstrated that pre- and early postnatal malnutrition in sheep induced depot- and sex-specific changes in adipose morphological features, metabolic outcomes, and transcriptome in adulthood, with perirenal (PER) as the major target followed by subcutaneous (SUB) adipose tissue. We aimed to identify coexpressed and hub genes in SUB and PER to identify the underlying molecular mechanisms contributing to the early nutritional programming of adipose-related phenotypic outcomes. Transcriptomes of SUB and PER of male and female adult sheep with different pre- and early postnatal nutrition histories were used to construct networks of coexpressed genes likely to be functionally associated with pre- and early postnatal nutrition histories and phenotypic traits using weighted gene coexpression network analysis. The modules from PER showed enrichment of cell cycle regulation, gene expression, transmembrane transport, and metabolic processes associated with both sexes' prenatal nutrition. In SUB (only males), a module of enriched adenosine diphosphate metabolism and development correlated with prenatal nutrition. Sex-specific module enrichments were found in PER, such as chromatin modification in the male network but histone modification and mitochondria- and oxidative phosphorylation-related functions in the female network. These sex-specific modules correlated with prenatal nutrition and adipocyte size distribution patterns. Our results point to PER as a primary target of prenatal malnutrition compared to SUB, which played only a minor role. The prenatal programming of gene expression and cell cycle, potentially through epigenetic modifications, might be underlying mechanisms responsible for observed changes in PER expandability and adipocyte-size distribution patterns in adulthood in both sexes

Is it possible to predict the methane emission intensity of Swedish dairy cows from milk spectra?

Emissions of greenhouse gases (GHG), especially methane (CH4), from dairy production have received much research attention in the past 15 years, with the main focus being to identify factors affecting CH4 production and measures to reduce CH4 emissions from dairy cows. However, measurement of CH4 production by dairy cows in commercial herds is time-consuming and requires expensive equipment, so there is a need to find alternative ways to estimate individual and herd CH4 emissions. Regular milk analyses are performed for many cows, so data from mid-infrared spectroscopy (MIRS) on individual milk samples could perhaps be utilised to predict CH4 emissions intensity (MI, CH4 g/kg milk production). This study investigated the potential and limitations of predicting individual MI by integrating data from CH4 measurements made by an infrared sniffer (IRS) and milk MIRS data taken from fortnightly morning milkings during the full lactation records of 37 multiparous cows. Partial least square regression was used to create prediction models for six-week lactation sub-periods and for full lactations, which were validated using leave-one-cow-out cross-validation. Coefficient of determination in predicting MI was low, indicating that the method is not suitable for predicting variations in individual MI, although it should further be evaluated at herd level