Related Experiment Video
Updated: Sep 5, 2026

An Efficient Single-Person Technique for Milk Sampling from Laboratory Mice
Published on: March 28, 2025
Temporal associations between changes in total mixed ration nutrients, maximum temperature-humidity index, and milk
A K G Lean1, A J Gunn2, H M Golder3
1School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University Boorooma Street, Wagga Wagga, NSW 2678; Gulbali Institute for Agriculture, Water and the Environment, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.; Scibus, Camden, NSW, 2570, Australia; Dairy UP Program, Camden NSW, 2570, Australia.
Abstract:
A multisite time series investigated effects of nutritional changes in TMR diets and temperature-humidity index (THI) on dairy cow milk yield. Weekly diet samples were collected and tested from 8 farms with a total of 9 strings or herds over an average of 58 weeks. Nutrient components of the diet examined for effects on milk yield were NDF, lignin, crude fat, CP, neutral detergent insoluble crude protein (NDICP), starch, and non-fiber carbohydrates (NFC). Milk yield was measured from daily shipped milk or individual milk volumes. Weather data were measured using on-farm or nearby publicly available weather stations to calculate the weekly average of the daily maximum temperature-humidity index (mTHI). The groups of cows on the diets monitored had mean daily milk yields between 31 and 52 L/cow. Principal components (PC) were created for nutritional covariables. The first PC (PC1) explained over 50% of the variability in nutrition and was positively correlated with slower-fermenting carbohydrates. The PC2 and PC3 were composed mainly of fat and protein variables, though PC3 had a negative loading with NDF. Univariable regression spline models were used to generate residual and smooth values for each variable. Lagged variables up to -5 weeks were generated for each covariable's residual within their farm diet. A Pearson correlation matrix of the milk yield residual and covariable residuals at each week's lag was created to determine the lag of greatest effect. These were included in univariable mixed models before a multivariable mixed model was created through backward stepping with farm diet as a random effect. Changes in milk yield were positively associated with changes in PC1 (carbohydrates) 4 weeks before and PC3 (crude fat/NDICP) at the time of milk yield changes in univariable and multivariable analysis. In the all-diet nutrient univariable model, changes in NDF 4 weeks earlier and NDICP at the time increased milk yield, NFC and starch 4 weeks earlier, and mTHI at the same time decreased milk yield. In the all-diets multivariable model, NFC 4 weeks earlier and mTHI at the time were negatively associated, crude fat 1 week earlier and NDICP at the time were positively associated with milk yield changes. Diets were grouped into low-starch (n = 3) and high-starch diets (n = 6) with a cut point of 24% mean dietary starch. Cows on high-starch diets had a higher average milk yield (high-starch = 41.8 L/cow; low-starch = 34.8 L/cow). The multivariable low-starch diet model showed a positive association between changes in milk yield and starch 1 week earlier and NDICP at the time, while DIM was negatively associated. For the high-starch diets, the multivariable model found positive associations for CP 3 weeks earlier, crude fat 4 weeks earlier and DIM, while starch 2 weeks earlier and mTHI 1 week earlier were negatively associated with milk yield changes. This study highlights the delayed effects of dietary changes, with carbohydrate components having their greatest association with milk yield 4 weeks later. This could have implications for study design and nutritional models. Differing starch levels led to variable associations of nutrients on milk yield.

