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Mid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model. [ABSTRACT FROM AUTHOR]

Two short-term grazing experiments were conducted with Norwegian Red cows. In Exp 1, 24 cows were randomly assigned to one of the following three pasture allocation methods (PAM): weekly pasture allowance (7RG), grazing 1/7 of 7RG each day (1SG), or grazing as 1SG but had access to grazed part of the paddock within one week (1FG). In Exp 2, 7RG was shortened to 5 days (5RG). We hypothesized that PAM will affect sward quality, quantity, intake and production differently. Pasture chemical composition changed with advancing grazing days but were not different between treatments. Pasture intake, milk yield, and methane emission were not affected by PAM. In Exp 1, 7RG cows spent less time on grazing, whereas in Exp 2, 1FG cows spent longer on grazing than others. Patterns observed in sward quality, and behavioural and physiological adaptations of cows to short-term changes in nutrient supply may explain the observed effects. [ABSTRACT FROM AUTHOR]

Progesterone profiles in Norwegian Red cows were categorized, and associations between the occurrence of irregularities in the profiles and the commencement of luteal activity were investigated. The cows were managed in 3 feeding trials from 1994 to 2001 and from 2005 to 2008 at the Norwegian University of Life Sciences. The cows were followed from calving, and the milk samples collected represented 502 lactations from 302 cows. Milk samples for progesterone analysis were taken 3 times weekly from 1994 throughout 1998 and from 2005 to 2008 and 2 times weekly from 1999 to 2001. Commencement of luteal activity was defined as the first day of 2 consecutive measurements of progesterone concentration ⩾3 ng/mL not earlier than 10 d after calving. Delayed ovulation type I was defined as consistently low progesterone concentration, <3 ng/mL for ⩾50 d postpartum. Delayed ovulation type II was defined as prolonged interluteal interval with milk progesterone measurements <3 ng/mL for ⩾12 d between 2 luteal phases. Persistent corpus luteum (PCL) type I was defined as delayed luteolysis with milk progesterone <3 ng/mL for ⩾19 d during the first estrous cycle postpartum. Persistent corpus luteum type II was defined as delayed luteolysis with milk progesterone ⩾3 ng/mL for ⩾19 d during subsequent estrous cycles before first artificial insemination. Delayed ovulation type I was present in 14.7%, delayed ovulation type II in 2.8%, PCL type I in 6.7%, and PCL type II in 3.3% of the profiles. Commencement of luteal activity was related to milk yield, parity, PCL type I, and the summated occurrence of PCL type I and II. The least squares means for the interval to commencement of luteal activity were 24.2 d when PCL type I and II were present and 29.5 d when PCL type I and II were absent. The likelihood of pregnancy to first service was not affected in cows with a history of PCL when artificial insemination was carried out at progesterone concentrations <3 ng/mL (i.e., during estrus); however, cows that had experienced PCL were more likely to be inseminated during a luteal phase. The occurrence of delayed ovulation and PCL in Norwegian Red cows was less than that reported in most other dairy populations. [ABSTRACT FROM AUTHOR]