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Attempts to lower the environmental footprint of milk production needs a sound understanding of the genetic and nutritional basis of methane (CH4) emissions from the dairy production systems. This in turn requires accurate and reliable techniques for the measurement of CH4 output from individual cows. Many of the available measurement techniques so far are either slow, expensive, labor intensive and are unsuitable for large-scale individual animal measurements. The main objectives of this study were to examine and validate a non-invasive individual cow CH4 measurement system that is based on photoacoustic IR spectroscopy (PAS) technique implemented in a portable gas analysis equipment (F10), referred to as PAS-F10 method and to estimate the magnitude of between-animal variations in CH4 output traits. Data were collected from 115 Nordic Red cows of the Minkiö experimental dairy farm, at the Natural Resources Institute Finland (Luke). Records on continuous daily measurements of CH4, milk yield, feed intake and BW measurements over 2 years period were compiled for data analysis. The daily CH4 output was calculated using carbon dioxide as a tracer method. Estimates from the non-invasive PAS-F10 technique were then tested against open-circuit indirect respiration calorimetric chamber measurements and against estimates from other widely used prediction models. Concordance analysis was used to establish agreement between the chamber and PAS-F10 methods. A linear mixed model was used for the analysis of the large continuous data. The daily CH4 output of cows was 555 l/day and ranged from 330 to 800 l/day. Dry matter intake, level of milk production, lactation stage and diurnal variation had significant effects on daily CH4 output. Estimates of the daily CH4 output from PAS-F10 technique compared relatively well with the other techniques. The concordance correlation coefficient between combined weekly CH4 output estimates of PAS-F10 and chamber was 0.84 with lower and upper confidence limits of 0.65 and 0.93, respectively. Similarly, when chamber CH4 measurements were predicted from PAS-F10 measurements, the mean of two separate weekly PAS-F10 measurements gave the lowest prediction error variance than either of the separate weekly PAS-F10 measurements alone. This suggests that every other week PAS-F10 measurements when combined would improve the estimation of CH4 output with PAS-F10 technique. The repeatability of daily CH4 output from PAS-F10 technique ranged from 0.40 to 0.46 indicating that some between-animal variation exist in CH4 output traits.

The effect of the rate of increase in concentrate allowance after calving with two concentrate mixes (A and B) differing in composition was evaluated using 64 Finnish Ayrshire cows during the first 100 days of lactation. After calving, the concentrate allowance of multiparous cows was increased stepwise from 4 to 17 kg/day, and of primiparous cows from 3 to 13.5 kg/day over 12 days (F rate of increase; multiparous 1.08 kg/day, primiparous 0.88 kg/day) or 24 days (S rate of increase; mutiparous 0.54 kg/day, primiparous 0.44 kg/day). The concentrates were formulated to have similar crude protein and metabolizable energy concentrations but differing starch and NDF concentrations. For concentrate A the starch and NDF concentrations were 421 and 167 g/kg dry matter (DM) and for concentrate B 258 and 251 g/kg DM. All cows received grass silage ad libitum. The higher concentrate intake during weeks 1 to 4 of lactation with F compared with the S rate of increase caused higher DM, energy and protein intake. The higher concentrate intake for F than for S treatment in early lactation did not cause a large decrease in silage intake (8.8 v. 8.3 kg DM/day). The intake of concentrate A and B after calving did not differ for S treatment. However, for F treatment the intake of fibrous concentrate B increased faster than starch-rich concentrate A during weeks 1 to 4 of lactation. The concentrate composition had no effect on energy-corrected milk (ECM) yield during weeks 1 to 4 of lactation for S treatments, but with F treatments the cows fed B concentrate produced more milk. The F rate of increase in concentrate allowance compared with the S rate increased the calculated energy balance after calving. The rate of increase in concentrate feeding post partum or concentrate composition had no effect on DM, energy or protein intake during the whole 100-day experiment. The average ECM yield over days 1 to 100 of lactation was higher for S than for F treatments and tended to be higher with concentrate B than A. Results of this study showed that by the fast rate of increase in concentrate allowance after calving on a grass silage diet, it was possible to improve the energy status of the cows in early lactation. This had, however, no effect on production later in lactation.

An empirical regression model for the prediction of total dry matter intake (DMI) of dairy cows was developed and compared with four published intake models. The model was constructed to include both animal and dietary factors, which are known to affect DMI. For model development, a data set based on individual cow data from 10 change-over and four continuous milk production studies was collected (n = 1554). Relevant animal (live weight (LW), days in milk (DIM), parity and breed) and dietary (total and concentrate DMI, concentrate composition, forage digestibility and fermentation quality) data were collected. The model factors were limited to those that are available before the diets are fed to animals, that is, standardized energy corrected milk (sECM) yield, LW, DIM and diet quality (total diet DMI index (TDMI index)). As observed ECM yield is a function of both the production potential of the cow and diet quality, ECM yield standardized for DIM, TDMI index and metabolizable protein concentration was used in modelling. In the individual data set, correlation coefficients between sECM and TDMI index or DIM were much weaker (0.16 and 0.03) than corresponding coefficients with observed ECM (0.65 and 0.46), respectively. The model was constructed with a mixed model regression analysis using cow within trial as a random factor. The following mixed model was estimated for DMI prediction: DMI (kg DM/day) = −2.9 (±0.56)+0.258 (±0.011) × sECM (kg/day) + 0.0148 (±0.0009) × LW (kg) −0.0175 (±0.001) × DIM −5.85 (±0.41) × exp (−0.03 × DIM) + 0.09 (±0.002) × TDMI index. The mixed DMI model was evaluated with a treatment mean data set (207 studies, 992 diets), and the following relationship was found: Observed DMI (kg DM/day) = −0.10 (±0.33) + 1.004 (±0.019) × Predicted DMI (kg DM/day) with an adjusted residual mean square error of 0.362 kg/day. Evaluation of the residuals did not result in a significant mean bias or linear slope bias, and random error accounted for proportionally >0.99 of the error. In conclusion, the DMI model developed is considered robust because of low mean prediction error, accurate and precise validation, and numerically small differences in the parameter values of model variables when estimated with mixed or simple regression models. The Cornell Net Carbohydrate and Protein System was the most accurate of the four other published DMI models evaluated using individual or treatment mean data, but in most cases mean and linear slope biases were relatively high, and, interestingly, there were large differences in both mean and linear slope biases between the two data sets.