G. André

Increasing the revenues from automatic milking by using individual variation in milking characteristics

The objective of this study was to quantify individual variation in daily milk yield and milking duration in response to the length of the milking interval and to assess the economic potential of using this individual variation to optimize the use of an automated milking system. Random coefficient models were used to describe the individual effects of milking interval on daily milk yield and milking duration. The random coefficient models were fitted on a data set consisting of 4,915 records of normal uninterrupted milkings collected from 311 cows kept in 5 separate herds for 1 wk. The estimated random parameters showed considerable variation between individuals within herds in milk yield and milking duration in response to milking interval. In the actual situation, the herd consisted of 60 cows and the automatic milking system operated at an occupation rate (OR) of 64%. When maximizing daily milk revenues per automated milking system by optimizing individual milking intervals, the average milking interval was reduced from 0.421 d to 0.400 d, the daily milk yield at the herd level was increased from 1,883 to 1,909 kg/d, and milk revenues increased from euro498 to euro507/d. If an OR of 85% could be reached with the same herd size, the optimal milking interval would decrease to 0.238 d, milk yield would increase to 1,997 kg/d, and milk revenues would increase to euro529/d. Consequently, more labor would be required for fetching the cows, and milking duration would increase. Alternatively, an OR of 85% could be achieved by increasing the herd size from 60 to 80 cows without decreasing the milking interval. Milk yield would then increase to 2,535 kg/d and milk revenues would increase to euro673/d. For practical implementation on farms, a dynamic approach is recommended, by which the parameter estimates regarding the effect of interval length on milk yield and the effect of milk yield on milking duration are updated regularly and also the milk production response to concentrate intake is taken into account.

Milk urea concentration as an indicator of ammonia emission from dairy cow barn under restricted grazing

Bulk milk urea concentration was evaluated to assess its potential as an indicator of ammonia emission from a dairy cow barn in a situation with restricted grazing. An experiment was carried out with a herd of, on average, 52 Holstein-Friesian dairy cows. The cows were housed in a naturally ventilated barn with cubicles and a slatted floor, were fed ensiled forages and feed supplements, and each day were allowed 8.5 h of grazing. The experiment was a balanced randomized block design, replicated 3 times. The experimental factor was the bulk milk urea level, which was adjusted to levels of 15, 35, and 55 mg of urea per 100 g of milk, respectively, by changing the level of nitrogen fertilization of the pasture, the herbage mass and grass regrowth age, and the level and type of feed supplement. Ammonia emission from the barn was measured using sulfur hexafluoride as the tracer gas. Ammonia emission generally increased upon an increase in adjusted milk urea levels. A dynamic regression model was used to predict ammonia emission from bulk milk urea concentration, temperature, and a slurry mixing index. This model accounted for 66% of the total variance in ammonia emission and showed that emission increases exponentially with increasing milk urea concentration. At levels of 20 and 30 mg of urea per 100 g of milk, ammonia emission increased by about 2.5 and 3.5%, respectively, when milk urea concentration increased by 1 mg/100 g. Furthermore, emissions from the barn increased 2.6% when temperature increased by 1°C. The study showed that bulk milk urea concentration is a useful indicator for ammonia emissions from a dairy cow barn in a situation with restricted grazing.

Economic potential of individual variation in milk yield response to concentrate intake of dairy cows

The objectives of the current study were to quantify the individual variation in daily milk yield response to concentrate intake during early lactation and to assess the economic prospects of exploiting the individual variation in milk yield response to concentrate intake. In an observational study, data from 299 cows on four farms in the first 3 weeks of the lactation were collected. Individual response in daily milk yield to concentrate intake was analysed by a random coefficient model. Marked variation in individual milk yield response to concentrate intake was found on all four farms. An economic simulation was carried out, based on the estimated parameter values in the observational study. Individual optimization of concentrate supply is compared with conventional strategies for concentrate supply based on averaged population response parameters. Applying individual economic optimal settings for concentrate supply during early lactation, potential economic gain ranges from €0·20 to €2·03/cow/day.

Adaptive models for online estimation of individual milk yield response to concentrate intake and milking interval length of dairy cows

Automated feeding and milking of dairy cows enables the application of individual cow settings for concentrate supply and milking frequency. Currently, general settings are used, based on knowledge about energy and nutrient requirements in relation to milk production at the group level. Individual settings, based on the actual individual response in milk yield, have the potential for a marked increase in economic profits. In the present study, adaptive dynamic models for online estimation of milk yield response to concentrate intake and length of milking interval are evaluated. The parameters in these models may change over time and are updated through a Bayesian approach for online analysis of time series. The main use of dynamic models lies in their ability to determine economically optimal settings for concentrate intake and milking interval length for individual cows at any day in lactation. Three adaptive dynamic models are evaluated, a model with linear terms for concentrate intake and length of milking interval, a model that also comprises quadratic terms, and an enhanced model (EM) in order to obtain more stable parameter estimates. The linear model is useful only for forecasting milk production and the estimated parameters of the quadratic model were found to be unstable. The parsimony of the EM leads to far more stable parameter estimates. It is shown that the EM is suitable for control and monitoring, and therefore promises to be a valuable tool for application within precision livestock farming