Reducing electricity consumption in Irish milk production is a topical issue for 2 reasons. First, the introduction of a dynamic electricity pricing system, with peak and off-peak prices, will be a reality for 80% of electricity consumers by 2020. The proposed pricing schedule intends to discourage energy consumption during peak periods (i.e., when electricity demand on the national grid is high) and to incentivize energy consumption during off-peak periods. If farmers, for example, carry out their evening milking during the peak period, energy costs may increase, which would affect farm profitability. Second, electricity consumption is identified in contributing to about 25% of energy use along the life cycle of pasture-based milk. The objectives of this study, therefore, were to document electricity use per kilogram of milk sold and to identify strategies that reduce its overall use while maximizing its use in off-peak periods (currently from 0000 to 0900 h). We assessed, therefore, average daily and seasonal trends in electricity consumption on 22 Irish dairy farms, through detailed auditing of electricity-consuming processes. To determine the potential of identified strategies to save energy, we also assessed total energy use of Irish milk, which is the sum of the direct (i.e., energy use on farm) and indirect energy use (i.e., energy needed to produce farm inputs). On average, a total of 31.73 MJ was required to produce 1 kg of milk solids, of which 20% was direct and 80% was indirect energy use. Electricity accounted for 60% of the direct energy use, and mainly resulted from milk cooling (31%), water heating (23%), and milking (20%). Analysis of trends in electricity consumption revealed that 62% of daily electricity was used at peak periods. Electricity use on Irish dairy farms, therefore, is substantial and centered around milk harvesting. To improve the competitiveness of milk production in a dynamic electricity pricing environment, therefore, management changes and technologies are required that decouple energy use during milking processes from peak periods.

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The primary objective of this experiment was to assess the effect of mouthpiece chamber vacuum on teat-end congestion. The secondary objective was to assess the interactive effects of mouthpiece chamber vacuum with teat-end vacuum and pulsation setting on teat-end congestion. The influence of system vacuum, pulsation settings, mouthpiece chamber vacuum, and teat-end vacuum on teat-end congestion were tested in a 2×2 factorial design. The low-risk conditions for teat-end congestion (TEL) were 40 kPa system vacuum (Vs) and 400-ms pulsation b-phase. The high-risk conditions for teat-end congestion (TEH) were 49 kPa Vs and 700-ms b-phase. The low-risk condition for teat-barrel congestion (TBL) was created by venting the liner mouthpiece chamber to atmosphere. In the high-risk condition for teat-barrel congestion (TBH) the mouthpiece chamber was connected to short milk tube vacuum. Eight cows (32 quarters) were used in the experiment conducted during 0400 h milkings. All cows received all treatments over the entire experimental period. Teatcups were removed after 150 s for all treatments to standardize the exposure period. Calculated teat canal cross-sectional area (CA) was used to assess congestion of teat tissue. The main effect of the teat-end treatment was a reduction in CA of 9.9% between TEL and TEH conditions, for both levels of teat-barrel congestion risk. The main effect of the teat-barrel treatment was remarkably similar, with a decrease of 9.7% in CA between TBL and TBH conditions for both levels of teat-end congestion risk. No interaction between treatments was detected, hence the main effects are additive. The most aggressive of the 4 treatment combinations (TEH plus TBH) had a CA estimate 20% smaller than for the most gentle treatment combination (TEL plus TBL). The conditions designed to impair circulation in the teat barrel also had a deleterious effect on circulation at the teat end. This experiment highlights the importance of elevated mouthpiece chamber vacuum on teat-end congestion and resultant decreases in CA.

No abstract

The objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤ 12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%)=8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%)=12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%)=10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions.