The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg⁻¹ Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg⁻¹ Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices.

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.

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.

The aim of this study was to conduct an investment appraisal for milk-cooling, water-heating, and milk-harvesting technologies on a range of farm sizes in 2 different electricity-pricing environments. This was achieved by using a model for electricity consumption on dairy farms. The model simulated the effect of 6 technology investment scenarios on the electricity consumption and electricity costs of the 3 largest electricity-consuming systems within the dairy farm (i.e., milk-cooling, water-heating, and milking machine systems). The technology investment scenarios were direct expansion milk-cooling, ice bank milk-cooling, milk precooling, solar water-heating, and variable speed drive vacuum pump-milking systems. A dairy farm profitability calculator was combined with the electricity consumption model to assess the effect of each investment scenario on the total discounted net income over a 10-yr period subsequent to the investment taking place. Included in the calculation were the initial investments, which were depreciated to zero over the 10-yr period. The return on additional investment for 5 investment scenarios compared with a base scenario was computed as the investment appraisal metric. The results of this study showed that the highest return on investment figures were realized by using a direct expansion milk-cooling system with precooling of milk to 15°C with water before milk entry to the storage tank, heating water with an electrical water-heating system, and using standard vacuum pump control on the milking system. Return on investment figures did not exceed the suggested hurdle rate of 10% for any of the ice bank scenarios, making the ice bank system reliant on a grant aid framework to reduce the initial capital investment and improve the return on investment. The solar water-heating and variable speed drive vacuum pump scenarios failed to produce positive return on investment figures on any of the 3 farm sizes considered on either the day and night tariff or the flat tariff, even when the technology costs were reduced by 40% in a sensitivity analysis of technology costs.

The objective of this study was to quantify the effect of d-phase (rest phase) duration of pulsation on the teat canal cross-sectional area during the period of peak milk flow from bovine teats. A secondary objective was to test if the effect of d-phase duration on teat canal cross-sectional area was influenced by milking system vacuum level, milking phase (b-phase) duration, and liner overpressure. During the d-phase of the pulsation cycle, liner compression facilitates venous flow and removal of fluids accumulated in teat-end tissues. It was hypothesized that a short-duration d-phase would result in congestion of teat-end tissue and a corresponding reduction in the cross-sectional area of the teat canal. A quarter milking device, designed and built at the Milking Research and Instruction Laboratory at the University of Wisconsin-Madison, was used to implement an experiment to test this hypothesis. Pulsator rate and ratios were adjusted to achieve 7 levels of d-phase duration: 50, 100, 150, 175, 200, 250, and 300ms. These 7 d-phase durations were applied during one milking session and were repeated for 2 vacuum levels (40 and 50kPa), 2 milking phase durations (575 and 775ms), and 2 levels of liner overpressure (9.8 and 18kPa). We observed a significant reduction in the estimated cross-sectional area of the teat canal with d-phase durations of 50 and 100ms when compared with d-phase durations of 150, 175, 225, 250, and 300ms. No significant difference was found in the estimated cross-sectional area of the teat canal for d-phase durations from 150 to 300ms. No significant interaction was observed between the effect of d-phase and b-phase durations, vacuum level, or liner overpressure.

Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff.