J. Upton

Energy demand on dairy farms in Ireland

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.

Comparison of modelling techniques for milk-production forecasting

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.

A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration

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.

Effect of pulsation rest phase duration on teat end congestion

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.

Investment appraisal of technology innovations on dairy farm electricity consumption

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.

Estimating teat canal cross-sectional area to determine the effects of teat-end and mouthpiece chamber vacuum on teat congestion

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.

Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms

Abstract This study analysed the performance of a range of machine learning algorithms when applied to the prediction of electricity and on-farm direct water consumption on Irish dairy farms. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, commercial Irish dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed for their predictive power of monthly electricity and water consumption, respectively. These variables were related to milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions. A CART decision tree algorithm, a random forest ensemble algorithm, an artificial neural network and a support vector machine algorithm were used to predict both water and electricity consumption. The methodology employed backward sequential variable selection to exclude variables, which added little predictive power. It also applied hyper-parameter tuning with nested cross-validation for calculating the prediction accuracy for each model on unseen data (data not utilised for model development). Electricity consumption was predicted to within 12% (relative prediction error (RPE)) using a support vector machine, while the random forest predicted water consumption to within 38%. Overall, the developed machine-learning models improved the RPE of electricity and water consumption by 54% and 23%, respectively, when compared to results previously obtained using a multiple linear regression approach. Further analysis found that during the January, February, November and December period, the support vector machine overpredicted electricity consumption by 4% (mean percentage error (MPE)) and water consumption by 21% (MPE), on average. However, overprediction was greatly reduced during the March – October period with overprediction of electricity consumption reduced to 1% while the overprediction of water consumption reduced to 8%. This was attributed to a phase shift between farms, where some farms produce milk all year round, some dry off earlier/later than others and some farms begin milking earlier/later resulting in an increased the coefficient of variance of milk production making it more difficult to model electricity and water accurately. Concurrently, large negative correlations were calculated between the number of dairy cows and absolute prediction error for electricity and water, respectively, suggesting improvements in electricity and water prediction accuracy may be achieved with increasing dairy cow numbers. The developed machine learning models may be utilised to provide key decision support information to both dairy farmers and policy makers or as a tool for conducting macro scale environmental analysis.

Assessing liner performance using on-farm milk meters

The primary objective of this study was to quantify and compare the interactive effects of liner compression, milking vacuum level, and pulsation settings on average milk flow rates for liners representing the range of liner compression of commercial liners. A secondary objective was to evaluate a methodology for assessing liner performance that can be applied on commercial dairy farms. Eight different liner types were assessed using 9 different combinations of milking system vacuum and pulsation settings applied to a herd of 80 cows with vacuum and pulsation conditions changed daily for 36d using a central composite experimental design. Liner response surfaces were created for explanatory variables milking system vacuum (Vsystem) and pulsator ratio (PR) and response variable average milk flow rate (AMF=total yield/total cups-on time) expressed as a fraction of the within-cow average flow rate for all treatments (average milk flow rate fraction, AMFf). Response surfaces were also created for between-liner comparisons for standardized conditions of claw vacuum and milk ratio (fraction of pulsation cycle during which milk is flowing). The highest AMFf was observed at the highest levels of Vsystem, PR, and overpressure. All liners showed an increase in AMF as milking conditions were changed from low to high standardized conditions of claw vacuum and milk ratio. Differences in AMF between liners were smallest at the most gentle milking conditions (low Vsystem and low milk ratio), and these between-liner differences in AMF increased as liner overpressure increased. Differences were noted with vacuum drop between Vsystem and claw vacuum depending on the liner venting system, with short milk tube vented liners having the greater vacuum drop than mouthpiece chamber vented liners. The accuracy of liner performance assessment in commercial parlors fitted with milk meters can be improved by using a central composite experimental design with a repeated center point treatment, rotating different clusters to different stalls (milk meters), and adjusting performance estimates for similar claw vacuum and pulsation conditions.

A load shifting controller for Cold Thermal Energy Storage systems

A method for controlling the generation of ice for Cold Thermal Energy Storage (CTES) is presented in this paper. A load shifting controller is developed to find the optimum trajectory for CTES in a dynamic environment. The variation in ice building efficiency due to the nonlinear relationship between changing ambient air temperature and ice charge level is accommodated by employing dynamic programming in a three dimensional space. The combined system learns from its errors and adapts during simulation. The optimum ice building trajectory is progressively updated as new information comes online. The energy saving performance of the controller is assessed using a simple optimization index.

Daily and seasonal trends of electricity and water use on pasture-based automatic milking dairy farms

The objective of this study was to identify the major electricity and water-consuming components of a pasture-based automatic milking (AM) system and to establish the daily and seasonal consumption trends. Electricity and water meters were installed on 7 seasonal calving pasture-based AM farms across Ireland. Electricity-consuming processes and equipment that were metered for consumption included milk cooling components, air compressors, AM unit(s), auxiliary water heaters, water pumps, lights, sockets, automatic manure scrapers, and so on. On-farm direct water-consuming processes and equipment were metered and included AM unit(s), auxiliary water heaters, tubular coolers, wash-down water pumps, livestock drinking water supply, and miscellaneous water taps. Data were collected and analyzed for the 12-mo period of 2015. The average AM farm examined had 114 cows, milking with 1.85 robots, performing a total of 105 milkings/AM unit per day. Total electricity consumption and costs were 62.6 Wh/L of milk produced and 0.91 cents/L, respectively. Milking (vacuum and milk pumping, within-AM unit water heating) had the largest electrical consumption at 33%, followed by air compressing (26%), milk cooling (18%), auxiliary water heating (8%), water pumping (4%), and other electricity-consuming processes (11%). Electricity costs followed a similar trend to that of consumption, with the milking process and water pumping accounting for the highest and lowest cost, respectively. The pattern of daily electricity consumption was similar across the lactation periods, with peak consumption occurring at 0100, 0800, and between 1300 and 1600 h. The trends in seasonal electricity consumption followed the seasonal milk production curve. Total water consumption was 3.7 L of water/L of milk produced. Water consumption associated with the dairy herd at the milking shed represented 42% of total water consumed on the farm. Daily water consumption trends indicated consumption to be lowest in the early morning period (0300-0600 h), followed by spikes in consumption between 1100 and 1400 h. Seasonal water trends followed the seasonal milk production curve, except for the month of May, when water consumption was reduced due to above-average rainfall. This study provides a useful insight into the consumption of electricity and water on a pasture-based AM farms, while also facilitating the development of future strategies and technologies likely to increase the sustainability of AM systems.