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The objective of this paper was to quantify the economic and environmental effects of changing a dairy farm’s milking start times. Changing morning and evening milking start times could reduce both electricity costs and farm electricity related CO2 emissions. However, this may also involve altering farmer routines which are based on practical considerations. Hence, these changes need to be quantified both in terms of profit/emissions and in terms of how far these milking start times deviate from normal operations. The method presented in this paper optimized the combination of dairy farm infrastructure setup and morning and evening milking start times, based on a weighting variable (α) which assigned relative importance to labor utilization, farm net profit and farm electricity related CO2 emissions. Multi-objective optimization was utilized to assess trade-offs between labor utilization and net profit, as well as labor utilization and electricity related CO2 emissions. For a case study involving a 195 cow Irish dairy farm, when the relative importance of maximizing farm net profit or minimizing farm electricity related CO2 emissions was high, the least common milking start times (06:00 and 20:00) were selected. When the relative importance of labor utilization was high, the most common milking start times (07:00 and 17:00) were selected. The 195 cow farm saved €137 per annum when milking start times were changed from the most common to the least common. Reductions in electricity related CO2 emissions were also seen when the milking start times were changed from most common to least common. However, this reduction in emissions was primarily due to the addition of efficient and renewable technology to the farm. It was deduced that the monetary and environmental benefits of altering farmer milking routines were unlikely to change normal farm operating procedures.
Michael Breen; Michael Murphy; John Upton. Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance. AgriEngineering 2021, 3, 266 -277.
AMA StyleMichael Breen, Michael Murphy, John Upton. Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance. AgriEngineering. 2021; 3 (2):266-277.
Chicago/Turabian StyleMichael Breen; Michael Murphy; John Upton. 2021. "Assessing the Effect of Modifying Milking Routines on Dairy Farm Economic and Environmental Performance." AgriEngineering 3, no. 2: 266-277.
The production of milk must be balanced with the sustainable consumption of water resources to ensure the future sustainability of the global dairy industry. Thus, this review article aimed to collate and summarize the literature in the dairy water-usage domain. While green water use (e.g., rainfall) was found to be largest category of water use on both stall and pasture-based dairy farms, on-farm blue water (i.e., freshwater) may be much more susceptible to local water shortages due to the nature of its localized supply through rivers, lakes, or groundwater aquifers. Research related to freshwater use on dairy farms has focused on monitoring, modeling, and analyzing the parlor water use and free water intake of dairy cows. Parlor water use depends upon factors related to milk precooling, farm size, milking systems, farming systems, and washing practices. Dry matter intake is a prominent variable in explaining free water intake variability; however, due to the unavailability of accurate data, some studies have reported moving away from dry matter intake at the expense of prediction accuracy. Machine-learning algorithms have been shown to improve dairy water-prediction accuracy by 23%, which may allow for coarse model inputs without reducing accuracy. Accurate models of on-farm water use allow for an increased number of dairy farms to be used in water footprinting studies, as the need for physical metering equipment is mitigated.
Philip Shine; Michael Murphy; John Upton. A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming. Sustainability 2020, 12, 7201 .
AMA StylePhilip Shine, Michael Murphy, John Upton. A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming. Sustainability. 2020; 12 (17):7201.
Chicago/Turabian StylePhilip Shine; Michael Murphy; John Upton. 2020. "A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming." Sustainability 12, no. 17: 7201.
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.
Philip Shine; John Upton; Paria Sefeedpari; Michael D. Murphy. Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies 2020, 13, 1288 .
AMA StylePhilip Shine, John Upton, Paria Sefeedpari, Michael D. Murphy. Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies. 2020; 13 (5):1288.
Chicago/Turabian StylePhilip Shine; John Upton; Paria Sefeedpari; Michael D. Murphy. 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses." Energies 13, no. 5: 1288.
The aim of this experiment was to assess strategies to reduce milking time in a pasture-based automatic milking system (AMS). Milking time is an important factor in automatic milking because any reductions in box time can facilitate more milkings per day and hence higher production levels per AMS. This study evaluated 2 end-of-milking criteria treatments (teatcup removal at 30% and 50% of average milk flowrate at the quarter-level), 2 milking system vacuum treatments (static and dynamic, where the milking system vacuum could change during the peak milk flowrate period), and the interaction of these treatment effects on milking time in a Lely Astronaut A4 AMS (Maassluis, the Netherlands). The experiment was carried out at the research facility at Teagasc Moorepark, Cork, Ireland, and used 77 spring-calved cows, which were managed on a grass-based system. Cows were 179 DIM, with an average parity of 3. No significant differences in milk flowrate, milk yield, box time, milking time, or milking interval were found between treatments in this study on cows milked in an AMS on a pasture-based system. Average and peak milk flowrates of 2.15 kg/min and 3.48 kg/min, respectively, were observed during the experiment. Small increases in maximum milk flowrate were detected (+0.09 kg/min) due to the effect of increasing the system vacuum during the peak milk flow period. These small increases in maximum milk flowrate were not sufficient to deliver a significant reduction in milking time or box time. Furthermore, increasing the removal setting from 30% of the average milk flowrate to 50% of the average milk flowrate was not an effective means of reducing box time, because the resultant increase in removal flowrate of 0.12 kg/min was not enough to deliver practical or statistically significant decreases in milking time or box time. Hence, to make significant reductions in milking time, where cows have an average milk flow of 2 kg/min and yield per milking of 10 kg, end-of-milking criteria above 50% of average milk flowrate at the quarter level would be required.
J. Upton; P. Silva Bolona; Douglas J. Reinemann. Short communication: Effects of changing teatcup removal and vacuum settings on milking efficiency of an automatic milking system. Journal of Dairy Science 2019, 102, 10500 -10505.
AMA StyleJ. Upton, P. Silva Bolona, Douglas J. Reinemann. Short communication: Effects of changing teatcup removal and vacuum settings on milking efficiency of an automatic milking system. Journal of Dairy Science. 2019; 102 (11):10500-10505.
Chicago/Turabian StyleJ. Upton; P. Silva Bolona; Douglas J. Reinemann. 2019. "Short communication: Effects of changing teatcup removal and vacuum settings on milking efficiency of an automatic milking system." Journal of Dairy Science 102, no. 11: 10500-10505.
The aim of this paper was to develop a Dairy Multi-Objective Optimization (DAIRYMOO) method to carry out multi-objective optimization of dairy farm equipment, management practices and electricity tariffs, optimizing based on a user specified economic and environmental weighting factor. Models of both solar thermal water heating and heat recovery systems were developed, validated, and used as part of a test case for DAIRYMOO. Optimizing dairy farm equipment, management practices and electricity tariffs both economically and environmentally is necessary because of the competing goals of increasing farm milk production and the necessity to reduce agricultural related greenhouse gas emissions. Models of solar thermal water heating and heat recovery systems were created using experimental data to evaluate the economic and environmental performance of these technologies. Multi-objective optimization was used to obtain the optimal selection of equipment, management practices and electricity tariffs which impact electricity related costs and CO2 emissions, based on a user specified economic and environmental weighting factor. A Genetic Algorithm was employed to maximize a combined objective function based on this weighting factor. For a test case with a 195 cow farm, over a ten year time horizon the optimal equipment, management and electricity tariff combination was found using 11 different weighting factor values ranging from 0 to 1. The combined objective function was gradually weighted towards the environmental criterion and away from the economic criterion. It was found that the optimal equipment combination changed incrementally relative to the change in the weighting factor. Furthermore, an analysis was carried out which performed the same multi-objective optimization on the same 195 cow farm, but with a mandatory real time pricing tariff in place. The optimal configurations using the mandatory real time pricing tariff showed that gas water heating was selected regardless of the weighting factor employed. This differed from the results with no mandatory tariff in which a day/night tariff was optimal for all weighting factor values, and electric water heating was optimal for weighting factor values of 0.9 or higher i.e. when the combined objective function was weighted heavily towards the economic criterion. For both analyses, heat recovery systems were not included in the optimal farm configuration unless the weighting factor was 0.2 or less, indicating that the economic performance of these systems was poor. Solar thermal water heating systems were not included in the optimal farm configuration regardless of the weighting factor value. The DAIRYMOO method described in this study will provide useful advice to farmers and policy makers relating to economic and environmental optimization around equipment, management and electricity tariff choices on dairy farms.
M. Breen; Michael D. Murphy; J. Upton. Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms. Applied Energy 2019, 242, 1697 -1711.
AMA StyleM. Breen, Michael D. Murphy, J. Upton. Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms. Applied Energy. 2019; 242 ():1697-1711.
Chicago/Turabian StyleM. Breen; Michael D. Murphy; J. Upton. 2019. "Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms." Applied Energy 242, no. : 1697-1711.
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.
J. Upton; J.F. Penry; M.D. Rasmussen; P.D. Thompson; D.J. Reinemann. Effect of pulsation rest phase duration on teat end congestion. Journal of Dairy Science 2016, 99, 3958 -3965.
AMA StyleJ. Upton, J.F. Penry, M.D. Rasmussen, P.D. Thompson, D.J. Reinemann. Effect of pulsation rest phase duration on teat end congestion. Journal of Dairy Science. 2016; 99 (5):3958-3965.
Chicago/Turabian StyleJ. Upton; J.F. Penry; M.D. Rasmussen; P.D. Thompson; D.J. Reinemann. 2016. "Effect of pulsation rest phase duration on teat end congestion." Journal of Dairy Science 99, no. 5: 3958-3965.
Michael D. Murphy; John Upton; Michael J. O'mahony. Corrigendum to “Rapid milk cooling control with varying water and energy consumption” [Biosyst Eng 116 (2013) 15–22]. Biosystems Engineering 2016, 143, 128 .
AMA StyleMichael D. Murphy, John Upton, Michael J. O'mahony. Corrigendum to “Rapid milk cooling control with varying water and energy consumption” [Biosyst Eng 116 (2013) 15–22]. Biosystems Engineering. 2016; 143 ():128.
Chicago/Turabian StyleMichael D. Murphy; John Upton; Michael J. O'mahony. 2016. "Corrigendum to “Rapid milk cooling control with varying water and energy consumption” [Biosyst Eng 116 (2013) 15–22]." Biosystems Engineering 143, no. : 128.
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.
J. Upton; Michael D. Murphy; Imke De Boer; P.W.G. Groot Koerkamp; P.B.M. Berentsen; L. Shalloo. Investment appraisal of technology innovations on dairy farm electricity consumption. Journal of Dairy Science 2015, 98, 898 -909.
AMA StyleJ. Upton, Michael D. Murphy, Imke De Boer, P.W.G. Groot Koerkamp, P.B.M. Berentsen, L. Shalloo. Investment appraisal of technology innovations on dairy farm electricity consumption. Journal of Dairy Science. 2015; 98 (2):898-909.
Chicago/Turabian StyleJ. Upton; Michael D. Murphy; Imke De Boer; P.W.G. Groot Koerkamp; P.B.M. Berentsen; L. Shalloo. 2015. "Investment appraisal of technology innovations on dairy farm electricity consumption." Journal of Dairy Science 98, no. 2: 898-909.
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
J. Upton; Michael D. Murphy; L. Shalloo; P.W.G. Groot Koerkamp; Imke De Boer. A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration. Journal of Dairy Science 2014, 97, 4973 -4984.
AMA StyleJ. Upton, Michael D. Murphy, L. Shalloo, P.W.G. Groot Koerkamp, Imke De Boer. A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration. Journal of Dairy Science. 2014; 97 (8):4973-4984.
Chicago/Turabian StyleJ. Upton; Michael D. Murphy; L. Shalloo; P.W.G. Groot Koerkamp; Imke De Boer. 2014. "A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration." Journal of Dairy Science 97, no. 8: 4973-4984.
Michael D. Murphy; M.J. O’Mahony; L. Shalloo; P. French; J. Upton. Comparison of modelling techniques for milk-production forecasting. Journal of Dairy Science 2014, 97, 3352 -3363.
AMA StyleMichael D. Murphy, M.J. O’Mahony, L. Shalloo, P. French, J. Upton. Comparison of modelling techniques for milk-production forecasting. Journal of Dairy Science. 2014; 97 (6):3352-3363.
Chicago/Turabian StyleMichael D. Murphy; M.J. O’Mahony; L. Shalloo; P. French; J. Upton. 2014. "Comparison of modelling techniques for milk-production forecasting." Journal of Dairy Science 97, no. 6: 3352-3363.
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 0900h). 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 1kg 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
J. Upton; J. Humphreys; P.W.G. Groot Koerkamp; P. French; P. Dillon; I.J.M. De Boer. Energy demand on dairy farms in Ireland. Journal of Dairy Science 2013, 96, 6489 -6498.
AMA StyleJ. Upton, J. Humphreys, P.W.G. Groot Koerkamp, P. French, P. Dillon, I.J.M. De Boer. Energy demand on dairy farms in Ireland. Journal of Dairy Science. 2013; 96 (10):6489-6498.
Chicago/Turabian StyleJ. Upton; J. Humphreys; P.W.G. Groot Koerkamp; P. French; P. Dillon; I.J.M. De Boer. 2013. "Energy demand on dairy farms in Ireland." Journal of Dairy Science 96, no. 10: 6489-6498.