• An Analysis of Abatement Potential of Greenhouse Gas Emissions in Irish Agriculture 2021-2030

      Lanigan, Gary; Donnellan, Trevor; Hanrahan, Kevin; Carsten, Paul; Shalloo, Laurence; Krol, Dominika; Forrestal, Patrick J.; Farrelly, Niall; O’Brien, Donal; Ryan, Mary; et al. (Teagasc, 2018-06-10)
      This report has been prepared by the Teagasc Working Group on GHG Emissions, which brings together and integrates the extensive and diverse range of organisational expertise on agricultural greenhouse gases. The previous Teagasc GHG MACC was published in 2012 in response to both the EU Climate and Energy Package and related Effort Sharing Decision and in the context of the establishment of the Food Harvest 2020 production targets.
    • Comparison of modelling techniques for milk-production forecasting

      Murphy, Michael D.; O’Mahony, Michéal J.; Shalloo, Laurence; French, Padraig; Upton, John (Elsevier for American Dairy Science Association, 2014-04-13)
      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.
    • Daily and seasonal trends of electricity and water use on pasture-based automatic milking dairy farms

      Shortall, John; O'Brien, Bernadette; Sleator, Roy D.; Upton, John; Teagasc Walsh Fellowship Programme; European Union; 2012015; SME-2012-2-314879 (Elsevier, 2017-11-15)
      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.
    • Daily and seasonal trends of electricity and water use on pasture-based automatic milking dairy farms

      Shortall, John; O'Brien, Bernadette; Sleator, Roy D.; Upton, John; Teagasc Walsh Fellowship programme; European Union; 2012015; SME-2012-2-314879 (American Dairy Science Association, 2017-11-15)
      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.
    • DSSED: Decision Support System for Energy use in Dairy Production

      Murphy, Michael D.; Shine, Philip; Breen, Michael; Upton, John (2021-11-22)
      The following report provides a comprehensive description of the background, implementation and dissemination of project RDD/00117. This project pertains to the development of an online portal for dairy farmers whereby the users of the portal will receive comprehensive information relating to energy use, electricity costs, carbon emissions, renewable energy and potential on-farm technology investments. With the rapid expansion of the Irish dairy industry resulting from the abolition of European Union milk quotas, the importance of decision support and information for dairy farmers has become extremely important. The first phase of the project involved monitoring the energy usage of 58 Irish dairy farms, and the subsequent development of a large database pertaining to energy usage on Irish dairy farms. In order to provide a detailed breakdown of energy use, electricity costs and carbon emissions on Irish dairy farms, a wide-ranging statistical analysis was carried out. The results of this analysis are available to farmers as part of the aforementioned portal, with a breakdown of mean energy consumption, cost and carbon emissions presented according to each energy consuming process on Irish dairy farms, as well as monthly trends relative to cow number and milk production. This statistical analysis is constantly updated due to continuous monitoring of 20 Irish dairy farms, with a dynamic information loop in operation between the farms in question and the statistical database. The second phase of the project involved the development of a dairy farm technology calculator which was included as part of the online portal. This tool provides a means for dairy farmers to input details of their current farm and calculates how investment in renewable and energy efficient technologies will affect their farm from economic, energy and environmental points of view. The technologies which may be analysed are plate coolers, variable speed drives (VSDs), heat recovery systems, solar water heating systems, solar photovoltaic (PV) systems and wind turbines. In addition, the technology calculator may be used as a tool for informing policy relating to incentivising the purchase of these technologies. It is anticipated that the online portal developed as part of this project will be used extensively in the future to assist farmers in making informed decisions pertaining to dairy farm energy, costs and carbon emissions. It can also be used by state bodies to aid them in policy related decisions.
    • Effect of different cleaning procedures on water use and bacterial levels in weaner pig pens

      Misra, Shilpi; van Middelaar, Corina E.; Jordan, Kieran; Upton, John; Quinn, Amy J.; de Boer, Imke J. M.; O’Driscoll, Keelin; Teagasc Walsh Scholarship; Teagasc Internal Funding; 2017147; et al. (Public Library of Science (PLoS), 2020-11-17)
      Pork is one of the most globally eaten meats and the pig production chain contributes significantly to the water footprint of livestock production. However, very little knowledge is available about the on-farm factors that influence freshwater use in the pig production chain. An experiment was conducted to quantify the effect of three different washing treatments on freshwater use, bacterial levels [(total bacterial counts; TBC), Enterobacteriaceae and Staphylococcus] and cleaning time in washing of pens for weaning pigs. Three weaner rooms were selected with each room having 10 pens and a capacity to hold up to 14 pigs each. Pigs were weaned and kept in the pens for 7 weeks. Finally, the pens were cleaned before the next batch of pigs moved in. The washing treatments used were power washing and disinfection (WASH); presoaking followed by power washing and disinfection (SOAK), and presoaking followed by detergent, power washing and disinfection (SOAK + DETER). A water meter was used to collect water use data and swab samples were taken to determine the bacterial levels. The results showed that there was no overall effect of washing treatments on water use. However, there was an effect of treatment on the washing time (p<0.01) with SOAK and SOAK+DETER reducing the washing time per pen by 2.3 minutes (14%) and 4.2 minutes (27%) compared to WASH. Nonetheless, there was an effect of sampling time (before or after washing) (p<0.001) on the levels of TBC and Staphylococcus, but no effect was seen on Enterobacteriaceae levels. Thus, the washing treatments used in this study had no effect on the water use of the pork production chain. Although there was no difference in both water use and bacterial load, from a producer perspective, presoaking and detergent use can save time and labour costs, so this would be the preferred option.
    • Effect of introducing weather parameters on the accuracy of milk production forecast models

      Zhang, Fan; Upton, John; Shalloo, Laurence; Shine, Philip; Murphy, Michael D. (Elsevier, 2019-04-13)
      The objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy.
    • Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

      Shine, Philip; Upton, John; Sefeedpari, Paria; Murphy, Michael D.; Sustainable Energy Authority of Ireland; 18/RDD/317 (MDPI AG, 2020-03-10)
      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
    • Energy demand on dairy farms in Ireland

      Upton, John; Humphreys, James; Groot Koerkamp, Peter W. G.; French, Padraig; Dillon, Pat; de Boer, Imke J. M.; INTERREG IVB North-West Europe (Elsevier, 2013-08-01)
      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.
    • A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration

      Upton, John; Murphy, Michael D.; Shalloo, Laurence; Groot Koerkamp, Peter W.G.; De Boer, Imke J.M.; INTERREG IVB North-West Europe (Elsevier, 2014-06-07)
      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 1 yr 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.
    • A method for assessing liner performance during the peak milk flow period

      Penry, J. F.; Upton, John; Leonardi, S.; Thompson, P. D.; Reinemann, D. J. (Elsevier, 2017-11-06)
      The objective of this study was to develop a method to quantify the milking conditions under which circulatory impairment of teat tissues occurs during the peak flow period of milking. A secondary objective was to quantify the effect of the same milking conditions on milk flow rate during the peak flow rate period of milking. Additionally, the observed milk flow rate was a necessary input to the calculation of canal area, our quantitative measure of circulatory impairment. A central composite experimental design was used with 5 levels of each of 2 explanatory variables (system vacuum and pulsator ratio), creating 9 treatments including the center point. Ten liners, representing a wide range of liner compression (as indicated by overpressure), were assessed, with treatments applied using a novel quarter-milking device. Eight cows (32 cow-quarters) were used across 10 separate evening milkings, with quarter being the experimental unit. The 9 treatments, with the exception of a repeated center point, were randomly applied to all quarters within each individual milking. Analysis was confined to the peak milk flow period. Milk flow rate (MFR) and teat canal cross sectional area (CA) were normalized by dividing individual MFR, or CA, values by their within-quarter average value across all treatments. A multiple explanatory variable regression model was developed for normalized MFR and normalized CA. The methods presented in this paper provided sufficient precision to estimate the effects of vacuum (both at teat-end and in the liner mouthpiece), pulsation, and liner compression on CA, as an indicator of teat-end congestion, during the peak flow period of milking. Liner compression (as indicated by overpressure), teat-end vacuum, vacuum in the liner mouthpiece, milk-phase time, and their interactions are all important predictors of MFR and teat-end congestion during the peak milk flow period of milking. Increasing teat-end vacuum and milk-phase time increases MFR and reduces CA (indicative of increased teat-end congestion). Increasing vacuum in the liner mouthpiece also acts to reduce CA and MFR. Increasing liner compression reduces the effects of teat-end congestion, resulting in increased MFR and increased CA at high levels of teat-end vacuum and milk-phase time. These results provide a better understanding of the balance between milking speed and milking gentleness.
    • Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

      Shine, Philip; Scully, Ted; Upton, John; Murphy, Michael D.; Institutes of Technology Ireland; Department of Agriculture, Food and the Marine; Sustainable Energy Authority of Ireland (Elsevier, 2018-03-05)
      An analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises.
    • Short communication: Effects of changing teatcup removal and vacuum settings on milking efficiency of an automatic milking system

      Upton, John; Bolona, P. Silva; Reinemann, D. J.; Teagasc Wash Fellowship Programme; University of Wisconsin-Madison; Lely, The Netherlands (Elsevier, 2019-08-22)
      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.