• 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 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
    • 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.