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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 main purpose of this study was to evaluate the use of an integrated life cycle assessment (LCA), artificial neural network, and metaheuristic optimization model to improve the sustainability of tomato-based cropping systems in Iran. The model outputs the combination of input usage in a tomato cropping system, which leads to the highest economic output and the least environmental impact. The LCA inventory was created using data from 114 open-field tomato farms in the Alborz Province of Iran during one growing period in 2015. Among all management components, the main focus was on irrigation management systems. The optimization problem was designed by integrating three indicators: carbon footprint (CF), benefit-cost ratio (BCR), and energy use efficiency (EUE) as the objective of field tomato production. The functional unit was 1 kg of tomato aligned with the system boundary of the cradle to market life cycle. Three artificial neural networks (ANNs) were applied to model relationships between the inputs and three indices (CF, BCR, and EUE) as the objective functions. Multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) were used to minimize the CF and maximize the BCR and EUE indicators. The abovementioned aims have been pursued by developing codes in MATLAB software. CF, BCR, and EUE were calculated to be 0.26 kg CO2−eq (kg tomato)−1, 1.8, and 0.5, respectively. MOGA results envisage the possibility of an increase of 86% and 50% in the EUE and BCR and a 43% reduction in the CF of tomato production systems. Moreover, EUE and BCR increased by 83% and 49%, and CF was reduced by 39% from the optimum results obtained from the MOPSO algorithm. It was revealed that in order to optimize field tomato production with the target objectives of this study, a large additional use for irrigation pipes, plastic, and machinery in comparison to current situation is required, while a large reduction of biocide, chemical fertilizer, and electricity consumption is indispensable. According to the results of our study, it was concluded that the optimal solutions require a modernization of irrigation systems and a decrease in the consumption of chemical fertilizers and pesticides. The implementation of management options for such solutions is discussed.
Seyyed Hassan Pishgar-Komleh; Asadollah Akram; Alireza Keyhani; Paria Sefeedpari; Philip Shine; Miguel Brandao. Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran. The International Journal of Life Cycle Assessment 2019, 25, 620 -632.
AMA StyleSeyyed Hassan Pishgar-Komleh, Asadollah Akram, Alireza Keyhani, Paria Sefeedpari, Philip Shine, Miguel Brandao. Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran. The International Journal of Life Cycle Assessment. 2019; 25 (3):620-632.
Chicago/Turabian StyleSeyyed Hassan Pishgar-Komleh; Asadollah Akram; Alireza Keyhani; Paria Sefeedpari; Philip Shine; Miguel Brandao. 2019. "Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran." The International Journal of Life Cycle Assessment 25, no. 3: 620-632.
Improper management of livestock manure has resulted in loss of nutrients and organic matter available in manure in addition to negative environmental impacts. This study developed and compared eight manure management scenarios across their entire life cycles, rom excretion to transport to land, considering technical, environmental and economic aspects. The scenarios based on combinations of collection, sand separation, solid/liquid (S/L) separation, anaerobic digestion (AD), composting, and storage were compared. Mass balances, costs and benefits and greenhouse emissions were evaluated. The model framework was tested and validated for a large-scale dairy farm with 9000 heads of cattle and daily manure production of approximately 505 t in Iran. The study indicated that sand separation and S/L separation did not contribute to a change in manure nutrients or emissions but reduced sand, maintenance cost, and transport requirements. AD followed by separation achieved the highest emission reduction (27.7 kg CO2eq t−1) due to the avoided emissions from replacing fossil fuels by renewable energy. Composting method had the lowest costs; however it resulted in a low nutrient recovery efficiency and high nitrous oxide emission. The assessment revealed that AD is a promising management option yielding a high potential greenhouse gas savings, nutrients recovery and nitrogen availability in fertilizer for plants. In spite of the high investment costs of AD, it could be a profitable strategy due to the high subsidies paid to renewable energy projects in Iran. In conclusion, this study showed that the choice of manure treatment method has a strong influence on nutrients, profitability and greenhouse gas balances by performing sensitivity analysis. The results of this study and the application of this model further indicate the need to consider various significant impacts, farm specifications and local conditions to decide the best manure management options.
Paria Sefeedpari; Theun Vellinga; Shahin Rafiee; Mohammad Sharifi; Philip Shine; Seyyed Hassan Pishgar-Komleh. Technical, environmental and cost-benefit assessment of manure management chain: A case study of large scale dairy farming. Journal of Cleaner Production 2019, 233, 857 -868.
AMA StyleParia Sefeedpari, Theun Vellinga, Shahin Rafiee, Mohammad Sharifi, Philip Shine, Seyyed Hassan Pishgar-Komleh. Technical, environmental and cost-benefit assessment of manure management chain: A case study of large scale dairy farming. Journal of Cleaner Production. 2019; 233 ():857-868.
Chicago/Turabian StyleParia Sefeedpari; Theun Vellinga; Shahin Rafiee; Mohammad Sharifi; Philip Shine; Seyyed Hassan Pishgar-Komleh. 2019. "Technical, environmental and cost-benefit assessment of manure management chain: A case study of large scale dairy farming." Journal of Cleaner Production 233, no. : 857-868.
This study utilised a previously developed support vector machine (SVM) (trained using empirical data from 56 dairy farms) for predicting and analysing annual dairy farm electricity consumption to help improve the sustainability of the projected expansion of milk production in Ireland. Firstly, the capability of the SVM to predict annual electricity consumption was investigated at both a farm and catchment-level (combined consumption). Electricity consumption data were attained from 16 pasture-based, Irish dairy farms between June 2016 and May 2017 in conjunction with farm data related to herd size, milk production, infrastructural equipment and managerial tendencies, required to generate predictions using the SVM. The SVM predicted annual electricity consumption of dairy farms to within 10.4% (relative prediction error). Concurrently, catchment-level electricity consumption was predicted with an error value less than 5.0%. Secondly, an investigation was carried out to assess the impact of increasing herd size and milk production on dairy farm related electricity consumption at a catchment-level across ten hypothetical infrastructural scenarios. The dairy expansion analysis showed electricity economies of scale across all ten infrastructural scenarios. The greatest reduction in electricity consumption per litre was observed when all farms employed ground water for pre-cooling milk with two additional parlour units, reducing by 4% in 2018, relative to a base scenario (no change to infrastructural equipment). The results presented in this article demonstrate the potential effectiveness of the SVM as a macro-level simulation forecast tool for dairy farm electricity consumption that may be used to quantify the impact of milk production on electricity resources, or to offer decision support to dairy farmers.
Philip Shine; T. Scully; J. Upton; M.D. Murphy. Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine. Applied Energy 2019, 250, 1110 -1119.
AMA StylePhilip Shine, T. Scully, J. Upton, M.D. Murphy. Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine. Applied Energy. 2019; 250 ():1110-1119.
Chicago/Turabian StylePhilip Shine; T. Scully; J. Upton; M.D. Murphy. 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine." Applied Energy 250, no. : 1110-1119.
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.
Fan Zhang; John Upton; Laurence Shalloo; Philip Shine; Michael D. Murphy. Effect of introducing weather parameters on the accuracy of milk production forecast models. Information Processing in Agriculture 2019, 7, 120 -138.
AMA StyleFan Zhang, John Upton, Laurence Shalloo, Philip Shine, Michael D. Murphy. Effect of introducing weather parameters on the accuracy of milk production forecast models. Information Processing in Agriculture. 2019; 7 (1):120-138.
Chicago/Turabian StyleFan Zhang; John Upton; Laurence Shalloo; Philip Shine; Michael D. Murphy. 2019. "Effect of introducing weather parameters on the accuracy of milk production forecast models." Information Processing in Agriculture 7, no. 1: 120-138.
Philip Shine; Michael Breen; John Upton; Adam O’Donovan; Michael D. Murphy. A decision support and optimization platform for energy technology investments on dairy farms. 2019 Boston, Massachusetts July 7- July 10, 2019 2019, 1 .
AMA StylePhilip Shine, Michael Breen, John Upton, Adam O’Donovan, Michael D. Murphy. A decision support and optimization platform for energy technology investments on dairy farms. 2019 Boston, Massachusetts July 7- July 10, 2019. 2019; ():1.
Chicago/Turabian StylePhilip Shine; Michael Breen; John Upton; Adam O’Donovan; Michael D. Murphy. 2019. "A decision support and optimization platform for energy technology investments on dairy farms." 2019 Boston, Massachusetts July 7- July 10, 2019 , no. : 1.
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.
Philip Shine; M.D. Murphy; J. Upton; T. Scully. Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture 2018, 150, 74 -87.
AMA StylePhilip Shine, M.D. Murphy, J. Upton, T. Scully. Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture. 2018; 150 ():74-87.
Chicago/Turabian StylePhilip Shine; M.D. Murphy; J. Upton; T. Scully. 2018. "Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms." Computers and Electronics in Agriculture 150, no. : 74-87.
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.
Philip Shine; T. Scully; J. Upton; M.D. Murphy. Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture 2018, 148, 337 -346.
AMA StylePhilip Shine, T. Scully, J. Upton, M.D. Murphy. Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture. 2018; 148 ():337-346.
Chicago/Turabian StylePhilip Shine; T. Scully; J. Upton; M.D. Murphy. 2018. "Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms." Computers and Electronics in Agriculture 148, no. : 337-346.
This study compared multiple linear regression (MLR) and support vector machine (SVM) models for predicting the annual electricity consumption of 20 Irish dairy farms, at a farm and catchment (combined) level. Model input variables were constrained to milk production, stock numbers, infrastructural equipment and managerial procedures to allow predictions to take place on a large scale without the use of specialized equipment. The SVM model has previously been shown to reduce the prediction error of monthly electricity consumption by 54% compared to the MLR model. Results found both the MLR and SVM models predicted annual electricity consumption per farm to within 20%. However, the error of the SVM model reduced to 9% when two farms with the greatest monthly prediction errors were removed. With herd sizes in excess of 190 dairy cows, these two farms were found to represent less than 3.3% of the Irish dairy farm demographic. Regarding the ability of each model to predict catchment level electricity consumption, the MLR model prediction resulted in an error of 4% while the SVM prediction resulted in an error of 9%. The improved accuracy of the MLR model when predicting electricity consumption at a catchment level was respective of a greater balance between the under and over prediction of electricity consumption across the 20 dairy farms. These models may be utilized to provide key decision support information to both dairy farmers and policy makers, or as a tool for conducting macro scale environmental analysis, for marketing Irish dairy products abroad.
Philip Shine; John Upton; Ted Scully; Laurence Shalloo; Michael D. Murphy. Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms. 2018 Detroit, Michigan July 29 - August 1, 2018 2018, 1 .
AMA StylePhilip Shine, John Upton, Ted Scully, Laurence Shalloo, Michael D. Murphy. Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms. 2018 Detroit, Michigan July 29 - August 1, 2018. 2018; ():1.
Chicago/Turabian StylePhilip Shine; John Upton; Ted Scully; Laurence Shalloo; Michael D. Murphy. 2018. "Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms." 2018 Detroit, Michigan July 29 - August 1, 2018 , no. : 1.
Philip Shine; T. Scully; J. Upton; L. Shalloo; Michael D. Murphy. Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis. Applied Energy 2018, 210, 529 -537.
AMA StylePhilip Shine, T. Scully, J. Upton, L. Shalloo, Michael D. Murphy. Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis. Applied Energy. 2018; 210 ():529-537.
Chicago/Turabian StylePhilip Shine; T. Scully; J. Upton; L. Shalloo; Michael D. Murphy. 2018. "Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis." Applied Energy 210, no. : 529-537.