Energy Use Analysis of Major Milking Center Components at a Dairy Experiment Station

Edens, W. C.; Pordesimo, L. O.; Wilhelm, L. R.; Burns, Robert T.

doi:10.13031/2013.15659

Cited by 16 publications

(11 citation statements)

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“…The EUIs (Energy Utilization Indices) [23,37,38] adopted to express on-farm electricity usage were referred to the cultivated land extent (kWh hectare −1 ), the herd dimension (kWh head −1 and kWh lactating cow −1 ) and the amount of milk produced (kWh kg FPCM −1 ). The requirements of diesel fuel and LPG for the EUIs measured were: kg head −1 , kg Lactating Cow −1 (LC), kg·hectare −1 and kg per kg FPCM.…”

Section: Energy Efficiency and Carbon Indicatorsmentioning

confidence: 99%

A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 1: Direct Energy Requirements

Todde¹,

Murgia²,

Caria³

et al. 2018

Energies

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Dairy cattle farms are continuously developing more intensive systems of management which require higher utilization of durable and not-durable inputs. These inputs are responsible of significant direct and indirect fossil energy requirements which are related to remarkable emissions of CO 2 . This study aims to analyze direct energy requirements and the related carbon footprint of a large population of conventional dairy farms located in the south of Italy. A detailed survey of electricity, diesel and Liquefied Petroleum Gas (LPG) consumptions has been carried out among on-farm activities. The results of the analyses showed an annual average fuel consumption of 40 kg per tonne of milk, while electricity accounted for 73 kWh per tonne of milk produced. Expressing the direct energy inputs as primary energy, diesel fuel results the main resource used in on-farm activities, accounting for 72% of the total fossil primary energy requirement, while electricity represents only 27%. Moreover, larger farms were able to use more efficiently the direct energy inputs and reduce the related emissions of carbon dioxide per unit of milk produced, since the milk yield increases with the herd size. The global average farm emissions of carbon dioxide equivalent, due to all direct energy usages, accounted for 156 kg CO 2 -eq per tonne of Fat and Protein Corrected Milk (FPCM), while farms that raise more than 200 heads emitted 36% less than the average value. In this two-part series, the total energy demand (Part 1 + Part 2) per farm is mainly due to agricultural inputs and fuel consumption, which have the largest quota of the annual requirements for each milk yield class. These results also showed that large size farms held lower CO 2 -eq emissions when referred to the mass of milk produced.

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Section: Energy Efficiency and Carbon Indicatorsmentioning

confidence: 99%

A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 1: Direct Energy Requirements

Todde¹,

Murgia²,

Caria³

et al. 2018

Energies

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“…However, Edens et al [24] (n/a), Hörndahl [25] (n/a), and Todde et al [29] (23%) found milk harvesting to be the largest consumer of electricity, while additionally, although no specific electricity consumption values were reported, Hartman and Sims [30] found water heating to be the largest consumer of electrical energy, consuming 31%, followed by milk cooling (21%), milk harvesting (18%), water pumping (18%), and miscellaneous usage throughout the farm (12%). Calcante et al [23] AMS-c ITL n/a n/a 11.13 n/a n/a Edens et al [24] Conv Concurrently, the one study that calculated total electricity consumption of AMS farms found 60.8 Wh kg −1 were consumed (24% greater than conventional farms) [28]. Four AMS farms reported on sub-metered energy consumption values, whereby on these farms (AMS-c and AMS-g), milk harvesting was the largest consumer of electrical energy, consuming 20.2 Wh kg −1 .…”

Section: Electrical Energymentioning

confidence: 98%

“…Edens et al [24] utilized empirical modelling, developing four multiple linear regression (MLR) models to predict the annual electricity consumption of each major electricity component in the USA. These included vacuum pumps, water heaters, refrigeration compressors, and air compressors, as well as a fifth MLR model to predict their combined consumption.…”

Section: Regression Modellingmentioning

confidence: 99%

“…These included vacuum pumps, water heaters, refrigeration compressors, and air compressors, as well as a fifth MLR model to predict their combined consumption. Using data from a single farm (herd size = 160 cows) throughout a 14 year period, Edens et al [24] developed the MLR models using data related to the volume of milk produced, the number of lactating cows, the percentage of butterfat in milk, pounds of fat-corrected milk, monthly low temperature, monthly high temperature, average monthly high temperature, and average monthly low temperature. They utilized five variable selection methods to help maximize the prediction capability of each MLR model to predict monthly unseen electricity consumption for each individual component.…”

Section: Regression Modellingmentioning

confidence: 99%

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Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

et al. 2020

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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.

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“…The dairy farms were located in valley. All the equipment found in the milking parlor were inventoried, reporting the operational power and their usage time in order to calculate the annual energy consumption [22,23].…”

Section: Dairy Farmsmentioning

confidence: 99%

Energy and Carbon Impact of Precision Livestock Farming Technologies Implementation in the Milk Chain: From Dairy Farm to Cheese Factory

et al. 2017

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Precision Livestock Farming (PLF) is being developed in livestock farms to relieve the human workload and to help farmers to optimize production and management procedure. The objectives of this study were to evaluate the consequences in energy intensity and the related carbon impact, from dairy farm to cheese factory, due to the implementation of a real-time milk analysis and separation (AfiMilk MCS) in milking parlors. The research carried out involved three conventional dairy farms, the collection and delivery of milk from dairy farms to cheese factory and the processing line of a traditional soft cheese into a dairy factory. The AfiMilk MCS system installed in the milking parlors allowed to obtain a large number of information related to the quantity and quality of milk from each individual cow and to separate milk with two different composition (one with high coagulation properties and the other one with low coagulation properties), with different percentage of separation. Due to the presence of an additional milkline and the AfiMilk MCS components, the energy requirements and the related environmental impact at farm level were slightly higher, among 1.1% and 4.4%. The logistic of milk collection was also significantly reorganized in view of the collection of two separate type of milk, hence, it leads an increment of 44% of the energy requirements. The logistic of milk collection and delivery represents the process which the highest incidence in energy consumption occurred after the installation of the PLF technology. Thanks to the availability of milk with high coagulation properties, the dairy plant, produced traditional soft cheese avoiding the standardization of the formula, as a result, the energy uses decreased about 44%, while considering the whole chain, the emissions of carbon dioxide was reduced by 69%. In this study, the application of advance technologies in milking parlors modified not only the on-farm management but mainly the procedure carried out in cheese making plant. This aspect makes precision livestock farming implementation unimportant technology that may provide important benefits throughout the overall milk chain, avoiding about 2.65 MJ of primary energy every 100 kg of processed milk.

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Energy Use Analysis of Major Milking Center Components at a Dairy Experiment Station

Cited by 16 publications

References 0 publications

A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 1: Direct Energy Requirements

A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 1: Direct Energy Requirements

Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

Energy and Carbon Impact of Precision Livestock Farming Technologies Implementation in the Milk Chain: From Dairy Farm to Cheese Factory

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