Factors affecting productivity and profitability in pasture-based automatic milking systems
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Open Access
Type
Conference paperAbstract
A large variability in productivity and profitability between farms milking with automatic milking systems (AMS) has been identified in Australia. This study aimed to identify the physical factors (i.e., production outputs, physical inputs, and productivity measures) associated ...
See moreA large variability in productivity and profitability between farms milking with automatic milking systems (AMS) has been identified in Australia. This study aimed to identify the physical factors (i.e., production outputs, physical inputs, and productivity measures) associated with profitability and productivity in pasture-based AMS, and to quantify how changes in these critical factors would affect farm productivity. Two different datasets with information from pasture-based AMS farms were utilized. The 'Business Performance Dataset' contained yearly farm business physical and economic data from 14 Australian AMS farms located across five states and collected during financial years 2015-2016, 2016-2017, and 2017-2018. The 'Robot-System Performance Dataset' contained monthly, detailed robot-system performance data from 24 AMS farms located in Australia, Ireland, New Zealand, and Chile, and collected during financial years 2015-2016, 2016-2017 and 2018-2019. Two linear mixed models were used to identify the physical factors associated with profitability (Model 1) and productivity (Model 2). A Monte Carlo simulation was conducted to simulate how changes in the factors identified in Model 2 would affect productivity. Each variable was sampled, individually, from the upper or lower quartile of its distribution to simulate different top-performing scenarios. Model 1 showed that the two physical factors associated with profitability were milk harvested per robot (MH), and total labor on-farm (Model 1 explained 69% of the variance in profitability). Model 2 showed that the physical factors associated with MH were cows per robot, milk flow, milking frequency, milking time and days in milk (Model 2 explained 88% of the variance in MH). The Monte Carlo simulation (Table 1) showed that if pasture-based AMS farms manage to increase the number of cows per robot from 55 (current average) to 71 (top 25% average), they will increase the chances of achieving more than 1500 kg MH (from 24% to 58%) and almost double the overall farm profitability.
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See moreA large variability in productivity and profitability between farms milking with automatic milking systems (AMS) has been identified in Australia. This study aimed to identify the physical factors (i.e., production outputs, physical inputs, and productivity measures) associated with profitability and productivity in pasture-based AMS, and to quantify how changes in these critical factors would affect farm productivity. Two different datasets with information from pasture-based AMS farms were utilized. The 'Business Performance Dataset' contained yearly farm business physical and economic data from 14 Australian AMS farms located across five states and collected during financial years 2015-2016, 2016-2017, and 2017-2018. The 'Robot-System Performance Dataset' contained monthly, detailed robot-system performance data from 24 AMS farms located in Australia, Ireland, New Zealand, and Chile, and collected during financial years 2015-2016, 2016-2017 and 2018-2019. Two linear mixed models were used to identify the physical factors associated with profitability (Model 1) and productivity (Model 2). A Monte Carlo simulation was conducted to simulate how changes in the factors identified in Model 2 would affect productivity. Each variable was sampled, individually, from the upper or lower quartile of its distribution to simulate different top-performing scenarios. Model 1 showed that the two physical factors associated with profitability were milk harvested per robot (MH), and total labor on-farm (Model 1 explained 69% of the variance in profitability). Model 2 showed that the physical factors associated with MH were cows per robot, milk flow, milking frequency, milking time and days in milk (Model 2 explained 88% of the variance in MH). The Monte Carlo simulation (Table 1) showed that if pasture-based AMS farms manage to increase the number of cows per robot from 55 (current average) to 71 (top 25% average), they will increase the chances of achieving more than 1500 kg MH (from 24% to 58%) and almost double the overall farm profitability.
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Date
2020Publisher
Online Dairy Research Foundation 2020 SymposiumFaculty/School
Faculty of Science, School of Life and Environmental SciencesShare