Factors associated with the technical, allocative and economic efficiency of Irish dairy farms

Transcription

1 Overview Factors associated with the technical, allocative and economic efficiency of Irish dairy farms E. Kelly 1,2, L. Shalloo 1, U. Geary 1, A. Kinsella 3, F. Thorne 3, M. Wallace 2 1 Teagasc, Livestock Systems epartment, Animal & Grassland and Innovation entre, Moorepark, Fermoy, o. ork, 2 School of Agriculture Food Science and Veterinary Medicine, NUI ublin, elfield, ublin 4, 3 Teagasc, Rural Economy Research entre, Athenry, o. Galway, Ireland ackground Objectives Efficiency measurement Technical, allocative and economic efficiency ata Envelopment Analysis (EA) Models Results onclusion ackground ackground Rising costs of inputs Volatile prices Policy changes Quota removal Subsidies Need to increase efficiency Management Widely regarded as associated with differences in efficiency

2 Objectives Objectives etermine the levels of technical, allocative and economic efficiency of Irish dairy farms Investigate the associations of key management, qualitative and demographic characteristics on efficiency Efficiency measurement efinitions of Efficiency (Farrell 1957) Technical: ability to maximise output from the current level of inputs Allocative: ability to react to market changes when choosing inputs Economic: technical x allocative efficiency

3 ata Envelopment Analysis (EA) ata Envelopment Analysis (EA) eveloped by harnes et al., (1978) Non-parametric methods eterministic Linear programming Efficiency scores range between and 1 EA Frontier EA Frontier Efficient Efficient

4 EA Frontier EA Frontier Economic Efficiency Projected Point θ = / θ = / Efficient Isocost Line EA Frontier Economic Efficiency EA Frontier Economic Efficiency Projected Point θ = / Projected Point θ = / Isocost Line Technical and economic efficient Isocost Line

5 Efficiency Models ataset National Farm Survey (NFS), 28 Annual survey of Irish dairy farms Over 3 farms Input Variables Land, cows, labour, concentrate, fertiliser, other direct and overhead costs (Physical and financial) Output Variables Milk Solids (MS) and other farm output (Physical and financial) Models Variable Returns to Scale (VRS) Second Stage Analysis Tobit Regression: Suitable as EA efficiency scores bound between and 1 Management Factors Qualitative Factors emographic Factors Results Technical efficiency No of producers % 8-89% 6-69% 4-49% 2-29% -9% Average Minimum Maximum St ev Efficiency (%)

6 Allocative efficiency Economic efficiency No of producers % 8-89% 6-69% 4-49% Efficiency (%) 2-29% -9% Average Minimum Maximum St ev No of 4 producers % 8-89% 6-69% 4-49% Efficiency (%) 2-29% -9% Average Minimum Maximum St ev Results summary Potential to increase technical, allocative and economic efficiency More technically efficient than economically efficient producers 85% of sample had technical efficiency > 6% with 13% fully technically efficient 83% had allocative efficiency>6% with 3% fully allocative efficient 42% had economic efficiency>6% with 3% fully economic efficient Factors associated with technical efficiency Need to focus on key factors associated with efficiency

7 Association of key factors with technical efficiency Model 1 Model 2 Model 3 Model 4 Model 5 Variable Estimate Estimate Estimate Estimate Estimate Intercept -.454*** *** *** ** * Grazing ays.13***.1**.7*.2.2 reeding ays -.5*** -.4** -.3* -.3* -.3* airy% Output Milk Penalties -.* -.* -.* -.** -.** Milk onuses.***.**.***.**.** Milk Recording iscussion *.472* alving Feb14th *.833* Factors associated with allocative efficiency alving March1st.997**.133**.176** Soil **.15* Age.173* * Significance PRO LifeReg SAS *** <.1, **.1-.1, *.1-.5 iscussion Group Member. Mean calving ate: ummy variable, 1 if calving 14 th February, 2 if calving 1 st March, 3 if calving 17 th March, 4 if calving 31 st March and 5 if calving April 1 st Soil quality ranges from 1-6 with 1 an indication of best soil with widest range of use and 6 the poorest soil quality with most limited range of use Association of key factors with allocative efficiency Model 1 Model 2 Model 3 Model 4 Model 5 Variable Estimate Estimate Estimate Estimate Estimate Intercept *** *** -.534*** *** -.54*** Grazing ays.6* reeding ays airy% Output.329***.2147**.2278**.2357**.2363** Milk Penalties..... Milk onuses.*.*.*.. Milk Recording.612**.631**.657**.648** AI use.431*.45*.497*.451* iscussion Factors associated with economic efficiency alving March 17th -.51* -.472* Soil 1.736*.73* Soil 2.956*.984* Age.14 * Significance PRO LifeReg SAS *** <.1, **.1-.1, *.1-.5 iscussion Group Member. Mean calving ate: ummy variable, 1 if calving 14 th February, 2 if calving 1 st March, 3 if calving 17 th March, 4 if calving 31 st March and 5 if calving April 1 st Soil quality ranges from 1-6 with 1 an indication of best soil with widest range of use and 6 the poorest soil quality with most limited range of use

8 Association of key factors with economic efficiency Model 1 Model 2 Model 3 Model 4 Model 5 onclusions Variable Estimate Estimate Estimate Estimate Estimate Intercept *** *** *** *** *** Grazing ays.25***.17**.15**.9.9 reeding ays -.4* -.4* airy% Output.634***.489**.389**.4241***.3968** Milk Penalties Milk onuses.***.***.***.***.*** Milk Recording.15**.138**.98**.958** AI use.1142**.117**.1147**.124** iscussion *.816* alving March1st.852* * Soil 1.143*.1269* Age.274* * Significance PRO LifeReg SAS *** <.1, **.1-.1, *.1-.5 iscussion Group Member. Mean calving ate: ummy variable, 1 if calving 14 th February, 2 if calving 1 st March, 3 if calving 17 th March, 4 if calving 31 st March and 5 if calving April 1 st Soil quality ranges from 1-6 with 1 an indication of best soil with widest range of use and 6 the poorest soil quality with most limited range of use onclusion onclusion Greater efficiency associated with Mean calving date Grazing days reeding season length Milk quality iscussion group membership Milk recording AI use airy specialisation Land Quality Age Potential to increase efficiency at farm level Identified KPI at farm level that are causing differences in efficiency among producers These factors can be used for the benchmarking of producers

9 Future work Acknowledgements Scale efficiency of Irish dairy producers Walsh Fellowship Supervisors Measure productivity over a longer period of time rian Moran NFS Farmers Sean ummins James O Loughlin rian Mcarthy Thank you for your attention!