Collecting georeferenced data in farm surveys
Philip Kokic, Kenton Lawson, Alistair Davidson and Lisa Elliston
Overview
Objectives ABARE farm surveys Georeferenced paddock data Data modelling Conclusions
Objectives
Improve responsiveness Improve timeliness Improve policy relevance
More appropriate analysis More detailed estimation Better modelling of data
Coverage
Survey ~ 2000 farms annually Broadacre and dairy industries only Stratified balanced random sample Estimates produced at ABARE region level
Survey regions
Collection of Georeferenced paddock data
Study region
Data modelling
Data modelling using spatial covariates
Intensity of agricultural operations (AAGIS) Arable hectares equivalent /ha operated
Pasture productivity index (AGO) Biophysical: incorporates climate and soil type
Vegetation density (AGO) Land capability measure (NSW Dept Ag) Distance to nearest town (ABS) Stream frontage (Geoscience Australia)
Land value reg. n=232, R2=80%
Estimate p-value (%)
Log intensity 0.42 < 0.01
Log PPI 1.16 < 0.01
Veg. density (%) -0.02 < 0.01
Log land capability index
-0.24 < 0.01
Log travel costs -0.45 < 0.01
Stream buffer prop. 4.46 < 0.01
Dependent variable: log (land value per hectare)
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#Roma
Dalby
Emerald
Goondiwindi
Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data
Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data
Probability of exceeding median wheat yields in 2003
Courtesy of QDPI
Remotely sensed crop classification
2003 Season 2004 Season2003 season 2004 season Courtesy of
QDPI
Benefits of geo-spatial data
Increase responsiveness Biophysical modelling of crop and pasture
data Reduced response burden Continuous in season crop estimates Improved accuracy of Small Area Estimation Econometric modelling
