Collecting georeferenced data in farm surveys

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Collecting georeferenced data in farm surveys

• Philip Kokic, Kenton Lawson, Alistair Davidson
  and Lisa Elliston

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Overview

• Objectives
• ABARE farm surveys
• Georeferenced paddock data
• Data modelling
• Conclusions

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Objectives

• Improve responsiveness
• Improve timeliness
• Improve policy relevance
• More appropriate analysis
• More detailed estimation
• Better modelling of data

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Coverage

• Survey 2000 farms annually
• Broadacre and dairy industries only
• Stratified balanced random sample
• Estimates produced at ABARE region level

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Survey regions
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Collection of Georeferenced paddock data
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Study region
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Data modelling
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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)

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Land value reg. n232, R280
Dependent variable log (land value per hectare)
Estimate p-value ()
Log intensity 0.42 lt 0.01
Log PPI 1.16 lt 0.01
Veg. density () -0.02 lt 0.01
Log land capability index -0.24 lt 0.01
Log travel costs -0.45 lt 0.01
Stream buffer prop. 4.46 lt 0.01
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Probability of exceeding median wheat yields in
2003
Courtesy of QDPI
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Remotely sensed crop classification
2003 season
2004 season
Courtesy of QDPI
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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