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. Overview. Objectives ABARE farm surveys Georeferenced paddock data Data modelling Conclusions. Objectives. Improve responsiveness Improve timeliness Improve policy relevance

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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


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%

Dependent variable: log (land value per hectare)


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Probability of exceeding median wheat yields in 2003

Courtesy of QDPI


Remotely sensed crop classification

2003 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


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