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Collecting georeferenced data in farm surveys

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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  1. Collecting georeferenced data in farm surveys Philip Kokic, Kenton Lawson, Alistair Davidson and Lisa Elliston

  2. Overview • Objectives • ABARE farm surveys • Georeferenced paddock data • Data modelling • Conclusions

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

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

  5. Survey regions

  6. Collection of Georeferenced paddock data

  7. Study region

  8. Data modelling

  9. 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)

  10. Land value reg. n=232, R2=80% Dependent variable: log (land value per hectare)

  11. # E m e r a l d R o m a # D a l b y # G o o n d i w i n d i # Probability of exceeding median wheat yields in 2003 Courtesy of QDPI

  12. Remotely sensed crop classification 2003 season 2004 season Courtesy of QDPI

  13. 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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