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