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Estimating pasture biomass with active optical sensors

Published online by Cambridge University Press:  01 June 2017

K. Andersson ,
M. Trotter ,
A. Robson ,
D. Schneider ,
L. Frizell ,
A. Saint ,
D. Lamb  and
C. Blore
K. Andersson*
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale NSW 2351Australia Cooperative Research Centre for Spatial Information, University of New England, Armidale NSW 2351Australia
M. Trotter
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale NSW 2351Australia Cooperative Research Centre for Spatial Information, University of New England, Armidale NSW 2351Australia
A. Robson
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale NSW 2351Australia Cooperative Research Centre for Spatial Information, University of New England, Armidale NSW 2351Australia
D. Schneider
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale NSW 2351Australia Cooperative Research Centre for Spatial Information, University of New England, Armidale NSW 2351Australia
L. Frizell
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale NSW 2351Australia
A. Saint
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale NSW 2351Australia
D. Lamb
Affiliation:
Precision Agriculture Research Group, University of New England, Armidale NSW 2351Australia Cooperative Research Centre for Spatial Information, University of New England, Armidale NSW 2351Australia
C. Blore
Affiliation:
Agriculture Services and Biosecurity Operations Division, Department of Economic Development, Jobs, Transport and Resources, Hamilton Victoria 3300
*
†Email: kander46@une.edu.au
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Abstract

We investigated relationship between pasture biomass and measures of height and NDVI (normalised difference vegetation index). The pastures were tall fescue (Festuca arundinacea), perennial ryegrass (Lolium perenne), and phalaris (Phalaris aquatica) located in Tasmania, Victoria and in the Northern Tablelands of NSW, Australia. Using the Trimble® GreenSeeker® Handheld active optical sensor (AOS) to measure NDVI, and a rising plate meter, the optimal model to estimate green dry biomass (GDM) during two years was a combination of NDVI and falling plate height index. The combined index was significantly correlated with GDM in each region during winter and spring (r2=0.62–0.77, P<0.001). Regional calibrations provided a smaller error in estimates of green biomass, required for potential application in the field, compared to a single overall calibration. Data collected in a third year will be used to test the accuracy of the models.

Keywords

NDVIplate metergreenseeker
Type
Precision Pasture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 754 - 757
Copyright
© The Animal Consortium 2017 

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