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RESEARCH ARTICLE
Contents Vol 60(9)

Advances in precision agriculture in south-eastern Australia. II. Spatio-temporal prediction of crop yield using terrain derivatives and proximally sensed data

N. J. Robinson A D , P. C. Rampant B , A. P. L. Callinan A , M. A. Rab C and P. D. Fisher C
+ Author Affiliations
- Author Affiliations

A Department of Primary Industries, Cnr Midland Hwy & Taylor Street, Epsom, Vic. 3554, Australia.

B Department of Environment & Conservation, Cnr Dodson Rd & SW Highway, Picton, WA 6230, Australia.

C Department of Primary Industries, 255 Ferguson Road, Tatura, Vic. 3616, Australia.

D Corresponding author. Email: Nathan.Robinson@dpi.vic.gov.au

Crop and Pasture Science 60(9) 859-869 https://doi.org/10.1071/CP08348
Submitted: 9 October 2008  Accepted: 18 August 2009   Published: 8 September 2009

Abstract

The effects of seasonal as well as spatial variability in yield maps for precision farming are poorly understood, and as a consequence may lead to low predictability of future crop yield. The potential to utilise terrain derivatives and proximally sensed datasets to improve this situation was explored.

Yield data for four seasons between 1996 and 2005, proximal datasets including EM38, EM31, and γ-ray spectra for 2003–06, were collected from a site near Birchip. Elevation data were obtained from a Differential Global Positioning System and terrain derivatives were formulated. Yield zones developed from grain yield data and yield biomass estimations were included in this analysis.

Statistical analysis methods, including spatial regression modelling, discriminant analysis via canonical variates analysis, and Bayesian spatial modelling, were undertaken to examine predictive capabilities of these datasets. Modelling of proximal data in association with crop yield found that EM38h, EM38v, and γ-ray total count were significantly correlated with yield for all seasons, while the terrain derivatives, relative elevation, slope, and elevation, were associated with yield for one season (1996, 1998, or 2005) only. Terrain derivatives, aspect, and profile and planimetric curvature were not associated with yield. Modest predictions of crop yield were established using these variables for the 1996 yield, while poor predictions were established in modelling yield zones.

Additional keywords: ECa, EMI, physio-chemical constraints.


Acknowledgments

This research was supported by co-funding from the Grains Research and Development Corporation project through the Precision Agriculture Initiative (SIP09), and the Victorian Department of Primary Industries. The authors thank Ian McLelland for the use of his paddock, and Grant Boyle, Garry O’Leary, Zoltan Beldi, Mohammad Abuzar, Austin Brown, Cherie Reilly, and Fiona Best for their contributions to data collection, analysis, and review of this paper.


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