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RESEARCH ARTICLE
Contents Vol 50(6)

Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia

G. E. Donald A , S. G. Gherardi B E , A. Edirisinghe C , S. P. Gittins B , D. A. Henry D and G. Mata C
+ Author Affiliations
- Author Affiliations

A CSIRO, Livestock Industries, Locked Bag 1, Armidale, NSW 2350, Australia.

B Department of Agriculture and Food Western Australia, Bentley, WA 6983, Australia.

C CSIRO, Livestock Industries, Private Bag 5, Wembley, WA 6913, Australia.

D CSIRO, Australian Animal Health Laboratory, Private Bag 24, Geelong, Vic. 3220, Australia.

E Corresponding author. Email: sgherardi@agric.wa.gov.au

Animal Production Science 50(6) 611-615 https://doi.org/10.1071/AN09159
Submitted: 27 November 2009  Accepted: 4 February 2010   Published: 11 June 2010

Abstract

Remote sensing of vegetation and its monitoring using the normalised difference vegetation index (NDVI) offers the opportunity to provide a coverage of agricultural land at a large scale. The availability of MODIS NDVI at a resolution of 250 m provided the opportunity to evaluate the hypothesis that pasture growth rate (PGR) of individual paddocks can be accurately predicted using a model based on MODIS NDVI in combination with climate and soil data and a light-use efficiency model. Model estimates of PGR were compared with field measurements of PGR recorded in grazing enclosure cages collected over 3 years from six farms located across the south-west region of Western Australia. The estimates attained from the model explained 70% of the variation in PGR for individual paddocks on farms over the 3 years of the study, with an average error at the paddock scale of 10.4 kg DM/ha.day over all growing seasons and years. Across all farms studied, there was generally good agreement between satellite-derived PGR and ground-based measurements, although estimates of PGR varied between years and farms. The model explained 47% of the variation in pasture growth early in the season (from break of season to end of July), compared with 62% late in the season (from August to pasture senescence). The present study demonstrated that PGR for individual paddocks can be predicted at weekly intervals from MODIS imagery, climate and soil data and a light-use efficiency model at an accuracy sufficient to facilitate on-farm pasture and livestock management.

Additional keywords: remote sensing.


Acknowledgements

We thank Dr Richard Smith, Richard Stovold, Ron Craig, Dr Jacki Marsden, Dr Stefan Meyer and John Adams of Landgate for providing NOAA AVHRR and MODIS NDVI data for this study. For the collection of field pasture information, we thank Murray Ellis, Tom Plaisted and Kazue Tanaka. This project was funded by CSIRO Livestock Industries, Department of Agriculture and Food WA and Landgate Satellite Remote Sensing Services.


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