PastureGrowth refers to the amount of carbon plants capture through photosynthesis and then store as above-ground plant mass. PastureGrowth provides an excellent indication of ecosystem productivity, pasture production and crop yield. As a biophysical variable, PastureGrowth can be used to compare differences in productivity between locations and through time.
CSIRO generates Australia-wide grids of grass PastureGrowth (pastures, crops and grasslands) at a 250 m spatial resolution and a 16-day timestep. These are derived from MODIS satellite imagery, which extends from 2001 up until the present. CSIRO continuously updates the PastureGrowth product approximately every three weeks as the latest imagery becomes available.
PastureGrowth data are in units of grams of carbon per square metre per day (gC/m2/d). Valid values range from around -1 to 5. PastureGrowth can go negative – that is, when plants lose more carbon in keeping themselves alive (via respiration) than they gain through photosynthesis. Hence there is a net loss of carbon. This happens annually in many places that experience really dry or cold conditions seasonally, or during extended drought etc. Note that the PastureGrowth estimates do not discriminate between different types of crops, pastures or grasses.
Here PastureGrowth is modelled as the rate of accumulation of carbon mass. This is not the same as the total mass (that is, biomass) which is what is observed and measured on the ground. As a rule of thumb, biomass is about twice carbon mass. To convert these PastureGrowth data to biomass they need to be doubled.
PastureGrowth data provide more powerful insights into productivity than the more commonly used Normalised Difference Vegetation Index (NDVI). The NDVI is a spectral index and, while it is strongly related to foliage cover, has no inherent biophysical meaning. To derive meaningful information from the NDVI it must be related (calibrated) to some local, physical attribute, such as biomass or cover. Even when locally calibrated, the NDVI by itself is not always a good indicator of productivity because it cannot capture real variability in productivity when cover is high (such as in the north’s wet season, the south’s dark winter months, in the peak of the cropping season and in irrigated pastures). And unless the NDVI is calibrated at each location of interest, it cannot be used to make absolute comparisons in productivity between locations separated by any significant distance. It is for these reasons that we have developed this new product, PastureGrowth .
How can I use these data?
As PastureGrowth estimates are produced every few weeks, they provide detailed insights into how variable productivity has been and how it is currently changing. They can be used to examine how the productivity of a given paddock has varied over the past 20 years, allowing for comparisons to made between current productivity and the average productivity for this time of year. The progression of the current season can be compared to that of previous seasons to get a feel for possible trajectories of the coming months (this is called seasonal profiling).
These PastureGrowth estimates can also be used to gauge how well a given paddock is performing compared to surround paddocks in a region. This can reveal whether a paddock’s productivity is lower or higher than what is being achieved regionally. Such benchmarking can be useful in assessing the effectiveness of management strategies.
When historical records of crop yields are available, these PastureGrowth estimates can be related to yields and used for assessing likely crop production. The same is true for pastoral systems when historical stocking rate records exist.
At a regional level, tracking the progress of a season’s PastureGrowth can provide indicators of the likely production of livestock or grain, or the demand for feed, or for transport and processing capacity.
PastureGrowth is priced as a monthly subscription. Purchasing from the shop will set up a recurring monthly charge to your nominated credit card.
Access and information
See the PastureGrowth page for further details and accessing the sample or subscription data.
1 Donohue, R.J., Hume, I.H., Roderick, M.L., McVicar, T.R., Beringer, J., Hutley, L.B., Gallant, J.C., Austin, J.M., van Gorsel, E., Cleverly, J.R., Meyer, W.S., & Arndt, S.K. (2014). Evaluation of the remote-sensing-based DIFFUSE model for estimating photosynthesis of vegetation. Remote Sensing of Environment, 155, 349-365. https://doi.org/10.1016/j.rse.2014.09.007.
2 Donohue, R.J., & Renzullo, L.J. (2015). C-Store: an Australian remote-sensing and observation-driven carbon assessment system. In. Canberra: CSIRO. https://doi.org/10.4225/08/5a3953b0a4d5c.