[Photo by Brad Collis]
By Dr Rob Kelly and Dr Wayne Strong, Queensland Department of Primary Industries and Fisheries
Precision agriculture (or PA) is becoming a simple evaluation tool for growers and consultants alike as they sort out how their season turned out.
Yield maps, created with a harvestermounted yield monitor and GPS, provide a good visual record of the spatial variation within a field in any year. Other spatial layers - like grain protein, airborne imagery or soil-based EM38 maps - are also needed to resolve why the yield map varied.
But it does not take many years to realise that more often than not, variation in crop growth and performance varies more from season to season than for a single season within the field.
A series of three yield maps taken from a 40-hectare sorghum field near Jimbour, Darling Downs, between 1999 and 2001 is a good example of this. Variation within the field for any single year, as expressed by the 20 to 80 per cent increments, was mostly around two to three tonnes per hectare - whereas variation within the field among the three seasons was over 5t/ha.
This season-to-season variation, known as temporal variation, is driven predominantly by seasonal conditions. Practitioners of PA will need to take notice of temporal variation as much as they do of spatial variation. What can be done about it - and how might seasonal forecasting help in practice with PA?
Linking PA with climate science is probably best done in two ways - either strategically or tactically.
Strategic integration of PA and climate science is about using what is known about past performance, combining this with the seasonal outlook using tools like Southern Oscillation Index (SOI), and to then plant and fertilise accordingly. Poor seasons will mean a backing-off of inputs, while potentially good seasons will likely boost the levels all round.
Temporal variation and yield stability - taking a series of yield maps - can help determine where inputs should be invested within the field. The higher the temporal variation in yield, the more yield instability and uncertainty in this part of the field.
Areas with a low temporal variation (less than 30 per cent) can be further classified into areas with relatively high yield or low yield. This approach helps to identify areas where a return on input is more likely (stable and high-yielding areas) than those where yield is completely up in the air (unstable).
Tactical integration of PA and climate science is about short-term adjustments being made as the season unfolds using Madden-Julian Oscillation (MJO) waves or other rain-stimulating events. This may mean inputs are more closely matched to outputs in the context of the season. Sites with higher yield potential that receive additional rain may be topped up with in-crop fertiliser application where appropriate. By linking PA technology with seasonal forecasting, yields are more likely to reflect the optimal use of in-season rain and stored moisture.
GRDC Research Code DAQ00067
For more information: Dr Rob Kelly, email@example.com; Dr Wayne Strong, firstname.lastname@example.org