INVITA stands for innovations
in variety testing in Australia.
It's actually derived from a similar
project in Europe, which is funded
by the European Commission.
That project was funded for about
12 million euros over five years,
and GRDC saw an opportunity to build
on that project and deliver
similar benefits for Australia.
INVITA's a project that's being
built on top of the National Variety
Trial. GRDC is
investing approximately
$5.4 million over four and a half
years in the INVTIA
project in order to achieve that.
So the objective of the INVITA
project is
to measure variety performance
in new ways.
So there are two issues in variety
trials that we want to deal with.
One is to improve the quality of the
data. We have spatial variability in
fields and we can apply
drone technology to try to
assess how that
how that variability can
be accommodated in analysis
of trials.
A second issue is
trying to connect data across
trials and trying to use the
environment information to
link to the data that we collect
across trials to to each other
and make improved performance
predictions for varieties.
The INVITA project has two types
of trials. It has a series
of trials that are co-located with
NVTtrials. There is almost 100 of
those trials.
So those in those trials, we're
planting special trials
that we can see from the satellite
alongside the NVT
and we're doing extra measurements
with drones and sensors that are
mounted in the field.
We also have 4G cameras in the field
that are watching the plants grow.
And we can use that information
combined with machine learning to
predict the biomass every day and to
understand how much stress the
trials are experiencing.
The second type of trial is what we
call reference trials, and we're
running those trials here at the
University of Queensland and
also on the CSIRO research station
at Boorowa, near Young.
And we also have another trial with
SARDI in South Australia.
And at those three sets of trials,
we're using high
throughput phenotyping technologies
like we have here, the all seeing
tractor, as we call it, at UQ.
And that tractor has Leida
thermal cameras, hyperspectral
systems and imagery.
And it's taking that
at very high resolution, submilimetre
some of that data.
And we use that information combined
with drones at these reference sites
in order to build better predictions
of from
the drone imagery.
And once we build those types of
predictions, we can then apply it to
the drone imagery we collect from
the rest of the country.
CSIRO is also a partner in
this project, and a major
role for CSIRO is to be
taking the historical weather data
and the imagery data and
connecting that in with computer
modelling so that we can
build indices of how
these trials have been affected
through the season.
And we can use that information to
also understand the relationship
between trials that are grown in
different sites so we can compare
trials grown in northern
New South Wales with trials that are
growing in Western Australia.
And we can work out what the
relationships between those are and
improve the prediction of variety
performance.
So the objective of the project
overall is that
on a single trial basis, we'll
be able to improve the the
value of the information, the
precision and which we we we
predict in a particular site,
because we'll be able to use
information from particularly
from the drone imagery
to understand how spatial
variability is occurred in the field
and we're able to use that
information to improve
the estimation of performance of
single varieties.
The second thing that we'll be able
to do is to
improve prediction across all
of these trials.
And so that means that our regional
predictions will be improved because
we'll be able to use environment
information.
A nice example is that this
year in the east,
we have a La Nina effect.
And so we're getting high rainfall.
We're getting we're going to have a
lot of trials that have
high yields, but
that's not always the case.
So how do we combine
that information with results
from trials this year or in past
years where there's been low
rainfall.
And part of what we can do is we can
use our monitoring of weather
and our monitoring of satellite
imagery to adjust for those effects.
Part of the advantage of
INVITA is that it's building on
investment in Europe.
We're working with one partner
in particular at Wageningen
University, which is one of
the leading agricultural
universities in the world.
And they've had an ongoing effort
in applying new statistical
and modelling techniques to
understand variability
and to understand genotype
environment interaction.
In Australia, we have a lot of
expertise in that space.
The Europeans are building more
in that area as well, and we're
working with them in particular to
try to apply those techniques and in
our trials.
INVITA is collecting a large amount
of data from
sensors on the ground, from weather
stations from satellites, and
then we use that information
and feed it into crop simulation
models, and that allows us to
produce an environment index
for each trial and in the
entire network.
And we'll also be doing that with
historical data.
So we'll be going back through
GRDC's history record
and and re-predicting in
all of those last 10 years
worth of trials, then we can
test those techniques and see
how much we can improve the
prediction of performance.
So that enables us to build
a system that allows
us to improve prediction and
performance in each season
and to augment that over time so
that farmers get a better prediction
of variety performance in any region.