RajKhoslaPrecisionAg

Good morning everybody and thank you for
extending me this opportunity to participate

in this international panel discussion on
big data in agriculture.

Now precision agriculture consists
of 5 R's.

Application of right input, at the right
time, in the right place, in the right

amount and in the right manner.

When you bring these 5 R's together,
that's when precision agriculture happens.

For farmers to be able to make decisions,
based on spatial and temporal variability

in their fields mandates quantification
of this variability.

Meaning they need to know how much
variability exists in their fields, where

does that variability exist in their
fields and what is the cause of variability.

If it can be managed with precision
application of inputs.

This desire to quantify variability in our
fields and farming operations has really

led to the collection of data, and I
mean a lot of data.

Here is an example that illustrates how
many data layers a famer may potentially

collect during a crop growing season.

He may start with a map of soil variability,
with soil electroconductivity, at

shallow and deeper depth.

He may come back with soil sampling
for nitrates, organic matter, sand silt and clay.

Likewise, for the purpose of water
management, mapping soil water

content using soil sensors.

Multiple times during the growing season
create data layers as well.

For the purpose of crop health, farmers
are using NDVI data many times during

the growing season to capture how
green their crops are.

Likewise, how well are equipment
doing with as applied data.

There is an opportunity that exists for
farmers to look at lead distribution data,

infestation data, how high the crops are
during the growing season.

In addition there are many other data
layers all the way down to the yield maps.

What you see here now is one farm field
in one crop growing season where a farmer

can collect millions and millions
of data points.

And hence the term "big data" in
agriculture.

It is one thing to be able to collect so
many data layers, but how do we process

this data. And how do we analyze this data?
Interpret this data? Integrate this data to

bring them together and create
this big picture together.

To translate this data into meaningful
information that farmers can use to

make better management decisions.

So that we could meet or exceed the
four goals of precision agriculture.

Which are to increase production, increase
efficiency, increase profitability of

farming operations and doing all of
this in sustainable manner.

In closing, I would like to add, though
that precision agriculture is a

relatively new discipline.

Yes we have the technology that can
deliver what we want, when we

want, and where we want.

But my question is, "do we really have the
science to take advantage of all these

data layers that we're now collecting?"

I think we have a long ways to go and a lot
of work needs to be done to really make

precision agriculture site specific, locally
adaptive, operationally feasible,

scale independent.

That means it can be practiced on small
scale farming systems and large

scale farming systems.

And above all, economically affordable.
Thank you.