This project is about making the crop production
system more efficient and more effective.
If you look at crop production in the 21st
century, it is facing lots of challenges.
On the one side, the population is growing
and the average living standards are also
improving.
So there's a higher demand for food and fiber
and fuel.
But on the other side, the environment is
changing.
We see a lot of weather and environment related
events happening.
So in the future crops have to adapt to the
changing environment.
So those are the challenges.
In order to solve these challenges the engineers
and agronomists are working together, we're
also partnering with three industry partners
to solve the problem.
This project is looking at how do we accelerate
crop adaptation to the changing environment.
So a farmer has a lot of decisions to make.
Among those decisions is which variety of
hybrid should they plant, how dense should
the planting be, when should the planting
be made, should they add fertilizer, should
they provide other inputs, should they put
it in with certain kinds of equipment.
There's a lot of decisions that have to be
made.
All of those decisions will affect their profitability.
Not only that but the question is, if I decide
to plant a hybrid this year that was designed
to solve a problem 10 years ago, and there
were a lot of drought-resistant hybrids that
came onto the market in the last couple years,
because 10 years ago drought was a big deal.
As you see from the pictures, drought has
not been a big deal in Iowa for quite a while.
In fact, the opposite problem is going to
be happening in the future.
So what are we going to do to design something
that will handle rapidly-changing environments.
Additionally, a lot of decisions in the crop
production process are made through trial
and error.
But because of the emergence of high throughput
genotype data and phenotype data, the conventional
decision making processes are not sufficient.
So people need more data analytics and decision
making.
That's where the engineers come into play.
In systems engineering and data analytics,
we have a lot of methods and models and algorithms
and software, particularly addressing the
problem of making decisions, and complex decisions
for large-scale systems with uncertainty.
During this five-year project we are planning
on designing a lot of engineering models and
algorithms and software to solve a lot of
research questions and hopefully decades down
the road we're hoping our algorithms will
be applied to the drop production system and
make it more efficient and more productive.
