Crop Insurance Today Fourth Quarter 2020 - 17

Data analytics is driving agriculture innovation in ways unimaginable ten years ago. The outcomes span from sub-acre field management to regional
and national policy.

generations of offspring. Data science is creeping into plant breeding-a discipline already
data intensive and rich in predictive modeling.
Plant breeders and data scientists are deploying
a wide range of analytical methods that were developed for other quantitative disciplines. These
methods are expected to revolutionize the way
that breeders improve cultivability of crops while
making crops more productive and yield-stable
in current and future environments. Today's crop
breeding data is multidimensional and includes
geospatial variables, plant response to different
environments, and the tiny pieces of genetic information stored on chromosomes.
For example, the standard process of developing a corn hybrid begins with line and varietal
development whereby parents and crosses are selected, by identifying the most fit and desirable
progeny. These steps are eventually followed by
commercialization, product development, and
placement in the market. At each step, the size of
the project gets larger and takes more manpower,
more time and space, and more money. The goal
is to find hybrids that are more productive than
their predecessors across multiple environmental
conditions (weather, disease, insects, etc.). However, data collected today, as mentioned, is mul-

tidimensional and the data sets are becoming extremely large. This poses challenges to standard
breeding because the desired traits are becoming
more difficult to predict with linear statistical
methods as breeders integrate more and different
types of data.
Using machine learning techniques, breeders
can better predict the response of a corn hybrid's
genetics under different management input scenarios in an environment without in-field testing
in that environment.  This allows breeding programs to focus on the most promising hybrids in
the most relevant environments and thus more
rapidly realize genetic gains on the farm. This
can be extended to future environmental scenarios with a greater degree of accuracy than in
the past. In the end, genes can be identified and
tested more rapidly, and their performance simulated across changing environments and landscapes. All with the end goal of developing crops
that will be able to handle the vagaries of nature
more efficiently and with greater yield.

The Big

Machine learning and artificial intelligence is
rapidly being developed to improve farm-level
efficiency. Machinery manufacturers are devel-

oping self-adjusting combines that monitor crop
intake and adjust reel and tractor speeds to transfer grain into thresher more efficiently. These
harvesters will be able to adjust rotor/cylinder
speed and clearance between the thresher and
the concave, thus optimizing concave clearance
and threshing speed based on specific moisture
of the crop, a variable that differs within and
across fields. On-the-fly adjustment of separator
fan and sieves, including the straw chopper and
particle size, can also be optimized for variable
crop conditions. These improvements increase
grain quality, reduce yield loss at the combine,
and allow farmers to harvest faster. While all of
these variables can increase yield and quality of
grain that is put into the bin, the ability to harvest
faster has broad implications on larger farms that
can suffer tremendous yield losses when endof-season weather events slow or halt harvest
completely.
Another integration of data science and farm
machinery is predictive maintenance. The idea
behind predictive maintenance is that time- or
usage-based maintenance scheduling typically
is either too early or too late. If the maintenance
is performed too early, the owner is over-caring
for their assets and wasting time and financial
CROPINSURANCE TODAY®

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Crop Insurance Today Fourth Quarter 2020

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