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DTN Digital Yield Tour 2018   08/14 17:18

   Digital Yield Tour Features Gro Intelligence Analysis

   As the 2018 corn and soybean crops head for harvest, DTN/The Progressive 
Farmer takes a high-tech look at yield predictions featuring the satellite 
analytics and modeling of Gro Intelligence and on-the-ground-reporting.

By Greg D. Horstmeier
DTN Editor-in-Chief

   OMAHA (DTN) -- The first high-tech, digital crop yield tour completely open 
to the public kicks off on various DTN/The Progressive Farmer digital platforms 
Aug. 15.

   The DTN/The Progressive Farmer 2018 Digital Yield Tour, powered by Gro 
Intelligence, is an in-depth look at how the 2018 corn and soybean crop is 
progressing, without the dust, sunburn and gasoline bills of a traditional 
multistate tour.

   The digital "tour" will cover corn and soybean yield expectations of 10 
Midwest states: Illinois, Indiana, Iowa, Kansas, Minnesota, Missouri, Nebraska, 
Ohio, South Dakota and Wisconsin. This year's tour is based on the real-time 
crop analytical models of Gro Intelligence, a data analytics company focused on 
the agriculture industry, but it also includes weather and other crop condition 
information from DTN plus on-the-ground reports from farmers within the DTN Ag 
Weather Station network.

   The keys to this crop outlook are the yield models and the visualization 
technologies of Gro Intelligence, headquartered in New York. Their modeling 
system is based on seven types of publically available crop and environmental 
data: normalized difference vegetation index (NDVI) from NASA satellite 
imagery, land surface temperature (LST) maps, rainfall data, USDA crop 
condition surveys, crop calendars, planted and harvested acreage data from the 
National Agricultural Statistics Service (NASS), cropland data and U.S. 
government soil surveys.

   Gro senior vice president for agribusiness James Heneghan said the Gro model 
uses the latest machine learning methods to take yield predictions to the next 
level of accuracy.

   "A classic modeling system compares historical data to present conditions 
and variables within the data," Heneghan said. Once set, those models apply the 
similar basic comparisons each season.

   "With machine learning, our system is learning which variables have the most 
weight at any particular time, and re-adjusts itself to those variables that 
are likely the most important at that time." When the model misjudges a 
variable, it automatically "remembers" that issue for the next time that 
scenario happens.

   Using satellite and other broad-swath data also gives a more complete 
picture of yields, Heneghan said. Gro's datasets start at the county level, 
then build from county data into statewide and national yield outlooks. In 
recent years, USDA has had data gaps in certain counties due to the lack of 
farmers participating in National Agricultural Statistics Service yield surveys.

   "For this digital yield tour, that county data means that farmers can follow 
web links to the state maps, then hover over counties they are interested in 
and get detailed yield and related numbers."  

   Constant updating "can make our models a bit more volatile early in the crop 
season," he said. "We at times have higher highs and lower lows compared to 
USDA and other trade estimates in the marketplace early on," as highly weighted 
variables within the model have sway over other data at that point in time.

   "As we move through July, the variables that will ultimately determine yield 
(like heat and rainfall patterns) begin to show through in the model." An 
example, he noted, was in 2017 when Gro models varied significantly from USDA 
trendline yield predictions. Those familiar predictions, based on USDA's 
historical yield patterns, smooth out early season weather extremes or pest 
issues that machine learning models may focus on. By harvest, the Gro corn 
model had set yields at 176.86 bushels per acre for the national average. 
USDA's final tally, announced during the January crop report, was 176.6 bpa.

   During much of the growing season, NDVI imagery from the network of NASA 
satellites orbiting Earth is a critical piece of the Gro model. "Vegetative 
health is the key variable in corn and soybean production," Heneghan said. That 
health, however, has to be balanced with the area of crops that show poor 
health and likely low production.

   A classic example that will weigh into the final DTNPF 2018 Digital Yield 
Tour national tally: Large swaths of eastern Kansas and northern Missouri have 
some of the driest conditions in years. Much of the corn in those areas is 
either being cut for silage or awaiting the crop insurance adjuster, not the 
combine. That loss of production, though, must be compared to the ideal crop 
conditions in parts of Nebraska, Iowa and Illinois.

   In early August, the Gro corn model set the national average yield at what 
research analyst Eric Moore called a bearish-at-the-time 177.5 bushels per 
acre. Trade estimates were around 176 bpa. When the Aug. 10 USDA report came 
out with a value of 178.4 bushels per acre, analysts said their trading 
customers were better positioned to profit from the even more bearish news.

   SADDLING UP FOR THE TOUR

   Each day of the digital tour, which runs Aug. 15-20, DTN reporters will dig 
into Gro datasets, looking at significantly high and low counties in each state 
and looking for reasons for those extremes. They will add in comments from 
farmers in those areas, when available, to give a fuller picture of what the 
crops look like. They will also refer back to weather, pest and disease issues 
that might have plagued an area and look for the conditions that led to some of 
the "catch up" stories we've been hearing.

   Articles will include links to the company's visuals database, so readers 
can look at the production patterns across their state or other areas of 
interest. They will be able to drill down into the county-level modeling 
numbers, as well, to compare what USDA and their own experience says about the 
county's production with what the models reveal.

   Gro models update almost constantly as new data from satellites and other 
sources comes in. The company's public website typically reveals yield 
prediction numbers that are several days behind the latest model "run." That is 
why the yield a viewer sees might be different than yields revealed in the DTN 
articles that focus on current-day conditions.

   It's rare for a commodity trading intelligence company to share its inside 
look at the markets with the public. It's Gro's philosophy to change that, 
which is one of the reasons the company is providing its data to DTN/The 
Progressive Farmer.

   Gro founder and CEO Sara Menker, a former Wall Street commodities trader, 
said her company's open approach comes from the desire to change how data is 
actually used to make decisions in agriculture and food systems. In a 2017 TED 
Talk, she noted that when founding the company, she wanted to create a way to 
make critical data "actionable by decision-makers at every level." In building 
her company, she said, "We realized that the world, not just world leaders, but 
businesses and citizens ... lacked an actionable guide on how we can avoid a 
coming global food security crisis."

   Heneghan encourages the public to look through the website and read the 
company's white paper on how the models were developed.

   "We don't want our methods to be just another black box that has limited 
trust." Trust in machine learning models will be critical, he said, in the 
fast-changing world of crop production. "The strides companies are making in 
hybrid performance, in mating the right seeds to right soil types and adding 
the correct fertilizers and pesticides, plus the better crop management farmers 
are doing today, just keeps pushing the U.S.'s ability to produce 
higher-yielding crops even in less-than-ideal conditions."

   Another reason for the openness is that much of the data Gro and other 
consultancies use in their yield models is from public sources, Heneghan said. 
"So we're taking a very public approach to this type of yield analysis in hopes 
to start a broader conversation around it. We hope it results in a product 
people can pick up and use as part of their current marketing strategy, as well 
as start a conversation around the subject in general."

   Developing that trust is also behind the conversation between Gro and DTN in 
the 2018 digital yield tour. "DTN and Progressive Farmer are long-established 
businesses with a reputation for supporting farmers and the agribusiness 
community," Heneghan said. "Being a newer, lesser-known company, we appreciate 
the ability to provide the analytics we've been working on alongside the 
weather data and the journalistic content that DTN/PF brings."

   "As a company focused on turning data into actionable insights for farmers 
and others in agriculture, DTN has for some time wanted to provide a high-tech 
answer to the traditional crop yield tour," said Mark Holland, senior vice 
president-Agriculture and Transportation at DTN. "The yield models created by 
Gro Intelligence cover every field in every county of a state, not just random 
fields as with traditional crop tours. We're excited to share these insights, 
combined with DTN's boots on the ground, to give an in-depth look at the 2018 
crops."

   LEARN MORE

   For more information on the DTN/The Progressive Farmer 2018 Digital Yield 
Tour, visit https://www.dtn.com/digital-yield-tour-18/   

   To learn more about Gro, see their website here: 
http://www.gro-intelligence.com/ 

   To read the research white paper on the modeling system, go here and select 
to "Download the corn yield model paper": 
https://gro-intelligence.com/us-corn-yield 

   Links to each state's yield and crop condition maps will be given in the 
articles to come.

   Note that the yields shown on the public website, which typically are one 
week delayed from the live data, will show a different yield estimate than the 
figures you will see reported in the daily tour stories. 

   Greg D. Horstmeier can be reached at greg.horstmeier@dtn.com 

   Follow him on Twitter @greghorstmeier


(KD/AG)

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