Jeff Bradshaw is the founder of Adaptris and Group CTO of Adaptris/F4F/DBT within Reed Business Information. He has spent his career integrating data wherever it resides and in-flight across a number of industries including Agriculture, Airlines, Telecommunications, Healthcare, Government and Finance. Jeff has worked with and contributed to a number of international standards bodies and continues to work with large enterprises to help them extract value from their data silos and share data seamlessly with their trading partners to achieve business benefit. For the last few years Jeff has been focusing on Big Data and how to gather that across a wide range of sources to help gain insight into the agri-food supply chain. Abstract Summary: Precision agriculture – Predicting outcomes for farmers using machine learning to help feed the world: Agricultural data is vast, often unstructured and includes many challenges when working with legacy farm systems on premise in rural areas. For instance, traditional farm equipment such as tractors, sprayers, and combines aren’t often from the same vendor, and it’s complex moving data between them. This is further complicated with the vast array of other systems used by our farmers. Furthermore, the number of sensors in agriculture is astonishing, whether it is sensors that measure the gait of the cow walking into the dairy parlor, or chickens that are pecking. All this data needs to turn into usable information on a global scale to improve the yields farmers get and provide greater visibility into what’s going on both in and out of the farm. In this session, a case study will be shared on how data was collected, normalized and analyzed leveraging the open source HPCC Systems platform from remote Farm Management Systems (used by farmers to manage their farms), and when merged with weather data, soil data and actual machinery data, the analyzed predictions is used to feed Agronomists and Crop Protection/Seed Manufacturers to get recommendations back. The goal is to deliver a precision agriculture solution, helping farmers increase their yield, which then helps feed the growing population of the world.

1. Using big data to help feed the world
Private and confidential

2. Who are Proagrica
Proagrica, the global agricultural division of RELX, drives growth and improves efficiency by
delivering high-value insight and data, critical tools and advanced technology solutions

3. Who are Proagrica
Proagrica, the global agricultural division of RELX, drives growth and improves efficiency by
delivering high-value insight and data, critical tools and advanced technology solutions

4. Agriculture is at the Centre of Global Change

5. How could data help?

6. Precision Agri: Our Data Landscape / Assets
Vast amounts of data spread across the Agricultural landscape. Proagrica is consolidating,
organising and enhancing this data to help drive value across the entire industry, from the
farm gate all the way to the super market shelf
Farm Machinery
Every piece of equipment on the
farm is now generating data and
wants to be precise
Agronomist
Providing farm advice, shape files
and data to farmers
Manufacturers & Distributors
Adaptris manages supply chain
connectivity between MFRS and
their Distributors
Weather Data
Global current and historical
weather and soil moisture data at
sub-field level
Farm Management Information
Systems (FMIS)
A wide spectrum of tools used by
Farmers all generating data
Satellites / Drones
Ability to identify yield and crop
issues from space / drones
Sensors
Ground and animal sensors
measuring everything from
animal fertility to soil moisture
Soil
Global soil type horizons

7. An overview of the approach

8. ProAgrica HPCC Platform

9. What does it deliver?
▶ Global insight through fully integrated ESB data,
Data As A Service and a range of Analytics tools
▶ An agile, scalable, resilient and secure platform that
can consume data from any source, consolidate,
enrich and expose global agricultural data from
everything soil to animals and all the way to satellites
▶ Precision Ag covering the full Ag value chain from
Mfr, through Agronomist, CO-OP, Farmer and
Distributor
▶ A range of Analytics solutions focused on Pesticides,
Herbicides, Fertilizers, Seeds, Cattle, Milk, etc. that
provide insight at market, region, farm, field and sub-
field levels
▶ Enabling the industry to increase yield and
profitability whilst reducing inputs and improving
environmental impact

10. Patterns of OSR using Principal Component Analysis
▶ Why was the 2016 harvest in the UK so awful?
▶ What correlates to higher yields?
▶ How effective are pesticides?
▶ Are hybrids better?

11. A few gotchas……..
▶ Correlation doesn’t equal causation……..
▶ Some unusal yields ……….
Maximum yield:
36,784,867 kg/ha

12. 570 million Farms, 25 million Tractors, 50
billion chickens, 1 billion sheep, 1 billion
pigs, 80 million turkeys, 1.5 billion cows
in the world with 100% of them with
passports in the UK vs 36% of the US
population….
…and Big Brother / Data is here, for
Animals at least as they are all being
monitored / reporting data

13. A few gotchas……..
▶ Growers aren’t very skilled at data entry
Planted Seed Variety
DK Excaliber
DK Excalibur
Excalibur + Coating
Excalibur Stock
Excalibur and Catana
Rolled
OSR + 15:10:28
Planted Seed Variety
Excalibur
Excalibur
Excalibur
Excalibur
Other
Other
Other

14. How has yield varied over the last 10 years?
▶ Average yield is 3,766 kg/ha

15. The spread in yield
3,750 kg/ha
5,250 kg/ha
2,250 kg/ha
▶ Most growers are within 1,496 kg/ha of the average

16. …. But this isn’t constant!

17. Could it be related to variety choice?

18. Popularity of hyrid varieties by location

19. Variety trait differences by location

20. How do we visualise the data?
▶ Over 150 pairs of variables to investigate
▶ No idea what is linked before we start……

21. Agriculture is at the Centre of Global Change

22. How about now?

23. How to read the graphs
Two variables
that are high
at the same
time

24. How to read the graphs
Two variables
that are high
at opposite
times

25. How to read the graphs
Two variables that
have nothing to do
with each other

26. The market for OSR varieties

27. What causes variation in yield – it’s a similar story
▶ Degree days
▶ Fertiliser treatments
▶ Fungicide treatments
▶ Insecticide treatments
▶ Month of first insecticide application
▶ High wind events
▶ Temperatures
▶ Average rainfall
▶ Coldest week
▶ Total radiation
▶ Wettest weeks
▶ Driest weeks
▶ Soil moisture
▶ Precipitation
▶ Lattitude & Longitude
▶ Soil content

28. How could data help?

29. The answers………..
▶ Why was the 2016 harvest in the UK so awful?
▶ Wet spring and/or dry winter
▶ What correlates to higher yields?
▶ Warm spring, wet winters and proper pesticide application
▶ How effective are pesticides?
▶ Yes for fungicide, “perhaps” for insecticide
▶ Are hybrids better?
▶ Not really

30. Take home messages
▶ If you’re a farmer
▶ There are probably too many varieties of OSR in the world!
▶ OSR does better in wet springs and warm winters
▶ If you’re in to analytics
▶ Working with big data is a lot of fun
▶ Dimension reduction is great for picking out correlations in
complex data

31. Over 3,600 integrated agricultural customers