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Jeff Bradshaw, Founder, Adaptris
Proagrica utilizes big data to enhance efficiency and insight in agriculture, consolidating vast data to drive value from farms to supermarkets. Their high-performance computing platform offers integrated analytics on various agricultural factors, helping to increase yield and profitability while minimizing environmental impacts. Key insights include the correlation of weather patterns and crop management practices with agricultural yields and data's crucial role in addressing challenges within the industry.
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Jeff Bradshaw, Founder, Adaptris
- 1. Using big datato 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 atthe Centre of Global Change
- 5. How could datahelp?
- 6. Precision Agri: OurData 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 ofthe approach
- 8.
- 9. What does itdeliver? ▶ 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 OSRusing 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 yieldvaried over the last 10 years? ▶ Average yield is 3,766 kg/ha
- 15. The spread inyield 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 thisisn’t constant!
- 17. Could it berelated to variety choice?
- 18. Popularity of hyridvarieties by location
- 19. Variety trait differencesby location
- 20. How do wevisualise the data? ▶ Over 150 pairs of variables to investigate ▶ No idea what is linked before we start……
- 21. Agriculture is atthe Centre of Global Change
- 22.
- 23. How to readthe graphs Two variables that are high at the same time
- 24. How to readthe graphs Two variables that are high at opposite times
- 25. How to readthe graphs Two variables that have nothing to do with each other
- 26. The market forOSR varieties
- 27. What causes variationin 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 datahelp?
- 29. The answers……….. ▶ Whywas 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 integratedagricultural customers