Advancement of Prescriptive Ag and Big...
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Advancement of Prescriptive Ag and Big Data John Fulton 2016 No-Till Oklahoma Conference, Stillwater, OK
Transcript of Advancement of Prescriptive Ag and Big...
Advancement of Prescriptive Ag and Big DataJohn Fulton
2016 No-Till Oklahoma Conference, Stillwater, OK
2015 Farm Evaluations & Decisions
‐ Stand evaluation (wet spring ‐‐‐ replant?)
‐ Soil Compaction (pinch rows, machine paths)
‐ N status in corn (Side‐dress: YES / NO)
‐ Disease (Fungicide: YES / NO)
‐ Hybrid selection and placement
Investment cost versus paycheck… $2/ac to $35/ac
Food, Agricultural and Biological Engineering
Precision Ag Evolutionary Phases
• The Start ‐ Early Pioneers ‐Mid‐1990’s• Technology Settlers ‐ Late‐1990’s and early 2000’s• Efficiency – Boom of Mid‐ to late‐2000’s• Automation ‐ 2010• Connectivity ‐ 2012• Digital Agriculture (decision agriculture) ‐ Today & Tomorrow
Has been a bit painful over this stretch but has brought opportunities.
Food, Agricultural and Biological Engineering
Precision Ag Evolutionary Phases
Automation & Collection ‐ 2010‐ Precision ag is main stream‐ Precision sampling along VR fertilizer and seeding standard services‐ Grid versus Zone???‐ Even larger equipment with embedded technologies to automate operation‐ iPADs‐ The Cloud‐ Incompatibility of hardware and software‐ Inputs tied to Precision Ag services‐ Sustainability discussions
Food, Agricultural and Biological Engineering
Precision Ag Evolutionary Phases
Connectivity ‐ 2012‐ Wireless and telemetry‐ Smartphones and tablets‐ APPs‐ Cloud technology on the full radar of agriculture‐ Data, data, data ‐‐‐‐‐ BIG DATA…
o Infusion of VC funding to data companieso Decision support tools…One‐stop Shopo CAN snifferso Agronomic & Machine datao Benchmarking
‐ Sustainability calculators‐ Incompatibility of hardware and software‐ Environmental concerns…
Food, Agricultural and Biological Engineering
Knuth Farms
Precision Ag Evolutionary Phases
Today & Tomorrow ‐ Digital Agriculture‐ Electronic drives versus mechanical and hydraulic for metering inputs (planter drives, PWM nozzles, etc.)
‐ Automating machinery…M2M, M2I‐ Prescriptive agriculture
o Data driven decisionso RIO?
‐ Online viewing dashboards (operational centers)‐ Agronomic, machine and imagery data (integration into individual platforms)
‐ Merger of agronomy‐technology‐business‐ Sustainability and Environmental Stewardship‐ Data growing pains…Incompatibility of hardware and software
Food, Agricultural and Biological Engineering
By‐row Prescription (Rx)• Hybrid• Population• Starter & pop‐up fertilizer• Down force• Row‐cleaner
Food, Agricultural and Biological Engineering
Rx Management
Digital Agriculture
Precision Agriculture
Prescriptive Agriculture
Enterprise Agriculture
Big Data in Agriculture
Based on information from an Iowa AgState / Hale Group report.
Precision Ag: +70% US acres
Prescriptive Ag: +15% of farms
+95% of farmers will outsource data management.
Adoption
• Preseason Fertility Management– Prescription P and K application (Precision Crop Services)
• Tillage Management– Prescription tillage maps (AGCO; CNH)
• Multi‐Hybrids– Prescription seeding of multi‐hybrids (Beck’s; Pioneer)
• SCN Management– Prescription application/use of nematicides (FMC)
• In‐Season Fertility Management– Prescription N application (DuPont Pioneer; Climate Corp)
• Irrigation Management– Prescription Irrigation (AgSmart)
• Disease Management– Prescription fungicide application (BASF)
Producer
Data Exchange for Growers
Data will need to move through multiple organizations and each organization will need different data sources.
Recommendations
Food, Agricultural and Biological Engineering
Types of Data
1) Agronomic – yield, as‐applied, as‐planted, etc.2) Machine (CAN) – engine parameters, tractor status variables,
implement mode & functions‐ CAN can also provide agronomic data
3) Production ‐ Information within home office, weather, notes, etc.4) Remote Sensed Imagery
Food, Agricultural and Biological Engineering
Food, Agricultural and Biological Engineering
Agronomic DataYield Maps, As‐applied…
As‐Planted Data
Machine DataCAN messages, Health, etc. Effective tool to evaluate operating costs and
capacity ‐‐‐ FUEL USAGE, UPTIME vs. DOWNTIME, ENGINE LOAD.
Bridging Agronomic and Machine Data
Moisture Content(%)
Ground Speed (mph)
Fuel Usage (gallons per
acre)Mean % Engine
LoadMean Field
Capacity (ac/hr)
Hybrid A 14.8 2.8 1.71 86 10.2
Hybrid B 14.3 5.2 0.86 44 18.9
BigData‐ Acceleratelearningthroughnewanalyticsandtherebyearlierselectionoffavorableeconomicresponse.
BIG DATA in Agricultural Production
Refers to the use of technology and advanced analytics for processing data in a useful and timely way. Big data may significantly affect many aspects of the agricultural industry, although the full extent and nature of its eventual impacts remain uncertain.
• Public‐level big data represent records that are collected, maintained, and analyzed through publicly funded sources.
• Private big data represent records generated at the production level and originate with the farmer or rancher.
Source: US Congressional Research Service
BigDatadoesnotexisttodayincropproductionbutbothag andexternaltoag companiesarebuildingcomponentstoenable.
MISSION:‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
To organize agriculture’s information and make it universally accessible and useful.
Big Data
• Internet‐related services and products• Founded 1998; Menlo Park, CA• Mission: to organize the world's information and make it universally accessible and useful.
• Revenue: $66 billion (2014)• Net Revenue: $14.4 billion (2014)• $502 Billion Market Capitalization• Google processes over 3.5 Billion searches PER DAY• Estimates of 530 Million Gmail users worldwide• 2 High‐use Data Services
‐ Gmail‐ Google Search
• Internet‐mobile app allowing consumers to submit and secure trip requests.
• Contracts with individual car owners to provide cab services
• Founded:March 2009, San Francisco, CA• Goal: connecting riders to drivers• Privately Held: Estimated 2015 worth $62.5B
Connecting Farmer Data and Transactions within the Ag “Ecosystem”
Internet example of linked companies “watching” my actions on 3 different websites.
New Age Business Models
• Notice these companies refer to “Users” not “Customers”• Income generating operations are unclear or kept offline• Basic model relies on data being fed in to the “system” at zero cost• There is no revenue sharing intended back to the providers of data
‐ Free Email Clients‐ Free Web Browsers‐ Free Search Engines‐ Free Social Media Site
The “Value” of Information is Changing….• Do not ignore the mountain of real‐time data being generated / collected.
• Make annual copies• Other new applications using farmer data are emerging
• “Trust a Data Steward”• Commit to learning more• Commit to being better…more competitive & improving your profitability!
Digital AgricultureProviding solutions to meet world demand
John [email protected]@fultojp
Ohio State Precision Ag Programwww.OhioStatePrecisionAg.com
Twitter: @OhioStatePA
Facebook: Ohio State Precision Ag
Food, Agricultural and Biological Engineering
