1 ‘ON Ag Organizations: educate & protect’Precision Agri‐Food Technology: Developing Ontario’s Vision and Strategy June 2016: A Precision Robot Dairy Perspective Ben Smink Lely North America Farm Management Support Audience: Precision Agri‐Food Technology = Developing Ontario’s Vision and Strategy = interdisciplinary workshop to review province scoping study = by specific invite only = gathering 140 leading/visionary minds with stake how field will be applied ON = collaborative funded by the Growing Forward 2 program (AAFC/OMAFRA) = led by: UofG, Grain Farmers, Vineland Research, Livestock Research. Fruit Vegetable Assoc At this meeting we will review:  What we learned. We would like to tell you everything we heard and learned.  Where we are going.  Continue the conversation. My background: Dutch => USA Precision tech and data exchange since 1990 Information tech since 1996 In 25 years not enough has changed (besides Holland ) ‘ON Ag Organizations: educate & protect’

2 2.550 7 6 Founded 1948 € 617 mln 5% growth Sales 2014Number of employees fte Investments in R&D from our product turnover 6% Number of living patents 2.550 Number of R&D units 7 Number of production units 6 Number of markets > 60

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4 A Precision Robot Dairy Perspective:In this presentation: What milking robots measure and present Decision support already used today What do producers expect in the future Future considerations for Ontario Just drawing the line…… what happened in the past to today Continue the line in the future Learning from other areas and industries

5 > 120 Values/cow/day from the robot:Observe Activity* Rumination* Milk Yield Milk Fat Milk Protein Milk Lactose Milk Speed Milk Temperature SCC* Robot visits Box times Feed intake Per Quarter: Yield contribution Teat position Pre Milk Time Milk Time Conductivity Color Data collections get huge Valuable only when aggregated….. to Weight* + combinations of all of the above ... + combinations with calendar + health events + combinations with external data points ….. * = option

6 Automatic Analysis and Attention:Observe Automatic Analysis and Attention: Aggregated data from sensors + cow calendar Automatic Analysis: Production Udder health Body health Reproduction Meaningful actions Decision Support Automatic Steering Automatic Analysis and Meaningful actions Automatic tuning, already on farm level, but how about over farm level……..?????

7 Producers: Thoughtful of resourcesVision Producers: Thoughtful of resources Get the best out of land and herd Unused production talents cows Secure cow’s health Utilize genetic potential Capitalize on genetic evolution Precision monitoring Precision handling Precision decision support Smart affordable efficient resource management. Combining real time on site data with reliable external info! Department of Economic and Social Affairs of the United Nations (2007) Global circumstances will drive economic use of ALL resources Key: Real Time and Reliable.

8 How could that look like?Video: “Farm Management Vision” … to build you a picture….

9 Decision Support used today:90% Lely milk robots connected to internet For remote technical support 80% Robot farms connected to Lely Benchmark = Global data cloud aggregated data = Incl. data from all robots in Canada Technology used today over the past 7 years

10 Decision Support used today:Producers: Dynamic milking and feeding Producers: Compare results with others and learn Producers: Automatic web advice based on own data Advisors: Monitor results, compare and improve Dealers: Monitor equipment for pro-active maintenance Dealers: Field Intelligence showing farms going up/down Lely Int.: Field Intelligence to improve quality equipment/services Lely Int.: Field Intelligence to optimize settings CRV NL: Compare Sires suitable for robots Inseminator: Insemination + cows ready in barn. What we do with it already starting 2010 and growing…. LELY DATA CHARTER…. ACADEMIC AND GOVERNMENT ONLY IF CUSTOMER SAYS OK $$$ INVESTED LEARNINGS / MISTAKES / GOOD DECISIONS

11 Decision Support tomorrow:Cluster Analysis: Enhance Decision support A.I. / Fuzzy logic: Auto tune output Connection producer partners: Seamless planning activities Genomics & Genetics Disease prevention / optimize yield External market info: Adjust to environment & resources Interconnection including sample labs, feed labs, field equipment with Artificial Intelligence (AI) embedded into everyday technology that save time, energy, and stress 100% resource management not only water, wind, sun, feed, but including nitrogen, phosphates etc full traceability of the milk supply management chain, which also means full traceability of medications etc AI decides where when which future load of milk will be (fully automatically using Google trucks) shipped. “just in time, just for me” advice at the point of care. Automatic detection of trends on the internet. Not search engine management, but automatic risk management trends of peers in the market, (peers in terms of production factors, like goals, genetics, generational stage, size, etc.

12 Many initiatives … : Examples: On-site: Lely T4COver-site: Lely Benchmark MyJohnDeere.com https://www.youtube.com/watch?v=sDUJ86NeOA4&list=PL1KGsSJ4CWk7e78CRA1w9DFDx0QHn5iXT&index=1 AGCO Precision farming: 365 Farmnet Claas: https://www.365farmnet.com/en/product/concept/ Vault milk broker: Smart Dairy Farming NL:

13 Future considerations:Three connection platform layers: On-site for sensors (‘Internet of things’) Over-site for system data (‘Internet of systems’) Global for aggregated data (‘Internet of clouds’) Ultimately, only few main providers will remain Connected to generic cloud solution Pay as you use The reason for these three levels: Redundancy: the system has to keep running at connection failures Owner of aggregated data and be able to do something meaningful with it. Platforms will weed out with only place for three or so at each level ‘Survival of the fittest’ ‘Imposed by: Legislation Best user experiences, reliability and added value of aggregated info. Sales volume of equipment and accounts NL + DK are easy: one dairy partner (CRV / DHI / Region) USA is a mess, Canada has great opportunity due coherent industry lead by ON

14 Future considerations Ontario:Take the lead in Canada … Look over the border … Don’t ‘own’ precision ag platforms … Team up with existing strong initiatives … Stimulate initiatives brought up by industry partners … Educate producers Educate consumers Protect producers Protect consumers NL + DK are easy: one dairy partner (CRV / DHI / Region) USA is a mess, Canada has great opportunity due coherent industry lead by ON WHAT ACADEMIA AND GOVERNMENT SHOULD DO: TO ACCELERATE INDUSTRY ENABLE SUCCESS CREATE OPPORTUNITY HOW INDUSTRY SHOULD PARTICIPATE FUNDING SHARING AGGREGEDATE DATA POLICY PROGRAMS ENABLE RESEARCH??