The Future of Farming: Ag Subcommittee Testimony
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Transcript of The Future of Farming: Ag Subcommittee Testimony
The Future of Farming: Technological Innovations,
Opportunities, and Challenges for Producers
Roger Royse
Royse Law Firm, PC
Royse AgTech Innovation Network
Dynamics
• Food Security
• The Rise of Consumerism
• The Declining Labor Market
• The Changing Agricultural Markets
• Increased Rural Internet Connectivity
• Cannabis
• The Introduction of Venture Capital
• The Rise of Big Data
Food Security
• Factors: climate change, growing global population, rising food prices & environmental pressures
• Impact people’s physical, social & economic access to sufficient, safe & nutritious food
The Rise of Consumerism
• Grow Local Movement
• Growing middle class’ desire for convenience & year-round food
• Tech development: urban & vertical farming, waste, transport & packaging
• Transparency in supply chain
• GMO & gene editing practices
• Environmental sustainability
The Declining Labor Market
• Farm labor shortages resulted in increased labor costs
• Technologies (e.g. Farmbot & Blue River Technology) replace manual labor part of farming process
The Changing Agricultural Markets
• Harvested cropland & agricultural labor work force declined over time
• Agricultural production increased
• Productivity increase
• Agricultural methods have changed
Increased Rural Internet Connectivity
• Adoption & application of AgTech rests on availability of the Internet
• AgTech is new rural driver for Internet utilization
• 70% of U.S. farms had Internet connectivity (2012)
Cannabis
• High margin & high cost
• Most high value (legal) crop in the world
• Easier to develop or implement tech compared to other crops
• Lighting (e.g. LED tech), water conservation, hydroponics, aeronautics & robots
The Introduction of Venture Capital
• AgTech startups raised more than $320 million (2017)
• Increase in Series B financings
• Diverse range of investment themes
• Willing & active venture investors
The Rise of Big Data
• Farm data: site-specific data (e.g. info about seeding rates, soil nutrients, fertilizer, pesticides, water, yield data)
• Meta data (e.g. info about number of acres, inputs applied, crops)
• Big data (e.g. aggregation of farm data from numerous operations)