Prassack Advisors

Industry Insights

Spring 2016

Human Behavior in Agriculture and the Technology Adoption Curve

by Dave Lundberg, Executive Consultant, Specialty: SAAS Software, Data Solutions, Agriculture

Their [farmers] evaluation criteria will always be the same – they’ll always ask themselves… So What? 

Farmer-collected data is the foundation of all data-driven decision systems in precision ag practices across the entire agricultural ecosystem.  This critical, high-resolution data feeds the rest of the data and analytics eco-system.  In fact, the business success of new technology solutions and potential increases in farm productivity are fundamentally dependent on sufficient numbers of farmers putting forth the additional effort and expense to consistently capture and maintain better data.  However, advanced data collection and data management is real work and real expense shouldered by the farmer:


  • Expenses

    • Buy and install the data collection equipment:  monitors, sensors, networks

    • Buy the data management software and services

    • Pay for data storage and back-up systems

    • Pay for training time by their own employees – full time or seasonal


  • Work effort

    • Learn to use the tools and train all employees

    • Maintain and calibrate the data collection equipment regularly

    • Take the extra time to actually use the equipment – even during the rush of planting and harvest

    • Manage and apply the actual data using software applications

    • Protect their data from unwanted use


Unlike the few early adopters who will spend more time to collect data without clearly proven value, the mass majority of farmers do not - and will not - until they know without hesitation that the additional work and expense will return sufficient value.  Market analysis indicates it likely that less than 10% of all farmers consistently capture and manage their own high-resolution data with quality.  To increase the quantity and quality of farmer-generated data, we must understand and communicate the threshold at which new value from data sufficiently motivates behavioral change at scale. 


The adoption rate of intense data collection and data management practices is not unlike the adoption of hybrid seed varieties during the 1930’s.  A sociological study on hybrid seed adoption rates was originally developed at Iowa State University in 1957 by Joe M. Bohlen, George M. Beal and Everett M. Rogers. This early research and its findings have become the benchmark of business and consumer technology adoption studies across an entire spectrum of industries and was the foundation for much of Geoffrey Moore’s work in Crossing the Chasm. 


In simple terms, the studies have shown a small percentage of consumers in any market will try a new practice before any proof of benefit is available – the Innovators.  A somewhat larger number of consumers will adopt new practices with limited proof of value, but are motivated to ‘improve’ and will try out new practices – the Early Adopters.  But far and away the largest majority of consumers fall in the middle of the curve; not willing to risk a loss without sufficient proof of value – the Majority.  The Majority represents from 70% to 80% of the target market.  Without adoption by this population, the long-term viability of any new technology is at risk.  Today’s adoption of consistent data collection and data management practices is clearly by the Innovators and Early Adopters. 


To increase the adoption rate of data collection and management practices by the majority of farmers – to get beyond the Innovators and Early Adopters – to move past the ‘hype’ stage - an increased effort must be made to prove and communicate exactly how an investment in better data management practices does in fact generate one or both of:

  • Increased profitability (higher net income through increased yields, reduced costs, or higher prices)

  • Reduced risk (reduced agronomic and economic risk as well as protection of personal data)


Fortunately for the ag technology industry, we can improve adoption in two ways:


  • Clearly communicate proof of value in quantifiable terms – show the proof of:

    • Reduced input costs

    • Increased yields

    • Higher prices (e.g better market and hedging decisions)

    • Reduced overhead for regulatory reporting requirements (e.g. traceability, nitrates)

    • Improved risk management and measurable risk offset


  • Reduce the effort and expense of data collection and data management:  

    • Provide easier data integration and sharing – eliminate need to be an advanced, tech-savvy user

    • Provide easier access to third party data

    • Improve usability of data collection systems – low/no training barrier to adoption 

    • Provide ready access to training and curriculums on data collection and data management

    • Negate the issues of data ownership and data security risks

It’s easy to be fascinated by new technology – many of us make a career out of it.  But, the most important question we should always ask when evaluating new technologies or data services is, “So what?”.  Will the new technology really make a viable difference across the majority of users?  Will most farmers really make the investment of time and dollars?  Even the Majority farmers – the 80% of your market - can get excited about new technologies, but their pragmatic nature will always drive their final decisions.  Their evaluation criteria will always be the same – they’ll always ask themselves… So What?