Report by Daniel Foy, Co-Founder & CEO, AgriGates

Workshop Hosts:


Opportunities & Conclusion


The workshop aimed to conduct and collect a (S.W.O.T) analysis with the participants from the workshop on a specific theme of interest to the attendees within food, agtech and livestock. These topic themes were:

  • What Funding is Needed to Accelerate the Uptake of Precision Livestock Systems?
  • Where is the ROI for Data Collection and Transparency in the Supply Chain?
  • How do We Improve the Accuracy of Hardware to Produce More Reliable Data?
  • How Could Systems Integration and Standardization Enhance the Effectiveness of PLF Platforms?


Digital literacy and the digital divide were common themes throughout the four groups, both from the farm level, where basic skills to intermediate digital skills training are required, but also across the supply chain and service industry, where more advanced and complex digital skills support and services are needed to better serve the future needs at the farm level. Rural broadband and connectivity are still a prevailing issue, inhibiting effective deployment and adoption of digital systems, while agritech suppliers are still building systems that need continuous and quality connectivity, which isn’t the case in all agricultural and rural locations at this time.

Large farms’ ability to deal with connectivity issues independently still leaves smaller and medium producers without access and reduced adoption ability and creates a digital divide between farms and the services that can be adopted and supplied to them. Ensuring the appropriate rollout of diverse, quality, and affordable rural connectivity methods is essential for the next wave of agritech tools and services to farms and for farms of all sizes and varieties (diversity) to operate sufficiently and successfully in the future. Future agritech systems should be resilient to intermittent connectivity and allow farms to operate continuously and smoothly with critical decision support.

The average age of our producers is compounded by low digital literacy rates within small to medium size farms, yet the producer is the domain expert at the center of the action and change. This is a linchpin to the future of on-farm digital adoption, for strategy and value for sustainability and welfare objectives in food animal agriculture.

The right to repair with agritech tools. Today this is not conducive to the first generation of hardware and software that is deployed to farms when yet, the models that are being deployed in other sectors emulate a symbiotic approach to an agile culture, the right to repair, and user skills growth.

The producers and their teams are at the center of agritech at the farm level and a critical part to success in sustainability and welfare in food animal agriculture, to which the consumer has demanded more accurate information on how and where their food comes from. Green-washing, or welfare-washing within food animal agriculture, is a large threat to the entire sector. It must be ensured that quality, validated, and valuable data is collected and used to inform the consumer. The need for farm data ownership is important, as the ability to determine if it’s a farm data issue (management) or an agritech issue will be significant as we move towards data use for standards, audits, and payments at the farm level.

Quality, validated, and valuable data will mean that data standards are adopted to form a common data production system at the farm level, which will be used for agile development and deployment of insights, intelligence, and decision support, while outputs are used to inform the consumer. Independent validation of on-farm outputs will begin to be a method of growing trust between the producers and their agritech on-farm and generate greater competition between suppliers and their tools. This should manifest to include:

  • The ability for farm-level validation of data, independent from the suppliers of the agritech data production tools. Reducing our reliability of validation from self-validation of tools or academic research herds to farm-specific data validation.
  • The emergence of a forum for all food animal species, data points of relevance, and data quality standards will allow faster innovation and development from wider use case assessments for agritech in food animal agriculture.
  • The emergence of regionality and farm style within the data, allowing for the recognition that no two farms are the same. The diversity of farm systems is an opportunity.
  • Integrated data for inter-data validation and sensor fusion.

Farm-level data has value. In Europe, the value of human digital identities are worth $1 trillion, while ‘Things’ are already generating data, predicted to create additional value-add of $1.9 trillion globally over the next five years (Future Agenda, 2022). Organizations with a strong data culture have nearly 2x the success rate and 3x the return from AI investments (Accenture). Value is predicated on quality vs big data, where we find ourselves today.

The importance of producers accessing the tangible value from their data as an asset represents new value to producers and their businesses. The ROI on agritech tools also has to be better displayed to the end user, and how / why they will adopt that data system, while in an agile approach, thinking about how this system or data, aids and benefits the next area of challenge and opportunity on farm.

The strategic deployment of agritech tools to the challenges and opportunities at each farm, within an agile approach to deployment is optimal. The sustainable deployment of agritech ‘can’t just be a blanket bombing of systems to collect all the data at the farm’, ‘let’s not try to boil the ocean in our attempt to solve food, animal agriculture and agritech opportunities’, ‘we can cut out our sustainability gains, by not thinking strategically about our agritech deployment at the farm-level’.

A longer life cycle of agritech tools toward five to ten years, over the current two to four years life-cycle, is essential for agritech sustainability, longevity, and value. The Interoperability of agritech solutions, with a reduced number and specific common APIs, will be vital for the agile and scalability of agritech, which reduces cybersecurity risk, at the farm level.

Cybersecurity and greater security posture within food, animal and agritech are needed, as food security is now data security (Foy 2023). As the quality, use, and value of data increases, so too does the cyber threats: Hacktivism, Terror Attack, Ransomware, and Foreign Adversaries. Farm data ownership, with a more nodal approach to farm-level data management, is necessary within that future cybersecurity strategy and model.

As we see with our European counterparts, government regulation will come to food and agritech. It may evolve differently in the US but will nonetheless mean that the sector should get ahead and prepare before we are told what to do, or the stick and carrot approach is deployed. Security risks, continuous supply, value, sustainability, and animal welfare are important, especially as food and animal products are a critical source of essential nutrients that are vital for a developed / developing economy. With the age of social media and the influence it can have on the consumer, it may lead to change in regulations that are non-science based, which the sector needs to be prepared. This can aid and combat with quality data and metrics, highlighting progress on the objectives in sustainability, animal welfare and the importance of food animal agriculture in soil and one health.

More farmers’ voices are needed to participate in this discussion on these topics. What they experience, what they need and what is unique to them is important as we move towards the next generation of on-farm agritech tools and develop our use cases. One in five farmers are dyslexic, when the general population is one in seven, and may be why we have so many innovative and deep thinkers in the sector. This higher population of neural diverse individuals within our sector begs the question are we creating the necessary UX/UI that impacts the learning and education styles of our users? When most agritech systems are developed and designed by degree-level and above companies and individuals who are off-farm?

Lastly, are the right agritech solutions getting funded? Is there a need for a specific food, animal and agritech fund that is inter-species? Does there need to be a re-education of VC’s on the appropriate runway length and funding appropriate objectives in food animal agriculture and agritech? When we talk about digital literacy, this must be extended to the funder level; VCs tend to be the gatekeepers yet may lack the specific industry or rural domain experience to appropriately fund future food, animal and agritech businesses that work within rural communities.


The future is exciting within food animal agriculture, as we are coming out of our first wave of agritech (the peak of inflated expectations, see image one). What may seem like a negative lull (the trough of disillusionment, see image one), but this is a natural point in any sectors tech deployment to reflect and look back, where are we, what’s worked, what’s not worked, how’s the landscape forming and what we should do next (i.e.. digital literacy). This is being agile.

As we look forward to the next decade of food, animal, and agritech deployment (or the slope of enlightenment and the path of productivity begins, see image one), there will be even more opportunities that will be illuminated, offering a clearer path to value for all in the food, animal and agritech sector, especially at the farm level, and from the farm up.

There is immense value in data, billions of dollars; this can become a tangible and decision-support value to producers, which aids the speed and degree to which we can tackle opportunities and challenges at the farm level. There is yet to be an agritech multiplier effect within food, animal and agriculture, that will be centered around the farm level.

The stickiness nature of animal agriculture businesses makes it a key strength for service suppliers to want to work with producers, as farm level opportunities and challenges can last / happen over decades and generations. Thus the need to better align with the funders and funding within agritech, as there are no short-term gains, in a long-term relationship.

There are many new jobs still to come to fruition within the animal agtech space, areas like IT support to farms, data validation and cybersecurity. These areas will generate many new jobs in food, animal farms and agritech, across both small and local businesses to multinational and international capabilities. Once we have the digital skills disseminated and training widely available within food animal agriculture, it offers an even greater opportunity in this area. Removal of subjective measurements of data collection and human error that has greater value across the supply chain and information for the consumer. While having independent validation of agritech data, where we need data forums to engage on this topic and aid in validated and volumes of data of quality (ICAR, ASABE, IEEE etc..)

The strategic, dedicated, and impactful food animal VC funds will start to emerge, which will ignite more innovative, novel technologies and ideas. This may even include farmers (like we see with Kipster Poultry Farm and Pasture Bird) and as we see farm data ownership emerge. As we continue to identify separately from that of crop agritech, which has had a head start, food animal agriculture will have divergent, sometimes similar challenges and concepts. Today we must ask are the right companies getting funded? Are the funders educated enough on the topic and unique challenges at the farm level in food animal agriculture? Are their runways and expectations suitable for animal agtech? But undoubtedly, we will see dedicated food animal agritech funds appear and increase over the next couple of years.

How will the US land grant universities and extension evolve to aid the farmer, and how will they participate in the equation? There seems to be a large opportunity in the US, as its Land grant universities and extension are a unique attribute and can aid in the regional diversity of food animal farming systems. Taking advantage of our academic institutions, these centers can refresh their raison d’être and become a critical part of the animal agritech ecosystem of the future.

The dissemination of digital literacy from the farm up and across services and supply chain is imperative as we look to the next generation of agritech tools in food animal agriculture and how we sustainably apply these tools for enhanced value. There are many use cases in this adoption cycle from other sectors, and they are overcoming this challenge, while what may be an opportunity for our US-based land-grant institutions?

The future is bright in food, animal and agritech. It might be a little complex, but complexity is an opportunity and can be overcome and simplified, while complexity illuminates that it’s not an impossible challenge.

Future Workshops:

  • Outline solutions to the SWOT analysis from 2022
  • Review progress from the 2022 SWOT analysis
  • Future workshops should have producers’ participation


  1. Foy, 2023 – (PDF) Introduction & Overview Data Value and Data Protection in Food Animal Agriculture ( 
  2. Foy, 2023 – Where Does Your Farm Data Go? – 
  3. Future agenda – Value of Data – Future Agenda 
  4. Accenture – Data Value & Data-led Transformation Services | Accenture 


Group 1: What Funding is Needed to Accelerate the Uptake of Precision Livestock Systems? 

Host: Matthew Rooda, President & CEO, SWINETECH


  • Can a range of funding models assist the introduction of new PLF solutions to help improve outcomes in health, welfare and productivity?
  • How much impact could the potential Precision Agriculture Loan Program Act to help drive uptake and what role can public spending play in encouraging PLF?
  • Where can PLF integrate with automated systems to further increase returns from their introduction?


  • “Stickiness” NIR 40 yr., Dairy 25 yr, ESG 40 yr, smart culture 10 yr 
  • Pure software play 


  • Infrastructure
  • Time
  • Environmental impact


  • Word of mouth
  • Consumer request
  • Retirement
  • Tech ecosystem
  • Lighthouse customers
  • Government regulation
  • Performance vs VALUE
  • Risks due to variation (Performance) (Responsible use of tech)
  • Consumer food preference (tech fit)
  • Multicultural (Work culture and traditional work)
  • Substitution of labor vs tech


  • Stickiness could change status quo of adoption 
  • Cost of innovation (stagnant) 
  • Lack of succession 
  • Government regulation 
  • Performance vs VALUE 
  • Risks due to variation (Performance) (Responsible use of tech) 
  • Consumer food preference (tech fit) 
  • Multicultural (Work culture and traditional work) 
  • Substitution of labor vs tech. 

Group 2: Where is the ROI for Data Collection and Transparency in the Supply Chain? 

Host: Rob Trice, Founding Partner, BETTER FOOD VENTURES 


  • How can producers leverage traceable improvements to sustainability, health and welfare to promote their products?
  • When companies are profiting from on-farm data, how do we determine the correct level of compensation for the data provider?
  • Will the increase of data in supply chains add value to products, and if so, how do we ensure this value is shared upstream?


  • Fasts / research is well known 
  • Baseline measurements are good 


  • Incremental improvements of data insights
  • Slow change rate (Genetics infer to)
  • Data capture is still hard
  • Not about to aggregate data (silos)
  • Need fresh, actionable data
  • Data rich decision poor
  • Standardized, interoperable, machine-readable data
  • Diversity of thought on data sharing across production systems and interspecies.
  • Cattle
  • Dairy
  • Pork
  • Poultry
  • Other small ruminants
  • Ancient / aging workforce, diverse workforce


  • Increase efficiency or gain “premium”
    (Reduce cost) 

Value =  Benefit 



  • Regulatory: Politics

Time / Speed of Exchange


  • Expectations
  • Trust
  • Power Imbalance
  • Ignorance
  • Duplicity
  • Digital divide of small / large producers
  • Small producers/ lifestyle – ‘apathy’
  • ROI must fit between cost: what the consumer/ buyer is willing to pay.
  • Laggard producers can dictate industry


Group 3: How Do We Improve the Accuracy of Hardware to Produce More Reliable Data? 

Host: Daniel Foy, CEO & Co-Founder, AgriGates 


  • How do we validate and improve the accuracy of PLF hardware to ensure that on-farm insights are usable and beneficial?
  • Where have sensors, cameras, tags, and other monitoring hardware shown the best results in data accuracy and how transferable are results across different farming systems?
  • How can legacy hardware be integrated into PLF systems to make data insights more accessible to producers?


  • Labor benefits 
  • Measuring objectively 
  • Integrity in products and practices 
  • Demanded by consumer
  • The partnerships with research and the uniqueness of land grant institutions for initial research 


  • Durability of tech 
  • Harsh environmental conditions 
  • Longevity 
  • Battery / energy example 
  • Cost (more for small to medium farms) 
  • How to choose hardware 
  • Reinventing the wheel on tools in food animal agriculture that are existing and deployed in other sectors
  • Digital + data literacy 
  • Appropriate validation 
  • Self-validations 
  • Translational validation 
  • Region to region 
  • Hardware scale and footprint 
  • Not currently smart IoT-Generation 


  • Remove human entry
  • Life cycle 
  • Right to repair 
  • New vale insights 
  • Sustainable measurements – ESR 
  • Digital Literacy 
  • Farm 
  • Services industry (e.g. up-skill vet) 
  • Standards forum or council 
  • Existing forums like IEEE, ICAR 
  • The importance of the independent and unbiased forum 
  • Standard APIs 
  • Reduce API vector threats 
  • Community approach and practice 
  • Retro fitting. No two farms the same 
  • Integrated data value for integrated service analytics and diagnostics. 
  • Hardware, Data, and software (three items) 
  • Centers of excellence 


  • Connectivity
  • Integrity
  • IP competition
  • Digital literacy
  • Appropriate validation techniques
  • Privacy + data ownership
  • Cybersecurity
  • Ability of existing forms (ICAR)
  • Change in regulation that are non science based

Group 4: How Could Systems Integration and Standardization Enhance the Effectiveness of PLF Platforms? 


  • Can data inputs and decision-making processes be standardized across systems to promote ease of use and switching to different platforms? 
  • How can communication between the platform provider and user ensure a better understanding of the decision-making process and improve outcomes from their systems? 
  • Should decision-making algorithms be available to observe, to enable full trust in their decisions? 


  • Domain Expertise 
  • Direct Communication Style 
  • ROI-Focused 
  • Data Driven 
  • Better Tech Economics 
  • Broadband Access 
  • Generational Change (Demographics) 


  • Status Quo 
  • Trust / once Burned. 
  • Brittle IT infrastructure 
  • Software as “equipment.” 
  • No standards 
  • Between system 
  • Within Systems 
  • Broadband access 
  • IT Support Networks 
  • Skilled labor 


  • Innovators / Mavericks 
  • Set of Standards 
  • Cloud Capture Value 
  • Market Access / Exports 
  • Stepwise plan (agile) 
  • Developed internally
  • Visibility 
  • Education / Customer Success