Research
Deep research conducted by the Metal Dog Labs team
Metal Dog Labs provides tailored research and analysis services for investors, agribusinesses, and startups as a paid service. Some examples of past research include
- Role of AI in herbicide discovery (for a large institutional investor)
- Role of GenAI in food ingredient management for packaged foods (for a FoodTech startup)
- Adoption challenges for automation services in South America (for a large agriholding company)
- Digitization of B2B marketplaces for specialty crops (for a tech startup)
- Workflow integration for equipment companies with agronomic recommendations (for a large agribusiness)
The following white papers are available for free for all members. Sign up for a free account to get the reports.
White papers (GenAI) - released in 2025
The following reports are available for free to members of SFTW.
“Software is Feeding the World” brings you two white papers on Generative AI. The white papers were commissioned by Bayer Crop Science, and supported by Digital Green, Kissan AI, and Traive.
In the first white paper, SFTW takes data from four agriculture focused organizations across different cropping systems, and regions of the world to provide a blueprint on how Food and Agriculture enterprises can get value from GenAI based capabilities.
In the second white paper, SFTW looks at value creation while using GenAI in agriculture and and provides a framework for AgriFood organizations around value identification, use cases, value creation and capture.
If you are a business or technology leader in an AgriFood organization these white papers are for you. Signup or Sign in to get access to the two white papers.
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POC to OMG - GenAI at the Farm Gate white paper (part 1 of 2)
Value Creation Frameworks for Generative AI in Agriculture (part 2 of 2)
Get both white papers together
White Paper on impact of AI on software adoption in AgTech (March 2024)
Agtech software has struggled with adoption.
Part of the reason has been that software and farmers haven’t always gotten along.
Communicating complex, data-rich insights is hard. And user interface paradigms like yield maps and NDVI may have made the problem worse - alienating prospective users with an unwelcoming user experience.
But poor user experience is just one challenge that has stymied adoption. Agtech software has also suffered because it has failed to earn enough trust from farmers, and because it continues to get stuck in challenging & unsustainable business models.
In this report, co-authored with Tenacious Ventures’ co-founders Sarah Nolet (Tenacious Ventures),and Matthew Pryor (Tenacious Ventures), we explore the reasons why agtech software has struggled with adoption, and then ask: can artificial intelligence (AI) address some of these challenges? And if so, how?
We also dig into existing and emerging use cases from corporates and Agtech startups that point to a possible future where agtech software unlocks its long-promised potential.