121. What would you do at age 79?

“Software is Feeding the World” is a weekly newsletter for Food/AgTech leaders about technology trends.

Greetings from the San Francisco Bay Area.

Analysis: What would you do at age 79?

What would you like to be doing when you are 79 years old? (Or what are you doing if you are 79 or older?)

My guess is most people will say they want to be retired, spend time with their grandkids, travel a bit, and relax.

Horace Clemmons of Paint Rock, Alabama does not agree with most people.

He is the founder of Ronnie Baugh Tractors. He learnt an important lesson as a marine in Vietnam.

If you believe something’s impossible, you’re dead!

Mr. Clemmons has a big and ambitious goal.

Clemmons says his ultimate goal is to give the millions of impoverished small farmers around the world access to mechanization and the economic empowerment that comes with it.

He says the whole concept behind Ronnie Baugh Tractors is to show the world what’s possible. Partners in Uganda, Senegal and the Philippines have already licensed the design and are now building Oggun tractors domestically. They’re even adding two-wheel and fully electric versions to the lineup.

Having access to low cost, highly customizable agricultural equipment that is also open-source technology has the potential to benefit millions of small farmers in the developing world.

I have found the Horace Clemmons story to be extremely inspiring.

If we think, we cannot solve the problems of climate change and improve our food and agriculture systems, then we won’t be able to.

The idea is gaining traction in the US as well, but for different reasons. The design is open and anybody can modify it. The design is flexible, and so others can tinker with the design. Some folks have made multiple modifications, by cutting and welding the frame to raise the floor bed to make room for nearly a dozen different custom tools. This could be very useful for small farms. (It reminds me of all the things a smartphone has replaced in the last 20 years)

Source: https://www.geckoandfly.com/13143/50-things-smartphone-replaced-will-replace-future/

In the past, I have written about an Android model for agriculture equipment, and I had only focused on the software side to make equipment smarter. For example, in edition 89 I wrote

Majors like Deere can follow an Apple approach with high end hardware, and software under their brand. They will service the high end large scale row crop farming in the developed world.

Is there space for a generic sub-$10K tractor, where the hardware is more or less a commodity, and the software on it is what makes it interesting and more useful? There are many types of operations depending on crop type, and so there might be different hardware configurations.

Could some manufacturer provide hardware in large volumes at low cost? Can a strong software and technology provider provide an equipment operating system, with room for application developers?

Could this strategy work in the developing smallholder space?

Mr. Clemmons is coming at it from the hardware side. I had written about another example of open source hardware and software in edition 80 (Open Source Software) called OWL.

Open source hardware and software green-on-brown weed detector that uses entirely off-the-shelf componentry, very simple green-detection algorithms and entirely 3D printable parts. OWL integrates weed detection on a Raspberry Pi with a relay control board in a custom designed case so you can attach any 12V solenoid, relay, lightbulb or device for low-cost, simple and open source site-specific weed control.

I was skeptical about open source in agriculture in edition 81 for both the Global North and Global South,

Given the small number of users in agriculture in the developed world (for example, there are less than half a million row crop farmers in the US), and the challenges with technology access for smallholders, I am skeptical about broad adoption of open source tools within agriculture, though tools like Farmstack from Digital Green hold promise.

I am a bit more bullish on Mr. Clemmons’ approach, as he has open sourced the design. Certain manufacturers in Africa have taken the design to manufacture versatile agriculture equipment. The price point of $ 22,000 for a tractor is still quite high, and I am sure at scale it can be brought down further.

When I spoke with Jehiel Oliver, CEO of Hello Tractor in Africa (edition 66), he had talked about a beautiful convergence for low horsepower tractors for smallholder farmers.

Compact tractors are the most cost-effective, highest-ROI investment in mechanization for smallholders, because you're jumping from plot to plot. For large horsepower equipment, the opportunity cost is high, any time the equipment is not doing a farming activity (for example, when it is on the road driving between fields.) The 75 horsepower tractor is the sweet spot for somebody who's servicing farmers between one to five hectares.

The big question is whether we can combine open source hardware design with open source software, to make an impact in the lives of millions of farmers in the Global South.

Open source versatile hardware is a key part of the big puzzle.

Mr. Clemmon’s story is fantastic as it shows how any and all of us can make an impact.

All it needs is conviction, passion, and hard work.

When innovation collides with personalization - machine learning edition

Personalization based on your preferences and data is everywhere around us. Netflix recommendations, including which image is shown to you for a particular movie is often personalized based on your preferences.

Echo chambers on social media are the extreme versions of personalization, which result in polarization. Machine learning and Artificial Intelligence play a big role in data and tech driven personalization, and it is different from my barista example.

There is “personalization” in agriculture as well. In the case of agriculture, personalization means understanding the context in which farming is being practiced, and fine tuning your actions to the context. For example, soil and weather conditions are different on different fields at different locations.

Operators on different farms have different objectives and preferences. Often the history of a farm is important to know what actions to take for the present and the future. For example, cropping rotations, history of diseases and pest infestations, will have a bearing on present and future decisions.

Machine learning and artificial algorithms have the potential to tease out these differences, if they can get access to high quality ground truth data, representative of different situations. Machine learning is one of the most powerful tools for personalization.

Recent advances in machine learning with unsupervised learning, transfer learning, and transformer models have enabled researchers and engineers to build large models, which are resilient and robust in a variety of situations, and require fewer ground truth data points. (If you want to know the difference between unsupervised and supervised learning, you can watch this video.)

Precision agriculture data has opened up the possibility to provide higher levels of personalization with variable rate seeding, variable rate fertilization, variable rate applications, and variable rate irrigation. See & spray technology currently being pursued by many players in the industry is a form of “personalization” as it treats weeds as weeds and plants as plants.

Noted political strategist, Peter Zeihan provides one future view for the US Midwest.

He imagines a future in which automated equipment with cameras will take photos of each individual plant. The equipment will identify if the plant is a weed, or a crop and assess the health of the crop. It will give it a little jolt of whatever is appropriate (herbicide, pesticide, fertilizer, water etc.). He believes we are on the verge of production increasing by a factor of 2-3 (“on the verge of” is subjective, though within the realm of possibility.) It will turn conventional farming into conventional gardening, with a lower pollution rating and a far lower carbon footprint. (Highlighted by me)

Jeremy Williams, Head of Climate LLC and Digital Farming, Bayer Crop Science recently wrote an article on LinkedIn, “Good Things Happen When Innovation and Personalization Collide” and made similar points.

Input companies often talk about digital transformation and business model transformation. They want to push outcome based models, instead of selling products by volume. This is a very difficult transition to make.

A key problem to solve underneath is “personalization” at the field and farm level.

Access to high quality data, and the tools and the skills to build robust and high performing machine learning models is critical to crack this problem. (To be clear, a digital transformation of your business is not just a technology problem, but also a cultural, organizational, and marketing problem).

Jeremy Williams gave some examples of tailored solutions for smart corn systems, farming practices like cover crops and no-till/low-till, and combination of corn hybrids and inputs tailored to local growing conditions in Ghana.

If we take the example of outcome based systems, the provider of the system of physical and digital product bundle, will have to characterize probability functions of yield (assuming that’s the outcome being managed and sold), understand major variables impacting yield and the magnitude of those variables, and then incorporate this understanding in the business model.

In the example below, Field 1, 2, and 3 have very different yield-probability functions, and so will have to be managed very differently. The only scalable way to characterize these functions and associated variables is through a combination of high quality ground truth data, and machine learning models.

Dealing with vast amounts of not-so-clean data is a challenge in itself. If you add the complexity and lack of sophistication of tools to deal with vast amounts of data creates significant challenges.

Another example is biological products.

The effectiveness of biological products is very much dependent on the local conditions in which they operate. Currently biological products have a sketchy reputation due to variable performance. There is a lack of high quality ground truth data to characterize the performance of biological products in different conditions. Even if ground truth is available, there is a lack of sophisticated machine learning tools to untangle the differences in performance. It creates a challenge to understand the potential of biological products, and the messaging around it.

There are many examples in breeding, scouting, crop trials, see & spray, each of which is a special case of innovation based on personalization.

From a technology standpoint (technology trends is the main topic of discussion in this newsletter), companies need to have access to high quality ground truth data, a data pipeline to process the data, and the right skill set and tools to train, build, and deploy high quality models.

This is an area of opportunity for technology providers, as there are generic tools available, but not many (or any) tailored to address the agriculture ecosystem.

In the News

AgTech and Agronomy

Chinese juggernaut PinDuoDuo has global ambitions for long term growth, with heavy investments in R&D to develop agriculture technology to enhance agriculture productivity.

On-farm data seen as possible fertilizer solution

Plant breeding innovation through democratization

Robotics and Automation

FieldPRO has developed an autonomous solution for Deere with hardware, software, and analytical tools to improve the efficiency of field operations.

Supply chain

One Mississippi, Two Mississippi, why can’t we send stuff down the Mississippi?

Supply chain woes boost meat substitutes in Asia

Supply Chain AgTech startup Falca (India) enables farmers to sell their products directly to consumers without extra costs such as middlemen, logistics, and packaging and has raised $ 3 M in pre-series A funding.

Arla Foods has set aside up to 3 euro cent per kilo of milk as part of a sustainability incentive to its farmers to motivate them to reach its 2030 emission reduction target on farms. To incentivise farmers to implement sustainable practices, from next year the milk price that individual Arla farmers will receive from the dairy cooperative will depend on their activities relating to environmental sustainability. The model operates on a points-based system, whereby farmers can collect points based on the model’s 19 separate levers. These levels include feed, protein and fertilizer efficiency, manure delivery to biogas, biodiversity, carbon farming and use of renewable electricity, and deforestation free soy.

Sustainability

A US-led sustainable farming initiative, which aims to raise billions of dollars to tackle climate change, has been criticized for favoring big business and promoting uncertain techno-fixes ahead of U.N. climate talks in Egypt in November.

Climate-smart potatoes!

And climate-smart sorghum

Sustainable cotton - innovation and data is driving sustainability in cotton. 51% of US cotton growers use GPS-enabled swath control to ensure they are not overlapping practices such as planting, fertilizer applications and crop-protection applications.

Extreme heat puts pollinators and crops at risk as bees support more than 100 commercial nut, fruit, and vegetable crops. Fewer bees means less pollination, and lower crop yields.

Indigenous communities are increasingly investing in agriculture to sustain their cultures and economies.

When it comes to reasons why cover crops are not grown, “it’s too cold here” and cost of changing equipment are some of the common reasons. How can some of those barriers be reduced?

Smallholder

Gramophone (India) evolves from a marketplace to a full stack platform. My friend Venky Ramachandran has written extensively about marketplaces and platforms in India.

E-Book: Global Perspectives on Agriculture Technology

The 12 conversations explore a wide range of topics like the venture capital model for Agricultural Technology (AgTech), different financing options for AgTech, the nature of innovation, automation, how farming will change in the future, the culture clash between technologists and agriculture industry veterans, history of US and Canadian agriculture, techno-optimism, role of policy and science, mental health issues for farmers, produce e-commerce, challenges with smallholder farming in countries like India, China, Indonesia, Malaysia, Kenya, Nigeria, and Zambia, and the potential of farm improvements to raise the standard of living of millions of farmers in the developing world.

All proceeds from the book (minus taxes, transaction fees etc.) will be donated to a non-profit or charity, working to improve the lives of people working in our food and agriculture systems. Rhishi will match the first $ 1001 of sales dollar-for-dollar with his own money.

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Newsletter Recommendations

My friend Connie Bowen has started a newsletter “Ag is for People”. I highly recommend this free newsletter! Also, if you are one of the few people who have not yet subscribed to Upstream Ag Insights by Shane Thomas, you definitely should.

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About me

My name is Rhishi Pethe. I lead the product management team at Project Mineral (focused on sustainable agriculture). The views expressed in this newsletter are my personal opinions.

Rhishi Pethe