Smallholder yields in Asia are well short of both international benchmarks and yields on larger farms in the region. At least part of this gap can be attributed to knowledge and information; including on seed selection, pest identification, climate and planting timing.
Digital advisory seeks to solve this problem by providing advice and information to farmers. A typical solution will provide some generic advice over a social media platform and seek to migrate farmers over to an Android application, where the farmer creates a profile, and receives more customized advice.
Digital advice has a range of advantages:
Once the material and interface are created, adding each additional farmer costs very little, and is much cheaper than in person advisory.
Advice can be highly targeted to the farmer’s specific plot, crop type and point in the crop cycle.
Once trust is established, the business can undergo viral growth, with one farmer suggesting the advisory application to another.
A number of types of digital advisory have been tested including Q and A with agronomists, advice driven by satellite imagery and pest identification using image recognition.
An advisory solution can be funded in two different ways, though farmer subscriptions or by selling farmer data. The farmer subscription model is proving to be difficult to scale, as the cost of building enough trust to secure each new paying farmer typically exceeds their lifetime value. Furthermore, paying farmers expect a high level of service, making this a more expensive offering to deliver.
Based on our observations, what appears to be more scalable is to provide the service for free; and monetize the data collected on the farms and farmers. Thus, while a subscription can be added over time as a premium offering, data sales will usually drive early adoption.
Much remains to be learned about how to build trust in digital advisory, what information farmers want, how to design the user interface, and leveraging existing trusting personal relationships (such as with retailers) to onboard farmers.
However, if data sales are going to drive revenue, considering who will pay for what data is a critical consideration. Potential data consumers include crop protection manufacturers, fertilizer companies, lenders and commodity buyers. In each case the advisory service is essentially selling a qualified lead, examples include:
Lender: this farmer is three weeks from planting and expressed an interest in hybrid seeds last season. Their potential seed buy is $100. Satellite images show consistent, high yields for three years.
Crop Protection Company: this farmer is four weeks into a rice crop, and the crop is inundated with Brown plant hopper.
Buyer: this farmer is one week away from harvest, with an estimated 6 tonnes of white maize.
The lead’s value is driven by three factors, the farmer’s spend on the data consumer’s product or service, the data consumer’s profit margin and the likelihood of conversation. For example:
Farmer crop protection spend: $100
Profit margin: 5%
Likelihood of conversion: 50%
Indicative lead value: $2.50
This calculation gives an indication of the value of the qualified lead to a crop protection company. Similar calculations could be run for a lender or buyer. The value of each lead will be low, but a particularly promising aspect of the business model is the ability to monetize the same farmer’s data multiple times with different classes of data consumer.
The model raises a number of ethical and legal issues around data protection and privacy, but with social media sites already operating at scale with a similar business model, solutions should be found.
The comparison with social media is instructive; as lenders, crop protection companies and buyers already use social media to reach pre qualified customers. To compete, advisory services need to offer better data at a lower cost. The diagram (above) highlights the key competitive pressure on this business model using Porter’s five forces approach.
Of the five models covered in this series, advisory stands out for its capacity to add value for farmers at scale. However, it's also a very challenging model, balancing on one hand the need to build trust with farmers by offering a good service, and the need to harness the right data to deliver value to data consumers.
Comment from Aukrit, Co-Founder & CEO of Ricult
"I generally agree with this piece regarding the business model and that the value of the advisory solution can create a huge impact in improving yields for the farmers. From Ricult experience in Thailand and Pakistan, monetizing from the small farmer is extremely difficult. Substantial value and trust need to be created and proved out over time before farmers are willing to pay for a digital app, especially when other day-to-day apps such as social media and messaging apps are free. Also, paying in itself is a huge challenge as most farmers in the developing world don’t have a credit card or digital banking. Finding other stakeholders such as banks or crop buyers to pay for the data or lead generation is the most promising way to go with."
Comment from Ajay, Associate at Wavemaker Partners
"Wavemaker Partners works with founders in smallholder agritech business models: from digital solutions providers (Ricult), to applied AI for agriculture (Adatos), to IoT hardware (eFishery). These companies have developed advisory models, that while challenging to ideate, provide their customers with unquestionable value.
What we specifically like to see amongst agritech businesses are solutions which are aimed at increasing yield and reducing waste - the two primary levers to improve farmer profit. The challenge is developing a technology that has a large enough ROI and payback period for the farmer to see immediate and long term value. The founders we invest in build trust with farmers by taking the time to understand the problems they face, develop solutions to tackle those problems specifically, and always look to improve farmer profit."