In his five years working on ICT for Agriculture (ICT4Ag) at CTA, Benjamin Addom has seen plenty of digital agricultural projects come and go. Here he discusses what it takes to ensure that promising ICT4Ag initiatives have staying power – and how big data analytics can help make the critical shift to self-sustaining business models.
For the past decade or so, we have seen the emergence and implementation of smallholder digital agricultural projects and initiatives across the developing world. Mainly driven by donors, foundations and international development organisations, investments have been fragmented, uncoordinated, and piecemeal. The result has been myriads of digital services, proof of concept cases, successful use cases, stories from the field, cases with potential for scale, and promising business models. Ultimately, while these have generated limited hard evidence on the impacts of digitalisation on agriculture, they have created a strong momentum for the sector to take off and a growing momentum towards digital applications and services. Thus, we see no apparent decline in interest in digital agribusiness.
A recurring critical question that we encounter is how to move from short-lived donor-funded projects to self-sustaining business-driven smallholder digital agriculture initiatives? And how to shift from traditional information and communication technologies for agriculture (ICT4Ag) projects to digitalisation for agriculture (D4Ag) businesses?
In answering these questions, I argue that adding big data analytics to digital services is one of the most promising ways to strengthen the business case for sustained agribusiness investment in smallholders - enhanced business insights and intelligence is the key to sustainable investment.
So what exactly is digitalisation for agriculture? D4Ag can be seen as an intersection between big data analytics, digital services and business drivers. It’s about proving the value of smallholder digital agriculture to businesses – private sector technology and agricultural businesses. It’s about moving away from proof of concept cases to sustainable businesses.
Digital applications have transformed the way people communicate and share information, not only within agriculture, but across all other sectors, such as health and education. In the agricultural sector, we have seen significant progress in areas that include youth entrepreneurship, digital skills and literacy for engagement in decent jobs, and incubation and coaching of young agripreneurs and digital start-ups. A wide range of solutions and platforms has been developed and tested, with the goal of addressing the information gap in areas such as climate variability, farm productivity, management of inputs, market linkages/access, supply chain management, postharvest management and financial inclusion. These services are being delivered through various channels such as online platforms, SMS, Interactive Voice Response (IVR) or combinations of these.
All these digital services increasingly depend on big data to function. A word of explanation here: big data are large data sets that can be analysed computationally to reveal patterns, trends, insights and associations, especially relating to human behaviour and interactions. But it takes analytics to determine the quality of the data and generate insights, and very few services are taking advantage of these to date. According to the CGIAR Big Data Platform, treating large data sets in isolation, with no relation to other forms of data, reduces the value that can be derived. So digital services for agriculture without analytics keeps the sector trapped in the same cycle of ‘project’ use cases. What then is the role of big data analytics in transforming digitalisation for agriculture?
A recent report from Deloitte predicted that by 2030, data collection and analysis will become the basis of all future service offerings and business models. Matt Shepherd, Head of Data Strategy at Bartle Borgle Hegarty (BBH), London also argues that like gold, data is a commodity in which the insight mined becomes the means to drive growth. Therefore while it is important to collect data, the real impact comes from the analytics. Examples of companies promoting data analytics within the agricultural sector include Gro Intelligence and Harvesting.co.
Investing in smallholder agriculture is a risky business, due to lack of data on farmers and their agribusinesses. Access to data on farmers and agribusinesses is a condition for building and providing them with tailored services and products. This can be done through a database with detailed profiles of farmers, their groups, cooperatives and agribusinesses through to the GPS coordinates of their fields, taking into consideration data privacy and protection. Digital agricultural products and services can then be built on these profiles, using remote sensing technologies – drones, satellite imageries, sensors and tracking devices – and other sources of data. Adoption and use of the services will generate valuable transaction, behavioural and credit history of individual farmers, and their engagement with other users. The analysis of metadata being generated through these services becomes a solid building block for businesses.
Proving the business case of smallholder digital agriculture to private sector investors has proved difficult over the years. For services to be continuous and relevant to farmers, someone has to pay for them. Studies have shown that smallholder farmers are willing to pay, but most do not have the ability. Modes of payment also affect the ability of users to pay for services. Combining digital services with big data analytics will help to set out a strong business case for private sector investment. A good example is the CTA-led MUIIS project in Uganda where digital farmer profile and remote sensing data is making smallholder farmers bankable, thereby attracting financial institutions to engage with farmers. It provides the basis to develop a business case for smallholder digital agriculture to financial institutions, agro-input dealers, aggregators, and other businesses, encouraging them to engage smallholder farmers. Connecting several examples of MUIIS across Africa results in big data. This should lead to improved business of digital agriculture for big technology firms, and help large agribusiness to generate revenue, ensuring profit to sustain the sector without depending on donor funding.
It is time for ICT4Ag to move on. It is time to make the shift from ‘integration of ICTs into agriculture’ to ‘digitalisation for agriculture’. It is time to think business with smallholder agriculture, and realise that a strong marriage between big data analytics and digital services has the potential to drive this process. Doing that will provide monetary value for large agribusinesses and technology firms, so that they can collaborate with smallholder farmers to address the supposedly insurmountable agricultural transformation challenges facing Africa.
This article was created through a CTA-led process to document and share actionable knowledge on 'what works' for ACP agriculture. It capitalises on the insights, lessons and experiences of practitioners to inform and guide the implementation of agriculture for development projects.