For digital agriculture to deliver on its promise of increasing farmers’ yields and incomes, it is critical to design interventions that consider the target populations’ needs and constraints and to assess whether a digital technology solution is appropriate.
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The need to make agriculture more profitable, productive, efficient, sustainable, and attractive to new groups, while making food more nutritious, provides a key opportunity for innovation in the field of digital agriculture. Digitization of the economy, or “cyber-physical” merging of human and machine, is often presented as the fourth industrial revolution.
Digital technologies are changing the way people access information, interact with others, provide services, sell and purchase products, and, ultimately, how they make decisions. The digitization of agricultural value chains is an opportunity to generate wealth, save time, and improve livelihoods throughout the world. The utilization of low and high technology solutions for agriculture has enormous potential to transform the agriculture sector. Currently, at least 96% of the world’s population is within range of a mobile signal.
Digital technologies have the potential to revolutionize agriculture and transform the sector and rural livelihoods. The integration of digital technology in agriculture can potentially lead to the modernization of the sector through better connected, informed farmers who have access to new information and markets while reducing hardships and ultimately improving their livelihoods. Agricultural researchers and implementers are actively developing new tools and solutions in agriculture that leverage digital technologies.
Service provision of drones for seed sowing, blockchain for traceability and certification, site-specific nutrient recommendations sent via text message, 3D printing of spare parts for farm machinery, and artificial intelligence algorithms that identify pests and diseases are a handful of examples that could lead this transformation.
Other examples of digital technologies and innovation practices in agriculture include:
Internet of Things-based decision support tool for irrigation scheduling and carbon footprints labeling
Site-specific nutrient precision management that enhances resource-use efficiency
Geospatial tools to estimate rice production and rapidly assess damage from floods and droughts to tie in the data to insurance schemes
Drone-based improvements in agronomic practices
The application of these technologies along the agricultural value chain has been equally diverse and varied: from market information on mobile phones, to drones that monitor pests and disease, to sensors that provide actionable irrigation and water management recommendations. Digital technologies can help smallholder farmers increase their incomes and yields if utilized effectively and efficiently.
Meanwhile, economists and policy analysts are developing relevant policies to foster digital technology adoption at scale in an interoperable, scalable, and affordable manner. The evidence for the transformative potential of digital agriculture continues to grow. Countries across South Asia, Southeast Asia, and Africa launch new digital agriculture platforms each year across the agricultural value chain.
In India, for example, states such as Andhra Pradesh, Odisha, and Telangana are at the forefront of the digital transformation. Sri Lanka, Vietnam, and the Philippines seek to partner with international and domestic technology companies to reap the potential benefits of digital agriculture. India and Sri Lanka, for example, have official programs to double farmers’ incomes through the adoption of technology-advanced agricultural innovations and more particularly digital technologies.
However, the realities and constraints that exist in agricultural production in the Global South temper the optimism that often accompanies unbridled advocacy of the potential promise of digital agriculture. Increasing agricultural productivity, for instance, has certain limitations. In geographies where markets for increased inputs do not exist because the private sector initiative and participation have not been sufficiently stimulated, pushing for higher-yielding technologies (such as modern crop varieties) to increase productivity merely ensures that input prices can be more readily controlled by the low number of agro-dealers.
As a result, the market power exercised by too few operators will lead to depressed farm-gate prices because of continuing high input prices. Furthermore, many of the binding constraints faced by smallholder farmers center on basic capacity issues that may be exacerbated, not alleviated, by digital technologies: they are not organized collectively, they have limited experience of market negotiation, and little appreciation of their capacity to influence the terms and conditions upon which they engage with the market, and they have little or no information on market conditions, prices, and quality of goods.
There is also the key issue of farmers’ aspirations; there are many farmers for whom increasing productivity and greater access to markets are not a priority, instead, they focus on off-farm or non-farm activities with a view to temporarily or permanently exiting from farming. Ultimately, improved preliminary assessments and targeting will maximize the likelihood that the spread of digital agriculture will contribute to improved access to markets and higher incomes for smallholder farmers.
Farmers are a heterogeneous group and for the poorest of the poor, digital agriculture will be an insufficient pathway out of poverty. Digital agriculture, like other technologies, should best be targeted at those farmers who aspire and are able to improve their livelihoods via farming, those farmers best placed to “step up” according to the typology proposed by Dorward et al. (2009). There also remains the challenge that has plagued agricultural development for decades, namely the scaling of technologies.
A digital decision support tool known as Rice Crop Manager (RCM) enables farmers to calculate field-specific rates of fertilizer N, P, and K for rice. The International Rice Research Institute (IRRI) and its partners developed the science behind RCM, site-specific nutrient management, in the 1990s to identify the best nutrient management practices for specific rice fields. RCM has been adapted for geographies such as the Philippines, India, Indonesia, Myanmar, and Bangladesh.
Digital technology, such as RCM, has the potential to transform agriculture in emerging markets, but like all technologies, it is not a panacea. The challenges of scaling faced by RCM are ones that almost all initiatives to promote agricultural technologies have to contend including digital agriculture.
Another Achilles heel of agricultural technologies is that they can lead to further marginalization of the most vulnerable populations. Information has always been a critical component of agricultural production as farmers exchange best practice, suggestions, and counsel. If the primary channel for information dissemination shifts to digital technology, farmers without access will face increased information asymmetries.
Exacerbating the digital divide generally and the social/gender divide more specifically, are two of the top risks of promoting digital agriculture, both of which have direct impacts on social and gender equity and parity. Digital divide: social and gender equity Behind the enthusiasm for this current wave of innovation and sophisticated technological advances, the experience with RCM illustrated the danger of marginalizing farmers who do not have the financial resources to access digital technologies, thereby exacerbating the digital divide.
There is a risk that some farmers will not get information about these new technologies at an early stage. Further, these farmers would neither have the physical and/or financial capacity to access new technologies nor would they receive sufficient capacity building and training to effectively use these technologies to transform not only their production system but also their lives and that of their families.
As a result, the benefits of digital agriculture may not accrue to the remote and marginalized communities and poorer farmers within them. Several factors underlie the digital divide, including, but not limited to, a lack of connectivity infrastructure, affordability, electricity, education, knowledge, skills, gender, age, and location.
For digital agriculture, the demand for evidence of what works and what does not work is getting increased attention. The evidence base for digital agriculture will guide further understanding of the enabling conditions for specific populations and geographies, advancing the understanding of the maturity of digital agriculture interventions, and providing impact and improved outcome data. Given the current evidence base, it is unclear whether digital technology will be accessible to most small farmers and automatically lead to increases in yields, income, and overall prosperity.
Furthermore, the design of agricultural interventions does not inherently focus on social equity, which must be a deliberate focus in the future. The utilization of low and high technology digital tools has enormous potential to transform the agriculture sector. Countries across South Asia, Southeast Asia, and Africa already promote new digital agriculture platforms across the agricultural value chain.
For digital agriculture to deliver on its promise of increasing farmers’ yields and incomes, it is critical to design interventions that consider the target populations’ needs and constraints and to assess whether a digital technology solution is appropriate.
Technologies affect different social groups in different ways, even in the same geographical/agro-ecological context. This leads to consideration of potential trade-offs between efficacy, efficiency, and fairness. Exacerbating the digital divide generally and the digital gender divide, more specifically, are two of the top risks of integrating digital tools in agricultural value chain development.
In addition, using technology for knowledge dissemination can further entrench the information asymmetries in the agricultural production chain that already exists. It is incumbent upon implementing research and funding organizations as well as governments that the enthusiasm and funding for digital agriculture consider the potential further marginalization of the most vulnerable populations they allegedly seek to benefit in the end.
Furthermore, as is the case with any technology, attention needs to be directed at scaling. RCM illustrates that integrating the globally recognized Principles for Digital Development is critical in program design, development, and deployment.
Scientific and technological advances in digital technology are already transforming agriculture around the world, helping to address challenges such as labor shortages and improve farming efficiency and sustainability. Digital agriculture can also advance progress on the agricultural sector’s contribution to realizing the Sustainable Development Goals (SDGs), but there are trade-offs, specifically with respect to agricultural productivity, poverty reduction, and social equity.
When it comes to digital agriculture’s contribution to the SDGs, there are two key issues to assess:
The extent to which digital technologies can help farmers move up the development ladder when an enabling environment for adoption is properly and consciously generated and maintained.
Even when the right technologies are identified and the enabling environment is in place, such new technologies are potentially socially, spatially, and economically disruptive and as a result can lead to the exclusion of some categories of farmers that are not equipped to successfully engage and reap the expected benefits. This raises the fundamental issues of social and gender equity and whether digital agriculture can and will mitigate or exacerbate these inequities.
For digital agriculture to deliver on its promise, it is critical to design digital agriculture interventions that consider the target populations’ needs and constraints, as well as the possibilities within the existing enabling environment to determine what – if any – digital technology solution is appropriate.
Read the full study:
Florey C, Balié J, and Hellin J. (2020) Digital agriculture and pathways out of poverty: the need for appropriate design, targeting, and scaling. Enterprise Development and Microfinance, 31:2, 126–140
