3.3 Mainstreaming digital solutions at scale
To fully employ digital solutions for DRR in agriculture, a shift is necessary in how we approach and address agricultural risk. Digital solutions for DRR support the transformation of agrifood systems. However, to maximize their potential, technological innovations and tools need to be embedded in digital agricultural policies, strategies and plans supported by the necessary finance for implementation. Moreover, to be truly people-centred and transformational, digital solutions for DRR must be affordable, accessible and tailored to the needs of the most vulnerable farmers.
Bold actions, policies, and the right regulations and investments are integral to accelerating the transformation necessary. This requires significant investments in key building blocks such as data governance, digital infrastructure, digital applications and services, enabling policy and environment, capacity development and institutionalization, and partnerships and finance. Embedding HCD principles in digital tools for agriculture is key. HCD not only creates more effective tools but also builds long-term capacity for innovation and empowers different actors to efficiently address agricultural challenges.
ENABLING ENVIRONMENT AND INFRASTRUCTURE
Data governance and interoperability
Effective data governance is essential to leveraging digital innovation. To support the integration of diverse datasets across sectors and facilitate data sharing, risk-related data must be accurate, accessible and interoperable. The development of interoperable systems, such as the EU Integrated and Control Management System (IACS), and the adoption of standardized protocols can streamline data sharing, reduce fragmentation and support comprehensive risk assessments. For example, India’s Digital Public Infrastructure for Agriculture integrates weather, soil and crop data in EWS. Indonesia’s One Disaster Data Initiative streamlines data from various sources, including data management and statistics on DRM and financing of related activities. The Philippines’ National DRR and Management Council (NDRRMC) uses a data-driven approach to supporting multi-sectoral disaster response.
Lack of reliable data hinders effective DRR efforts; therefore, strong data governance is essential to sustain risk-informed and data-driven policy interventions and coordinated response. Data governance encompasses technical, policy and regulatory frameworks to manage data throughout its value cycle – from creation to deletion – and across policy domains. However, in many countries, the absence of standardized protocols for data collection, sharing and value creation limits the ability to generate meaningful insights from data.
For disaster tracking systems to succeed, adopting a common framework is necessary. This includes scientifically agreed hazard definitions and taxonomies (such as those developed by the International Science Council), and well-tested post-disaster assessment methodologies (e.g. DaLa and PDNA). Disaster tracking systems should also be customizable to reflect local specificities on asset categories, data contributors, unit of analysis, currency or languages.
Using APIs, geospatial layers of vulnerability and exposure can be added to disaster impact data, permitting new directions of analysis, enhanced data integration and processing and visualization functionalities. Data visualization options to showcase impact, such as per sector, hazard and disaster events, can help summarize impacts and better communicate dimensions, such as disruption in production or access to services, which are not always visible when using monetary valuation of losses. Furthermore, a mobile-first software system with advanced geospatial data collection and analysis functionalities can support users in digitalizing data collection, while facilitating workflows for data sharing, validation and coordination.
The DELTA Resilience system developed by UNDRR aims to fill this gap by equipping local governments, sectoral and specialized agencies to collect and share better data related to impact on people, assets and services under their jurisdiction. DELTA Resilience system impact data also helps in measuring the effectiveness of DRR, climate and losses and damages. Showcasing the value of better data for better action is meant to encourage governments to invest in a disaster tracking system by enhancing data ecosystem and governance, institutional mechanisms and operational procedures.
Digital infrastructure requirements
A robust digital infrastructure can provide the foundation for effective DRR. This includes energy solutions, connectivity, devices and efforts to address socioeconomic challenges to co-create digital solutions and foster innovation. Ensuring digital tools and content are context-specific, linguistically relevant and economically viable is crucial for bridging the digital divide, including the digital divide for women.210 Despite significant progress in global connectivity, 2.6 billion people remain offline. Of these, an estimated 38 percent live within mobile broadband coverage but do not use it, while 5 percent are still not covered by mobile broadband at all.
People with access to digital resources can benefit from powerful services and opportunities unavailable to those who remain offline. To address this challenge, Farm Radio International in sub-Saharan Africa combines radio broadcasts with mobile and interactive voice response systems to reach farmers with limited internet access in remote areas. This ensures the timely dissemination of localized weather advisories and emergency alerts, enabling them to respond quickly and effectively to potential disasters.
Policy frameworks and strategies
Mainstreaming digital solutions in both agricultural and institutional DRR strategies fosters agrifood system transformation. For example, national digital agriculture strategies (DAS) like the one in Madagascar pave the way for the sector’s transformation by integrating technology, data and innovation. Supported by FAO, the Digital Transformation Strategy for Agriculture in Madagascar 2024–2028 aims to improve food security and farmers’ incomes, while developing the country’s economy through digital technologies. Its vision is to transform Malagasy agriculture through people-centred digital technologies such as satellite imagery, mobile applications and data analytics. These tools seek to empower farmers with real-time information for better decision-making, optimize supply chain logistics, and improve access to markets and financial services. This initiative is particularly vital in Madagascar, where agriculture is central to the local economy and livelihoods, yet is frequently threatened by natural hazard-induced disasters and climate variability
DAS can support centralization of diverse datasets and establish interoperable systems to ensure comprehensive and seamless data sharing for agrifood and DRR. In Rwanda, integrating DRR into the national DAS aims to both address immediate disaster impacts and strengthen the long-term resilience of agrifood systems. The development of DAS, supported by FAO (such as Rwanda ICT4AG 2016–2020 and the National Digital Agriculture Strategy 2021–2026), marks a significant step in leveraging emerging technologies to improve agricultural productivity and sustainability. By aligning the DAS with the Strategic Plan for Agrifood Systems Transformation 2024–2029 (PSTA5), Rwanda is taking a forward-looking approach to agricultural transformation, incorporating DRR. DAS underscores Rwanda’s commitment to creating a resilient, sustainable agriculture sector that can withstand shocks and stresses such as those induced by climate and market fluctuations. Through digital tools such as data analytics, mobile apps and precision agriculture, farmers can access real-time weather, soil health and crop data. DAS focuses on four key areas: (1) service digitalization to promote interoperability and agricultural technology; (2) data-driven decision-making through agricultural data governance; (3) digital competence development; and (4) the adoption of emerging technologies for agricultural traceability, supply chain automation and smart agriculture.
DAS can also inform the development of evidence-based policies, standards and regulations for data management, as well as digital platforms for disaster insurance, subsidies, compensation and EWS. They can provide a clear roadmap for developing digital solutions for DRR in agriculture, based on the assessment of needs and the digital readiness of the country. The FAO-ITU DAS guide provides a framework to assist countries in shaping their national DAS and identifying sustainable digital solutions.
Financing and partnership models
Specific resource allocation and business models are important for ensuring the long-term sustainability of digital solutions for DRR. Funding models through public–private partnerships, donor funding or blended finance models, and securing cost-effective pricing models that suit agriculture stakeholders will ensure accessibility and scalability of such digital solutions. India’s Digital Agriculture Mission has committed public funding and, in partnership with the private sector, delivers digital services to the agriculture sector. In South Africa, Kuronga offers a tiered subscription model for farmers and applies AI-based grading tools to help them meet market needs and quality standards. Pula, the microinsurance company, leverages technology, data analytics and AI to develop innovative climate insurance solutions bundled with agricultural inputs. Pay-as-you-go models for digital agricultural services, implemented in partnership with local cooperatives, have also proven successful in various contexts.
Effective partnerships are crucial to reducing the digital infrastructure gap in rural areas. In many countries, multisectoral public–private collaborations with the private sector, academia, civil society and technology providers effectively supported rural communities in moving towards a digital economy. It also significantly strengthens data and knowledge sharing, interoperability and seamless service delivery for DRR. The partnership between FAO and Google exemplifies how digital tools like the Google Earth Engine (GEE) can transform DRR in agriculture.
The FAO–Google Earth Engine Partnership (GEEP) has launched dashboards to track submissions to United Nations Framework Convention on Climate Change (UNFCCC) and to monitor deforestation-free commodity value chains. These tools leverage GEEP’s processing power to provide real-time data, aiding countries in meeting their climate commitments. Over 500 people in Ethiopia, Viet Nam, and the Plurinational State of Bolivia have been trained in using GEEP through workshops, webinars and e-learning courses, enhancing capacities in climate monitoring and environmental protection. GEEP has also provided 1 500 people, including farmers, local small- and medium-sized enterprises (SMEs), and vulnerable communities, with access to critical data and tools for managing agricultural risks. This partnership increased the use of geospatial analysis in FAO projects, contributing to better production, nutrition, environment and life. It also underscores the importance of effective collaboration with big technology companies in bridging the digital infrastructure gap and enhancing resilience.
India’s rural connectivity revolution has seen partnerships between government and telecommunication companies to extend coverage and empower communities. For example, the Grameen Foundation has helped rural communities access mobile money and other financial services. In many African countries, M-Pesa supported financial inclusion through partnerships with companies and financial institutions. M-Pesa has also partnered with Microsoft to develop digital skills for micro-, small- and medium-sized enterprises (MSMEs). In Rwanda, the government-supported platform e-Soko disseminates real-time market prices and weather updates via SMS and web portals. Meanwhile, the Private Sector Alliance for Disaster Resilient Societies (ARISE) partnership has also enabled the integration of the private sector into national DRR efforts.
IMPLEMENTATION PATHWAYS AND COUNTRY EXPERIENCES
Integrated systems and platforms
The Philippines is taking a transformational shift towards ecosystem-based governance, integrating scientific data and interoperable systems for better decision-making and enforcement. In August 2024, bathymetric data were incorporated into the Integrated Marine Environment Monitoring System (IMEMS), redefining municipal waters based on ecological realities. This shift aligns with broader environmental policies emphasizing data interoperability and technology. The Philippine Ecosystem and Natural Capital Accounting System (PENCAS) Act supports this by standardizing environmental and economic data. Innovative tools, in particular rapid visualization methods such as heat maps, are developed to respond quickly to complex data, such as critical insights into coral reef health, fishing activity and enforcement gaps.
An integrated solution that connects data visualization and resource management was designed to allow fishers to access real-time data, such as depth and ecological characteristics, enhancing their fishing efficiency and environmental knowledge without additional costs. This solution includes layers like marine protected areas and legal boundaries to provide comprehensive environmental information. The solution integrates concepts developed in the environmental witness model,211 enabling users to report pain points directly in real time. These can range from perceived violations of environmental laws, maritime accidents, safety hazards to environmental characteristics (e.g. algal blooms) and heat-related observations (e.g. coral bleaching). This aims at mitigating, for example, illegal, unreported and unregulated fishing, alongside building communities of best practices through the concept of social reporting and whistleblowing. The system also enables managers to visualize resource usage through heat maps, facilitating better management decisions. The platform’s data can be enriched by the ability of the API to import contributions from researchers, government agencies and ongoing fieldwork, ensuring comprehensive and up-to-date information. For aggregating data into the system from specialized tasks like predictive modelling and ecosystem niche modelling, ArcGIS is recommended due to its advanced capabilities in ML for feature classification and geostatistical forecasting. ArcGIS also offers valuable communication tools like story maps and mobile apps to create a more connected and responsive fisheries management systems. The data collection process and flow are designed to follow a cycle of validation and approval as defined in PENCAS. After methodology standardization and validation, data can be processed at scale using ML resources.
While three systems were considered – ArcGIS by ESRI, Quantum GIS (QGIS) and GEOVS from SRT – GEOVS was identified as the most suitable platform for integrating real-time environmental data into a unified framework. GEOVS enables natural resource management agencies to make informed decisions based on real-time insights. However, it requires ongoing scaling based on project priorities, as well as extended deployment to enable customized and context-specific features. This highlights that digital solutions should be context specific, and that investing in them is necessary for creating integrated solutions, alongside investments in capacity development along the entry points of the system.
A pathway for deploying sensor-equipped buoys to enhance the Philippines’ marine resource management has been formulated too. These buoys are equipped with tools such as transponders, cameras, acoustic sensors and environmental monitoring instruments. They provide continuous real-time data on marine conditions and activities. These buoys can be integrated into the IMEMS GEOVS system and provide actionable insights for decision-makers and improve enforcement, biodiversity protection and the sustainability of marine resources.
GEOVS also aims to enhance socioeconomic resilience by incorporating risk reduction, disaster response and recovery systems. Its three components include using real-time monitoring and geospatial tools to identify environmental disruptions and trigger local response protocols; allowing fishers to document and report post-disaster damages through a mobile interface, linking data to insurance and aid programmes; and engaging communities in data-sharing and disaster preparedness through app notifications and real-time updates. This approach ensures that vulnerable fishing communities have access to recovery resources, turning passive monitoring into proactive resource management, and reinforcing national food security and socioeconomic stability.
Capacity development and digital literacy
Moreover, human capital critically enables digital transformation in DRR. Public institutions and advisory services can drive digital innovation by investing in digital DRR capacity development (e.g. skills development, digital and financial literacy, livelihood diversification with a focus on women, youth and equitable access) to strengthen individual, institutional and community resilience. In Barbados, FAO is strengthening the capacity of extension services through the introduction of precision agriculture to improve crops. The development of a decision support system provides accurate data to inform critical crop management decisions. It offers precise recommendations on input use – including application rates, timing and safety intervals – along with other essential guidelines to enhance nutrient and pest management efficiency and reduce food safety risks.
Enhanced digital capabilities also improve decision-making through data-driven insights and scenario planning. For example, in Grenada, FAO supported the creation of a drone mapping and GIS team within the Ministry of Agriculture and Lands, Fisheries and Cooperatives. This allows for better utilization of agriculture data collection and planning techniques, for acquiring updated spatial information for farmers and for better communication with communities dealing with flooding. In India, the National Disaster Management Authority (NDMA) trains and equips officials, stakeholders and the community on disaster response. Building the capacity and skills of the public sector to effectively leverage data, extract actionable intelligence and strengthen data-driven policy interventions is crucial for enhancing DRR.
Education equips farmers with the necessary knowledge and skills to implement effective DRR practices and measures. Farmers learn about practices (e.g. sustainable farming techniques, soil conservation, water management and crop diversification) that mitigate the effects of disasters and ensure productivity standards and livelihood protection. Leveraging human capital in rural communities is therefore crucial. For example, the Bangladesh Cyclone Preparedness Programme (CPP) has trained 76 thousand volunteers, 50 percent of them women, to disseminate early warning and coordinate response to cyclones.
Moreover, agricultural education fosters a better understanding of risk information, including climate services and EWS and disaster preparedness plans. Farmers are trained to recognize signs of impending disasters and take proactive measures to safeguard their crops and livestock. This knowledge not only reduces the immediate impact of disasters but also aids in quicker recovery and long-term sustainability. By promoting community-based approaches and encouraging collaboration among farmers, agricultural education strengthens the overall resilience of agricultural communities.
Mobile apps, online platforms and digital advisory services offer tailored advice and training, helping farmers adopt good practices and innovative techniques. Digital solutions also facilitate farmers’ access to markets, buyers, and financial services and institutions. This improves their ability to secure fair prices and obtain necessary credit and insurance. An effective example is that of Kenya’s Kilimo Salama project, which provides climate risk insurance to farmers through mobile phones. Integrating digital solutions into agriculture also empowers farmers to build sustainable livelihoods and better withstand the impacts of climate extremes. In Uganda, FAO is helping women in remote rural areas without access to the internet and smart devices to inform and inspire rural communities to drive social change. Using Amplio Talking Book as a technology, FAO sensitized around 8 000 people on women’s land rights and land management in the West Nile region.
Similarly, FAO’s Virtual Learning Centers (VLC) strengthen rural communities to be better prepared for animal disease outbreaks. VLCs are virtual hubs that develop and deliver online and blended courses for professionals supporting farmers and rural communities. They are crucial in building preparedness and response capacities for animal health emergencies. The courses on transboundary animal diseases (TADs) can be quickly adapted and translated to train large audiences at low cost.
The VLCs use decentralized and innovative approaches and methodologies to train professionals in remote areas that otherwise would not have access to similar training opportunities. In the Pacific Islands, the VLCs have delivered multiple courses for veterinary paraprofessionals (VPPs) and community health workers (CHW), focusing on empowering women, helping them to become One Health leaders in their communities, and training them on how to provide community-responsive services. This has provided more effective community-driven health and animal disease responses.
HUMAN-CENTRED APPROACHES AND FUTURE DIRECTIONS
Finally, adopting a human-centred design approach helps improve usability and engage farmers and stakeholders in developing digital agriculture solutions. For example, the Consultative Group on International Agricultural Research (CGIAR) has made efforts in integrating HCD to reduce adoption risks, align solutions with real needs and enhance user satisfaction. The redesign of the feedback mechanism for triadic comparison of technology options (tricot) allows farmers to test new crop varieties directly on their farms and provide feedback on their preferences and observations. Similarly, the Artemis project focuses on AI-supported digital phenotyping for crop breeding programmes across Africa. These initiatives have proven that HCD is not only an effective tool but also builds long-term capacity for innovation, empowering farmers, researchers and communities to effectively face agricultural challenges.
Human-centred design principles
HCD offers a solution by prioritizing empathy, inclusivity and iterative design to deeply understand user needs and integrate them into every stage of development. The HCD process follows five key stages: scoping, exploration, creation, validation and implementation. Scoping defines the problem and goals. Exploration focuses on understanding user contexts and constraints. Creation involves developing prototypes based on insights, followed by validation through user testing and iterative refinements. Implementation ensures solutions are deployed effectively. Several successful digital agriculture solutions were co-designed with farmers to understand their challenges, preferences and local contexts.
For organizations to effectively implement HCD, several foundational elements must be in place. First, institutional commitment and leadership buy-in are necessary to prioritize HCD across projects and workflows. Leaders are crucial in fostering a culture of innovation and user-centric thinking. Second, capacity building is essential to equip teams with the necessary HCD skills and methodologies. Training workshops, hands-on design sprints and regular knowledge-sharing sessions can build expertise across teams. Third, cross-disciplinary collaboration is key. HCD requires teams to include designers, developers, domain experts and end-users working together throughout the design process. Fourth, organizations must establish iterative feedback loops to continuously gather and act on user feedback during each phase of a project’s lifecycle. This involves investing in tools and systems for usability testing, prototyping and iterative refinement with multiple tools available. Finally, sufficient resources and infrastructure must be allocated for prototyping, user research, and long-term monitoring and evaluation. Without these foundational elements, HCD efforts risk being superficial and ineffective.
HCD prioritizes user needs, iterative improvement and context-specific design. It ensures that digital solutions for risk reduction are not only technically sound but also socially and culturally aligned with the users’ realities. The iterative and community oriented nature of HCD fosters trust, enhances adoption, and ensures that digital innovations contribute meaningfully to resilience and sustainability in agriculture.
Human-centred design in practice
In Rwanda, the International Institute of Tropical Agriculture (IITA) and Viamo were interested in understanding the usability and user experience with a diet quality survey delivered through unstructured supplementary service data (USSD). A heuristic evaluation revealed significant usability barriers due to interface design flaws and inconsistent language translations. To address these issues, a usability lab was set up where participants completed the survey while being observed and interviewed. This revealed several challenges, including unclear instructions, language mismatches in messages and technical difficulties when navigating long questions on small screens. Key insights stated the need for clearer prompts, improved user control (e.g. the ability to undo mistakes) and simplified navigation. Adjustments included better communication between the survey platform and telecom providers to reduce system errors, as well as clearer instructions and question framing. These changes significantly improved survey completion rates from 58 to 70 percent, with women’s completion rates increasing to 76 percent. Data quality also improved, demonstrating the tangible benefits of applying HCD principles to digital tools.
The SEED Hub was developed with the HCD approach. The platform was co-developed by CSI in collaboration with Rwanda’s Ministry of Agriculture, the Department of Meteorology, and the Hector Kobbekaduwa Agrarian Research and Training Institute (HARTI). Local stakeholders contributed by populating the SEED Hub with context-specific information, ensuring it addressed local needs while leveraging existing institutional expertise. Capacity-building initiatives enabled organizations to maintain and update the platform. In addition, four workshops were conducted by the CSI team to train stakeholders on developing effective, user-friendly messages for farmers and on uploading information to the platform.
Zalo (a messaging platform providing farmers access to a 10-day Agro-Climatic Bulletin [ACB] in Viet Nam) provides another example. To understand how farmers accessed and interacted with ACB through the messaging app, a usability test was conducted. The test involved guiding farmers through accessing ACB on their phones, completing navigation tasks, and providing feedback on accessibility, design and readability, comprehension and usefulness of the bulletin. The test uncovered issues such as difficulty locating ACB in messaging group chats, small fonts and dense text and confusion caused by technical terms. Farmers appreciated the ACB’s role in reducing weather-related risks but preferred more actionable, specific recommendations and more frequent updates to match their decision-making and their needs in critical periods, including droughts or salinity intrusion. The usability testing also captured non-verbal cues or negative reactions, like lack of attention, disengagement and confusion, which farmers might not express verbally.
Future directions and emerging technologies
As we look to the future of digital solutions for DRR in agriculture, several emerging technologies and trends are poised to transform the landscape. The convergence of AI, IoT, blockchain, and advanced satellite technologies promises to create even more sophisticated and integrated risk management systems.
The next generation of digital DRR solutions will likely feature greater automation and predictive capabilities, with AI systems capable of autonomously detecting and responding to emerging threats. Edge computing will enable real-time data processing at the field level, reducing dependency on connectivity and enabling faster response times. Quantum computing may revolutionize our ability to process complex climate models and predict agricultural risks with unprecedented accuracy. However, it is important to recognize that AI and emerging technologies alone are not a panacea; effective digital DRR for agriculture requires human oversight of AI systems, risk informed governance and robust institutional frameworks that are aligned with international ethical AI guidelines, such as those outlined in the United Nations Educational, Scientific, and Cultural Organization’s (UNESCO) Recommendation on the Ethics of Artificial Intelligence.212 These are key to ensuring that technology is applied transparently, accountably and fairly, while respecting human rights.
Realizing this potential requires addressing several critical challenges. The digital divide remains a significant barrier, with many rural communities still lacking basic connectivity and digital literacy. Ensuring that advanced technologies benefit smallholder farmers and vulnerable communities will require continuous focus on accessibility, affordability and human-centred design.
Moreover, as digital systems become more integrated and autonomous, questions of data sovereignty, privacy and control become increasingly important. Establishing governance frameworks that protect farmers’ rights while enabling innovation will be crucial for sustainable digital transformation in agriculture.
The journey towards fully digitalized agricultural DRR is ongoing, but the foundations laid today through innovative tools, accessible governance and enabling policies, and human-centred approaches are paving the way for more resilient and sustainable global agrifood systems. By continuing to prioritize the needs of farmers and vulnerable communities while responsibly embracing technological innovation, we can build agrifood systems that not only withstand shocks but are also capable of thriving in the face of mounting challenges of climate variability, biodiversity loss, pollution and environmental degradation. Now is the moment to act towards transforming risk into resilience and innovation into impact.
