Sub-Sahara Africa is characterized by high incidence of hunger and malnutrition due to low agricultural productivity. Low soil fertility, declining nutrient stocks, erratic rainfall, limited use of improved inputs, and inadequate access to knowledge and advisory services continue to constrain crop production.

Women are central to African agriculture. They account for approximately 70% of the agricultural workforce and contribute most of the labour required for planting, weeding, harvesting, and post-harvest handling (USAID, 2024). They also provide much of the family care-related work, including cooking, cleaning, water carrying, and elderly care (Chivenge and Connor, 2026). Women also have inequitable access to agricultural inputs and services and fewer land rights; 15% of land is controlled by women (USAID, 2024). Socio-economic, productivity, and time burden challenges result in a large gender productivity gap that can be greater than 30%.

Africa also has the world’s youngest population (Oluwatayo and Ojo, 2024). Despite agriculture offering significant opportunities for employment, innovation, agribusiness development, and climate resilience, many young people face high levels of unemployment and underemployment, limited access to land and finance, inadequate technical skills, weak market linkages, and negative perceptions of agriculture as a low-return sector (Mac Té and Sy, 2025). Young women are particularly disadvantaged because of intersecting gender and age-related constraints.

Reducing these inequalities is not only a social objective but is also essential for improving agricultural productivity. PA provides an opportunity to address some of these challenges, but only if technologies are designed and delivered in ways that respond to the different realities faced by women and youth.

PA is a data-driven approach that reduces temporal, metrical, structural, and translational uncertainties in smallholder farming systems, enabling more informed and site-specific management decisions. Time-, location-, and individual-level data can be used, combined with other information, to guide variable-aware management decisions that boost efficiency, yields, quality, profits, and sustainability (Andujar, 2023). PA decision support tools, such as mobile phones, satellites, drones, sensors, the Internet of Things, robotics, and farm automation, help monitor weather, soil, yield, diseases, and pests (UNDP, 2021). Across Africa, digital advisory platforms, mobile applications, remote sensing, geographic information systems, and soil diagnostic tools are becoming increasingly available to smallholder farmers. These technologies can support better decisions on planting dates, fertilizer management, irrigation scheduling, pest control, and harvesting (Njoroge et al., 2026).

Women and youth in Africa commonly use digital decision support tools (e.g., smartphone applications, SMS advisory systems, and AI-enabled extension tools), soil and crop sensing tools (e.g., portable nutrient meters), and drone services for field scouting, often provided as bundled services. For women, PA can reduce labor demands, improve access to information, increase productivity, and strengthen household food security. For youth, it creates new opportunities for employment and entrepreneurship through digital advisory services, data collection, equipment hires, and technology support businesses.

However, access to technology alone is not enough. Farmers must also have the skills, resources, confidence, and enabling environment needed to use these tools effectively. Without deliberate efforts to address existing inequalities, PA risks widening rather than narrowing the gap between those who benefit from innovation and those who do not.

Long-standing challenges faced by female and youth farmers in traditional agricultural settings also apply to PA. These challenges are grouped into broad categories including infrastructure, information, skill, participation and inclusion, technology-based, financial, policy, etc.

Some of these barriers are common to all forms of digital agriculture. Others are unique to PA because they relate directly to the affordability, accessibility, and usability of advanced technologies such as sensors, drones, digital platforms, and data-driven decision-support systems. Distinguishing between these different types of constraints is important because it helps identify where targeted interventions are needed to ensure that PA is both productive and inclusive. Table 1 summarizes the major barriers affecting women and youth and assesses their implications for the successful implementation of PA.

PA is designed to reduce decision uncertainty across the production cycle; however, men and women experience fundamentally different types of uncertainty due to divergent access to devices, skills, decision-making power, finance, and policy support. Women face uncertainty rooted in socio-cultural exclusion from technology use and credit discrimination, while youth face uncertainty due to inadequate financial products and mentorship gaps.

Based on Rowe (1994) and Marra et al. (2003), several types of uncertainty are associated with each challenge. These include technical uncertainty (yield and production variability), price uncertainty (market fluctuations), tenurial uncertainty (land ownership), political uncertainty (government policy), personal uncertainty (family welfare), and people’s uncertainty (relationships with others).

Marra et al. (2003) further categorized uncertainty in the context of technology adoption, including subjective (perceptions of risk and outcomes), epistemic (lack of knowledge or experience), random (inherent variability), and learning uncertainty (uncertainty reduced through experience).

Women and youth face fundamentally different uncertainty profiles than men in PA. Men primarily face technical and price uncertainty (inherent to farming: will crops yield? what will prices be?) Women face additional uncertainty due to information access gaps (epistemic), social exclusion from decision-making (people’s), and institutional discrimination in credit/subsidies (political). This means PA’s promise to reduce decision uncertainty is unequally distributed: men benefit from data-driven precision decisions, while women face compound uncertainty from both production risks and structural barriers.

A theoretical risk management framework can be created by merging Rowe’s classification of decision-making uncertainty (temporal, metrical, structural, and translational) with the statistical concepts of Type I and Type II (Table 2). Type I and II errors are framed as a concrete checklist that redirects advice toward the prevention of Type I errors (harm/unintended consequences) and Type II errors (missed opportunities for women’s agricultural domains). This classification demonstrates that a woman managing a secondary plot does not just need “more information”; she needs a targeted reduction in specific uncertainties. For instance, a raw weather forecast (e.g., “20 mm of rain expected”) reduces metrical uncertainty for a meteorologist, but it creates translational uncertainty for a smallholder farmer who must calculate what that means for her freshly planted seeds.

Similarly, if PA designers deploy satellite imagery to optimize nitrogen on the husband’s cash crop, they have successfully reduced his uncertainty. If they claim that this helps the entire household, but the woman’s weeding burden increases due to the larger, fertilized canopy, the project has committed a Type I error (false positive impact on women). Conversely, if developers assume that IoT sensors are too advanced for post-harvest storage (a female-dominated domain) and only deploy them for commercial irrigation, they commit a Type II error (missed opportunity), leaving women entirely unserved by the PA revolution.

The framework outlined in Table 2 demonstrates that successful PA depends not only on technological innovation but also on understanding how different groups make decisions and manage risk.

First, this framework distinguishes PA-specific barriers from generic digital agriculture barriers, which is important for design. This helps PA projects avoid over-investing in low-level digital fixes when the real constraints are hardware access, usability, and affordability.

Second, the framework improves the targeting of interventions by uncertainty type and decision area, so PA can be tailored to specific decisions rather than offered as a one-size-fits-all package. This makes PA more effective because advisory tools, training, and financing can be matched to the actual production decisions being made.

Lastly, the framework strengthens the case for gender-responsive and youth-inclusive PA as a productivity strategy and equity strategy. The framework goes beyond diagnosing exclusion by providing a practical approach for designing PA interventions that reduce decision uncertainty for different farmer groups.

PA strategies must address gendered uncertainty types (epistemic through training, people through participatory decision support, and political through inclusive policies) to achieve equitable uncertainty reduction across the production cycle. Therefore, effective PA strategies must be gender-transformative, addressing not only device access but also participatory decision support, tailored skills development, inclusive credit systems, and youth-specific innovation grants to ensure PA sustainability.