| Sumario: | Fertilizer use has long been a cornerstone of global agricultural intensification. However, blanket fertilizer recommendations that overlook variation in soils, topography, moisture, and crop responses often produce suboptimal outcomes, including low agronomic efficiency, nutrient losses, environmental degradation, and uneven economic returns across fields (Desta et al., 2025). Yield gaps persist due to declining soil fertility, generalized fertilizer recommendations, and limited access to tailored advisory services (CGIAR, Data-Driven Fertilizer Optimization). Even when fertilizer adoption has increased, efficiency and profitability have remained low, largely because “one-size-fits-all” approaches fail to reflect strong within-field and landscape variability. To address these challenges, more precise and context-sensitive fertilizer management is urgently needed. More recently, data-driven and machine learning–enhanced recommendations for wheat, teff, maize, and sorghum have delivered higher yields and greater resource-use efficiency compared with blanket recommendations in multi-location trials (Alliance of Bioversity International & CIAT, 2024). Despite these promising results, scaling remains constrained by several barriers: (a) variability in piloting and scaling performance, (b) limited institutional capacity to maintain and update advisory, (c) affordability and accessibility challenges for resource-poor farmers; and (d) gaps in inclusion of women and youth, both as users and service providers. Addressing these requires an integrated scaling strategy that combines robust piloting and scaling, inclusive delivery channels (public, private, and digital), capacity development, and policy alignment.
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