| Summary: | Agricultural production, particularly in smallholder systems, faces increasing risks from climate hazards such as floods, droughts, and typhoons, which directly threaten livelihoods. Timely access to climate advisories is paramount for strengthening adaptation strategies and minimizing climate change impacts. However, a significant systemic challenge persists, widely recognized as the "climate information application gap". This gap exists because valuable climate forecast data, often produced by sophisticated meteorological agencies, fails to translate effectively into timely, location-specific, and actionable farm-level management decisions (Seid et al., 2020). Historically, agricultural extension services have relied on generic, often outdated, fertilizer recommendations derived from limited field trials conducted decades ago. This static, "blanket" recommendations fail to account for the crucial spatial heterogeneity of soils and the immense temporal variability inherent in modern seasonal rainfall patterns (AFR-2314, 2023) The resulting disconnect undermines farmer resilience and resource efficiency. The strategic goal for the AgWise1 Climate Intelligence (CI) integration is to engineer a fundamental paradigm shift: climate predictions must evolve from passive informational products into active decision-input variables that directly parameterize and modulate underlying agronomic optimization algorithms. Achieving genuine climate resilience necessitates a dual-timeframe strategy, a core lesson learned from successful implementations like WeRise, Climate+ and EDACaP-NextGen (Seid et al., 2023). This strategy requires integrating climate information across two distinct temporal scales: (i) Strategic Planning (3-6 Months): Utilizing seasonal forecasts to guide critical long-term decisions, including optimal variety selection, defining the best sowing or planting windows, and facilitating access to bundled financial services (AFR-2419, 2024) (ii) Tactical Execution (10-Day): Employing dynamic, high-frequency sub-seasonal forecasts to provide precise, in-season adjustments necessary for critical management actions, such as fine-tuning fertilizer application schedules, managing supplementary irrigation, and determining optimal harvest timing(CGIAR, 2024). The proposed framework for AgWise CI is structurally designed to overcome two specific technical challenges identified across foundational projects: (1) the translation problem converting complex, probabilistic climate outputs into agronomically meaningful parameters; (2) the integration problem embedding these climate-conditioned algorithms seamlessly within existing digital advisory architectures.
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