Uganda Training of Trainers on Enhancing Forecasting Capacities and Crop Capability Prediction Tools and Models

The negative impact of hydro-meteorological hazards on the agricultural sector oftentimes leads to food insecurity, especially in Sub-Saharan Africa (SSA). It is, therefore, incumbent upon policy-makers to formulate appropriate strategies to minimize hydro-meteorological hazards' effects on communit...

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Detalles Bibliográficos
Autores principales: Garanganga, Bradwell, Nyakutambwa, Trymore, Barungi, Julian, Ssebwana, Achilley, Ndigire, Regina, Recha, John W.M.
Formato: Informe técnico
Lenguaje:Inglés
Publicado: Accelerating Impacts of CGIAR Climate Research for Africa 2025
Materias:
Acceso en línea:https://hdl.handle.net/10568/177075
Descripción
Sumario:The negative impact of hydro-meteorological hazards on the agricultural sector oftentimes leads to food insecurity, especially in Sub-Saharan Africa (SSA). It is, therefore, incumbent upon policy-makers to formulate appropriate strategies to minimize hydro-meteorological hazards' effects on communities and economies. Increased availability of timely and tailored climate-related knowledge, information, and products supports decision-makers in reducing climate-related losses and enhancing benefits. However, smallholder farmers do not use the majority of the available weather and agrometeorological information, resulting in low agricultural productivity. To address this, the African Climate Policy Centre (ACPC)/United Nations Economic Commission for Africa (UNECA), in collaboration with its regional partners, commissioned a study to develop a set of simple and rigorous scientific tools that can be used to make evidence-based decisions in agriculture planning and policy. The study took place in three southern African countries: Malawi, Mozambique, and Zimbabwe. The study was validated in 2021. Use of the Tool leads maximizing agricultural productivity while limiting the consequences of hydro-meteorological risks on the food system. This tool can assist policy-makers and user communities decide on the most up-to-date crop capability based on seasonal climate forecast (SCF). Following the validation of the Tool, the Accelerating Impact of CGIAR Climate Research for Africa (AICCRA), in collaboration with Centre for Coordination of Agricultural Research and Development for Southern Africa (CCARDESA), UNECA-ACPC, World Meteorological Organization (WMO) are building a cohort of CIS practitioners on CIS-Based Decision Support Tools to assist user communities in improving their decisions in agricultural production systems in order to operationalize and bring maximum impact. Several roving Training of Trainers (ToT) workshops were successfully conducted for agricultural yield prediction users, SCF providers, researchers, and academics in Southern Africa Development Community (SADC). CCARDESA and AICCRA coordinated most of these initiatives in SADC. Following the successes of the ToT Workshops in SADC, the Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA), felt it prudent to commence the ToT Workshops within its jurisdiction. In this regard, the first of such ToT Workshop was held in Kampala, Uganda, in September 2025. This ToT workshop covered a wide range of topics, including providing a conceptual framework for the Climate Agriculture Modelling and Decision Tool (CAMDT) - Decision Support System for Agrotechnology Transfer (DSSAT) platform; the importance of SCF; a ‘Hands-on’ Exercise in data management (quality control and missing values, as well as a specific template/format); data acquisition; model descriptions (assumptions and uncertainties); and model analysis (simulation and validation). Participants' feedback indicated that running the model and interpreting its outputs were easy and acknowledged the feasibility of the tool for future applications. However, despite the availability of a user manual, participants preferred a simpler programme-assisted method so that individuals with less computer knowledge could run the model for immediate use and application. They also thought the training was extremely relevant and valuable to the user communities. From the hands-on exercise, participants emphasized that the proper use of the SFC-driven crop capability prediction model and its timely deployment will result in improved efficiencies in agricultural productivity in the ASARECA area. Participants also recommended that the model be improved by including local circumstances and cultivars for its comprehensive applicability in Uganda and beyond. Hence, the incorporation of common crops (e.g., legumes) into the model is absolutely key. They recommended that, for this capacity-building programme to be successful and have a lasting impact, there is needs for the full support of pertinent national and regional organizations, projects, and governments in the area. More resources are also required to guarantee that developers continued to engage in model improvement and skill transfer within ASARECA jurisdiction.