UGANDA TRAINING OF TRAINERS ON ENHANCING FORECASTING CAPACITIES AND CROP CAPABILITY PREDICTION TOOLS AND MODELS Training Report Bradwell Garanganga, Trymore Nyakutambwa, Julian Barungi, Achilley Ssebwana, Regina Ndigire, John Recha September 2025 TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) To cite this report Garanganga, B., Nyakutambwa, T., Barungi, J., Ssebwana, A., Ndagire, R., & Recha, J. (2025). Uganda Training of Trainers on Enhancing Forecasting Capacities and Crop Capability Prediction Tools and Models. AICCRA Training Report. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA). Acknowledgments The facilitator extends sincere appreciation to Dr. Sylvester Dickson Baguma, Executive Director of ASARECA, for the opportunity to conduct the Training of Trainers Workshop on Enhancing Forecasting Capacities and Developing Crop Capability Prediction Models and Tools in Uganda. The United Nations Economic Commission for Africa (UNECA) / African Climate Policy Centre (ACPC) is gratefully acknowledged for providing the resources and technical guidance that led to the development of this Tool. Through the strong partnership among ASARECA, AICCRA, CCARDESA, WMO, and ACPC, the Training of Trainers Workshop has been successfully upscaled. Digitron appreciates the financial support provided by ASARECA and its partners, which made this workshop possible. Special thanks go to Ms. Julian Barungi, Ms. Racheal Namuzibwa, Mr. Achilley Kiwanuka Ssebwana, and Ms. Regina Ndagire of ASARECA for their excellent organizational and logistical support. Heartfelt appreciation is extended to Dr. John Walker Recha of the Accelerating Impacts of CGIAR Climate Research for Africa project at the International Livestock Research Institute for his guidance and dedicated efforts in ensuring the successful implementation of the Uganda ToT Workshop. Finally, sincere gratitude is expressed to the World Bank–IDA for its invaluable support to the AICCRA program, without which this Training of Trainers Workshop would not have been possible. About AICCRA Reports Titles in this series aim to disseminate interim research on scaling climate services and climate-smart agriculture in Africa and stimulate feedback from the scientific community. Photos Cover photo: © ASARECA Disclaimer This report has not been peer-reviewed. Any opinions stated herein are those of the author(s) and do not necessarily reflect the policies or opinions of AICCRA, donors, or partners. Licensed under a Creative Commons Attribution–Noncommercial 4.0 International License. © 2025 Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Partners TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) EXECUTIVE SUMMARY 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. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) ABOUT THE AUTHORS Bradwell Garanganga is a Senior Researcher at Digitron Business Systems, Zimbabwe Trymore Nyakutambwa is a Researcher at Digitron Business Systems, Zimbabwe Julian Barungi is Programme Officer Planning the Association for Strengthening Agricultural Research in Eastern and Central Africa Achilley Ssebwana is a Software Developer at the Association for Strengthening Agricultural Research in Eastern and Central Africa Regina Ndigire is Software Developer at the Association for Strengthening Agricultural Research in Eastern and Central Africa John Recha is a Scientist - Climate Smart Agriculture and Policy at Accelerating Impacts of CGIAR Climate Research for Africa, International Livestock Research Institute TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) CONTENTS EXECUTIVE SUMMARY ....................................................................... 4 BACKGROUND ................................................................................... 8 1. INTRODUCTION ......................................................................... 9 WELCOMING REMARKS ..................................................................... 9 OFFICIAL OPENING REMARKS BY REPRESENTATIVE OF ASARECA .. 11 OBJECTIVES OF THE TOT WORKSHOP .............................................. 12 Expected Outputs of ToT Workshop ............................................... 12 2. CROP YIELD PREDICTION MODELLING FOR OPTIMAL AGRICULTURAL PRODUCTION ......................................................... 14 2.1. Introduction to crop capability prediction model ................. 15 2.2. Hands-on exercise on CAMDT/DSSAT analysis ...................... 17 Data and Model Implementation Session........................................ 17 Breakout Session ....................................................................... 23 3. GENERAL DISCUSSION AND RECOMMENDATIONS.................... 24 CLOSING REMARKS ......................................................................... 26 WAY FORWARD ............................................................................... 28 ANNEX I: PARTICIPANTS' FEEDBACK .............................................. 30 ANNEX II: LIST OF PARTICIPANTS .................................................. 33 ANNEX III: PROGRAMME FOR ToT WORKSHOP, KAMPALA, UGANDA, 22-26 SEP 2025 .............................................................................. 36 TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) ACRONYMS ACPC African Climate Policy Centre AICCRA Accelerating Impacts of CGIAR Climate Research for Africa ASARECA The Association for Strengthening Agricultural Research in Eastern and Central Africa CCARDESA Centre for Coordination of Agricultural Research and Development for Southern Africa CCAFS Climate Change, Agriculture and Food Security Programme of CGIAR CAMDT Climate Agriculture Modelling and Decision Tool CGIAR Consultative Groups on International Agricultural Research CIS Climate Information Services CSA Climate Smart Agriculture DES Department of Extension Services DPS Department of Planning Services DST Decision Support Tools DSS Decision Support System DSSAT Decision Support System for Agrotechnology Transfer GUI Graphical User Interface ILRI International Livestock Research Institute MAAIF Minister of Agriculture, Animal Industry and Fisheries MWE Ministry of Water and Environment NARIs National Agricultural Research Institutes NARL National Agricultural Research Laboratories NARO National Agricultural Research Organization NARS National Agricultural Research Systems NASA National Aeronautics and Space Administration of USA NMHSs National Meteorological and Hydrological Services SADC Southern African Development Community SCF Seasonal Climate Forecast SEBs Socioeconomic Benefits ToT Training of Trainers UNECA United Nations Economic Commission for Africa UNMA Uganda National Meteorological Authority WISER (Weather and Climate Information Services) WMO World Meteorological Organization WTD File with daily Weather data aggregated over many years WTH File with one Year’s daily Weather data WISER Weather and climate Information Service programme WMO World Meteorological Organization TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 8 BACKGROUND Despite significant advances in climate research and technology, smallholder farmers do not use the majority of the available weather and agrometeorological information, resulting in low agricultural productivity. To address this, the 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 produced a tool for measuring crop capability in different agro-ecological zones, with the aim of improving agricultural production and food security. A group of relevant experts from the three countries' National Meteorology and Hydrology Services (NMHSs), Ministries responsible for Agriculture and their affiliated research institutes, and relevant departments from Academia endorsed the tool at a Validation Workshop held in Lilongwe, Malawi. This crop capability prediction tool is critical for increasing agricultural productivity, identifying yield deficits and surpluses with exceptional lead times, and providing greater opportunities for nations to attain food security. This tool can also be used in the livestock sector. For example, if poor fodder production is predicted due to drought, it may help farmers make evidence-based decisions to destock or purchase extra stock feed to sustain their animals through the dry season. This suggests that improved access, uptake, and use of Climate Information Services (CIS) can reduce the vulnerability of smallholder farmers to the impacts of climate extremes and changes. In this context, training on effectively utilizing CIS in decision-making processes is critical to limiting the adverse effects of climate-induced risks and enhancing production under favorable climate conditions. This will yield enormous benefits to the economy. As a result, 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, is building a cohort of CIS practitioners on CIS-Based Decision Support Tools to assist user communities in improving their decisions in agricultural production systems. This is achieved by organizing roving Training of Trainers (ToT) workshops in Zimbabwe, followed by Mozambique, Zambia, and Malawi. The specific objectives are to provide hands-on training on a modified CAMDT/DSSAT crop yield prediction model using national data. Through these workshops, the goal is to equip participants with the knowledge and skills necessary to effectively apply the crop capability prediction tool, thereby enhancing their ability to manage agricultural risks and improve food security in their respective countries. The tool’s integration into decision-making processes will empower stakeholders to better anticipate crop yields and make proactive, informed decisions that promote resilience in the face of climate variability and change. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 9 1. INTRODUCTION Climate information services (CIS) in agriculture provide critical information to agricultural institutions, input suppliers, local cooperatives, and community-based groups and assist them in making practical, realistic, and relevant decisions in the face of climate change. Given the reality of extremes in climate variations and climate change and their negative consequences on agriculture, it is vital to deploy technologies like crop intelligence tools, such as crop yield prediction models, to strengthen the sector's resilience to climate change and variability impacts. Crop growth simulation models have evolved into helpful tools for agricultural research and production systems that benefit end users. User communities can, for example, use a Climate Agriculture Modelling and Decision Tool (CAMDT) linked Decision Support System for Agrotechnology Transfer (DSSAT) to correlate crop biological requirements to physical attributes of land in order to give management greater opportunities for enhanced agricultural planning. These models require climatic factors such as temperature, solar radiation, and precipitation, which all influence crop growth and yield development. The models also require the evolution of these variables daily during the season. However, the majority of the publicly available seasonal climate forecasts (SCF) released by Regional Climate Outlook Forums (RCOFs) and other Centres are provided in typically three-monthly means in tercile probabilities (for rainfall and temperature), i.e., below-normal (BN), near-normal (NN), and above-normal (AN). Thus, SCFs alone could fail to deliver highly relevant information for enhancing farm-level decisions and policy-level actions. As a result, the CAMDT-DSSAT (i.e., SCFs connected with crop simulation models) become crucial CIS-based decision support tools to assist user communities in improving their strategic and tactical decisions to maximize benefits and minimize any climate-related risks in the growing season. The DSSAT platform has been used in over 174 countries for over 30 years by researchers, educators, consultants, extension agents, farmers, private industry, policy and decision- makers, and many others. The DSSAT package includes 16 distinct crops and software for evaluating and deploying crop models for various purposes. The DSSAT crop simulation modelling can also help forecast the impacts of future global climate change and can, therefore, contribute to developing national adaptation and mitigation policies. Other policy challenges that might benefit from crop yield prediction modelling studies include yield projections, agribusiness planning, operations management, and the effects of management activities on environmental issues. User groups can also minimize losses due to unforeseen inter-annual climate variability by using CIS, such as crop yield prediction modelling (CAMDT- DSSAT combination), thereby maximizing productivity more efficiently under favorable climatic patterns when these are predicted in advance. WELCOMING REMARKS At the onset of the workshop there were welcoming remarks by the organizers of the ToT Workshop. Remarks by the Facilitator Bradwell Garanganga recalled that the study on Enhancing Forecasting Capacities and Developing Crop Capability Prediction Models/Tools was commissioned by UNECA/ACPC had its inception during the ‘Building Back Better’ workshop held in Oct 2020, Harare, Zimbabwe, to respond to Cyclone Idai that devastated Malawi, Mozambique and Zimbabwe. Following this, there was a high-level request for UNECA/ACPC and its partners to develop a Decision Support Tool (DST) by which TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 10 governments could make informed agricultural decisions using climate information. ACPC then commissioned a crop capability prediction model as a pilot in the three countries. The tool was subsequently developed and validated. Welcoming Remarks by the AICCRA project Representative from ILRI In his opening remarks, the representative of International Livestock Research Institute (ILRI), Dr John Recha noted that the participants were gathered today at a pivotal moment. Agriculture remains the backbone of our economies, and yet it is also the sector most vulnerable to climate variability and climate change. Farmers and livestock keepers continue to face uncertainties from climate variability translating to food insecurity. He noted that the development and application of tools such as the modified Climate Agro- Meteorological and Crop Forecasting Tool (CAMDT) are so vital. The tool enables users to anticipate climate impacts and make evidence-based decisions well ahead of time. It increases crop production, improves farm profitability, reduces losses from adverse climate hazards, and ultimately enhances food security. Moreover, livestock producers can also benefit from timely forecasts; making informed decisions to adjust stocking rates, plan for supplementary feeding, or safeguard their herds. He noted that, tools alone are not enough. There was need for experts, people like Trainer gathered who can interpret, apply, and communicate these forecasts effectively to farmers, policymakers, and communities. This training is designed to develop a cadre of specialists who will champion the use of climate information services (CIS) and crop prediction models to transform agriculture across our region. Over the course of this training, the participants will strengthen your capacity to use seasonal climate forecast-driven crop capability prediction tools, engage in hands-on exercises with the modified CAMDT/DSSAT model, and gain access to practical user guides and software packages. Just as importantly, this workshop will strengthen the collaboration among institutions and stakeholders involved in climate services, ensuring that knowledge flows seamlessly from scientists to policymakers and farmers. Dr Recha noted that the expected outcome is clear: a network of trained professionals who will enhance the resilience of our food systems. By applying these models and tools, we can save millions of dollars in agricultural productivity, reduce the devastating impacts of climate shocks, and build a more secure and sustainable future for our nations. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 11 OFFICIAL OPENING REMARKS BY REPRESENTATIVE OF ASARECA There were official opening remarks by Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA). On behalf of Dr. Sylvester Dickson Baguma, Executive Director, ASARECA, MS Julian Barungi, made opening remarks. MS Barungi informed that: • The Association for • Strengthening Agricultural Research in Eastern and Central Africa (ASARECA) is an intergovernmental subregional organization of 15 member countries in Eastern and Central Africa including: Uganda, Kenya, Tanzania, Rwanda, Burundi, Central African Republic, Republic of Congo, Democratic Republic of Congo, Eritrea, Ethiopia, Sudan, South Sudan, Somalia, Madagascar and Cameroon. • ASARECA coordinates agricultural research for development in Eastern and Central Africa through focusing on four major thematic areas: capacity building; agricultural technologies and innovations; enabling policy environment and functional markets; and knowledge management. • The training on crop capability tool aims to equip agricultural scientists, climate scientists and policy makers with the skills to better utilize climate forecast data for a particular season to predict yield expected in the same season and inform production decisions in that particular season. • The training is convened under ASARECA’s Accelerating Impacts for CGIAR Climate Research in Africa (AICCRA) project which promotes the utilization and scaling of climate smart agriculture technologies and climate information services. The AICCRA project is funded by the World Bank and managed by CIAT. MS Barungi wished fruitful deliberations and declared the workshop officially opened. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 12 OBJECTIVES OF THE TOT WORKSHOP The facilitator, Bradwell J Garanganga, stated the Objectives of ToT Workshop as to train experts on: ▪ Seasonal climate forecast-driven crop capability prediction tool to benefit policymakers and the user community for the strategic provision of appropriate inputs to the Agriculture (livestock and crops) and Food Security Sector; ▪ Strengthening the platform for collaboration by key stakeholders involved in the production and application of timely climate information; ▪ Strengthening capacity for improved production, better access, and sustainable operations for CIS; ▪ Developing a methodology for predicting crop capability in the various agro-ecological zones to enhance agricultural productivity and food security; and ▪ Hands-on exercise on modified CAMDT/DSSAT crop yield prediction model using country data. Expected Outputs of ToT Workshop The facilitator, shared the Expected Outputs of ToT Workshop which were as follows: ▪ Enhanced capacity in incorporation of climate services into agricultural planning and decision-making frameworks. ▪ Establishing a platform for cohort of producers and users of climate information services TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 13 ▪ Participants to be capacitated to develop methodologies to forecast crop performance in different agroecological zones, which helps identify optimal crops, optimize land use, and enhance agricultural productivity. ▪ The practical approach to ensure that producers and users can effectively apply these tools to enhance agricultural outcomes. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 14 2. CROP YIELD PREDICTION MODELLING FOR OPTIMAL AGRICULTURAL PRODUCTION Bradwell J Garanganga from Digitron made two presentations: 1) Crop Yield Prediction Modelling for Optimal Agricultural Production Systems; and 2) Introduction to Crop Capability Prediction Modelling Techniques. These set the scene in the quest for understanding the background of the seasonal climate forecast- driven Crop Capability Prediction Tool, and its corresponding theoretical basis. The presentations showed that with resources permitting, the Tool could be further developed for greater benefits by extending the lead time. The presentation highlighted the development of the Tool as an example of the considerable Socioeconomic Benefits of investing in CIS, as it showed that the DST potentially enabled farmers and policymakers to make informed decisions, which potentially saved millions of dollars in avoided costs in unfavourable climate conditions. In particular, he presented the importance of crop yield prediction modelling for optimal agricultural production systems. In his presentation, the following points were covered: ▪ Agricultural production vs. climate change impacts – how the subSaharan African (SSA) countries' agricultural productivity has been significantly less than the global average due to inefficiencies. This is worsened by climatic extremes and shocks, which are manifested in floods, storms, and droughts. ▪ Justifications for enhanced CIS investment – hydro-meteorological hazards accounted for 90% of total disaster losses worldwide, and improved uptake and use of CIS is critical in the agriculture sector. The claim is backed up with UNECA’s findings showing that the use of CIS in support of making strategic and tactical decisions resulted in a benefit-to- cost ratio of 10 to 1. ▪ Types of crop growth simulation models – background descriptions and how they become usual tools for agricultural research and production systems; and common types of crop growth models, including those listed under statistical models, mechanistic models, deterministic models, stochastic models, and simulation models. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 15 ▪ Climate Agriculture Modelling and Decision Tool (CAMDT) linked crop-yield simulation- the Decision Support System for Agrotechnology Transfer (DSSAT) – how climate-informed crop intelligence technologies are critical in attempts to properly plan and direct agricultural adaptation actions; and why CAMDTDSSAT was chosen for this study. ▪ The applications of crop capability tools include their roles in increasing agricultural productivity, improving farm-level and policy-level interventions, selecting potential management practices, estimating crop yield early enough, and estimating the performance of different crops under different scenarios, among other things. ▪ Need for establishing a cohort of experts – Rationale for AICCRA and its regional partners to participate in the Crop Capability Prediction Model Initiative and Training of Trainers (ToT) Workshops. At the end of this training, the participants of ToT’s will be able to: o analyse the expected impact of technology options and climate on crop yields, o provide information on “what inputs to procure well before the agricultural season commences” to enhance agricultural productivity, o optimize agronomic practices according to expected climate conditions, and o reduce losses in agricultural production systems under different adaptation/mitigation scenarios. 2.1. Introduction to crop capability prediction model Bradwell Garanganga of DIGITRON presented the conceptual framework of crop capability prediction modeling. His presentations were made in two sessions where he discussed the overview of weather and CIS and their utility for crop yield prediction, motivation for developing actionable DST in agriculture, a brief overview of methodology, concepts, and application of DSSAT, and rationale for using CAMDT. The following main points were highlighted: ▪ Overview of crop capability model – the DSSAT is not just a software programme but an ecosystem for crop model users, crop model trainers, and crop model developers, and how the model is important for predicting growth in food, feed, fibre, and fuel crops, etc. ▪ DSSAT model architecture considers the environment (weather, soils), genes, and phenology of crops, as well as management (fertilizers, irrigation), to simulate crop yields with the required measure of fidelity. ▪ Growth simulation – the simulation models are constructed using a series of historical data of relevant parameters. DSSAT requires environmental parameters, such as solar irradiance, minimum and maximum temperature and precipitation, soil type and its profiles, seed variety, and management system, to run the required crop simulations. ▪ Running DSSAT required a database management system for soil, weather, genetic coefficients, management inputs, crop phenology simulations, and a series of other utility programmes. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 16 ▪ Why SCF is an important input in crop yield prediction models – to have a long planning window, SCFs are critical. However, the utility of SCFs for driving DSSAT requires weather generators. These are algorithms used to reconfigure SCF into daily weather realizations as input into crop growth simulation models to predict yield. An example of such weather generators is the CAMDT developed by Han and co-workers at the International Institution for Climate and Society. CAMDT converts tercile- based SCF into daily weather sequences, which are critical inputs that the crop yield prediction models need. ▪ Applications of the training manuals – these manuals combine two User Guides to be used in tandem. The crop capability prediction modelling platforms used in this User Guide are the CAMDT as the weather generator for the DST for DSSAT, the crop weather simulation model. CAMDT/ DSSAT platforms are very sensitive to the data formats. In this regard, there is a need to carefully manipulate the climatological data for exporting into CAMDT/DSSAT programmes to be compatible for ingestion into these environments. ▪ Introduction to CAMDT software – the agricultural systems are modified ecosystems that need to be managed through systems models, which are possible only through classical engineering expertise in modelling. It was noted that models have interacting components such that a change in one component affects changes in other components. ▪ Assumptions made in CAMDT as a weather generator – by their very nature, some assumptions were made in developing the CAMDT model. It is used to drive DSSAT to simulate crop yields. In the crop capability prediction model, the following assumptions are made: fidelity in seasonal climate forecasting, a good hit rate, thresholds for above or below normal probabilities, and the expectation that crop cultivars will be replaced by local ones after calibration work, acknowledging the limitations of predictability. ▪ Some inherent uncertainties in any model – There will be some uncertainties due to the fact that, for instance: the model is applied in a new situation (e.g., switching to a new variety), the processes are not all fully understood to be always ideally simulated; and model performance is limited to the quality of input data of parameters to be modelled: e.g., (meteorological data used in the model need to be reliable and complete). ▪ Downscale SCF to daily pattern– the distribution of rainfall episodes, for instance, taking into account intensity, amount, or duration, is assumed by the scheme of the model. The validation of the model is done by routinely analyzing the model outputs and assessing whether they are consistent with realistic crop yield products. There was also a demonstration of how generated outputs from running CAMDT-DSSAT can be presented as actionable products to the user community. Trymore Nyakutambwa from Digitron installed and successfully ran the necessary suite of software for the Tool. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 17 2.2. Hands-on exercise on CAMDT/DSSAT analysis The trainers gave participants a URL to download the software, which included Python and DSSAT 4.6, and an instruction manual comprised of two user guides. In addition, Digitron built a data gathering and processing programme in response to requests from previous workshop attendees. The trainers demonstrated the key steps in the acquisition of NASA data and preparing it for ingestion into the model. Automated data handling techniques in this respect include saving into appropriate pre-configured hierarchical folders. Following that, trainers demonstrated how to execute CAMDT/DSSAT analysis using the instruction manual. In this regard, participants were then guided through the processes necessary for running the simulation of crop yield prediction. The following sections were covered during the hands-on exercise: Data and Model Implementation Session Following the usage of the digiSoft tool, participants were taught data collection frameworks for crop yield prediction modeling, namely the CAMDT/DSSAT template. Participants were shown how to perform agricultural yield prediction data analytics simulations, including quality control and missing data concerns. This was a participative session with questions aimed at clarifying the procedures to correct errors that sometimes happened. Demonstration of Data and Model Implementation Data Acquisition and Processing Once the appropriate software suite were installed and operational, participants were directed to NASA's data sources website. They retrieved the necessary data from the website for ingestion into the CAMDT/DSSAT platform to generate crop capability predictions. These were historical daily climatological characteristics (solar irradiance, minimum and maximum temperatures, and rainfall) for a specific meteorological station from 1 January 1984 to August 31, 2025. This time frame was chosen because developing a crop yield prediction model required at least 30 years of consistent daily data in those four parameters. Participants downloaded data as an Agro-Climatology Community to receive the required files, particularly solar irradiance values, which change depending on the needs of distinct community users. The files had to be saved in CSV format. However, the use of the digiSoft App, developed by Digitron, for data handling dispensed with the previous intermediate manual process of “Formatting and TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 18 Splitting NASA Data to CAMDT Format” and “Exporting to Text Format and Renaming WTH Files.” These procedures were used in the early version of the manual, which took up to a whole day for the users/trainees to master. Using the new App saved considerable time, which made it possible for participants to begin running CAMDT much earlier than before, within a few hours. Downloading the required software Trainers guided participants on downloading the digiSoft CAMDT NASA Autoformat software tool, used to split and format the NASA file into *.WTD (global data) and into *.WTH (yearly data) files as provided in the MS OneDrive/Google Drive, as in the provided link. This entailed the running the installation by: ▪ double-clicking the shortcut digiSoft; or ▪ typing in digiSoft; or ▪ or searching for digiSoft in the search box. Trainees were also guided to download the Python software package used for the modelling environment https://www.python.org/downloads/release/python-3110/ by carrying out: ▪ Running a custom installation ensuring that PIP option is selected. ▪ Using the Python PIP utility to add the options required by CAMDT; and ▪ Locating the given CAMDT files and TEST RUN the GUI display Instructions to connect to NASA website were for trainees to: ▪ Connect to the NASA Climate Data Web Portal; (https://power.larc.nasa.gov/data-access-viewer/) ▪ Download and format NASA Data for Ndola and desired data range (Jan 1984- August 2025) and the parameters required for ingesting into CAMDT. Running the digiSoft NASA Data Tool in order to: ▪ Create CAMDT Data folders for the Weather station ▪ Run the extraction process to produce *.WTD (global data) and *.WTH (yearly data) files into CLIMATEDATA folder ▪ Verify the climatological parameters data files ▪ Copy extracted climatological parameters data to CAMDT Working folders Identify appropriate tabs to run crop yield projection https://www.python.org/downloads/release/python-3110/ https://www.python.org/downloads/release/python-3110/ https://www.python.org/downloads/release/python-3110/ https://www.python.org/downloads/release/python-3110/ https://power.larc.nasa.gov/data-access-viewer/ https://power.larc.nasa.gov/data-access-viewer/ https://power.larc.nasa.gov/data-access-viewer/ https://power.larc.nasa.gov/data-access-viewer/ https://power.larc.nasa.gov/data-access-viewer/ https://power.larc.nasa.gov/data-access-viewer/ https://power.larc.nasa.gov/data-access-viewer/ TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 19 Trainees were given instructions on how to identify appropriate tabs on the GUI in order to run crop yield projection through: ▪ Selecting a simulation setup ▪ Selecting a target station ▪ Selecting a disaggregation mode ▪ Providing a seasonal climate forecast ▪ Input for DSSAT ▪ Writing scenario set up ▪ Providing additional “what-if if scenarios” ▪ Selecting an appropriate working directory ▪ Determining threshold ▪ Creating DSSAT experimental files ▪ Running DSSAT for all scenarios ▪ Displaying graphs ▪ Simulation horizons ▪ Downscaling process; and ▪ Above-normal, near-normal and below-normal rainfall terciles; and ▪ Required iterations for test runs Trainees were also guided on how to: ▪ Copy CAMDT system files to the Working Directory ▪ Run Rice and Maize Automated Modelling for Big Bend ▪ Generate Simulated Rice/Maize Crop Yield Graphs ▪ Duplicate and modified Big Bend Model to other Stations TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 20 Running CAMDT graphical user interface Some of the participants encountered difficulties installing applications due to hardware and Windows version limitations. These were resolved by assisting them with downloads to guarantee all participants had the necessary software on their laptops. Creating folders for storage Participants were also instructed on how to create two folders and name them CAMDT and DSSAT, in the laptop's C: directory. Within the CAMDT folder, there were sub-folders for rice, maize, and sorghum, each of which had sub-folders for the relevant meteorological station, down to two sub-folders for above-normal rain and below-normal rain at the same level of hierarchy. These were the working directories where CAMDT would be pointed to simulate specific crop yield predictions depending on whether the anticipated seasonal forecast for the station in above-normal or below-normal rain. The results from the simulation would be output to the respective working directory. Running rice crop yield simulation The 2017 edition of CAMDT is only automated for the RICE crop and was originally developed to model rice production in the Philippines, mostly the PILI meteorological station. The participants were shown how to add a working directory related to their meteorological station into the file “CAMDT_2017_0310-py”. As part of continuous improvements, Digitron changed the codes in the programme in order for CAMDT to automatically run other crops without recourse to MS Windows Explorer or Command Prompt environments made use of previously to enable the user to run maize and sorghum probability of yield exceedances successfully. Participants familiarized with CAMDT displaying some of the outputs (predicted yields, water stress, and gross margin) easily. Running CAMDT/DSSAT crop modelling for non-rice cereals Digitron made changes to some of the CAMDT system file in order to enable the automatic simulation of other crops yield prediction. Participants were shown how to copy the successful outputs of the working directory as per an appropriate seasonal climate forecast, say that related to abovenormal rain into a corresponding working directory for the new crop variety. After that, participants were shown how to TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 21 make the necessary changes to some of the rice output files. This was so that respective working directory now had the specifications of the new cereal, maize or sorghum, such as the cultivar variety type its variety number, its population distribution at planting. Trainee analysing model outputs Once the working directory had these new specifications, participants were made to run CAMDT. The outputs were now for the respective crop, either maize or sorghum. The participants were shown how to further process the outputs to display graphical information on the non-rice cereal. Group discussion of modelling outputs Trainees were split into groups to experiment with the tool by generating model outputs. Trainers demonstrated how this was to be done. All groups were able to generate experimental model outputs. Each of the group model outputs was analysed to gauge: ▪ What outputs were generated ▪ How realistic the outputs were, and ▪ How the output could be used in decision-making. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 22 Trainee showing a Crop’s Yield Forecast using Whisker Box-Plots The main conclusions were that the Tool was a very important aid that could be effectively used in decision-making by farmers, input suppliers, and policymakers around agricultural production systems more efficiently. There was a need to refine the outputs by examining the impacts of greater smoothing of the generation processes. Experienced scientists assisting in the interpretation of modeling outputs by trainees Techniques for synthesizing data for seasonal climate forecasting to improve lead time for decision-making The trainers demonstrated that in addition to SCF, CAMDT requires near-real-time observed data up to the month preceding the month for planting. In this regard, the Tool requires projected daily climatic data up to one month before planting (October). Typically, this takes two additional months after the RCOF Statement (Forecast), i.e., November of the forecast season. Digitron is developing and testing a scheme that needs to be perfected and implemented in the CAMDT simulation to achieve an additional two-month lead time. Digitron developed some techniques for this connection and conducted initial tests on meteorological stations in Chegutu and Gwanda, Zimbabwe. The expanded experiments covered both high and low rainfall areas through different above-normal and below-normal rainfall conditions. The tests were run for various SARCOFs and SCFs. The technique yielded viable results, such that the crop yield forecasts based on synthesized data products and actual data (only available two months later) are closely matched for all the crops. With resources permitting, this scheme needs to be developed across other meteorological stations to increase the crop capability prediction lead time by an additional two months at every specified location. This will immediately make the TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 23 Tool more usable; SCFs from SARCOFs/NCOFs are issued (early September) rather than wait issuing the crop yield prediction in November as per original CAMDT limitation. The benefit to the economy will be immense. Breakout Session The participants have a breakout session to deliberate more on the Tool in terms of the following: ▪ Workings of the model ▪ Limitations of the model ▪ Potential for country application/adaptation Reporting Back Session After the breakout session, the groups made presentations at the plenary. Trainees during a breakout TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 24 3. GENERAL DISCUSSION AND RECOMMENDATIONS Following the demonstration of the Crop Capability Prediction Model commissioned by ACPC, a Breakout Session was held to allow participants to analyze the utility of the model. This session was followed by a Report Back to the Plenary, the details of which are included in Annex I. In the plenary, there was consensus that the tool would add significant value to agricultural production systems and food security. However, areas requiring improvement, particularly in terms of ease of use, were also noted. The Training of Trainers (ToT) Workshop highlighted the following primary outcomes: ▪ The participants highly endorsed the Tool, recognizing it as a significant resource for assisting agricultural production and food security stakeholders. ▪ The Tool helps estimate crop yield based on different forecasts (AN or BN), thereby aiding in the planning of the cropping season, including selecting crops and/or varieties to plant. ▪ The model is robust and enables the formulation of effective strategies and decisions to improve agricultural production. ▪ The Tool needs to be tailored for different users: e.g., experts versus on- farm workers. ▪ The model addresses gaps in agricultural season planning, leading to more informed decision-making. ▪ National seasonal climate forecasting skills need to be enhanced, as they are essential for driving crop capability predictions; the model serves as an important Early Warning System (EWS) Tool. ▪ Local IT (software) developers should undergo the ToT to assist other participants and support the broader implementation of the model. ▪ The model should be continuously improved to increase its applicability based on feedback and observations from the user community. ▪ Consolidating all relevant information into one folder or container to avoid "back-and-forth" processes, which could lead to confusion and errors. ▪ The model should automatically save generated graphs into designated folders for better organization and ease of use. ▪ The participants generally expressed appreciation of the ToT Workshop and the Tool itself based on the observed results generated and integrated from it. The Training of Trainers (ToT) Workshop highlighted the following primary outcomes: ▪ More explanations on the climatological terms used in the tool to cater for other users such as agricultural practitioners. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 25 ▪ Integrate the model to suit acceptance of own observed data by the climatologist scientists. ▪ Design a module to Integrate the model simulation results with other agricultural related systems data at MAAIF, NARO etc to inform and improve decision making. ▪ Avail the model on other operating systems such as Ubuntu and Linux. ▪ Use the model results to generate articles for publication. ▪ Drafting a video manual for more guidance on the utilization of the model. ▪ Add more parameters to be examined by the tool for scalability and yielding of more effective results. There is need to have statistical metrics of the outputs. ▪ Have direct contact with the developers of the tool to enhance support for tool workability and avail a technical support team for the tool users. ▪ Further integrate the model with mapping of the crop yields. ▪ There is a need to incorporate more cultivars and local environmental conditions into the model to ensure it reflects local conditions in Uganda fully. In this regard, Digitron has committed to being available to support such requests. ▪ The Uganda Met noted that the Tool will greatly assist in their work in having actionable products for the users and policy makers. ▪ Uganda Met requested further training in order to customize the Tool. ▪ There was interest by two participants to utilize the tool in doctoral (PhD) studies. ▪ It was noted that there was need for resources to be made available in order for it to be possible to carry out the recommendations. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 26 NEXT STEPS The Tool to be further developed and scaled for greater local applicability, countries and partners will need to secure additional resources to enhance and improve the Tool. To maintain the momentum so far generated, there is need for the form a consortium of participants and related field practitioners to enhance usability and development of the Tool. The Consortium would: ▪ actively utilize the tool while advising areas for its improvement and additions; ▪ apply for proposals; and ▪ mobilize resources to engage more training on Tool utilization and improvements. The Consortium could also form a What’s App Google Teams or other platforms. CLOSING REMARKS Facilitator There were closing remarks made by the respective AICCRA and ASARECA representatives as follows: Closing Speech – Training of Trainers on Enhancing Forecasting Capacities and Developing Crop Capability Prediction Models/Tools, 26/09/2025 Dr Recha of ILRI began by expressing my sincere appreciation to all for your active participation, commitment, and contributions over the past days. He noted that this was not been just another training session – it was a platform of learning, exchange, and collaboration, and above all, a collective step towards strengthening the ability to transform agriculture in the face of climate variability and change. He noted that the training, convened under the thematic area of agricultural technologies and innovations by ASARECA and supported by the AICCRA Project, had been driven by a clear and urgent purpose: to enhance the forecasting capacities of our institutions and to equip climate scientists, agrometeorologists, and policy makers with practical tools for crop capability prediction. Over the course of the programme, there was the achievement of several milestones together: strengthened the capacity in the use of seasonal climate forecast-driven prediction tools. practiced hands-on exercises with the modified CAMDT/DSSAT model using own country data, demonstrating its application in real-world contexts. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 27 explored how the tools can inform agriculture and food security decisions, ensuring that forecasts are not left on the shelves of meteorological services, but rather reach farmers, extension agents, and decision-makers who need them most. received a user guide that will allow the participants to take these tools back to the institutions, embed them in systems, and share them with colleagues who could not be at the ToT Workshop. Dr Recha noted that the concept behind this training is powerful: a combined seasonal climate and crop forecasting tool that allows professionals to look ahead of climate hazards, anticipate deficits or surpluses, and take timely action. Such tools have the potential to save millions of dollars in avoided losses, enhance productivity, and assure food security for our nations. Dr Recha observed that importantly, this training has not only focused on the technical side but also on building the platform for collaboration among key stakeholders. Participants were leaving the ToT Workshop not just as individuals who have been trained, but as a cohort – a consortium of experts who will carry forward the knowledge, continue skill transfer, and jointly work to advance the use of SCF-driven crop yield prediction modelling in Uganda and the region. He emphasize three key calls to action: Collaboration – Let us keep this network alive, continue sharing experiences, and support each other in refining and applying these tools. Fundraising and Resource Mobilization – As recommended, urged for a proactive engagement in fundraising by preparing Concept Notes, responding to Calls for Proposals, and Mobilizing the Resources needed to scale up this work. Practical Application and Advocacy – Most importantly, there is need to apply what had been learned, ensuring that the models and forecasts are not only technically sound but also translated into advisories that directly benefit farmers, pastoralists, and communities. He urged participants to draft Concept Notes and share with ASARECA and ILRI for exploring future training and collaborations on the same. Dr Recha wished participants safe travels back to their institutions and looked forward to the impact that will undoubtedly flow from this training. MS. Julian Barungi, ASARECA’s AICCRA Project Manager delivered the closing remarks during the crop capability prediction tool training on 26/09/2025. MS Barungi noted that: We started training on the first day knowing that the crop capability prediction tool will be used to predict crop yield expected in a particular season given the climate forecast given for that particular season. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 28 Today the trainees can attest that beyond yield prediction in a particular season, the tool can serve several other purposes including informing decision making on investments in fertilizer use, irrigation, credit, pest and disease management among others. Do you see the power of giving people an opportunity to experiment with technology? It doesn’t matter whether they are agricultural scientists/meteorologists/agro-meteorologists or any others….it will trigger curiosity, innovation, unlock new possibilities and create demand for the technology. Already participants have made brilliant suggestions on how the tool can be improved and some are willing to participate actively in improving the tool directly or indirectly through documenting and publishing experiences in utilization of the tool. The willingness to utilize and adopt the crop capability prediction tool by the participants is impressive! This is indicative of its usefulness, relevance and timeliness. I wish to thank the participants for their active participation and enthusiasm in utilizing the tool. I thank the trainer – Mr. Bradwell Garanganga for the great facilitation, knowledge shared and skills imparted over the past 5 days. I also wish to thank the co-facilitators – Achilley and Regina for the great work done. I thank Dr. Recha from our regional AICCRA project office for funding such an important training. WAY FORWARD As a way forward, ASARECA has taken note of all the suggestions that have come through during the training and these will be shared with the developer for improvement. Subject to availability of funds, ASARECA will plan (2026 project work plan) to facilitate documentation of experiences in utilization of the tool as well as impact- based forecasting and wide-spread awareness and dissemination of improved and more relevant agro-advisories resulting from contextualized utilization of the tool. ASARECA will support publications resulting from experiences and lessons in utilization of the crop capability prediction tool. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 29 Subject to availability of funds, ASARECA will support regional sharing of knowledge, experiences and lessons resulting from adoption of the tool in different member countries. ASARECA will create a WhatsApp Group for the trainees for continue with discussions and sharing of relevant knowledge and information. She wished participants the best as she urged them to continue to collaborate in utilization of the Crop Capability Prediction Tool. TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 30 ANNEX I: PARTICIPANTS' FEEDBACK GROUP 1 Members Kwaka Lorna (Rapporteur) Okee Joseph (Chair person) Godfrey Mujuni Joseph Isaac Mugagga Kisakye Angella GROUP 2 Members TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 31 Godfrey Seruwu Joyce Adokorach Peace Byiringiro Isaac Mugume Wilber Sekandi Stephen Magume GROUP 3 TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 32 Members Jeanifer Oyuru Ndagire Regina Angella Namyenya Jonathan Lwanga Ainebyona Jasper TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 33 ANNEX II: LIST OF PARTICIPANTS The following was the list of Participants at the ToT Workshop 1 Mr. Bradwell Garanganga (Facilitator) Zimbabwe bjgaranganga@gmail.com +263772220330 +26371922033 2 Dr. John Recha (ILRI) Kenya j.recha@cgiar.org +254 721 264936 3 Ms. Joyce Adokorach (NARO) Uganda joyceadokorach@gmail.com +256 788 567139 4 Dr. Godfrey Sseruwu (NARO) Uganda seruwugo@gmail.com +256 782 485063 5 Dr. Isaac Mugagga (NARO) Uganda jmugagga@gmail.com +256 772 322460 6 Mr. Jonathan Lwanga (NARO) Uganda lwangajonahb@gmail.com +256 392 968387 7 Ms. Lorna Kwaka (NARO) Uganda lornakwaka@gmail.com +256 773 714144 8 Dr. Everline Komutunga (NARL) Uganda komutungae@gmail.com +256 772 573687 9 Ms. Angela Kisakye (NARL) Uganda kisakyeangela19@gmail.com +256 777 605410 10 Dr. Wilber Ssekandi (NACRRI) Uganda ssekandiwilber34@gmail.com +256 774 001434 11 Ms. Eunice Kesiime (NARO) Uganda eunicekesiime@gmail.com +256 782 465655 12 Ms. Angela Namyenya MAAIF (Crop production) Uganda anamyenya@gmail.com +256 772 618130 mailto:bjgaranganga@gmail.com mailto:j.recha@cgiar.org mailto:joyceadokorach@gmail.com mailto:seruwugo@gmail.com mailto:jmugagga@gmail.com mailto:lwangajonahb@gmail.com mailto:lornakwaka@gmail.com mailto:komutungae@gmail.com mailto:kisakyeangela19@gmail.com mailto:ssekandiwilber34@gmail.com mailto:eunicekesiime@gmail.com mailto:anamyenya@gmail.com TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 34 13 Mr. Joseph Okee MAAIF (Crop production) Uganda jjcoko@yahoo.com +256 777 296296 14 Ms. Jennifer Oyuru (MAAIF Ext) Uganda joyuru@yahoo.com +256 772 873699 15 Mr. Stephen Magume (MAAIF Extension) Uganda stephenrma@gmail.com +256 782 225728 16 Mr. Charles Damba (MAAIF Extension) Uganda damcharle@gmail.com +256 752 808006 18 Ms. Reginah Ndagire (IT Specialist) Uganda ndagilereginah2@gmail.com +256 772 887079 19 Ms. Jasper Ainebyona (Met Department) Uganda jainebyona@yahoo.com; +256 779 721822; 0701 062555 20 Ms. Peace Byiringiro (Met Department) Uganda peacebyiringiro@gmail.com +256 773 941 707; 0701 504122 21 Mr. Abubaker Kalema (Met Department) Uganda kalema.unma@gmail.com +256 772 258373 22 Mr. Godfrey Mujuni (Met Department) Uganda grmujuni@gmail.com +256 772 568977 23 Dr. Isaac Mugume Amooti (Met Department) Uganda amooti23@gmail.com +256 779 721822; 0701 062555 24 Mr. Michael Wakabi (Journalist) Uganda owakabi@hotmail.com +256 772-484163 ASARECA Staff mailto:jjcoko@yahoo.com mailto:joyuru@yahoo.com mailto:stephenrma@gmail.com mailto:damcharle@gmail.com mailto:ndagilereginah2@gmail.com mailto:jainebyona@yahoo.com; mailto:peacebyiringiro@gmail.com mailto:kalema.unma@gmail.com mailto:grmujuni@gmail.com mailto:amooti23@gmail.com mailto:owakabi@hotmail.com TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 35 25 Ms. Julian Barungi Uganda j.barungi@asareca.org +256 772-745608 26 Mr. Ben Moses Ilakut Uganda b.ilakut@asareca.org +256 772 798632 27 Mr. Achilley Ssebwana Uganda a.ssebwana@asareca.org +256 772 750005 28 Ms. Racheal Namuzibwa Musisi Uganda r.namuzibwa@asareca.org +256 772 367750 mailto:j.barungi@asareca.org mailto:b.ilakut@asareca.org mailto:a.ssebwana@asareca.org mailto:r.namuzibwa@asareca.org TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 36 ANNEX III: PROGRAMME FOR ToT WORKSHOP, KAMPALA, UGANDA, 22-26 SEP 2025 TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 37 Draft Programme, Kampala, Uganda, 22 -26 September 2025 WORKSHOP DAY 1: Monday 22 September 2025 Time Events Responsible Chair 08:30 – 09:00 Registration ASARECA Ministry of Agriculture 09:00 – 09:15 Welcoming Remarks Ministry of Agriculture, TBD 09:15 – 09:20 Introduction of Participants Participants 09:20 – 09:30 Opening Remarks by UNECA/ACPC To Be Decided (TBD) (UNECA/ACPC) 09:30 – 09:40 Opening Remarks by ILRI Dr John W Recha (ILRI) 09:40 – 09:50 Opening Remarks by ASARECA TBD (ASARECA) 09:50 – 10:10 Official opening by MoA Permanent Secretary 10:10– 10:20 Objectives of the Workshop and Agenda Julian Barungi 10:20 – 10:30 Discussion and Group photo Participants 10:30 – 11:00 HEALTH BREAK Organizers 11:00 – 11:30 Crop Yield Prediction Modelling for Optimal Agricultural Production Systems - Climate variability and change - Impacts on agriculture and food security - Importance of crop prediction model under the changing climate - Crop capability model development at ACPC - Supports of AICCRA to crop capability work TBD (UNECA) Bradwell Garanganga/ (DIGITRON) Met Department TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 38 WORKSHOP Day 2: Tuesday 23 September, 2025 Time Events Responsible Chair 11:30 – 12:00 Introduction to Crop Capability Prediction Modelling Techniques - Overview of Weather and Climate Information Services and Their Utility for Crop Yield Prediction - Concepts and Application - Rationale for using CAMDT - The Key Procedures in the CAMDT (Data acquisition, formatting, and modelling) TBD (UNECA) and Bradwell Garanganga/ (DIGITRON) 12:00 – 13:00 Discussions TBD and/ Bradwell 13:00 – 14:00 LUNCH BREAK Organizers 14:00 – 15:00 CAMDT/DSSAT and digiSoft Software loaded onto each participants laptops and tested for successful applications Participants Ministry of Agriculture 15:00 – 16:30 DATA Acquisition (Example NASA) Steps to follow - Identifying and accessing sites of data sources; - Identifying the data climatological period; - Downloading required parameters Participants 16:30 – 17:00 CAMDT/DSSAT and digiSoft Software loaded onto each participants laptops and tested for successful applications continued Participants Ministry of Agriculture 16:30 – 17:00 HEALTH BREAK 17:00 – 17:30 Discussion/ Q & A Participants Ministry of Agriculture TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 39 09:00 - 09:15 Recap of second day Bradwell G. ILRI 09: 15 – 10:15 DATA Acquisition (Example NASA) Steps to follow - Identifying and accessing sites of data sources; - Identifying the data climatological period; - Downloading required parameters Participants 10:15 – 11:00 Crop Capability Prediction Model on CAMDT – Model descriptions (assumption and uncertainties) – Model analysis (simulation and validation) Bradwell Garanganga (DIGITRON) 11:00 – 11:30 HEALTH BREAK 12:00 – 13:00 Data Implementation into the CAMDT Model Data Collection Framework for Crop Yield Prediction Modelling; Four Steps to Implementation: – Collect. The first step seems simple, but there's a caveat: Look beyond your immediate data sources and immediate needs when collecting and compiling data. ... – Validate. Raw data should be complete and consistent. ... – Analyse. Detect missing values… – Data Management: Quality Control including replacing missing values…. Bradwell Garanganga (DIGITRON) 13:00 – 14:00 LUNCH BREAK Organizers 14:00- 15:30 Data Implementation into the CAMDT Model Data Collection Framework for Crop Yield Prediction Modelling; Four Steps to Implementation: – Collect. The first step seems simple, but there's a caveat: Look beyond your immediate data sources and immediate needs when collecting and compiling data. ... – Validate. Raw data should be complete and consistent. ... Bradwell Garanganga (DIGITRON) Ministry of Agriculture TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 40 – Analyse. Detect missing values… – Data Management: Quality Control including replacing missing values…. 15:30 – 16:00 Overview of DSSAT parameters – Plant populations – Soil compositions – Plant cultivars – Cultivar and soil files Bradwell Garangang (DIGITRON) 16:00 – 16:30 HEALTH BREAK 16:30 – 17:00 Hands-on exercise with CAMDT software using data Steps to follow: – Running the model – Analysing model results Bradwell Garanganga (DIGITRON) Ministry of Agriculture 17:00 – 17:30 Continuing with hands-on exercise with CAMDT software using data Bradwell Garanganga (DIGITRON) END OF DAY 2 WORKSHOP Day 3: Wednesday 24 September, 2025 Time Events Responsible Chair 09:00 - 09:15 Recap of second day Bradwell G. AICCRA 09:15 – 10:00 Continuing with hands-on exercise with CAMDT software using data Bradwell, (DIGITRON) 10:00 – 10:30 Crop Management - Irrigation/water - Fertilization All Participants TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 41 10:30 – 11:100 HEALTH BREAK Organizers 11:00 – 12:00 Crop Management - Irrigation/water - Fertilisation All Participants Ministry of Agriculture 12:00 – 13:00 Options for selected crops - Gross Margins - Additional graphical outputs for selected crops - Discussions All Participants 13:00 – 14:00 LUNCH BREAK 14:00 – 16:00 Options for selected crops continuation - Gross Margins - Additional graphical outputs for selected crops - Graphs for Unmanaged versus Managed crops - Crop comparison Graphs(e.g. Maize vs Sorghum) - Varieties within Species Comparison Graphs - Discussions All Participants Ministry of Agriculture 16:00 – 16:30 HEALTH BREAK Organizers 16:30 – 17:30 Presentation of Modelling outputs Participants by Groups Ministry of Agriculture END OF DAY 3 WORKSHOP Day 4:Thursday 25 September, 2025 Time Events Responsible Chair TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 42 09:00 - 09:15 Recap of third day Bradwell G. UNECA 09:15 - 09:45 Further Work for Synthesizing Data For Seasonal Climate Forecasting – Observed data – Synthesized data – Forecast data – Simulation horizon set up Bradwell, (DIGITRON) 09:45 - 11:00 Steps for Operationalising Products for Decision Support – Rainfall regions and seasonal climate forecast – Crop varieties and seasonal climate forecast Bradwell G. 11:00 - 11:30 HEALTH BREAK 11:30 - 13:00 Breakout Session – Workings of the model – Limitations of the model – Potential for country application/adaptation All participants Ministry of Agriculture 13:00 – 14:00 LUNCH BREAK 14:00 – 15:00 Reporting back major points on – Model workability – Limitation – Potential for country application/adaptation All Participants Ministry of Agriculture 15:00 – 16:00 Discussion All Participants 16:00 – 16:30 HEALTH BREAK END OF DAY 4 TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 43 WORKSHOP Day 5:Friday 26 September, 2025 Time Events Responsible Chair 09:30 - 10:30 Demonstration of Crop Yield Model Using Near Real data for Input to a Weather Station in Uganda Interpretation of BoxPlot and Linear Graphs Trymore Bradwell G Academia 10:30 – 11:00 HEALTH BREAK 11:00- 12:30 General Discussions and Recommendations 12:30- 13:15 Closing Remarks TBD (UNECA/ACPC) Dr John W Recha (ILRI) TBD (ASARECA) Ministry of Agriculture (Director) Ministry of Agriculture Networking For more information, contact: TRAINING REPORT Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 44 1) MS Julian Barungi Julian Barungi 2) Bradwell GARANGANGA bjgaranganga@digitron.co.zw; bjgaranganga@gmail.com mailto:bjgaranganga@digitron.co.zw; mailto:bjgaranganga@digitron.co.zw;