Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Participants
2.2. Digital and Rainfall Monitoring Tools
2.3. Data Collection and Sharing
2.4. Workshops, Training, and Monitoring
2.5. Analysis of Design and Lessons Learned
3. Results
3.1. Design Phase of the Digital and Rain Monitoring Tools
3.2. Evaluation of the Testing Phase
3.2.1. Participant Engagement
3.2.2. Usability of the Digital Technology
3.2.3. Usefulness of Tools, Weather Forecasts, and Data
3.2.4. Outreach to Other Farmers
3.2.5. Monthly Monitoring and Assistance Activities
4. Discussion
4.1. Evaluation of the Coproduction Experiment
4.2. Design Criteria for Weather and Climate Information Services for Smallholders
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Digital Tools | Data Collected and Shared |
---|---|
Weather app (collection) | Daily biophysical local forecast indicators as observed and reported by farmers in their various locations |
Daily rainfall observations as measured by farmers using the provided rain gauges | |
WhatsApp group (sharing) | Daily local forecasts based on the processed and aggregated local forecast indicators [29] |
Daily local forecasts derived from scientific sources (e.g., meteoblue) [29] | |
Daily rainfall observations as measured with the provided rain gauges |
Digital Tools | Features | Important Characteristics |
---|---|---|
Weather app (for collection of daily observations on local forecast indicators and rainfall data) | Images | Images for local forecast indicators were chosen and refined with farmers and presented on the app interface. |
Symbols | Symbols were used for easy selection of options, such as heavy, light, low, or no rain and confidence levels (see Figure 5a). | |
Text | Most farmers could read (see socio-demographic details in Figure 3); thus, short phrases were used to describe, for example, signal indicators, rainfall levels, and farmer forecasts. | |
App manipulation | The app was designed for easy scrolling, selection, and submission of data, with a confirmation message sent upon successful submission. A training session helped farmers to quickly master it. | |
WhatsApp (for sharing daily local and scientific forecasts, and daily rainfall data) | Forecast graphs | To illustrate the probabilistic nature of both local and scientific forecasts, simple pie charts were used to show the probability of, for example, rain or no rain (see Figure 5b). |
Text | Chats among farmers, extension agents, and scientists required that each participant be able to read and write. Most farmers could do so. Low-literacy farmers were assisted by relatives at home. | |
Appmanipulation | Most farmers had never used this app; thus, training was provided to help them find the app, launch it, and read and write messages. | |
Internet (medium for transmitting digital weather forecasts and data) | Set-up and handling | Internet connections were preconfigured on each smartphone with a subscription from a local provider in Ghana. Farmers were trained in how to turn mobile data on and off. |
Rain gauges (for measuring daily rainfall amounts) | Set-up of manual rain gauges | An experienced meteorologist from the Ghana Meteorological Agency trained farmers to set up conventional rain gauges on their farms or near their homes (Figure 5c). |
Recording of daily rainfall amounts | Farmers were trained to record daily rainfall amounts at 9:00 a.m. and to specify the start and end times and dates of each rainfall event | |
Reporting of daily rainfall amounts | Farmers could report the data collected in several ways, including the weather app, WhatsApp, or a notebook (e.g., if internet service was unavailable or the telephone battery was dead). |
Farmers | Extension Agents | |
---|---|---|
Number of participants in coproduction experiment | 22 | 6 |
Number of farmers with whom forecast information and/or data were shared. | 350+ | 504+ |
Period | Monitoring and Technical Assistance Provided during the Testing Phase | Observations from the Monitoring and Assistance during the Testing Phase |
---|---|---|
Monthly/Weekly |
|
|
April 2019 |
| |
May 2019 |
| |
June 2019 |
| |
July 2019 |
|
Design Criteria Recommendations | |
---|---|
(1) Goal of coproduction of a weather information service | Defining the goal of the WCIS is important for design tailoring. The WCIS designed in our experiment used ICT-based tools and engagement with farmers, extension agents, and scientists to collect local forecasts and weather indicators (with rainfall data for validation), combined with scientific model-based forecasts and group interaction. |
(2) User interface of the application (front-end and back-end design) | The ICT-based tool should have a simple and clean design with emphasis on visualization. Consensus and visual design facilitate understanding by low-literacy farmers. Additional voice messages can be used to further facilitate farmers’ understanding. The two-way information sharing system (i.e., both sending and receiving data and forecasts) could be integrated within a single application that uses algorithms in the back-end design which automatically process and display forecasts. |
(3) Capacity building of both farmers and research scientists | Training is necessary to learn from farmers and ensure appropriate design, good usage of tools, and the quality of the data collected. Training can be delivered through workshop sessions with farmers. |
(4) Monitoring and technical assistance during the development phase | During the development phase of the information service, monitoring and technical assistance are important to ensure appropriate use of tools and quality of the local forecast knowledge and data, as well as coaching to keep the participants motivated. Monitoring and technical assistance also helps in detecting problems and making the adjustments needed to solve the technical and non-technical issues that arise. |
(5) Sample size of the coproduction participants | Sample size is important. At least one farmer should be included from each community targeted. This will help achieve a good distribution of the dataset across the district or area considered. We also learned that availability, knowledge, and engagement are more important for the quality of data than having a large number of farmers. However, the coproduced information can be shared with a larger group of farmers in the district. |
(6) Socio-demographic characteristics of the coproduction participants | We learned that it is important to include both older and younger farmers in the coproduction process and to balance gender as much as possible. This facilitates knowledge harnessing, sharing, and transfer between generations. It is also important to include agricultural and meteorological extension agents in the coproduction process, as they are in contact with a large network of farmers and, thus, can boost sharing of the results. |
Design Criteria Recommendations | |
---|---|
(1) Trade-off between cost (investment) and quality of intervention | Costs are involved in the acquisition of tools (e.g., smartphones and rain gauges), in providing training sessions, and in monitoring and lending assistance to farmers to ensure appropriate usage of tools and the quality of data and forecasts. To optimize these investments, we recommend intensifying the coproduction intervention within a limited but representative group of farmers and extension agents (see notes on sample size and socio-demographic characteristics in Table 5). This will help ensure the quality of the data and its continuous improvement. The coproduced information can be made available and disseminated publicly in the targeted district. |
(2) Dissemination of weather and climate information | This case study found that extension agents played a key role in dissemination of weather forecast information, as they were in contact with a larger network of farmers. This demonstrates that both farmers and extension agents involved in the experiment can provide a base for sharing knowledge across the communities of the district. |
(3) Transferability of the design criteria to other areas | The design principles can be applied to other areas where local or traditional forecasting knowledge exists and can be used to boost uptake of scientific model-based weather and climate information. However, internet coverage is essential for real-time data collection. Moreover, location-specific information needs have to be identified first. Moreover, local forecast indicators will vary from place to place, and need to be identified for each new target community. |
(4) Sustainability and inclusiveness | Regarding sustainability and inclusive development, it is important to reflect on the way forward with local authorities and to choose together an appropriate approach for scaling up. For example, as a follow-up to this study, we decided together with district authorities to create a business model for development of an app that combines the functionalities of the two apps used in this experiment. That app is now under development and provisionally called “FarmerSupport” (http://www.waterapps.net/en-us/ghana-updates/farmersupport-mobile-app-now-online/). The coproduction process can be incorporated into the “farmer field school system”, which offers a location-specific environment for intensive, technically rigorous knowledge exchange [53]. Farmer field schools are often supported by a multilevel institutional platform that includes international, national, and sub-national actors. Hence, they can provide a setting and resources for farmers to coproduce and access weather and climate information and related agrometeorological services. |
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Share and Cite
Gbangou, T.; Sarku, R.; Slobbe, E.V.; Ludwig, F.; Kranjac-Berisavljevic, G.; Paparrizos, S. Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles. Atmosphere 2020, 11, 902. https://doi.org/10.3390/atmos11090902
Gbangou T, Sarku R, Slobbe EV, Ludwig F, Kranjac-Berisavljevic G, Paparrizos S. Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles. Atmosphere. 2020; 11(9):902. https://doi.org/10.3390/atmos11090902
Chicago/Turabian StyleGbangou, Talardia, Rebecca Sarku, Erik Van Slobbe, Fulco Ludwig, Gordana Kranjac-Berisavljevic, and Spyridon Paparrizos. 2020. "Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles" Atmosphere 11, no. 9: 902. https://doi.org/10.3390/atmos11090902
APA StyleGbangou, T., Sarku, R., Slobbe, E. V., Ludwig, F., Kranjac-Berisavljevic, G., & Paparrizos, S. (2020). Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles. Atmosphere, 11(9), 902. https://doi.org/10.3390/atmos11090902