Víctor Tablas

06/12/2023

Climate Information Products for farmers

The experimental activity design focused on understanding how farmers respond to climate adaptation messages. We conducted surveys with randomly assigned items, delivered through CAPI (Computer-Assisted Personal Interviewing) to local farmers. By dividing participants into treatment and control groups, we tested if peer actions influence farmers’ willingness to adopt adaptive practices. Additionally, framing experiments examined how different contexts impact attitudes toward adaptation, and traditional knowledge tests assessed whether integrating indigenous practices with forecasts can build trust in official weather data.

If farmers observe that their peers are adopting climate adaptation practices, then they will be more likely to adopt similar practices themselves. If climate adaptation information is framed positively (e.g., emphasizing potential benefits), then farmers’ attitudes toward adopting adaptation measures will improve. If traditional knowledge (such as wildlife behavior and lunar phases) is combined with official climate forecasts, then farmers’ trust in the accuracy and reliability of weather information will increase. If climate information is delivered through accessible and familiar channels (e.g., WhatsApp groups), then farmers will be more likely to regularly use this information in their decision-making.

We designed and administered surveys where certain items (questions or statements) were randomized, meaning that different farmers received different versions of the survey. This random assignment helps us understand how varying pieces of information or framing can influence responses, as it controls for potential biases. The surveys were delivered directly to farmers in person using CAPI (Computer-Assisted Personal Interviewing), a data collection method where enumerators use electronic devices, like tablets, to input responses in real time. This method improves data accuracy and speeds up the data collection process.

The hypotheses were generally supported by the experiment's results. We hypothesized that if climate adaptation practices were framed as already adopted by local peers, farmers would show a higher motivation to implement these practices themselves. The results confirmed this: 87.5% of the treatment group—those informed about peer adoption—expressed motivation to adopt similar measures, compared to 76.7% in the control group. Additionally, framing adaptation benefits versus risks differently influenced farmer perception. When information was framed negatively (risks of not adapting), it had a stronger impact, with 75% of farmers seeing climate change impacts as likely compared to 41% when presented positively. These findings highlight the influence of peer examples and loss aversion in climate adaptation motivation.

Peer Influence Enhances Motivation: Farmers were more motivated to adopt climate adaptation practices when they perceived them as popular among their peers. This shows that a social proof approach can drive higher adoption rates in climate adaptation. Framing Matters: Presenting information with a negative framing (emphasizing risks of inaction) proved more effective in making farmers feel the urgency of adapting to climate change than positive framing (emphasizing benefits of action). This highlights that loss aversion is a strong motivator in this context. Trust in Traditional Knowledge: Farmers showed high confidence in traditional climate knowledge, and the combination of traditional insights with meteorological data seemed to foster even more trust. This indicates that integrating familiar, culturally resonant information with scientific data is likely more effective. Preference for Mobile Access: Despite low internet access, farmers showed a preference for receiving information on mobile devices. This suggests that efforts to improve digital infrastructure could greatly enhance information reach.

Limited Internet Access: Data collection was hampered by limited internet availability in rural areas, making it difficult to upload survey data in real time and requiring offline workarounds. Geographic and Logistical Constraints: Reaching remote agricultural communities required extensive planning, both in terms of transport logistics and ensuring that data collection tools (e.g., tablets) were fully functional offline. Technological Familiarity and Comfort: Many farmers were unfamiliar with digital devices, and some preferred traditional communication methods, which required careful adaptation of survey tools and materials. Language and Cultural Nuances: Conveying climate information required adapting language and framing that resonated with the local culture and knowledge systems.