Abstract
The International Maize and Wheat Improvement Center’s (CIMMYT’s) BACKFEED project is designing and testing different models of mobile phone-enabled agricultural advisories (m-advisories) to reach and benefit a diversity of smallholder farmers in sub-Saharan Africa with climate-smart agricultural (CSA) practices. BACKFEED aims to put diverse farmers at the center of agricultural information channels, giving them an opportunity to share their knowledge and experiences, voice their demands and concerns, and be originators (not just recipients) of content (Steinke et al. 2021; Ortiz-Crespo et al. 2021). In Zambia, BACKFEED is deploying a hybrid farmer feedback system, “Atubandike,” which means “let’s have a conversation” in the local language, Tonga. Atubandike provides beneficiary farmers access to a toll-free hotline using Viamo’s interactive voice response (IVR), capacitates village-based digital champions to build trust in and support hotline use, and engages communities in gender transformative activities. Atubandike is free to call in (up to 10 free calls per month, then a nominal charge thereafter), does not require internet access, and can be used with a basic phone, a technology many farmers have at their fingertips right now and one with a limited gender gap. Furthermore, by using Viamo’s IVR capabilities in local languages, BACKFEED can reach and hear from farmers who might otherwise be excluded due to limited literacy (ICT, reading and writing ability, national languages).
Farmers registered by the project (56% women, 34% youth) can call into the hotline and then navigate through several menu options to access both static and dynamic content. Static content is a set of 30 pre-recorded messages on sustainable agricultural practices developed at a content development workshop. Dynamic content is continuously developed during the project life based on farmer feedback. Atubandike allows farmers to share their farming experiences and ask pressing agricultural questions, by recording messages with their phone. Feedback recordings are then developed into “shared experiences” and “talk shows” by a content development committee comprising farmers and representatives from government (Ministry of Agriculture and Government Agricultural Extension staff) and CIMMYT (an agricultural scientist and communications staff). Personal testimonials are also being developed and shared on the hotline and on posters placed in intervention communities. New dynamic content will be released each week. Continuous engagement with village-based digital champions and regular community engagement activities will give farmers voice and enable design corrections to be undertaken during the project, ultimately leading to enhanced reach and benefits to a diverse group of farmers.
The project is being implemented in the Southern Province (Zambia) districts of Kalomo, Monze, and Choma, purposely selected for their importance in Zambian smallholder agriculture, high climatic variability, and minimal reach (so far) by m-advisories. To evaluate different approaches to digital agricultural advisory, Atubandike is implemented as a randomized control trial with randomization at the agricultural camp level to minimize spillovers. The five intervention treatments are:
• push digital advisory (T1);
• push digital advisory with feedback opportunities (T2);
• hybrid feedback (digital plus community digital champions) (T3);
• hybrid feedback plus communication skills and gender responsiveness training of community digital champions (T4); and
• hybrid feedback with community engagement for social inclusion (T5).
A total of 2,800 beneficiary farmers were selected using a multi-stage sampling approach. Data from baseline and endline surveys with beneficiary farmers will enable evaluation of the effectiveness of different approaches to m-advisories for (a) building trust in mobile phones as sources of agricultural information, (b) encouraging participation in m-advisories, and (c) improving awareness and knowledge of CSA practices. Outcomes will be compared across demographic groups (gender, age, and indicators of class) to assess social inclusion in m-advisories.