Information and knowledge play an important role in the uptake of technologies to boost agricultural productivity. While information on its own is not a panacea, information can help farmers make better decisions about what agricultural inputs and practices to adopt and it can give them more bargaining power in their interactions with buyers. Particularly when combined with efforts to address other key bottlenecks to productivity, information has a potentially transformative role to play.
Some of the lessons and ideas from this brief are synthesised in the following list of recommendations for policymakers:
- Solutions need to start by understanding farmers’ needs and perspectives. The specific barriers to technology take-up in a given context need to be well-understood before policy solutions can be designed. This includes understanding, for instance, whether the primary binding constraint is a lack of information about agricultural technologies or poor quality inputs and technologies.
- Tackling poor quality and counterfeit agricultural inputs like fertilisers and seeds should be prioritised. A starting point is to replicate research like the Uganda study described in this brief in other contexts to illuminate the extent of the poor quality inputs problem. Similarly, research and monitoring to detect food quality and toxin contamination is important for consumer food safety (Hoffmann 2014). Long-term strategies such as building seed certification systems and establishing credible product brands to improve input quality are needed.
- New ICT-based approaches to sharing agricultural information could replace or complement less effective traditional agricultural extension programmes. While there is still a need to prove the impacts of different approaches, mobile-based agricultural information platforms and consulting services are starting to demonstrate their potential to deliver information to farmers about new technologies, weather forecasts, and market conditions at lower cost and with more customized information than traditional extension services. Key challenges include building sustainable business models, effectively leveraging social networks, and exploring ways that machine learning and crowd- sourced data on farmers’ experiences can be used to generate better agricultural advice.
- Information-based solutions may require special strategies to ensure that vulnerable groups such as women and the poor are reached. Agricultural extension efforts often tend to reach better-off farmers despite their goals of reaching the poor. Information-based solutions have the potential to break down access barriers but the risk of deepening inequalities must also be mitigated.
- Innovative models of collaboration across the public and private sectors deserve further testing. Innovative funding and delivery models may involve cooperation across the public and private sectors to support agricultural technology adoption, taking advantage of the comparative strengths of each. ‘Smart’ subsidy models that can help build access to modern agricultural training and mobile-based information services may have some potential, for example.
- Focus on behavioural biases that inhibit technology adoption. Simple strategies to overcome psychological blockages like procrastination, forgetfulness, and risk aversion may have surprising impacts. Timing matters – encouraging farmers to invest in inputs is likely to be more effective right after harvest time when they are least credit-constrained, for example.