Mitigating the aversion to climate-smart agriculture on Ghanaian smallholdings

Maize crops in a field

Evidence shows that climate-smart agriculture is crucial for climate risk mitigation. Yet farmers, particularly in developing countries, may be reluctant to adopt new technologies precisely because of uncertainty about the economic returns from their adoption. Uncertainty may arise not only because the income from a particular farming method will vary from year to year – due, for example, to changes in weather conditions, prices, availability of inputs –, but also because the extent of this variability itself may be unknown. The latter, called ‘ambiguity’ in the academic literature, is a potentially important consideration for the adoption of climate-smart technologies among smallholder farmers but is difficult to measure in practice.

Dr Adelina Gschwandtner, Dr Christian Crentsil and Professor Zaki Wahhaj from the School of Economics, and Professor Victor Owusu from Kwame Nkrumah University of Science and Technology, Kumasi, Ghana are piloting an innovative approach to understanding the impact of ambiguity in an ongoing study with smallholder farmers in Ghana. They have invited study participants to make choices based on their beliefs about the yields on neighbouring farms that use climate-smart technologies. The study is yielding new insights about farmers’ aversion to ambiguity and their potential role in the adoption of climate-smart technologies.

Read on for Dr Adelina Gschwandtner’s detailed account of the project.

The Challenge

Climate change poses a major threat to smallholder farming and food security particularly for farmers in sub-Saharan Africa.  Rising temperatures and increased risks of floods, droughts and other natural hazards are expected to lower agricultural yields and increase their variability because of the region’s proximity to the equator, low elevation, and weak infrastructure. Climate Smart Agriculture (CSA) such as crop diversification, planting of drought/flood-resistant crops or agroforestry, have been developed to mitigate the risk from climate change, but adoption remains very low.  This is due to several constraints, such as high upfront costs and limited access to credit, uncertainty/ambiguity about the returns, and limited information related to these practices.

Our research team in the School of Economics aimed to address these constraints by firstly identifying potential uncertainty related to CSA using a newly developed methodology, and secondly by performing an information intervention tailored specifically to reduce this ambiguity.  As lack of information drives ambiguity/aversion, we anticipate that better informing farmers will increase the uptake of CSA and resilience to climate change.

The Approach

As part of this project, we piloted an innovative method to elicit ambiguity aversion among smallholder farmers in northern Ghana. The pilot involved 70 maize farmers in two communities cultivating two Drought Tolerant Maize (DMT) varieties in the Upper West region of Ghana. The pilot took place between the end of July and the beginning of August 2022. The two DMT varieties were Sanzal-sima and Bihilifa.  These two varieties were bred to be resistant to parasites and droughts and have been on the market in Ghana since 2012. However, while Sanzal-sima is more widespread, the uptake of Bihilifa appears to be rather low in spite of similar parameters.

The pilot survey comprised of two parts; a farm household survey to collect information on farming practices and experiences, and a set of choice/lottery experiments to elicit the potential ambiguity aversion related to the two DMT technologies. The experiments were based on an approach developed by Baillon et al. (2018) which measures ambiguity attitudes towards naturally occurring uncertainty, without the need to measure or make assumptions about the individual’s subjective view of the  likelihood of the event in question. In our experiment, farmers were instructed to choose between a series of lottery pairs; in each pair, the winnings of the first one depended on the future yield of a farm plot planted with DMT, and the winnings of the second one were based on a random draw with known probabilities. As the method is based on natural events, we expected it to yield more accurate and context-relevant estimates of ambiguity aversion than previously used methods based purely on artificial events.

The Result

We computed and compared measures of ambiguity aversion based on the newly developed method by Baillon et al. (2018) and an older method developed by Ellsberg (1961) that is widely used in research. Both measures indicate that, on average, farmers are ambiguity averse. Reassuringly, the two measures were significantly associated with each other.

We also analysed the associations of the old and new measures with various farmer-related characteristics. We found that while both measures were associated with some of these characteristics, the newly developed measure had a stronger association with farmers’ demographic characteristics. For example, older and male farmers have lower ambiguity aversion while married farmers have higher ambiguity aversion, but Ellsberg’s measure does not detect these associations.  These associations seem plausible as:

1. Older farmers have more experience, which is likely to reduce ambiguity

2. Studies have shown that men are, in general, less ambiguity averse than women

3. Farmers who have the responsibility of a family may be more cautious and therefore more averse to ambiguities associated with the adoption of climate smart agricultural technologies.

We also found (using both methods to measure ambiguity aversion) that farmers that live closer to their farms are less ambiguity averse. This may be because proximity to the farm give farmers better control over the farming process and thus reduce ambiguity.

Overall, the results suggest that whilst both the new and the classical measures capture differences in ambiguity aversion due to farming-related factors, the newly developed measure by Baillon et al. is better able to detect differences in a more nuanced manner, for example, capturing differences due to age, gender, and marital status.

Key Takeaways

  • The pilot in northern Ghana demonstrated that the approach developed by Baillon et al. can be used to elicit reasonable measures of ambiguity aversion among smallholder farmers towards climate smart agriculture.
  • The pilot has also yielded insights about how to implement the experiments better in the future (e.g. providing the study participants some basic preparatory training in probability, and conducting the experiments before the survey to minimise the effects of fatigue).
  • This approach to measuring ambiguity aversion can serve as an important instrument for testing the efficacy of different interventions that provide smallholder farmers knowledge and exposure to climate smart agriculture.
  • In a next step we plan to apply the method to a much larger number of communities and different types of climate smart technologies in Ghana and other African countries, in order to design policy interventions that will reduce ambiguity aversion towards them and increase their uptake.
  • This research builds on our previous work related to risk and ambiguity with respect to aquaculture technologies in Ghana: Crentsil, C., Gschwandtner, A. and Wahhaj, Z., 2020. The effects of risk and ambiguity aversion on technology adoption: evidence from aquaculture in Ghana. Journal of Economic Behaviour & Organization179, pp.46-68.