Risk and time preferences for participating in forest landscape restoration: The case of coffee farmers in Uganda

In recent years, Uganda has experienced widespread forest loss and degradation, mainly driven by agricultural expansion and rising demand for forest products. The adoption of agroforestry is regarded as one of the key strategies in forest landscape restoration in agriculture. While the benefits of agroforestry are widely acknowledged, adoption among smallholder farmers is sluggish. This study analyzes how individual risk and time preferences affect smallholder farmers' choice of attributes of companion trees within coffee agroforestry systems in the Mt. Elgon region in Uganda. Farmers' risk and time preferences are elicited using lottery-based experiments, whereas farmers' choices of preferred attributes for companion trees are determined using a discrete choice experiment. The data from the different experimental designs are combined to establish how risk and time preferences affect the decision to integrate companion trees into coffee farms. Farmers' choices of tree attributes are analyzed based on random utility models, and farmers' risk and time preferences are measured using cumulative prospect theory and quasi hyperbolic discounting. The results reveal that most farmers are both risk and loss averse with high discount rates (impatience), and they are willing to pay more for quality tree seedlings. Analyzing the behavioral parameters in combination with discrete choice data on the preferred choice of tree attributes reveals a close association between farmers' aversion to risk and loss and high discount rates with preferences for trees that grow fast, improve soil fertility, and provide fuelwood. This study offers unique insights for researchers, extension officers, and policymakers, on how farmers' risk and time preferences and preferred attributes can be used to tailor agroforestry interventions to be attractive for farmers in different contexts in pursuit of broader forest landscape restoration goals. (c) 2021 Elsevier Ltd. All rights reserved.