Risk Preferences and Technology Adoption in Agriculture

Adoption of new agricultural technologies—from high-yield seed varieties and precision tools to climate-smart practices—is fundamentally shaped by farmers’ attitudes towards risk. Risk preferences determine whether an individual is inclined to embrace innovations that promise higher returns but carry uncertainty. Across diverse contexts, farm households exhibit heterogeneity in risk aversion, loss aversion and probability weighting, influenced by factors such as past experiences, access to information and social norms. Risk-averse producers often favour incremental changes or tried-and-tested inputs, whereas those with more moderate or complex preferences may invest in digital advisory platforms, novel crop varieties or insurance products. Understanding these behavioural drivers is essential for designing interventions that lower perceived uncertainties, tailor financial instruments and strengthen extension services. At the policy level, bridging the gap between potential benefits and farmers’ uptake requires aligning technology development with local risk perceptions, improving the reliability of agronomic recommendations and facilitating peer learning. Recognising the interplay between individual decision-making under uncertainty and the diffusion of innovation is critical for building resilient and productive agricultural systems worldwide.

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Risk Preferences and Technology Adoption in Agriculture publication trend

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Technical terms

Risk aversion: Preference for options with lower uncertainty, even if they offer lower expected returns.

Loss aversion: Tendency to experience losses more intensely than equivalent gains.

Probability weighting: Non-linear treatment of probabilities, often overweighting unlikely events and underweighting likely ones.

Cumulative Prospect Theory: Behavioural model describing decision making under risk by weighting gains and losses differently and transforming probabilities.

High-yield variety: Seed type bred for increased productivity under optimal agronomic conditions.

Instrumental-variable probit model: Statistical method addressing endogeneity in binary choice models by using external instruments correlated with the predictor.

References

1. Relating risk preferences and risk perceptions over different agricultural risk domains: Insights from Ethiopia. World Development (2023).
2. The roles of risk aversion and climate-smart agriculture in climate risk management: Evidence from rice production in the Jianghan Plain, China. Climate Risk Management (2019).
3. Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference. Agriculture (2021).