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1. Development and Validation of the Calculated and Spontaneous Risk‐Taking Scale (CASPRT)

   https://doi.org/10.1002/bdm.70020  

   Zhang, Don C; Smith, Rachel Williamson (July 2025, Journal of Behavioral Decision Making)

   ABSTRACT Risk preference is a key concept across social, economic, and decision sciences. While existing measures assess risk taking either as domain‐specific preferences (e.g., finance and health) or as a general trait, they have largely overlooked individual differences in the narrow, domain‐general aspects of risk preference. Drawing from a dual‐process framework, we advance a multidimensional domain‐general measure of risk preference. We develop and validate the Calculated and Spontaneous Risk‐Taking Scale across seven studies (N = 2116). Results show (1) the two risk styles are moderately correlated and align with existing risk preference measures; (2) they are distinct from personality traits like the Big Five and cognitive traits like decision style; (3) calculated risk‐takers show more variability in risk attitudes across contexts; (4) calculated risk‐taking predicts adaptive outcomes (e.g., creativity and entrepreneurship), while spontaneous risk‐taking predicts maladaptive behaviors (e.g., crime, safety violations); and (5) the scale is invariant across sex and age. Overall, calculated risk‐takers engage in more adaptive risks, leading to healthier, more meaningful lives. 

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2. Volatility and the Gains From Trade

   https://doi.org/10.3982/ECTA14411  

   Allen, Treb; Atkin, David (January 2022, Econometrica)

   Trade liberalization changes the volatility of returns by reducing the negative correlation between local prices and productivity shocks. In this paper, we explore these second‐moment effects of trade. Using forty years of agricultural micro‐data from India, we show that falling trade costs due to expansions of the Indian highway network reduced the responsiveness of local prices to local yields but increased the responsiveness of local prices to yields elsewhere. In response, farmers shifted their production toward crops with less volatile yields, especially so for those with poor access to risk mitigating technologies such as banks. We then characterize how volatility affects farmers' crop allocation using a portfolio choice framework where returns are determined in general equilibrium by a many‐location, many‐good Ricardian trade model with flexible trade costs. Finally, we structurally estimate the model—recovering farmers' risk‐return preferences from the gradient of the mean‐variance frontier at their observed crop choices—to quantify the second‐moment effects of trade. The simultaneous expansion of both the highway and rural bank networks increased the mean and the variance of farmer real income, with the first‐moment effect dominating such that expected welfare rose 4.4%. But had rural bank access remained unchanged, welfare gains would have been only half as great, as risk mitigating technologies allowed farmers to take advantage of higher‐risk higher‐return allocations. 

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3. Managing Crop Yield Risk at the Kellogg Biological Station, Hickory Corners, MI (2022 to 2023)

   https://doi.org/10.6073/pasta/b0e1cd7e724c1b5d7579ab2a810c6e8b  

   Swinton, Scott; Loduca, Natalie (January 2025, Environmental Data Initiative)

   Dataset Abstract As farmers adapt to changing climate, they modify practices and technologies to manage evolving risk. Adaptive changes may be as small as adjusting a crop insurance coverage level or as large as investing in an irrigation system. Farmer attitudes toward risk and their subjective perceptions of the evolving probability distributions of crop yields drive adaptation decisions. To understand climate change adaptation behavior by farmers, we undertook the study “Elicitation and Estimation of Risk Preference and Subjective Probabilities to Understand Farmer Decisions on Climate Change Adaptation.” We interviewed 44 Michigan corn and soybean farmers to elicit mathematical expressions of their risk attitudes. During the interviews, each completed two sets of lottery choices, the first using 25 general risky gambles and the second using 18 risky gambles in a crop farming context that enable econometric estimation of risk attitudes (using variants of Expected Utility Theory). Next, they answered questions about corn yield probability distributions over the past ten years and the next ten years (triangular distributions of minimum, most likely, and maximum values) with no water management, irrigation, tile drainage, and drought-resistant seed. After that, they reported on water management investments that they have made in past and intend to make in future. Finally, they provided background information about themselves and their farms. This study (MSU Study ID: STUDY00007871) was submitted to the Michigan State University Institutional Review Board (IRB) by principal investigator Scott Swinton. On July 5, 2022, it was determined to be exempt under 45 CFR 46.104(d) 3(i)(B). Data collection took place during September 2022 through March 2023. Farmer respondents completed the survey instrument on Qualtrics with assistance from graduate students in Agricultural, Food, and Resource Economics at Michigan State University at various MSU Extension offices and restaurants around southern Michigan. Respondents received lunch plus $50 for participating and a credit of $40 that could be gained or lost based on the outcome from one of the risky gambles (included to encourage truthful responses [“incentive compatibility”]). original data source http://lter.kbs.msu.edu/datasets/249 

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4. Usability of Seasonal Climate Forecasts in the Guatemalan Dry Corridor

   https://doi.org/10.1175/WCAS-D-25-0001.1  

   Anderson, Talia; Pons, Diego; Guido, Zack; Tun, Luis; Anchukaitis, Kevin (January 2026, Weather, Climate, and Society)

   Climate services, including seasonal climate forecasts, have been promoted to foster proactive risk management and adaptation to climate variability and change across multiple sectors of society. In smallholder farming and rain-fed systems, where climate variability has an important influence on crop production and food security, information from seasonal climate forecasts has the potential to support and enhance agricultural decision-making. Here, we assess the usability of seasonal climate forecasts in Guatemala, drawing on aspects of both forecast quality and value. We analyze multiple attributes of quality for published forecasts made by the Guatemalan National Meteorological Service between 2019 and 2023 and for a comprehensive set of model hindcasts. We evaluate seasonal forecast value among smallholder farmers based on a survey of over 700 households in the departments of Totonicapán and Chiquimula, Guatemala. While the quality of seasonal forecasts varies regionally, there is evidence of marginal skill for some attributes across the multiple model configurations used in operational practices over the last 5 years. However, the household survey results reveal that very few smallholder farmers actually receive seasonal climate information. We make recommendations to enhance the usability of existing seasonal climate forecasts in Guatemala, including improving reproducibility and transparency of operational processes, better communication and presentation of forecast uncertainty, and updating and expanding dissemination efforts. Our findings underscore the need for greater engagement and collaboration between forecast providers and end users throughout the development and dissemination processes to improve the quality and usability of climate information. Significance StatementSeasonal climate forecasts can help farmers prepare for the upcoming variability in climate that influences the success of their crops each year. We investigate the quality of seasonal climate forecasts produced by the Guatemalan National Meteorological Service between 2019 and 2023 and their value to farmers in two regions. We show that the forecast quality is highly variable but that forecasts over the last 5 years could have provided some useful information. However, we also find that very few households receive or have access to these forecasts. We recommend future efforts focus on improving collaboration with farmers, enhancing communication surrounding the limitations and uncertainties in the forecasts, and increasing the transparency of the forecasting methods. 

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5. When crops fail, forests follow: Agricultural shocks and deforestation in Zambia

   https://doi.org/10.1073/pnas.2427156122  

   Ordóñez, Pablo J; Hadunka, Protensia; Del_Rossi, Gemma; Baylis, Kathy (October 2025, Proceedings of the National Academy of Sciences)

   As climate change makes agricultural production shocks more frequent and severe, it is vital to understand their effect on farmer welfare, land use, and deforestation. Theoretically, a change in agricultural productivity could increase or decrease deforestation by changing demand for agricultural land and/or through the consumption of forests as a coping strategy. This paper uses the introduction of a crop pest to sub-Saharan Africa to estimate the effect of a negative agricultural productivity shock on deforestation. Using primary household data, we first find that farmers who were exposed to higher levels of fall armyworm saw substantial decreases in yield and food security. Using estimates of fall armyworm suitability in conjunction with machine-learning models of maize yields and deforestation, we find that the introduction of the fall armyworm induced a doubling of the deforestation rate in Zambia in the 3 y following the outbreak. This increase was driven both by increased agricultural land expansion and increased charcoal production as a coping strategy. These responses vary substantially over space. More remote areas experienced 23% lower FAW-induced deforestation compared with the sample average, suggesting that farmers with access to maize and charcoal markets may have increased deforestation as a response. Wealthier areas were also less likely to deforest in response to FAW pressure. In sum, our results suggest that negative agricultural productivity shocks may lead to a negative climate feedback, with farmers engaging in emissions-increasing strategies in response. 

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