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Abstract Weather uncertainty is a significant source of agricultural production risks, and is increasingly salient in the context of climate change. Short- to medium-range weather forecasts could help farmers optimize the timing of agricultural practices, increasing returns to agricultural investment. However, smallholder farmers in most parts of the developing world do not have access to highly localized, accurate forecasts, nor are forecasts customized to convey weather conditions relevant for agricultural decision-making. In this study, we investigate whether accurate, relevant-for-context, probabilistic medium-range weather forecasts can aid farmer decision-making through a lab-in-the-field experiment. We will analyze whether probability training, climate change awareness, prior luck impact choices in the experiment and willingness-to-pay for weather forecasts. In addition, we will also analyze whether the format in which weather forecasts are communicated impacts choices/decisions. Weather uncertainty is a significant source of agricultural production risks, and is increasingly salient in the context of climate change. Short- to medium-range weather forecasts could help farmers optimize the timing of agricultural practices, increasing returns to agricultural investment. However, smallholder farmers in most parts of the developing world do not have access to highly localized, accurate forecasts, nor are forecasts customized to convey weather conditions relevant for agricultural decision-making. In this study, we investigate whether accurate, relevant-for-context, probabilistic, medium-range weather forecasts can aid farmer decision-making through a lab-in-the-field experiment. We will analyze whether climate change salience, probability training, prior luck impact choices in the experiment and willingness-to-pay for weather forecasts.
Trial Start Date July 03, 2023 August 05, 2023
Last Published June 15, 2023 04:43 PM August 10, 2023 06:29 PM
Intervention (Public) Farmers are randomly assigned to four groups: (1) probability training treatment; (2) climate change awareness treatment; (3) probability training and climate change awareness treatments; (4) neither (control). 1. The probability training treatment involves farmers watching an interactive probability training video 2. The climate change awareness treatment involves farmers watching a video describing changing weather patterns globally and locally, the impact it has on coffee farming, and how weather forecasts might help time practices better to mitigate weather effects on production. 3. In round 1 of the experiment, farmers will receive a few hypothetical scenarios that involve choosing a location with specific weather conditions out of two locations, for which they see weather forecasts. Once a location is chosen, farmers will indicate their confidence in their choice, and then in-experiment-round weather will be realized. Farmers’ payoffs will depend on the realized weather. 4. In round 2 of the experiment, farmers will receive a few hypothetical cultivation practice-related scenarios that involve choosing an action following a weather forecast. Once an action is taken, in-experiment-round weather will be realized. Farmers’ payoffs will depend on the realized weather. 5. Finally, we will elicit farmers’ willingness-to-pay for accurate, customized, probabilistic weather forecasts. Farmers are randomly assigned to one of the following groups: (1) climate change salience information treatment; (2) probability training and climate change salience information treatments; (3) control. 1. The climate change salience information treatment involves farmers watching a video describing changing weather patterns globally and locally, and the impact it has on coffee farming. 2. The probability training information treatment involves farmers watching a probability training video. 3. In round 1 of the experiment, farmers will receive a few hypothetical scenarios that involve choosing a location with specific weather conditions out of two locations, for which they see weather forecasts. Once a location is chosen, farmers will indicate their confidence in their choice ('points-at-stake'), and then in-experiment-round weather will be realized. Farmers’ payoffs will depend on the realized weather. 4. In round 2 of the experiment, farmers will receive a few hypothetical cultivation practice-related scenarios that involve choosing an action following a weather forecast. Once an action is taken, in-experiment-round weather will be realized. Farmers’ payoffs will depend on the realized weather. 5. Finally, we will elicit farmers’ willingness-to-pay for accurate, customized, probabilistic weather forecasts.
Intervention Start Date July 03, 2023 August 05, 2023
Primary Outcomes (End Points) Willingness-to-pay for accurate, customized weather forecasts; choice of correct forecast; confidence in chosen forecast; choice of action appropriate for realized weather in hypothetical agricultural decisions; game score/payoff. Willingness to pay for probabilistic medium-range weather forecasts; choosing the correct forecast; choosing the action with the higher expected payoff.
Primary Outcomes (Explanation) Willingness-to-pay for weather forecasts will be elicited from farmers using the Becker–DeGroot–Marschak (BDM) method. Choice of correct forecast will be a binary outcome taking a value 1 if the chosen forecast is more (less) likely and 0 otherwise. Confidence in chosen forecast will be elicited by asking farmers to choose the number of ‘points-at-stake’. So, if the desired weather event occurs, the ‘points-at-stake’ are added to their score, and if the desired weather event does not occur, the ‘points-at-stake’ are deducted from their score. Choice of action appropriate for realized weather in the hypothetical agricultural decisions will be a binary outcome taking a value 1 if the action is desired in the realized state of the world; and 0 otherwise. Game score/payoff will be constructed by adding points earned over each decision in the experiment.
Experimental Design (Public) We will recruit small- and medium-holder coffee farmers in Karnataka, India. Farmers will be randomly assigned to 2 cross-randomized treatments (probability training, climate change awareness). Each farmer will answer survey questions, watch their assigned video(s), and play a hypothetical decision-making game where decisions depend on weather realizations and decisions are made after receiving a weather forecast. The game consists of two rounds. In the first round, farmers will receive hypothetical scenarios in which they choose between two locations with different weather forecasts, picking the location where a weather event is more (less) likely to occur. After their choice, a weather realization will occur in-game. Farmer payoffs depend on realized weather. In the second round, farmers will receive hypothetical agricultural scenarios, and choose whether to take an action or not, following a weather forecast. After their choice of action, a weather realization will occur in-game. Farmer payoffs depend on realized weather. After the end of the game, we will elicit farmers’ willingness-to-pay for accurate, medium-range probabilistic weather forecasts. We will recruit small- and medium-holder coffee farmers in Karnataka, India. Farmers will be randomly assigned to (1) climate change salience information treatment, (2) probability training and climate change salience information treatment, (3) control. Each farmer will answer survey questions, watch their assigned video(s), and play a hypothetical decision-making game where decisions depend on weather realizations and decisions are made after receiving a weather forecast. The game consists of two rounds. In the first round, farmers will receive hypothetical scenarios in which they choose between two locations with different weather forecasts, picking the location where a weather event is more (less) likely to occur. After their choice, a weather realization will occur in-game. Farmer payoffs depend on realized weather. In the second round, farmers will receive hypothetical agricultural scenarios, and choose whether to take an action or not, following a weather forecast. After their choice of action, a weather realization will occur in-game. Farmer payoffs depend on realized weather. After the end of the game, we will elicit farmers’ willingness-to-pay for accurate, medium-range probabilistic weather forecasts.
Planned Number of Clusters 1200 farmers At least 1200 farmers (actual number depends on number of participants enrolled).
Planned Number of Observations 18,000 decisions (1200 farmers X 15 decisions) 1200 observations for willingness to pay At least1200 individuals (actual number depends on number of participants enrolled).
Sample size (or number of clusters) by treatment arms 300 farmers in control (placebo video); 300 farmers in treatment 1 (probability training); 300 farmers in treatment 2 (climate change awareness); 300 farmers in treatment 1 + 2 (probability training + climate change awareness) 504 farmers receiving the climate change salience information treatment; 348 farmers receiving the climate change salience and probability training information treatment; 348 farmers in the control group (actual numbers depends on number of participants enrolled).
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