Yu, JisangHendricks, Nathan P.2022-03-292022-03-292019-08-20https://hdl.handle.net/2097/42045Emerging precision agriculture technologies allow farms to make input decisions with greater information on crop conditions. This greater information occurs by providing improved predictions of crop yields using remote sensing and crop simulation models and by allowing farms to apply inputs within the growing season when some crop conditions are already realized. We use a stylized model with uncertainty in yield and price to examine how greater information on crop conditions (i.e., a “forecast”) affects input use for insured and uninsured farms. We show that moral hazard decreases—farms apply more inputs—as the forecast accuracy improves when the forecast indicates good yields, and vice versa when the forecast indicates bad yields. In the long run, moral hazard decreases in response to an improvement in forecast accuracy. Even though moral hazard decreases in the long run, indemnity payments are likely to increase in the long run—driven by the increase in moral hazard when the forecast indicates bad crop conditions. We use the results of our model to discuss the potential impact of different technologies and types of inputs on the federal crop insurance program and the environment.This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).Crop InsuranceMoral HazardForecastValue of InformationPrecision AgricultureText