Performing Department
Morrison School of Agribusiness and Resource Management
Non Technical Summary
Asian Citrus Psyllid (ACP, Diaphorina citri Kuwayama) is an invasive insect species that has been present in Florida since 1998, but was not sighted in California (San Diego County) until August 2008. ACP damages citrus by feeding on the leaf and depositing a large amount of honeydew, as well as vectoring the bacterium that causes Citrus Greening Disease (CGD, huanglongbing). If left unchecked, the California Department of Food and Agriculture (CDFA) estimates potential damage at $224.0 million annually. Given the financial constraints facing CDFA, the decision of whether to commit resources to: (1) excluding pests from entering, (2) allowing entry, but eradicating upon discovery, or (3) allowing entry, and controlling dispersion through existing techniques, is a critically important problem. This study proposes to develop a spatio-temporal optimal control modeling framework to determine the optimal allocation of resources to these three activities, taking into account: (1) whether it will be possible to incentivize the grower community to mount a cooperative management effort on their own, and (2) the market impacts of an uncontrolled ACP infestation. We plan to communicate the practical application of our results to CDFA officials through seminars and industry stakeholders (growers and processors) through industry meetings organized by the California Citrus Mutual (CCM).
Animal Health Component
(N/A)
Research Effort Categories
Basic
40%
Applied
60%
Developmental
(N/A)
Goals / Objectives
To determine the most economically efficient way to minimize the risk faced by the California citrus industry to ACP infestation and, thereby, exposure to citrus greening disease (CGD). The operational objectives necessary to achieve this long term objective are to: The long-term objective of the proposed research is to determine the most economically efficient way to minimize the risk faced by the California citrus industry to ACP infestation and, thereby, exposure to citrus greening disease (CGD). The operational objectives necessary to achieve this long term objective are to: 1. Develop an economic-optimization framework that incorporates the spatial-temporal movement of invasive insects and dynamic optimization methods that can be used to optimally allocate resources among exclusionary, eradication and control efforts. The framework will be designed on multiple levels using analytical analysis for intuition and numerical methods for solving the specific policy problem under study. 2. Estimate the relationship between ACP infestation levels and citrus quality, citrus quantity, control costs and reproduction rates in order to parameterize the objective function of the proposed optimal control model. 3. Use the dynamic optimization model to analyze alternative first-best ACP management control strategies, or those that are optimal from the perspective of a "central planner," here defined as the combined efforts of the US Department of Agriculture (USDA) and CDFA. If the dynamic optimization model suggests that control should be part of an optimal management strategy, we will recommend optimal resource levels to commit to existing biological, chemical and IPM programs. 4. Use the dynamic optimization model to analyze alternative second-best policy instruments, or those that are optimal under the assumption that individual growers are to be held responsible for controlling the spread of ACP. 5. Design and implement an extension program that will effectively communicate our results to citrus growers, processors, government agents and private landowners in California, as all stakeholder groups are essential to our concept of a community-based management system that will minimize cost and the public good nature of insect control. The primary outputs of our research will be to recommend an optimal allocation of resources dedicated to either: (1) exclusion before arrival, (2) eradication after arrival, or (3) control after arrival. The menu of expected outcomes from this research include: (1) a description of economically-optimal ACP management strategies, including the method, timing and intensity of exclusion or control, (2) estimates of the likely divergence between population outcomes if firms are left to make decisions on their own relative to a socially-optimal outcome, or one that balances the marginal costs and benefits of control from an social perspective, (3) new methods of modeling the spatio-temporal dynamics of the spread of insect-invasive species, and (4) the relatively desirability of incentivizing individual growers through tax programs or tradable permits to achieve socially optimal levels of insect control.
Project Methods
The proposed research program consists of five stages. Stage 1 involves developing and specifying a stochastic optimal control model in which the decision maker chooses the amount of resources to commit to each of three types of management activity: (1) pre-arrival control, or exclusion, that reduces the probability of discovering an new invasive insect, (2) post-arrival exclusion or eradication activities that reduce the probability of subsequent discoveries and (3) on-going control activities that reduce the growth rate of a population once established. The level of these three activities will be chosen to maximize the present value of social economic surplus over cost (equivalent to minimizing all economic costs, including damage and control). In stage 2 we will parameterize the objective function and state equations that comprise the optimal control model, using spatio-temporal data gathered by entomologists at CDFA (CDFA, 2009). During stage 3, we will generate numerical solutions to the optimal control model that will indicate the optimal level of each management activity (exclusion, eradication, control) and timing (the threshold population level) of control activities that maximize the present value of economic surplus. In stage 4 we will conduct numerical simulations designed to demonstrate how optimal spatio-temporal growth and dispersion patterns change under alternative parametric and policy scenarios (comparative dynamic analysis). In stage 5, we use the policy simulation results to recommend policy measures that are likely to be both effective and efficient in managing ACP numbers at economically sustainable levels.