Introduction to the Economics of Animal and Plant Biosecurity

Introduction to the Economics of Animal and Plant Biosecurity

David Schimmelpfennig, Janie M. Chermak
Copyright: © 2022 |Pages: 31
DOI: 10.4018/978-1-7998-7935-0.ch001
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Abstract

The economics of plant and animal health protection influence country policies through rapidly evolving benefit-cost tradeoffs that are difficult to forecast. Increased threat of infestation by invasive species following novel trade pathways is one recent trend, being counteracted by advances in data analytics to target interventions on higher risk pathways. The availability of increasingly large, complicated datasets generated from daily enforcement of regulations are available to safeguarding analysts. These data resources used to monitor and evaluate pathways are increasingly available electronically with shorter time lags. But the efficacy of increased analytic capabilities requires a clear objective of what is optimal. Economic frameworks can help focus the analytics. For example, increased protection that costs more than the benefit generated is not efficient. Economic theory provides a systematic method with which to develop policy or to assess existing programs. This chapter provides basic economic concepts and examples relevant to biosecurity safeguarding.
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Introduction To The Economics Of Plant And Animal Biosecurity

International export markets for U.S. domestic products, and U.S. markets for international products, are pathways for the spread of agricultural and environmental pests and diseases. International shipping and air travel, as well as trade in non-agricultural products, even hurricanes and storms can lead to pest and disease spread. These factors increase the likelihood of novel introductions and re-introduction of previously controlled pests and diseases.

The economics of plant and animal health protection in the U.S. influence policy through a labyrinth of benefit-cost tradeoffs that are difficult to predict and rapidly evolving. Two opposing forces are gaining strength in this landscape: increases in the likelihood of infestations by invasive species following novel pathways; versus analysts’ ability to use data analytics to target interventions on higher risk pathways to lower the risk of infestations taking place.

Historically, plant and animal protection has operated in a command-and-control environment (see glossary) which was inherently reacting to an introduction. Ideally, an agricultural safeguarding continuum requires a proactive rather than reactive response to agricultural pest and disease threats, and more simultaneous than sequential consideration of economic implications of different approaches to dealing with threats. There was a time when we could address the cost of pest and disease exclusion, followed by an economic analysis of what dangers were avoided and at what cost. Increased threats, increased mobilization, and limited economic resources to address them has led to a proactive consideration of the value of exclusion, relative to the risk of introduction and establishment for various agricultural commodities.

Large datasets are generated from daily enforcement of animal and plant protection regulations to safeguard from pests and pathogens associated with the different arrival pathways for various plant and animal commodities. Risk-based sampling of cargo and conveyances for pests and pathogens is based on interpretation of that data. At the same time, data resources to monitor and evaluate plant and animal pests and diseases are increasingly available electronically with shorter time lags. Analytical capability that can be applied to this data is growing in both rigor and ease of implementation including statistical and geographic information system (GIS) map-layering capability. Data mining, simulation, and optimization algorithms have gained sophistication and can be used to predict future events or discover patterns that would be otherwise undetectable.

The news media regularly reports the environmental, social and agricultural costs of introduced pests and diseases, and have even created “glamor pests” that catch the public’s imagination like the “murder hornet” or Asian giant hornet (AGH), one of the world’s largest hornets and one that is extremely aggressive towards honeybees, threatening their pollinator functions (Kumar, 2020). This celebrity is likely to influence the economic tradeoffs associated with different exclusion, treatment, and AGH control strategies. The prioritization and timing of AGH actions are likely to be influenced towards proactive strategies and away from reactive ones. The result is more resources are likely to be allocated to develop an early warning system, rather than a more standard detect and react approach.

These opposing forces of expanding exposure and increasing analytical capability leads to some important agricultural safeguarding questions. Can practitioners predict the next costly invasive pest? Can they forecast how it might get here, or how they could stop it before it ever reaches our shores? Can policymakers develop a best practices approach for managing a domestic outbreak before it happens? This chapter discusses the economics of these opposing forces. It relates those economic principles to specific, recent examples of plant and animal pests and diseases impacting U.S. agriculture, and how enhanced analytical capability has helped control or eradicate problems for individual agricultural products.

The Animal and Plant Health Inspection Service (APHIS) agency of the U.S. Dept. of Agriculture strongly supports the publication of a chapter that describes the rapidly evolving landscape of plant and animal health safeguarding and protection as it supports the basic mission of the agency to protect and promote U.S. agricultural health (APHIS, 2021).

Key Terms in this Chapter

Framing Effects: The presentation of options with a positive or a negative connotation. The context within which problems and solutions are presented often impact how positive or negative they are perceived to be.

Pest and Disease Exclusion: Actions and procedures used to intercept pests and diseases before they become established in a country or jurisdiction.

Monopoly: A market structure where one producer or firm produces and supplies all the product to a market.

Command-and-Control Environment: A type of regulation that sets the outcome and mandates the technology that must be used.

Perfectly Competitive or a Price Taking Market: A market where consumers have no ability to influence prices they pay, and suppliers have no ability to influence the prices they receive. Prices are determined in the market and are taken as given by consumers and suppliers.

Vertical Integration: An arrangement a single company operates or controls multiple activities within the supply chain.

Market: A mechanism that allows buyers and sellers to strike an agreement on price and quantity traded.

Negative Externality: A cost that accrues to a third party outside of a private market transaction.

Market Structure: The characteristics of a specific market that impact how prices are determined and if any participants in the market have market power.

Marginal Benefit: The incremental benefit, or the benefit of one additional unit of a good or service.

Positive Externality: A benefit that accrues to a third party outside of a private market transaction.

Monopsony: A market structure where a single consumer is the only buyer of the good in a market.

Market Power: The ability by participant, or group of participants to influence the outcome of a market.

Optimal Level of Detection: For budgetary reasons, there will likely be a low level of detection that is considered acceptable. An optimal level of detection balances the costs of additional detections against the damage caused by the remaining pests or pathogens.

Nash Equilibrium: A stable state of interactions between participants where the outcome for each participant depends on their choice as well as others' choices and no individual will be better off switching their choice if no one else switches.

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