Regulation of a Spatial Externality: Refuges versus Tax for Managing Pest Resistance

Ambec, Stéfan; Desquilbet, Marion

doi:10.1007/s10640-011-9489-3

Cited by 17 publications

(14 citation statements)

References 23 publications

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“…Our biological model is very close to that of Secchi et al () . It is also close to the biological model of Ambec and Desquilbet (), who focus on spatial instead of intertemporal aspects as we do here. However, as we detail below, it differs along several dimensions from models by Laxminarayan and Simpson (), Grimsrud and Huffaker (), and Qiao et al (, ).…”

Section: Biological Modelingsupporting

confidence: 68%

“…This allows us to simplify slightly the spatial interaction between pests from the Bt and refuge fields in comparison with Alstad and Andow (1995) who assume that 95% of pests migrate from the field in which they emerge. 6 We also assume that genotype proportions in each field are not affected by 3 Ambec and Desquilbet (2012) show that scattered refuge fields perform better than a tax on Bt seeds when pest prevalence is heterogenous among farmers and pest dispersal is imperfect across landscapes. Note that this explicit modeling of spatial pest prevalence and pest dispersal is tractable in their model because they simplify the dynamic aspects by modeling only two time periods.…”

Section: A Biological Model Consistent With the Population Genetics Lmentioning

confidence: 99%

“…Ambec and Desquilbet () show that scattered refuge fields perform better than a tax on Bt seeds when pest prevalence is heterogenous among farmers and pest dispersal is imperfect across landscapes. Note that this explicit modeling of spatial pest prevalence and pest dispersal is tractable in their model because they simplify the dynamic aspects by modeling only two time periods.…”

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confidence: 99%

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The Dynamics of Pest Resistance Management: The Case of Refuge Fields for Bt Crops

Desquilbet

Herrmann

2015

Canadian J Agri Economics

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We examine the optimal time-variant refuge policy to manage pest resistance to Bacillus thuringiensis (Bt) crops in a finite, discrete-time horizon model. We identify analytically the intertemporal effects of refuge fields on the pest population and its susceptibility. The shape of the optimal refuge policy and whether or not pest susceptibility should be exhausted completely at the end of the time horizon depend crucially on the values of a cost premium of Bt seeds and the fitness cost of resistance (over mortality of resistant pests) and are addressed via numerical simulations. We demonstrate the importance of modeling the dynamics of the biological system accurately, of defining a diploid (and not haploid) biological model, and of using a discrete-time (rather than continuous-time) framework. Nous analysons la politique optimale d'un refuge variable dans le temps afin de gérer la résistance d'insectes nuisibles face aux semences Bt dans le cadre d'un modèleà horizon fini et en temps discret.Nous identifions de manière analytique les effets intertemporels des zones de refuge sur la population d'insectes et leur susceptibilité aux semences Bt. La forme de la politique optimale de zone de refuge ainsi que l'extraction complète de la susceptibilité des insectes nuisibles en horizon fini -si elle a lieu -dépendent de manière cruciale des valeurs du surcoût des semences Bt et du coût de "fitness" (mortalité des insectes résistants), ce qui est analysé via des simulations numériques. Nous montrons l'importance de modéliser la dynamique du système biologique de manière exacte, de définir un modèle biologique diplo ide (et non haplo ide) et d'utiliser un cadre d'analyse en temps discret (au lieu de continu).

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Section: Biological Modelingsupporting

confidence: 68%

Section: A Biological Model Consistent With the Population Genetics Lmentioning

confidence: 99%

mentioning

confidence: 99%

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The Dynamics of Pest Resistance Management: The Case of Refuge Fields for Bt Crops

Desquilbet

Herrmann

2015

Canadian J Agri Economics

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“…1 These characteristics suggest that growers would overuse Bt varieties, relative to the social optimum. Conventional economic prescriptions for resistance management therefore focus on the standard pecuniary instruments associated with common-pool resources: enforceable (and ideally transferable) quotas or user fees that internalize the user costs of resistance (Ambec and Desquilbet, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…This research is important to consider against application of traditional common-pool resource economics to pesticide resistance management. The transferable quotas and user fees traditionally advocated for managing commons aim to institute well-defined property rights to incentivize economically efficient conservation of pesticide resistance (Vacher et al 2006;Ambec and Desquilbet, 2012). Yet current refuge mandates, as implemented in the US and elsewhere, are deficient in two of the three requirements for well-defined property rights: enforceability and transferability.…”

Section: Introductionmentioning

confidence: 99%

Voluntary Programs To Encourage Refuges for Pesticide Resistance Management: Lessons from a Quasi‐Experiment

Brown

2018

American J Agri Economics

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Economists often treat pesticide resistance as a common-pool resource problem. While pecuniary economic incentives are the standard prescription for open-access market failures arising with such resources, non-pecuniary behavioral approaches are also effective in some cases. Yet nonpecuniary instruments have not previously been evaluated for managing pesticide resistance. I empirically evaluate the performance of such an intervention to manage pest resistance to genetically engineered Bacillus thuringiensis (Bt) corn. Regulations by the US Environmental Protection Agency mandate refuges to delay the evolution of Bt resistance. To satisfy Bt product permitting requirements with the EPA, the agricultural company Monsanto piloted a social marketing program to promote refuge in 17 North Carolina counties in 2013-2014. Using seed sales data from 2013 to 2016 for the whole of North Carolina, I estimate multiple econometric models, including linear difference-indifferences (DID), fractional DID, discrete changes-inchanges, and matching to identify the average treatment effect (ATE) of the program on growers' refuge planting decisions. Results suggest that if it had covered all corn growers in North Carolina, the intervention would have led the average grower to plant between 2.6% and 5.8% (depending on model) more refuge in the 2014 season immediately following the program, than would have been the case without the program. The ATE roughly halves in 2015 and vanishes by 2016. Econometric analysis suggests the program increased by 12% the average probability of planting any refuge in 2014. Evidence is mixed for effects of the intervention on grower compliance with mandated refuge thresholds. Informed by behavioral economics research on other environmental and resource policies, I discuss the implications of these findings for pesticide resistance management.

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Developing practical measures of the price of pesticide resistance: A flexible computational framework with global sensitivity analysis

Cho,

Brown,

Gross

et al. 2024

J of Agr & App Econ Assoc

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Pesticide resistance poses an increasing challenge for agricultural sustainability. Pesticide susceptibility is a depletable biological resource, but resistance management rarely quantifies marginal, forward‐looking economic costs to users of depletion. To facilitate the development of such costs, we use a generic stochastic bioeconomic model of resistance evolution in a crop pest population, stochastic dynamic programming, and global sensitivity analysis to analyze the “marginal user costs” of resistance. The most impactful parameters are population density dependence and pesticide prices. The least impactful is the fitness cost of resistance, which is noteworthy because of prior emphasis on this parameter in the resistance management literature.

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Regulation of a Spatial Externality: Refuges versus Tax for Managing Pest Resistance

Abstract: Pest resistance, Pesticides, Transgenic crop, Tax, Refuge, Spatial, Externalities, Q16, Q18, D62,

Cited by 17 publications

References 23 publications

The Dynamics of Pest Resistance Management: The Case of Refuge Fields for Bt Crops

The Dynamics of Pest Resistance Management: The Case of Refuge Fields for Bt Crops

Voluntary Programs To Encourage Refuges for Pesticide Resistance Management: Lessons from a Quasi‐Experiment

Developing practical measures of the price of pesticide resistance: A flexible computational framework with global sensitivity analysis

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