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Enregistrement W2105786871 · doi:10.22004/ag.econ.9917

Genetically Modified Food and International Trade: The Case of India, Bangladesh, Indonesia and the Philippines

2007· preprint· en· W2105786871 sur OpenAlexaboutno aff
Guillaume Gruère, Antoine Bouët, Simon Mevel

Notice bibliographique

RevueAgEcon Search (University of Minnesota, USA) · 2007
Typepreprint
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueGenetically Modified Organisms Research
Établissements canadiensnon disponible
Organismes subventionnairesUnited States Agency for International Development
Mots-clésProductivityAgricultureComputable general equilibriumAgricultural economicsMarket accessFood securityAgricultural biotechnologyBusinessDeveloping countryEconomicsCropInternational tradeAgricultural scienceGeographyEconomic growthAgronomyEnvironmental scienceBiology

Résumé

récupéré en direct d'OpenAlex

In the last eleven years, the global production of genetically modified (GM) crops has increased dramatically. Yet more than 95% of GM crops are produced in four countries: the United States, Argentina, Canada and China. During the same period, a group of countries with consumer opposition to GM food, lead by the European Union (EU) and Japan, have implemented stringent policies regulating the approval and import of GM crops and the marketing of GM food. In the context of increased international agricultural trade, the regulations of these importers and the lack of demand for GM food in these countries likely limited the expansion of agricultural biotechnology to many developing countries. Cohen and Paarlberg (2002) argued that the restricted adoption and availability of GM crops in developing countries may not be due to science incapacity, intellectual property rights constraints, biosafety risks or food safety fears, but mainly to the fear of lost export sales to the EU and East Asian countries because of their approval, labeling and traceability regulations. For several years, a number of Asian countries have been actively developing programs of research on agricultural biotechnology, focusing on GM crops with potentially beneficial agronomic traits (Runge and Ryan 2004). Some of these countries have developed biosafety regulatory frameworks, but until now only a few have approved one or more GM crops. Recent studies show that the introduction of Bt cotton in India and China, have generated yield and revenue gains for farmers (Bennett et al. 2004, Pray et. al 2002). But these two countries only approved the large scale production of GM cotton, in part because unlike other GM crops, the main products of cotton are not used for food, and thus are not subject to food safety approval, traceability and labeling regulations in major importing countries. In particular, neither Japan nor the European Union directly regulates textile products derived from GM cotton. At the same time, the Philippines have approved the commercialization of GM maize, but maize remains largely imported in the Philippines. Following a detection of unapproved US rice in the EU and Japanese markets, prompting rapid import bans, Thailand and Vietnam have recently announced that they would not produce GM rice. In this context, most Asian countries that have invested in research and regulations on crop biotechnology are confronted with three possible alternatives: 1) allowing the production of GM food crops with the risk of losing potential exports, 2)reject the commercialization of any GM food crop, 3) producing both GM and non-GM crops separately at a marketing cost. The purpose of this paper is to provide an integrated economic assessment of these three strategies focusing on India, Bangladesh, Indonesia and the Philippines. More specifically, this paper has two main objectives. First, the study assesses the impacts of large importers' regulations (such as the EU and Japan) on the potential benefits of adopting particular GM crops in the four countries. Secondly, we evaluate the opportunity cost of GM/non-GM segregation for these crops under the external constraints previously defined. We focus on four major traded commodities: rice, wheat, maize and soybeans. For each crop, we selected a set of biotic or abiotic stress resistance traits (such as drought resistance) according to the status of research, and its productivity and income potential in these countries. To achieve our two specific objectives, we first conduct multiple focus groups and interviews of local scientific experts on the future productivity potential of the selected GM crops in the four countries (in the summer of 2005). Second, we analyzed the drought and salinity constraints affecting each crop locally in irrigated versus rainfed conditions using GIS data analysis, and we reviewed the literature on biotic stresses for each of these crops. Third, we coupled these data with projections of irrigated versus rainfed areas at the water basin level in the four countries in a 10-15 year horizon. The derived estimated ranges of regional productivity potentials are then aggregated at the national level. These productivity changes are used as factor-biased productivity shocks calibrated into a multi-market, computable general equilibrium (CGE) model that accounts for the specificity of the GM/non-GM commodity markets and the international regulatory policies of GM food at all major importers. We build on previous literature using CGE models by improving the representation of trade policies and refining the assumptions on the productivity effects of biotechnology. First we account for the trade filter effect of GM food marketing policies, e.g., accounting for the fact that the EU is importing large quantities of GM products for animal feed. We also allow the costly segregation of non-GM crops for export, and we model GM crop adoption as a factor-biased productivity shock based on water-basin level data of agricultural constraints. These assumptions help us to obtain robust estimates of the economic and welfare effects of adopting GM crops under trade regulations and allow us to derive the opportunity cost of segregation of GM and non-GM crops. Many developing countries have delayed the adoption of GM crops for fear of losing export markets to the European Union and other countries with stringent regulations on the approval and marketing of GM food. Yet, previous trade studies showed that despite the presence of these importing countries' regulations, the production of relevant GM crops in Africa and China is still expected to provide significant net welfare gains. Similarly, our study will provide estimates of the potential economic benefits of adopting specific GM crops in India, Bangladesh, Indonesia and the Philippines based on a disaggregated assessment of the potential effects of the technology and of current international trade related regulations. The results of our paper will support policy recommendations suggesting which strategy would be best for India, Bangladesh, Indonesia and the Philippines to maximize expected welfare gains from the use of GM food crops in the presence of stringent international regulations. References Bennett, R.M., Ismael, Y., Kambhampati, U., and S. Morse (2004). Economic impact of genetically modified cotton in India. AgBioForum, 7(3), 96-100. Cohen, J. I., and R. Paarlberg (2002). Explaining restricted approval and availability of GM crops in developing countries. AgBiotechNet, 4, Review Article, October. Pray, C., Rozelle, S., Huang, J., and Q. Wang (2002). Plant biotechnology in China. Science, 295, 674-677. Runge, C. F., and B. Ryan (2004). The Global Diffusion of Plant Biotechnology: International Adoption and Research in 2004. Technical Report, Center for International Food and Agricultural Policy, December.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,960
Score d'incertitude au seuil0,737

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,002
Communication savante0,0000,000
Science ouverte0,0010,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,042
Tête enseignante GPT0,245
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2007
Routes d'admission1
Résumé présentoui

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