#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Genetic modification and yield risk: A stochastic dominance analysis of corn in the USA


Autoři: Elizabeth Nolan aff001;  Paulo Santos aff002
Působiště autorů: Affiliation School of Economics, The University of Sydney, Sydney, NSW, Australia aff001;  Affiliation Dept Economics, Monash University, Caulfield, Vic, Australia aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222156

Souhrn

Production risk has been ignored in most of the analysis of GM technology, which has mostly focused on its effects on mean yield. We use stochastic dominance to quantify the effect of GM traits on the entire distribution of yields in corn in the USA under a wide range of growing conditions. Although no GM hybrid outperforms conventional hybrids under all growing conditions, we present evidence that most GM hybrids can be considered as improvements of the yield distribution.

Klíčová slova:

Maize – Probability distribution – Agricultural soil science – Production functions – Edaphology – Cereal crops – Clay mineralogy – Herbicides


Zdroje

1. Day RH. Probability Distributions of Field Crop Yields. Journal of Farm Economics. 1965;47(3):713–741. doi: 10.2307/1236284

2. Anderson JR. Sparse Data, Climatic Variability, and Yield Uncertainty in Response Analysis. American Journal of Agricultural Economics. 1973;55(1):77–82. doi: 10.2307/1238667

3. Just RE, Pope RD. Production Function Estimation and Related Risk Considerations. American Journal of Agricultural Economics. 1979;61(2):276–284. doi: 10.2307/1239732

4. Moschini G, Hennessy DA. Uncertainty, risk aversion, and risk management for agricultural producers. In: Gardner B, Rausser G, editors. Handbook of Agricultural Economics. vol. 1, Part A; 2001. p. 88–153.

5. Hurley TM, Mitchell PD, Rice M. Risk and the Value of Bt Corn. American Journal of Agricultural Economics. 2004;86(2):345–358. doi: 10.1111/j.0092-5853.2004.00583.x

6. Ortman EE, et al. Transgenic Insecticidal Corn: The Agronomic and Ecological Rationale for Its Use. BioScience. 2001;51(11):900–905. doi: 10.1641/0006-3568(2001)051%5B0900:TICTAA%5D2.0.CO;2

7. Fernandez-Cornejo J, Wechsler SJ, Livingston M, Mitchell L. Genetically Engineered Crops in the United States; 2014.

8. Qaim M. The Economics of Genetically Modified Crops. Annual Review of Resource Economics. 2009;1(1):665–694. doi: 10.1146/annurev.resource.050708.144203

9. Areal FJ, Riesgo L, Rodriguez-Cerezo E. Economic and agronomic impact of commercialized GM crops: a meta-analysis. The Journal of Agricultural Science. 2013;151(1):7–33. doi: 10.1017/S0021859612000111

10. Klümper W, Qaim M. A Meta-Analysis of the Impacts of Genetically Modified Crops. PLOS ONE. 2014;9(11):1–7.

11. Yassour J, Zilberman D, Rausser CG. Optimal Choices among Alternative Technologies with Stochastic Yield. American Journal of Agricultural Economics. 1981;63(4):718–723. doi: 10.2307/1241217

12. Just RE, Pope RD. Stochastic specification of production functions and economic implications. Journal of Econometrics. 1978;7(1):67–86.

13. Antle JM. Testing the Stochastic Structure of Production: A Flexible Moment-Based Approach. Journal of Business and Economic Statistics. 1983;1(3):192–201. doi: 10.1080/07350015.1983.10509339

14. Shi G, Chavas JP, Lauer J. Commercialized transgenic traits, maize productivity and yield risk. Nature Biotechnology. 2013;31(2):111–114. doi: 10.1038/nbt.2496 23392505

15. Chavas JP, Shi G. An economic analysis of risk, management and agricultural technology. Journal of Agricultural and Resource Economics. 2015;40:63–79.

16. Coble KH, Dismukes R, Thomas S. Policy Implications of Crop Yield and Revenue Variability at Differing Levels of Disaggregation; 2007.

17. Meyer J. Choice among distributions. Journal of Economic Theory. 1977;14(2):326–336.

18. Mas-Colell A, Whinston M, Green J. Microeconomic Theory. Oxford University Press; 1995.

19. Hardaker JB, Huirne R, Anderson J. Coping with risk in agriculture. CAB International; 1997.

20. Chavas J. Risk analysis in theory and practice. Academic Press; 2004.

21. Griliches Z, Mairesse J. Production functions: the search for identification. In: Griliches Z, editor. Practicing Econometrics: Essays in Method and Application. Edward Elgar; 1998.

22. Just RE, Pope RD. The agricultural producer: Theory and statistical measurement. In: Gardner B, Rausser G, editors. Handbook of Agricultural Economics. vol. 1, Part A. Elsevier; 2001. p. 629–741.

23. Mundlak Y. Production and supply. In: Gardner B, Rausser G, editors. Handbook of Agricultural Economics. vol. 1, Part A. Elsevier; 2001. p. 3–85.

24. Huang J, Hu R, Rozelle S, Pray C. Insect-Resistant GM Rice in Farmers’ Fields: Assessing Productivity and Health Effects in China. Science. 2005;308:688–690. doi: 10.1126/science.1108972 15860626

25. Davidson R, Duclos JY. Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality. Econometrica. 2000;68(6):1435–1464. doi: 10.1111/1468-0262.00167

26. Araar A, Duclos JY. DASP: Distributive Analysis Stata Package Version 2.3; 2013.

27. Shi G, Chavas J, Lauer J, Nolan E. An analysis of selectivity in the productivity evaluation of biotechnology: an application to corn. American Journal of Agricultural Economics. 2013;95:739–754. doi: 10.1093/ajae/aas169


Článok vyšiel v časopise

PLOS One


2019 Číslo 10
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#