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Production risk, pesticide use and GM crop technology in South Africa

Author

Listed:
  • Bhavani Shankar
  • Richard Bennett
  • Stephen Morse

Abstract

Technology involving genetic modification of crops has the potential to make a contribution to rural poverty reduction in many developing countries. Thus far, pesticide-producing Bacillus thuringensis (Bt) varieties of cotton have been the main GM crops under cultivation in developing nations. Several studies have evaluated the farm-level performance of Bt varieties in comparison to conventional ones by estimating production technology, and have mostly found Bt technology to be very successful in raising output and/or reducing pesticide input. However, the production risk properties of this technology have not been studied, although they are likely to be important to risk-averse smallholders. This study investigates the output risk aspects of Bt technology by estimating two 'flexible risk' production function models allowing technology to independently affect the mean and higher moments of output. The first is the popular Just-Pope model and the second is a more general 'damage control' flexible risk model. The models are applied to cross-sectional data on South African smallholders, some of whom used Bt varieties. The results show no evidence that a 'risk-reduction' claim can be made for Bt technology. Indeed, there is some evidence to support the notion that the technology increases output risk, implying that simple (expected) profit computations used in past evaluations may overstate true benefits.

Suggested Citation

  • Bhavani Shankar & Richard Bennett & Stephen Morse, 2008. "Production risk, pesticide use and GM crop technology in South Africa," Applied Economics, Taylor & Francis Journals, vol. 40(19), pages 2489-2500.
  • Handle: RePEc:taf:applec:v:40:y:2008:i:19:p:2489-2500
    DOI: 10.1080/00036840600970161
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    Citations

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    Cited by:

    1. Theodoros Skevas & Teresa Serra, 2016. "The role of pest pressure in technical and environmental inefficiency analysis of Dutch arable farms: an event-specific data envelopment approach," Journal of Productivity Analysis, Springer, vol. 46(2), pages 139-153, December.
    2. Sanglestsawai, Santi & Rodriguez, Divina Gracia P. & Rejesus, Roderick M. & Yorobe, Jose M., 2017. "Production Risk, Farmer Welfare, and Bt Corn in the Philippines," Agricultural and Resource Economics Review, Cambridge University Press, vol. 46(03), pages 507-528, December.
    3. Evita Pangaribowo & Nicolas Gerber & Pascal Tillie, 2013. "Assessing the FNS impacts of technological and institutional innovations and future innovation trends," FOODSECURE Working papers 11, LEI Wageningen UR.
    4. Vijesh Krishna & Matin Qaim & David Zilberman, 2016. "Transgenic crops, production risk and agrobiodiversity," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 43(1), pages 137-164.
    5. Klara Fischer & Camilla Eriksson, 2016. "Social Science Studies on European and African Agriculture Compared: Bringing Together Different Strands of Academic Debate on GM Crops," Sustainability, MDPI, Open Access Journal, vol. 8(9), pages 1-17, August.
    6. Hurley, Terrance M., 2010. "A review of agricultural production risk in the developing world," Working Papers 188476, HarvestChoice.
    7. Nolan, Elizabeth & Santos, Paulo, 2012. "Insurance premiums and GM traits," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 125942, International Association of Agricultural Economists.
    8. Klara Fischer & Elisabeth Ekener-Petersen & Lotta Rydhmer & Karin Edvardsson Björnberg, 2015. "Social Impacts of GM Crops in Agriculture: A Systematic Literature Review," Sustainability, MDPI, Open Access Journal, vol. 7(7), pages 1-23, July.
    9. Sanglestsawai, Santi & Rejesus, Roderick M. & Yorobe, Jose M., 2014. "Do lower yielding farmers benefit from Bt corn? Evidence from instrumental variable quantile regressions," Food Policy, Elsevier, vol. 44(C), pages 285-296.

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