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Estimated Contribution of Four Biotechnologies to New Zealand Agriculture

Listed author(s):
  • Kaye-Blake, William
  • Saunders, Caroline M.

The impact of biotechnology is an important consideration for New Zealand. The country depends significantly on agricultural production and exports (Ministry of Agriculture and Forestry, 2004), and has relied in part on modern biotechnology for productivity increases over the last 20 years (Evenson & Gollin, 2003; Jacobsen & Scobie, 1999; Ovenden, Anderson, Armstrong, & Mitchel, 1985). A recent survey of individuals in agriculture and biotechnology generated a comprehensive list of products and processes that are derived from four specific biotechnologies and are commercially significant in agriculture (Kaye-Blake, Saunders, Emanuelsson, Dalziel, & Wreford, 2005). This innovative research generated primary data on the actual impacts that biotechnology is currently having on agricultural production and produced a unique dataset of biotechnology products and processes and their value to New Zealand agriculture. Analysis found that these four biotechnologies are contributing approximately $206 million per year to agriculture. This analysis, however, assumed perfectly elastic international prices, and thus that New Zealand agricultural producers would capture the benefits of increased productivity. Literature on the impacts of productivity increases suggests that the distribution of benefits from increased productivity depends on how widely a technology is adopted. For example, genetic improvements in the crops of one country can allow domestic producers to increase producer surplus at the expense of producers in the rest of the world (Frisvold, Sullivan, & Raneses, 2003). By contrast, domestic farmers may be worse off if innovations are adopted in both the home country and the rest of the world (Moschini, Lapan, & Sobolevsky, 2000). The literature also suggests that specific impact of a novel technology is important to its impacts on agricultural producers. For example, technology that increases yields may be less beneficial for farmers than technology that reduces costs (Moschini et al., 2000). In addition, innovations that increase productivity of commodity products with low price elasticities of demand may not benefit farmers as much as innovations that increase consumer demand for agricultural products (Saunders & Cagatay, 2003). These findings are relevant because some features of New Zealand's primary sector suggest that international price impacts may be important. New Zealand is an open economy (Ministry of Agriculture and Forestry (MAF), 2004) and a significant exporter on world markets, particularly in pastoral products (Ministry of Agriculture and Forestry (MAF), 2004; Saunders & Cagatay, 2003). Modelling the movement of international prices may be done in several ways. The general equilibrium GTAP model (Hertel, 1997), for example, has been used to examine the potential impacts of biotechnology on producer and consumer welfare assuming different levels of adoption and consumer acceptance (e.g., Anderson & Jackson, 2005; Stone, Matysek, & Dolling, 2002). These impacts have also been analysed with partial equilibrium models, in particular models derived originally from the Uruguay Round of trade negotiations (Roningen, 1997), such as SWOPSIM (Frisvold et al., 2003; Roningen, Dixit, Sullivan, & Hart, 1991) and LTEM (Saunders & Cagatay, 2003). Partial equilibrium models are particularly appropriate for analysing impacts on a single sector of the economy: they allow substantial disaggregation by commodity and examination of the linkages that lead to model results (Gaisford & Kerr, 2001). In order to investigate the possible impact of biotechnological innovations on commodity prices and agricultural producers, the results of the original findings based on elastic prices were incorporated into a partial equilibrium model of world agricultural commodity trade (Cagatay & Saunders, 2003; Saunders & Cagatay, 2003). The model contained 19 commodities, including the major trade commodities for New Zealand (dairy products and meat). World trade was divided into 17 countries and the rest of the world, including New Zealand as a separate entity as well as the US, EU, Australia, Japan and others. As a partial equilibrium model, it examined the agricultural sector in isolation from other sectors of the economy. The base year was 2000, and impacts were modelled to 2005. The base solution modelled current production, which included biotechnological innovations. Alternative scenarios modelled the impact of the absence or loss of biotechnological innovations. The first scenario modelled the absence of innovations in all countries, while the second scenario examined the impact of innovations specific to New Zealand. The contribution of biotechnology to productivity was assessed separately for each commodity, using the original dataset (Kaye-Blake et al., 2005). For each commodity in the model, the analysis calculated the change in producer prices and total producer returns (price x quantity). The modelling results conformed to expectations. In the first scenario, a worldwide reduction in productivity in the primary sector led market prices to adjust upward in response to the lower production. For the second scenario, the price impacts were smaller for sectors with innovations specific to New Zealand. These changes were then combined with the original, constant-price estimate to calculate price-adjusted figures. The constant price analysis found that the contribution of the biotechnologies was $206 million. The first modelling scenario found that the economic benefit of the biotechnologies was only $19 million because increased productivity reduced commodity prices. The second scenario yielded an economic benefit of $191 million, suggesting that adopting New Zealandspecific innovations might not have a large impact on aggregate trade and might have allowed domestic producers to capture much of the increased welfare from innovations. Economic impacts, however, were spread unevenly across the commodities. In both trade scenarios, dairy producers increased producer returns through biotechnology, regardless of how widely the innovations were adopted. Meat producers, on the other hand, improved their returns when the innovations were specific to New Zealand, but were somewhat worse off when the innovations were available worldwide. This research contributes to understanding of the impacts of biotechnology in several ways. First, the productivity impacts were based on empirical findings regarding estimated impacts of actual commercially released biotechnologies; these were estimates of impacts that have actually occurred. Secondly, the productivity effects varied by commodity in the model, so that the impacts on different commodities could be estimated. Finally, by using a disaggregated, multi-commodity model, the cross-effects from resources shifting into other agricultural uses could be captured.

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Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2006 Annual meeting, July 23-26, Long Beach, CA with number 21133.

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Date of creation: 2006
Handle: RePEc:ags:aaea06:21133
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  1. Kym Anderson & Lee Ann Jackson, 2005. "GM crop technology and trade restraints: economic implications for Australia and New Zealand ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 49(3), pages 263-281, 09.
  2. Saunders, Caroline M. & Cagatay, Selim, 2003. "Commercial release of first-generation genetically modified food products in New Zealand: using a partial equilibrium trade model to assess the impact on producer returns in New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 47(2), June.
  3. Harvey E. Lapan & Giancarlo Moschini, 2004. "Innovation and Trade with Endogenous Market Failure: The Case of Genetically Modified Products," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(3), pages 634-648.
  4. Stone, Susan F. & Matysek, Anna & Dolling, Andrew, 2002. "Modelling Possible Impacts of GM Crops on Australian Trade," Staff Research Papers 31913, Productivity Commission.
  5. Kaye-Blake, William & Saunders, Caroline M. & Emanuelsson, Martin, 2006. "Current Contribution of Four Biotechnologies to New Zealand's Primary Sector," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25411, International Association of Agricultural Economists.
  6. Colman, David R., 1983. "A Review of the Arts of Supply Response Analysis," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 51(03), December.
  7. José Benjamin Falck-Zepeda & Greg Traxler & Robert G. Nelson, 2000. "Surplus Distribution from the Introduction of a Biotechnology Innovation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 360-369.
  8. Andrei Sobolevsky & GianCarlo Moschini & Harvey E. Lapan, 2002. "Genetically Modified Crop Innovations and Product Differentiation: Trade and Welfare Effects in the Soybean Complex," Center for Agricultural and Rural Development (CARD) Publications 02-wp319, Center for Agricultural and Rural Development (CARD) at Iowa State University.
  9. Frisvold, George B. & Sullivan, John & Raneses, Anton, 2003. "Genetic improvements in major US crops: the size and distribution of benefits," Agricultural Economics, Blackwell, vol. 28(2), pages 109-119, March.
  10. Matin Qaim & Greg Traxler, 2005. "Roundup Ready soybeans in Argentina: farm level and aggregate welfare effects," Agricultural Economics, International Association of Agricultural Economists, vol. 32(1), pages 73-86, January.
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