دانلود مقاله ISI انگلیسی شماره 28603
عنوان فارسی مقاله

برآورد تاثیر تراریخته پنبه Bt در غرب و مرکز آفریقا: روش تعادل عمومی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
28603 2004 16 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Estimating the Impact of Transgenic Bt Cotton on West and Central Africa: A General Equilibrium Approach
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : World Development, Volume 32, Issue 12, December 2004, Pages 2049–2064

کلمات کلیدی
پنبه - بیوتکنولوژی - بهره وری محصول - غرب آفریقا - تعادل عمومی کاربردی -
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پیش نمایش مقاله برآورد تاثیر تراریخته پنبه Bt در غرب و مرکز آفریقا: روش تعادل عمومی

چکیده انگلیسی

The growth of the cotton sector in West and Central Africa (WCA)1 over the last four decades is one of the few bright spots in economic development of sub-Saharan Africa. Since the 1960s, cotton production in WCA has expanded substantially, making cotton one of the drivers of regional economic growth. Over 1961–2000, WCA cotton production grew by 20-fold while yields increased by more than four-fold (Figure 1). In many WCA countries, cotton is the main engine of rural employment, affecting the economic livelihood of over two million in Burkina Faso (16% of total population), and 2.5 million in Mali (18% of total) (Table 1). For five countries (Benin, Burkina Faso, Chad, Mali and Togo) the cotton sector represents between 5% and 19% of GDP, and cotton is the most important export commodity for several countries. Currently, the WCA’s share of world cotton exports stands at around 15%, second only to the United States. Full-size image (34 K) Figure 1. Patterns of cotton area and yields for West and Central African region (Source: FAOSTAT, 2003). Figure options Table 1. Importance of the cotton sector to West and Central African economies (1999) Number of cotton farms (×1,000) Cotton-dependent rural population (million) Cotton share of total GDP (percent) Cotton share of total export value (percent) Ratio of cotton exports to food imports (% value) Benin NA NA 8.8 44 88 Burkina Fas 250 2.0 6.9 58 99.5 Chad 400 NA 5.1 46 143.8 Mali 160 2.5 5 41 160.9 Togo 200 NA 4.9 19 169.6 Côte d’Ivoire 150 1.0 1.7 5 45.3 Cameroon 250 1.5 1.3 4 78.2 Central African republic NA NA 1.3 7 62.5 Sources: FAOSTAT, 2003 and Coton et Developpement, 1999; NA: not available. Table options Several factors, both institutional and technological, have contributed to cotton growth in WCA. First, cotton production and marketing are vertically integrated, with state enterprises typically providing input credit and technical support, and purchasing all produced cotton from farmers. Access to credit and steady prices—often higher than alternative crops—has attracted farmers to cotton. Improved technologies, such as introduction of animal traction, fertilizers and insecticides have been critical in raising yields and expanding cotton areas. In the 1970s, other pest management innovations such as the ultra-light volume (ULV) spraying, the switch to more effective pyrethroid pesticides and to higher yielding upland (or US) cotton varieties also helped expand cotton area and production (Follin & Deat, 1999). More recently, however, the WCA cotton sector has been showing declining yields, rising costs of production and eroding profitability (Badiane et al., 2002 and Tefft et al., 1998). These factors are compounded by WCA vulnerability to world price fluctuations in response to global demand and supply shifts. Moreover, the CFA franc devaluation in 1994 and the phasing out of input subsidies have induced short-term production costs leading to an extensification of cotton production with few input use. These changes have revealed the underlying weaknesses of the sector, and drawn attention to the need for longer-term productivity gains. The emphasis on efficiency-boosting cost reduction requires a re-examination of chemical-based pest management at the core of the cotton production system in WCA, and the source of much of past yield gains (Follin & Deat, 1999). In recent years, however, yields have been falling even while pesticide use continues to increase (Ajayi et al., 2002) revealing both short-term inefficiencies and long-term unsustainability. The increasing incidences of pest resistance to pyrethroids, particularly cotton bollworm (Helicoverpa armigera) are contributing to the declining effectiveness of pesticides ( Martin, Chandre, Ochu, Vaissayre, & Fournier, 2002). Alternative approaches to calendar-based spray schedules, such as threshold applications or integrated pest management (IPM) methods are being tested in some WCA countries but the success is relatively slow ( Ochut et al., 1998 and Silvie et al., 2001). Low levels of literacy, farmers’ aversion to risk, and high requirements for insect scouting are all contributing factors. Heavy reliance on insecticides is characteristic of most cotton production systems in the world. Indeed cotton uses 25% of world pesticides while covering only 2.5% of world cropland (Krattiger, 1997). But, rising pesticides resistance and increasing attention to environment and human health impact has motivated a re-examination of pesticide use. Up until 1996, there were very few real alternatives to pesticide use, and efforts to develop varietal resistance, biological-control and IPM had limited effect (Chaudhry, 1993). In this light, the release in the United States of transgenic Bt cotton in 1996 with resistance to bollworm-type insects represents a major technological breakthrough. Pest resistance to chemicals has become a serious problem in many cotton-growing regions. In the United States, cotton bollworm and tobacco budworm resistance to organophosphates has been rising since late 1980s and to pyrethroids since the early 1990s ( Livingston, Carlson, & Fackler, 2003). In Pakistan and India, cotton bollworm developed into a major pest exhibiting increasing resistance to pesticides since early 1990s. The most dramatic case was China, where after decades of over-reliance on insecticides, a major outbreak of cotton bollworm in 1992 caused substantial crop and economic damage in several eastern provinces ( Du, 2001). In subsequent years, a major share of China’s cotton shifted to Western drier provinces with low pest pressures. In WCA, resistance of cotton bollworm to pyrethroids developed in many countries by 1996 ( Martin et al., 2002). Adoption of transgenic Bt cotton was quite rapid in many countries. In 2003 over 37% of total cotton acreage in the United States was planted to Bt varieties; this compared to 25% in Australia, 30% in Mexico, 58% in China, 25% in South Africa, and 5% in Argentina ( James, 2003). Direct farm-level benefits from the adoption of transgenic Bt cotton, through input cost reduction and increased yields, have been documented for the United States ( Deville et al., 2002 and Marra et al., 2002); China ( Du, 2001, Huang et al., 2002 and Pray et al., 2001); South Africa ( Ismail, Bennett, & Morse, 2002); Mexico ( Traxler, Godoy-Avila, Falck-Zepeda, & Espinosa-Arellano, 2001); Argentina ( Qaim & de Janvry, 2002), and India ( Qaim, 2003). Because of different ecological conditions and pest problems, not all cotton regions could equally benefit from the transgenic Bt varieties. But many other countries/regions such as WCA could benefit from the technology since cotton bollworm is the leading pest in the region and has become increasingly resistant to pyrethroid pesticides ( ICAC, 2000 and Martin et al., 2002). A key question is whether the WCA cotton industry can afford to fall behind technologically at a time when costs of production are rising and yields are trending downward. In this paper, we examine the economic impact of transgenic Bt cotton adoption in WCA. We use a multiregion applied general equilibrium (AGE) model to quantify the effects on production, prices, returns to factors, and welfare resulting from Bt -induced productivity boost. The application of the multiregion AGE framework in technology evaluation is justified on several grounds. First, given the economic significance of cotton to WCA, the impact of Bt cotton will extend to rural employment, GDP and exports, all of which are more suitably examined within an economy-wide framework. Second, given that transgenic Bt cotton adoption is pervasive in many regions, the impact on WCA depends not only on adoption within the region but also the extent of adoption in other regions. Moreover, the high dependency of WCA on world cotton trade makes the multiregional framework more suitable to explore the trade implications of technical change. In this analysis we pay particular attention to the estimation of crop productivity gains due to transgenic Bt cotton for all adopting regions. Total factor productivity (TFP) is estimated from farm-level economic impact analyses of Bt cotton and a comprehensive multicountry 2001 cost of production survey for cotton by ICAC (2001a). In simulating the impact of transgenic technology, we consider both factor-neutral and factor-biased technical change assumptions. The remainder of the paper is as follows. Section 2 reviews the recent global trends in cotton yields, pesticide use, and the transgenic technology. Section 3 reviews the evolution of WCA cotton productivity trends and underlying determinants of the sector’s inefficiencies. Section 4 describes the modeling framework and database, while section 5 presents the simulation scenarios. Section 6 presents the results while Section 7 provides a summary and conclusion.

مقدمه انگلیسی

The growth of the cotton sector in West and Central Africa (WCA)1 over the last four decades is one of the few bright spots in economic development of sub-Saharan Africa. Since the 1960s, cotton production in WCA has expanded substantially, making cotton one of the drivers of regional economic growth. Over 1961–2000, WCA cotton production grew by 20-fold while yields increased by more than four-fold (Figure 1). In many WCA countries, cotton is the main engine of rural employment, affecting the economic livelihood of over two million in Burkina Faso (16% of total population), and 2.5 million in Mali (18% of total) (Table 1). For five countries (Benin, Burkina Faso, Chad, Mali and Togo) the cotton sector represents between 5% and 19% of GDP, and cotton is the most important export commodity for several countries. Currently, the WCA’s share of world cotton exports stands at around 15%, second only to the United States. Full-size image (34 K) Figure 1. Patterns of cotton area and yields for West and Central African region (Source: FAOSTAT, 2003). Figure options Table 1. Importance of the cotton sector to West and Central African economies (1999) Number of cotton farms (×1,000) Cotton-dependent rural population (million) Cotton share of total GDP (percent) Cotton share of total export value (percent) Ratio of cotton exports to food imports (% value) Benin NA NA 8.8 44 88 Burkina Fas 250 2.0 6.9 58 99.5 Chad 400 NA 5.1 46 143.8 Mali 160 2.5 5 41 160.9 Togo 200 NA 4.9 19 169.6 Côte d’Ivoire 150 1.0 1.7 5 45.3 Cameroon 250 1.5 1.3 4 78.2 Central African republic NA NA 1.3 7 62.5 Sources: FAOSTAT, 2003 and Coton et Developpement, 1999; NA: not available. Table options Several factors, both institutional and technological, have contributed to cotton growth in WCA. First, cotton production and marketing are vertically integrated, with state enterprises typically providing input credit and technical support, and purchasing all produced cotton from farmers. Access to credit and steady prices—often higher than alternative crops—has attracted farmers to cotton. Improved technologies, such as introduction of animal traction, fertilizers and insecticides have been critical in raising yields and expanding cotton areas. In the 1970s, other pest management innovations such as the ultra-light volume (ULV) spraying, the switch to more effective pyrethroid pesticides and to higher yielding upland (or US) cotton varieties also helped expand cotton area and production (Follin & Deat, 1999). More recently, however, the WCA cotton sector has been showing declining yields, rising costs of production and eroding profitability (Badiane et al., 2002 and Tefft et al., 1998). These factors are compounded by WCA vulnerability to world price fluctuations in response to global demand and supply shifts. Moreover, the CFA franc devaluation in 1994 and the phasing out of input subsidies have induced short-term production costs leading to an extensification of cotton production with few input use. These changes have revealed the underlying weaknesses of the sector, and drawn attention to the need for longer-term productivity gains. The emphasis on efficiency-boosting cost reduction requires a re-examination of chemical-based pest management at the core of the cotton production system in WCA, and the source of much of past yield gains (Follin & Deat, 1999). In recent years, however, yields have been falling even while pesticide use continues to increase (Ajayi et al., 2002) revealing both short-term inefficiencies and long-term unsustainability. The increasing incidences of pest resistance to pyrethroids, particularly cotton bollworm (Helicoverpa armigera) are contributing to the declining effectiveness of pesticides ( Martin, Chandre, Ochu, Vaissayre, & Fournier, 2002). Alternative approaches to calendar-based spray schedules, such as threshold applications or integrated pest management (IPM) methods are being tested in some WCA countries but the success is relatively slow ( Ochut et al., 1998 and Silvie et al., 2001). Low levels of literacy, farmers’ aversion to risk, and high requirements for insect scouting are all contributing factors. Heavy reliance on insecticides is characteristic of most cotton production systems in the world. Indeed cotton uses 25% of world pesticides while covering only 2.5% of world cropland (Krattiger, 1997). But, rising pesticides resistance and increasing attention to environment and human health impact has motivated a re-examination of pesticide use. Up until 1996, there were very few real alternatives to pesticide use, and efforts to develop varietal resistance, biological-control and IPM had limited effect (Chaudhry, 1993). In this light, the release in the United States of transgenic Bt cotton in 1996 with resistance to bollworm-type insects represents a major technological breakthrough. Pest resistance to chemicals has become a serious problem in many cotton-growing regions. In the United States, cotton bollworm and tobacco budworm resistance to organophosphates has been rising since late 1980s and to pyrethroids since the early 1990s ( Livingston, Carlson, & Fackler, 2003). In Pakistan and India, cotton bollworm developed into a major pest exhibiting increasing resistance to pesticides since early 1990s. The most dramatic case was China, where after decades of over-reliance on insecticides, a major outbreak of cotton bollworm in 1992 caused substantial crop and economic damage in several eastern provinces ( Du, 2001). In subsequent years, a major share of China’s cotton shifted to Western drier provinces with low pest pressures. In WCA, resistance of cotton bollworm to pyrethroids developed in many countries by 1996 ( Martin et al., 2002). Adoption of transgenic Bt cotton was quite rapid in many countries. In 2003 over 37% of total cotton acreage in the United States was planted to Bt varieties; this compared to 25% in Australia, 30% in Mexico, 58% in China, 25% in South Africa, and 5% in Argentina ( James, 2003). Direct farm-level benefits from the adoption of transgenic Bt cotton, through input cost reduction and increased yields, have been documented for the United States ( Deville et al., 2002 and Marra et al., 2002); China ( Du, 2001, Huang et al., 2002 and Pray et al., 2001); South Africa ( Ismail, Bennett, & Morse, 2002); Mexico ( Traxler, Godoy-Avila, Falck-Zepeda, & Espinosa-Arellano, 2001); Argentina ( Qaim & de Janvry, 2002), and India ( Qaim, 2003). Because of different ecological conditions and pest problems, not all cotton regions could equally benefit from the transgenic Bt varieties. But many other countries/regions such as WCA could benefit from the technology since cotton bollworm is the leading pest in the region and has become increasingly resistant to pyrethroid pesticides ( ICAC, 2000 and Martin et al., 2002). A key question is whether the WCA cotton industry can afford to fall behind technologically at a time when costs of production are rising and yields are trending downward. In this paper, we examine the economic impact of transgenic Bt cotton adoption in WCA. We use a multiregion applied general equilibrium (AGE) model to quantify the effects on production, prices, returns to factors, and welfare resulting from Bt -induced productivity boost. The application of the multiregion AGE framework in technology evaluation is justified on several grounds. First, given the economic significance of cotton to WCA, the impact of Bt cotton will extend to rural employment, GDP and exports, all of which are more suitably examined within an economy-wide framework. Second, given that transgenic Bt cotton adoption is pervasive in many regions, the impact on WCA depends not only on adoption within the region but also the extent of adoption in other regions. Moreover, the high dependency of WCA on world cotton trade makes the multiregional framework more suitable to explore the trade implications of technical change. In this analysis we pay particular attention to the estimation of crop productivity gains due to transgenic Bt cotton for all adopting regions. Total factor productivity (TFP) is estimated from farm-level economic impact analyses of Bt cotton and a comprehensive multicountry 2001 cost of production survey for cotton by ICAC (2001a). In simulating the impact of transgenic technology, we consider both factor-neutral and factor-biased technical change assumptions. The remainder of the paper is as follows. Section 2 reviews the recent global trends in cotton yields, pesticide use, and the transgenic technology. Section 3 reviews the evolution of WCA cotton productivity trends and underlying determinants of the sector’s inefficiencies. Section 4 describes the modeling framework and database, while section 5 presents the simulation scenarios. Section 6 presents the results while Section 7 provides a summary and conclusion.

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