Centre for Agricultural and Food Economics, K.U.LeuvenCentre for Agricultural and Food Economics, K.U.Leuven
GMO’s in Food: Economic Impact on Various Stakeholders in the EU and in
the WorldThis presentation can be downloaded at
http://www.biw.kuleuven.be/aee/clo/euwab.htmEmail: [email protected]
Koen DillenErik MathijsEric Tollens
Course ‘Social and Ethical Aspects of Biotechnology’, VUB, Brussels, 29 November 2007.
IntroductionIntroduction GM experience gap EU vs. ROW EU has chosen the option to wait through the
1998 moratorium and current coexistence regulation process, postponing release
This option has a value and a cost, i.e. potential welfare effects forgone
The trade-off of both needs to be assessed – in order to know the ex post implications of
our decision in the past, i.e. 1998– in order to know the ex ante implications of
future decisions to be taken
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IntroductionIntroductionSystemic Approach is Needed:Systemic Approach is Needed:
IntroductionIntroduction
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GOVERNMENTGOVERNMENT
FARMERSFARMERS
MARKETING SYSTEMMARKETING SYSTEM
CONSUMERSCONSUMERS
Research Expenditures Regulatory ApprovalIPR Legislation
Labelling Policy Trade Regulation
ENVIRONMENTENVIRONMENT
ACTIVISTS, LOBBY GROUPS, MEDIA
ACTIVISTS, LOBBY GROUPS, MEDIA
INPUT SUPPLIERSINPUT SUPPLIERSbiotechnology seeds,
pesticides, ...technology fee, contract
GMO crops or GMO fed livestock productscontract
marketing GM products
environmental benefits and risks
Downstream
Upstream
IntroductionIntroduction Most of the recent agbiotech innovations
have been developed by private sector (upstream), mostly because of very stringent regulations and as such high costs for legislation
Therefore, the central focus of societal interest is not on the ROR of R&D, but on distribution of benefits among stakeholders in the technology diffusion chain
But what are the « benefits » and « costs » arising from GM crops?
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IntroductionIntroduction4 Quadrants of Research in B/C Analyses:4 Quadrants of Research in B/C Analyses:
Scope
Reversibility
Private
Social
Reversible
Quadrant 1
Private Reversible Benefits (PRB) Private Reversible Costs (PRC)
Net Private Reversible Benefits (W): W = PRB-PRC
EUWABSIM (Demont and Tollens, 2003)
Quadrant 2
Social Reversible Benefits (SRB)
Social Reversible Costs (SRC)
Irreversible
Quadrant 3
Private Irreversible Benefits (PIB)
Private Irreversible Costs (PIC)
Quadrant 4
Social Irreversible Benefits (R)
Social Irreversible Costs (I)
• gene flow, outcrossing and weediness
• development of resistance (insects, weeds)
• decline biodiversity (less varieties)
• impacts on non-target species (lepidopteran, birds,
wildlife, …)
• health benefits (Bt
crops)• fixed cost
engendered by e.g. identity preservation
system on the farm
• ethical cons, perception of
non-sustainable and non
environment-friendly
agriculture• decline
of environme
ntal externalities due to
less pesticide
use
• ethical pros, perception of
sustainable and environment-friendly
agriculture• less damage on honey bees due to less pesticide use
• increase biodiversity in field (herbicide tolerant
beet)
• yield increase• pest control cost decline
• labour savings• non-pecuniary benefits like management savings
and ease of use• market effects like price
declines and consumer surplus
• technology fee• other variable costs associated
with the introduction of
GM crops (irrigation)
• market effects like price declines
IntroductionIntroduction
EUWAB-project (European Union Welfare effects of Agricultural Biotechnology)
Pre-coexistence (although some work on coexistence as well)
What have we learned so far from ex post and ex ante agbiotech impact assessments in the EU?
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Table 1: Global welfare distribution of the first generation of transgenic crops Country Crop Year Adoption Welfare Welfare distribution
(%) (m$) Domestic farmers
Innovators Domestic consumers
Net ROW
USA Bt cotton 1996 14% 134 43% 47% 6% 4% USA Bt cotton 1996 14% 240 59% 26% 9% 6% USA Bt cotton 1997 20% 190 43% 44% 7% 6% USA Bt cotton 1998 27% 213 46% 43% 7% 4% USAa Bt cotton 1996-98 20% 151 22% 46% 14% 18% USAb Bt cotton 1997 20% 213 29% 35% 14% 22% USAc Bt cotton 1997 20% 301 39% 25% 17% 19% USA HT cotton 1997 11% 232 4% 6% 57% 33% USAd HT soyb. 1997 17% 1,062 76% 10% 4% 9% USAe HT soyb. 1997 17% 437 29% 25% 17% 28% USA HT soyb. 1999 56% 804 19% 45% 10% 26% USA HT soyb. 1997 17% 308 20% 68% 5% 6% Canadaf HT canola 2000 54% 209 19% 67% 14% . Argentina HT soyb. 2001 90% 1,230 25% 34% 0.3% 41% Argentina Bt cotton 2001 5% 0.4 21% 79% . . China Bt cotton 1999 11% 95 83% 17% 0%g . India Bt cotton 2002 7% 6.2 67% 33% 0%g . Mexico Bt cotton 1998 15% 2.8 84% 16% . . South Africah Bt cotton 2000 75% 0.1 58% 42% . . South Africai Bt cotton 2001 80% 1.2 67% 33% 0%g 0%
Global Case StudiesGlobal Case Studies Farmers capture sizeable gains Size and distribution of welfare effects of the
first generation of GE crops are function of:1. Adoption rate2. Crop3. Biotech trait4. Geographical region5. Year6. National policies and IPR protection7. Assumptions and underlying dataset
On average, domestic farmers and consumers extract 2/3 of the benefits while 1/3 is captured by the seed industry
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Table 1: Global welfare distribution of the first generation of transgenic crops Country Crop Year Adoption Welfare Welfare distribution
(%) (m$) Domestic farmers
Innovators Domestic consumers
Net ROW
USA Bt cotton 1996 14% 134 43% 47% 6% 4% USA Bt cotton 1996 14% 240 59% 26% 9% 6% USA Bt cotton 1997 20% 190 43% 44% 7% 6% USA Bt cotton 1998 27% 213 46% 43% 7% 4% USAa Bt cotton 1996-98 20% 151 22% 46% 14% 18% USAb Bt cotton 1997 20% 213 29% 35% 14% 22% USAc Bt cotton 1997 20% 301 39% 25% 17% 19% USA HT cotton 1997 11% 232 4% 6% 57% 33% USAd HT soyb. 1997 17% 1,062 76% 10% 4% 9% USAe HT soyb. 1997 17% 437 29% 25% 17% 28% USA HT soyb. 1999 56% 804 19% 45% 10% 26% USA HT soyb. 1997 17% 308 20% 68% 5% 6% Canadaf HT canola 2000 54% 209 19% 67% 14% . Argentina HT soyb. 2001 90% 1,230 25% 34% 0.3% 41% Argentina Bt cotton 2001 5% 0.4 21% 79% . . China Bt cotton 1999 11% 95 83% 17% 0%g . India Bt cotton 2002 7% 6.2 67% 33% 0%g . Mexico Bt cotton 1998 15% 2.8 84% 16% . . South Africah Bt cotton 2000 75% 0.1 58% 42% . . South Africai Bt cotton 2001 80% 1.2 67% 33% 0%g 0%
UpstreamAverage
= 37%
Global Case StudiesGlobal Case Studies Hence, benefit sharing seems to
follow a general rule of thumb:1/3 upstream vs. 2/3 downstream (Demont, Dillen et al.) IntroductionIntroduction
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GOVERNMENTGOVERNMENT
FARMERSFARMERS
MARKETING SYSTEMMARKETING SYSTEM
CONSUMERSCONSUMERS
Research Expenditures Regulatory ApprovalIPR Legislation
Labelling Policy Trade Regulation
ENVIRONMENTENVIRONMENT
ACTIVISTS, LOBBY GROUPS, MEDIA
ACTIVISTS, LOBBY GROUPS, MEDIA
INPUT SUPPLIERSINPUT SUPPLIERSbiotechnology seeds,
pesticides, ...technology fee, contract
GMO crops or GMO fed livestock productscontract
marketing GM products
environmental benefits and risks
Downstream
Upstream
Global Case StudiesGlobal Case Studies This 2:1 rule of thumb seems to be
valid for both industrial and developing countries
Typical for large exporting countries: international trade of both the innovation (multinationals) and the commodity international spillover effects possibility of immiserising growth (Bhagwati, 1958)
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EU Case StudiesEU Case Studies De facto moratorium on GM crops: October 1998
– May 2004 (Syngenta Bt 11 maize) 1998-2002: Adoption stagnated at 25,000 ha Bt
maize in Spain, doubled afterwards 2007: 6 Bt maize growing EU Member States:
Spain, Portugal, France, Czech Republic, Germany, Slovakia (but still only MON810)
De facto moratorium and the postponement nowadays implies a cost to society = deadweight cost or benefits foregone of GM crops
But we need a representative EU case study to show this!
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GM crops in EUGM crops in EUHectares 2005 2006 2007Spain 53,225 53,667 75,148
France 492 5000 21,174
Czech Republic
150 1,290 5,000
Portugal 750 1,250 4,500
Germany 400 950 2,685
Slovakia 30 900
Romania 110,000 90,000 350
Poland 100 320
TOTAL 62,187 110,077
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EU Case StudiesEU Case StudiesPreferable conditions of a good EU case study:1. Crop representative for EU agriculture 2. Crop problem representative for EU agriculture3. Important EU export commodity (spillover)4. Acceptance of GM variety realistic5. GM variety near commercialization6. Some impact data available, e.g. field trials
Sugar beet fullfills most criteriaAnd we have ex post impact evidence from Spain
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Table 1: Accordance of selected EU case studies on the impact of GE crops with criteria Crop
Criterion HT sugar beet Bt maize
1. Representativeness of the crop +++ grown in all EU regions
+ grain maize more important in southerly
regions 2. Representativeness of the pest +++
weed control is crucial to profitability
+ corn borers more important in southerly
regions 3. Representativeness of trade +++
EU provides 20% of global trade
– EU-15 and EU-25 are net importers of
maize, only internal EU trade 4. Availability of genetic resources +++
presence of wild relatives, e.g. sea beet
– no wild relatives in Europe,
primary centre of origin is Mexico 5. Realistic acceptance –
main impediments are manufacturers
+++ widely accepted in Spain, entirely used for animal feed, no labelling required
6. Realistic commercialisation ++ registrations are
pending
+++ already commercialised in Spain, France, Germany, Portugal and the Czech Republic
7. Availability of impact data + research capacity has declined since 2001
+ very little data publicly available
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DiscussionDiscussion 0
10
20
30
40
50
60
70
80
90
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Maize Sugar beet
Figure 1: Evolution of the number of field trials of maize and sugar beet in the EU-25 Source: SNIF database (European Commission, 2006a)
EU Case StudiesEU Case Studies1. Bt maize resistant against European corn borer
(ECB) [Ostrinia nubilalis (Hübner)] and Mediterranean corn borer (MCB) [Sesamia nonagrioides (Lefebvre)] in Spain (Demont and Tollens, 2004b)
2. Herbicide tolerant (HT) sugar beet in the former EU-25 (Dillen,Demont and Tollens, 2007)
3. Bt maize resistant against ECB in Hungary /Czech Republic(Demont et al., 2007)
4. Bt maize resistant against Western corn rootworm (WCR) [Diabrotica virgifera virgifera LeConte] in Hungary/Czech
5. Herbicide tolerant maize in Hungary/Czech6. Herbicide tolerant sugar beet in Hungary/Czech7. Herbicide tolerant oilseed rape in
Hungary/czech
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BtBt Maize in Spain Maize in Spain
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BtBt Maize in Spain Maize in Spain
2 corn borers important losses in Spanish maize production: 9% on average
Syngenta 2 Bt maize varieties: Compa CB & Jordi CB
Today: only MON810 varieties Government 20.000 ha limit
= 5,2% adoption (in this period) Analyze 1998-2003
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BtBt Maize in Spain Maize in Spain
1. Farm level analysis: - standard damage abatement function- damage = stochastic (lognormal)- calibrated on real corn borer damage data
2. Aggregation to national level- Alston, Norton & Pardey (1995) (ANP)- small, open economy- Oehmke & Crawford (2002) & Qaim (2003) (OCQ)
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HT Sugar BeetsHT Sugar Beets
BtBt Maize Maize
EnvironmentEnvironment
ConclusionConclusion
BtBt Maize in Spain Maize in Spain
Table 1: Economic impact of Bt maize on Spanish agriculture and the seed industry, 1998-2003 Year
1998
1999
2000
2001
2002
2003 Average
1998-2003 Aggregated value 2004
Adoption (%) 4.8% 7.6% 4.6% 5.0% 5.4% 6.8% 5.7% 5.7% Bt maize adopters (€/ha) 50.5 50.6 47.9 46.8 45.1 45.7 47.8 415.5 Agriculture (m€) 1.1 1.5 1.0 1.2 1.1 1.5 1.2 10.5 Seed industry (m€) 0.5 0.7 0.5 0.6 0.6 0.8 0.6 5.2 Total impact (m€) 1.6 2.2 1.4 1.8 1.7 2.2 1.8 15.8 Agriculture share (%) 67.9% 67.9% 66.7% 66.2% 65.3% 65.6% 66.6% 66.8% Seed industry share (%) 32.1% 32.1% 33.3% 33.8% 34.7% 34.4% 33.4% 33.2%
Herbicide Tolerant Sugar BeetsHerbicide Tolerant Sugar Beets Effective weed control = crucial Yield losses up to 100% due to weed
competition Glyphosate and Glufosinate-ammonium
= broad-spectrum post-emergence herbicides, low toxicity
Introduction of genes from soil bacteria in beet genome Roundup Ready ™ (Monsanto)
Broad-spectrum weed control Less applications Less volume active ingredient More flexibility in timing
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Herbicide Tolerant Sugar BeetsHerbicide Tolerant Sugar Beets1. Farm level analysis:
- assume standard HT replacement programs - compare costs with observed programs-model the heterogeneity among farmers-some farmers rationaly decide to adopt, others not choose not to-calculate the optimal technology fee
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f(hc)
hc)( cgg nnah gw
g
maxmin
Herbicide Tolerant Sugar BeetsHerbicide Tolerant Sugar Beets
Uniform monopolistic price setting
Part of monopolistic rent accrues to farmers
Third degree price discrimination preferable->Bt-maize, Bt cotton
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Herbicide Tolerant Sugar Herbicide Tolerant Sugar BeetsBeets Data: ex ante
- No adoption of the new technology- No farm level impact data, only field trials- Assumptions: 1. Yield impact
2. Form of the density curve- Sources: expert opinions, literature, economic theory, national surveys, Eurostat- Stochastic simulation (monte carlo)
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Year Benchmark 1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 1996/97
Price effects World sugar price (%) 100% 99.65% 99.59% 99.35% 99.09% 98.92% 98.73% 98.65% 98.49% 98.58% A sugar price (%) 100% 99.99% 99.99% 99.99% 99.99% 99.97% 99.98% 99.97% 99.98% 99.99% B sugar price (%) 100% 99.77% 99.77% 99.73% 99.70% 99.17% 99.48% 99.16% 99.26% 99.56% Welfare effects
Belgium 0.00 2.46 3.61 4.13 5.70 6.72 8.67 8.67 9.02 15.21 Denmark 0.00 1.90 3.23 4.59 6.28 7.75 9.56 9.91 11.04 12.61 Germany 0.00 12.70 18.53 24.46 31.12 39.79 48.13 48.17 49.08 44.21 Greece 0.00 1.82 3.17 3.44 5.47 7.84 8.87 9.51 8.33 5.38 Spain 0.00 9.10 15.47 22.04 30.27 38.07 44.49 44.88 50.76 45.90 France 0.00 5.10 7.40 9.71 11.74 11.50 19.09 15.78 16.84 44.74 Ireland 0.00 1.32 2.09 3.20 4.45 5.08 5.98 6.45 7.13 0.21 Italy 0.00 5.67 9.21 13.64 18.61 22.74 27.09 28.66 34.37 21.91 The Netherlands 0.00 3.71 6.60 10.50 13.88 15.83 20.00 20.27 22.47 34.09 Austria 0.00 1.42 2.09 2.74 3.39 3.93 5.34 5.24 5.15 8.22 Portugal 0.00 0.03 0.83 1.17 1.75 1.90 2.54 2.36 2.96 3.00 Finland 0.00 1.02 1.48 2.57 3.24 4.11 4.83 5.13 5.78 4.94 Sweden 0.00 0.64 1.09 1.58 2.32 2.93 3.45 3.97 4.19 4.88 United Kingdom 0.00 2.62 3.97 4.93 6.03 6.50 9.51 8.56 8.98 13.25 Czech Republic 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 15.44 Hungary 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 9.12 Poland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 58.17 EU-15 producers 0.00 49.51 78.77 108.69 144.25 174.68 217.53 217.54 236.09 341.29 EU-15 consumers 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ROW cane 0.00 -116.46 -115.22 -148.77 -164.49 -238.67 -289.76 -300.04 -296.12 -310.02 ROW beet 0.00 39.56 67.42 89.95 115.36 154.36 161.54 187.03 181.99 175.09 Net ROW producers 0.00 -76.90 -47.80 -58.83 -49.12 -84.31 -128.22 -113.00 -114.14 -134.93 ROW consumers 0.00 148.80 147.01 184.16 202.99 295.45 341.47 335.27 349.94 373.88 Net ROW 0.00 71.90 99.21 125.33 153.86 211.14 213.25 222.27 235.80 238.95 Input suppliers 0.00 75.33 113.68 169.04 233.93 277.06 307.68 352.17 363.58 359.78
Total 0.00 196.75 291.66 403.06 532.05 662.89 738.46 791.98 835.48 940.02
Year 2006/07 2007/08 2008/09 2009/10 2010/11 2011/2012 2012/2013 2013/2014 2014/2015 AGGR Land supply response Price effects
World sugar price (%)
98.33% 98.36% 98.40% 98.40% 98.40% 98.40% 98.40% 98.40% 98.40%
Welfare effects
Belgium 16.50 14.30 14.92 15.26 15.29 15.37 15.50 15.58 15.70 222.73 0.18% Denmark 10.87 8.75 8.32 7.97 7.97 7.96 7.95 7.93 7.92 177.93 -3.11% Germany 45.45 43.54 45.56 46.63 46.76 47.04 47.47 47.73 48.15 887.94 0.20% Greece 5.12 2.35 2.21 2.10 2.11 2.11 2.11 2.11 2.11 115.02 -2.77% Spain 27.00 22.18 21.18 20.35 20.33 20.30 20.26 20.23 20.19 691.01 -3.26% France 56.81 43.62 46.29 47.78 48.02 48.45 49.09 49.51 50.14 603.32 0.29% Ireland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 63.75 -1.99% Italy 10.83 9.69 9.17 8.75 8.74 8.73 8.72 8.70 8.69 389.45 -2.23% The Netherlands
22.57 18.07 17.14 16.37 16.36 16.35 16.34 16.32 16.30 389.05 -3.17%
Austria 9.40 7.84 8.15 8.31 8.33 8.36 8.42 8.45 8.51 127.25 0.63% Portugal 1.60 0.85 0.81 0.78 0.78 0.78 0.78 0.78 0.77 36.01 -3.64% Finland 4.78 2.68 2.56 2.46 2.45 2.44 2.43 2.42 2.40 78.91 -3.58% Sweden 5.35 4.46 4.23 4.05 4.04 4.04 4.03 4.03 4.02 74.06 -1.99% United Kingdom
14.59 12.10 12.79 13.17 13.22 13.33 13.49 13.60 13.75 209.75 0.30%
Czech Republic
10.68 7.09 6.71 6.40 6.39 6.39 6.38 6.37 6.37 80.30 -4.29%
Hungary 5.90 3.79 3.62 3.48 3.48 3.47 3.46 3.45 3.44 45.07 -2.19% Poland 40.36 32.45 30.84 29.51 29.47 29.41 29.35 29.29 29.23 331.44 -4.06% EU-15 producers
287.81 233.76 234.52 233.37 233.74 234.53 235.78 236.49 237.70 4523.00 -1.20%
EU-15 consumers
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 .
ROW cane -520.77 -478.29 -508.50 -537.68 -548.45 -561.27 -576.74 -589.95 -606.16 -7222.11 -0.37% ROW beet 199.78 190.78 197.62 199.85 198.69 199.95 201.69 202.84 204.53 3460.72 -2.75% Net ROW producers
-320.99 -287.51 -310.88 -337.83 -349.76 -361.32 -375.04 -387.11 -401.63 -3761.38 -0.70%
ROW consumers
601.25 569.38 597.32 629.07 644.44 661.78 682.95 701.22 723.31 8609.72 .
Net ROW 280.26 281.87 286.44 291.24 294.67 300.46 307.91 314.11 321.68 4848.33 . Input suppliers
321.29 306.54 302.39 298.31 294.13 293.57 292.94 292.28 291.61 6068.94 .
Total 889.36 822.17 823.34 822.93 822.55 828.55 836.63 842.88 850.99 15440.27 -0.73%
Welfare distribution
EU-15 producers (%) . 25% 27% 27% 27% 26% 30% 28% 28% 36% EU-15 consumers (%) . 0% 0% 0% 0% 0% 0% 0% 0% 0% Net ROW (%) . 36% 34% 31% 29% 32% 29% 28% 28% 25% Input suppliers (%) . 38% 39% 42% 44% 42% 42% 45% 44% 38%
Total (%) . 100% 100% 100% 100% 100% 100% 100% 100% 100%
Welfare distribution
EU-15 producers (%)
32% 29% 29% 28% 29% 28% 28% 28% 28% 29%
EU-15 consumers (%)
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Net ROW (%) 31% 34% 35% 35% 36% 36% 37% 37% 38% 31% Input suppliers (%)
36% 37% 37% 36% 36% 35% 35% 35% 34% 39%
Total (%) 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
BtBt Maize in Hungary Maize in HungaryEuropean Corn Borer (European Corn Borer (Ostrinia nubilalisOstrinia nubilalis Hübner) Hübner)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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BtBt Maize in Hungary Maize in HungaryWestern Corn Rootworm (Western Corn Rootworm (Diabrotica virgifera virgiferaDiabrotica virgifera virgifera
LeConte)LeConte)
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WCR in Czech RepublicWCR in Czech Republic
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Micro-economic level: Develop bio-economic pest damage abatement models Calibrate on real field data (surveys, expert opinions,
literature) Model heterogeneity Pre-coexistence Incorporate uncertaintyMacro-economic level: Model GM crop adoption through partial equilibrium
displacement model (EDM) Incorporate market structure and response Incorporate trade policies Incorporate uncertainty
DataData
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Ex ante: no adoption data available Data mining, combine different data sources:
– National and international statistics– National and regional farmer surveys– Field trials– Expert opinions– Literature– Assumptions– Economic theory
Importance of modelling data uncertainty and conducting sensitivity and scenario analyses
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Total benefits per hectare are fairly robust measure of value or “size” of the innovation
This value is distributed among input industry and farmers (who share it with consumers)
Market power of input industry is constrained by 5 factors:1. Farmer heterogeneity (e.g. Bt maize)2. Uncertainty and irreversibility3. Competition from chemical industry4. Competition within biotechnology industry5. Coexistence regulation (EU)
Immiserising growth unlikely due to:1. Smaller scale & heterogeneous innovation pattern2. Common Agricultural Policy (CAP) protecting farmers against
eroding world prices
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Non-Pecuniary Benefits of HT Crops:
Management Flexibility and Convenience
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Coexistence, the last hurdle to GM crops?European Commission (2003):
“Coexistence refers to the ability of farmers to make a practical choice between conventional, organic and GM [genetically modified] crop production, in compliance with the legal obligations for labelling and/or purity standards. The adventitious presence of GMOs [genetically modified organisms] above the tolerance threshold set out in Community legislation triggers the need for a crop that was intended to be a non-GMO crop, to be labelled as containing GMOs. This could cause a loss of income, due to a lower market price of the crop or difficulties in selling it. Moreover, additional costs might incur to farmers if they have to adopt monitoring systems and measures to minimise the admixture of GM and non-GM crops. Coexistence is, therefore, concerned with the potential economic impact of the admixture of GM and non-GM crops, the identification of workable management measures to minimise admixture and the cost of these measures.”
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What is coexistence? A cost or an incentive? Ex-ante measure The right to choose (farmers & consumers) Gene flow, pollen drift, contamination, commingling Coexistence is only relevant
– if there is a significant long-term domestic or international (export) consumer demand for non-GM crops (e.g. not cotton)
– if this demand translates into market signals (e.g. price premiums for non-GM crops)
– if there is a significant farmer demand for cost-reducing transgenic crops (e.g. not ECB-resistant Bt maize in Belgium)
– Costs proportional to economic incentives
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adoption
co-e
xist
ence
co
sts rupture
point
Coexistence costs borne by 2 incentives:1. Farmer profits of GM crops (“GM rent”)2. Price premium of identity preserved (IP)
crops (“IP rent”)
Phase I Phase II Phase III
clustering, reallocation of land
IP rentseekingGM rents
Coexistence Coexistence measuresmeasures
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Definitions: Isolation distances = rigid minimum
distance rules between GM and non-GM crop fields of the same species and imposed on GM crop producers
Buffer zones = flexible segregation measures by using field surroundings (which serve as cross-pollination zones) with non-GM crops of the same species, planted on (negotiable between farmers):
• Donor fields (System 1)• Recipient fields (System 2)
and planted and cultivated by• Owner (System a)• Neighbor (System b)
ArcView ModellingArcView Modelling Hypothetical adoption of GMHT OSR in Beauce
Blésoise region in Central France Sample square of 100 km² = 6% area GIS dataset of sample square: 1,508 field polygons
covering 4,233 ha Constrained randomization process:
Generate 10 random allocations of GM and non-GM OSR fields in the landscapeSubject to
• OSR planting density = 26% (= 2x regional one)• GM adoption rate = 50%
Represents most stringent scenario of coexistence in a single season
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AssumptionsAssumptions
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The domino-effect caused by rigid coexistence regulations
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ArcView ModellingArcView Modelling
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Economic IncentivesEconomic Incentives
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The domino-effect caused by rigid coexistence regulations
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The domino-effect caused by rigid coexistence regulations
Domino-effectDomino-effectTable 2 Domino-effect on costs (€/ha) of rigid coexistence regulations in oilseed rape cultivation in Central France Phase OSR
area (ha)
Intended GM OSR
area (ha)
Adoption of GM OSR (%
of the area)
GM-free OSR area (ha)
Coexistence costs (€/ha)
IP price premium
Isolation distance of 50 m 8% 4% 0% Phase 1 1,097 548 (6) 50% (0%) 310 (44) 0 16 (2) 32 (4) Phase 2 1,097 238 (44) 22% (4%) 47 (12) 0 6 (1) 0 Phase 3 1,097 191 (42) 17% (4%) 5.0 (5.6) 0 0.6 (0.6) 0 Phase 4 1,097 186 (38) 17% (3%) 0.4 (1.0) 0 0.1 (0.1) 0 Cumulative 1,097 186 (38) 17% (3%) 362 (38) 0 22 (3) 0 Domino-effect
-66% (7%)
-66% (3%) +17% (6%) +0%
+40% (10%) +0%
Isolation distance of 100 m Phase 1 1,097 548 (6) 50% (0%) 361 (42) 0 18 (2) 37 (4) Phase 2 1,097 187 (42) 17% (4%) 50 (20) 0 7 (2) 0 Phase 3 1,097 136 (34) 12% (3%) 8 (8) 0 1.6 (1.3) 0 Phase 4 1,097 128 (29) 12% (3%) 1 (2) 0 0.2 (0.4) 0 Cumulative 1,097 128 (29) 12% (3%) 420 (25) 0 28 (3) 0 Domino-effect
-77% (5%)
-77% (3%) +17% (9%)
+0% +50% (19%) +0%
Notes: OSR = oilseed rape. Areas and costs are averages, based on 10 random allocations of GM and non-GM OSR fields. Standard deviations are shown between brackets. The domino-effect expresses the relative difference in per cent between the cumulative value and the value in Phase 1. Source: Authors’ calculations based on GIS dataset of the sample square (Pessel et al., 2001).
Conclusion (coexistence)Conclusion (coexistence) Rigid regulations may impose severe burden on GM
crop production in Europe Even under low demand for IP crops, and hence,
low demand for coexistence Costly, not proportional to incentives and hence not
consistent with EC’s objectives Flexible measures are preferable as they are less
costly and proportional to incentives Should be negotiable between adopters and non-
adopters as both farmer segments have economic incentives to ensure coexistence in long-run
Conclusion (coexistence)Conclusion (coexistence) Trade-off between GM and IP rent depends on market
signals from consumers IP incentive only sustainable if consumers
• Have strong & sustainable preferences for non-GM• Are willing to pay significant IP price premiums
Otherwise no coexistence issue strictu sensu and cost = pure regulatory burden
EU policy makers: under absence of clear market signals for IP, we recommend to shift regulatory rigidity from ex ante ex post
To avoid jeopardizing economic incentives for coexistence of GM/non-GM in Europe
ConclusionConclusion
System approach needed Case by case Producers capture an important part of
the benefits of transgenic crops: most often between 2/3 and 3/4
Government’s trade policy can influence the impact of biotechnology (e.g. sugar sector)
Coexistence only relevant when 2 incentives are both present at the same time: GM rent & IP rent
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The EndThe End
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