Katholieke Universiteit Leuven Faculty of Bioscience Engineering Working Paper 2005 / 92 POTENTIAL IMPACT OF BIOTECHNOLOGY IN EASTERN EUROPE: TRANSGENIC MAIZE, SUGAR BEET AND OILSEED RAPE IN HUNGARY Matty DEMONT, Eric TOLLENS and József FOGARASI January 2005 EUWAB-Project (European Union Welfare effects of Agricultural Biotechnology). This paper (pdf) can be downloaded following the link: http://www.agr.kuleuven.ac.be/aee/clo/wp/demont2005a.pdf The authors are grateful to Monsanto for equally sharing the funding for this study with the Katholieke Universiteit Leuven. We would also like to thank Janos Soos for his valuable work in translating scientific Hungarian articles. Centre for Agricultural and Food Economics Katholieke Universiteit Leuven Willem de Croylaan 42, B-3001 Leuven – Belgium Tel. +32-16-321614, Fax +32-16-321996
Transcript
Katholieke Universiteit Leuven Faculty of Bioscience Engineering
Working Paper 2005 / 92
POTENTIAL IMPACT OF BIOTECHNOLOGY IN EASTERN EUROPE: TRANSGENIC MAIZE, SUGAR BEET AND OILSEED RAPE IN HUNGARY
Matty DEMONT, Eric TOLLENS and József FOGARASI
January 2005
EUWAB-Project (European Union Welfare effects of Agricultural Biotechnology). This paper (pdf) can be downloaded following the link:
The authors are grateful to Monsanto for equally sharing the funding for this study with the Katholieke Universiteit Leuven. We would also like to thank Janos Soos for
his valuable work in translating scientific Hungarian articles.
Centre for Agricultural and Food Economics Katholieke Universiteit Leuven
Willem de Croylaan 42, B-3001 Leuven – Belgium Tel. +32-16-321614, Fax +32-16-321996
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Demont, M., Tollens, E. and J. Fogarasi. "Potential Impact of Biotechnology in Eastern Europe: Transgenic Maize, Sugar Beet and Oilseed Rape in Hungary." Working Paper, n° 92, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, 2005.
Matty Demont, Centre for Agricultural and Food Economics, K.U.Leuven,
The EUWAB-project (European Union Welfare Effects of Agricultural Biotechnology) http://www.agr.kuleuven.ac.be/aee/clo/euwab.htm
Since 1995, genetically modified organisms (GMO's) have been introduced commercially into US agriculture. These innovations are developed and commercialised by a handful of vertically coordinated “life science” firms who have fundamentally altered the structure of the seed industry. Enforcement of intellectual property rights for biological innovations has been the major incentive for a concentration tendency in the upstream sector. Due to their monopolistic behaviour, these firms are able to extract a part of the total social welfare through. In the US, the first ex post welfare studies
reveal that farmers and input suppliers are receiving the largest part of the benefits. However, few ex ante studies exist for the European Union. Hence, the K.U.Leuven presents the EUWAB-project (European Union Welfare effects of Agricultural Biotechnology), assessing the economic impact of agricultural biotechnology innovations in the EU and their welfare distribution among Member States, producers, processors, consumers, input suppliers, governments and the environment. This project has been financed by the Flanders Interuniversity Institute for Biotechnology (VIB), the European Commission's Sixth Framework Programme and Monsanto.
Copyright 2005 by Matty Demont, Eric Tollens and József Fogarasi. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.
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Abstract
In January 2005, the Hungarian farm ministry announced that it would not allow
Monsanto’s MON 810 maize to be planted or imported until tests had established
whether transgenic crops contaminated other cultures. The present study is the first
attempt to estimate the size and distribution of the ex ante welfare effects of
transgenic crops in Hungary developing a partial equilibrium model following a
counterfactual approach for the agricultural season 2003. Our model and data are
conservative, such tha t the results have to be interpreted as lower estimates of the true
impact. For uncertain parameters, we include subjective prior distributions. In 2003,
maize, sugar beet and oilseed rape were planted on an area of respectively 1,150,000
ha, 53,000 ha and 71,000 ha in Hungary. Total benefits of Bt maize resistant to the
European corn borer amount to an estimated 3 million euros, of which 74% accrues to
the farmers and 26% to the seed industry. The adoption of Bt maize resistant to the
Western corn rootworm translates into a total welfare increase of 16 million euros, of
which farmers gain 65% and the industry 35%. The introduction of herbicide tolerant
maize potentially generates 14 million euros, of which 73% is shared by the farmers
and 27% is extracted by the industry. Herbicide tolerant sugar beet involves a welfare
gain of 3 million euros, of which 50% flows to farmers and 50% to the seed industry.
The adoption of herbicide tolerant oilseed rape could potentially engender a total
benefit of 0.8 million euros, of which 61% is absorbed by Hungarian farmers and
39% is captured by the seed industry. We then conduct a stochastic sensitivity
analysis through Monte Carlo simulation techniques to analyze the robustness and
sensitivity of the model to the underlying parameter estimates and assumptions.
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Introduction
During the eight-year period from 1996 to 2003, global area of transgenic crops
increased more than 47 fold, i.e. from 1.7 million to 81.0 million hectares (James,
2004). The first United States (US) ex post welfare studies reveal that the benefits of
these innovations are essentially shared among farmers and the seed industry (Falck-
Zepeda et al., 2000, Moschini et al., 2000, Price et al., 2003). On the level of the
European Union (EU), the first ex post welfare study on transgenic maize adoption in
Spain has now been published, reporting a total welfare gain of 15.5 million euros
during the six-year period 1998-2003, of which Spanish farmers captured two thirds,
the rest accruing to the seed industry (Demont and Tollens, 2004b). Some ex ante
impact results on transgenic sugar beets are documented as well (Demont and Tollens,
2004a, Demont et al., 2004a, Demont et al., 2004b), reporting a global welfare
increase of 1.2 billion euros during the five-year period 1996-2000, shared among EU
producers (33%), the seed industry (16%) and the rest of the world (52%).
As of May 1st, 2004, 10 new Central and Eastern European Member States joined the
EU, i.e. Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland,
Slovakia and Slovenia. However, up to now no ex ante study has been published on
the potential impact of transgenic crops in these new Member States (NMS). In the
present paper, we analyze the case of Hungary and assess the potential economic
impact of transgenic crops, more specifically insect resistant Bt (Bacillus
etc., due to data limitations we base our analysis on the most important factor in the
adoption of HT crops, i.e. actual herbicide use and costs. Herbicide costs are typically
distributed following a lognormal curve, as can be seen in the study of Desquilbet et
al. (2001). This curve is consistent with the nonnegativity of herbicide costs and their
right-skewed nature. Following this assumption, the density function f of herbicide
costs wc (euros/ha) can be expressed as (Figure 4):
≤
>=
−−
)0(0
)0(2
1)(
2
2
2
))(ln(
c
c
w
cc
w
wewwf
c
σ
µ
πσ (4)
with mean 2
2σµ+
e , variance )1(222 −+ σσµ ee and median µe .
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In this paper, we will assume ‘competitive pricing’, i.e. the innovator will price the
new technology such that the recommended replacement program based on HT seed
and the associated herbicide, i.e. glyphosate, will reflect the average herbicide
expenditure cw (euros/ha) in conventional weeding (Figure 4):
gdwwfwwdwwfwwgw cccgccccg γγ ∫∫∞∞
−=⇔==+00
)()( (5)
with wg (euros/ha) the price premium of the transgenic seed, γ (euros/l) the average
glyphosate price and g (l/ha) the recommended glyphosate rate of the replacement
program.
Theoretically, given this price premium wg, only farmers facing higher herbicide
expenditures at the right tail of the lognormal distribution (Figure 4), i.e. wc > wg +
γ g , are willing to adopt the transgenic seed. In doing so, they make an additional
profit p(wc, wg, g ) (euros/ha) equal to:
p(wc, wg, g ) = wc – wg – γ g (6)
The adoption rate ρg(wg, g ) can then be predicted as:
)(1)(),( gwg
ccgg wgFdwwfgwg
+−== ∫∞
+
γργ
(7)
with F(wc) the cumulative distribution function of f(wc). If we define fa(wc) as the
adopters’ density function of herbicide costs, it follows that:
+≤
+>=
)(0
)(),(
)(
)(
gc
gcgg
c
ca
wgw
wgwgw
wf
wfγ
γρ (8)
The average benefits ),( gwgπ (euros/ha) of all adopting farmers then amounts to:
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∫∞
+
=gwg
ccagcg dwwfgwwgwγ
ππ )(),,(),( (9)
It is important to note that the assumption of competitive pricing endogenizes the
price premium in such a way into the model that π is only a function of the mean and
standard deviation of the herbicide costs’ distribution, i.e. independent of g and γ.
The yield boost ß (%) generated by the HT system and essentially caused by the lower
toxicity of the weeding operation on the crops, is incorporated using equation 3.
Following equation 2, the per-unit cost reduction ∆C (%) due to the conversion from
the conventional (k = c) to the HT system (k = g) is estimated as:
cc
ggccc
ycw
ygwcwycwC
/)(
/)],([/)(
+
−+−+=∆
π (10)
Aggregation to the National Level
Next, we model the innovation as a technology spill- in into Hungary, mainly from the
US which started to adopt transgenic crops first. The low presence of Hungary in
global maize, sugar beet and oilseed rape production in 2003 (Table 1) suggests
modelling Hungary as a small open economy5, i.e. not able to influence world prices
significantly through the adoption of the new technology. This argument suggests
modelling demand in Hungary as infinitely elastic and modelling the change in
producer surplus ∆PS (euros) as (Alston et al., 1995, p. 227):
∆PS = p Q K (1 + 0.5 K ε) (11)
with ε the supply elasticity and Q (t) total production in 2003. The proportionate
vertical supply-shift K (%) is calculated as:
K = ρg ∆C (12)
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with ρg (%) the adoption rate of the transgenic crop. The gross profit Π (euros)
captured by the seed industry6 is:
Π = wg L ρg (13)
with L (ha) the total amount of land allocated to the crop. It is important to stress that
this represents a gross profit, i.e. not taking into account any marketing and
distribution expenditures associated with the commercialization of transgenic seed.
Finally, total welfare increase Wtot (euros) in Hungary in 2003 is:
Wtot = W + Π (14)
Data
Due to its nature, an ex ante assessment of the economic impact of transgenic crops
requires data which is not available. We use the methodology of data mining in which
all available information surrounding the ‘missing link’ is collected, including
subjective estimates and information from economic theory. Hungary has an
outstanding agricultural research basis, but unfortunately most of the research results
are published in Hungarian language, more specifically in three important scientific
journals: Növényvédelem (Crop Protection), Növénytermelés (Crop Production) and
Gyakorlati Agrofórum. In order to incorporate valuable information from Hungarian
agricultural research, we collected 87 scientific Hungarian articles, screened them for
information and translated them in cooperation with a Hungarian agricultural
scientist.
In order to deal with the scarcity of data and the uncertainty surrounding some of our
central assumptions, following recommendations in recent literature (Davis and
Espinoza, 1998, Zhao et al., 2000), we use stochastic simulation techniques through
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the software @Risk of Palisade Corporation (2002). For uncertain parameters we
introduce prior subjective stochastic distributions, based on literature review and
subjective estimates, and through Monte Carlo simulation techniques we generate
posterior distributions for the outcomes in our model. The parameter estimates and
subjective distributions are collected in Table 6.
Bt Maize Resistant to European Corn Borer
The annual loss s due to ECB in Hungary varies considerably from year to year.
Therefore, analogous to Demont and Tollens (2004b), we build a bio-economic
stochastic distribution for this parameter. While gamma as well as lognormal
distributions are used to model insect damage, Hurley et al. (2004) observed a better
statistical fitting for the lognormal distribution. In Table 5 we combine different data
sources from literature to estimate the parameters of the lognormal distribution. Since
only 10 annual averages are available, we use the overall median instead of the
overall average for the mean of the lognormal distribution. The median is more robust
for outliers than the average in the case of such a small skewed sample. Using the
standard deviation for the lognormal distribution, we assume s ~ Lognormal(5.0%;
4.5%). The variability of ECB infestations, measured by a coefficient of variation
(CV) of 74%, is equal to the one observed in Spain (Demont and Tollens, 2004b).
Finally, since no negative losses or losses greater than 100% can be incurred, we
truncate the lognormal distribution to the interval [0,1].
In Hungarian maize cultivation, efficacy of chemical control of ECB is low, i.e. about
46-50% (Herczig et al., 1987, Szõke et al., 2002). On the other hand, chemical
treatment is expensive, i.e. 30 euros/ha, and does not pay the yield benefit of 14
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euros/ha on average. Since we assume a shift from no chemical treatment to Bt maize,
the parameter ‘all other costs’ c equals total production costs. Table 8 summarizes
real farm level survey data collected by the Hungarian Research and Information
Institute for Agricultural Economics (AKII, 2004).7 In column 11 of Table 8, we find
an estimate of the total production costs.
The monopolistic innovator’s pricing behaviour is limited by the low yield benefit of
chemical treatments and, secondly, by the large heterogeneity of ECB damage within
the region of Hungary in a given year. The seed supplier has to set its price low
enough to provide adoption incentives to a sufficiently large segment of farmers.
Therefore, we assume a price premium following a triangular distribution with a
minimum, average and maximum of respectively 0 euros/ha, 6 euros/ha8 and 13
euros/ha or 0%, 10% (Czepó, 2004) and 20% of the seed cost and a maximum
adoption potential of 10%.
Bt Maize Resistant to Western Corn Rootworm
Since WCR is still in its build-up phase, we conservatively assume that the average
root rating of the adoption area amounts to 4, one root rating above the economic
threshold of 3 (Hataláné Zsellér et al., 2004). Since no field trials with MON 863
have been carried out in Hungary yet, we base our yield assumptions of MON 863 on
US findings. Mitchell (2002) reports yield benefits of MON 863 relative to chemical
control for different root ratings (Table 7). The right-skewed nature of the data
suggests modelling ß through a lognormal distribution. We shape the distribution to
the lower and upper percentiles and adjust the median until the estimated mean equals
4.2%. This results in a standard deviation of 7.3%.
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Tóth (2004) reports chemical treatment costs for WCR control in maize of 28-32
euros/ha for seed treatment (pelleting), 44-71 euros/ha for soil treatment and 28-56
euros/ha for plant treatment. In column 8 of Table 8, we report the insecticide costs of
only those farmers who use insecticides, i.e. 18% of the total sample of 827 farms.
Seed treatment costs are included in the seed costs. This information allows us to
conservatively model insecticide costs through a triangular distribution with a
minimum of 28 euros/ha, an average of 50 euros/ha and a maximum of 71 euros/ha,
i.e. on average 79% of the seed cost. We assume that the price premium wg will
follow this pattern and define a triangular distribution for it, characterized by a
minimum of 0%, a most likely value of 40% of the seed cost, i.e. 25 euros/ha9 or half
of the average chemical treatment cost, and a maximum of 79%, i.e. 50 euros/ha or
the average chemical treatment cost. By subtracting this cost from the total production
costs (column 11 in Table 8), we obtain an estimate of ‘all other costs’ c. We assume
that the adoption of WCR-resistant maize is between the reported share of the area
under economic damage, i.e. Ld/L, and the share of the total area under continuous
maize, i.e. Lc/L. To allow all possible scenarios between the two extremes to occur
with an equal probability, we model ρg through a uniform distribution (Table 6).
Herbicide Tolerant Maize, Sugar Beet and Oilseed Rape
We calibrate the lognormal distributions of the herbicide cost of the three crops on the
survey data from the AKII (2004) using the average and the standard deviation of the
observed herbicide costs (Table 8, Table 9 and Table 10). The average glyphosate
price γ was around 6 euros/l in 2003 (Czepó, 2004). The recommended glyphosate
rates g are 5 l/ha for HT maize, 6 l/ha for HT sugar beet and 2.5 l/ha for HT oilseed
rape.
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In literature, there is quite some discussion about the yield effects of HT crops. Some
advocate that HT crops should be compared with their isogenic 10 counterparts (Marra
et al., 2002). In the case of sugar beet breeding on the other hand, the incorporation of
traits into accepted cultivars can be a time-intensive process because of the biennial
nature of sugar beet. The time involved is amplified when dealing with transgenic
traits. In the time it takes breeders to produce a transgenic cultivar that is
commercially acceptable, newer, higher-yielding conventional cultivars will have
entered the market. If this is the case, economic analyses should include side-by-side
comparisons of locally adapted, top-yielding cultivars regardless of whether a HT
version of the cultivar is available (Kniss et al., 2004). Moreover, if the regulatory
system in the EU is such that transgenic crops require more registration time than
their non-transgenic counterparts, we systematically might have to assume a yield
drag instead of a yield boost in our ex ante analyses. Some have found a yield drag for
HT soybeans in the US (Benbrook, 1999). Recent research on North Carolina’s
farmers did not reveal any statistically significant yield differences at the 95% level
between HT maize, cotton and soybeans and their conventional counterparts (Marra et
al., 2004, p. 43). Likewise, European field trials showed no increase in any HT crop
(Schütte, 2003). Based on these findings, we conservatively assume a normal
distribution for the yield boost of HT crops with a mean of 0% and a standard
deviation of 2.5%. This allows this parameter to vary in a 95% confidence interval
between a yield drag of 5% and a yield boost of 5% (Table 6).
Aggregation to the National Level
We use the producer price estimates from the survey carried out by the AKII (2004).
The average and standard deviation from the sample (Table 8, Table 9 and Table 10)
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allows us to introduce price uncertainty into the analysis through normal distributions
(Table 6). National produced quantities Q, allocation of land L and yields yk of the
different crops are derived from the FAOSTAT dataset (FAO, 2004).
For our aggregation to the national level, we need estimates of the supply elasticities
of the different crops. Hallam (1998) questions the applicability of standard dynamic
econometric models in the transition context where structural breaks are present and
time series data are limited. Therefore, elasticities are taken from an updated version
of the European Simulation Model (ESIM), where they have been simulated through
behavioural equations instead of econometrics. Crop production is modelled as a two-
tiered function, which means separate yield and area supply function. The area supply
function included also own and cross price elasticities while the yield function is
driven by the own-prices only. The functional form is an isoelastic Cob-Douglas type
function where the intercepts are calculated to reproduce the base data (Banse et al.,
2004). To derive supply elasticities for the analyzed crops, we simply sum the area
supply and yield elasticities. To reflect the high uncertainty11 surrounding the
calculation of supply elasticities, mentioned above, we introduce triangular
distributions for these parameters, based on the strict positivity of elasticities from
theory and a maximum of twice the base value (Table 6).
Results
Descriptive Statistics
As mentioned, our model results have to be interpreted as conservative estimates. To
obtain an idea of the variability of the impact estimates and the sensitivity of the
model to our parameter estimates and assumptions, we generate 100,000 iterations in
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@Risk to obtain posterior distributions for the outcomes of the model. In Table 11,
Table 12, Table 14, Table 15 and Table 16 for each biotechnology innovation, the
mean and 95% confidence interval limits of the farmer’s surplus, industry’s rent and
total welfare gain are tabulated. In the last column, the average distribution of the
benefits is represented.
In the case of Bt maize resistant to ECB, a total welfare gain of 3 million euros is
estimated for the agricultural season 2003, of which 2 million euros accrues to
farmers and 0.7 million euros to the seed industry (Table 11). Some authors have
observed a negative correlation between farm heterogeneity regarding the utility of a
new technology and monopolistic pricing behaviour (Oehmke and Wolf, 2002). In
Hungary, due to the low average importance of ECB and the high heterogeneity of
ECB attacks, the innovator probably will not be able to extract a large share of the
benefits through a high price premium. Therefore, we assumed a conservative price
premium of 10% of the seed cost. As a result, three quarters is absorbed by Hungarian
agriculture and only one quarter by the seed industry. In 12% of the cases, farmer
benefits were negative. These losses can be considered as ex ante risk premiums paid
by the farmers in order to protect their production from high ECB attacks.
WCR, although still in its build-up stage, is a more homogenous pest than ECB. This
is reflected in the price premium of WCR-resistant maize which we assumed four
times higher than in the case of ECB-resistant maize. Despite this higher price
premium, two thirds is absorbed by the agricultural sector (Table 12). Total benefits
amount to 16 million euros, of which farmers gain 11 million euros and the industry 6
million euros. Given our parameter estimates and model assumptions, only in 0.01%
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of the cases negative farmer benefits are recorded. But even in the case of net
financial losses, farmers could consider the investment in WCR-resistant seed as a
risk premium in order to protect themselves from WCR attacks. Moreover, we did not
account for the so-called nonpecuniary benefits. Alston et al. (2002) were the first to
estimate these benefits through ex ante willingness-to-pay (WTP) surveys in the US.
They estimated an average nonpecuniary benefit for adopters of the technology of 16
euros/ha, consisting in handling and labour time savings, human safety,
environmental safety, consistent control (reduced yield risk), equipment cost savings
and better standability. The interviews we had with Hungarian farmers reveal a WTP
for these nonpecuniary attributes of about 59-99 euros/ha, i.e. much higher than the
estimate in the US and mostly higher than the price premiums we calculated for the
different crops (Table 13).
Regarding HT crops, we used heterogeneity in herbicide use to estimate the
competitive price premium, farmer benefits and adoption rate of the transgenic
variety. The results are presented in (Table 13). The competitive price premium of 8
euros/ha12 for HT maize is modest due to low average herbicide costs (Table 8), but
comparable to the low price premium of 6 euros/ha we assumed for Bt maize resistant
to ECB. In the case of sugar beet, the crop is such a bad competitor against weeds that
herbicide expenditures are very high to achieve economic production (Table 8). The
HT system represents a drastic innovation in this case, allowing the innovator to
charge a high price premium (Table 13), i.e. 81 euros/ha13 or 63% of the seed price,
and extracting half of the total benefits (Table 15). The estimated price premium of
HT oilseed rape is about 12 euros/ha14 or 34% of the seed price. The predicted
adoption rates of 40%, 38% and 35% for respectively HT maize, sugar beet and
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oilseed rape are modest and reflect the expected adoption rates of early adopters. For
oilseed rape it is in line with Monsanto’s seed market share of 40% in oilseed rape
seed breeding (Kiss and Bói, 2001, Nagy, 2004).
In the case of HT maize, a total welfare gain of 14 million euros is estimated, of
which 10 million euros flows to farmers and 4 million euros to the seed industry.
These impact estimates and benefit sharing, i.e. three quarters versus one quarter, are
very comparable with the case of ECB-resistant maize. However, in 4% of the
iterations, farmers suffer losses, mainly due to the possibility of a yield drag. Even in
these cases, it is possible that farmers find it profitable to adopt the HT technology,
because of non-pecuniary benefits not included in our analysis. To date, only one
complete study exists attempting to estimate the non-pecuniary benefits of HT crops
by distinguishing between convenience factors (management time savings, equipment
savings, etc.), conservation tillage benefits (cost savings, time savings, environmental
benefits, etc.), less adverse effects from herbicide drift on livestock production,
human safety benefits and environmental benefits (Marra et al., 2004).
The total benefits of adopting HT sugar beets are about 3 million euros, equally
distributed among farmers and the industry. Negative farmer benefits only occur in
0.2% of the cases. The total welfare increase of HT oilseed rape is much smaller, i.e.
0.8 million euros, due to the low importance of this crop in Hungary (Table 3). Of
these benefits, 0.5 million euros accrues to farmers and 0.3 million euros to the
industry. The benefit sharing, i.e. two thirds versus one third, is in line with the case
of WCR-resistant maize. Only in 0.5% of the cases, farmers lose due the possibility of
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a yield drag. Finally, Table 17 shows that the average farmers’ surplus per adopted
hectare of transgenic crops ranges from 19-81 euros/ha.
Sensitivity Analysis
In Table 18 and Table 19 we report the results of a stochastic sensitivity analysis by
running a linear regression analysis on the 100,000 simulated iterations. The
coefficients listed are statistically significant 15 normalized regression coefficients
associated with each input. A regression value of 0 indicates that there is no
significant relationship between the input and the output, while a regression value of 1
or -1 indicates a 1 or -1 standard deviation change in the output for a 1 standard
deviation change in the input. The coefficient of determination R2 listed at the bottom
of the column is simply a measurement of the percentage of variation that is explained
by the linear relationship (Palisade Corporation, 2002).
In all cases, R2 is satisfactory high, signifying that the linear relationship sufficiently
explains the variation in the iterations. In the case of Bt maize resistant against ECB,
most of the variation is explained by the wide dis tribution we assumed for ECB
damage (Table 18). Logically, the technology fee is negatively correlated, but only
plays a role in the distribution of the benefits, not in the total welfare gain. Due to the
linear model we assumed in equation 13, the correlation between price premium and
industry’s profit is 100%. Producer price plays a non-negligible role, as well as the
efficacy of the new technology. Given our assumed subjective distributions and
model parameters, supply elasticity does not seem to drive the model results.
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In the case of WCR-resistant maize, essentially the same sensitivities are observed.
The assumed wide uniform distribution of potential adoption is now mainly driving
the results, followed by the yield benefit. Again, the assumed cost of the new
technology negatively influences farmers’ benefits but has a much smaller effect on
total welfare gain. The cost of the conventional technology also plays an important
role. Industry’s profit is now driven by both the adoption rate and the price premium.
Again, supply elasticity hardly seems to influence the results.
Since we completely exploited heterogeneity in conventional weeding to estimate
price premiums, benefits and adoption of HT crops, re- introducing this variability into
the analysis has but a limited sense. Instead, we only introduce variability for the
yield boost, supply elasticity and producer prices (Table 6), reflected in Table 14,
Table 15 and Table 16. The sensitivity analysis essentially produces the same
normalized regression coefficients for all three crops, i.e. between 0.999 and 1.000 for
the yield boost, between 0.007 and 0.017 for the supply elasticity and 0.000 for the
producer prices. The yield boost assumption clearly drives the results. As mentioned
before, literature does not provide strong arguments for the presence of a yield boost
due to the planting of HT crops. The 2.5% and 97.5% quantiles in Table 14, Table 15
and Table 16 illustrate the effect of a yield boost of respectively -5% and 5%. In the
case of maize, a yield drag of less than 5% would cancel out all benefits from the new
technology. For sugar beet and oilseed rape, it would reduce the benefits to
respectively one third and one quarter. Assuming a yield boost of 5% on the other
hand would roughly double the benefits of HT crops.
33
Environmental Effects
It is important to note that our ex ante welfare calculation only contains private
reversible effects. In reality, technologies also engender non-private effects, the so-
called externalities. The number of biosafety related publications concerning
transgenic organisms has increased within the decade 1990–2004 to 4,896 citations
according to one of the most comprehensive databases, published online by the
ICGEB (2002). The Hungarian Biosafety Homepage published online by the
Agricultural Biotechnology Center (2004) contains a complete database of
Genetically Modified Organisms (GMOs) emitted in Hungary as well as some
biosafety related documents and links.
A growing body of scientific literature about the non-private effects of Bt maize is
available, reviewed by James (2003a). The major concerns include (1) effects on non-
target organisms, (2) gene flow, (3) the impact of Cry1Ab proteins in soil and surface
water, (4) the evolution of pest resistance, (5) the development of antibiotic resistance
and (5) food and feed safety aspects of Bt maize. However, also positive externalities
are reported, such as (1) lower contamination of aquifers with insecticides, (2) lower
farmers’ exposure to insecticides and (3) lower levels of the mycotoxin fumonisin in
Bt maize.
HT systems also potentially entail irreversible environmental externalities. First of all,
glyphosate, the herbicide that substitutes for the conventional herbicide mixes, has
been widely studied for its environmental and human health impacts, extensively
documented in Sullivan and Sullivan’s (1997) latest compendium of 763 references
and abstracts, of which the earlier edition had been criticised by Zammuto (1994).
34
The second concern relates to gene flow from HT crops to closely related weeds, the
so-called ‘weediness’. Ammann (2000) provides a critical review of the literature on
these issues for HT sugar beet and HT oilseed rape.
The impact of HT sugar beet systems on field biodiversity is questioned (Elmegaard
and Pedersen, 2001, Gura, 2001). However, the major concerns comprise the transfer
of genes from transgenic sugar beet by pollen (Saeglitz et al., 2000) to bacteria
(Gebhard and Smalla, 1998, Gebhard and Smalla, 1999) or wild relatives (Santoni and
Bervillé, 1992, Boudry et al., 1993, Fredshavn and Poulsen, 1996, Madsen et al.,
1997, Dietz-Pfeilstetter and Kirchner, 1998, Danish EPA, 1999, Pohl-Orf et al., 1999,
Gestat de Garambe, 2000, Darmency et al., 2000, Crawley et al., 2001, Desplanque et
al., 2002, Bartsch and Schuphan, 2002) engendering a hybrid offspring invading farm
fields. Most of those studies suggest that field trials cannot predict what will happen
once HT crops are planted away from the controlled conditions of an experiment. The
possibility of non-private irreversible costs exists.
While the before-mentioned environmental effects are surrounded by uncertainty,
there is less uncertainty on the irreversible effect the introduction of transgenic crops
will have on long-term biodiversity resources for society (Demont et al., 2004a). The
most significant source of biodiversity in crop production is provided by the number
of cultivated species and by the number of cultivars within the species. Genetic
reserves of cultivated plants, maintained in genebanks for succeeding generations,
form one element of biodiversity. In Hungary about 80,000 specimens of agricultural
and horticultural crops can be found in genebanks. The other element of biodiversity
is provided by the choice of agricultural species and cultivars. In Hungary the number
35
of species used in plant production is 350, of which 80-85 are used in arable crop and
vegetable plant production. These numbers have not changed significantly in the last
50 years. The extent of biodiversity within several cultivated species is significantly
enlarged by the great number of cultivars. On the one hand, Hungary’s biodiversity
will increase due to its accession to the EU in 2004, engendering an increase in the
number of cultivars. On the other hand, the introduction of new transgenic cultivars
will first contribute to biodiversity, but if the current process of consolidation in the
seed industry is continued, the numbers of cultivars might decrease at the expense of
biodiversity in the long-run. This could be significant in the first decades of the 21st
century in the case of maize, oilseed rape, soybean and sugar beet (Heszky et al.,
1999).
However, herbicides also cause systematic losses of seed banks and difficulties in
reversing it and consequently play a prevailing role in damaging biodiversity. If HT
crops result in less herbicide applications, the introduction provides additional
benefits. In the US Heartland, only in one of two years, a significant reduction in the
number of herbicide treatments has been observed due to the adoption of HT maize,
but no significant difference has been found in the total volume of active ingredients
(Fernandez-Cornejo et al., 2003). In Europe, a reduction of application frequencies is
only expected for HT sugar beet (Schütte, 2003). However, number of treatments and
volume measures are poor indicators of the potential environmental and human health
effects. Therefore, two authors have attempted to bridge this gap. First, Bennett et al.
(2004) assess the expected decline of emissions from herbicide manufacture, transport
and field operations due to the decline in herbicide treatments associated with the
adoption of HT sugar beets. Fewer emissions lead to lower negative environmental
36
impacts, such as global warming, ozone depletion, ecotoxicity of water and
acidification and nitrification of soil and water and lower negative human health
impacts, such as summer smog, toxic particulate matter and carcinogenicity.
Secondly, the herbicide glyphosate that substitutes for the conventional one has a
lower toxicity and is metabolised fast and without residues in the soil and therefore
has a better environmental and toxicological profile than most of the herbicides it
replaces (Märländer and Bückmann, 1999, May, 2000). To prove this, Nelson and
Bullock (2003) present a type 2 economic environmental indicator of the effect of
herbicides based on a standardized mammalian oral toxicity measure and use it to
simulate the effect of HT soybeans relative to conventional soybeans. Their
simulations suggest that HT systems are more environmentally friendly than
conventional ones in at least one dimension.
In conclusion, the release of transgenic crops not only produces irreversible costs but
also irreversible benefits, a term introduced by Pindyck (2000) in the context of
greenhouse gas abatement. Those environmental implications have to be considered
in an ex ante assessment of economic costs and benefits of any newly introduced
transgenic or non-transgenic crop. Unfortunately, this has been neglected in most of
the ex ante economic studies on transgenic crops (Ando and Khanna, 2000). For a
detailed review on uncertainty and irreversibility, how to include it into welfare
analysis and the application of the concept on a concrete case study, we refer to
Demont et al. (2004a, 2005). These elements are not included in this paper, but will
be extensively analyzed in a follow-up paper.
37
Discussion and Conclusions
We find conservative total welfare gains ranging from 0.8 million euros to 16 million
euros on average, depending on the importance of the crop and the size of the
innovation. Farmers’ share of the benefits varies from one half to three quarters and
this mainly depends on the heterogeneity of expenditures of the conventional
technology and the size of the innovation. At one extreme, Bt maize is a marginal
innovation with limited potential for rent creation by the seed industry. At the other
extreme, HT sugar beet is a drastic innovation allowing the innovator to capture a
sizable part of the benefits.
Four elements limit the monopolistic seed industry’s ability to charge a high price
premium and extract a large part of the benefits. The first is farmer heterogeneity
(Oehmke and Wolf, 2002, Weaver and Kim, 2002). Bt maize resistant to ECB, for
example, has to be sufficiently cheap to provide sufficient adoption incentives to a
highly heterogeneous group of farmers with respect to the damage they suffer from
ECB attacks. The second is uncertainty and irreversibility (Weaver and Wesseler,
2004). The farmer faces ex ante uncertainty regarding future ECB damage, input and
output prices, agricultural and biotechnology policy regulations in the EU and the
potential for irreversible environmental costs. The existence of irreversible benefits on
the other hand will strengthen the seed industry’s pricing power. The third is
competition from the chemical industry leading to ‘restricted monopoly pricing’
(Weaver and Kim, 2002) and incomplete adoption (Lapan and Moschini, 2000). An
exception would be the case of commercial maize in Hungary where hardly any
insecticides are used. As a result, the seed industry will face limited competition from
the pesticide sector. The fourth is competition within the biotechnology industry
38
which has led to technology price declines in all countries where transgenic crops
have been introduced (Gianessi et al., 2002).
This benefit sharing is consistent with the majority of biotechnology impact
distribution studies in literature. Demont and Tollens (2004b) found that, due to the
adoption of Bt maize in Spain in 1998-2003, farmers gained two thirds (65%) of the
total benefits, while one third (35%) accrued to the seed industry. Price et al. (2003)
review eight published studies and add four own-calculated estimates. Adding Qaim
(2003) and Demont and Tollens (2004b), we have a sample of 14 impact distribution
estimates of transgenic crops. Our meta-analysis of these estimates suggest that on
average, farmers and consumers extract 61% of total domestic benefits, or with a 95%
confidence interval between 52% and 70%, the rest accruing to the seed industry.
In our analysis we model Hungary as a small producer facing an infinite elastic
demand. As a result, no price declines are generated by our model impeding
Hungarian consumers to capture any gains from the new technology. The innovation
is essentially a technology spill- in, mainly from the US. The only way Hungarian
consumers can benefit from the innovation is through declining world prices due to
the large scale adoption of transgenic crops in large exporting economies, such as the
US (e.g. for maize), Canada (e.g. for oilseed rape) and the EU as a whole (e.g. for
sugar beet) (Demont and Tollens, 2004a).
Transgenic Maize
Prospects on EU maize markets are expected to remain positive, especially in the new
Member States (European Commission, 2004). Hungary is traditiona lly a wheat and
39
maize producing country (Vizvári and Bacsi, 2003). It is widely known that Hungary
has a comparative advantage in maize growing and maize represents the most
profitable crop in Hungarian arable farming (Nemes, 2003). Therefore, investments in
biotechnology in the maize sector will have the largest society-wide effects.
Conventional weeding in maize is very efficient. However, improvements are still
possible and HT maize will probably present the most important biotechnology
innovation in Hungarian maize cultivation (Czepó, 2000). It also leads to the highest
benefits of all new technologies we analyzed, mainly due to its high adoption
potential (Table 13 and Table 14). WCR-resistant maize is the second most important
biotechnology innovation, but will largely depend on the evolution of the new
invasive pest. Hungary also possesses a historic tradition in maize seed production.
The first maize hybrid registered in Europe, Mv 5, was developed and registered in
Hungary in 1953 (Hadi et al., 2004). This is also an important obstacle for the
introduction of transgenic maize varieties in Hungary. An interview with the largest
seed producer in Hungary reveals a strong opposition towards this new technology.
This is mainly based on fears of contamination of seed production, loss of purity and,
hence, loss of international markets.
Transgenic Sugar Beet
The price of sugar has been a sensitive issue in the Hungarian industry for some time,
as processors’ purchase prices have been much lower than those applied in the EU
(Agra Europe, 2002). Some analysts predict that sugar beet production will be
phasing out after EU accession. Moreover, the majority of the current sugar beet
varieties do not provide a high yield with high sugar content (Popp, 2000). On the
other hand, the evolution to a warmer climate in Hungary would extend the growing
40
season of sugar beet and would enhance the profitability of this crop (Chloupek et al.,
2004). From our analysis, it is clear that biotechnology can provide important benefits
both to the sugar sector and the seed industry (Table 15). The question is to which
degree farmers are willing to pay such a high price premium in advance for the
improved seed. The new common market organization (CMO) for sugar, currently
under review, will largely determine the future profitability of sugar beet farming and
of new technologies, such as biotechnology. It is questionable whether the new CMO
will still be able to protect EU farmers from the adverse effects of technology
adoption through the corrosion of world market prices (Demont and Tollens, 2004a),
i.e. the possibility of ‘immiserizing growth’ (Bhagwati, 1958). However, without
doubt the major impediment will come from the highly concentrated sugar industry,
which has been blocking the introduction of the new technology everywhere in the
world due to market concerns (Lilleboe, 2000).
Transgenic Oilseed Rape
The domestic and international competitiveness of oilseeds is mainly determined by
the characteristics of available oilseed varieties. Despite the projected rise in oilseed
production, the EU-25 will remain a large net importer of oilseeds (European
Commission, 2004). Hungary has both in sunflower and oilseed rape production a
broad variety of seeds approved by the state. Most of the oilseed rape and sunflower
seeds correspond to international standards. With regard to the necessary technology
for the production of major industrial plants the production of sunflower and sugar
beet is based on large-scale technology, oilseed rape can be produced on small and
large size farms. Considering the current regulations of the EU there is a possibility to
produce energy, especially bio-diesel from oilseeds. All of the oilseed plants
41
(sunflower, soybean, oilseed rape) can be used for bio-diesel processing but the most
likely source would be oilseed rape and sunflower (Popp, 2000). This provides an
important scope for biotechnology innovation in the sector since bio-diesel produced
from transgenic oilseed rape will probably be less vulnerable to consumer opposition.
Despite the fact that oilseed rape is a winter crop with lower weed problems than
maize and sugar beet, HT oilseed rape systems are able to provide non-negligible
benefits to small farmers in Hungary (Table 16).
42
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Table 1 : Global Importance of Maize in 2003 Area (ha) Yield (t/ha) Production (t) % EU-25 % AC % Australia 60,000 5.3 316,000 . Africa 27,113,910 1.6 43,522,313 6.8% Asia 42,695,723 3.9 164,667,617 25.8% Canada 1,226,100 7.8 9,587,300 1.5% South America 18,111,433 3.9 70,647,646 11.1% US 28,789,240 8.9 256,904,560 40.3% EU-15 4,397,737 7.7 33,667,379 5.3% 81.4% Belgium 52,724 10.5 554,743 0.1% 1.3% France 1,667,000 7.1 11,898,000 1.9% 28.8% Germany 472,700 7.2 3,415,000 0.5% 8.3% Italy 1,159,370 7.7 8,978,180 1.4% 21.7% Netherlands 24,500 8.0 196,000 0.0% 0.5% Spain 472,000 9.1 4,300,800 0.7% 10.4% UK . . . . . Rest of EU-15 549,443 7.9 4,324,656 0.7% 10.5% NMS 1,780,725 4.3 7,697,634 1.2% 18.6% 100.0% Czech Republic 85,426 5.6 476,371 0.1% 1.2% 6.2% Hungary 1,150,000 3.9 4,534,000 0.7% 11.0% 58.9% Poland 356,337 5.3 1,883,677 0.3% 4.6% 24.5% Rest of NMS 188,962 4.3 803,586 0.1% 1.9% 10.4% EU-25 6,178,462 6.7 41,365,013 6.5% 100.0% Rest of Europe 8,663,027 3.2 27,786,952 4.4% World 142,685,295 4.5 638,043,432 100.0% (FAO, 2004) NMS: new Member States Table 2 : Global Importance of Sugar Beet in 2003 Area (ha) Yield (t/ha) Production (t) % EU-25 % AC % Australia . . . . Africa 123,000 51.5 6,328,500 2.7% Asia 1,095,021 29.0 31,771,059 13.6% Canada 12,100 56.2 680,400 0.3% South America 36,807 57.6 2,119,266 0.9% US 545,480 50.9 27,764,390 11.9% EU-15 1,777,990 59.5 105,744,868 45.3% 84.7% Belgium 91,177 70.7 6,449,682 2.8% 5.2% France 402,000 72.7 29,238,000 12.5% 23.4% Germany 444,900 59.3 26,400,000 11.3% 21.1% Italy 215,000 38.6 8,300,000 3.6% 6.6% Netherlands 110,000 58.2 6,400,000 2.7% 5.1% Spain 100,200 64.0 6,413,500 2.7% 5.1% UK 162,000 57.4 9,296,000 4.0% 7.4% Rest of EU-15 252,713 52.4 13,247,686 5.7% 10.6% NMS 494,929 38.6 19,122,378 8.2% 15.3% 100.0% Czech Republic 77,325 45.2 3,495,148 1.5% 2.8% 18.3% Hungary 53,000 34.0 1,802,000 0.8% 1.4% 9.4% Poland 286,300 38.1 10,900,000 4.7% 8.7% 57.0% Rest of NMS 78,304 37.4 2,925,230 1.3% 2.3% 15.3% EU-25 2,272,919 54.9 124,867,246 53.5% 100.0% Rest of Europe 1,779,449 22.5 39,956,212 17.1% World 5,864,776 39.8 233,487,073 100.0% (FAO, 2004) NMS: new Member States
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Table 3 : Global Importance of Oilseed Rape in 2003 Area (ha) Yield
(t/ha) Production (t) % EU-25
% AC %
Australia 1,005,000 1.6 1,622,000 4.5% Africa 45,000 1.1 47,443 0.1% Asia 12,212,399 1.3 15,725,687 43.5% Canada 4,689,200 1.4 6,669,200 18.5% South America 45,680 1.7 79,450 0.2% US 432,700 1.6 686,470 1.9% EU-15 3,197,044 2.9 9,388,142 26.0% 86.1% Belgium 4,644 3.6 16,768 0.0% 0.2% France 1,080,000 3.1 3,341,000 9.2% 30.6% Germany 1,268,000 2.9 3,638,000 10.1% 33.4% Italy 4,185 1.7 6,919 0.0% 0.1% Netherlands 605 3.5 2,100 0.0% 0.0% Spain 6,000 1.9 11,300 0.0% 0.1% UK 542,000 3.3 1,771,000 4.9% 16.2% Rest of EU-15 291,610 2.1 601,055 1.7% 5.5% NMS 918,709 1.6 1,512,823 4.2% 13.9% 100.0% Czech Republic 250,959 1.5 387,805 1.1% 3.6% 25.6% Hungary 71,000 1.5 104,000 0.3% 1.0% 6.9% Poland 426,270 1.8 753,556 2.1% 6.9% 49.8% Rest of NMS 170,480 1.6 267,462 0.7% 2.5% 17.7% EU-25 4,115,753 2.6 10,900,965 30.2% 100.0% Rest of Europe 385,214 1.0 396,448 1.1% World 22,943,446 1.6 36,145,663 100.0% (FAO, 2004) NMS: new Member States Table 4 : Spread of the Western Corn Rootworm in Hungary, 1998-2003 1998 1999 2000 2001 2002 2003 Area infested (103 ha) 3,000 4,000 5,000 7,000 7,800 9,300 Area with economic adult activity (103 ha) . . 0.2 1,000 2,000 3,000 Area facing economic larval damage (ha) . . 3,130 3,058 5,381 5,995 (Kiss and Edwards, 2001, Ripka and Princzinger, 2001a, Ripka and Princzinger, 2001b, Princzinger et al., 2002, Hataláné Zsellér et al., 2004, Kiss et al., 2005) Table 5 : Data Mining on European Corn Borer Losses in Hungary 1961 1971 1975 1976 1977 1978 1979 1990 1998 2001 ECB damage 4.8%a 4.2%a 11.0%a 3.1%c 17.5%d 5.7%e 5.6% f
Average 6.0% Median 5.0% StDev 4.5% Coefficient of variation (CV) 73.8% a Hertelendy (1976) b Pálfy (1983) c Gyurkó (1980) d Szõke (2002) e Keszthelyi et al. (2000) f Keszthelyi and Najat (2002) g Keszthelyi et al. (2002)
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Table 6 : Estimates and Subjective Prior Distributions of Model Parameters Parameter Symbol Distribution Unit Sources Bt maize resistant to ECB ECB damage s Lognorm(5.0; 4.5) % Table 5 ECB Bt maize efficacy ag Triang(95; 97.5;
100) % (Labatte et al.,
1996) Bt maize price premium (% of seed cost)
wg Triang(0; 10; 20) % assumption (Czepó, 2004)
Bt maize price premium wg Triang(0; 6; 13) euros/ha assumption Bt maize adoption rate ρg 10 % assumption Bt maize resistant to WCR Area under continuous maize Lc 460,000 ha (Magonette, 2004) Area under economic WCR damage, Iowa scale > 3
Ld 5,995 ha (Hataláné Zsellér et al., 2004)
Yield benefit of WCR-resistant maize relative to chemical control
ß Lognorm(4.2; 7.3) % Table 7 (Mitchell, 2002)
WCR cost of chemical control wc Triang(28; 50; 71) euros/ha (Tóth, 2004, AKII, 2004)
Maximum 2160 7.9 162 167 167 67 67 75 386 746 158 Sample size 827 827 827 827 827 827 145 827 827 827 827 a This cost has been averaged over all 827 farmers, including 682 farmers that do not use any insecticides. b This cost has been averaged over only the group of 145 farmers that use some insecticides. (AKII, 2004) Table 9 : Survey Data of Sugar Beet Cultivation in Hungarian Farms
Table 11 : Descriptive Statistics of the Impact of Bt Maize Resistant against ECB in Hungary in 2003 2.5% quantile Mean 97.5% quantile Average share Farmers’ surplus (euros) -510,285 2,010,140 8,704,366 74% Industry’s rent (euros) 160,874 719,593 1,278,250 26% Total welfare gain (euros) 374,411 2,729,733 9,419,571 100% Table 12 : Descriptive Statistics of the Impact of Bt Maize Resistant against WCR in Hungary in 2003 2.5% quantile Mean 97.5% quantile Average share Farmers’ surplus (euros) 565,773 10,666,550 34,563,060 65% Industry’s rent (euros) 300,518 5,771,842 16,479,330 35% Total welfare gain (euros) 1,101,732 16,438,390 43,336,200 100%
Table 13 : Predicted Price Premiums and Adoption Rates of HT Crops in Hungary in 2003 Maize Sugar beet Oilseed rape Price premium (euros/ha) 8 81 12 Price premium (% of seed cost) 13% 63% 34% Adoption rate (%) 40% 38% 35% Table 14 : Descriptive Statistics of the Impact of HT Maize in Hungary in 2003 2.5% quantile Mean 97.5% quantile Average share Farmers’ surplus (euros) -1,192,280 10,083,380 20,702,820 73% Industry’s rent (euros) 3,718,113 3,718,113 3,718,113 27% Total welfare gain (euros) 2,525,833 13,801,490 24,420,940 100% Table 15 : Descriptive Statistics of the Impact of HT Sugar Beet in Hungary in 2003 2.5% quantile Mean 97.5% quantile Average share Farmers’ surplus (euros) 532,952 1,638,328 2,675,734 50% Industry’s rent (euros) 1,646,186 1,646,186 1,646,186 50% Total welfare gain (euros) 2,179,139 3,284,515 4,321,921 100%
Table 16 : Descriptive Statistics of the Impact of HT Oilseed Rape in Hungary in 2003 2.5% quantile Mean 97.5% quantile Average share Farmers’ surplus (euros) 115,709 479,262 821,899 61% Industry’s rent (euros) 306,186 306,186 306,186 39% Total welfare gain (euros) 421,894 785,448 1,128,084 100% Table 17 : Average Farmers’ Surplus per Adopted Hectare
Table 18 : Sensitivity Analysis of the Impact of Bt Maize Resistant against ECB in Hungary in 2003 Farmers’ surplus Industry’s rent Total welfare gain Theoretical damage 0.979 0.000 0.986 Price premium Bt maize -0.121 1.000 -0.008 Maize producer price 0.099 0.000 0.100 Efficacy Bt maize 0.011 0.000 0.011 Maize supply elasticity 0.002 0.000 0.002 R2 98.3% 100.0% 98.3% Table 19 : Sensitivity Analysis of the Impact of Bt Maize Resistant against WCR in Hungary in 2003 Farmers’ surplus Industry’s rent Total welfare gain Adoption Bt maize 0.633 0.734 0.795 Yield benefit of MON 863 0.570 0.000 0.465 Price premium Bt maize -0.302 0.591 -0.024 Chemical treatment cost 0.239 0.000 0.196 Maize supply elasticity 0.006 0.000 0.005 Maize producer price 0.000 0.000 0.000 R2 81.6% 89.0% 88.5%
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Figure 1 : Geographical Dispersion of European corn borer in Hungary in 2004 (ONTSZ, 2004)
Figure 2 : Geographical Dispersion and Spread of the Western corn rootworm in Europe 1992-2003 (Kiss and Edwards, 2004)
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Figure 3 : Economic Activity of Western corn rootworm in Europe 1998-2003 (Kiss and Edwards, 2004)
f(wc)
wcwg cwgγ Figure 4 : Theoretical Distribution of Herbicide Costs and Pricing Decision of the Innovator
62
1 The voltinism expresses the annual number of generations produced by a species.
2 Univoltine species annually produce one single generation.
3 Note that Hungarians refer to this insect as ‘amerikai kukoricabogár’.
4 This represents the area with a population level such that economic larval damage will occur if maize
is planted. Adults lay their eggs in the soil of maize fields during the summer and larvae hatching from
overwintering adults will damage the maize root system. If the full economic adult activity area is
rotated, no economic larval damage will be suffered. If no rotation is applied, theoretically the whole
economic adult activity area will be economic larval damage area.
5 The same assumption has been made in the construction of the European Simulation Model (ESIM)
(Banse and Tangermann, 1996).
6 The ‘seed industry’ includes the gene developers, e.g. Monsanto, and the seed suppliers, e.g. Pioneer
in Hungary.
7 The data are calculated as a weighted average of the individual groups of sample farms. For
weighting purposes, the AKII used the data of the General Agricultural Census of year 2000. The
weight shows how many farms in the similar group of the population a farm in the sample represents.
This way the data does not only characterize the farms in the sample group but also the statistical
population they represent (Kovács and Keszthelyi, 2003).
8 This is much lower than in areas with a higher ECB pressure. As a comparison, the technology fee of
Bt maize in the US is estimated at 26 €/ha in 1997, 22 €/ha in 1998 and 1999 and 16-17 €/ha in 2001
(Gianessi et al., 2002), while Benbrook (2001) reports a higher fee, i.e. 25 €/ha during the same period.
The price premium of Bt maize in France was projected in ex ante at 36 €/ha (Lemarié et al., 2001).
Brookes (2002) reports a technology fee of 29-31 €/ha in Spain. This price is recommended by the seed
industry but many farmers pay lower prices through local cooperatives, i.e. 18-19 €/ha, capturing 70%
of the Spanish maize seed market. Up to 2003, Bt maize seed was only supplied by one company, i.e.
Syngenta. In 2003 five new varieties were registered and four new companies have entered the market,
i.e. Pioneer, Monsanto, Nickerson and Limagrain. With this additional competition in mind it is likely
that technology fees will fall. This has happened in all other countries where transgenic crops have
been introduced (Gianessi et al., 2002). Therefore, our conservative seed price premium assumption
might be representative for the future introduction of Bt maize in Hungary.
63
9 Alston et al. (2002) found a comparable competitive seed price premium of 27 €/ha.
10 Isogenic varieties have exactly the same genetic composition with the exception of the inserted gene.
11 The authors of ESIM have been criticized as expecting unrealistically high price responses in the
countries of Central Europe, especially for Hungary (Banse and Tangermann, 1996). Therefore we
incorporate this uncertainty into the model. In the sensitivity section of the paper it will become clear
that supply elasticities have a negligible effect on the model outcomes (Table 18 and Table 19).
12 Marra et al. (2004) did not find any significant difference in seed costs between HT and conventional
maize for North Carolina farmers. Back in 2001 there was relatively little adoption of HT maize and as
a result Monsanto was lowering the price to that of conventional corn. There was a significantly higher
seed cost for cotton and for soybeans relative to their conventional counterparts (Marra, 2004).
13 Since nowhere in the world HT sugar beet has been commercialized, the seed price premium has not
been established yet. Our estimate is in line with the premium of 77 €/ha estimated in ex ante for
France (Lemarié et al., 2001) and is situated between the high estimated price premiums in the US, i.e.
133 €/ha (Burgener et al., 2000), 157 €/ha (Rice et al., 2001), 128 €/ha (Gianessi et al., 2002) and 164
€/ha (Kniss et al., 2004), and the lower price premiums assumed for the EU, i.e. 32-48 €/ha (May,
2003) and 38 €/ha (Gianessi et al., 2003b).
14 This is lower than the Technology User Agreement of 23 €/ha and calculated price premium of 32
€/ha reported for HT canola in Canada in 1999 (Fulton and Keyowski, 1999) and projected 49 €/ha for
HT oilseed rape in France (Desquilbet et al., 2001).
15 The degree of significance is 5%.
64
List of Available Working Papers
1. BEERLANDT, H. en L. DRIESEN, Criteria ter evaluatie van 'duurzame landbouw', Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 35 p.
2. BEERLANDT, H. en L. DRIESEN, Evaluatie van herbicide-resistente planten aan criteria voor duurzame landbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 39 p.
3. BEERLANDT, H. en L. DRIESEN, Evaluatie van bovine somatotropine aan criteria voor duurzame landbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 63 p.
4. BEERLANDT, H. en L. DRIESEN, Evaluatie van gemanipuleerde planten met biopesticide eigenschappen afkomstig van Bacillus thuringiensis aan criteria voor duurzame landbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 32 p.
5. BEERLANDT, H. en L. DRIESEN, Evaluatie van haploide planten aan criteria voor duurzame landbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 17 p.
6. BEERLANDT, H. en L. DRIESEN, Evaluatie van genetische technieken voor diagnosebepaling, immunologische technieken ter verbetering van de landbouwproduktie en transgene dieren en planten als bioreactor aan criteria voor duurzame landbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 28 p.
7. BEERLANDT, H. en L. DRIESEN, Evaluatie van verbetering van de stikstoffixatie bij planten aan criteria voor duurzame landbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 17 p.
8. BEERLANDT, H. en L. DRIESEN, Evaluatie van porcine somatotropine aan criteria voor duurzamelandbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, januari 1994, 29 p.
9. BEERLANDT, H. en L. DRIESEN, Evaluatie van tomaten met een langere houdbaarheid aan criteria voor duurzame landbouw, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, februari 1994, 30 p.
10. CHRISTIAENSEN, L., Voedselzekerheid: van concept tot actie: een status questionis, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, april 1994, 106 p.
11. CHRISTIAENSEN, L. and J. SWINNEN, Economic, Institutional and Political Determinants of Agricultural Production Structures in Western Europe, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, May 1994, 40 p.
12. GOOSSENS, F., Efficiency and Performance of an Informal Food Marketing System, The case of Kinshasa, Zaire, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, July 1995, 41 p.
13. GOOSSENS, F., Failing Innovation in the Zairian Cassava Production System, A comparative historical analysis, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, July 1995, 18 p.
14. TOLLENS, E., Cadre conceptuel concernant l'analyse de la performance économique des marchés, Projet-FAO "Approvisionnement et Distribution Alimentaires des Villes de l'Afrique Francophone", Afdeling Landbouweconomie, Katholieke Universiteit Leuven, août 1995, 35 p. (Deuxiè me version, avril 1996, 77 p.)
15. TOLLENS, E., Les marchés de gros dans les grandes villes Africaines, diagnostic, avantages et éléments d'étude et de développement, Projet-FAO "ApprovisioMement et Distribution Alimentaires des Villes de l'Afrique Francophone", Afdeling Landbouweconomie, Katholieke Universiteit Leuven, août 1995, 23 p. (Deuxieme version, septembre 1996, 32 p.)
16. ENGELEN, G., Inleiding tot de landbouwvoorlichting (heruitgave), Afdeling Landbouweconomie, Katholieke Universiteit Leuven, augustus 1995, 17 p.
17. TOLLENS, E., Agricultural Research and Development towards Sustainable Production Systems: I. Information Sources, Surveys; II. Conceptualisation of the Change Process,
65
NATURA-NECTAR course: "Agricultural Economics and Rural Development", module 1, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, August 1995
18. TOLLENS, E., Planning and Appraising Agricultural Development programmes and Projects: I. Farm Planning; II. Aggregation, Sensitivity Analyses and Farm Investment Analysis; III. Guidelines on Informal Surveys and Data Collection, NATURA-NECTAR course: "Agricultural Economics and Rural Development", module 2, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, September 1995
19. TOLLENS, E., Structural Adjustment and Agricultural Policies: I. Market Theory: the State and the Private Sector; II. Output Markets and Marketing Institutions; III. Input Markets; IV. Case Study: Cameroon, NATURA-NECTAR course: "Agricultural Economics and Policy Reforms", module 1, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, September 1995
20. TOLLENS, E., Theory and Macro-Economic Measures of Structural Adjustment – Methods of Evaluation and Linkages to the Agricultural Sector: I. Development Models and the Role of Agriculture, NATURA -NECTAR course: "Agricultural Economics and Policy Reforms", module 2, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, September 1995
21. TOLLENS, E., Theory and Macro-Economic Measures of Structural Adjustment – Methods of Evaluation and Linkages to the Agricultural Sector: II. Implementation of Policy Reforms: Case Study of Market Liberalisation in Cameroon for Cocoa and Coffee, NATURA-NECTAR course: "Agricultural Economics and Policy Reforms", module 2, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, September 1995
22. TOLLENS, E., Supply Response within the Farming Systems Context: I. Input Supply and Product Markets; II. Agricultural Supply Response Assessment, NATURA-NECTAR course: "Agricultural Economics and Policy Reforms", module 3, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, September 1995
23. GOOSSENS, F., Agricultural Marketing and Marketing Analysis: I. Agricultural Marketing Research Frameworks. II. Agricultural Market Performance Criteria and The Role of Government Intervention, NATURA-NECTAR course: "Agricultural Economics and Rural Development", module 3, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, September 1995
24. GOOSSENS, F., Agricultural Marketing and Marketing Analysis: Demand Analysis, NATURA--NECTAR course: "Agricultural Economics and Rural Development", module 3, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, September 1995
25. CHRISTIAENSEN, L. en H. BEERLANDT, Belgische voedselhulp geanalyseerd met betrekking tot voedselzekerheid, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, november 1994, 15 p.
26. CHRISTIAENSEN, L. en H. BEERLANDT, De Belgische ontwikkelingssamenwerking met Rwanda geanalyseerd met betrekking tot voedselzekerheid, Afdeling Landbouweconomie, KU.Leuven, november 1995, 36 p.
27. BEERLANDT, H., Identificatie van de meest kwetsbaren in Monduli distrikt, Arusha regio, Tanzania, A.C.T.- Afdeling Landbouweconomie, Katholieke Universiteit Leuven, april 1995, 40 p.
28. BEERLANDT, H., TOLLENS, E. and DERCON, S., Methodology for Addressing Food Security in Development Projects, Identification of the Food Insecure and the Causes of Food Insecurity based on Experiences from the Region of Kigoma, Tanzania, Department of Agricultural Economics and Centre for Economic Research, Katholieke Universiteit Leuven, Leuven, December 1995, 19 p.
29. BEERLANDT, H., Koppelen van noodhulp en strukturele ontwikkelingssamenwerking: opties voor een Belgisch beleid, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, december 1995, 23 p.
30. TOLLENS, E., La crise agraire au Zaïre: pour quelle politique de développement dans la phase de transition?, Une contribution au colloque “Le Zaïre en Chantier: Quels Projets de Société”, Anvers, 18 février 1993, December 1995, 14 p.
31. GOOSSENS, F., Rôle des systèmes d'alimentation dans la sécurité alimentaire de Kinshasa, Une contribution au projet GCP/RAF/309, AGSM, FA0, mai 1996, 78 p.
66
32. BEERLANDT, H., DERCON, S., and SERNEELS, I., (Project co-ordinator: E. TOLLENS), Tanzania, a Food Insecure Country?, Department of Agricultural Economics, Center for Economic Research, Katholieke Universiteit Leuven, September 1996, 68 p.
33. TOLLENS, E., Food security and nutrition 2. Case study from Tanzania, Nectar Programme, Agricultural Economics and Policy Reforms, module 4, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, Septembre 1996, 47 p.
34. BEERLANDT, H., en SERNEELS, J., Voedselzekerheid in de regio Kigoma, Tanzania, Afdeling Landbouweconomie en Centrum voor Economische Studiën, Katholieke Universiteit Leuven, september 1996, 45 p.
35. BEERLANDT, H., Identificatie van verifieerbare indicatoren ter toetsing van de voedselzekerheidssituatie in de regio Arusha, Tanzania, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, november 1996, 60 p.
36. GOOSSENS, F., Commercialisation des vivres locaux en Afrique Subsaharienne, le secteur informel dans un perspectif dynamique, Une contribution au projet GCP/RAF/309, AGSM, FAO, novembre 1996, 58 p.
37. GOOSSENS, F., The Economics of Livestock Systems: I. Marketing Problems and Channels of Livestock in Subsahara Africa, NATURA-NECTAR course: "Agricultural Economics and Rural Development", module 4, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, November 1996.
38. GOOSSENS, F., The Economics of Livestock Systems: II. Price Stabilization in the Livestock Sector, NATURA-NECTAR course: "Agricultural Economics and Rural Development", module 4, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, November 1996.
39. GOOSSENS, F., The Economics of Livestock Systems: III. Consumer Demand for Livestock Products, NATURA -NECTAR course: "Agricultural Economics and Rural Development, module 4, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, November 1996.
40. JASPERS, N., I. La Seguridad Alimenticia en el departamento de Quiché: Identificación e Impacto del Programa de Créditos, II. Informe Sobre Estudio Seguridad Alimenticia, ACT - Afdeling Landbouweconomie, Katholieke Universiteit Leuven, November 1996, 39 p.
41. TOLLENS, E., Social indicators with an illustration from Thailand, NATURA-NECTAR course: "Agricultural Economics and Policy Reforms", module 4, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, January 1997, 38 p.
42. BEERLANDT, H., en SERNEELS, J., Handleiding voor een voedselzekerheidsdiagnose, Afdeling Landbouweconomie en Centrum voor Economische Studiën, Katholieke Universiteit Leuven, februari 1997, 131 p.
43. BEERLANDT, H., and SERNEELS, J., Manual for a Food Security Diagnosis, Department of Agricultural Economics and Center for Economic Research, Katholieke Universiteit Leuven, March 1997, 125 p.
44. GOOSSENS, F., Aangepaste vormen van samenwerking als hefboom voor de sociaal-economische promotie van boeren in het zuiden - algemene conclusies, Seminarie georganizeerd door Ieder Voor Allen, Brussel, 17-18 maart 1997, 8 p.
45. GOOSSENS, F., Commercialisation des vivres locaux en Afrique Subsaharienne - neuf études de cas, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, Mai 1997, 50 p.
46. BEERLANDT, H., en SERNEELS, J., Food Security in the Kigoma Region of Tanzania, Department of Agricultural Economics and Center for Economic Research, Katholieke Universiteit Leuven, May 1997, 42 p.
47. BEERLANDT, H., and SERNEELS, J., Manuel Pour un Diagnostic de Securité Alimentaire, Département d’Economie Agricole et le Centre d’Etudes Economiques, Katholieke Universiteit Leuven, Juillet 1997, 134 p.
48. GOOSSENS, F., Rural Services and Infrastructure - Marketing Institutions, NATURA-NECTAR course: "Agricultural Economics and Policy Reforms", module 4, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, June 1997, 20 p.
67
49. TOLLENS, E., International Trade and Trade Policy in Livestock and Livestock Products, NATURA -NECTAR COURSE: "Agricultural Economics and Rural Development", module 4, Afdeling Landbouweconomie, Katholieke Universiteit Leuven, October 1997,43 p.
50. DESMET, A., Working towards autonomous development of local farmer organisations: which role for development agencies?, Department of Agricultural Economics and Center for Economic Research, March 1998, 49 p.
51. TOLLENS, E., Catalogue de titres dans la bibliotheque ALEO sur le Zaïre - Congo, Département d'Economie Agricole, Katholieke Universiteit Leuven, Mars 1998, 96 p.
52. DEMONT, M., JOUVE, P., STESSENS, J., et TOLLENS, E., Evolution des systèmes agraires dans le Nord de la Côte d’Ivoire: les débats « Boserup versus Malthus » et « compétition versus complémentarité » révisités, Département d’Economie Agricole et de l’Environnement, Katholieke Universiteit Leuven, Avril 1999, 43 p.
53. DEMONT, M., and TOLLENS, E., The Economics of Agricultural Biotechnology: Historical and Analytical Framework , Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, October 1999, 47 p.
54. DEMONT, M., en TOLLENS, E., Biologische, biotechnologische en gangbare landbouw : een vergelijkende economische studie, Afdeling Landbouw- en Milieueconomie, Katholieke Universiteit Leuven, Maart 2000, 53 p.
55. DEMONT, M., JOUVE, P., STESSENS, J., and TOLLENS, E., The Evolution of Farming Systems in Northern Côte d’Ivoire: Boserup versus Malthus and Competition versus Complementarity, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, August 2000, 25 p.
56. DEMONT, M., and TOLLENS, E., Economic Impact of Agricultural Biotechnology in the EU: The EUWAB-project, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, January 2001, 16 p.
57. DEMONT, M., and TOLLENS, E., Reshaping the Conventional Welfare Economics Framework for Estimating the Economic Impact of Agricultural Biotechnology in the European Union , Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, March 2001, 32 p.
58. DEMONT, M., and TOLLENS, E., Uncertainties of Estimating the Welfare Effects of Agricultural Biotechnology in the European Union, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, April 2001, 81 p.
59. DEMONT, M., and TOLLENS, E., Welfare Effects of Transgenic Sugarbeets in the European Union: A Theoretical Ex-Ante Framework , Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, May 2001, 39 p.
60. DE VENTER, K., DEMONT, M., and TOLLENS, E., Bedrijfseconomische impact van biotechnologie in de Belgische suikerbietenteelt, Afdeling Landbouw- en Milieueconomie, Katholieke Universiteit Leuven, Juni 2002, 65 p.
61. DEMONT, M., and TOLLENS, E., Impact of Agricultural Biotechnology in the European Union’s Sugar Industry, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, June 2002, 55 p.
62. DEMONT, M., and TOLLENS, E., The EUWAB-Project: Discussion, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, August 2002, 20 p.
63. DEMONT, M., DELOOF, F. en TOLLENS, E., Impact van biotechnologie in Europa: de eerste vier jaar Bt maïs adoptie in Spanje , Afdeling Landbouw- en Milieueconomie, Katholieke Universiteit Leuven, Augustus 2002, 41 p.
64. TOLLENS, E., Food Security: Incidence and Causes of Food Insecurity among Vulnerable Groups and Coping Strategies, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, September 2002, 30 p.
65. TOLLENS, E., La sécurité alimentaire: Incidence et causes de l’insécurité alimentaire parmi les groupes vulnérables et les strategies de lutte, Département d’Economie Agricole et de l’Environnement, Katholieke Universiteit Leuven, Septembre 2002, 33 p.
68
66. TOLLENS, E., Food Security in Kinshasa, Coping with Adversity, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, September 2002, 35 p.
67. TOLLENS, E., The Challenges of Poverty Reduction with Particular Reference to Rural Poverty and Agriculture in sub-Saharan Africa, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, September 2002, 31 p.
68. TOLLENS, E., Het voedselvraagstuk , Afdeling Landbouw- en Milieueconomie, Katholieke Universiteit Leuven, December 2002, 59 p.
69. DEMONT, M., WESSELER, J., and TOLLENS, E., Biodiversity versus Transgenic Sugar Beet: The One Euro Question, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, November 2002, 33 p.
70. TOLLENS, E., and DEMONT, M., Biotech in Developing Countries: From a Gene Revolution to a Doubly Green Revolution?, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, November 2002, 8 p.
71. TOLLENS, E., Market Information Systems in Liberalized African Export Markets: The Case of Cocoa in Côte d’Ivoire, Nigeria and Cameroon, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, November 2002, 19 p.
72. TOLLENS, E., Estimation of Production of Cassava in Bandundu (1987-1988) and Bas Congo (1988-1989) Regions, as Compared to Official R.D. Congo statistics, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, December 2002, 29 p.
73. TOLLENS, E., Biotechnology in the South: Absolute Necessity or Illusion?, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, December 2002, 29 p.
74. DEMONT, M., BONNY, S., and TOLLENS, E., Prospects for GMO’s in Europe, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, January 2003.
75. FRANCHOIS, L., and MATHIJS, E., Economic and Energetic Valuation of Farming Systems: A Review, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, February 2003, 36 p.
76. VANDERMERSCH, M. en MATHIJS, E. , Performantie en bedrijfsprofiel in de melkveehouderij, Afdeling Landbouw- en Milieueconomie, Katholieke Universiteit Leuven, Februari 2003, 33 p.
77. TOLLENS, E., L’état actuel de la sécurité alimentaire en R.D. Congo : Diagnostic et perspectives, Département d'Economie Agricole et de l'Environnement, Katholieke Universiteit Leuven, Février 2003, 40p.
78. VANDERMERSCH, M., MESKENS, L. en MATHIJS, E., Structuur van de Belgische melkveehouderij, Afdeling Landbouw- en Milieueconomie, Katholieke Universiteit Leuven, Februari 2003, 60 p.
79. DEMONT, M., HOUEDJOKLOUNON, A., HOUNHOUIGAN, J., MAHYAO, A., ORKWOR, G., STESSENS, J., TOLLENS, E. et TOURE, M., Etude comparative des systèmes de commercialisation d’igname en Côte-d’Ivoire, au Bénin et au Nigeria, Département d'Economie Agricole et de l'Environnement, Katholieke Universiteit Leuven, Juin 2003, 30 p.
80. TOLLENS, E., Current Situation of Food Security in the D.R. Congo: Diagnostic and Perspectives, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, August 2003, 37 p.
81. TOLLENS, E., Poverty and Livelihood Entitlement, How It Relates to Agriculture, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, August 2003, 30 p.
82. TOLLENS, E., Sécurité alimentaire à Kinshasa: un face à face quotidien avec l’adversité, Département d'Economie Agricole et de l'Environnement, Katholieke Universiteit Leuven, Septembre 2003, 33 p.
83. DEMONT, M. and TOLLENS, E., Impact of Biotechnology in Europe: The First Four Years of Bt Maize Adoption in Spain, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, October 2003, 28 p.
69
84. TOLLENS, E., Fair Trade: An Illusion?, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, October 2003, 17 p.
85. TOLLENS, E., DEMONT, M. and SWENNEN, R., Agrobiotechnology in Developing Countries: North-South Partnerships are a Key, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, December 2003, 29 p.
86. TOLLENS, E., Les défis : Sécurité alimentaire et cultures de rente pour l’exportation – Principales orientations et avantages comparatifs de l’agriculture en R.D. Congo, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, Mars 2004, 67 p.
87. DEMONT, M., JOUVE, P., STESSENS, J. et TOLLENS, E., Boserup versus Malthus revisités: Evolution des exploitations agricoles dans le Nord de la Côte d’Ivoire, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, Avril 2004, 20 p.
88. DEMONT, M., JOUVE, P., STESSENS, J. and TOLLENS, E., Boserup versus Malthus Revisited: Evolution of Farms in Northern Côte d’Ivoire, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, April 2004, 17 p.
89. DEMONT, M., OEHMKE, J. F. and TOLLENS, E., Alston, Norton and Pardey Revisited: The Impact of Bt Maize in Spain, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, April 2006, 31 p.
90. VANDENBOSCH, T., NANOK, T. and TOLLENS, E., The Role of Relevant Basic Education in Achieving Food Security and Sustainable Rural Development, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, August 2004, 23 p.
91. VANDERMERSCH, M. and MATHIJS, E., Consumer willingness to pay for domestic milk , Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, October 2004, 34 p.
92. DEMONT, M., TOLLENS, E. and FOGARASI, J., Potential Impact of Biotechnology in Eastern Europe: Transgenic Maize, Sugar Beet and Oilseed Rape in Hungary, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, January 2005, 63 p.
93. DAEMS, W., DEMONT, M., MUŠKA, F., SOUKUP, J. and TOLLENS, E., Potential impact of biotechnology in Eastern Europe: Transgenic maize, sugar beet and oilseed rape in the Czech Republic, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, May 2006, 74 p.
94. TOLLENS, E., Manual on Cocoa Market Information Systems Based on Experiences in Nigeria, Cameroon and Côte d’Ivoire, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, October 2006, 29 p.
95. TOLLENS, E., Markets and Institutions for Promoting Rice as a Tool for Food Security and Poverty Reduction in Sub-Sahara Africa, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, October 2006, 27 p.
96. DAEMS, W., DEMONT, M. and TOLLENS, E., Economics of Spatial Coexistence of Transgenic and Conventional Crops: Oilseed rape in Central France, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, May 2006, 48 p.
97. DILLEN, K., DRIES, L. and TOLLENS, E., The impact of the EU sugar reform on sugar and sugar substitutes industries, Centre for Agricultural and Food Economics, Katholieke Universiteit Leuven, October 2006, 24p.
