SIGMEA. Antoine Messéan Frédérique Angevin INRA, France

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1 SIGMEA Antoine Messéan Frédérique Angevin INRA, France

2 SIGMEA partnership FP6 STREP project (24-27) 44 partners from 12 European countries; Principal programmes studying crop-to-crop gene flow over a wide number of countries across Europe with a diversity of agricultural systems including organic and landrace cropping; Links with other research projects (FP5/FP6)

3 Background A large amount of information is available on direct effects of GMOs but remains fragmented; Indirect and long-term effects highly depend on regional environments, local landscapes, farming systems and practices; Field experiments are necessary but not sufficient Modelling is a key issue for taking into account the various factors.

4 General objective of SIGMEA To set up a science-based framework, strategies, methods and a practical toolbox for: Assessing ecological and economic impacts of GM crops at the landscape level Designing management rules and scenarios Implementing monitoring schemes

5 SIGMEA Objectives To collate and synthesize existing and developing experimental information on gene flow of the major GM crops targeted for this study (oilseed rape, maize) as well as for other crops considered: sugar beet, rice and wheat; To conduct landscape scale evaluations of gene flow and develop bio-geographical models of outcrossing, seed dispersal and persistence covering whole farms and regional scales for beet, maize and oilseed rape;

6 SIGMEA Objectives To design a landscape generator simulating agricultural landscapes (field patterns, cropping systems allocation) from statistical descriptors. To design and implement an operational, practical and dynamic generic gene flow modeling platform at the landscape level. To identify changes in farming practices that minimize gene flow and adventitious mixing of GM and non-gm crops and the feasibility of applying these measures on the farm;

7 SIGMEA Objectives To propose various scenarios, ensuring coexistence in six regional case studies and evaluate their technical feasibility, economical costs and acceptability by local operators. To determine socio-economic and environmental impacts coming from the adoption or not of GM crops by the farmers. To build up an integrated and dynamic decisionsupport system for assessing the sustainability of regional farming systems taking into account both ecological and economical aspects.

8 SIGMEA Objectives To design on-site novel methods for GMO detection, identification and quantification as well as sampling procedures for maize, oilseed rape and sugar beet; To provide a long-term monitoring strategy for EU including recommendations for regulation, relevant biological indicators, sampling and detection methods and analyze its economical implications; To analyze the current regulatory regimes of EU and member states, their implementation as well as the interest and practice of insurance and re-insurance companies related to liability issues on the release of GMOs. To provide practical recommendations for the decisionmaking processes relating to the market release of GM crops under progress.

9 Real landscapes Landscape Modelling Socio-economics of GMOs Gene flow and fate of transgenes at the landscape level Simulated landscape Gene flow and Ecological processes Elaboration, feasibility and acceptability of scenarios of introduction of GMOs Decision-support systems and tools for GMO assessment and management, monitoring schemes

10 WP4: Why models are necessary? The landscape fragmentation, climate and farmer practices have a great influence on gene flow and adventitious presence Many factors and interactions to be addressed on a long-term basis, e.g., crop rotations, seed spillage, effect of soil tillage Field studies no longer sufficient

11 Why models are necessary? Landscape Isolation distances Different decision levels should be taken into account Farm Field clusters Field and associated cropping system Sowing date Variety type Discarding Border management

12 Coexistence in maize crop production

13 Field pattern Sowing date and density Maize varieties For each non GM plant in each field: number of grains with the transgene Proportion of GM grains in non OGM harvest Climate

14 Applications to coexistence Can GM crops co-exist with conventional or organic crops? Official threshold:.9% of adventitious presence Lower thresholds ~.1% Where? Agricultural practices required? Additional costs?

15 Three types of approach Field scale study (CoExII) Estimation of major factors and of potential individual coexistence measures decision tables Landscape scale study (CoExII + SIGMEA) Assess the feasibility of coexistence at the landscape level Regional scale studies (SIGMEA) How to organize GM production at the regional level? Messéan A., Angevin F., Gómez-Barbero M., Menrad K., Rodríguez-Cerezo E., 26. New case studies on the coexistence of GM and non-gm crops in European agriculture, Technical Report Series of the Joint Research Center of the European Commission, EUR 2212 En, 112 p.

16 Estimating gene flow at the field scale level Isolation distance Non-GM crop: Area Discard width Earliness of flowering Spatial layout of the field with respect to wind direction Non-GM width GM crop (15 ha) Wind

17 Wind situation Non GM field Flowering area time-lag day 3 days <5ha 6 days Decision rule table 9 days Isolation distances to meet thresholds «upwind» situation day 3 days Upwind 5ha<x<1ha 6 days 9 days Heterozygous case day GM field 15 ha 3 days >1ha 6 days 9 days Non GM width Cross-pollination rates.9%.8%.7%.6%.5%.4%.3%.2%.1%.5%.1%

18 Non GM field Flowering area time-lag day 3 days <5ha 6 days 9 days Decision rule table day Isolation distances to meet thresholds 3 days 5ha<x<1ha «downwind» situation 6 days 9 days Heterozygous case day GM Field 15 ha 3 days >1ha 6 days 9 days Non GM width Cross-pollination rates Contamination rates.9% %.7%.6%.5%.4% %.5%.1% X 3 5 X X 2 15 X 5 1 X X % % X X X X

19 An cluster organisation: maize plots are grouped around water supply points Each cluster differs from the others by: Its area which depends on the number and the area of the fields in the cluster The number of farmers who owns one or many fields in the cluster We have considered four clusters Situatio Situatio nn Numbe Numbe r of r of cluster clusters s Mean area of clusters (ha) Mean number of fields by cluster ,5 1 Mean distanc e betwee n clusters (m) , , XXX Mean number of farmers by cluster

20 Diagnostic.1.11%.2.16% %.1.14% %.3.33%.1.12% %.1.8%.3.29%.2.17% -.3% -.2% -.1% % %

21 Wind distribution (in %) according to direction (in ) %.5.56%.2.18% %.2.24% %.2.3%.7.72% % % %.1.13% %.18.19%.4.49% %.8.84%.9.9% % %.3.32%.4.38%.5.49%.1.13%.2.18%.3.33%.2.3% %.298% %.6.63%.1.5%.1.7%.1.8% % %.3%.1.3%.1.4%.44.41%.17.4% %.14.31% %.11.24%.2.27% %.33.31% % (6) % %.2%.2%.2%.7.52%.3.25%.2.19%.2.14%.1.14%.1.12%.1.9%.6.4%.4.32% %.68.51% % % % % % 1.974% (5) % % % %.18.15% % %

22 SIGMEA WP7: Elaboration of scenarios The overall objective of WP7 is to assess the impacts of introduction of two GM crops (Bt-maize and HT-OSR) in selected SIGMEA regions (France, Scotland, Spain, Denmark, Germany, Czech Rep); Potential scenarios will be derived by integrating independent measures; These scenarios will be assessed in terms of their technical and economic feasibility as well as their acceptability by involving the major actors and operators of the production chain.

23 Regional scale study: Alsace, France Maize = 85%AUA Maize = 7%AUA Ensisheim studyarea area Heiwiller study Total area area Total Number of of fields fields Number Number of of farmers farmers Number 885 ha ha

24 Regional databases: Surveys: Work hypothesis: Meteorological data Field pattern Maize allocation For each maize field: Sowing date and density Maize precocity Allocation of GM maize varieties Simulations Situations of cross-pollination between GM and non GM field on real agricultural landscape Debates with stakeholders Surveys Surveys For each farm: Determinant for technical decision Leeways to implement innovations For collecting basin: Organization of country elevators

25 Impact of sowing 1% of maize area with GM varieties on the non-gm area to be downgraded (25 maize allocation, CAP database) ENSISHEIM Maize on 85% of AUA HEIWILLER Maize on 7% of AUA

26 Conclusions Explicit gene flow models are necessary for addressing the diversity of cropping systems and practices as well as the diversity of decision-making situations Compatibility between kernels and DNA based contents remains to be taken into account in predictive models Most of the results and models would be useful for «conventional» coexistence issues (IP supply chains) Models as a mediation tool with stakeholders Analysis of post-farm supply chains: identification of critical points and modelling FP6 Co-Extra Integrated project

27 Thank you for your attention

28 WP1 Coordination Project Advisory Board Project Executive Committee Project Management Office Scientific Coordination Administrative Coordination Antoine Messéan Jeremy Sweet EU Design of generic models WP3/WP4 WP2 Gene flow and field ecological studies WP3 Landscape modelling and Generator Landscape generator Datasets on gene flow and impacts WP4 Modelling fate of transgenes at the landscape level Ecological impact models WP5 Socio-Economics of GM and costs of coexistence GM adoption factors, economic balances, impacts on farming systems, costs of co-existence Operational generic platform WP7 Scenarios for GMO introduction Potential scenarios Scenario feasibility Management and stewardship of GMOs; WP8 Monitoring of environmental effects Monitoring schemes Biological indicators Sampling protocols WP9 Detection for GMO presence Methods WP6 Social, legal and liability issues. Insurance schemes and costs WP1 Integration and decision-making Co-existence rules, Management and monitoring tools, Decision support system for economical and environmental balances and how to use them