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MODEL FOR GMO IMPURITY QUANTIFICATION IN SOY BEANS SEED PRODUCTION Lidia Esteve-Agelet 25 th April, 2005.

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Presentation on theme: "MODEL FOR GMO IMPURITY QUANTIFICATION IN SOY BEANS SEED PRODUCTION Lidia Esteve-Agelet 25 th April, 2005."— Presentation transcript:

1 MODEL FOR GMO IMPURITY QUANTIFICATION IN SOY BEANS SEED PRODUCTION Lidia Esteve-Agelet 25 th April, 2005

2 THE GMO IMPURITY CONCERN The adventitious presence of genetically engineered material Where do impurities come from?  Natural sources Cross pollination Segregation Residual seeds  Human handling factors

3 THE GMO IMPURITY CONCERN Varietal identity preservation Consumers pressure Environmental risks Health risks Lower impurity thresholds

4 THE GMO IMPURITY CONCERN The consequences:  Loss of market  Rejected or penalized shipments  Economic loss

5 OBJECTIVES Create a simple screening model for predicting the total impurity in soy bean seed production Identify the critical stages of contamination in the process

6 THE MODEL - STAGES- Initial Impurity Planter Combine Truck Negligible: -Cross pollination -Dormant seeds

7 THE MODEL -ASSUMPTIONS- 30 inches row space in field Seed rates and yield considered independent Medium seed size : seeds = 1 lb Contamination is additive Not seed losses, only replaced Germination of 90% Homogeneity/perfect mixture/isotropic

8 THE MODEL -VARIABLES- X1: Rate seeds/acre: – seeds/acre. X2: Seed initial impurity in %: 0.5-2%. X3: Planter impurity in %: over total planted seed. for 1 acre around 0.05% max. (Hanna, 2004) X4: Yield in bushels per acre bu/ac. X5: Combine impurity in %: 1 acre around 0.06% max. (Hanna, 2004) X6: Truck capacity in pounds: lb. X7: Impurity in trucks % over capacity: 0-0.2% (Ruiz-Diaz, 2005) Independent variables

9 THE MODEL -VARIABLES- Dependent variables Y1: Total impurity in truck (lb) Y2 : % impurity over the total loaded in truck Y3: weight of impurity (lb) /acre of crop

10 THE MODEL -MAIN EQUATIONS- Y1 =(((((X2 / 100) * X1 + (X3 /100)*X1)*0.9*X4*(60 / X1)) + + ((X5 / 100) * (X4 / 2800) * 60)) * (X7 / (X4 * 60))) + + ((X6 / 100) * X7) Y2 = (Y1 / X7) * 100 Y3 = Y1 / (X7 / (X4 * 60))

11 Excel Program of the model (Visual Basic macro)

12 CONCLUSIONS The variables required for the model have to be taken from the given ranges in order to get consequent results Results in accordance with several reports  GMO impurity about 0.1-1% (Quisel, 2004)  % impurity in oilseeds (Genetic Engineering Newsletter – Special Issue 11/12, 2003) The impurity from trucks and initial impurity in seeds are the most relevant pollution sources in the model. Impurity from combine seems to be almost negligible.

13 ACKNOWLEDGEMENTS Dr. Thomas J. Brumm for his advises and assessment about the topic Dr. H. Mark Hanna for the clean out information Dorivar Ruiz-Diaz for the collaboration in programming and agronomic assessments Thanks;


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