MSc. Adriano Gomes Garcia Entomology Department

1. Applied Computational Modelling to support studies on agricultural pest management, considering the landscape MSc. Adriano Gomes Garcia Entomology Department

2. Agriculturalpest management

3. Effects associated to insecticides

4. Agricultural pest management • Growing concern on the impact of chemicals • Integrated pest management: Strategies to reduce insecticide applications, maximizing natural control. • It is necessary to understand better the population dynamics on agricultural systems.

5. Landscape Ecology • Isthepopulation dynamics enoughtounderstandallcomplexinteractionsinsideanecosytem?

6. Challenges in working with the landscape • High heterogeneity. • Instability. • Howtorepresent it ?

7. Computational approaches • Allow to create hypothetical landscapes or working with real areas. • Good approach for studies whose experiment fields are inviable. • Prediction or identification of patterns: simulations by using different programming languages (C, C++) or softwares (GIS). • Example: Cellular Automata.

8. Cellular automata • Mathematical discrete model • Grid of cells • Cell states change over time steps • Transition rules

9. Cellular automata-EXAMPLE In each time steptheadultcanlayeggs in 3 neighbourcells In each time steptheadultcan die withprobability 0.5. In each time steplarvaecan emerge from 2 in 3 eggs In each time steplarvaecaneither die withprobability 0.5 or emerge in adultwithprobability 0.5

10. How to apply CA in agricultural pest management? • Intercropping systems • Refuge areas • Control strategies based on manipulation of the landscape

11. Intercropping systems • Practice in growing two or more crops in proximity • Most used arrangement: alternated rows. • Strategy based on the nutritional ecology of invader insects: Combining non-suscetible hosts to suscetible hosts.

12. Intercropping systems Oviposition,mortality,development Ecological parameters Nutritional Ecology

13. How to simulate insect dynamics in an intercropping system by using CA Study case: • Diabroticaspeciosa: Polyphagousbeetle • Hosts: Corn, soybean, beanandpotato • Different fitness for adultand larva stage in each host.

14. Ávila &Parra, 2002

15. Oviposition Larva mortality Larva-adult development Adult mortality Oviposition’ Larva mortality’ Larva-adult development’ Adult mortality’

16. CA 2: adult dynamics CA 1: larva dynamics

17. Transition rules CA1: • a) a celloccupiedby a larva canbecomeemptywithprobabilityμ +αdueto larval mortalityoradultemergence , respectively. • b)anemptycellcanbecomeoccupiedby a larva ifanadultlayseggson it with a probabilityβ. CA2: • a) a celloccupiedbyanadultfemalecanbecomeemptywithprobabilityδ duetoadultmortality. • b) anemptycellcanbeoccupiedwithprobabilityα /2 if a larva in thecorrespondentcell in CA1 turnsinto a femaleadult. The fraction ½ isrelatedto sex ratio.

18. Spatio-temporal evolution (larva) soybean-corn soybean-bean corn-bean soybean-potato corn-potato bean-potato

19. Population density per row (horizontal view) population density population density population density row row row soybean-bean soybean-corn corn-bean population density populational density population density row row row soybean-potato corn-potato bean-potato

20. Average distance reached over the time Average distance Average distance Average distance time time time corn-bean soybean-corn soybean-bean Average distance Average distance Average distance time time time bean-potato soybean-potato corn-potato

21. Considerations • By mean of CA, it was possible to predict the population behavior of D.speciosa on different combinations of crops in intercropping systems. • Corn has shown the better crop to be inserted in an intercropping system since the population density and dispersion ability were reduced

22. Refuge areas and resistance evolution • Transgeniccrop: geneticallymodifiedcrop • Cultivationofnontransgeniccrops in associationwithtransgeniccropstomanageofinsectresistance. • Computationalprogrammingbyusing celular automata (methodology similar tointercropping systems

23. Refuge Areas

24. Possible study cases • Helicoverpa armigera and Spodoptera frugiperda: polyphagous lepidopterous pests that are the main target of Bt-crops. • Understanding the whole resistance evolution when a new pest arrives to the agriculture environment would provide importante results for agriculture • Incipient project: no results achieved yet

25. Working with satellite images • Because of the high diversity in real landscapes, it is necessary to work with real images (from satellite). • Geographic information system

26. Geographic Information System Hardware, software and data for capturing, managing, analyzing, and displaying geographically referenced information. GPS use. Georreferenced image. Softwares: ArcGIS, MapInfo, Fragstat (free).

27. ArcGIS Geographic information system for working with maps and geographic information. Create, share, and manage geographic data, maps, and analytical models. Geostatistical Analyst Tools e Spatial Statistics Tools: Regression Analysis, Krigging,Cellular Automata

28. Lygus spp : western tarnished plant bug Local: Cotton field from San Joaquin Valley Hypothesis: Verify if Lygus hesperus density in cotton fields is correlated to the density of the same specie in other crops close to the fields.

30. Chilo partellus is one of the main lepdopterous that attack maize and sorgum • Cotesia flavipes is a promise for biological control since it is a larval endoparasitoid of C.partellus. • Objective: Predict distribution of C.partellus and C.flavipes in all Ethiopia.

32. Final Considerations • It is importante to understand how landscape elements Interact with insect populations. • Computational approaches are useful to represent and analyse landscape factors. • There is still a great potential to work with computational modelling in landscape management for controlling pests.

33. Research group Profª.Drª.Cláudia Pio Ferreira Prof.Dr.Fernando Cônsoli Prof.Dr.Wesley A.C.Godoy

34. Thank you! webmail: adrianogomesgarcia@gmail.com