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Agrometeorological Monitoring and Forecasts for Pest and Disease Control

Agrometeorological Monitoring and Forecasts for Pest and Disease Control. Simone Orlandini Department of Plant, Soil and Environmental Science University of Florence. International Workshop on Addressing the Livelihood Crisis of Farmers Belo Horizonte, Brazil, 12-14 July 2010. Outline.

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Agrometeorological Monitoring and Forecasts for Pest and Disease Control

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  1. Agrometeorological Monitoring and Forecasts for Pest and Disease Control Simone Orlandini Department of Plant, Soil and Environmental Science University of Florence International Workshop on Addressing the Livelihood Crisis of Farmers Belo Horizonte, Brazil, 12-14 July 2010

  2. Outline • Background • Input data • Models for crop protection • Use and application • Dissemination of information

  3. Outline • Background • Input data • Models for crop protection • Use and application • Dissemination of information

  4. Background Widening of biological knowledge

  5. Background Development of computer science and telecommuncations

  6. Background • Constantlevelofcroplosses Most affected regions: tropics and developing Countries Causes: • Lack of technologies • Crop successions • High temperatures • Possibility to have more than one cycle per year current 1950 1960-1970 pest disease weed

  7. Background • High levelofpesticideutilisation

  8. Needsof information • There is the need of information disseminated to the growers to rationalise crop protection. • Usually farmers carry out their decision in condition of high risk and uncertainty. • The lack of knowledge increases the level of risk in farm management, and farmers have to increase the quantity of chemical and energy inputs, without solving the problems. • A way to help growers during their activity is represented by the acquisition of high quality elaborated information, so reducing decision making uncertainty minimising the use of chemical and energy inputs. Agrometeorological modelling can be the suitable tool to provide this information

  9. Outline • Background • Input data • Models for crop protection • Use and application • Dissemination of information

  10. Field stations

  11. Leaf wetness sensors

  12. Remote sensing – input data Ground stations Radar (RAINFALL) LWD model maps of downy mildew infection Epidemiological model

  13. Remote sensing – identification of symptoms on crop canopies using multispectral images

  14. Numerical weather models

  15. Seasonal forecast

  16. GIS Map of number of days for the outbreak of the current infection Number of generation of Bactrocera oleae

  17. Crop models Simulatedimpactsofleaf-damagingpestinfestation on maizeyield at regional scale (30-arcminute grids in Tanzania). Leafdamageswasimplementedthrough a leaf area couplingpoint in the DSSAT model www.regional.org.au

  18. Outline • Background • Input data • Models for crop protection • Use and application • Dissemination of information

  19. Mechanistic Empirical Cornell University in Geneva, New York http://www.ipm.ucdavis.edu

  20. yes or no T fuzzification de-fuzzification RH fuzzification Quantitative information base of rules & Qualitative information Other approaches: fuzzy, neural network fuzzy inference

  21. R. D. Magarey, T. B. Sutton, and C. L. Thayer Department of Plant Pathology, North Carolina State University, Raleigh 27696..

  22. Main models

  23. Source: Erick D. DeWolf and Scott A. Isard, 2007. DiseaseCycleApproachtoPlantDiseasePrediction Annu. Rev. Phytopathol. 2007. 45:203–20

  24. Year of formulation

  25. Continent

  26. Input example: Plasmoparaviticola simulation models

  27. Output example: Plasmoparaviticolasimulation models

  28. Exampleofvariablesincludedintodifferentkindsofmodels L. M. Contreras-Medina, I. Torres-Pacheco, R. G. Guevara-González, R. J. Romero-Troncoso, I. R. Terol-Villalobos, R. A. Osornio-Rios, 2009. Mathematicalmodelingtendencies in plantpathology. African Journal ofBiotechnology Vol. 8 (25), pp. 7399-7408

  29. Outline • Background • Input data • Models for crop protection • Use and application • Dissemination of information

  30. Condition of application • Climatic classification • Future climatic scenario for climate change and variability analysis • Field monitoring and forecast for crop protection

  31. Climatic classification

  32. Potato late blight risk Climaticriskfor potato late blight in the Andesregionof Venezuela (BeatrizIbetLozada Garcia; Paulo Cesar Sentelhas; Luciano Roberto Tapia; GerdSparovek, 2008)

  33. Climate change impact a)1960-1990 Predicted severity of phoma stem canker (L. maculans) at harvest (Sc) on winteroilseed rape crops. b)2020 LO c)2020 HI d) 2050 LO e) 2050 HI Zhouet al. 1999)

  34. Probablenumberofgenerationsofleafminer (Leucopteracoffeella) on coffee plant in Brazil Source: Ghini R. et al., 2008. Riskanalysisofclimatechange on coffee nematodes and leafminer in Brazil. Pesq. agropec. bras. vol.43  n.2.

  35. Totreat Nottotreat Field monitoring and forecast for crop protection

  36. Information utilisation For using information obtained by models or by decision making systems in order to define the field treatment epochs, different aspects have to be highlighted  Necessary to treat when ·the pathogen is present ·the crop is susceptible ·the treatment is efficacious  To avoid treatments ·in advance, for losses of efficacy due to the product degradation and to the growth of plants ·late, for losses of efficacy due to a too developed infective process Factors to consider ·character of the farmer ·need to have all the information concerning the disease and the crop ·position of the threshold of action and damage ·application with strategic or tactical aims

  37. Model application economic benefits • Dr Peter Gladders, ADAS Boxworth, Cambridge. LK0944:Validationofdiseasemodels in PASSWORD integrateddecisionsupportforpests and diseases in oilseed rape. HGCA conference 2004: Managingsoil and rootsforprofitable production • Phipps PM, Deck SH,Walker DR. 1997.Weather-based crop and disease advisories for peanuts in Virginia. Plant Dis. 81:236–44 • L. Massetti, A. Dalla Marta and S. Orlandini, Preliminary economic evaluation of an agrometeorological system for Plasmoparaviticola infections management. • Alka Bhatia, P. D. Roberts, L. W. Timmer, 2003. Evaluation of the Alter-Rater Model for Timing of Fungicide Applications for Control of Alternaria Brown Spot of Citrus. Plant Disease / September 2003.

  38. Costs and benefitsofAlter-Rater Model

  39. Benefits from the IPM impact studies Economic Impacts of Integrated Pest Management in Developing Countries: Evidence from the IPM CRSP TatjanaHristovska Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University, 2009

  40. Other benefits reduction of chemical inputs in the ecosystem soil fertility conservation smaller amount of chemical residuals in food work quality improvement reduction in the development of resistant forms safeguarding of natural predatory reduction of new diseases

  41. Implementationof the model Tables for manual calculations Simplicity of application, difficulty to obtain information for an efficacious use Electronic plant stations Collocation in field, complete automation, imprecise results, frequent damages Computer Rapidity of intervention (tactic), possibility to analyse past conditions, possible simulation with future scenarios (strategic), automatic collection of data, use for different aims, precision of results

  42. Manual calculation: Mills table (apple scab)

  43. Electronic plant station

  44. Personal computer and network of meteorological sensors and stations

  45. Outline • Background • Input data • Models for crop protection • Use and application • Dissemination of information

  46. Conditionsofapplication Farm: in this case the model is applied directly by farmers, with evident benefits in the evaluation of real epidemiological condition and microclimate evaluation. On the other hand, the management of the simulations and the updating of the systems represent big obstacles. Territory: it is probably preferable because it allows a better management and updating of the system. This solution requires the application of suitable methods for the information dissemination among the users.

  47. Information dissemination: the bulletins • Advises and information to the users can be disseminated by using: personal contact, newspaper and magazines, radio and television, videotel, televideo, telefax, mail, phone, INTERNET, SMS.

  48. Mobile phone From OmondiLwande and Muchemi Lawrence (2008)

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