Introduction to a forecasting and decision support system in tree fruit: The Swiss system SOPRA

Transcription

1 Federal Department of Economic Affairs DEA Agroscope Changins-Wädenswil Research Station ACW Introduction to a forecasting and decision support system in tree fruit: The Swiss system SOPRA Jörg Samietz, Heinrich Höhn, Benno Graf, Sibylle Stöckli, Martin Hirschi et al. Agroscope Changins-Wädenswil Research Station ACW

2 Overview Aims Species implemented What do we simulate? Modelling approach behind it Background regarding temperature dependent processes modelling of habitat temperatures weather data Internet platform

3 Decision finding process increasing certainty saving time (off tractor) Monitoring pest Optimum Timing Control measures No Treatment Risk assessment increasing efficacy, reduction of side effects, reduction/avoiding residues reduction of the number of treatments Treatment required

4 Aims of SOPRA Timing of pest monitoring and surveys increasing certainty, saving time (off tractor) Timing of pest control measures increasing efficacy, reduction of side effects, number of treatments Advancing environmentally friendly control measures (since those measure especially depend on precise timing: mating disruption, growth regulators, viruses) Providing decision support to farmers AND consultants Robust, easy to use, very few clicks to access current local information

5 Species implemented (1) Codling Moth Cydia pomonella Smaller Fruit Tortrix Grapholita lobarzewskii Rosy Apple Aphid Dysaphis plantaginea Apple Saw Fly Hoplocampa testudinea Apple Blossom Weevil Anthonomus pomorum Red Spider Mite Panonychus ulmi

6 Species implemented (2) Summer Fruit Tortrix Adoxophyes orana Europ. Cherry Fly Ragoletis cerasi Europ. Pear Psylla Cacopsylla pyri Plum Fruit Moth Grapholita funebrana

7 What do we simulate? Establish relative phenologies of life stages over season 100% 90% 80% Identify key events of the life cycle Relate them to monitoring or control measurers Provide decision support on that basis Larvae hatch Example: Smaller fruit tortrix 70% 60% 50% 40% 30% Start of adult flight Start of oviposition Pupae Females Pupae Males Adults Females Adults Males Eggs Larvae Larvae Diapause 20% 10% 0%

8 How do we simulate? Development as any physiol. process depends on temperature (e.g. after diapause) For each single insect stage, knowledge of 1.) the relationship between temperature and development and 2.) the temperature of the insects, allows for simulating the occurrence of the stages by use of a computer model on base of current and projected weather data Codling moth stages

9 How do we simulate? Modelling approach: Time varying distribute delays for implemented stages of the life cycle on base of Erlang density function Arrays of first order differential equations describe the transition between substages (k i ) properly reproduce the stage specific variability in life history Driven by temperature dependent processes (hourly basis) using current weather data and climatology for projection (also hourly means)

10 How do we simulate? Example European Cherry fly, (on of the simplest models in SOPRA) All relevant stages: Developmental rates i.s.l. Reproductive stages (adults): Reproductive rate Mortality rate dependent processes

11 dependent processes For all relevant stages: Individual-based studies of temperature dependent development Individual caging Summer tortrix larvae

12 dependent processes For all relevant stages: Individual-based studies of temperature dependent development or temperature dependent activity (Apple blossom weevil, Pear psylla) Activity monitoring Apple blossom weevils

13 dependent processes in delay models Example Summer Tortrix 3 Generations; Delays allow for overlapping life stages Embryo (Egg) Early Larvae Late Larvae Pupae Adult moths Reproduction Mortality Hibern. Generation Rep. rate Age of female Mort. rate Age 1. Gen. Rep. rate Mort. rate Age of female Age 2. Gen. Diapause, Hibernation

14 dependent processes in delay models Codling moth 4 Generations; Delays allow for overlapping life stages Embryo (Egg) Larvae Late Larvae+ Pupae (Postdiapause) Pupae Adult moths Reproduction Mortality Hibern. Generation Rep. rate Age of female Mort. rate Age Day length 1 st foll. Gen. rate Dev. rate Dev. rate Dev. r rate Rep. r Age of female rate Mort. r Age Day length 2 nd foll. Gen. Rep. rate Mort. rate Age of female Age 3 rd foll. Gen. Diapause, Hibernation

15 Habitat selection, modelling of habitat temperatures Measured air temperature is not yet temperature of the insects Simulation of stem temperatures: Current weather plus a) Azimuth angle of the sun throughout the season b) Transmission of sunlight through the leaf area with changing season Codling moth stages Overwintering on tree strems IRAD= RAD exp(4 (005. ( JULD 50))

16 Habitat selection, modelling of habitat temperatures Habitat selection is essential: 1.0 Wädenswil / In Smaller fruit tortrix Basis stem temperature far to early Relative sim mulated phenology Simulated stem temperature Relativ ve trap catches Day of the year Smaller fruit tortrix: temperature basis for phenology

17 Habitat selection, modelling of habitat temperatures Habitat selection is essential: 1.0 Wädenswil / In Smaller fruit tortrix Basis stem temperature far to early Basis air Relative sim mulated phenology Simulated stem temperature Air temperature Relativ ve trap catches temperature far to late Day of the year Smaller fruit tortrix: temperature basis for phenology

18 Habitat selection, modelling of habitat temperatures Habitat selection Smaller fruit tortrix Destructive potted tree experiments Larvae bore in the wood (~ 2 cm)

19 Habitat selection, modelling of habitat temperatures Habitat selection Smaller fruit tortrix Empirical field measurements

20 Habitat selection, modelling of habitat temperatures Habitat selection Smaller fruit tortrix Empirical field measurements Simulation of Internal stem temperature temperature ( C) by very well relationship with seasonal irradiation model and air temperature Internal stem t Ambient air temperature ( C) Solar radiation flux corrected for season (W m -2 ) IRAD = RAD exp(4 (0. 05 ( JULD 50))

21 Habitat selection, modelling of habitat temperatures Habitat selection Smaller fruit tortrix 1.0 Wädenswil / Relative sim mulated phenology Simulated stem temperature Air temperature Relativ ve trap catches Day of the year

22 Habitat selection, modelling of habitat temperatures Habitat selection Smaller fruit tortrix 1.0 Wädenswil / Validation: Inner stem temperature Relative sim mulated phenology Simulated stem temperature Simulated inner stem temperature Air temperature Relativ ve trap catches Day of the year

23 Weather data Weather stations for representative regions: current weather data and long-time hourly means for projection (10 years) Hourly data: Air temperature (+2m) Soil temperature (-5cm) Solar radiation

24 From weather data to the Internet Weather data base (daily automatic update) Phenology data base (in season daily update with the simulation tool) Web interface, decision support

25 Internet platform: Entered through digital map of climatic regions (click on desired locality) Weather-based regional prognosis; graphics, interpretations, decision support

26 Internet platform: Overview species prognoses for a climatic region Alert status in colour code with "pre- and post warning"

27 Internet platform: Specific species prognosis for a climatic region Interpretation and Decision support including "Prewarning"

28 Internet platform: SOPRA-Link Suggestions with extensive information about biology and management strategies (incl. full list of registered plant protection agents) SOPRA-Link