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Sparks_KSPP_II.pdf

Adam H. Sparks
October 21, 2016
22

 Sparks_KSPP_II.pdf

Botanical epidemiology is the study of how plant pathogens and the diseases that they cause are affected by the environment and plant host. To study these interactions botanical epidemiologists often turn to models to help understand how diseases develop, what the potential risks might be and how they could be or when they should be controlled. Many different modelling approaches can be used, which range from statistical models to biophysical crop growth models with functions for yield losses due to pests and disease. Geographic information systems (GIS) help us to piece together information that have geographic relationships to develop better understanding and promote further inquiries through the use of maps. Using modelling alone or by linking it with GIS we can extend our research to the field.

Spatial modelling of the effects of climate change on rice diseases in Tanzania and mapping common diseases in India for breeders to understand where different stresses occur or co-occur. Using these combined approaches can help us as plant pathologists to understand and communicate what is happening in the rice field and what possible risks may be and make recommendations. These recommendations can range from identifying stress-prone areas for research priorities or targeted deployment of resistant or tolerant varieties to providing farmers with recommendations for pest management giving us insights into what is happening in the rice field.

Adam H. Sparks

October 21, 2016
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  1. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    Using modelling and mapping
    for digital insights into diseases
    in the rice field
    Adam H. Sparks, Associate Professor
    Centre for Crop Health

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  2. –G. E. P. Box
    “Essentially, all models are wrong, but some are useful.”
    “All models are wrong; some models are useful”
    “Since all models are wrong the scientist
    cannot obtain a "correct" one by excessive
    elaboration.”
    "Is the model illuminating and useful?"

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  3. Two Tools
    From
    breeders’
    plots to
    farmers’
    fields

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  4. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    Tool # 1 - Models for rice diseases
    (No equations were harmed during the making of this presentation)

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  5. Losses due to
    pest injuries
    Actual Yield
    Production-Situation
    Driven Crop Growth
    Model
    (Attainable Yield)
    RICEPEST

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  6. Bacterial Leaf Blight - Asia
    Average AUDPC
    1983-1998
    EPIRICE

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  7. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    Biotic Stresses in India as a part of STRASA
    An EPIRICE example

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  8. The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by IRRI.

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  9. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    An example of the study of effects of climate change on
    diseases
    Linking EPIRICE and RICEPEST

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  10. Duku, C., Sparks, A. H. and Zwart, S. 2016. Spatial modelling of rice yield losses in Tanzania due to bacterial leaf
    blight and leaf blast in a changing climate. Climatic Change 135(3).
    EPIRICE RICEPEST
    2000 2030 2050
    0
    10
    20
    30
    40
    25
    50
    75
    100
    125
    25
    50
    75
    100
    125
    25
    50
    75
    100
    125
    Day of Season
    Leaf Coverage by Bacterial Leaf Blight Lesions (%)
    Emission
    Scenario
    A1B
    A2
    B1
    Base
    Bacterial Blight in TZA

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  11. Change in Yield Losses Due to Bacterial Blight, Tanzania
    Duku, C., Sparks, A. H. and Zwart, S. 2016. Spatial modelling of rice yield losses in Tanzania due to bacterial leaf
    blight and leaf blast in a changing climate. Climatic Change 135(3).

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  12. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    Tool # 2 - Surveys
    Creating actionable information
    from in-field data

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  13. View Slide

  14. View Slide

  15. Smart phones replace
    pen and paper data
    collection
    • Data collection forms
    • GPS for location and time
    • Camera for documentation
    and observations
    • Direct transfer of field data
    to cloud platform using
    mobile network
    • Rapid field information
    in event of a calamity or
    outbreak

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  16. Source PRISM

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  17. Source Natural Earth Data and PRISM

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  19. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    Using Survey Data
    Designing Crop Protection Regimes

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  20. Field BB BS DH DP FS GS HB LB LF LM LS WM
    F1
    F2
    F3
    F4
    F5
    F6
    F7
    Injury variables (31)
    high
    low
    Farmers’ fields
    (458)
    Incidence
    Syngenta/IRRI Scientific Knowledge Exchange Program

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  21. Data (relational)
    Node
    Edge
    Network model
    Graph theory
    Network analysis
    What a network can identify
    Highly central nodes
    Identification of important
    connectors
    A clustered region

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  22. Correlation analysis of an injury co-
    occurrence network in dry season in
    Central Plain, Thailand
    Syngenta/IRRI Scientific Knowledge Exchange Program

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  23. Correlation analysis of an injury co-
    occurrence network in wet season in
    Central Plain, Thailand
    Syngenta/IRRI Scientific Knowledge Exchange Program

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  24. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    RICE-PRE
    A prescription for rice crop health

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  25. RICE-PRE
    • A crop health syndrome model,
    • Inspired by EPIPRE by Zadoks (1981),
    • Based on agroecologies as defined by Nelson
    et al., and
    • Based on survey data from 456 lowland rice
    farmers’ fields in tropical and sub-tropical
    Asia
    Philippine Department of Agriculture FSSP

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  26. Agroecologies
    Agroecology code Description
    1 - IR Single season, irrigated rice, no other crop
    2 - IR / other Double season, irrigated rice / irrigated other crop
    3 - IR / IR Double season, irrigated rice / irrigated rice OR Triple season, irrigated rice /
    irrigated rice / irrigated rice
    4 - IR / IR / other Triple season, irrigated rice / irrigated rice / irrigated other
    5 - RF Single season, rainfed rice, no other crop
    6 - RF / RF Double season, rainfed rice / rainfed rice
    7 - RF / RF other Double season, rainfed rice / rainfed other
    8 - RF Dry/Upland Single season, rainfed rice, not bunded

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  27. RICE-PRE Plot Locations
    2011 to 2015
    N
    Philippine Department of Agriculture FSSP

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  28. • Use certified seed
    • Thorough land preparation and leveling
    • Varieties with good local adaptation (BPH, WH)
    • 5 cm of standing water up to DVS=80
    • IF TR = post emergence herbicide application at DVS=10 and DVS=20 or
    hand weeding or mechanical weeding
    • IF DS = pre-emergence herbicide and post emergence herbicide at DVS= 10
    • Mineral fertilizer (NPK) application suitable for location, per Rice Crop
    Manager
    • Do not spray insecticides
    Philippines Prescription

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  29. Results
    Philippine Department of Agriculture FSSP
    Sparks et al. unpublished

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  30. RICEPEST and EPIRICE
    • Mapped relative disease severity
    • Effects of climate change
    Surveys
    • PRiSM
    • Network Analysis
    • RICE-PRE
    Two Tools
    From breeders’ plots to farmers’ fields

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  31. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    Digital Insights
    From the field to the computer and
    back again
    Developing new ways of gathering,
    analysing and sharing data and
    information

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  33. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    Acknowledgements
    Bill and Melinda Gates Foundation – STRASA
    Syngenta – Syngenta/IRRI SKEP
    GIZ – MICCORDEA
    Philippine Department of Agriculture - PRiSM
    Sith Jaisong (IRRI, UPLB and Thai Rice
    Department)
    Mr Gertrudo Arida (PhilRice)
    Mr Edwin Martin (PhilRice)
    Dr. Nancy Castilla (IRRI)
    Dr. Joselito Villa (IRRI)

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  34. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
    adamhsparks
    @adamhsparks

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