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.

Transcript

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
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?"
3. Two Tools From breeders’ plots to farmers’ fields
4. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Tool # 1 - Models for rice diseases (No equations were harmed during the making of this presentation)
5. Losses due to pest injuries Actual Yield Production-Situation Driven Crop Growth Model (Attainable Yield) RICEPEST
6. Bacterial Leaf Blight - Asia Average AUDPC 1983-1998 EPIRICE
7. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Biotic Stresses in India as a part of STRASA An EPIRICE example
8. The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by IRRI.
9. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 An example of the study of effects of climate change on diseases Linking EPIRICE and RICEPEST
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
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).
12. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Tool # 2 - Surveys Creating actionable information from in-field data
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
16. Source PRISM
17. Source Natural Earth Data and PRISM
19. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Using Survey Data Designing Crop Protection Regimes
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
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
22. Correlation analysis of an injury co- occurrence network in dry season in Central Plain, Thailand Syngenta/IRRI Scientific Knowledge Exchange Program
23. Correlation analysis of an injury co- occurrence network in wet season in Central Plain, Thailand Syngenta/IRRI Scientific Knowledge Exchange Program
24. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 RICE-PRE A prescription for rice crop health
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
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
27. RICE-PRE Plot Locations 2011 to 2015 N Philippine Department of Agriculture FSSP
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
29. Results Philippine Department of Agriculture FSSP Sparks et al. unpublished
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
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
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)
34. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 adamhsparks @adamhsparks