1. Mapping the extent of crop pests & diseases and their associated yield losses
Andy Nelson, ITC, University of Twente, Netherlands

2. Acknowledgements
Serge Savary and Laetitia Willocquet (INRA) Paul Esker (Penn State) Neil McRoberts (UC Davis) Sarah Pethybridge (Cornell)

3. My main interests in this field
Can we develop tools to enable the scoping of the potential importance or importance of plant diseases? And, can this be done on a global scale?
The idea being that it would help:
Research prioritization
Assess the risks of epidemics occurring
Identify geographic gaps in knowledge

4. Two examples of past and current work
1 Global scale estimates of potential disease epidemics 2 Global scale estimates of yield losses from pests & diseases

5. Mapping potential epidemics with a simple, generic GIS based model
Scoping the potential importance of diseases with EPRICE
EPIRICE was developed as a general model framework to address any rice (or plant) disease.
It was designed to be as simple as possible and can be linked to spatial data on weather, crop extent and crop calendars.
The EPIRICE model is a simple Suscept-Exposed-Infectious-Removed (SEIR) model.
It is parameterized using literature data for each disease and involves four state variables of a crop stand: healthy (H), latent (L), infectious (I), and post-infectious sites (P).
It maps the annual simulated potential epidemic.
Savary, S., Nelson, A. D., Willocquet, L., Pangga, I., & Aunario, J. (2012). Modeling and mapping potential epidemics of rice diseases globally. Crop Protection, 34, 6-17.

6. The EPIRICE model
EPIRICE models potential epidemics, meaning what could occur if no plant protection measures are taken to prevent or reduce disease.
The spatial component means that the outputs shows areas where the prevailing management and climate is conducive to rice cultivation.

7. Parameterising the model for five rice diseases
The model is parameterised for five rice diseases brown spot, leaf blast, bacterial blight, sheath blight, and rice tungro disease (a similar model has been developed for wheat diseases).
Example for Sheath Blight
Left: Observed disease dynamics
Right : Simulated dynamics: thick solid lines: proportion of total infected sites (I).
DACE = days after crop establishment

8. Making it spatial
The STELLA model was translated to R and linked to spatial data
Daily weather data at 1 degree resolution, globally
Date of crop establishment from collated crop calendars
Resulting in maps that show annual simulated potential epidemics (represented as the area under the disease progress curve, AUDPC)

9. Simulated potential epidemics for five rice diseases (12 year averages)

10. Possible improvements
Since then
The model has been expanded to wheat (EPIWHEAT) for brown rust and Septoria tritici blotch
We have improved
global crop calendar data (RICEATLAS) to estimate establishment dates and maturities.
rice crop extent maps to limit the simulation to rice growing areas
more detailed daily weather data and forecasts, allowing epidemic simulation with higher detail
So,
Can results from these simulations be used as a plausible geographic envelope for a disease?
Would incorporating climate and crop change information help prioritise areas for surveillance?

11. Mapping/estimating yield losses from pests & diseases
The frequency and extent of crop losses caused by plant diseases and pests is a gap in our knowledge and understanding of agrifood systems.
This information is crucial for developing sustainable strategies to manage crop health but, information losses is fragmented, heterogeneous, and incomplete.
So,
Is it possible to quantify the importance of crop diseases and pests?
Where to get the information to enable us to quantify this?

12. What sort of information do we need?
Can we
Estimate global and regional losses per crop on a pest and disease basis?
Determine if these estimates are plausible (inline with crop x region studies)?
Do this for a number of crops and a number of pests and diseases at the same time?

13. Five simple questions for crop health experts
In late 2016, the ISPP (International Society for Plant Pathology) Global Crop Loss Survey was launched to collect expert assessments on crop losses in five major staple crops. The survey asked the experts five simple questions:
Which crop are you reporting on? (wheat, maize, rice, potato or soybean)
Which pest/disease are you reporting on? (select from list or add your own)
Where does the pest/disease occur? (approximate location on a Google Map interface)
How often does it occur? (every season, every other season, 1 season in 5, less than 1 in 5)
What is the level of yield loss? (< 1%, 1 - 5%, %, %, > 60%)

14. Responses from the expert survey
Over a period of three months, we reached out to the ISPP network and elsewhere.
We received 990 responses from 219 crop health experts covering 67 countries:
wheat – 326 responses across 31 pests/diseases
maize – 138 responses across 38 pests/diseases
rice – 247 responses across 26 pests/diseases
potato – 154 across 17 pests/diseases
soybean – 125 responses across 25 pests/diseases

15. Expert responses per country
None
1 – 5
6 – 10
11 – 50
None
1 – 5
6 – 10
11 – 50
The boundaries, colours, denominations, and other information shown on this map do not imply any judgment on the part of the ISPP or the authors or the respondents concerning the legal status of any territory or the endorsement or acceptance of such boundaries.
globalcrophealth.org

16. relation to crop production
Responses per crop in
relation to crop production
The boundaries, colours, denominations, and other information shown on this map do not imply any judgment on the part of the ISPP or the authors or the respondents concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

17. Lessons from the survey
Reported yield losses from experts aligned well with the small number of available reports from crop and country specific studies (huge geographic gaps in available information)
Initial results suggest that global losses across these five staples are in the 20 to 30% range
We could estimate losses in a number of global regions, some hotspots are the Indo Gangetic Plain (40% losses for rice) and China (28% for wheat)
These can be broken down in to yield losses per region per pest and disease (in progress)
Some emerging or re-emerging pests were identified: Wheat (stem rust, stripe rust, wheat blast), rice (bacterial panicle blight, false smut), maize (fall armyworm, maize lethal necrosis, striga), potato (brown rot), soybean (soybean rust)

18. The role/value of global scale models, maps and surveys
Global scale representations are often coarse – but hopefully useful – generalisations that can help to raise the profile of an issue
Estimating the potential geographic extent of pests and diseases can provide clues to the location of future epidemics. We can do this with a spatial detail of 5-10km and incorporate forecasts, but we would need better information on current production situations (i.e. better socio-economic layers)
An expert survey can contribute to global information on the presence, frequency and yield losses across a range of crops on a pest and disease basis but completeness, bias and accuracy need to be addressed

19. Thanks More info dx.doi.org/10.1016/j.cropro.2011.11.009
globalcrophealth.org
research.utwente.nl/en/persons/andy-nelson

20. Table 1 from Savary, S. , Nelson, A. D. , Willocquet, L. , Pangga, I
Table 1 from Savary, S., Nelson, A. D., Willocquet, L., Pangga, I., & Aunario, J. (2012). Modeling and mapping potential epidemics of rice diseases globally. Crop Protection, 34, 6-17.

21. Table 2 from Savary, S. , Nelson, A. D. , Willocquet, L. , Pangga, I
Table 2 from Savary, S., Nelson, A. D., Willocquet, L., Pangga, I., & Aunario, J. (2012). Modeling and mapping potential epidemics of rice diseases globally. Crop Protection, 34, 6-17.