Mapping the extent of crop
pests & diseases and their
associated yield losses
Acknowledgements
Serge Savary and Laetitia Willocquet (INRA)
Paul Esker (Penn State)
Neil McRoberts (UC Davis)
Sarah Pethybridge (Cornell)
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
1 Global scale estimates of potential disease epidemics
2 Global scale estimates of yield losses from pests & diseases
Two examples of past
and current work
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.
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.
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
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)
Simulated potential epidemics for
five rice diseases (12 year averages)
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?
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?
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?
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%, 5 - 20%, 20 - 60%, >
60%)
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
Expert responses per country
None 1 – 5 6 – 10 11 – 50 51 - 200 None 1 – 5 6 – 10 11 – 50 51 - 200 Responsesglobalcrophealth.org 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.
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.
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)
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
Thanks
More info
dx.doi.org/10.1016/j.cropro.2011.11.009
www.nature.com/articles/sdata201774
globalcrophealth.org
www.nature.com/articles/541464a
www.isppweb.org/newsletters/pdf/46_11.pdf
www.isppweb.org/newsletters/apr.html
research.utwente.nl/en/persons/andy-nelson
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.
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.