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

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

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

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? www.nature.com/articles/541464a

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? www.isppweb.org/newsletters/pdf/46_11.pdf

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%) www.isppweb.org/newsletters/pdf/46_11.pdf

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 www.isppweb.org/newsletters/apr.html

Expert responses per country None 1 – 5 6 – 10 11 – 50 51 - 200 None 1 – 5 6 – 10 11 – 50 51 - 200 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

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.

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 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 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.