Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-23T22:40:43.108Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved weather-based late blight risk management: comparing models with a ten year forecast archive

Published online by Cambridge University Press:  13 March 2014

K. M. BAKER ,
T. LAKE ,
S. F. BENSTON ,
R. TRENARY ,
P. WHARTON ,
L. DUYNSLAGER  and
W. KIRK
K. M. BAKER*
Affiliation:
Department of Geography, Western Michigan University, Kalamazoo, Michigan, USA
T. LAKE
Affiliation:
Department of Computer Science, Western Michigan University, Kalamazoo, Michigan, USA
S. F. BENSTON
Affiliation:
Department of Geography, Western Michigan University, Kalamazoo, Michigan, USA
R. TRENARY
Affiliation:
Department of Computer Science, Western Michigan University, Kalamazoo, Michigan, USA
P. WHARTON
Affiliation:
Aberdeen Research and Extension Center, University of Idaho, Aberdeen, Idaho, USA
L. DUYNSLAGER
Affiliation:
Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
W. KIRK
Affiliation:
Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Agroecosystem decision support systems typically rely on some types of weather data. Although many new digital weather and forecast datasets are gridded data, the current authors feel that evaluating previous methods with data of increased archive length is critical in aiding the transition to new datasets that lack extensive archives. To that end, the present paper reviews the improvements made to an artificial neural network for forecasting weather-based potato late blight (Phytophthora infestans) risk at 26 locations in the Great Lakes region. Accuracies of predictions made using an early model, developed in 2007, are compared with accuracies of predictions made using a new 10-year hourly optimized model. In nearly every comparison by month, forecast lead time and spatial region, the newly optimized model is more accurate, especially when the weather is conducive to high disease levels.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 2 , March 2015 , pp. 245 - 256
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Baker, K. M. & Kirk, W. W. (2007). Comparative analysis of models integrating synoptic forecast data into potato late blight risk estimate systems. Computers and Electronics in Agriculture 57, 23–32.Google Scholar
Baker, K. M., Kirk, W. W., Andresen, J. A. & Stein, J. M. (2004). A problem case study: influence of climatic trends on late blight epidemiology in potatoes. Acta Horticulturae 638, 37–42.Google Scholar
Baker, K. M., Kirk, W. W., Stein, J. M. & Andresen, J. A. (2005). Climatic trends and potato late blight risk in the Upper Great Lakes region. HortTechnology 15, 510–518.Google Scholar
Baker, K. M., Lake, T., Roehsner, P. & Schrantz, K. (2012). Forecasting disease with 10-year optimized models: moving toward new digital datasets. In First International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2012) , pp. 400–407. New York: IEEE.Google Scholar
Bondalapati, K. D., Stein, J. M. & Baker, K. M. (2012). Neural network model to predict deoxynivalenol (DON) in barley using historic and forecasted weather conditions. In First International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2012) , pp. 96–99. New York: IEEE.Google Scholar
Carroll, K. L. & Maloney, J. C. III (2004). Improvements in extended-range temperature and probability of precipitation guidance. In Preprints Symposium on the 50th Anniversary of Operational Numerical Weather Prediction, pp. 4.6–4.13. College Park, Maryland, USA: American Meteorological Society.Google Scholar
Caruana, R., Lawrence, S. & Giles, C. L. (2000). Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. Advances in Neural Information Processing Systems 13, 402–408.Google Scholar
COAPS: Center for Ocean-Atmospheric Prediction Studies (2009). ENSO Index According to JMA SSTA (1868–present) . Tallahassee, FL, USA: COAPS. Available from: http://www.coaps.fsu.edu/jma.shtml (accessed 20 November 2013).Google Scholar
Guenthner, J. F., Michael, K. C. & Nolte, P. (2001). The economic impact of potato late blight on US growers. Potato Research 44, 121–125.CrossRefGoogle Scholar
Hijmans, R. J., Forbes, G. A. & Walker, T. S. (2000). Estimating the global severity of potato late blight with GIS-linked disease forecast models. Plant Pathology 49, 697–705.Google Scholar
Japkowicz, N. (2000). Learning from imbalanced data sets: a comparison of various strategies. AAAI Workshop on Learning from Imbalanced Data Sets 68, 10–15.Google Scholar
Kirk, W. W. (2010). Potato Late Blight Update and Late Season Recommendations. East Lansing, MI, USA: Michigan State University Extension. Available from: http://msue.anr.msu.edu/news/potato_late_blight_update_and_late_season_recommendations (accessed 20 November 2013).Google Scholar
Kunst, R. M. (2008). Cross validation of prediction models for seasonal time series by parametric bootstrapping. Austrian Journal of Statistics 37, 271–284.Google Scholar
MacKenzie, D. (1981). Scheduling fungicide applications for potato late blight with Blitecast. Plant Disease 65, 394–399.Google Scholar
Maloney, J. C., Gilbert, K. K., Baker, M. N. & Shafer, P. E. (2010). GFS-based MOS Guidance: The Extended-range Alphanumeric Messages from the 0000/1200 UTC Forecast Cycles. Meteorological Development Laboratory Technical Procedures Bulletin 2010–01. Silver Spring, MD, USA: NOAA, US Department of Commerce. Available from: http://www.nws.noaa.gov/mdl/synop/tpb/mdltpb2010-01.pdf (accessed 20 November 2013).Google Scholar
MSU: Michigan State University. (2005). Michigan Potato Diseases: Late Blight Risk Monitoring. East Lansing, MI, USA: Michigan State University. Available from: http://www.lateblight.org/forecasting.php (accessed 20 November 2013).Google Scholar
NCDC: National Climactic Data Center (2013). Quality Controlled Local Climatological Data (QCLCD). Washington, DC: NCDC. Available from: http://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/quality-controlled-local-climatological-data-qclcd (accessed 20 November 2013).Google Scholar
NOAA ESRL-PSD: National Oceanic and Atmospheric Administration Earth System Research Laboratory Physical Sciences Division (2012). Climate Timeseries: AMO (Atlantic Multidecadal Oscillation) Index. Washington, DC: NOAA. Available from: http://www.esrl.noaa.gov/psd/data/timeseries/AMO/ (accessed 20 November 2013).Google Scholar
Roberts, M. J., Schimmelpfennig, D., Ashley, E., Livingston, M., Ash, M. & Vasavada, U. (2005). The Value of Plant Disease Early-warning Systems: A Case Study of USDA's Soybean Rust Coordinated Network. Economic Research Report 18. Washington, DC: USDA.Google Scholar
Savary, S., Nelson, A., Sparks, A. H., Willocquet, L., Hodson, D., Duveiller, E., Mahuku, G., Padgham, J., Forbes, G., Pande, S., Sharma, M., Garrett, K. A., Yuen, J. & Djurle, A. (2011). International agricultural research tackling the effects of global and climate changes on plant diseases in the developing world. Plant Disease 95, 1204–1216.Google Scholar
SRCC: Southern Regional Climate Center (2013). Climate Trends – State: MI. Baton Rouge, LA, USA: SRCC. Available from: http://charts.srcc.lsu.edu/trends/ (accessed 20 November 2013).Google Scholar
USDA: United States Department of Agriculture. (2013). USA Blight: A National Project on Tomato and Potato Late Blight in the United States. Late Blight Map. Washington, DC: USDA. Available from: http://www.usablight.org/map (accessed 20 November 2013).Google Scholar
Wallin, J. R. (1962). Summary of recent progress in predicting late blight epidemics in the United States and Canada. American Potato Journal 39, 306–312.Google Scholar
Wallin, J. R. & Schuster, M. L. (1960). Forcasting potato late blight in western Nebraska. Plant Disease Reporter 44, 896–900.Google Scholar
Wharton, P. S., Kirk, W. W., Baker, K. M. & Duynslager, L. (2008). A web-based interactive system for risk management of potato late blight in Michigan. Computers and Electronics in Agriculture 61, 136–148.Google Scholar