| Year |
Citation |
Score |
| 2023 |
Shah DA, De Wolf E, Paul PA, Madden LV. Into the trees: random forests for predicting Fusarium head blight epidemics of wheat in the United States. Phytopathology. PMID 36880796 DOI: 10.1094/PHYTO-10-22-0380-R |
0.613 |
|
| 2022 |
De Wolf E, Andersen Onofre K, Lollato R. Early Season Environmental Indicators of Wheat Stripe Rust Epidemics in Kansas and the Central Great Plains Region of United States. Plant Disease. PMID 36471459 DOI: 10.1094/PDIS-08-22-1873-RE |
0.306 |
|
| 2021 |
Shah DA, De Wolf ED, Paul PA, Madden LV. Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models. Plos Computational Biology. 17: e1008831. PMID 33720929 DOI: 10.1371/journal.pcbi.1008831 |
0.603 |
|
| 2019 |
Shah DA, Paul PA, De Wolf ED, Madden LV. Predicting plant disease epidemics from functionally represented weather series. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 374: 20180273. PMID 31056045 DOI: 10.1098/Rstb.2018.0273 |
0.606 |
|
| 2019 |
Ali S, Francl LJ, De Wolf ED. First Report of Pyrenophora tritici-repentis Race 5 from North America. Plant Disease. 83: 591. PMID 30849852 DOI: 10.1094/Pdis.1999.83.6.591A |
0.701 |
|
| 2018 |
Shah DA, De Wolf E, Paul PA, Madden LV. Functional data analysis of weather variables linked to Fusarium head blight epidemics in the United States. Phytopathology. PMID 29897307 DOI: 10.1094/Phyto-11-17-0386-R |
0.576 |
|
| 2014 |
Shah DA, De Wolf ED, Paul PA, Madden LV. Predicting Fusarium head blight epidemics with boosted regression trees. Phytopathology. 104: 702-14. PMID 24450462 DOI: 10.1094/Phyto-10-13-0273-R |
0.653 |
|
| 2013 |
Shah DA, Molineros JE, Paul PA, Willyerd KT, Madden LV, De Wolf ED. Predicting fusarium head blight epidemics with weather-driven pre- and post-anthesis logistic regression models. Phytopathology. 103: 906-19. PMID 23527485 DOI: 10.1094/Phyto-11-12-0304-R |
0.694 |
|
| 2007 |
Paul PA, Lipps PE, De Wolf E, Shaner G, Buechley G, Adhikari T, Ali S, Stein J, Osborne L, Madden LV. A Distributed Lag Analysis of the Relationship Between Gibberella zeae Inoculum Density on Wheat Spikes and Weather Variables. Phytopathology. 97: 1608-24. PMID 18943722 DOI: 10.1094/Phyto-97-12-1608 |
0.623 |
|
| 2006 |
Dufault NS, De Wolf ED, Lipps PE, Madden LV. Role of temperature and moisture in the production and maturation of Gibberella zeae perithecia Plant Disease. 90: 637-644. DOI: 10.1094/Pd-90-0637 |
0.463 |
|
| 2003 |
De Wolf ED, Madden LV, Lipps PE. Risk assessment models for wheat Fusarium head blight epidemics based on within-season weather data Phytopathology. 93: 428-435. DOI: 10.1094/Phyto.2003.93.4.428 |
0.646 |
|
| 2001 |
Friesen TL, De Wolf ED, Francl LJ. Source strength of wheat pathogens during combine harvest Aerobiologia. 17: 293-299. DOI: 10.1023/A:1013045517513 |
0.679 |
|
| 2000 |
De Wolf ED, Francl LJ. Neural network classification of tan spot and stagonospora blotch infection periods in a wheat field environment. Phytopathology. 90: 108-13. PMID 18944597 DOI: 10.1094/PHYTO.2000.90.2.108 |
0.754 |
|
| 1998 |
De Wolf ED, Francl LJ. Empirical infection period models for tan spot of wheat Canadian Journal of Plant Pathology. 20: 394-395. |
0.751 |
|
| 1998 |
De Wolf ED, Effertz RJ, Ali S, Francl LJ. Vistas of tan spot research Canadian Journal of Plant Pathology. 20: 349-370. |
0.686 |
|
| 1997 |
De Wolf ED, Francl LJ. Neural networks that distinguish infection periods of wheat tan spot in an outdoor environment Phytopathology. 87: 83-87. |
0.748 |
|
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