Predicting wheat stripe rust epidemics according to influential climatic variables
Bita Naseri 1, A-F  
,   Farhad Sharifi 2
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Plant Protection Research Department, Kermanshah Agricultural & Natural Resources Research & Education Center, Kermanshah, Iran
Plant Protection Office, Agriculture Organization, Kermanshah, Iran
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Bita Naseri   

Plant Protection Research Department, Kermanshah Agricultural & Natural Resources Research & Education Center, Plant Protection Research Department, Kermanshah A, 67145-1661, Kermanshah, Iran
Submission date: 2019-04-09
Acceptance date: 2019-06-18
Online publication date: 2020-01-22
Journal of Plant Protection Research 2019;59(4):519–528
From 2009 to 2018, a total of 80 wheat crops were studied at plot and regional scales to predict stripe rust epidemics based on influential climatic indicators in Kermanshah province, Iran. Disease onset time and epidemic intensity varied spatially and temporarily. The disease epidemic variable was classified as having experienced nonepidemic, moderate or severe epidemics to be used for statistical analysis. Principal component analysis (PCA) was used to identify climatic variables associated with occurrence and intensity of stripe rust epidemics. Two principal factors accounting for 70% of the total variance indicated association of stripe rust epidemic occurrence with the number of icy days with minimum temperatures below 0°C (for subtropical regions) and below −10°C (for cool temperate and semi-arid regions). Disease epidemic intensity was linked to the number of rainy days, the number of days with minimum temperatures within the range of 7−8°C and relative humidity (RH) above 60%, and the number of periods involving consecutive days with minimum temperature within the range of 6−9°C and RH% > 60% during a 240-day period, from September 23 to May 21. Among mean monthly minimum temperatures and maximum relative humidity examined, mean maximum relative humidity for Aban (from October 23 to November 21) and mean minimum temperature for Esfand (from February 20 to March 20) indicated higher contributions to stripe rust epidemic development. Confirming PCA results, a multivariate logit ordinal model was developed to predict severe disease epidemics. The findings of this study improved our understanding of the combined interactions between air temperature, relative humidity, rainfall, and wheat stripe rust development over a three-season period of autumn-winter-spring.
The authors have declared that no conflict of interests exist.
This research was financially supported by the Iranian Agricultural Research, Education & Extension Organization, project no. 4-55-16-92197. The authors acknowledge all wheat growers who were involved in this study.
Afzal S.N., Haque M.I., Ahmedani M.S., Bashir S., Rattu A.R. 2007. Assessment of yield losses caused by Puccinia striiformis triggering stripe rust in the most common wheat varieties. Pakistan Journal of Botany 39 (6): 2127−2134.
Coakley S.M., Line R.F., McDaniel L.R. 1988. Predicting stripe rust severity on winter wheat using an improved method for analyzing meteorological and rust data. Phytopathology 78: 543−550. DOI: 10.1094/Phyto-78-543.
de Vallavieille-Pope C., Huber L., Leconte M., Goyeau H. 1995. Comparative effects of temperature and interrupted wet periods on germination, penetration, and infection of Puccinia recondita f. sp. tritici and P. striiformis on wheat seedlings. Phytopathology 85: 409−415. DOI: 10.1094/Phyto-85-409.
El Jarroudi M., Kouadio A., Bock C.H., El Jarroudi M., Junk J., Pasquali M. 2017. A threshold-based weather model for predicting stripe rust infection in winter wheat. Plant Disease 101 (5): 693–703. DOI: https://doi.org/10.1094/PDIS-1....
Gladders P., Langton S.D., Barrie I.A., Hardwick N.V., Taylor M.C., Paveley N.D. 2007. The importance of weather and agronomic factors for the overwinter survival of yellow rust (Puccinia striiformis) and subsequent risk in commercial wheat crops in England. Annals of Applied Biology 150 (3): 371−382. DOI: 10.1111/j.1744-7348.2007.00131.x.
Grabow B.S., Shah D.A., DeWolf E.D. 2016. Environmental conditions associated with stripe rust in Kansas winter wheat. Plant Disease 100 (11): 2306−2312. DOI: 10.1094/PDIS-11-15-1321-RE.
Jeger M.J. 2004. Analysis of disease progress as a basis for evaluating disease management practices. Annual Review of Phytopathology 42 (1): 61−82. DOI: 10.1146/annurev.phyto.42.040803.140427.
Milus E.A., Kristensen K., Hovmøller M.S. 2009. Evidence for increased aggressiveness in a recent widespread strain of Puccinia striiformis f. sp. tritici causing stripe rust of wheat. Phytopathology 99 (1): 89−94. DOI: 10.1094/PHYTO-99-1-0089.
Naseri B., Marefat A. 2019. Wheat stripe rust epidemics in interaction with climate, genotype and planting date. European Journal of Plant Pathology 154 (4): 1077–1089. DOI: 10.1007/s10658-019-01729-8.
Newlands N.K. 2018. Model-based forecasting of agricultural crop disease risk at the regional scale, integrating airborne inoculum, environmental, and satellite-based monitoring data. Frontiers in Environmental Science 6: 1−16. DOI: 10.3389/fenvs.2018.00063.
Park R.F. 1990. The role of temperature and rainfall in the epidemiology of Puccinia striiformis f. sp. tritici in the summer rainfall area of eastern Australia. Plant Pathology 39 (3): 416−423. DOI: https://doi.org/10.1111/j.1365....
Rapilly F. 1979. Yellow rust epidemiology. Annual Review of Phytopathology 17: 59–73.
Sharma S. 1996. Applied Multivariate Techniques. Wiley, New York, USA, 512 pp.
Sharma-Poudyal D., Chen X.M. 2011. Models for predicting potential yield loss of wheat caused by stripe rust in the U.S. Pacific Northwest. Phytopathology 101 (5): 544−554. DOI: 10.1094/PHYTO-08-10-0215.
Stubbs R.W. 1985. Stripe rust. p. 61−101. In: “The Cereal Rusts II. Diseases, Distribution, Epidemiology and Control” (A.P. Roelfs, W.R. Bushnell, eds.). Academic Press, New York, USA.
Te Beest D.E., Paveley N.D., Shaw M.W., van den Bosch F. 2008. Disease-weather relationships for powdery mildew and yellow rust on winter wheat. Phytopathology 98 (5): 609−617. DOI: 10.1094/PHYTO-98-5-0609.
van den Berg F., van den Bosch F. 2007. The elasticity of the epidemic growth rate to observed weather patterns with an application to yellow rust. Phytopathology 97 (11): 1512−1518. DOI: 10.1094/PHYTO-97-11-1512.
Zadoks J.C. 1961. Yellow rust on wheat: studies in epidemiology and physiologic specialization. Netherland Journal of Plant Pathology 67 (3): 69−259. DOI: https://doi.org/10.1007/BF0198....