پیش آگاهی همه‌گیری بیماری زنگ زرد گندم دیم در استان کرمانشاه

نوع مقاله : بیماری‌شناسی گیاهی

نویسنده

بخش تحقیقات گیاهپزشکی، مرکز تحقیقات کشاورزی و منابع طبیعی کزمانشاه،

10.22092/jaep.2022.356751.1423

چکیده

این پژوهش با هدف پیش‌آگاهی همه‌گیری زنگ زرد، گندم‏زارهای دیم درچهار پهنه‌ی کانون آلودگی به این بیماری در شهرستان‌های سرپل‌ذهاب و گیلان‌غرب (گرمسیری) و اسلام‌‌آباد غرب و ماهیدشت (معتدل) استان کرمانشاه در سال‌های  97-1388 انجام شد و به‌منظور ارزیابی همبستگی داده‌های هواشناسی با رخداد همه‏گیری، ویژگی‌های زمینه‌ساز گسترش بیماری شناسایی و پایش شد. بیماری در سال‌های 91-1390 و 96-1395 و 97-1396 بدون بروز هیچ‌گونه جوش زنگ زرد در کانون‌های آلودگی استان، سال‌های 90-1389 و 92-1391 و 93-1392 و 94-1393 با آلودگی پایین و پراکنده، و سال‌های 89-1388 و 95-1394 با آلودگی بالا همه‌گیر شد. آزمون آماری تجزیه به مؤلفه اصلی (Principal component analysis) داده‌های هواشناسی و پایش بیماری در این دوره ده ساله نشان ‌داد که شمار روزهای بارانی از مهر تا اردیبهشت‌، دوره‌های دربرگیرنده روزهای پیاپی با دمای کمینه‌ی 9-6 درجه سلسیوس و نم بالای 60 درصد و بلندترین دوره روزانه با این ویژگی، میانگین بیشینه درصد نم بهمن‌ماه و میانگین کمینه دمای اسفندماه برترین نشانگرهای رخداد همه‌گیری زنگ زرد در هر چهار پهنه‌ی مورد بررسی بودند. دو ویژگی روزهای یخبندانی و روزهای با دمای کمینه‌ی زیر10- درجه سلسیوس برترین نشانگرهای پیش‌بینی زمان بروز بیماری بودند. همخوانی این یافته‌ها با مدل رگرسیون ترتیبی برازش شده نشان داد که پیش‌بینی سال همه‌گیری زنگ زرد در استان کرمانشاه و زمان بروز بیماری در دیم‌زارهای هر پهنه با کمک این شناسه‌های کلیدی در پایان اسفندماه هر سال انجام‌پذیر است. سپس کارآیی مدل برازش شده در پیش‌بینی صحیح، از نبود رخداد بیماری زنگ زرد در سال 1396 و آلودگی پایین در سال 1397 راستی‌آزمایی گردید.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting rain-fed wheat yellow rust epidemics in Kermanshah province

چکیده [English]

During ten-year study (2009-2018), rainfed wheat fields were selected to evaluate disease and examine associations of climatic data with wheat yellow rust disease epidemics across four regions, Sarpolzohab and Gilangharb (tropical), Eslamabad Gharb and Mahidasht (temprate). No disease was evident in 2009-2010, 2016-2017 and 2017-18 and there was low and sparce disease levels in remainder years. Principal component analysis of climatic and disease data indicated that number of rainy days from October to May, periods of consequential days with minimum temperature within 6-9˚C and maximum relative humidity > 60%, and longest period with these climatic characters were the best indicators of yellow rust disease epidemics occurrence across four study regions. Two characters of monthly average of maximum relative humidity for February and minimum temperature of March were also identified as important disease epidemics predictors. Two characteristics, number of icy days and days with minimum temperature under -10˚C were recognized as best indicators to predict disease onset time. To test efficiency of developed stripe-rust-predicting model, lack of or low disease occurrence for two years of 2017 and 2018 were predicted correctly.
 

کلیدواژه‌ها [English]

  • Disease occurrence
  • epidemic
  • rainfed land
  • stripe rust
ANONYMOUS. 2020. Agricultural Production Report. The Iranian Ministry of Agriculture, Tehran, Iran.
CHEN, X.M. 2005. Epidemiology and control of stripe rust Puccinia striiformis f.sp. tritici on wheat. Canadian Journal of Plant Pathology, 27: 314-337.
CHEN, X.M. 2007. Challenges and solutions for stripe rust control in the United States. Australian Journal of Agricultural Research, 58: 648-655.
COAKLEY, S.M., W.S. Boyd and R.F., Line. 1982. Statistical models for predicting stripe rust on winter wheat in the Pacific Northwest. Phytopathology, 72: 1539-1542.
COAKLEY, S.M., R.F., Line. and L.R., McDaniel. 1988. Predicting stripe rust severity on winter wheat using an improved method for analyzing meterological and rust data. Phytopathology, 78: 543-550.
DE VALLAVIEILLE-POPE C., L. HUBER., M. LECONTE. and H. GOYEAU. 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.
ELAHINIA, S.A. 2000. Assessment of urediniospore germination of Puccinia striiformis at various temperature on agar and detached leaves of wheat. Journal of Agricultural Science and Technology, 2:1-8.
EVERSMEYER, M.G. and J.R. BURLEIGH. 1969. A method of predicting epidemic development of wheat leaf rust. Phytopathology, 60: 805-811.
GRABOW, B.S., D.A. SHAH and E.D. DEWOLF. 2016. Environmental conditions associated with stripe rust in Kansas winter wheat. Plant Disease, 100: 2306-2312.
GLADDERS, P., S.D. LANGTON, I.A. BARRIE, N.V. HARDWICK, M.C. TAYLOR and N.D. PAVELEY. 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: 371-382.
JARROUDI, M.E., L. KOUADIO., C.H. BOCK., M.E. JARROUDI., J. JUNK., M. PASQUALI., H. MARAITE and P. DELFOSSE. 2017. A threshold-based weather model for predicting stripe rust infection in winter wheat. Plant Disease, 101:693-703.
LANDSCHOOT, S., W., WAEGEMAN., K. AUDENAERT., G. HAESAERT and D. BAETS. 2013. Ordinal regression models for predicting deoxynivalenol in winter wheat. Plant Pathology, 62:1319-1329.
LARGE, E.C. 1954. Growth stages in cereals. Plant Pathology, 3:128-129.
MILAM A.L., A.L. CARRIQUIRY., J. ZHAO and X.B. YANG. 2003. Impact of management practices on prevalence of soybean Sclerotinia stem rot in the north-central United States and on farmers’ decisions under uncertainty. Plant Disease, 87: 1048-1058.
NASERI, B. and F. JALILIAN. 2021. Characterization of leaf rust progress in wheat cultivars with different resistance levels and sowing dates. European Journal of Plant Pathology, 159:665-672.
NASERI, B. and H. KAZENI. 2020. Structural characterization of stripe rust progress in wheat crops sown at different planting dates. Heliyon, 6: e05328.
NASERI, B. and A.R. MAREFAT. 2018. Wheat stripe rust epidemics in interaction with climate, genotype and planting date. European Journal of Plant Pathology, 154:1077-1089.
NASERI, B. and P. SABETI. 2021. Analysis of the effects of climate, host resistance, maturity and sowing date on wheat stem rust epidemics. Journal of Plant Pathology, 103:197-205.
NASERI, B. and F. SHARIFI. 2019. Predicting wheat stripe rust epidemics according to influential climatic variables. Journal of Plant Protection Research, 59:519-528. 
NASERI, B. and M. SHEIKHOLESLAMI. 2021. Powdery mildew development is highly associated with a combination of sowing date, weather, wheat cultivar and maturity. Journal of Agricultural Science and Technology, 23:1367-1378.
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: 416-423.
RAPILLY, F. 1979. Yellow rust epidemiology. Annual Review of Phytopathology, 17: 59–73.
SHARMA-POUDYAL, D. and X.M. CHEN. 2011. Models for predicting potential yield loss of wheat caused by stripe rust in the U.S. Pacific Northwest. Phytopathology, 101: 544-554.
SIMKO, I. and H.P. PIEPHO. 2012. The area under the disease progress stairs: Calculation, advantage, and application. Phytopathology, 102:381-389.
TE BEEST, D.E., N.D. PAVELEY, M.W. SHAW and F. VAN DEN BOSCH. 2008. Disease-weather relationships for powdery mildew and yellow rust on winter wheat. Phytopathology, 98: 609-617.
VAN DEN BERG F. and F. VAN DEN BOSCH. 2007. The elasticity of the epidemic growth rate to observed weather patterns with an application to yellow rust. Phytopathology, 97: 1512-1518.
ZADOKS, J.C. 1961. Yellow rust on wheat: studies in epidemiology and physiologic specialization. Netherlands Journal of Plant Pathology, 67: 69-259.