پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,996,904 |
| تعداد دریافت فایل اصل مقاله | 89,670,231 |
بررسی اثرات تنش های آب و هوایی بر شدت آسیب آفات و بیماری های میوه خرما | ||
| دانش گیاهپزشکی ایران | ||
| دوره 51، شماره 1، شهریور 1399، صفحه 39-53 اصل مقاله (905.34 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijpps.2020.290086.1006912 | ||
| نویسنده | ||
| مسعود لطیفیان* | ||
| سازمان تحقیقات، آموزش و ترویج کشاورزی، موسسه تحقیقات علوم باغبانی | ||
| چکیده | ||
| ریزش میوه، کرم میوه خوار (Batrachedra amydraula Meyrick)، کنه تارتن(Oligonychus afrasiaticus McGregor)، عارضه خشکیدگی خوشه و بیماری خامج (Mauginiella scaettae Cavara) از عوامل مهم خسارت درخت خرما میباشد. این تحقیق در طی سال‌های 1385 تا 1394 به مدت 10 سال در منطقه آبادان برای بررسی اثرات تنش‌های حرارتی و رطوبتی بر شدت آسیب و شبیه‌سازی مدل پیش‌آگاهی عوامل خسارت‌زای نخل خرما انجام گرفت. چهار نخلستان از چهار روستا به صورت تصادفی انتخاب و ماهانه از آنها تا هنگام برداشت میوه برای درصد آسیب میوه خرما نمونه‌برداری میشد. دادههای هواشناسی از ایستگاه سینوپتیک آبادان جمعآوری گردید. در طراحی سیستم از مدل‌های حرارتی، رطوبتی و رگرسیون چند متغیره استفاده شد. نتایج نشان داد که مقدار آسیب ریزش میوه، کرم میوه خوار، کنه تارتن، عارضه خشکیدگی خوشه و بیماری خامج به ترتیب در ماه‌های فروردین، خرداد، تیر، شهریور و فروردین همزمان با مرحله فنولوژیکی حبابوک، کیمری، خارک، تبدیل خارک به رطب و حبابوک به حداکثر میرسد. میزان آسیب این عوامل به ترتیب از دمای 4/21، 21، 7/26، 2/30 و 4/21 درجه سلسیوس و رطوبت نسبی 7/14، 20، 7/14، 3/21 و 9/27 درصد شروع شده و تا دمای9/40، 36، 50، 50 و 6/37 درجه سلسیوس به تدریج افزایش مییابد. مدل پیش‌آگاهی عومل آسیب‌زای خرما به ترتیب در سطح 1، 5، 5، 5 و 5 درصد معنی‌دار بوده‌اند. در تمام مدل‌های پیش‌آگاهی ضریب تبیین بالاتر از 7/0 و خطای تشخیص کم‌تر از 25 درصد بود. در میان شاخص‌های هواشناسی میزان رطوبت‌نسبی و بارندگی بیشترین تأثیر را در تغییرات شدت آسیب عوامل مورد بررسی داشتند. | ||
| کلیدواژه‌ها | ||
| خرما؛ عوامل آسیب؛ تنش های آب و هوایی | ||
| عنوان مقاله [English] | ||
| Effects of climatic stress on the severity of date palm fuits pests and diseases damages | ||
| نویسندگان [English] | ||
| Masoud Latifian | ||
| Agricultural Research, Extension and education organization, Horticulture science Research Institute | ||
| چکیده [English] | ||
| Fruits dropping, the lesser moth (Batrachedra amydraula Meyrick), spider mite (Oligonychus afrasiaticus McGregor), Date bunch fading and Date palm inflorescence rot diseases (Mauginiella scaettae Cavara) are important injurious factors of date palm. This research was carried out in Abadan region from 2005 to 2014 to study the effects of temperature and humidity stresses on injury severity and simulation of date palm damages prediction model. Four different date palm orchards from four villages were selected and they were sampled monthly for the percentage of date fruit damage until harvest. Climatic data were obtained from Abadan meteorology station. Multivariate regression, thermal and humidity models were used to design the system. Results showed that fruits dropping, the lesser moth, spider mite, date bunch fading and Khamedje diseases damages reached to the maximum at thhe months of April, June, July, September and April coincide with the phenological stage of the Hababok, Kimri, Khark, turning Khark into Rotab and Hababok respectively. The damage of these factors started at temperature 21.4, 21, 26.7, 30.2, 21.4oc and relative humidity 14.7, 20, 14.7, 21.3 and 27.9 gradually increases to 40.9, 36, 50, 50 and 37.6°C respectively. Forecasting model of damage factors have been significant at level 1, 5, 5, 5 and 5 percent respectively. All of the forecasting models had coefficient higher than 0.7 and the detection error less than 25 percent. Among the meteorological indices, relative humidity and rainfall had the most influence on the variations in the severitis of damages. | ||
| کلیدواژه‌ها [English] | ||
| Date palm, damage causing factors, weather stress | ||
| مراجع | ||
10. Chatterjee, HJaydeb, G., Senapti, S.K. & Ghosh, J. (2000). Influence of important weather parameters on population fluctuation on major insect pest of mandarin orange at darjeeling district of west Bengal. Journal of the Entomological Research Society, 24(3), 229-233.
11. Cunniffe, N. J., Koskella, B., Metcalf, J. E., Parnell, S., Gottwald, T. R. & Gilligan, C. A. (2015). Thirteen challenges in modelling plant diseases. Epidemics, 10, 6–10.
12. Dent, D. R. & Walton, M. P. (1999). Methods in ecological & Agricultural Entomology. CAB international, 387pp.
13. Dent, D. (1995) Integrated pest management. Chapmans & Hall. London, PP: 356.
14. Diaz, H. F., & Michael G. B. (1982). Inventory of sources of long term climatic data in microfilm and publication form. Asheville, N.C., National Climatic Center. 79 pp.
15. Esker, P. D., Savary, S. & McRoberts, N. (2012). Crop loss analysis and global food supply: focusing now on required harvests. CAB Reviews: perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 052. CAB Reviews 2012, pp. 1–14 7.
16. Gaston, K. J. (2003) The Structure and Dynamics of Geographic Ranges. Oxford University Press. Oxford. Loiselle, B.A. 2003. Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology, 17, 1591–1600.
17. Gendi, S. M. (1998) Population fluctuation of Thrips tabaci on onion plants under environmental condition. Arab Universities Journal of Agriculture science, 69(11). 267-276.
18. Gramaje, D., Baumgartner, K., Halleen, F., Mostert, L., Sosnowski, M. R., Urbez-Torres, J. R. & Armengol, J. (2016). Fungal trunk diseases: a problem beyond grapevines? The Plant Pathology Journal, 65, 355–356.
19. Gregory, P. J., Johnson, S. N., Newton, A. C. & Ingram, J. S. (2009). Integrating pests and pathogens into the climate change/food security debate. Journal of Experimental Botany, 60, 2827–2838.
20. Guedes, R. N. C., Zanuncio, T. V., Zanuncio, J. C. & Medeiros, A. G. (2000). Species richness and fluctuation of defolier Lepidoptera population in Brazilian plantation of Eucalyptus grandis as affected by plant age and weather factors. Forest ecology and management, 137, 179-184.
21. Harrington, R. & Clark, S. J. (2007). Environmental change and the phenology of European aphids. Global Change Biology, 13(8), 1550-1564.
22. Holzworth, D. P., Snow, V. O., Janssen, S., Athanasiadis, I. N., Donatelli, M., Hoogenboom, G., White, J. W. & Thorburn, P. (2015). Agricultural production systems modelling and software: current status and future prospects. Environmental Modelling & Software, 72, 276–286.
23. Khamis, H. J. (1990). The delta-corrected Kolmogorov-Smirnov test for goodness of fit. Journal of Statistical Planning and Inference, 24, 317-335.
24. Latifian, M. & Zaerae, M. (2009). The effects of climatic conditions on seasonal population fluctuation of date palm scale Parlatoria blanchardi Targ. (Hem.: Dispididae). Plant Protection Journal, 1, 277-286.
25. Lamichhane, J. R., Barzman, M., Booij. K., Boonekamp, P., Desneux, N., Huber, L., Kudsk, P., Langrell, S. R. H., Ratnadass, A., Ricci, P., Sarah, J. L. & Messéan, A. (2015). Robust cropping systems to tackle pests under climate change. A review. Agronomy for Sustainable Development, 35, 443–459.
26. Latifian, M. & Zare, M. (2003). The forecasting model of the date lesser moth (Batrachedra amydraula) based on climatic factors. Journal of Agricultural Science, 2, 27-36.
27. Latifian, M. & Solimannejadian, N. E. (2009). Study of the Lesser moth Batrachedra amydraula (Lep.: Batrachedridae) distribution based on geostatistical models in Khuzestan province. Journal of the Entomological Research, 1, 43-55.
28. Latifian, M. (2012). Study the effects of drought on date palm pests and diseases damage fluctuations. First National Conference Dates and food security. Ahwaz, Iran, 237-239.
29. Latifian, M. (2014a). Date palm spider mite (Oligonychus afrasiaticus McGregor) forecasting and monitoring system. WALIA Journal, 30, 79-85.
30. Latifian, M. (2014b). Study the effects of dusts phenomenon on date palm important pests and diseases. Int. Journal of Agricultural Research Science, 2, 8-15.
31. Latifian, M. (2015). Study the Effects of Dusts Phenomenon on Date Palm Important Pests and Diseases. International Journal of Research in Agricultural Sciences, 2(1), 8-15.
32. Lees, A. K., & Hilton, A. J. (2003). Black dot (Colletotrichum coccodes): an increasingly important disease of potato. Plant Pathology, 52, 3–12.
33. Lee, K. J., Kang, J. Y., Lee, D. Y., Jang, S. W., Lee, M. S., Lee, B. W. & Kim, K. S. (2015) Use of an empirical model to estimate leaf wetness duration for operation of a disease warning system under a shade in a ginseng field. Plant Diseases, 100, 25–31.
34. Machekano, H., Mvumi, B. M. & Nyamukondiwa, C. (2017). Diamondback moth, Plutella xylostella (L.) in Southern Africa: Research trends, challenges and insights on sustainable management options. Sustainability, 9, 91.
35. Madden, L. V. & Ellis, M. A. (1988). How to develop plant disease forecasters. Pages 191-208. in: Experimental Techniques in Plant Disease Epidemiology Rotem ed. Springer-Verlag., New York.
36. Masters, G., Baker, P. & Flood, J. (2010). Climate change and agricultural commodities. CABI Work, 2, 1–38
37. Mawby, W. D. & Gold, H. J. (1984). A stochastic simulation model for large-scale southern pine beetle (Dendroctonus frontalis Zimmerman) infestation dynamics in the southeastern United States. Researches in Population Ecology, 26, 275-283.
38. Newman, J. A., Gibson, D. J., Parsons, A. J. & Thornley, J. H. M. (2003). How predictable are aphid population responses to elevated CO2. Journal of Animal Ecology, 52, 556–566.
39. Parker, M. & Warmund, M. (2011). Effect of Temperature on Apple Trees - eXtension. Extension. http://articles.extension.org/pages/60619/effect-of-temperature -onapple- trees.
40. Savary, S., Teng, P. S., Willocquet, L., Nutter Jr., F. W. (2006). Quantification and modeling of crop losses: a review of purposes. The Annual Review of Phytopathology,44, 89–112.
41. Whish, J. P. M., Herrmann, N. I., White, N. A., Moore, A. D. & Kriticos, D. J. (2015). Integrating pest population models with biophysical cropmodels to better represent the farming system. Environmental Modelling & Software, 72, 418–425.
42. Wiedner, C. & Rucker, J. (2007). Climate change affects timing and size of populations of an invasive cyanobacterium in temperate regions. Oecologia, 152(3), 473-484.
43. Yang, X. B. & Navi, S. S. (2005). First report of charcoal rot epidemics caused by Macrophomina phaseolina in soybean in Iowa. Plant Diseases, 89, 526.
| ||
|
آمار تعداد مشاهده مقاله: 359 تعداد دریافت فایل اصل مقاله: 295 |
||