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Bazgeer, S., Kamali, Gh., Mortazavi, A.. (1386). Wheat yield prediction through agrometeorological indices for Hamedan, Iran. , 12(1), 33-38. doi: 10.22059/jdesert.2008.31063
S. Bazgeer; Gh. Kamali; A. Mortazavi. "Wheat yield prediction through agrometeorological indices for Hamedan, Iran". , 12, 1, 1386, 33-38. doi: 10.22059/jdesert.2008.31063
Bazgeer, S., Kamali, Gh., Mortazavi, A.. (1386). 'Wheat yield prediction through agrometeorological indices for Hamedan, Iran', , 12(1), pp. 33-38. doi: 10.22059/jdesert.2008.31063
Bazgeer, S., Kamali, Gh., Mortazavi, A.. Wheat yield prediction through agrometeorological indices for Hamedan, Iran. , 1386; 12(1): 33-38. doi: 10.22059/jdesert.2008.31063

Wheat yield prediction through agrometeorological indices for Hamedan, Iran

Desert
مقاله 5، دوره 12، شماره 1، شهریور 2007، صفحه 33-38    اصل مقاله (87.98 K)
شناسه دیجیتال (DOI): 10.22059/jdesert.2008.31063
نویسندگان
S. Bazgeer* 1؛ Gh. Kamali2؛ A. Mortazavi3
1Agrometeorological Division of I.R. of Iran Meteorological Organization,( IRIMO), Iran
2Scientific member of Atmospheric science & Meteorological Research center,(IRIMO), Iran
3Data Bank Management Division of I.R. of Iran Meteorological Organization,(IRIMO), Iran
چکیده
Yield prediction before harvesting is one of the tools in order to planning food production supply in future.Yield prediction was carried out for Wheat(Triticum aestivum) using different meteorological variables with agrometeorological indices in Hamedan district during 2003-04 and 2004-05. According to correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agrometeorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapour pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (27th of May). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agrometeorological indices.
کلیدواژه‌ها
Wheat yield prediction؛ Agrometeorological indices؛ Statistical models
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