A St AtiS tic Al A ppro Ach for E Stim Ating Wh EAt Yi Eld USing Boot Str Ap rE SAmpling f or rA in- fEd fArming: A cASE St UdY of K UrdiS t An p rovinc E, ir An
Year: 2014
Type: article
Abstract: For the purpose of modeling and predicting rainfed wheat (Triticum aestivum) yield in Kurdistan province, Iran, fiveweather parameters, as well as three agrometeorological indices were used, as independent variables in linear regression modelsduring 1991-2003. The independent variables were extracted for different phenological phases during the plant-growingseason from sowing to harvest. Backward regression models were used to model rain-fed wheat yield and sensitivity analysiswas carried out on the models. On the basis of choosing the best models for each district and Kurdistan province (in the northwest of Iran), the bootstrap resampling method was run on them. Both above-mentioned models were validated for 2003-2006years data by estimating the rain-fed wheat yield. The results show that using bootstrap resampling method for modeling andestimating the crop yield increases the interior accuracy (increasing r, multiple correlation coefficient, from 0.84 to 0.98, anddecreasing SEOE , standard error of estimate, from 166 to 47 kg/ha) of the models.
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W2186012752