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Winter Wheat Yield Prediction Using Normalized Difference Vegetative Index and Agro‐Climatic Parameters in Oklahoma
Author(s) -
Zhang Ning,
Zhao Chen,
Quiring Steven M.,
Li Jinlin
Publication year - 2017
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2017.03.0133
Subject(s) - yield (engineering) , normalized difference vegetation index , anthesis , winter wheat , environmental science , moisture , vegetation (pathology) , regression analysis , index (typography) , mathematics , growing degree day , agronomy , stepwise regression , climatology , atmospheric sciences , leaf area index , statistics , meteorology , geography , cultivar , phenology , biology , geology , medicine , materials science , pathology , world wide web , computer science , metallurgy
Core Ideas Normalized difference vegetative index has a stronger correlation with yield than moisture and temperature indices. Optimal winter wheat model includes normalized difference vegetative index, moisture and temperature variables. All three variables have different periods during which they are important. Model can accurately predict winter wheat yield one month before harvest. Gridded data outperform station‐based data for county‐level yield prediction.This article develops a model for predicting winter wheat ( Triticum aestivum L.) yield variations in Oklahoma, based on vegetation, moisture, and temperature conditions. A common model structure is identified using stepwise regression with one vegetation indicator (normalized difference vegetative index, NDVI) during wheat jointing and anthesis stages (March and April), one moisture indicator at emergence period (October and November), and one temperature indicator (temperature index, TI) at emergence, jointing and anthesis stages (October, March, and April). The final model accounts for ∼70% of the variation in winter wheat yield and can be used to forecast yields 1 mo before harvest. Spatially, it performs best in the northern and central portions of the Oklahoma winter wheat belt. Model performance is similar regardless of which moisture index is used. The correctly predicted yield variations in at least 9 of the 14 counties every year, and in the best case it correctly predicted yield variation in all counties. Our results also demonstrate that the gridded meteorological data generally outperforms the station‐based data for yield prediction at county level. The methods used in this study can be applied to identify the most significant variables and growth stages for winter wheat yield prediction in other regions.