Publications

Zhang, N; Zhao, C; Quiring, SM; Li, JL (2017). Winter Wheat Yield Prediction Using Normalized Difference Vegetative Index and Agro-Climatic Parameters in Oklahoma. AGRONOMY JOURNAL, 109(6), 2700-2713.

Abstract
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 similar to 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.

DOI:
10.2134/agronj2017.03.0133

ISSN:
Feb-62