Saeed Bazgeer - Assistant Professor, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran

Crop yield prediction is important for advanced planning, formulation and implementation of policies relating to food procurement, distribution, and import-export decision. This paper aimed to predict corn yield using statistical models. Meteorological variables including maximum and minimum temperature (Tmax and Tmin, respectively), relative humidity (RH), sunshine hours (SH) and evaporation (E) and NDVI (Normalized Difference Vegetation Index) were used during corn growth season (July ۱۱ to November ۲۱) from ۲۰۰۱ to ۲۰۲۰. The regression models were developed during crop growth stages of corn using regression model in Darab agrometeorological research center, Fars province, South of Iran. The estimated corn yield models were developed. The applicable time to estimate corn yield was on the basis of data from anther development stage, approximately ۲ months before harvesting time (September ۱st). The model explained ۷۵% corn yield variability. The NDVI with Tmax incorporated in regression model as predictors.

Corn Grain Yield, Meteorological Variables, Regression models, Fars province.