Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)

Optical sensors have grown in popularity for estimating plant health, and they form the basis of midseason yield estimations nitrogen (N) fertilizer recommendations, such as Oklahoma State University (OSU) fertilization optimization algorithm (NFOA). That uses measurements normalized difference vegetative index (NDVI), yet not all producers access to required make these measurements. In contrast, most smartphones, which can measure fractional green canopy cover (FGCC) using Canopeo app, but usefulness remains untested. Our objectives were (1) quantify relationship between NDVI FGCC, (2) assess potential FGCC values place current OSU Yield Prediction Model, (3) compare performance FGCC-based prediction models from collected dataset. This project, implemented on 13 winter wheat sites over 2019-2020 growing season, used a range rates (0, 34, 67, 101, 134 kg N ha−1) provide different levels yield. results indicated that while are highly correlated (r2 = 0.76), is suitable direct insertion into model. However, model derived provided similar estimates compared (Nash Sutcliffe Efficiency −3.3). new will give more sensor-based rate recommendations.