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

• Published in: International journal of agronomy Vol. 2021; pp. 1 - 8
• Main Authors: Reed, Vaughn, Arnall, Daryl B., Finch, Bronc, Bigatao Souza, Joao Luis
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; Hindawi Limited
• Subjects: Agricultural production; Algorithms; Canopies; Crop yield; Crops; Estimates; Farm machinery; Fertilization; Fertilizers; Growing season; Nitrogen; Normalized difference vegetative index; Optical measuring instruments; Optimization; Optimization algorithms; Prediction models; Sensors; Smartphones; Soils; Vegetation; Vegetation index; Wheat; Winter; Winter wheat; United States > US; Oklahoma
• ISSN: 1687-8159; 1687-8167
• DOI: 10.1155/2021/1443191

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