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

Vaughn Reed,Joao Luis Bigatao Souza,Bronc Finch,Daryl B Arnall,Mathias N Andersen

doi:10.1155/2021/1443191

Vaughn Reed, Joao Luis Bigatao Souza + Show 3 more

Open Access

https://doi.org/10.1155/2021/1443191

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Abstract

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.

Highlights

Sensor-based yield prediction technology can be an important decision support tool for producers across the United States, with the resulting near real time yield estimates allowing farmers to optimize fertilizer application rates
Lukina et al [4] noted that normalized difference vegetation index (NDVI) measurements could be useful for in-season yield estimates, which could in turn be used to develop N rate recommendations through the nitrogen fertilization optimization algorithm (NFOA)
While NDVI and fractional green canopy cover (FGCC) are grounded on inherently different technologies, we found that these measurements are strongly correlated (r2 0.76, Figure 2)

Summary

Introduction

Sensor-based yield prediction technology can be an important decision support tool for producers across the United States, with the resulting near real time yield estimates allowing farmers to optimize fertilizer application rates. Associated fertilizer recommendations, can be made using normalized difference vegetation index (NDVI) measurements collected using instruments mounted to farm equipment or handheld sensors. While such sensors provide valuable information, the costs and availability of NDVI sensors can deter producers from adopting them [1]. Lukina et al [4] noted that NDVI measurements could be useful for in-season yield estimates, which could in turn be used to develop N rate recommendations through the nitrogen fertilization optimization algorithm (NFOA). NDVI is based on reflectance of near infrared light (NIR) and red light, ranging from 0 to 1, with higher values coming from healthier (i.e., greener) plants [6]: NDVI

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