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 and nitrogen (N) fertilizer recommendations, such as the Oklahoma State University (OSU) nitrogen fertilization optimization algorithm (NFOA). That algorithm uses measurements...

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Autores principales:	Vaughn Reed, Daryl B. Arnall, Bronc Finch, Joao Luis Bigatao Souza
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/140ce488ec9f4836a8df704f77c3aa0f
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spelling	oai:doaj.org-article:140ce488ec9f4836a8df704f77c3aa0f2021-11-29T00:56:23ZPredicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)1687-816710.1155/2021/1443191https://doaj.org/article/140ce488ec9f4836a8df704f77c3aa0f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1443191https://doaj.org/toc/1687-8167Optical 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.Vaughn ReedDaryl B. ArnallBronc FinchJoao Luis Bigatao SouzaHindawi LimitedarticleAgriculture (General)S1-972ENInternational Journal of Agronomy, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Agriculture (General) S1-972
spellingShingle	Agriculture (General) S1-972 Vaughn Reed Daryl B. Arnall Bronc Finch Joao Luis Bigatao Souza Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)
description	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.
format	article
author	Vaughn Reed Daryl B. Arnall Bronc Finch Joao Luis Bigatao Souza
author_facet	Vaughn Reed Daryl B. Arnall Bronc Finch Joao Luis Bigatao Souza
author_sort	Vaughn Reed
title	Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)
title_short	Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)
title_full	Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)
title_fullStr	Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)
title_full_unstemmed	Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)
title_sort	predicting winter wheat grain yield using fractional green canopy cover (fgcc)
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/140ce488ec9f4836a8df704f77c3aa0f
work_keys_str_mv	AT vaughnreed predictingwinterwheatgrainyieldusingfractionalgreencanopycoverfgcc AT darylbarnall predictingwinterwheatgrainyieldusingfractionalgreencanopycoverfgcc AT broncfinch predictingwinterwheatgrainyieldusingfractionalgreencanopycoverfgcc AT joaoluisbigataosouza predictingwinterwheatgrainyieldusingfractionalgreencanopycoverfgcc
_version_	1718407690823139328

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

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