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A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments
Seeding rate in hard red spring wheat (HRSW; Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can eliminate the need for costly seeding rate research and growers using OSRs can maximize yield and seeding efficiency. Dat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326012/ https://www.ncbi.nlm.nih.gov/pubmed/32655595 http://dx.doi.org/10.3389/fpls.2020.00779 |
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author | Stanley, Jordan D. Mehring, Grant H. Wiersma, Jochum J. Ransom, Joel K. |
author_facet | Stanley, Jordan D. Mehring, Grant H. Wiersma, Jochum J. Ransom, Joel K. |
author_sort | Stanley, Jordan D. |
collection | PubMed |
description | Seeding rate in hard red spring wheat (HRSW; Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can eliminate the need for costly seeding rate research and growers using OSRs can maximize yield and seeding efficiency. Data were compiled from seeding rate studies conducted in 32 environments in the Northern Plains United States to determine the OSR of HRSW cultivars grown in diverse environments. Twelve cultivars with diverse genetic and phenotypic characteristics were evaluated at five seeding rates in 2013–2015, and nine cultivars were evaluated in 2017–2018. OSR varied among cultivar within environments. Cultivar x environment interactions were explored with the objective of developing a decision support system (DSS) to aid growers in determining the OSR for the cultivar they select, and for the environment in which it is sown. A 10-fold repeated cross-validation of the seeding rate data was used to fit 10 decision tree models and the most robust model was selected based on minimizing the value for model variance. The final decision tree model for predicting OSR of HRSW cultivars in diverse environments was considered the most reliable as bias was minimized by pruning methods, and model variance was acceptable for OSR predictions (RMSE = 1.24). Findings from this model were used to develop the grower DSS for determining OSR dependent on cultivar straw strength (as a measure of lodging resistance), tillering capacity, and yield of the environment. Recommendations for OSR ranged from 3.1 to 4.5 million seeds ha(–1). Growers can benefit from using this DSS by sowing at OSR relative to their average yields; especially when seeding new HRSW cultivars. |
format | Online Article Text |
id | pubmed-7326012 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73260122020-07-09 A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments Stanley, Jordan D. Mehring, Grant H. Wiersma, Jochum J. Ransom, Joel K. Front Plant Sci Plant Science Seeding rate in hard red spring wheat (HRSW; Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can eliminate the need for costly seeding rate research and growers using OSRs can maximize yield and seeding efficiency. Data were compiled from seeding rate studies conducted in 32 environments in the Northern Plains United States to determine the OSR of HRSW cultivars grown in diverse environments. Twelve cultivars with diverse genetic and phenotypic characteristics were evaluated at five seeding rates in 2013–2015, and nine cultivars were evaluated in 2017–2018. OSR varied among cultivar within environments. Cultivar x environment interactions were explored with the objective of developing a decision support system (DSS) to aid growers in determining the OSR for the cultivar they select, and for the environment in which it is sown. A 10-fold repeated cross-validation of the seeding rate data was used to fit 10 decision tree models and the most robust model was selected based on minimizing the value for model variance. The final decision tree model for predicting OSR of HRSW cultivars in diverse environments was considered the most reliable as bias was minimized by pruning methods, and model variance was acceptable for OSR predictions (RMSE = 1.24). Findings from this model were used to develop the grower DSS for determining OSR dependent on cultivar straw strength (as a measure of lodging resistance), tillering capacity, and yield of the environment. Recommendations for OSR ranged from 3.1 to 4.5 million seeds ha(–1). Growers can benefit from using this DSS by sowing at OSR relative to their average yields; especially when seeding new HRSW cultivars. Frontiers Media S.A. 2020-06-10 /pmc/articles/PMC7326012/ /pubmed/32655595 http://dx.doi.org/10.3389/fpls.2020.00779 Text en Copyright © 2020 Stanley, Mehring, Wiersma and Ransom. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Stanley, Jordan D. Mehring, Grant H. Wiersma, Jochum J. Ransom, Joel K. A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments |
title | A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments |
title_full | A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments |
title_fullStr | A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments |
title_full_unstemmed | A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments |
title_short | A Decision Support System to Guide Grower Selection of Optimal Seeding Rates of Wheat Cultivars in Diverse Environments |
title_sort | decision support system to guide grower selection of optimal seeding rates of wheat cultivars in diverse environments |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326012/ https://www.ncbi.nlm.nih.gov/pubmed/32655595 http://dx.doi.org/10.3389/fpls.2020.00779 |
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