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Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn
Recent research shows significant economic benefit if the processing sweet corn [Zea mays L. var. rugosa (or saccharata)] industry grew crowding stress tolerant (CST) hybrids at their optimum plant densities, which may exceed current plant densities by up to 14,500 plants ha(-1). However, optimum pl...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006923/ https://www.ncbi.nlm.nih.gov/pubmed/32032371 http://dx.doi.org/10.1371/journal.pone.0228809 |
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author | Dhaliwal, Daljeet S. Williams, Martin M. |
author_facet | Dhaliwal, Daljeet S. Williams, Martin M. |
author_sort | Dhaliwal, Daljeet S. |
collection | PubMed |
description | Recent research shows significant economic benefit if the processing sweet corn [Zea mays L. var. rugosa (or saccharata)] industry grew crowding stress tolerant (CST) hybrids at their optimum plant densities, which may exceed current plant densities by up to 14,500 plants ha(-1). However, optimum plant density of individual fields varies over years and across the Upper Midwest (Illinois, Minnesota and Wisconsin), where processing sweet corn is concentrated. The objectives of this study were to: (1) determine the extent to which environmental and management practices affect optimum plant density and, (2) identify the most appropriate recommendation domain for making decisions on plant density. To capture spatial and temporal variability in optimum plant density, on-farm experiments were conducted at thirty fields across the states of Illinois, Minnesota and Wisconsin, from 2013 to 2017. Exploratory factor analysis of twelve environmental and management variables revealed two factors, one related to growing period and the other defining soil type, which explained the maximum variability observed across all the fields. These factors were then used to quantify the strength of associations with optimum plant density. Pearson’s partial correlation coefficients of ‘growing period’ and ‘soil type’ with optimum plant density were low (ρ(1) = -0.14 and ρ(2) = -0.09, respectively) and non-significant (P = 0.47 and 0.65, respectively). To address the second objective, six candidate recommendation domain models (RDM) were developed and tested. Linear mixed effects models describing crop response to plant density were fit to each level of each candidate RDM. The difference in profitability observed at the current plant density for a field and the optimum plant density under RDM level represented the additional processor profit ($ ha(-1)) from a field. The RDM built around ‘Production Area’ (RDM(PA)) appears most suitable, because plant density recommendations based on RDM(PA) maximized processor profits as well grower returns better than other RDMs. Compared to current plant density, processor profits and grower returns increased by $448 ha(-1) and $82 ha(-1), respectively at plant densities under RDM(PA). |
format | Online Article Text |
id | pubmed-7006923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70069232020-02-20 Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn Dhaliwal, Daljeet S. Williams, Martin M. PLoS One Research Article Recent research shows significant economic benefit if the processing sweet corn [Zea mays L. var. rugosa (or saccharata)] industry grew crowding stress tolerant (CST) hybrids at their optimum plant densities, which may exceed current plant densities by up to 14,500 plants ha(-1). However, optimum plant density of individual fields varies over years and across the Upper Midwest (Illinois, Minnesota and Wisconsin), where processing sweet corn is concentrated. The objectives of this study were to: (1) determine the extent to which environmental and management practices affect optimum plant density and, (2) identify the most appropriate recommendation domain for making decisions on plant density. To capture spatial and temporal variability in optimum plant density, on-farm experiments were conducted at thirty fields across the states of Illinois, Minnesota and Wisconsin, from 2013 to 2017. Exploratory factor analysis of twelve environmental and management variables revealed two factors, one related to growing period and the other defining soil type, which explained the maximum variability observed across all the fields. These factors were then used to quantify the strength of associations with optimum plant density. Pearson’s partial correlation coefficients of ‘growing period’ and ‘soil type’ with optimum plant density were low (ρ(1) = -0.14 and ρ(2) = -0.09, respectively) and non-significant (P = 0.47 and 0.65, respectively). To address the second objective, six candidate recommendation domain models (RDM) were developed and tested. Linear mixed effects models describing crop response to plant density were fit to each level of each candidate RDM. The difference in profitability observed at the current plant density for a field and the optimum plant density under RDM level represented the additional processor profit ($ ha(-1)) from a field. The RDM built around ‘Production Area’ (RDM(PA)) appears most suitable, because plant density recommendations based on RDM(PA) maximized processor profits as well grower returns better than other RDMs. Compared to current plant density, processor profits and grower returns increased by $448 ha(-1) and $82 ha(-1), respectively at plant densities under RDM(PA). Public Library of Science 2020-02-07 /pmc/articles/PMC7006923/ /pubmed/32032371 http://dx.doi.org/10.1371/journal.pone.0228809 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Dhaliwal, Daljeet S. Williams, Martin M. Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn |
title | Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn |
title_full | Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn |
title_fullStr | Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn |
title_full_unstemmed | Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn |
title_short | Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn |
title_sort | understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006923/ https://www.ncbi.nlm.nih.gov/pubmed/32032371 http://dx.doi.org/10.1371/journal.pone.0228809 |
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