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Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica)
Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflict...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3824108/ https://www.ncbi.nlm.nih.gov/pubmed/24273548 http://dx.doi.org/10.3389/fpls.2013.00449 |
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author | Parent, Serge-Étienne Parent, Léon E. Rozane, Danilo Eduardo Natale, William |
author_facet | Parent, Serge-Étienne Parent, Léon E. Rozane, Danilo Eduardo Natale, William |
author_sort | Parent, Serge-Étienne |
collection | PubMed |
description | Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflicting results and wrong inferences. Our objective was to present an unbiased statistical approach of plant nutrient diagnosis using a balance concept and mango (Mangifera indica) as test crop. We collected foliar samples at flowering stage in 175 mango orchards. The ionomes comprised 11 nutrients (S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Traditional multivariate methods were found to be biased. Ionomes were thus represented by unbiased balances computed as isometric log ratios (ilr). Soil fertility attributes (pH and bioavailable nutrients) were transformed into balances to conduct discriminant analysis. The orchards differed more from genotype than soil nutrient signatures. A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalanced and high/low-yielders. The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree(−1) proved to be a fairly informative test (area under curve = 0.84–0.92). The [P | N,S] and [Mn | Cu,Zn] balances were found to be potential sources of misbalance in the less productive orchards, and should thus be further investigated in field experiments. We propose using a coherent pan balance diagnostic method with median ilr values of top yielders centered at fulcrums of a mobile and the critical Mahalanobis distance as a guide for global nutrient balance. Nutrient concentrations in weighing pans assisted appreciating nutrients as relative shortage, adequacy or excess in balances. |
format | Online Article Text |
id | pubmed-3824108 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-38241082013-11-22 Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) Parent, Serge-Étienne Parent, Léon E. Rozane, Danilo Eduardo Natale, William Front Plant Sci Plant Science Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflicting results and wrong inferences. Our objective was to present an unbiased statistical approach of plant nutrient diagnosis using a balance concept and mango (Mangifera indica) as test crop. We collected foliar samples at flowering stage in 175 mango orchards. The ionomes comprised 11 nutrients (S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Traditional multivariate methods were found to be biased. Ionomes were thus represented by unbiased balances computed as isometric log ratios (ilr). Soil fertility attributes (pH and bioavailable nutrients) were transformed into balances to conduct discriminant analysis. The orchards differed more from genotype than soil nutrient signatures. A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalanced and high/low-yielders. The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree(−1) proved to be a fairly informative test (area under curve = 0.84–0.92). The [P | N,S] and [Mn | Cu,Zn] balances were found to be potential sources of misbalance in the less productive orchards, and should thus be further investigated in field experiments. We propose using a coherent pan balance diagnostic method with median ilr values of top yielders centered at fulcrums of a mobile and the critical Mahalanobis distance as a guide for global nutrient balance. Nutrient concentrations in weighing pans assisted appreciating nutrients as relative shortage, adequacy or excess in balances. Frontiers Media S.A. 2013-11-12 /pmc/articles/PMC3824108/ /pubmed/24273548 http://dx.doi.org/10.3389/fpls.2013.00449 Text en Copyright © 2013 Parent, Parent, Rozane and Natale. http://creativecommons.org/licenses/by/3.0/ 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) or licensor 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 Parent, Serge-Étienne Parent, Léon E. Rozane, Danilo Eduardo Natale, William Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
title | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
title_full | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
title_fullStr | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
title_full_unstemmed | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
title_short | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
title_sort | plant ionome diagnosis using sound balances: case study with mango (mangifera indica) |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3824108/ https://www.ncbi.nlm.nih.gov/pubmed/24273548 http://dx.doi.org/10.3389/fpls.2013.00449 |
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