Diagnosis of nutrient composition in fruit crops: Major developments
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/B978-0-12-818732-6.00012-5 http://hdl.handle.net/11449/205148 |
Resumo: | Early methods of tissue diagnosis opposed the concept of critical nutrient concentrations supported by the laws of the minimum and the optimum to the more complex one of nutrient balances. But this proved to be a futile debate. Dual interactions and individual nutrient concentrations were later integrated into the Diagnosis and Recommendation Integrated System (DRIS). Methods of compositional data analysis (CoDa) corrected biases in DRIS on strong mathematical basis using centered (clr) and isomeric (ilr) log ratios. Log ratios were designed to adjust each nutrient to the level of others within the closed space or subspace of compositional data. Nutrient imbalance was assessed using a multivariate distance between given composition or ionome and a reference composition. In the near future, machine learning techniques and ionomics will allow predicting crop yield and quality using many more metadata collected in large data sets. Lines of research are proposed to improve models under international collaboration. |
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Diagnosis of nutrient composition in fruit crops: Major developmentsCritical concentrationMultivariate nutrient diagnosisNutrient balanceEarly methods of tissue diagnosis opposed the concept of critical nutrient concentrations supported by the laws of the minimum and the optimum to the more complex one of nutrient balances. But this proved to be a futile debate. Dual interactions and individual nutrient concentrations were later integrated into the Diagnosis and Recommendation Integrated System (DRIS). Methods of compositional data analysis (CoDa) corrected biases in DRIS on strong mathematical basis using centered (clr) and isomeric (ilr) log ratios. Log ratios were designed to adjust each nutrient to the level of others within the closed space or subspace of compositional data. Nutrient imbalance was assessed using a multivariate distance between given composition or ionome and a reference composition. In the near future, machine learning techniques and ionomics will allow predicting crop yield and quality using many more metadata collected in large data sets. Lines of research are proposed to improve models under international collaboration.Department of Soil and Agri-food Engineering Université LavalSão Paulo State University UNESPInstitute of Technical Assistance and Rural Extension of Paraná (EMATER-PR)Federal University of CearáSão Paulo State University UNESPUniversité LavalUniversidade Estadual Paulista (Unesp)Institute of Technical Assistance and Rural Extension of Paraná (EMATER-PR)Federal University of CearáParent, Léon EtienneRozane, Danilo Eduardo [UNESP]Deus, José Aridiano Lima deNatale, William2021-06-25T10:10:39Z2021-06-25T10:10:39Z2019-11-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart145-156http://dx.doi.org/10.1016/B978-0-12-818732-6.00012-5Fruit Crops: Diagnosis and Management of Nutrient Constraints, p. 145-156.http://hdl.handle.net/11449/20514810.1016/B978-0-12-818732-6.00012-52-s2.0-85087502171Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengFruit Crops: Diagnosis and Management of Nutrient Constraintsinfo:eu-repo/semantics/openAccess2021-10-23T10:45:19Zoai:repositorio.unesp.br:11449/205148Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T10:45:19Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Diagnosis of nutrient composition in fruit crops: Major developments |
title |
Diagnosis of nutrient composition in fruit crops: Major developments |
spellingShingle |
Diagnosis of nutrient composition in fruit crops: Major developments Parent, Léon Etienne Critical concentration Multivariate nutrient diagnosis Nutrient balance |
title_short |
Diagnosis of nutrient composition in fruit crops: Major developments |
title_full |
Diagnosis of nutrient composition in fruit crops: Major developments |
title_fullStr |
Diagnosis of nutrient composition in fruit crops: Major developments |
title_full_unstemmed |
Diagnosis of nutrient composition in fruit crops: Major developments |
title_sort |
Diagnosis of nutrient composition in fruit crops: Major developments |
author |
Parent, Léon Etienne |
author_facet |
Parent, Léon Etienne Rozane, Danilo Eduardo [UNESP] Deus, José Aridiano Lima de Natale, William |
author_role |
author |
author2 |
Rozane, Danilo Eduardo [UNESP] Deus, José Aridiano Lima de Natale, William |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Université Laval Universidade Estadual Paulista (Unesp) Institute of Technical Assistance and Rural Extension of Paraná (EMATER-PR) Federal University of Ceará |
dc.contributor.author.fl_str_mv |
Parent, Léon Etienne Rozane, Danilo Eduardo [UNESP] Deus, José Aridiano Lima de Natale, William |
dc.subject.por.fl_str_mv |
Critical concentration Multivariate nutrient diagnosis Nutrient balance |
topic |
Critical concentration Multivariate nutrient diagnosis Nutrient balance |
description |
Early methods of tissue diagnosis opposed the concept of critical nutrient concentrations supported by the laws of the minimum and the optimum to the more complex one of nutrient balances. But this proved to be a futile debate. Dual interactions and individual nutrient concentrations were later integrated into the Diagnosis and Recommendation Integrated System (DRIS). Methods of compositional data analysis (CoDa) corrected biases in DRIS on strong mathematical basis using centered (clr) and isomeric (ilr) log ratios. Log ratios were designed to adjust each nutrient to the level of others within the closed space or subspace of compositional data. Nutrient imbalance was assessed using a multivariate distance between given composition or ionome and a reference composition. In the near future, machine learning techniques and ionomics will allow predicting crop yield and quality using many more metadata collected in large data sets. Lines of research are proposed to improve models under international collaboration. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-11-27 2021-06-25T10:10:39Z 2021-06-25T10:10:39Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/B978-0-12-818732-6.00012-5 Fruit Crops: Diagnosis and Management of Nutrient Constraints, p. 145-156. http://hdl.handle.net/11449/205148 10.1016/B978-0-12-818732-6.00012-5 2-s2.0-85087502171 |
url |
http://dx.doi.org/10.1016/B978-0-12-818732-6.00012-5 http://hdl.handle.net/11449/205148 |
identifier_str_mv |
Fruit Crops: Diagnosis and Management of Nutrient Constraints, p. 145-156. 10.1016/B978-0-12-818732-6.00012-5 2-s2.0-85087502171 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fruit Crops: Diagnosis and Management of Nutrient Constraints |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
145-156 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965247682904064 |