Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe

n the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increasefood production while keeping pace with continued population growth. Conservation agriculture(CA) has been proposed to enhance soil health and productivity to respond to this situation.Maize is the main staple foo...

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Autores: Gracia-Romero, Adrian, Vergara Díaz, Omar, Thierfelder, Christian, Cairns, Jill E., Kefauver, Shawn Carlisle, Araus Ortega, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/145217
Acceso en línea:https://hdl.handle.net/2445/145217
Access Level:acceso abierto
Palabra clave:Àfrica
Agricultura de conservació
Teledetecció
Fenotip
Africa
Agricultural conservation
Remote sensing
Phenotype
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spelling Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in ZimbabweGracia-Romero, AdrianVergara Díaz, OmarThierfelder, ChristianCairns, Jill E.Kefauver, Shawn CarlisleAraus Ortega, José LuisÀfricaAgricultura de conservacióTeledeteccióFenotipAfricaAgricultural conservationRemote sensingPhenotypen the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increasefood production while keeping pace with continued population growth. Conservation agriculture(CA) has been proposed to enhance soil health and productivity to respond to this situation.Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes andmanagement practices for CA conditions has been explored using remote sensing tools. They may playa fundamental role towards overcoming the traditional limitations of data collection and processing inlarge scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) andmultispectral indexes were evaluated for assessing maize performance under conventional ploughing(CP) and CA practices. Eight hybrids under different planting densities and tillage practices weretested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmannedaerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution thatdid not have any negative impact on the performance of the indexes. Most of the calculated indexes(Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affectedby tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-imagesrelated to canopy greenness performed better at assessing yield differences, potentially due to thegreater resolution of the RGB compared with the multispectral data, although this performance wasmore precise for CP than CA.The correlations of the multispectral indexes with yield were improvedby applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels withvegetation. The results of this study highlight the applicability of remote sensing approaches basedon RGB images to the assessment of crop performance and hybrid choice.MDPI2019201920182019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion21 p.application/pdfapplication/pdfhttps://hdl.handle.net/2445/145217Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.3390/rs10020349Remote Sensing, 2018, vol. 10, num. 349https://doi.org/10.3390/rs10020349cc-by (c) Gracia Romero, Adrián et al., 2018http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1452172026-05-29T05:05:01Z
dc.title.none.fl_str_mv Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
title Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
spellingShingle Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
Gracia-Romero, Adrian
Àfrica
Agricultura de conservació
Teledetecció
Fenotip
Africa
Agricultural conservation
Remote sensing
Phenotype
title_short Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
title_full Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
title_fullStr Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
title_full_unstemmed Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
title_sort Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
dc.creator.none.fl_str_mv Gracia-Romero, Adrian
Vergara Díaz, Omar
Thierfelder, Christian
Cairns, Jill E.
Kefauver, Shawn Carlisle
Araus Ortega, José Luis
author Gracia-Romero, Adrian
author_facet Gracia-Romero, Adrian
Vergara Díaz, Omar
Thierfelder, Christian
Cairns, Jill E.
Kefauver, Shawn Carlisle
Araus Ortega, José Luis
author_role author
author2 Vergara Díaz, Omar
Thierfelder, Christian
Cairns, Jill E.
Kefauver, Shawn Carlisle
Araus Ortega, José Luis
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Àfrica
Agricultura de conservació
Teledetecció
Fenotip
Africa
Agricultural conservation
Remote sensing
Phenotype
topic Àfrica
Agricultura de conservació
Teledetecció
Fenotip
Africa
Agricultural conservation
Remote sensing
Phenotype
description n the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increasefood production while keeping pace with continued population growth. Conservation agriculture(CA) has been proposed to enhance soil health and productivity to respond to this situation.Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes andmanagement practices for CA conditions has been explored using remote sensing tools. They may playa fundamental role towards overcoming the traditional limitations of data collection and processing inlarge scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) andmultispectral indexes were evaluated for assessing maize performance under conventional ploughing(CP) and CA practices. Eight hybrids under different planting densities and tillage practices weretested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmannedaerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution thatdid not have any negative impact on the performance of the indexes. Most of the calculated indexes(Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affectedby tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-imagesrelated to canopy greenness performed better at assessing yield differences, potentially due to thegreater resolution of the RGB compared with the multispectral data, although this performance wasmore precise for CP than CA.The correlations of the multispectral indexes with yield were improvedby applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels withvegetation. The results of this study highlight the applicability of remote sensing approaches basedon RGB images to the assessment of crop performance and hybrid choice.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/145217
url https://hdl.handle.net/2445/145217
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/rs10020349
Remote Sensing, 2018, vol. 10, num. 349
https://doi.org/10.3390/rs10020349
dc.rights.none.fl_str_mv cc-by (c) Gracia Romero, Adrián et al., 2018
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Gracia Romero, Adrián et al., 2018
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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