Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s00484-021-02147-4 http://hdl.handle.net/11449/208689 |
Resumo: | Studies on the use of deficit irrigation and application of models for estimating agronomic performance of crops can help in more sustainable agricultural managements. The objective of this study was to evaluate the effect of irrigation levels on the agronomic performance of white oat (Avena sativa L.) and accuracy of the CERES-Barley model in simulating white oat growth and yield, as well as performing long-term simulation to identify the best sowing time for each irrigation management. The experiment consisted of five irrigation levels (11%, 31%, 60%, 87%, and 100%), being conducted in two agricultural years in southeastern Brazil. The model was calibrated with data of the treatment without water deficit (100%) of the first year and validated with the data of the other treatments in the 2 years. Long-term analyses, with a historical series of 16 years, were performed to recommend the best sowing dates for each irrigation management. Deficit irrigation linearly reduces the agronomic performance of white oat. The high accuracy of white oat yield estimation (R2 = 0.86; RMSE = 616 kg ha−1) using the CERES-Barley model allowed the long-term simulation for establishing the best sowing date for each irrigation level. For higher irrigation levels, sowing in periods with lower temperature (May and June) is more appropriate, as the 1 °C increment in the average temperature before flowering reduces crop yield by 600 kg ha−1. At irrigation levels with higher deficit, sowing in periods with higher rainfall (March and April) promotes higher crop yield. |
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Optimization of sowing date and irrigation levels for white oats using the CERES-Barley modelAvena sativa LDSSATGrain yieldModelingSowing timeStudies on the use of deficit irrigation and application of models for estimating agronomic performance of crops can help in more sustainable agricultural managements. The objective of this study was to evaluate the effect of irrigation levels on the agronomic performance of white oat (Avena sativa L.) and accuracy of the CERES-Barley model in simulating white oat growth and yield, as well as performing long-term simulation to identify the best sowing time for each irrigation management. The experiment consisted of five irrigation levels (11%, 31%, 60%, 87%, and 100%), being conducted in two agricultural years in southeastern Brazil. The model was calibrated with data of the treatment without water deficit (100%) of the first year and validated with the data of the other treatments in the 2 years. Long-term analyses, with a historical series of 16 years, were performed to recommend the best sowing dates for each irrigation management. Deficit irrigation linearly reduces the agronomic performance of white oat. The high accuracy of white oat yield estimation (R2 = 0.86; RMSE = 616 kg ha−1) using the CERES-Barley model allowed the long-term simulation for establishing the best sowing date for each irrigation level. For higher irrigation levels, sowing in periods with lower temperature (May and June) is more appropriate, as the 1 °C increment in the average temperature before flowering reduces crop yield by 600 kg ha−1. At irrigation levels with higher deficit, sowing in periods with higher rainfall (March and April) promotes higher crop yield.Department of Rural Engineering School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), Via de Acesso Prof. Paulo Donato Castellane, s/n, CEPDepartment of Rural Engineering School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), Via de Acesso Prof. Paulo Donato Castellane, s/n, CEPUniversidade Estadual Paulista (Unesp)Coelho, Anderson Prates [UNESP]de Faria, Rogério Teixeira [UNESP]Leal, Fábio Tiraboschi [UNESP]de Arruda Barbosa, José [UNESP]2021-06-25T11:16:17Z2021-06-25T11:16:17Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s00484-021-02147-4International Journal of Biometeorology.1432-12540020-7128http://hdl.handle.net/11449/20868910.1007/s00484-021-02147-42-s2.0-85106046332Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Biometeorologyinfo:eu-repo/semantics/openAccess2021-10-23T19:02:25Zoai:repositorio.unesp.br:11449/208689Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:27:37.868884Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model |
title |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model |
spellingShingle |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model Coelho, Anderson Prates [UNESP] Avena sativa L DSSAT Grain yield Modeling Sowing time |
title_short |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model |
title_full |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model |
title_fullStr |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model |
title_full_unstemmed |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model |
title_sort |
Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model |
author |
Coelho, Anderson Prates [UNESP] |
author_facet |
Coelho, Anderson Prates [UNESP] de Faria, Rogério Teixeira [UNESP] Leal, Fábio Tiraboschi [UNESP] de Arruda Barbosa, José [UNESP] |
author_role |
author |
author2 |
de Faria, Rogério Teixeira [UNESP] Leal, Fábio Tiraboschi [UNESP] de Arruda Barbosa, José [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Coelho, Anderson Prates [UNESP] de Faria, Rogério Teixeira [UNESP] Leal, Fábio Tiraboschi [UNESP] de Arruda Barbosa, José [UNESP] |
dc.subject.por.fl_str_mv |
Avena sativa L DSSAT Grain yield Modeling Sowing time |
topic |
Avena sativa L DSSAT Grain yield Modeling Sowing time |
description |
Studies on the use of deficit irrigation and application of models for estimating agronomic performance of crops can help in more sustainable agricultural managements. The objective of this study was to evaluate the effect of irrigation levels on the agronomic performance of white oat (Avena sativa L.) and accuracy of the CERES-Barley model in simulating white oat growth and yield, as well as performing long-term simulation to identify the best sowing time for each irrigation management. The experiment consisted of five irrigation levels (11%, 31%, 60%, 87%, and 100%), being conducted in two agricultural years in southeastern Brazil. The model was calibrated with data of the treatment without water deficit (100%) of the first year and validated with the data of the other treatments in the 2 years. Long-term analyses, with a historical series of 16 years, were performed to recommend the best sowing dates for each irrigation management. Deficit irrigation linearly reduces the agronomic performance of white oat. The high accuracy of white oat yield estimation (R2 = 0.86; RMSE = 616 kg ha−1) using the CERES-Barley model allowed the long-term simulation for establishing the best sowing date for each irrigation level. For higher irrigation levels, sowing in periods with lower temperature (May and June) is more appropriate, as the 1 °C increment in the average temperature before flowering reduces crop yield by 600 kg ha−1. At irrigation levels with higher deficit, sowing in periods with higher rainfall (March and April) promotes higher crop yield. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T11:16:17Z 2021-06-25T11:16:17Z 2021-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s00484-021-02147-4 International Journal of Biometeorology. 1432-1254 0020-7128 http://hdl.handle.net/11449/208689 10.1007/s00484-021-02147-4 2-s2.0-85106046332 |
url |
http://dx.doi.org/10.1007/s00484-021-02147-4 http://hdl.handle.net/11449/208689 |
identifier_str_mv |
International Journal of Biometeorology. 1432-1254 0020-7128 10.1007/s00484-021-02147-4 2-s2.0-85106046332 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Biometeorology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1808128655502606336 |