Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/191584 |
Resumo: | The complex and interactive effects of genotype (G), environment (E), and management (M) can be a barrier to the development of sound agronomic recommendations. We hypothesize that long-term variety performance trials (VPT) can be used to understand these effects and improve regional recommendations. Our objective was to explore long-term VPT data to improve management and variety-selection recommendations using winter wheat (Triticum aestivum L.) in the U.S. central Great Plains as a case-study. Data of grain yield, variety, and trial management were collected from 748 wheat VPT conducted in the states of Colorado, Kansas, and Oklahoma over nineteen harvest years (2000-2018) and 92 locations, resulting in 97,996 yield observations. Using 30-yr cumulative annual precipitation and growing degree-days, we partitioned the study region into 11 contiguous sub-regions, which we refer to as growing adaptation regions (GAR). We used variance component analysis, gradient boosted trees, and conditional inference trees to explore the management and variety trait effects within each GAR. For the variety trait analysis, the VPT dataset was reduced to account for varieties for which 17 agronomic traits and 11 disease/insect reaction ratings were available (65,264 yield observations). GAR accounted for 46% of the total variation in grain yield, M for 32%, residuals (including interactions) for 13%, year for 7%, and G for 2%. Conditional inference trees identified interactions among management practices and their effects on yield within each GAR. For instance, water regime was the most important practice influencing wheat yield in the semi-arid western portion of the study region, followed by sowing date and fungicide. In dryland trials, there was typically an interaction between fungicide, sowing date, and tillage system, depending on GAR. Other management practices (e.g. dual-purpose management, crop rotation, and tillage practice) also significantly affected yield, depending on GAR. The main variety trait associated with increased yields depended on region and management combination. For instance, drought tolerance was the most important trait in dryland trials while stripe rust tolerance was more relevant in irrigated trials in the semi-arid region. In this research, we demonstrated an approach that uses widely available long-term VPT data to improve management and variety selection recommendations and can be used in other regions and crops for which long-term VPT data are available. |
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Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheatExplorando ensaios de longa duração de performance de variedades para melhorar as recomendações de genótipo, manejo e ambiente: Um estudo de caso para o trigo de invernoConditional inference treesExploratory analysisTriticum aestivum L.Long-term dataManagement practicesAnálise exploratóriaDados de longa duraçãoPráticas de manejoThe complex and interactive effects of genotype (G), environment (E), and management (M) can be a barrier to the development of sound agronomic recommendations. We hypothesize that long-term variety performance trials (VPT) can be used to understand these effects and improve regional recommendations. Our objective was to explore long-term VPT data to improve management and variety-selection recommendations using winter wheat (Triticum aestivum L.) in the U.S. central Great Plains as a case-study. Data of grain yield, variety, and trial management were collected from 748 wheat VPT conducted in the states of Colorado, Kansas, and Oklahoma over nineteen harvest years (2000-2018) and 92 locations, resulting in 97,996 yield observations. Using 30-yr cumulative annual precipitation and growing degree-days, we partitioned the study region into 11 contiguous sub-regions, which we refer to as growing adaptation regions (GAR). We used variance component analysis, gradient boosted trees, and conditional inference trees to explore the management and variety trait effects within each GAR. For the variety trait analysis, the VPT dataset was reduced to account for varieties for which 17 agronomic traits and 11 disease/insect reaction ratings were available (65,264 yield observations). GAR accounted for 46% of the total variation in grain yield, M for 32%, residuals (including interactions) for 13%, year for 7%, and G for 2%. Conditional inference trees identified interactions among management practices and their effects on yield within each GAR. For instance, water regime was the most important practice influencing wheat yield in the semi-arid western portion of the study region, followed by sowing date and fungicide. In dryland trials, there was typically an interaction between fungicide, sowing date, and tillage system, depending on GAR. Other management practices (e.g. dual-purpose management, crop rotation, and tillage practice) also significantly affected yield, depending on GAR. The main variety trait associated with increased yields depended on region and management combination. For instance, drought tolerance was the most important trait in dryland trials while stripe rust tolerance was more relevant in irrigated trials in the semi-arid region. In this research, we demonstrated an approach that uses widely available long-term VPT data to improve management and variety selection recommendations and can be used in other regions and crops for which long-term VPT data are available.Os complexos efeitos de genótipo, ambiente e manejo podem ser uma barreira para o desenvolvimento de recomendações agronômicas sólidas. Nossa hipótese é que dados de longa duração de ensaios de performance de variedades (VPT) podem ser utilizados para entender esses efeitos e melhorar as recomendações regionais. Nosso objetivo foi explorar dados de longa duração de VPT para melhorar as recomendações de manejo e seleção de variedades utilizando trigo de inverno (Triticum aestivum L.) na região central das grandes planícies dos Estados Unidos da América como um estudo de caso. Dados de rendimento de grãos, variedades e manejo dos ensaios foram coletados de 748 VPT de trigo conduzidos nos estados do Colorado, Kansas e Oklahoma durante dezenove anos (2000-2018) e 92 locais, resultando em 97.996 observações de rendimento. Utilizando dados de 30 anos de precipitação anual acumulada e graus-dia, nós dividimos a região de estudo em 11 sub-regiões contíguas, as quais nos referimos como regiões de adaptação de cultivo (GAR). Nós utilizamos “variance componente analysis”, “gradiante boosted trees” e “conditional inference trees” para explorar os efeitos de manejo e características das variedades em cada GAR. Para a análise das características das variedades o conjunto de dados de VPT foi reduzido para considerar as variedades cuja informação referente a 17 características agronômicas e 11 reações a doença/inseto estava disponível (65.264 observações de rendimento). GAR representou por 46% da variação total no rendimento de grãos, manejo por 32%, resíduo (incluindo interações) por 13%, ano por 7% e genótipo por 2%. “Conditional inference trees” identificaram a presença de interação entre práticas de manejo e seus efeitos no rendimento de grãos em cada GAR. Por exemplo, regime hídrico foi a prática mais importante influenciando o rendimento de grãos de trigo na porção oeste e semiárida da região de estudo, seguido por data de semeadura e fungicida. Em ensaios de sequeiro, normalmente há uma interação entre aplicação de fungicida, data de semeadura e sistema de preparo do solo, dependendo da GAR. Outras práticas de manejo (e.g. manejo de duplo propósito, rotação de culturas e práticas de preparo do solo) também afetaram significativamente o rendimento de grãos, dependendo da GAR. A principal característica varietal associada a maiores rendimento de grãos foi dependente da combinação de região e manejo. Por exemplo, tolerância a seca foi a característica mais importante em ensaios de sequeiro, enquanto tolerância a ferrugem estriada foi mais relevante em ensaios irrigados na região semiárida. Nesta pesquisa demonstramos uma abordagem que utiliza dados de longa duração de VPT, amplamente disponíveis, para melhorar recomendações de manejo e de seleção de variedades e pode ser utilizado em outras regiões ou culturas para as quais dados de VPT são disponíveis.Universidade Estadual Paulista (Unesp)Unêda Trevisoli, Sandra Helena [UNESP]Universidade Estadual Paulista (Unesp)Munaro, Lucas Berger2020-02-12T14:25:51Z2020-02-12T14:25:51Z2019-12-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/11449/19158400092892633004102029P6enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESP2024-06-04T19:26:29Zoai:repositorio.unesp.br:11449/191584Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:23:24.157909Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat Explorando ensaios de longa duração de performance de variedades para melhorar as recomendações de genótipo, manejo e ambiente: Um estudo de caso para o trigo de inverno |
title |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat |
spellingShingle |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat Munaro, Lucas Berger Conditional inference trees Exploratory analysis Triticum aestivum L. Long-term data Management practices Análise exploratória Dados de longa duração Práticas de manejo |
title_short |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat |
title_full |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat |
title_fullStr |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat |
title_full_unstemmed |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat |
title_sort |
Exploring long-term performance variety trials to improve genotype, management, and environment recommendations : A case study for winter wheat |
author |
Munaro, Lucas Berger |
author_facet |
Munaro, Lucas Berger |
author_role |
author |
dc.contributor.none.fl_str_mv |
Unêda Trevisoli, Sandra Helena [UNESP] Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Munaro, Lucas Berger |
dc.subject.por.fl_str_mv |
Conditional inference trees Exploratory analysis Triticum aestivum L. Long-term data Management practices Análise exploratória Dados de longa duração Práticas de manejo |
topic |
Conditional inference trees Exploratory analysis Triticum aestivum L. Long-term data Management practices Análise exploratória Dados de longa duração Práticas de manejo |
description |
The complex and interactive effects of genotype (G), environment (E), and management (M) can be a barrier to the development of sound agronomic recommendations. We hypothesize that long-term variety performance trials (VPT) can be used to understand these effects and improve regional recommendations. Our objective was to explore long-term VPT data to improve management and variety-selection recommendations using winter wheat (Triticum aestivum L.) in the U.S. central Great Plains as a case-study. Data of grain yield, variety, and trial management were collected from 748 wheat VPT conducted in the states of Colorado, Kansas, and Oklahoma over nineteen harvest years (2000-2018) and 92 locations, resulting in 97,996 yield observations. Using 30-yr cumulative annual precipitation and growing degree-days, we partitioned the study region into 11 contiguous sub-regions, which we refer to as growing adaptation regions (GAR). We used variance component analysis, gradient boosted trees, and conditional inference trees to explore the management and variety trait effects within each GAR. For the variety trait analysis, the VPT dataset was reduced to account for varieties for which 17 agronomic traits and 11 disease/insect reaction ratings were available (65,264 yield observations). GAR accounted for 46% of the total variation in grain yield, M for 32%, residuals (including interactions) for 13%, year for 7%, and G for 2%. Conditional inference trees identified interactions among management practices and their effects on yield within each GAR. For instance, water regime was the most important practice influencing wheat yield in the semi-arid western portion of the study region, followed by sowing date and fungicide. In dryland trials, there was typically an interaction between fungicide, sowing date, and tillage system, depending on GAR. Other management practices (e.g. dual-purpose management, crop rotation, and tillage practice) also significantly affected yield, depending on GAR. The main variety trait associated with increased yields depended on region and management combination. For instance, drought tolerance was the most important trait in dryland trials while stripe rust tolerance was more relevant in irrigated trials in the semi-arid region. In this research, we demonstrated an approach that uses widely available long-term VPT data to improve management and variety selection recommendations and can be used in other regions and crops for which long-term VPT data are available. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-16 2020-02-12T14:25:51Z 2020-02-12T14:25:51Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/11449/191584 000928926 33004102029P6 |
url |
http://hdl.handle.net/11449/191584 |
identifier_str_mv |
000928926 33004102029P6 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
publisher.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.source.none.fl_str_mv |
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_ |
1808129514714169344 |