Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review

Detalhes bibliográficos
Autor(a) principal: Barriguinha, André
Data de Publicação: 2021
Outros Autores: Neto, Miguel de Castro, Gil, Artur José Freire
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.3/6108
Resumo: Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.
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spelling Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature ReviewEstimationForecastingPredictionSystematic Literature ReviewVineyardYieldPurpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.MDPIRepositório da Universidade dos AçoresBarriguinha, AndréNeto, Miguel de CastroGil, Artur José Freire2021-11-13T14:07:35Z2021-092021-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.3/6108engBarriguinha, A., Neto, M. C. & Gil, A. (2021). Vineyard yield estimation, prediction, and forecasting: a systematic literature review. "Agronomy", 11(9), 1-27. [1789]. DOI:10.3390/agronomy1109178910.3390/agronomy110917892073-4395000699161500001info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-12-20T14:34:28Zoai:repositorio.uac.pt:10400.3/6108Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:28:14.377110Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
title Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
spellingShingle Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
Barriguinha, André
Estimation
Forecasting
Prediction
Systematic Literature Review
Vineyard
Yield
title_short Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
title_full Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
title_fullStr Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
title_full_unstemmed Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
title_sort Vineyard Yield Estimation, Prediction, and Forecasting : A Systematic Literature Review
author Barriguinha, André
author_facet Barriguinha, André
Neto, Miguel de Castro
Gil, Artur José Freire
author_role author
author2 Neto, Miguel de Castro
Gil, Artur José Freire
author2_role author
author
dc.contributor.none.fl_str_mv Repositório da Universidade dos Açores
dc.contributor.author.fl_str_mv Barriguinha, André
Neto, Miguel de Castro
Gil, Artur José Freire
dc.subject.por.fl_str_mv Estimation
Forecasting
Prediction
Systematic Literature Review
Vineyard
Yield
topic Estimation
Forecasting
Prediction
Systematic Literature Review
Vineyard
Yield
description Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.
publishDate 2021
dc.date.none.fl_str_mv 2021-11-13T14:07:35Z
2021-09
2021-09-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.3/6108
url http://hdl.handle.net/10400.3/6108
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Barriguinha, A., Neto, M. C. & Gil, A. (2021). Vineyard yield estimation, prediction, and forecasting: a systematic literature review. "Agronomy", 11(9), 1-27. [1789]. DOI:10.3390/agronomy11091789
10.3390/agronomy11091789
2073-4395
000699161500001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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