Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review
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 inte...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/9/1789 |
| _version_ | 1797520621198901248 |
|---|---|
| author | André Barriguinha Miguel de Castro Neto Artur Gil |
| author_facet | André Barriguinha Miguel de Castro Neto Artur Gil |
| author_sort | André Barriguinha |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T07:59:21Z |
| format | Article |
| id | doaj.art-b35972692bee491b9812f2317890ade4 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T07:59:21Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-b35972692bee491b9812f2317890ade42023-11-22T11:38:23ZengMDPI AGAgronomy2073-43952021-09-01119178910.3390/agronomy11091789Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature ReviewAndré Barriguinha0Miguel de Castro Neto1Artur Gil2NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, PortugalNOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, PortugalIVAR-Research Institute for Volcanology and Risks Assessment, University of the Azores, 9500-321 Ponta Delgada, PortugalPurpose—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.https://www.mdpi.com/2073-4395/11/9/1789vineyardyieldestimationpredictionforecastingsystematic literature review |
| spellingShingle | André Barriguinha Miguel de Castro Neto Artur Gil Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review Agronomy vineyard yield estimation prediction forecasting systematic literature review |
| title | 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_short | Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review |
| title_sort | vineyard yield estimation prediction and forecasting a systematic literature review |
| topic | vineyard yield estimation prediction forecasting systematic literature review |
| url | https://www.mdpi.com/2073-4395/11/9/1789 |
| work_keys_str_mv | AT andrebarriguinha vineyardyieldestimationpredictionandforecastingasystematicliteraturereview AT migueldecastroneto vineyardyieldestimationpredictionandforecastingasystematicliteraturereview AT arturgil vineyardyieldestimationpredictionandforecastingasystematicliteraturereview |