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  Assessing the grapevine crop water stress indicator over the flowering-veraison phase and the potential yield lose rate in important European wine regions

Yang, C., Menz, C., Fraga, H., Costafreda-Aumedes, S., Leolini, L., Ramos, M. C., Molitor, D., van Leeuwen, C., Santos, J. A. (2022): Assessing the grapevine crop water stress indicator over the flowering-veraison phase and the potential yield lose rate in important European wine regions. - Agricultural Water Management, 261, 107349.
https://doi.org/10.1016/j.agwat.2021.107349

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 Creators:
Yang, Chenyao1, Author
Menz, Christoph2, Author              
Fraga, Helder1, Author
Costafreda-Aumedes, Sergi1, Author
Leolini, Luisa1, Author
Ramos, Maria Concepción1, Author
Molitor, Daniel1, Author
van Leeuwen, Cornelis1, Author
Santos, João A.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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Free keywords: Grapevine modelling, Drought stress, Yield gap, Phenology network, STICS, Regional crop modelling
 Abstract: In Europe, most of vineyards are managed under rainfed conditions, where water deficit has become increasingly an issue. The flowering-veraison phenophase represents an important period for vine response to water stress, which is known to depend on variety characteristics, soil and climate conditions. In this paper, we have carried out a retrospective analysis for important European wine regions over 1986–2015, with objectives to assess the mean Crop Water Stress Indicator (CWSI) during flowering-veraison phase, and potential Yield Lose Rate (YLR) due to seasonal cumulative water stress. Moreover, we also investigate if advanced flowering-veraison phase can lead to alleviated CWSI under recent-past conditions, thus contributing to reduced YLR. A process-based grapevine model is employed, which has been extensively calibrated for simulating both flowering and veraison stages using location-specific observations representing 10 different varieties. Subsequently, grid-based modelling is implemented with gridded climate and soil datasets and calibrated phenology parameters. The findings suggest wine regions with higher mean CWSI of flowering-veraison phase tend to have higher potential YLR. However, contrasting patterns are found between wine regions in France-Germany-Luxembourg and Italy-Portugal-Spain. The former tends to have slight-to-moderate drought conditions (CWSI<0.5) along with a negligible-to-moderate YLR (<30%), whereas the latter is found to have severe-to-extreme drought (CWSI>0.5) and substantial YLR (>40%). Wine regions prone to a high drought risk (CWSI>0.75) are also identified, which are concentrated in southern Mediterranean Europe. Advanced flowering-veraison phase over 1986–2015, could have benefited from more spring precipitation and cooler temperatures for wine regions of Italy-Portugal-Spain, leading to reduced mean CWSI and YLR. For those of France-Germany-Luxembourg, this can have reduced flowering-veraison precipitation, but prevalent reductions of YLR are also found, possibly due to shifted phase towards a cooler growing-season with reduced evaporative demands. Our study demonstrates flowering-verasion water deficit is critical for potential yield, which can have different impacts between Central and Southern European wine regions. This phase can be advanced under a warmer climate, thus having important implications for European rainfed vineyards. The overall outcome may provide new insights for appropriate viticultural management of seasonal water deficits under climate change.

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Language(s): eng - English
 Dates: 2021-08-122021-11-122021-11-242022-03-01
 Publication Status: Finally published
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.agwat.2021.107349
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Hydroclimatic Risks
Regional keyword: Europe
Research topic keyword: Adaptation
Research topic keyword: Food & Agriculture
Research topic keyword: Economics
Research topic keyword: Weather
Research topic keyword: Climate impacts
Research topic keyword: Extremes
Model / method: Machine Learning
Model / method: Open Source Software
MDB-ID: yes - 3414
OATYPE: Hybrid Open Access
 Degree: -

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Title: Agricultural Water Management
Source Genre: Journal, SCI, Scopus, oa as of 2023
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Pages: - Volume / Issue: 261 Sequence Number: 107349 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180814
Publisher: Elsevier