A method for using monthly average temperatures in phenology models for grapevine (Vitis vinifera L.)

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Garcia-Tejera, O (Author) Other; Marcos-Matamoros, R (Author) Other; Basile, B (Author) Other; Mataffo, A (Author) Other; Scognamiglio, P (Author) Other; Prieto, H (Author) Other; Mancha, L (Author) Other; Cabral, I (Author) Other; Jorge Queiroz (Author) FCUP; Valente, J (Author) Other; Alves, F (Author) Other; Gonzalez-Reviriego, N (Author) Other; Hernandez-Barrera, S (Author) Other; Mata, M (Author) Other; Girona, J (Author) Other

Journal

Title: OENO One

Vol. 56

Pages: 173-182

ISSN: 2494-1271

Publisher: Vigne et Vin Publications Internationales

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 173 Citations

Other information

Authenticus ID: P-00X-G06

DOI: 10.20870/oeno-one.2022.56.4.5401

Abstract (EN): In recent years, there have been increasing efforts to link phenology models with seasonal climate predictions in so-called Decision Support Systems (DSS) to tailor crop management strategies. However, temporal discrepancies between phenology models with temperature data gathered on a daily basis and seasonal forecasting systems providing predictability on monthly scales have limited their use. In this work, we present a novel methodology to use monthly average temperature data in phenology models. Briefly stated, we modelled the timing of the appearance of specific grapevine phenological phases using monthly average temperatures. To do so, we computed the cumulative thermal time (Sf) and the number of effective days per month (eff(d)). The eff(d) is the number of days in a month on which temperatures would be above the minimum value for development (T-b). The calculation of eff(d) is obtained from a normal probability distribution function derived from historical weather records. We tested the methodology on four experimental plots located in different European countries with contrasting weather conditions and for four different grapevine cultivars. The root mean square deviation (RMSD) ranged from 4 to 7 days for all the phenological phases considered, at all the different sites, and for all the cultivars. Furthermore, the bias of observed vs predicted comparisons was not significantly different when using either monthly mean or daily temperature values to model phenology. This new methodology, therefore, provides an easy and robust way to incorporate monthly temperature data into grapevine phenology models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10