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

• Published in: OENO one Vol. 56; no. 4; pp. 173 - 182
• Main Authors: Garcia-Tejera, Omar, Marcos-Matamoros, Raül, Basile, Boris, Mataffo, Alessandro, Scognamiglio, Pasquale, Prieto, Henar, Mancha, Luis, Cabral, Inês, Queiroz, Jorge, Valente, Joana, Alves, Fernando, González-Reviriego, Nube, Hernández-Barrera, Sara, Mata, Mercè, Girona, Joan
• Format: Journal Article
• Language: English
• Published: International Viticulture and Enology Society 01-01-2022
• Subjects: forecast; grapevine; phenology modelling; seasonal
• ISSN: 2494-1271; 2494-1271
• DOI: 10.20870/oeno-one.2022.56.4.5401

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 (effd). The effd is the number of days in a month on which temperatures would be above the minimum value for development (Tb). The calculation of effd 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.