Antonio Volta1,2*, Paola Tessarin1, Giulia Villani2,3, Adamo Domenico Rombolà1, Mario Rega4, Benjamin Bois4, VittorioMarletto2
1 Dipartimento di Scienze Agrarie, Università di Bologna, viale Fanin 44, 40127 Bologna (Italy) 2 Servizio Idro-Meteo-Clima, ARPA-ER, viale Silvani 6, 40122 Bologna (Italy)
3 Dipartimento di Scienze e Tecnologie Agro-Alimentari, viale Fanin 50, 40127 Bologna (Italy)
4 Centre de Recherches de Climatologie, UMR 6282 Biogéosciences CNRS - Université de Bourgogne, 6 boulevard Gabriel, 21000 Dijon, France * [email protected]
Abstract
The Sangiovese di Romagna wines are considered in recent years interesting for their quality and for their rising demand. In this work we present an algorithm based on simple agrometeorological data in order to support wine growers for quality productions. The algorithm enables to assess at veraison the maximum reachable sugar concentration. Moreover, if fed by weather forecasts, through a mathematical model we are able to estimate the time course of berry ripening. Here we test our methodology with field data collected in a vineyard located in Tebano within the Emilia-Romagna region.
Keywords
grapevine, plant modelling, sugar concentration, berry ripening
Parole chiave
vite; modellistica della pianta; concentrazione zuccherina; maturazione della bacca
Introduction
Global warming is changing the management in the whole agricultural sector. Seasons, from the weather point of view, are much more variable than in the past decades and extreme events are more frequent. Thus, this brings to outstanding differences in the grapevine development and growth, rising uncertainty on agronomic scheduling and on harvest quality. On the other hand, models are a helpful tool for monitoring and forecasting the behaviour of plants, avoiding several field observations. In this work it is simulated berry ripening in terms of sugar concentration whose we assess the maximal value already at veraison and we provide the time course from veraison onwards. The model is here tested by means of 2 years field data collected in a Sangiovese vineyard located in Tebano (Faenza, Italy).
Materials and Methods
The berry ripening model is thought to be simple and easily usable, having a few parameters to be calibrated. The daily weather variables which feed the model are maximum temperature, minimum temperature and global radiation. The idea is to assess the maximum reachable soluble solids concentration at veraison by analyzing the cumulated global radiation from 400 °D (calculated with null base temperature) after full flowering to veraison. Afterwards the time course of berry ripening is calculated through the conversion of the Force state (Caffarra and Eccel, 2010) into sugar concentration expressed in Brix degrees. This conversion is given by a third polynomial equation. Computation of the Force state was slightly different from
the original in the range of mean daily temperature lower than 18 °C in order to avoid a delay in ripening, as shown in Bois et al., 2014. Calibration was performed using the year 2013, whereas 2014 was used as validation year. The weather data come from a weather station located in Tebano (lat. 44°17’, lon. 11°50’). The field data have been collected in a mature organic vineyard of cv. Sangiovese (clone FEDIT 30 ESAVE), Vitis vinifera L., grafted on Kober 5BB, trained to cordon de Royat training system (VSP). The vineyard is located in Tebano (Faenza, RA), Italy (44°17’7’’ N, 11°52’59’E, 117 m a.s.l.), in a medium hill slope, with South-East/North-West and downhill oriented rows. Vines were spaced 2.8 m x 1.0 m (intra- and interrow), 3,571 plants ha-1. Starting in 2007, the vineyard was managed as organic in accordance with Reg. EC 834/2007 (EC., 2007). Since 2007, no irrigation water has been supplied and no fertilizers have been provided.
Phenological and ripening data were collected throughout the summer in both years 2013 and 2014.
Results and Discussion
In Table 1 we list the phenological dates recorded in the last two growing seasons, the comparison between the phenological dates show that spring 2013 was colder with respect to spring 2014. This brought to a delay of about one week up to the beginning of veraison.
In Fig. 1 we show the trend of cumulated global radiation from 15/06 until the end of July, i.e. the period of the year used by the model to assess the maximal sugar concentration and it could be seen that the difference is
about 200 MJ m-2. The conversion factor to estimate the maximal sugar concentration for Sangiovese is 0.019 (°Bx m2 MJ-1). This means that for the two season it was expected a difference of about 200 x 0.019 = 4° Bx.
In Fig. 2 we show the ripening curves of both years. The lines represent the simulation outputs which are compared with field observations, represented by three replicates of the field experiment. Simulations are in good agreement with field data in most of cases. In 2014 we notice the highest discrepancies. The model overestimates in the first 15 days of September, where temperatures resulted definitely colder than climate.
Conclusions
We applied a simple model for simulating berry ripening to Sangiovese variety. A two years time series was used to calibrate (year 2013) and to validate (year 2014) the model. The first results obtained are promising. In particular the model is able to assess correctly the final plateau of sugar concentration in both years. Future publications will provide a longer simulation to strengthen model validation. In addition the model will be tested to more grapevine varieties. Further improvements will take into account topography and details of training system to assess more precisely light interception.
Tab.1 – Phenological dates of flowering and veraison. Tab.1 – Rilievi fenologici di fioritura e invaiatura.
2013 2014 Budburst 08/04/2013 28/03/14 Beginning of flowering 27/05/2013 22/05/14 End of flowering 06/06/2013 27/05/14 Beginning of veraison 07/08/2013 24/07/14 End of veraison 20/08/2013 18/08/14 Harvest 01/10/2013 25/09/14 References
Bois B., Volta A., Rega M., Caffarra A., Costa F., Antolini G., Tomei F., Galizia S., Nascimben J., Crestini C., Baret F., Neri M., Bertozzi B., Lughi G., Roffilli M., Botarelli L., Bauer-Marschallinger B., Hasenauer S., Campagnolo S., Dellavalle D., Brossaud F., Grosso V., Marletto V. , 2014: Mechanistic modelling and ontology: a performing mix for precision and sustainable viticulture. Proceedings of the 37th World Congress of Vine and Wine, 9-14 November, Mendoza, Argentina.
Caffarra A., Eccel E., 2010. Int. J. Biometeorol. 54: 255– 267.Parker A. et al., 2013. Agric. For. Meteorol. 180: 249– 264.
Fig.1- Cumulated global radiation from late spring to midsummer in 2013 (red dashed line) and in 2014 (blue solid line).
Fig.1 –Radiazione globale cumulata dalla tarda primavera a mezza estate per gli anni 2013 (linea rossa tratteggiata) e 2014 (linea blu continua).
Fig.1- Berry ripening time course. Simulated curves: red dashed line for 2013 and blue solid line for 2014. Field data consist of three replicates (circles, triangles and squares) per date: red for 2013 and blue for 2014 respectively.
Fig.1 –Andamento della maturazione della bacca. Curve simulate: linea rossa tratteggiata per il 2013 e linea azzurra continua per il 2014. I rilievi di campo sono costituiti da tre repliche (cerchi, triangoli e quadrati) per data: rossi per il 2013 e azzurri per il 2014.
0 200 400 600 800 1000 1200 1400 15/06 25/06 05/07 15/07 25/07 04/08 C u m u la te d glo b a l ra dia ti on ( M J m -2 ) 2014 2013 9 11 13 15 17 19 21 23 25 30/07 09/08 19/08 29/08 08/09 18/09 28/09 08/10 S u g a r c o n cen tr at io n ( B x) 2013 2014