TECNOVINE at the World Wine Congress (Genevra)

TECNOVINE work has been accepeted for poster pubblication at the   42nd World Wine Congress, July 15-17 2019.

Following the abstract of the presented poster (download)

 

PO-135: VALIDATION OF CROP GROWTH FUNCTION FOR ASSESSING CLIMATE CHANGE IMPACT ON VINEYARDS

Scope
The present work deal with the verification, against field data coming from three years of monitoring of three center Italy sites, of some typical growing functions:
– Logistic
– Expo-linear
– Beta-function.
The considered growing functions have been applied to simulate the growing of the following parameters:
– Leaf area
– Leaf dry matter accumulation
– Berry diameter
– Berry weight
The scope of comparison is to identify the accuracy of growth prediction of a specific cultivar to investigate impact of climate change in the main vine parameters, to optimize vineyard management and increase production quality

Methods and Models

Within the life cycle of an organ, a plant or a crop, the total growth duration can be divided into three sub-phases: an early accelerating phase, a linear phase and a saturation phase for ripening. Therefore, the growth pattern typically follows a sigmoid curve, and the growth rate a bell-shaped curve. Several growing functions are available in the literature to quantify the dynamics of growth, enabling the daily growth rates being integrated to e to evaluate the vine development during a given time frame. http://www.saisautolinee.it/regionali.htm this work three different approaches have been evaluated against field data:
– The beta function introduced by Yin et al. (2003) to describe the phasic development rate as a function of temperature.
– The expolinear function modified by Goudriaan (1994) for describing the pattern of leaf area index to allow a smooth transition among growing phases
– The logistic equation, modified by Richards (1959) considering an additional parameter to allow asymmetrical behaviour

Results

The performances of selected functions have been tested against a field data collected during a monitoring campaign performed in three different vineyards located in The Tuscany region (Montecarlo di Lucca, Terricciola and Cortona, Italy) The accuracy of the selected growing function to predict the parameters previously indicated has been retained fairly acceptable, especially for berry diameter and leaf area evaluation, making possible the use of this simple approach to foreseen the possible vegetative development of a given vineyard.
The results obtained by the model using the monitored vineyard show an error of about 20% in evaluating the above-mentioned parameters. Among considered models, the beta-function seems the most suitable in vineyard parameters prediction, due to the flexibility of its mathematical form.
Conclusions
The growth functions are widely used to simulate the vegetative development and production of both annual and perennial crops. Here these have been applied to predict the main parameters related to vine and grapevine growing, to evaluate their suitability to identify the impact of meteorological changes in the grapevine development.
The results in the application of the selected functions show a sufficient agreement with field data (within 20% of average error for the considered parameters). Among considered models, the beta-function seems the most suitable in vineyard parameters prediction, due to the flexibility of its form.
However, the analyzed methods suffer of the following limitations which might reduce their range of applicability:
– A constant rate of growth assumption for a vine is a rude approximation of its phenological development, leading unpredictable errors in vegetative development under particular meteorological conditions
– Several correction coefficients for different cultivar have to be selected to match field data, with apparently little physical and phenological meaning
– Little impact of environmental and nutritional conditions on prediction
Due to the above, a wider test on different sites and cultivar is required, to generalize some of the results so far achieved, as well as a better mathematical reformulation of these functions to be able to include specific environmental parameters characterizing a given terroir

 

 

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