Héctor Valdés-Gómez

Gas exchange relations of ungrafted grapevines (cv. Carménère) growing under irrigated field conditions

A study was carried out to evaluate the leaf gas exchange relations and water-use efficiency (WUE) of ungrafted Carmenere (Vitis vinifera L.) grapevines growing under field conditions and different levels of water stress. Stomatal conductance (g s ), transpiration (E), net CO 2 assimilation (A N ) and stem water potential (Ψs) were measured at midday in a drip-irrigated commercial vineyard located in the Maule Valley (Chile) during three growing seasons (2005 to 2009). In addition, the instantaneous (A N /E) and intrinsic (A N /g s ) water-use efficiencies and stomatal sensitivity factor (k) were estimated for the own-rooted grapevines. In this study a significant non-linear relationship was observed between A N and g s (r 2 = 0.82), with values of A N decreasing from 14.9 to 3.5 μmol/m 2/ sec as g s diminished from 0.5 to 0.05 mol/m 2 /sec. This resulted in a progressive increase in WUEi (intrinsic water use efficiency). A significant linear relationship was observed between Ψs and g s (r 2 = 0.39) for measurements taken before and after veraison, with an increasing scattering from -1.6 to -0.4 MPa. Finally, k decreased as water stress increased, with values of 234 and 120 for no and severe water stress respectively, while k ranged from 264 to 480 and 255 to 297 for the measurements taken before and after veraison respectively. Based on the results obtained in the present study, the cultivar Carmenere could be classified as drought tolerant at low water potentials, with a large range of physiological parameters changing in response to water stress.

Using ancillary yield data to improve sampling and grape yield estimation of the current season

This paper proposes a methodology aiming at using historical yield data to improve yield sampling and yield estimation. The sampling method is based on a collaboration between historical data (at least three years) and yield measurements of the year performed on some sites within the field. It assumes a temporal stability of within field yield spatial patterns over the years. The first factor of a principal component analysis (PCA) is used to summarize the stable temporal patterns of within field yield data and it represents a large part of the variability of the different years assuming yield temporal stability and a high positive correlation between this factor and the yield. This main factor is then used to choose the best sites to sample (target sampling). Yield measurements are then used to calibrate a model that relates yield values to coordinates on the first factor of the PCA. This sampling method was tested on three vine fields (Vitis vinifera L.) in Chile and France with different varieties (Chardonnay, Cabernet Sauvignon and Syrah). For each of these fields, yield data of several years were available at the within field level. After temporal stability of yield patterns was verified for almost all the fields, the proposed sampling method was applied. Results were compared to those of a classical random sampling method showing that the use of historical yield data allows sampling sites selection to be optimized. Errors in yield estimations were reduced by more than 10% in all the cases, except when yield stable patterns are affected by specific events, i.e. early frost occurring on Chardonnay field.

Assessment of an empirical spatio-temporal model of the grapevine phenology at the within-field scale

The aim of this work is to calibrate and validate an empirical approach to predict the date of occurrence of the grapevine phenology (budburst, flowering and veraison) temporally and spatially at the within-field scale. It is based on the collaboration between a classical model of phenology based on climate data and a spatial model calibrated with ancillary data of phenology observations. This approach was tested and validated on a field of cv Cabernet Sauvignon. Results showed that the spatial component improved the fit of the climatic model, allowing the generation of maps of the grapevine phenology with errors lower than 5 days of prediction. Spatio-temporal model errors were mainly associated with the temporal component of the model.