Cover crops competition for water in vineyards: case studies in Mediterranean terroirs

Vineyard cover cropping is a cultural practice widely used in many of the world’s winegrowing regions being one of the most recommended practices to face climate changes and to promote vineyard environmental sustainability. The benefits of using cover crops are many ranging from environmental protection (e.g. control of soil erosion, enhancement of soil structure and biodiversity, sequestering carbon) to vineyard management, including control of vigor and improvement of berry composition. Despite those potential benefits, the adoption of cover crops in Mediterranean non-irrigated vineyards has been limited by the concern of excessive water competition between cover crops and vines. However the level of this competition should be better understood as in warm and dry terroirs, like the case of Mediterranean winegrowing regions, water competition by the cover crops is effective mainly during spring. During summer, the almost absence or rainfall induces the dry out of the sward vegetation which residues became dead mulch that can even reduce soil evaporation. Furthermore, some research has also demonstrated that, after some years of competition with swards, the vines were able to develop deeper roots, therefore increasing the capacity for water extraction from deeper soil layers. In order to further elucidate the above mentioned topics, in this paper data on water use and grapevine performance obtained in three floor management experiments (soil tillage vs. inter-row swards), carried out in three different winegrowing regions of the Mediterranean Portugal (covering rainfed and irrigated vineyards), will be presented. Discussion will be focus on water competition by the swards and corresponding effects on grapevine vigor, yield and berry composition. The effect of terroir on grapevine responses will be also underlined. From the data presented it can be concluded that cover crops is a vineyard management practice that can have a positive influence on water use efficiency, either by preventing vine excessive vigor when water is fully available during spring or by maximizing the volume of soil explored by vine roots through the enhancement of the exploitation of soil water reserves into deeper layers. However, in the case of low vigor vineyards located in dry terroirs, the degree of water competition between cover crops and vine must be carefully monitored and managed (e.g. by increasing mowing frequency, reducing the sward strip and/or choosing less competitive species) and adjustments in conventional irrigation management are necessary in order to avoid detrimental effects on grapevine yield and longevity.info:eu-repo/semantics/publishedVersio

Grapevine bunch weight estimation using image-based features: comparing the predictive performance of number of visible berries and bunch area

Recent advances in machine vision technologies have provided a multitude of automatic tools for recognition and quantitative estimation of grapevine bunch features in 2D images. However, converting them into bunch weight (BuW) is still a big challenge. This paper aims to compare the explanatory power of the number of visible berries (#vBe) and the bunch area (BuA) in 2D images, in order to predict BuW. A set of 300 bunches from four grapevine cultivars were picked at harvest and imaged using a digital RGB camera. Then each bunch was manually assessed for several morphological attributes and, from each image, the #vBe was visually assessed while BuA was segmented using manual labelling combined with an image processing software. Single and multiple regression analysis between BuW and the image-based variables were performed and the obtained regression models were subsequently validated with two independent datasets. The high goodness of fit obtained for all the linear regression models indicates that either one of the imagebased variables can be used as an accurate proxy of actual bunch weight and that a general model is also suitable. The comparison of the explanatory power of the two image-based attributes for predicting bunch weight showed that the models based on the predictor #vBe had a slightly lower coefficient of determination (R2) than the models based on BuA. The combination of the two image-based explanatory variables in a multiple regression model produced predictor models with similar or noticeably higher R2 than those obtained for single-predictor models. However, adding a second variable produced a higher and more generalised gain in accuracy for the simple regression models based on the predictor #vBe than for the models based on BuA. Our results recommend the use of the models based on the two image-based variables, as they were generally more accurate and robust than the single variable models. When the gains in accuracy produced by adding a second image-based feature are small, the option of using only a single predictor can be chosen; in such a case, our results indicate that BuA would be a more accurate and less cultivardependent option than the #vBeinfo:eu-repo/semantics/publishedVersio

Seasonal changes in berry fluorescence induced by different levels of uv-radiation

Plant stress is usually diagnosed via physiological measurements on leaves such as water potential, photosynthetic rate or chlorophyll fluorescence. Since stress in the case of grapevines is of concern with respect to the quality of the fruit, we attempted to use one of the above mentioned techniques, chlorophyll fluorescence, directly on the fruit itself in order to investigate stress responses in vivo. Berry fluorescence was measured in an experiment with different levels of UV-B and UV-A radiation in the field in 2000 in Geisenheim with the variety Riesling. UV radiation has been shown to affect chlorophyll and was thus likely to show differences in fluorescence signals. Berry fluorescence was measured periodically between veraison and harvest on darkadapted berries (20min). For each berry, a light response curve was recorded followed by a recovery phase in the dark. The maximum quantum yield of PSII (qY) was determined after a saturation pulse at each light intensity. The qY decreased exponentially with increasing light intensity. The response level of the quantum yield of PSII decreased during ripening indicating a loss in chlorophyll and/or PSII capacity for all treatments. Effects of the different levels of UV-radiation on the light curve were only detected at the end of the ripening period, with higher quantum yield values recorded for the treatments protected against UV-A and UV-A+UV-B. Similar results were obtained after recovery in the dark. Changes in fluorescence signals were accompanied by a visual change in berry colour indicating changes in skin pigmentation, which may also have some effect on fruit qualityinfo:eu-repo/semantics/publishedVersio

Validation of an empirical model for grapevine leaf area estimation with data from simplified pruning systems

The performance of a mathematical model developed for non-destructive estimation of primary leaf area per shoot of Tempranillo grapevines, was tested using independent datasets from two vineyards with simplified pruning techniques. The first dataset was collected in Portugal on Cabernet Sauvignon grapevines subjected to mechanical hedge pruning and the second one in Germany on minimal pruned Riesling grapevines. For both datasets the model presented a very good fit between observed and estimated values with the error increasing with the increase in leaf area per shoot. The mean absolute percent error for all systems was lower or equal to 10% with lower absolute values (7.7%) for the Riesling dataset. Both linear regression between observed (dependent variable) and estimated (independent variable) leaf area had high and significant R2 with an intercept not significantly different from zero. Fitted lines were not significantly different from 1 for Cabernet Sauvignon, but slightly yet significantly different from 1 for Riesling fitted line (1.03), indicating that the model underestimated the leaf area per shoot. The good results obtained with this validation test show that the model can be used to accurately predict primary leaf area per shoot independent of variety, training system and climatic conditionsinfo:eu-repo/semantics/publishedVersio

Validation of an empirical model for grapevine leaf area estimation with data from the varieties ‘Cannonau’ and ‘Vermentino’ grown in Sardinia

The performance of two mathematical models for non-destructive estimation of primary and lateral leaf area per shoot of Tempranillo grapevines was tested in Sardinia using independent datasets from two main traditional varieties. One collected on Cannonau grapevines from Nurra wine region, and another from Vermentino grapevines grown in Gallura wine region. The models presented good fit between observed and estimated values with high modeling efficiency. For primary leaf area estimation the mean absolute percent error for both varieties was lower than 10%. Both linear regressions between observed and estimated primary leaf area had high and significant R2 but while Vermentino fitted line presented a slope not significantly different from 1, Cannonau fitted line showed a slope significantly < 1, indicating that the model overestimated the primary leaf area per shoot. The validation of the model for lateral leaf area presented lower goodness of fit as that reported for primary leaf area. Linear regressions had a very high and significant R2 but the slopes were significantly <1 indicating that the model overestimated lateral leaf area per shoot. The positive validation shows that these models can accurately predict leaf area per shoot independently of ecological conditions, variety, year, growth stage and training system. Low goodness of fit for lateral leaf area model may be avoided building the model on each variety data. The generalized use of this type of model represents a powerful tool for grapevine research, for consultants and advanced growers, allowing the evaluate vine leaf area more frequently and with low costinfo:eu-repo/semantics/publishedVersio

Comparing water relations and stomatal regulation of Touriga Nacional and Syrah under mild water stress

Proceedings - IX International Terroir Congress, 2012Aiming to compare the physiological responses of the Portuguese red variety Touriga Nacional (TN) with Syrah (SY), we studied during the 2007 growing season five-year-old grapevines growing in a commercial non-irrigated vineyard located at the Lisbon winegrowing region. Predawn (Ypd) and midday (YM) leaf water potential, leaf stomatal conductance (gs) and photosynthetic rate (A) were periodically measured between fruit set and harvest. Ypd displayed a decreasing pattern throughout the growing cycle from -0.10 MPa at flowering to -0.44 MPa at harvest. Both varieties showed similar values except during the two measurements made in August when TN presented significantly higher values than SY. Ymid also showed a decreasing pattern from the end of June towards harvest date, with significant differences between varieties being observed during the ripening period, with SY showing lower values than TN. A measured either at mid-morning and midday presented, for most part of the cases, lower values in SY than in TN, although the differences were only significant during the ripening period. gs pattern was parallel to A and, in general, the relative differences between varieties mirrored those reported for A. No significant effect of the variety was detected on the relationships between A or gs measured at mid-morning and Ypd. However, when analyzing the set of data collected at midday it was observed that the regression lines of the relationships between A or gs (dependent variables) and Ypd (independent variable) presented a significantly higher slope in SY as compared to those showed by TN. These results show that the rate of decrease of A and gs with the decrease of Ypd was lower in TN than in SY suggesting that the two varieties have different stomatal regulation, with a more “optimistic” behavior in TN

Micromorfoanatomia foliar de cultivares tintas de Vitis vinifera SSP.vinifera (Vitaceae)

Aiming to characterize and discriminate between four red grapevine cultivars – ‘Aragonez’ (AR), ‘Cabernet Sauvignon’ (CS), ‘Syrah’ (SY) and ‘Touriga Nacional’ (TN) – grown under Mediterranean field conditions, we studied their leaf micromorphoanatomic characteristics under light (LM) and scanning electron microscopy (SEM). The studied characteristics included those of the epidermis, stomata and hair distribution, and the mesophyll structure. The individual primary leaf area revealed significant differences between cultivars, with the highest value presented by AR and the lowest by CS, while SY and TN gave intermediate values. CS presented a significantly higher leaf specific dry weight value than the other three cultivars, which returned similar values. Under SEM magnification three types of stomata were identified in all the studied genotypes: sunken, at the same level, and raised above the other epidermal cells. Each cultivar displayed different percentages of these types of stomata: the highest raised-above values were observed in AR; TN had the highest same-level values and the lowest sunken ones; CS revealed the highest values for sunken stomata; while SY returned average values for all the types of stomata. Stomatal density was higher in AR and SY and lower in CS and TN. The hairs on the lower surface presented a similar woolly aspect in all the studied cultivars, but the mesophyll structure was quite different: CS presented the highest and AR the lowest values for total thickness of the lamina, thickness of palisade and spongy parenchyma, and length and thickness of upper and lower epidermal cells; the values for these leaf features in TN and SY fell between those for CS and AR. The data suggest that differences in leaf micromorphoanatomy can be used to distinguish between grapevine cultivars. Further studies are needed to confirm whether there is any association between some of these leaf traits – e.g. stomata type and mesophyll structure – and the physiological behaviour observed under field conditions

The effect of topography on the spatial variability of grapevine vegetative and reproductive components

I Congresso Luso-Brasileiro de Horticultura. Sessão ViticulturaTopography variation is one of the main causes for vineyard variability. Terrain attributes, such as slope, altitude and aspect are highly variable and have an impact on soil depth, water holding capacity, air and soil temperature, radiation exposure, among other factors. Patterns of topographic variability tend to be stable over time, therefore recognizing such patterns can potentially provide the winegrower with relevant economic returns. A study was conducted in 2015, in a vineyard located at Tapada da Ajuda, Lisbon (slope range from 7% to 9%; southern orientation). Four white varieties (Alvarinho, Viosinho, Encruzado and Arinto) were analyzed regarding their vegetative development, yield and grape quality. This study had two main objectives: (i) to evaluate the magnitude of the spatial variability among varieties and (ii) to evaluate the effect of the terrain position (TP) in each variety, individually. Smart points (SP) were selected for each variety, organized according to their slope position (uphill, mid-slope and downhill) and vegetative and reproductive data was collected at relevant phenological stages (pre-flowering, flowering, veraison and full maturation). Alvarinho and Arinto varieties presented the highest spatial variability, regardless of their position along the slope. Yield and leaf-to-fruit ratio were the most variable parameters (coefficient of variation>30% in all varieties) with no correlation with TP. Encruzado showed higher vegetative development (+36% leaf area index and +18% exposed leaf area) in downhill SPs, while Arinto presented higher bud burst percentage (+49%) and lower water-shoot development (-30%) in downhill SPs. In these cases, canopy development parameters were influenced by TP. Such information can be used for a differentiated scheduling of canopy management activities e.g. canopy thinning and water-shoot removal, tasks that are expensive and time consuming. This study created a basis for further research that can lead to more accurate vineyard design planning and managementinfo:eu-repo/semantics/publishedVersio

Estimation de la surface foliaire du cépage Syrah avec des modèles empiriques

Aiming to obtain empirical models for the estimation of Syrah leaf area a set of 210 fruiting shoots was randomly collected during the 2013 growing season in an adult experimental vineyard, located in Lisbon, Portugal. Samples of 30 fruiting shoots were taken periodically from the stage of inflorescences visible to veraison (7 sampling dates). At the lab, from each shoot, primary and lateral leaves were separated and numbered according to node insertion. For each leaf, the length of the central and lateral veins was recorded and then the leaf area was measured by a leaf area meter. For single leaf area estimation the best statistical models uses as explanatory variable the sum of the lengths of the two lateral leaf veins. For the estimation of leaf area per shoot it was followed the approach of Lopes & Pinto (2005), based on 3 explanatory variables: number of primary leaves and area of the largest and smallest leaves. The best statistical model for estimation of primary leaf area per shoot uses a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves. For lateral leaf area estimation another model using the same type of calculated variable is also presented. All models explain a very high proportion of variability in leaf area. Our results confirm the already reported strong importance of the three measured variables (number of leaves and area of the largest and smallest leaf) as predictors of the shoot leaf area. The proposed models can be used to accurately predict Syrah primary and secondary leaf area per shoot in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialized staff or expensive equipment

A Multicultivar Approach for Grape Bunch Weight Estimation Using Image Analysis

The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach. A set of 192 bunches from four cultivars, collected at sites located in Portugal and South Africa, were imaged using a conventional digital RGB camera, followed by image analysis, where several bunch features were extracted, along with physical measurements performed in laboratory conditions. Image data features were explored as predictors of bunch weight, individually and in a multiple stepwise regression analysis, which were then tested on 37% of the data. The results show that the variables bunch area and visible berries are good predictors of bunch weight (R2 ranging from 0.72 to 0.90); however, the simple regression lines fitted between these predictors and the response variable presented significantly different slopes among cultivars, indicating cultivar dependency. The elected multiple regression model used a combination of four variables: bunch area, bunch perimeter, visible berry number, and average berry area. The regression analysis between the actual and estimated bunch weight yielded a R2 = 0.91 on the test set. Our results are an important step towards automatic yield estimation in the vineyard, as they increase the possibility of applying image-based approaches using a generalized model, independent of the cultivarinfo:eu-repo/semantics/publishedVersio