The performance of grapevines on identified terroirs in Stellenbosch, South Africa

Understanding vineyard variability to target management strategies, apply inputs efficiently and deliver consistent grape quality to the winery is essential. However, despite inherent vineyard variability, the majority are managed as if they are uniform. VitiCanopy is a simple, grower-friendly tool for precision/digital viticulture that allows users to collect and interpret objective spatial information about vineyard performance. After four years of field and market research, an upgraded VitiCanopy has been created to achieve a more streamlined, technology-assisted vine monitoring tool that provides users with a set of superior new features, which could significantly improve the way users monitor their grapevines. These new features include:
• New user interface
• User authentication
• Batch analysis of multiple images
• Ease the learning curve through enhanced help features
• Reporting via the creation of colour maps that will allow users to assess the spatial differences in canopies within a vineyard.
Use-case examples are presented to demonstrate the quantification and mapping of vineyard variability through objective canopy measurements, ground-truthing of remotely sensed measurements, monitoring of crop conditions, implementation of disease and water management decisions as well as creating a history of each site to forecast quality. This intelligent tool allows users to manage grapevines and make informed management choices to achieve the desired production targets and remain profitable.

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

There is a growing trend on the transition from conventional to agroecological management of vineyards. However, the impact of practices, such as reduced-tillage, organic fertilization and cover crops, is not well-understood regarding the soil microbial diversity, and its relationship with the soil physicochemical properties in the subsurface depth near the rooting zone. Soil bacterial diversity is an important contributor towards plant health, productivity and response to environmental stresses. A field experiment was conducted by sampling subsurface soil bacterial community (NGS and qPCR) near to the root zone of Macabeu and Xarel·lo vineyards, located at the Penedes. 3 organic (ECO) and 3 conventional (CON) vineyards, with more than 10 years of respective management were sampled (n=5 each plot). ECO practices did not affect bacterial and fungal abundance but increased significantly the ammonium oxidizing bacteria and alpha-diversity (Inv.Simpson). Interestingly beta-diversity was significantly affected by the management strategy. ANOSIM-tests revealed a significative effect of the management (ecological vs conventional) and plot, on the soil microbial structure (ASV abundance). Main phyla depicted were Proteobacteria, Actinobacteria and Acidobacteria, whose relative abundances were not affected by the management. EdgeR assay revealed a significant increase of Cyanobacteria and decrease of Gemmatimonadetes and Firmicutes phyla in ECO. Interestingly, the grapevine variety was not correlated with the soil microbial community structure. Mantel-test revealed an important correlation (Spearman) of some physicochemical parameters with the soil microbiota structure, in order of importance: texture, EC, pH Ca/Mg, Mg/P, K+, Mg2+, Ca2+, SO42-, and OM. N-NH4 and NTK, which were higher in the ECO managed soils, did not correlated significantly with the soil microbiome population. The results revealed the importance of combining a deep physicochemical characterization of each replicate with the microbial diversity assessment to gain better insights on the relationship between soil microbiome and vineyard management.

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The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.