Terroir aspects in development of quality of Egri bikavér

Global warming is one of the biggest environmental, social, and economic threats. In the Douro Valley, change to the climate are expected in the coming years, namely an increase in average temperature and a decrease in annual precipitation. Since vine cultivation is extremely vulnerable and influenced by the climate, these changes are likely to have negative effects on the production and quality of wine. Adaptation is a major challenge facing the viticulture sector where the choice of plant material plays an important role, particularly the rootstock as it is a driver for adaptation with a wide range of effects, the most important being phylloxera, nematode and salt, tolerance to drought and a complex set of interactions in the grafted plant. In an experimental vineyard, established in the Douro Region in 1997, with four randomized blocs, with five varieties, Touriga Nacional, Tinta Barroca, Touriga Franca and Tinta Roriz, grafted in four rootstocks, Rupestris du Lot, R110, 196-17C, R99 and 1103P, data was collected consecutively over 20 years (2001-2020). Phenological observations were made two to three times a week, following established criteria, to determine the average dates of budbreak, flowering and veraison. During maturation, weekly berry samples were taken to study the dynamics of sugar accumulation, amongst other parameters. Climate data was collected from a weather station located near the vineyard parcel, with data classified through several climatic indices. The results achieved show a very low coefficient of variations in the average date of the phenophases and an important contribution from the rootstock in the dynamic of the phenology, allowing a delay in the cycle of up to10-12 days for the different combinations. The Principal Component Analysis performed, evaluating trends in the physical-chemical parameters, highlighted the effect of the climate and rootstock on fruit quality by grape varieties.

The Campanha Gaúcha is located in the pampa biome and has unique characteristics, as it is the hottest producing region with the lowest volume of rain in Southern Brazil. Furthermore, the large extensions of flat or low-sloping areas, harsh winters and great sunshine during the ripening period, made this the second largest producer of fine wines in Brazil.

Table grape production requires adequate nitrogen (N) supply to sustain vine performance and obtain high yields. However, excess agricultural N fertilization is a major source of groundwater contamination and air pollution. Therefore, there is a strong need for empirically based precision N fertilization schemes in vineyards, for optimizing grape yield and quality while minimizing their environmental impact.
Our aim was to unequivocally quantify table grape N requirements, elucidate the drivers of daily N uptake, and quantify the relationship between fertigation N levels and vine growth, fruit yield, composition, and quality. For this, forty ‘Early Sweet’ (early-maturing, white) and ‘Crimson seedless’ (late-maturing, red) vines were grown in 500L drainage-lysimeters for 2 fruiting seasons, while subjected to five continuous N fertigation treatments ranging from 10 to 200 ppm.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.

There is an increasing need in agriculture to adopt site-specific management (precision farming) because of economic and environmental pressures. Geophysical on-the-go sensors, such as the ARP (Automatic Resistivity Profiling) system, can effectively support soil survey by optimizing sampling density according to the spatial variability of apparent electrical resistivity (ER).