The characterization of vineyard soil is a key issue for crop management, which directly affects the quality and yield of grapes. However, traditional laboratory analysis of soil properties is tedious and both time and cost consuming, which is not suitable for precision viticulture. For this reason, a fast and convenient soil characterization technique is needed for soil quality assessment and precision soil management. Here, spectroscopy appears as a suitable alternative to assist laboratory analysis. This work focuses on estimating soil properties by spectroscopy. Our study was carried out using 96 soil samples collected from three vineyards in Rias Baixas Designation of Origen (Galicia, Spain). The soils that were characterized include nitrogen (N), organic matter (OM) and clay content (Clay). The presented work compared two regression techniques (partial least squares (PLSR) and random forest (RF)) and four spectral ranges: visible—VIS (350–700 nm), near infrared—NIR (701–1000 nm), short wave infrared—SWIR (1001–2500 nm) and VIS-NIR-SWIR (350–2500 nm) in order to identify the more suitable prediction models. Moreover, the effect of pre-treatments in reflectance data (smoothing Svitzky–Golay, SG, baseline normalization, BN, first derivative, FD, standard normal variate, SNV, logarithm of 1/reflectance or spectroscopy (SP) and detrending, SNV-D) was evaluated. Finally, continuous maps of the soil properties were created based on estimated values of regression models. Our results identified PLSR as the best regression technique, with less computation time than RF. The data improved after applying transformation in reflectance data, with the best results from spectroscopy pre-treatment (logarithm of 1/Reflectance). PLSR performances have obtained determination coefficients (R²) of 0.69, 0.73 and 0.52 for nitrogen, organic matter, and clay, respectively, with acceptable accuracy (RMSE: 0.03, 1.06 and 2.90 %) in a short time. Furthermore, the mapping of soil vineyards generates information of high interest for the precision viticulture management, as well as a comparison between the methodologies used.

In the context of climate change, water management is crucial for controlling the reproductive growth and quality of grapes. In this study, we aim to determine the effects of different water regimes on the yield components and chemical composition of grape Vitis vinifera Albariño grown in Galicia. Four treatments were tested in the trial: rainfed (T0), irrigated at 30% of ET_o from veraison to harvest (T1), from pea-size to harvest (T2), and throughout the whole vegetative cycle (T6). To analyse the effects of irrigation, measures of stem water potential (Ψ_stem) were obtained fortnightly, from flowering to harvest. During the harvest, the average weight per bunch and the number of bunch and yield per vine were determined. In addition, the Ravaz index was also calculated. The grapes from each experimental treatment were subjected to chemical (OIV methods) and volatile composition (GC-MS) analyses. The results indicated a greater yield in the T0 group. The lowest yield was observed for T2 over the three years of the study. The results also showed a stronger influence of year than treatment on Albariño must include chemical composition parameters. Treatment affected malic acid, total acidity, free amino acids (FAN), and assimilable yeast nitrogen (YAN); meanwhile, of the volatile chemical groups analysed, esters, acetates, and volatile acids were affected by the treatment, while the season affected all chemical families of volatile compounds, with the exception of C6 compounds. The interaction effect of Tr × Y was observed on esters, acetates, and total volatile concentration. In the contrast analysis (p < 0.05), we observed increases in alcohols and terpenes in T6 vs. T2; however, esters and acetates were increased under rainfed treatment (T0) vs. T6.