Phenological models applied to grapevines are valuable tools to assist in the decision of cultural practices related to winegrowers and winemakers. The two-parameter sigmoid phenological model was used to estimate the three main phenological stages of the grapevine development, i.e., budburst, flowering, and veraison. This model was calibrated and validated with phenology data for 51 grapevine varieties distributed in four wine regions in Portugal (Lisboa, Douro, Dão, and Vinhos Verdes). Meteorological data for the selected sites were also used. Hence, 153 model calibrations (51 varieties × 3 phenological stages) and corresponding parameter estimations were carried out based on an unprecedented comprehensive and systematized dataset of phenology in Portugal. For each phenological stage, the centroid of the estimated parameters was subsequently used, and three generalized sigmoid models (GSM) were constructed (budburst: d = −0.6, e = 8.6; flowering: d = −0.6, e = 13.7; veraison: d = −0.5, e = 13.2). Centroid parameters show high performance for approximately 90% of the varieties and can thereby be used instead of variety-specific parameters. Overall, the RMSE (root-mean-squared-error) is < 7 days, while the EF (efficiency coefficient) is > 0.5. Additionally, according to other studies, the predictive capacity of the models for budburst remains lower than for flowering or veraison. Furthermore, the F-forcing parameter (thermal accumulation) was evaluated for the Lisboa wine region, where the sample size is larger, and for the varieties with model efficiency equal to or greater than 0.5. A ranking and categorization of the varieties in early, intermediate, and late varieties was subsequently undertaken on the basis of F values. These results can be used to more accurately monitor and predict grapevine phenology during a given season, thus supporting decision making in the Portuguese wine sector.

Weed control in vineyards demands regular interventions that currently consist of the use of machinery, such as plows and brush-cutters, and the application of herbicides. These methods have several drawbacks, including cost, chemical pollution, and the emission of greenhouse gases. The use of animals to weed vineyards, usually ovines, is an ancestral, environmentally friendly, and sustainable practice that was abandoned because of the scarcity and cost of shepherds, which were essential for preventing animals from damaging the vines and grapes. The SheepIT project was developed to automate the role of human shepherds, by monitoring and conditioning the behaviour of grazing animals. Additionally, the data collected in real-time can be used for improving the efficiency of the whole process, e.g., by detecting abnormal situations such as health conditions or attacks and manage the weeding areas. This paper presents a comprehensive set of field-test results, obtained with the SheepIT infrastructure, addressing several dimensions, from the animals’ well-being and their impact on the cultures, to technical aspects, such as system autonomy. The results show that the core objectives of the project have been attained and that it is feasible to use this system, at an industrial scale, in vineyards.

In the present study, we calibrated and validated thermal models to predict the DOY date at which the grape maturity index, potential alcohol/total acidity (PA/TA), reaches 0.75 (MS_0.75), 1.0 (MS₁), 1.5 (MS_1.5), and 2.0 (MS₂) for two grapevine Portuguese varieties, Touriga Nacional (TN) and Encruzado (EN), growing in the Dão wine region, Portugal. Daily rates of forcing calculated with the Sigmoid (SM) function and the Degree Day (DD) function were used. The outcomes show that the best performance of the models was obtained for the heat accumulation starting at flowering (t_x = EL23). The analysis of model sensitivity to changes in forcing rate coefficients (T₀, e, and d) enabled the selection of the same models for all maturity stage of each variety. The selected models revealed significant predictability, though dependent on the grape maturity stage and variety (EFF > 0.81 for TN and EFF > 0.75 for EN). The non-linear regression analyses of sugar concentration (SC) and total acidity (TA) with heat accumulation, calculated using the select models, demonstrated that a high fraction of SC and TA variance was explained by the variation of these temperature-based indices. Comparatively to SC and TA, the results highlight that the thermal conditions accumulated from flowering had a lower influence on pH juice variance.

Quando bem aproveitados, os resíduos orgânicos conduzem a grandes benefícios disponibilizando matéria orgânica e nutriente ao solo. Neste contexto, objetivou-se, no presente estudo, verificar o efeito da aplicação de doses de composto orgânico em dois solos submetidos a diferentes compactações na biomassa seca do milho. Para isto se aplicaram, em 54 vasos, três doses de composto orgânico nas quantidades 0 (testemunha), 40 e 80 g dm-3, em um Argissolo Vermelho Escuro e em um outro Argissolo Vermelho-Amarelo, coletados no horizonte Ap, no perfil de 0 a 0,20 m, conduzidos em três diferentes densidades (1,3; 1,5 e 1,7 kg dm-3) e três repetições. A cultura utilizada no experimento foi o milho (Zea mays), cultivar Itapuã 700. As variáveis avaliadas foram biomassa seca da parte aérea do milho (MS) e os teores de N, P, K, Ca, Mg e carbono orgânico total (COT) do solo. Os resultados obtidos indicaram que a aplicação do composto orgânico causou efeito significativo sobre os teores dos macronutrientes disponíveis e COT e a compactação do solo pouco afetou as variáveis estudadas.