Abstract
Pollen quality of 15 cultivars of Vitis vinifera L. was studied in this work. Pollen viability was tested by the fluorochromatic reaction and germination was analyzed by in vitro assays, using two different media. Differences among cultivars in the number of pollen apertures were observed under light microscope. All the cultivars studied showed a higher percentage of tricolporated pollen, however, pollen grains containing one, two or four apertures were also observed. The cultivar Loureiro was the one with the higher percentage of pollen grains with four apertures (3.8%) and Touriga Nacional presented 100% of tricolporated pollen grains. The viability analysis showed that 13 cultivars presented values higher than 50%, with 8 cultivars reaching values above 75%. The pollen germination rates vary greatly for the grapevine cultivars studied, three cultivars show low values of germination (under 14%) in the two media tested, which were Touriga Nacional, Cabernet Franc, and Cabernet Sauvignon while others presented high values of germination like Casteldo, Loureiro, Malbec and Petit Verdot. No significant statistical differences between the percentages of germination in the two media studied were found for the majority of cultivars analyzed.

Abstract
In this work, fungi present in the grapevine's phyllosphere collected from the main demarcated wine regions of Portugal were identified, and their phylogenetic relationships were analysed. A total of 46 vine samples (leaves and berries) were collected from different parts of the country, being isolated a total of 117 fungal colonies that were identified to the genus level and sequenced in the following genetic regions: internal transcribed spacer region and 18S rRNA and -tubulin gene. Next, a phylogenetic tree reconstruction for each genetic region was built. The isolates retrieved from environmental samples belonged to the genera Alternaria (31%), Cladosporium (21%), Penicillium (19%), Aspergillus (7%) and Epicoccum (3%). No genetic signatures of exchange of genetic material were detected, and consequently, the reconstructed phylogenetic trees allowed to distinguish between these different species/genera. In the fungal composition of the Vitis vinifera phyllosphere, several potential pathogens were identified that can be associated with decreases in crop productivity. Knowledge of fungi identification and genetic diversity is pivotal for the development of more adequate crop management strategies. Furthermore, this information will provide guidelines for a more specific and wiser use of fungicides. Significance and Impact of the StudyThe knowledge on the composition of the phyllosphere microbial community is still limited, especially when fungi are concerned. These micro-organisms not only play a crucial role in crop health and productivity but also interact with the winemaking process, determining the safety and quality of grape and grape-derived products. The elucidation of the micro-organisms present in the phyllosphere will have a notorious impact on plant breeding and protection programmes and disease management strategies, allowing a better control of pesticide applications.

Abstract

Abstract
Phenological models for predicting the grapevine flowering were tested using phenological data of 15 grape varieties collected between 1990 and 2014 in Vinhos Verdes and Lisbon Portuguese wine regions. Three models were tested: Spring Warming (Growing Degree Days - GDD model), Spring Warming modified using a triangular function - GDD triangular and UniFORC model, which considers an exponential response curve to temperature. Model estimation was performed using data on two grape varieties (Loureiro and Fernao Pires), present in both regions. Three dates were tested for the beginning of heat unit accumulation (t(0)( )date): budburst, 1 January and 1 September. The best overall date was budburst. Furthermore, for each model parameter, an intermediate range of values common for the studied regions was estimated and further optimized to obtain one model that could he used for a diverse range of grape varieties in both wine regions. External validation was performed using an independent data set from 13 grape varieties (seven red and six white), different from the two used in the estimation step. The results showed a high coefficient of determination (R-2 : 0.59-0.89), low Root Mean Square Error (RMSE: 3-7 days) and Mean Absolute Deviation (MAD: 2-6 days) between predicted and observed values. The UniFORC model overall performed slightly better than the two GDD models, presenting higher R-2 (0.75) and lower RMSE (4.55) and MAD (3.60). The developed phenological models presented good accuracy when applied to several varieties in different regions and can be used as a predictor tool of flowering date in Portugal.

Abstract
Phenology is one of the most reliable indicators of vegetation dynamics. Assessing and monitoring the dynamics of phenology is relevant to support several decisions in order to improve the efficiency of several farming practices. An open source application QPhenoMetrics - implemented in QGIS software that estimates vegetation phenology metrics is presented, using Earth Observation Systems (EOS) based time-series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as proxies for phenology. QPhenoMetrics is characterized by freely-usable and updatable code, acceptance of satellite images or text formats, time-series analysis toolbox allowing the selection of region of interest with statistical quality assessment for Vegetation Indices (VI), and estimation of ensemble metrics. The application is structured in three components: (i) input data; (ii) pre-processing of the VI time-series and several fitting methods and (iii) computation of the phenological metrics. QPhenoMetrics produces a plot with the VI time-series and corresponding phenology metrics, and a spreadsheet is created with a list of NDVI or EVI values estimated using the selected fitting method. To evaluate the application, two main Portuguese crops, vineyards and maize, and MOD13 data from MODIS sensor during 2011-2012 were considered. QPhenoMetrics was validated with vineyard phenology observations (2007-2011). A comparative analysis with software products TimeSat and Spirits was also performed. It was concluded that QPhenoMetrics can be very useful for common users to extract phenology information for 16 daily MODIS data in HDF format, text files with NDVI/EVI data and ASCII files, through a simple and intuitive graphic interface. Furthermore, the user can evaluate the quality assessment of VI of the images used. QPhenoMetrics is an effective open source tool that in addition to being free, is readily modifiable by user according to the study requirements.

Abstract
Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDI(d: 725; 715; 565)) for the hyperspectral dataset and the modified simple ratio (mSR(c: 740; 705; 865)) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.