Abstract
PhenoSat is a software tool that extracts phenological information from satellite based vegetation index time-series. This chapter presents PhenoSat and tests its main characteristics and functionalities using a multi-year experiment and different vegetation types – vineyard and semi-natural meadows. Three important features were analyzed: (1) the extraction of phenological information for the main growing season, (2) detection and estimation of double growth season parameters, and (3) the advantages of selecting a sub-temporal region of interest. Temporal NDVI satellite data from SPOT VEGETATION and NOAA AVHRR were used. Six fitting methods were applied to filter the satellite noise data: cubic splines, piecewise-logistic, Gaussian models, Fourier series, polynomial curve-fitting and Savitzky-Golay. PhenoSat showed to be capable to extract phenological information consistent with reference measurements, presenting in some cases correlations above 70% (n=10; p=0.012). The start of in-season regrowth in semi-natural meadows was detected with a precision lower than 10-days. The selection of a temporal region of interest, improve the fitting process (R-square increased from 0.596 to 0.997). This improvement detected more accurately the maximum vegetation development and provided more reliable results. PhenoSat showed to be capable to adapt to different vegetation types, and different satellite data sources, proving to be a useful tool to extract metrics related with vegetation dynamics. © Springer International Publishing AG 2016.

Abstract
Non-destructive, accurate, user-friendly and low-cost approaches to determining crop leaf area (LA) are a key tool in many agronomic and physiological studies, as well as in current agricultural management. Although there are models that estimate cut rose LA in the literature, they are generally designed for a specific stage of the crop cycle, usually harvest. This study aimed to estimate the LA of cut “Red Naomi” rose stems in several phenological phases using morphological descriptors and allometric measurements derived from image processing. A statistical model was developed based on the “multiple stepwise regression” technique and considered the stem height, the number of stem leaves, and the stage of the flower bud. The model, based on 26 stems (232 leaves) collected at different developmental stages, explained 95% of the LA variance (R2 = 0.95, n = 26, p < 0.0001). The mean relative difference between the observed and the estimated LA was 8.2%. The methodology had a high accuracy and precision in the estimation of LA during crop development. It can save time, effort, and resources in determining cut rose stem LA, enhancing its application in research and production contexts. © 2016 by the authors.

Abstract
Soil erosion is a serious environmental problem. An estimation of the expected soil loss by water-caused erosion can be calculated considering the Revised Universal Soil Loss Equation (RUSLE). Geographical Information Systems (GIS) provide different tools to create categorical maps of soil erosion risk which help to study the risk assessment of soil loss. The objective of this study was to develop a GIS open source application (in QGIS), using the RUSLE methodology for estimating erosion rate at the watershed scale (desktop application) and provide the same application via web access (web application). The applications developed allow one to generate all the maps necessary to evaluate the soil erosion risk. Several libraries and algorithms from SEXTANTE were used to develop these applications. These applications were tested in Montalegre municipality (Portugal). The maps involved in RUSLE method-soil erosivity factor, soil erodibility factor, topographic factor, cover management factor, and support practices-were created. The estimated mean value of the soil loss obtained was 220 ton km(-2) year(-1) ranged from 0.27 to 1283 ton km(-2) year(-1). The results indicated that most of the study area (80 %) is characterized by very low soil erosion level (<321 ton km(-2) year(-1)) and in 4 % of the studied area the soil erosion was higher than 962 ton km(-2) year(-1). It was also concluded that areas with high slope values and bare soil are related with high level of erosion and the higher the P and C values, the higher the soil erosion percentage. The RUSLE web and the desktop application are freely available.

Abstract
In this paper, we analyse the impact of spring temperature (ST) and soil water (SW) on wine production volume (WPV) for the period 1933 to 2013 in the Douro region. We employ a state-space regression model to capture possible structural changes in wine production caused by a change in ST and/or SW. We find that the ST explains about 65 % of the variability of WPV. In contrast, the summer SW level increases the R (adj)-square to 83 % and the Akaike criterion value was lower. We also find interesting dynamic properties of SW and ST. The immediate impact of an increase in SW is negative for WPV, while the SW that is in the ground, i.e. from the previous 2 and 3 years, have a positive effect on actual WPV. Moreover, the individual changes of ST and SW have similar dynamic impact on WPV. Our main finding is that climate change does not only change the variables in question but also the winegrape vineyards adding to the negative impact on WPV levels. As a result we observe a shift of the relative importance away from ST to SW.

Abstract
A wine forecast model for one of the most arid wine regions of the Europe-Alentejo was improved and tested for the period 1998-2014. During this period, Alentejo region had strong upward trends in wine production associated to the increase of vineyard area. The forecast model was supported on a hierarchical analysis, including the determination of the potential production at flowering by quantifying airborne pollen concentration, followed by a climate based evaluation of the possible impact of fruit-set conditions in the limitation of production. Through the monitoring of airborne pollen flows it is possible to define an accurate main pollen season and determine the regional pollen index that will be used as independent variable in the regional forecast model. The time trend, which was initially removed from data, was then added back to obtain the forecast. Stepwise regression and cross-validation were employed during the period 1998-2014 for calibration of the model used for predicting annual wine production. The developed model explained about 86% of wine variance over the years with absolute average error of 6% for the cross validation and 87% of cases had differences between actual and forecasted wine production below 10%. The reliability and early-indication ability of the proposed forecast model justify their use to respond to a number of government agencies and wine industry concerns and activities.

Abstract
In northern Spain, the use of biomass to produce bioenergy has led to increased exploitation of both natural pedunculate oak (Quercus robur L.) stands and fast-growing plantations of natural or exotic species. In this study, we developed a model for estimating aboveground biomass, carbon and nutrient contents in different pedunculate oak components at individual-tree and at stand level. Six harvesting methods were simulated in an average stand, ranging from whole-tree to stem wood extraction (stem without bark) and including the conventional harvesting method used in the region (extraction of stem plus branches of diameter >7 cm). The biomass and macronutrients extracted were compared with those removed during harvesting of fast-growing tree species (Eucalyptus globulus Labill., Pinus radiata D. Don and Pinus pinaster Ait.) on the same temporal basis (mean annual values). Harvesting pedunculate oak stands generally extracted lower amounts of nutrients than harvesting fast-growing species, although the differences depended on the species, macronutrients and harvesting regime considered.