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.

Abstract
This paper analyzes the impact of climate dynamics on vegetation growth for a rural mountainous region in northeastern Portugal. As a measure of vegetation growth, we use the normalized difference vegetation index (NDVI), which is based on the ten-day synthesis data set (S10) from Satellite Pour l'Observation de la Terre (SPOT-VEGETATION) imagery from 1998 to 2011. We test whether the dynamic growth pattern of the NDVI has changed due to climate variability, and we test the relationship of NDVI with temperature and available soil water (ASW). In order to do so, we use a time-frequency approach based on Kalman filter regressions in the time domain. The advantage of our approach is that it can be used even in the case where the sample size is relatively small. By estimating the important relationships in the time domain first and transferring them into the frequency domain, we are still able to derive a complete spectrum over all frequencies. In our example, we find a change of the cyclical pattern for the spring season and different changes if we take into account all seasons. In other words, we can distinguish between deterministic changes of the vegetation cycles and stochastic changes that only occur randomly. Deterministic changes imply that the data-generating process has changed (such as climate), whereas stochastic changes imply only temporary changes. We find that individual seasons undergo cyclical changes that are different from other seasons. Moreover, our analysis shows that temperature and ASW are the main drivers of vegetation growth. We can also recognize a shift of the relative importance away from temperature to soil water.

Abstract
Water and energy balance interactions with vegetation in mountainous terrain are influenced by topographic effects, spatial variation in vegetation type and density, and water availability. This is the case for the mountainous areas of northern Portugal, where ancestral irrigated meadows (lameiros) are a main component of a complex vegetation mosaic. The widely used surface energy balance model METRIC was applied to four Landsat images to determine the spatial and temporal distribution of the energy balance terms in the identified land cover types (LCT). A discussion on the variability of evapotranspiration (ET) through the various vegetation types was supported by a comparison between the respective crop coefficients and those available in the literature corresponding to the LC, which has shown the appropriateness of METRIC estimates of ET. METRIC products derived from images of May and June - NDVI, surface temperature, net radiation, soil heat flux, sensible heat flux, and ET - were used to characterize the LCTs, through application of principal component analysis. Three principal components explained the variance of observed variables and their varimax rotated loadings allowed a good explanation of the behaviour of the explanatory variables in association with the LCTs. Information gained contributes to improve the characterization of the study area and may further support conservation and management of these mountain landscapes.

Abstract
Nutrient quantification in hydroponic systems is essential. Reagent-less spectral quantification of nitrogen, phosphate and potassium faces challenges in accessing information-rich spectral signals and unscrambling interference from each constituent. Herein, we introduce information equivalence between spectra and sample composition, enabling extraction of consistent covariance to isolate nutrient-specific spectral information (N, P or K) in Hoagland nutrient solutions using orthogonal covariance modes. Chemometrics methods quantify nitrogen and potassium, but not phosphate. Orthogonal covariance modes, however, enable quantification of all three nutrients: nitrogen (N) with R = 0.9926 and standard error of 17.22 ppm, phosphate (P) with R = 0.9196 and standard error of 63.62 ppm, and potassium (K) with R = 0.9975 and standard error of 9.51 ppm. Including pH information significantly improves phosphate quantification (R = 0.9638, standard error: 43.16 ppm). Results demonstrate a direct relationship between spectra and Hoagland nutrient solution information, preserving NPK orthogonality and supporting orthogonal covariance modes. These modes enhance detection sensitivity by maximizing information of the constituent being quantified, while minimizing interferences from others. Orthogonal covariance modes predicted nitrogen (R = 0.9474, standard error: 29.95 ppm) accurately. Phosphate and potassium showed strong interference from contaminants, but most extrapolation samples were correctly diagnosed above the reference interval (83.26%). Despite potassium features outside the knowledge base, a significant correlation was obtained (R = 0.6751). Orthogonal covariance modes use unique N, P or K information for quantification, not spurious correlations due to fertilizer composition. This approach minimizes interferences during extrapolation to complex samples, a crucial step towards resilient nutrient management in hydroponics using spectroscopy.