Abstract
Optimising the design of water distribution networks (WDNs) is a well-known problem that has been studied by numerous researchers. This work proposes a heuristic based on simulated annealing and improved by using concepts from the cross-entropy method. The proposed optimization approach is presented and used in two case studies of different complexity. The results show not only a fall in the computational effort of the new approach relative to simulated annealing but also include a comparison with other heuristic results from the literature, used to solve the same problems.

Abstract
Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential ((pd)). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and (pd). A linear regression was defined using a parameterization dataset. The correlation analysis between (pd) and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r(2)) smaller than 0.67. However, the results of r(2) highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r(2) and stability index results. The equations obtained through the regression between measured (pd) ((pd_obs)) and optimal VARI and between (pd_obs) and optimal NDGI when using the parameterization dataset were adopted for predicting (pd) using a testing dataset. The comparison of (pd_obs) with (pd) predicted based on VARI led to R-2 = 0.79 and a regression coefficient b = 0.96. Similar R-2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating (pd), supporting vineyards irrigation management.

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.

Abstract
The Diagnosis and Recommendation Integrated System (DRIS) is an alternative tool for the evaluation of nutritional status and fertilizer recommendation of several crops. However, as this methodology implies the establishment of norms or standards, without which one cannot infer about the nutritional status of a crop, in Portugal this tool has little application. The aim of this study was to establish preliminary DRIS norms for vineyards in Portugal. From 2007 to 2009, petiole samples were collected on a set of 199 selected plots. The DRIS norms were established according to the proposed by Beaufils (1973), based on the results of the laboratory procedures. The results suggest the need for further studies in order to validate the DRIS norms presented. In the future it will be important to increase the number of observations for the establishment of DRIS norms, as well as to determine the relevance of establishing specific nutritional standard according the edaphic, climatic and varietal variability of Portuguese wine regions.

Abstract
Knowledge regarding Ecosystem Services (ES) delivery and the socio-ecological factors that influence their proficiency is essential to allow cities to adopt policies that lead to resource-efficient planning and greater resilience. As one of the matrix elements of urban ecological structure, vegetation may play a major role in promoting ES proficiency through planting design. This research addresses the heterogeneity of ES delivered by the urban vegetation of Porto, a Portuguese city. A methodology is proposed to investigate associations between socioeconomic indicators and structural variables of the urban forest, and also which structural variables of the urban forest, if any, differ along a socioeconomic gradient. Our results reveal that before setting planning and management goals, it is crucial to understand local patterns of ES and their relationships with socioeconomic patterns, which can be affected by variables such as building age. This should be followed by the identification of structural variables of the urban forest that better explain the differences, in order to target these through planning and management goals. The conceptual framework adopted in this research can guide adaptation of our methodology to other cities, providing insights for planning and management suitable to site-specific conditions and directly usable by stakeholders.

Abstract
In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Psi(pd)) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive Modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Psi(pd), with an average determination coefficient (R-2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R-2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Psi(pd) observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support irrigation scheduling in vineyard.