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Accurate evaluation of grapevine water use is essential for optimizing water management and maximizing grapevine yield and berry quality in Mediterranean climates. Understanding the water and heat flux dynamics in a vineyard during grapevine berry maturation is of utmost importance. This study focuses on evaluating sensible and latent energy fluxes at the canopy, the soil beneath the canopy, and the interrow areas. The primary objective is to develop a model framework for accurately estimating these energy fluxes, contributing to a better understanding of their behavior during berry ripening. The model's accuracy was assessed by comparing the estimated fluxes with those measured by an eddy-covariance system installed at a reference height of three meters in the experimental vineyard. This validation step was essential to confirm the model's ability to capture the intricate energy flux dynamics of the vineyard, especially during grape maturation. The results revealed a high level of agreement between the observed and estimated fluxes, confirming the model's reliability. This comprehensive evaluation of energy fluxes provides valuable insights for optimizing irrigation strategies. By doing so, this study contributes to improving grape quality, ensuring sustainable water resource use, and ultimately enhancing vineyard productivity in arid and water-scarce regions. [ABSTRACT FROM AUTHOR]

Power converters are the basic elements of any power electronics system in many areas and applications. Among them, the push–pull converter topology is one of the most widespread due to its high efficiency, versatility, galvanic isolation, reduced number of switching devices and the possibility of implementing high conversion ratios with respect to non-isolated topologies. Optimal design and control requires very accurate models that consider all the non-idealities associated with the actual converter. However, this leads to the use of high-order models, which are impractical for the design of model-based controllers in real-time applications. To obtain a trade-off model that combines the criteria of simplicity and accuracy, it is appropriate to assess whether it is necessary to consider all non-idealities to accurately model the dynamic response of the converter. For this purpose, this paper proposes a methodology based on a sensitivity analysis that allows quantifying the impact of each non-ideality on the converter behaviour response as a function of the converter topology, power and frequency. As a result of the study, practical models that combine the trade-off between precision and simplicity are obtained. The behaviour of the simplified models for each topology was evaluated and validated by simulation against the most complete and accurate non-ideal model found in the literature. The results have been excellent, with an error rate of less than 5% in all cases. [ABSTRACT FROM AUTHOR]

The current need to reduce carbon emissions makes hydrogen use essential for self-consumption in microgrids. To make a profitability analysis of a microgrid, the influence of equipment costs and the electricity price must be known. This paper studies the cost-effective electricity price (EUR/kWh) for a microgrid located at ''La Rábida Campus" (University of Huelva, south of Spain), for two different energy-management systems (EMSs): hydrogen-priority strategy and battery-priority strategy. The profitability analysis is based, on one hand, on the hydrogen-systems' cost reduction (%) and, on the other hand, considering renewable energy sources (RESs) and energy storage systems (ESSs), on cost reduction (%). Due to technological advances, microgrid-element costs are expected to decrease over time; therefore, future profitable electricity prices will be even lower. Results show a cost-effective electricity price ranging from 0.61 EUR/kWh to 0.16 EUR/kWh for hydrogen-priority EMSs and from 0.4 EUR/kWh to 0.17 EUR/kWh for battery-priority EMSs (0 and 100% hydrogen-system cost reduction, respectively). These figures still decrease sharply if RES and ESS cost reductions are considered. In the current scenario of uncertainty in electricity prices, the microgrid studied may become economically competitive in the near future. [ABSTRACT FROM AUTHOR]

This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET0) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates. [ABSTRACT FROM AUTHOR]

Current climate crisis makes the need for reducing carbon emissions more than evident. For this reason, renewable energy sources are expected to play a fundamental role. However, these sources are not controllable, but depend on the weather conditions. Therefore, green hydrogen (hydrogen produced from water electrolysis using renewable energies) is emerging as the key energy carrier to solve this problem. Although different properties of hydrogen have been widely studied, some key aspects such as the water and energy footprint, as well as the technological development and the regulatory framework of green hydrogen in different parts of the world have not been analysed in depth. This work performs a data-driven analysis of these three pillars: water and energy footprint, technological maturity, and regulatory framework of green hydrogen technology. Results will allow the evaluation of green hydrogen deployment, both the current situation and expectations. Regarding the water footprint, this is lower than that of other fossil fuels and competitive with other types of hydrogen, while the energy footprint is higher than that of other fuels. Additionally, results show that technological and regulatory framework for hydrogen is not fully developed and there is a great inequality in green hydrogen legislation in different regions of the world. [ABSTRACT FROM AUTHOR]

The state of ripeness at harvest is a key piece of information for growers as it determines the market price of the yield. This has been traditionally assessed by destructive chemical methods, which lead to low-spatiotemporal resolution in the monitorization of crop development and poor responsiveness for growers. These limitations have shifted the focus to remote-sensing, spectroscopy-based approaches. However, most of the research focusing on these approaches has been accomplished with expensive equipment, which is exorbitant for most users. To combat this issue, this work presents a low-cost, hand-held, multispectral device with original hardware specially designed to face the complexity related to in-field use. The proposed device is based on a development board (AS7265x, AMS AG) that has three sensor chips with a spectral response of eighteen channels in a range from 410 to 940 nm. The proposed device was evaluated in a red-grape field experiment. Briefly, it was used to acquire the spectral signature of eighty red-grape samples in the vineyard. Subsequently, the grape samples were analysed using standard chemical methods to generate ground-truth values of ripening status indicators (soluble solid content (SSC) and titratable acidity (TA)). The eighteen pre-process reflectance measurements were used as input for training artificial neural network models to estimate the two target parameters (SSC and TA). The developed estimation models were evaluated through a leave-one-out cross-validation approach obtaining promising results (R2 = 0.70, RMSE = 1.21 for SSC; and R2 = 0.67, RMSE = 0.91 for TA). [ABSTRACT FROM AUTHOR]