Este estudio explora el nivel motivacional y de inteligencia emocional del alumnado de Educación Secundaria Obligatoria, y la relación existente entre ambos componentes esenciales en la personalidad del individuo. Se ha trabajado desde una metodología cuantitativa con una muestra de 500 alumnos de centros públicos de secundaria, utilizando el Cuestionario de Motivación y Estrategias de Aprendizaje (CMEA) y la versión en castellano de la Trait Meta-Mood Scale (TMMS-24). Los resultados sostienen un buen nivel motivacional en todos los factores (metas intrínsecas, valor de las tareas, creencia de control, autoeficacia para el rendimiento, ansiedad y metas extrínsecas), con una adecuada percepción, regulación y comprensión emocional. Así mismo, evidencian una correlación positiva y significativa de ambas, excepto entre el factor motivacional “ansiedad” y los factores “comprensión” y “regulación” que conforman la inteligencia emocional. En conclusión, tanto motivación como inteligencia emocional, son susceptibles de ser entrenadas y mejoradas en el ámbito educativo, con capacidad para poner en marcha las habilidades sociales y sacar el máximo rendimiento de cada adolescente.
<p>The eGROUNDWATER project aims to improve sustainable, participatory groundwater management in the Mediterranean region by developing and testing Enhanced Information Systems (EIS) that integrate citizen science and information and communication technology (ICT) tools. A key component of the EIS is a mobile app that will allow farmers and other groundwater users to report groundwater levels in their wells and the amount of water used for irrigation and other purposes. At the same time, the app will provide users with information about the state of the aquifer and recommendations for sustainable water use (for example, short-term and seasonal predictions of irrigation water needs).</p> <p>The eGROUNDWATER app has the potential to be a valuable tool for improving the sustainable use and management of aquifers in the Mediterranean region. By gathering real-time data from a wide range of users, the app will help to create a more complete and accurate picture of groundwater conditions and usage. Policymakers and resource managers can use this information to make informed decisions about the allocation and use of water resources. It can also help to identify potential problems and areas where conservation efforts may be needed.</p> <p>The development of an app that meets the needs of the users required first to understand their perspectives and experiences related to the groundwater body. eGROUNDWATER has organized interviews and meetings in each case study to characterize the vision of the different agents on the groundwater bodies, and to try to build a collective framing of the current groundwater status and use in the area. On these meetings, the users identified lack of information about the aquifer as a critical issue to solve in order to advance towards a sustainable management of the resource.</p> <p>In addition to providing valuable data and information, the eGROUNDWATER app has the potential to engage and educate farmers and other users about the importance of sustainable water management. The app can foster a sense of ownership and responsibility for the aquifer's health among users, as it offers personalized feedback and information about the use of water and the aquifer&#8217;s health. This, in turn, could lead to more responsible water use practices and help to preserve groundwater resources for the long-term.</p> <p>The eGROUNDWATER project and its accompanying mobile app offer a promising approach to improving the sustainable use and management of aquifers in the Mediterranean region. The engagement of stakeholders in the development process and the collection and sharing of useful data and information through the app can help promoting education and awareness about water resource management. These efforts can help to foster a greater understanding of the importance of these resources and encourage more sustainable use of aquifers in the region, significantly contributing to the long-term sustainability of Mediterranean aquifers.</p> <p><strong>Acknowledgements:</strong></p> <p>This study has received funding from the eGROUNDWATER project (GA n. 1921) a project from the PRIMA programme, supported by Horizon 2020, the European Union's Framework Programme for Research and Innovation.</p>
<p>Irrigated agriculture is a major contributor to global groundwater use, and can sometimes lead to the overexploitation of aquifers. The Requena-Utiel, Campina de Faro and Ain Timguenay aquifers in Spain, Portugal and Morocco, respectively, are facing such a situation, with excessive pumping raising concerns about the aquifer's water levels and the long-term health of the groundwater body. Accurate estimation and remote monitoring of crop water needs are crucial for effectively managing the limited water resources in the region by providing farmers with accurate recommendations on water use.</p> <p>The eGROUNDWATER project aims to address this issue by applying a water balance method based on Vegetation Index data of croplands. The method uses the Fractional Vegetation Cover (FVC) to estimate bare soil evaporation and vegetation transpiration, agro-climatic data and optical data (CopernicusESA/EROS-USGS). Potential evapotranspiration was calculated using the FAO method. The result of this process was a model for determining the irrigation water needs of crops within the region that allows researchers to differentiate stressed and over-irrigated areas with a high degree of precision.</p> <p>The model was developed for the Spanish case study and was successfully applied to the Moroccan and Portuguese cases, where data scarcity at the local scale is also an issue. Remote sensing allows for more accurate detection of crop water needs, enabling the alignment of water requirements and agricultural demands. Although evapotranspiration estimates based on remote sensing may be subject to bias, these biases can be identified and corrected using reliable ground data. If daily images are not available, it is possible to upscale daily evapotranspiration estimates to seasonal or annual estimates. At the end, annual crop water needs can be modeled using a yearly map of irrigated areas, which is helpful for planning and managing water resources at the plot scale.</p> <p>In conclusion, this research has shown that remote sensing can be a valuable tool for accurately estimating and monitoring crop water needs and for improving water resource management in three Mediterranean regions. By using the described methods, it is possible to align water use with agricultural demands more effectively and to ensure sustainable use of the aquifer's limited resources.</p> <p><strong>Acknowledgements:</strong></p> <p>This study has received funding from the eGROUNDWATER project (GA n. 1921) a project from the PRIMA programme, supported by Horizon 2020, the European Union's Framework Programme for Research and Innovation.</p>
<p>The Requena-Utiel aquifer in the Jucar River Basin (Mediterranean Spain) is mined mainly for the irrigation of vineyards (Denominaci&#243;n de Origen Utiel-Requena), and some olive and nut trees. It has been recently declared as in bad quantitative status by the Jucar River Basin Agency (Confederaci&#243;n Hidrogr&#225;fica del J&#250;car, CHJ). Among the measures taken to control water abstraction, a pumping cap for the irrigation season (May-September) has been agreed between the CHJ and the groundwater user association. This limit depends on the cumulative precipitation from December to April (classifying the year in wet, normal or dry), although that irrigation amount is in any case below the crop requirements. Consequently, predicting the type of year beforehand is a piece of valuable information for the water users in order to optimally schedule groundwater pumping and foresee crop production.</p><p>This study analyses the ability of seasonal meteorological forecasts from the Copernicus Climate Change Service (C3S) to anticipate the type of year in the agricultural areas of the Requena Utiel aquifer considering different periods ahead. The following seasonal forecasting services were used: ECMWF SEAS5, UKMO GloSEA5, M&#233;t&#233;oFrance System, DWD GCFS, and CMCC SPS. Seasonal forecasts issued between November 1<sup>st</sup> and April 1<sup>st</sup> were downloaded and post-processed using a month-dependent linear scaling against historical records. Once post-processed, the skill of seasonal forecasts to predict the type of year has been evaluated for the 1995-2015 period, depending on the anticipation time.</p><p>Results show that, on a broader view, the type of year cannot be safely anticipated before April 1<sup>st</sup>. However, we have identified that, for particular types of year and forecasting services, the anticipation time can be enlarged (e.g predicting wet years in December). Furthermore, we have found a direct relationship between the strength of the signal (number of ensemble members that predict the same type of year) and the forecasting skill, meaning that seasonal forecasts showing a strong signal, if properly identified, could offer valuable information months in advance to the beginning of the irrigation season.</p><p><em>Acknowledgements:</em></p><p>This study has received funding from the eGROUNDWATER project (GA n. 1921), part of the PRIMA programme supported by the European Union&#8217;s Horizon 2020 research and innovation programme. It has been also supported by the ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Economia y Competitividad (MINECO) of Spain and with EU FEDER funds.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.