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Percorrer Coleção CV por Objetivos de Desenvolvimento Sustentável (ODS) "01:Erradicar a Pobreza"
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- Analysis of EU investment on rural development and its impact on Portuguese rural regions in the period 2011-2021Publication . Isabel Abreu; Joaquim Mourato; Francisco J. MesiasEU's rural areas are an essential part of the European way of life, covering more than 75 % of the Union's territory and housing over 25 % of its population. However, they experience major imbalances compared to urban areas. Thus, it is important to analyse whether EU's rural development (RD) policy is effective in promoting the development of rural areas. This study uses a RD index for 9 Portuguese NUTS 3 regions, with data from 2011 and 2021. The results are then compared with the financial support delivered by EU funds and show their impact on RD in Portugal
- Artificial Intelligence in Learning: An Integrative Framework for Education 4.0Publication . Lourenço, Abílio A.; Valente, Sabina; Corresponding author: Lourenço, Abílio A..The integration of artificial intelligence into learning represents a paradigmatic transformation in contemporary education, offering unprecedented scale personalisation whilst also posing ethical and equity challenges. This article presents a critical and practical perspective on the potential and limitations of educational technologies and artificial intelligence to support academic success, analysing collaborative digital tools, adaptive systems and intelligent tutors, artificial intelligence as support for self-regulated learning (Zimmerman model) and systemic risks such as privacy, algorithmic biases and the global digital divide. Through systematic review of recent literature (Scopus/ERIC/WoS databases), empirical evidence is mapped regarding gains in motivation, retention and autonomy, balanced by technical and social limitations. The chapter’s flow ranges from basic platforms to advanced applications, critical challenges, and sustainable, integrative proposals. It is concluded that artificial intelligence potentiates self-regulation cycles but requires ethical frameworks for equitable implementation, and proposes practical recommendations for educators, institutions and public policies towards Education 4.0. This analysis contributes to the academic debate on responsible artificial intelligence and promotes autonomous and inclusive learning on a global scale.
- Digitization of Crop Nitrogen Modelling: A ReviewPublication . Luís Silva; Luís Alcino Conceição; Fernando Cebola Lidon; Manuel Patanita; Paola D’Antonio; Costanza FiorentinoApplying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The current predictive models of yield and soil–crop dynamics during the crop growing season currently combine information about soil, climate, crops, and agricultural practices to predict the N needs of plants and optimize its application. Recent advances in remote sensing technology have also contributed to digital modelling of crop N requirements. These sensors provide detailed data, allowing for real-time adjustments in order to increase nutrient application accuracy. Combining these with other tools such as geographic information systems, data analysis, and their integration in modelling with experimental approaches in techniques such as machine learning (ML) and artificial intelligence, it is possible to develop digital twins for complex agricultural systems. Creating digital twins from the physical field can simulate the impact of different events and actions. In this article, we review the state-of-the-art of modelling N needs by crops, starting by exploring N dynamics in the soil−plant system; we demonstrate different classical approaches to modelling these dynamics so as to predict the needs and to define the optimal fertilization doses of this nutrient. Therefore, this article reviews the currently available information from Google Scholar and ScienceDirect, using relevant studies on N dynamics in agricultural systems, different modelling approaches used to simulate crop growth and N dynamics, and the application of digital tools and technologies for modelling proposed crops. The cited articles were selected following the exclusion criteria, resulting in a total of 66 articles. Finally, we present digital tools and technologies that increase the accuracy of model estimates and improve the simulation and presentation of estimated results to the manager in order to facilitate decision-making processes.
- Dynamic Cross-Correlation Between BRICS Markets, Commodities and Green BondsPublication . Eder J. A. L. Pereira; Letícia S. Anjos; Paulo Ferreira; Derick D. Quintino; Dora AlmeidaThis paper evaluated the cross-correlation between the BRICS (Brazil, Russia, India, China and South Africa) markets with commodities and green bonds. For this purpose, the detrended moving-average cross-correlation coefficient (ρDMCA) was used, based on a sliding windows approach, with data covering a sample before the COVID-19 pandemic, during the COVID-19 pandemic and after Russia invaded Ukraine. The results show a positive cross-correlation between BRICS markets and commodities and green bonds after the COVID-19 pandemic, mainly for long time scales. This result can contribute to financial risk analysis, especially regarding hedge funds.
- The European tango between market risk and credit risk: A non-linear approachPublication . Dora Almeida; Paulo Ferreira; Andreia DionísioFinancial markets are closely connected, with credit and market risks dynamically influencing each other, particularly during extreme events. While their interdependence is well-documented in the literature, the direction and intensity of information flow remain uncertain. Using transfer entropy on European credit and stock volatility indices, we quantify this flow and its dynamics during the most recent extreme events. Our findings reveal a shifting dominance, with the credit market leading during extreme uncertainty, challenging the conventional view of risk market leadership. These patterns underscore the need to monitor the credit market as a potential early warning sign of financial instability.
- Optimising Legume Integration, Nitrogen Fertilisation, and Irrigation in Semi-Arid Forage SystemsPublication . Luís Silva; Sofia Barbosa; Fernando Cebola Lidón; Benvindo Maçãs; Salvatore Faugno; Maura Sannino; João Serrano; et al.Monoculture systems depend on high nitrogen (N) fertilisation. Incorporating legumes into forage crops offers a sustainable alternative with agronomic and economic benefits. This study assesses the impact of legumes in fodder systems, evaluating yield, N use efficiency (NUE), and profitability while identifying the best cropping strategy under semi-arid conditions. The experiment, conducted at Herdade da Comenda, Elvas, Portugal, used a split–split-plot randomised block design to analyse N doses, forage species, and irrigation. Economic metrics, including costs, net revenue, return on investment, and risk analysis, were also assessed. Moderate N doses (120 kg ha−1 ) resulted in significantly higher NUE (15.67 kg kg−1 N) than higher doses (200 kg ha−1 ), which yield only 1.40 kg kg−1 N (p < 0.05), particularly in mixed fodder crops. Irrigation improved N absorption, crop nutrition, and yield, leading to higher profitability despite increased initial costs. Agronomically, irrigation and N doses influenced yield and nutrient uptake, while no significant differences were observed between different forage crops in terms of yield or NUE. Economically, the irrigated mixed crop showed the highest return on investment (ROI = 247.37 EUR ha−1 ), whereas ryegrass presented lower financial risk (BE = 2213.24 kg ha−1 ) due to lower establishment costs. Yield was the strongest predictor of net profit (R2 = 0.89). Looking ahead, optimising N management, irrigation strategies, and mixed grass–legume crops will be crucial to maximising economic returns while minimising environmental impacts.
- Optimising Legume Integration, Nitrogen Fertilisation, and Irrigation in Semi-Arid Forage SystemsPublication . Luís Silva; Sofia Barbosa; Fernando Cebola Lidón; Benvindo Maçãs; Salvatore Faugno; Maura Sannino; João Serrano; et al.Monoculture systems depend on high nitrogen (N) fertilisation. Incorporating legumes into forage crops offers a sustainable alternative with agronomic and economic benefits. This study assesses the impact of legumes in fodder systems, evaluating yield, N use efficiency (NUE), and profitability while identifying the best cropping strategy under semi-arid conditions. The experiment, conducted at Herdade da Comenda, Elvas, Portugal, used a split–split-plot randomised block design to analyse N doses, forage species, and irrigation. Economic metrics, including costs, net revenue, return on investment, and risk analysis, were also assessed. Moderate N doses (120 kg ha−1 ) resulted in significantly higher NUE (15.67 kg kg−1 N) than higher doses (200 kg ha−1 ), which yield only 1.40 kg kg−1 N (p < 0.05), particularly in mixed fodder crops. Irrigation improved N absorption, crop nutrition, and yield, leading to higher profitability despite increased initial costs. Agronomically, irrigation and N doses influenced yield and nutrient uptake, while no significant differences were observed between different forage crops in terms of yield or NUE. Economically, the irrigated mixed crop showed the highest return on investment (ROI = 247.37 EUR ha−1 ), whereas ryegrass presented lower financial risk (BE = 2213.24 kg ha−1 ) due to lower establishment costs. Yield was the strongest predictor of net profit (R2 = 0.89). Looking ahead, optimising N management, irrigation strategies, and mixed grass–legume crops will be crucial to maximising economic returns while minimising environmental impacts.
- Quantifying Market Sentiment Influence on Systemic Connectedness: A Quantile-Based Analysis Across Asset ClassesPublication . Almeida, Dora; Ferreira, Paulo; Dionísio, Andreia; Quintino, Derick; Aslam, Faheem; Corresponding author: Almeida, Dora.Financial markets are becoming more interconnected, leading to a better understanding of how shocks can spread across different markets. Investor sentiment is crucial in market dynamics, especially during high volatility. However, traditional mean-based connectedness measures fail to capture asymmetric dependence and tail-risk dynamics and are therefore less suitable under extreme market conditions, such as crises or periods of heightened uncertainty. Against this backdrop, this study employs a quantile connectedness approach to analyze the transmission of shocks among ten financial indices, including a crypto assets-related index and an economic-news-related sentiment index. The results show significant changes in shock transmission patterns, with the Daily News Sentiment Index (DNSI) being the primary transmitter during bull markets, reinforcing the influence of news sentiment on investor behavior. Traditional indices like MSCI USA (MSCI_USA) and MSCI Europe (MSCI_EUR) are key transmitters in bear and normal market conditions, while MSCI China (MSCI_CN) and the Dow Jones Commodity Index (DJCI) are more sensitive to external shocks and act as net receivers. Furthermore, the dynamic analysis highlights the evolving role of sentiment-based indicators, particularly in extreme market regimes, supporting the role of sentiment-driven contagion effects and emphasizing their relevance for risk management. These findings highlight the importance of sentiment and traditional and emerging markets in risk management and portfolio diversification for investors and policymakers.
- Reflectance-based assessment of nitrogen status in ryegrass and mixed ryegrass-clover intercropping fodder cropsPublication . Luís Silva; Sofia Barbosa; Teresa Carita; Paola D’Antonio; Fernando Cebola Lidon; Luís Alcino ConceiçãoEffective nitrogen (N) management is essential for optimizing crop yields and minimizing environmental impacts, particularly in semi-arid regions where climate risks and natural resource constraints complicate decisionmaking. These low-energy systems require precise N strategies tailored to their unique challenges. This study evaluated a sensor-driven data analysis workflow for assessing N status in ryegrass-based fodder crops under semi-arid conditions and identified the most effective bands and vegetation indices (VIs) for use. Field trials conducted at Herdade da Comenda in Portugal employed a split-plot design, testing three N topdressing rates (0, 120, and 200 kg ha⁻¹) across varying crop types and irrigation systems. Both physical and remote measurements of crop parameters and N nutrition indicators were taken to address the limitations of current approaches in these conditions. The study found that vegetation pixels dominate spectral imagery, making additional filtering, such as ExG masks, unnecessary at ryegrass tillering and stem-elongation in ryegrass-based fodders. This simplification reduces processing time, costs, and digital footprints. Key VIs—NDRE, RERVI, and CIRE—proved robust for monitoring variables such as crop type, growth stage, and N treatments, showing strong correlations with N status indicators (NNI and CNI). Additionally, the study contrasted the efficiency of the entirely remote NNI method with the enhanced accuracy of the hybrid CCCI-CNI approach, providing valuable insights for tailored N management in semi-arid systems.
- Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A ReviewPublication . Silva, Luís; Conceição, Luís Alcino; Lidon, Fernando Cebola; Maçãs, BenvindoNitrogen use efficiency (NUE) is a central issue to address regarding the nitrogen (N) uptake by crops, and can be improved by applying the correct dose of fertilizers at specific points in the fields according to the plants status. The N nutrition index (NNI) was developed to diagnose plant N status. However, its determination requires destructive, time-consuming measurements of plant N content (PNC) and plant dry matter (PDM). To overcome logistical and economic problems, it is necessary to assesses crop NNI rapidly and non-destructively. According to the literature which we reviewed, it, as well as PNC and PDM, can be estimated using vegetation indices obtained from remote sensing. While sensory techniques are useful for measuring PNC, crop growth models estimate crop N requirements. Research has indicated that the accuracy of the estimate is increased through the integration of remote sensing data to periodically update the model, considering the spatial variability in the plot. However, this combination of data presents some difficulties. On one hand, at the level of remote sensing is the identification of the most appropriate sensor for each situation, and on the other hand, at the level of crop growth models is the estimation of the needs of crops in the interest stages of growth. The methods used to couple remote sensing data with the needs of crops estimated by crop growth models must be very well calibrated, especially for the crop parameters and for the environment around this crop. Therefore, this paper reviews currently available information from Google Scholar and ScienceDirect to identify studies relevant to crops N nutrition status, to assess crop NNI through non-destructive methods, and to integrate the remote sensing data on crop models from which the cited articles were selected. Finally, we discuss further research on PNC determination via remote sensing and algorithms to help farmers with field application. Although some knowledge about this determination is still necessary, we can define three guidelines to aid in choosing a correct platform.