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Research Center for Endogenous Resource Valorization
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Optimising Legume Integration, Nitrogen Fertilisation, and Irrigation in Semi-Arid Forage Systems
Publication . Silva, Luís; Barbosa, Sofia; Lidón, Fernando Cebola; Maçãs, Benvindo; Faugno, Salvatore; Sannino, Maura; Serrano, João; D’Antonio, Paola; Fiorentino, Costanza; Cellini, Francesco; Ferreira, Paulo; Conceição, Luís Alcino; DCT - Departamento de Ciências da Terra; GeoBioTec - Geobiociências, Geoengenharias e Geotecnologias; MDPI - Multidisciplinary Digital Publishing Institute
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.
Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models
Publication . Silva, Luís; Conceição, Luís Alcino; Lidon, Fernando Cebola; Maçãs, Benvindo; DCT - Departamento de Ciências da Terra; GeoBioTec - Geobiociências, Geoengenharias e Geotecnologias; MDPI - Multidisciplinary Digital Publishing Institute
Nitrogen 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.
LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection Sensors †
Publication . Correia, Sérgio D.; Roque, Pedro M.; Matos-Carvalho, João P.; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; CTS - Centro de Tecnologia e Sistemas; MDPI - Multidisciplinary Digital Publishing Institute
In this paper, the concept of symmetry is used to design the efficient inference of a fall-detection algorithm for elderly people on embedded processors—that is, there is a symmetric relation between the model’s structure and the memory footprint on the embedded processor. Artificial intelligence (AI) and, more particularly, Long Short-Term Memory (LSTM) neural networks are commonly used in the detection of falls in the elderly population based on acceleration measures. Nevertheless, embedded systems that may be utilized on wearable or wireless sensor networks have a hurdle due to the customarily massive dimensions of those networks. Because of this, the algorithms’ most popular implementation relies on edge or cloud computing, which raises privacy concerns and presents challenges since a lot of data need to be sent via a communication channel. The current work proposes a memory occupancy model for LSTM-type networks to pave the way to more efficient embedded implementations. Also, it offers a sensitivity analysis of the network hyper-parameters through a grid search procedure to refine the LSTM topology network under scrutiny. Lastly, it proposes a new methodology that acts over the quantization granularity for the embedded AI implementation on wearable devices. The extensive simulation results demonstrate the effectiveness and feasibility of the proposed methodology. For the embedded implementation of the LSTM for the fall-detection problem on a wearable platform, one can see that an STM8L low-power processor could support a 40-hidden-cell LSTM network with an accuracy of (Formula presented.).
Properties and Uses of Biochars Incorporated into Mortars
Publication . Mota-Panizio, Roberta; Carmo-Calado, Luís; Assis, Ana Carolina; Matos, Vítor; Hermoso-Orzáez, Manuel Jesus; Romano, Pedro; Gonçalves, Margarida; Brito, Paulo; DCTB - Departamento de Ciências e Tecnologia da Biomassa (ex-GDEH); MEtRICS - Centro de Engenharia Mecânica e Sustentabilidade de Recursos; MDPI AG
The construction industry is responsible for a large amount of CO2 emissions and an intensive energy consumption. Cement production is the third largest source of anthropogenic CO2 emissions and is responsible for about 1.8 Gt of CO2 emissions into the atmosphere. The use of waste materials to replace a fraction of cement in the mortar makes it more economically and ecologically friendly. In this work, the main objective was to test incorporations of biochar produced at temperatures of 300, 350, and 400 °C, as a partial replacement for cement in the production of mortar. The materials used for the tests were residual lignocellulosic biomass (WBL) and electrical cable insulation waste (WIEC) mixed in a ratio of 1:1. The biochars produced were crushed and sieved after production to reduce the particles. A sample of biochar was used and tested under these conditions and another sample was washed in water and dried before being incorporated; all tests were carried out with a 5% replacement. Waste recovery tests were also carried out without thermochemical treatment. The specimens were studied for compressive strength and water absorption by immersion. All tests were replicated and were analyzed and compared with a control mixture with no incorporation of biochar in the mixture. It was possible to observe that the tests with the incorporation of biochars at 400 °C showed better results, with only a 24% reduction in resistance to compression.
Brief Overview of Refuse-Derived Fuel Production and Energetic Valorization
Publication . Santos, Santa Margarida; Nobre, Catarina; Brito, Paulo; Gonçalves, Margarida; DCTB - Departamento de Ciências e Tecnologia da Biomassa (ex-GDEH); MEtRICS - Centro de Engenharia Mecânica e Sustentabilidade de Recursos; Molecular Diversity Preservation International (MDPI)
A large part of municipal solid waste (MSW) still goes to landfills, representing an environmental concern. A circular economy approach can enable safe management of MSW while mitigating the increasing energy needs when waste is used as a feedstock in energy production processes (waste to energy). Currently, MSW can be converted into refuse-derived fuel (RDF) through mechanical and biological treatment processes. This study analyzes the status of MSW and RDF production, as well as its main destinations in Portugal and Europe. The legislation applied, possible energy-recovery routes, and challenges associated with energy recovery are discussed throughout this paper. This research finds that the production of RDF in Portugal has been neglected, mostly because of RDF composition being quite heterogeneous and its poor fuel properties. Therefore, the need to improve and upgrade the characteristics and properties of RDF for waste-to-energy processes was detected. RDF can be pretreated to be further applied to waste-to-energy and waste-to-gas processes, such as incineration and gasification. The technology readiness level data, costs, and SWOT analysis allowedto assess that although incineration is the most mature and widely used technology, gasification becomes more attractive, having lower costs and gaseous emissions, proving to be more efficient and sustainable for MSW and RDF conversion.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/05064/2020