Repositório Científico do Instituto Politécnico de Castelo Branco

From food systems to gut microbiota: Dietary substrates, microbial exposure and one health

Publication . Barreto, Inês R.; Eugénio, Ana; Cristóvão, Mário; Rodrigues, Francisco; Espírito Santo, Christophe; Brandão, Inês

Food systems are usually discussed in terms of nutrition, food safety, productivity, sustainability or emissions. Less attention is given to the microbial dimension of the farm-to-fork pathway and to the way food systems shape the dietary substrates, food matrices and microbial exposures that reach the gut. Soils, plants, foods, processing environments, animals and the human gut all host microbial communities that influence nutrient cycling, plant performance, food characteristics, metabolism, immune regulation and ecological resilience. This review examines how food systems may modulate gut microbiota and microbiome resilience within a One Health framework. Evidence from soil, crop and food microbiome studies suggests that local conditions and farming practices can leave detectable microbial signatures on plants and edible tissues. However, the soil–food–gut continuum should not be understood as a simple transfer route. Microorganisms and microbial products are repeatedly filtered by plant traits, farming systems, animal-production interfaces, harvesting, processing, storage, preparation and host physiology. The review also considers how this continuity may be weakened or redirected. Agricultural intensification, pollutants, postharvest processing, antimicrobial use, ultra-processed foods, additive mixtures, low-fibre diets, early-life microbial disruption and reduced contact with environmental biodiversity may alter microbial communities at different points of the food system. Antimicrobial resistance is also discussed as a functional microbial trait that can circulate across human, animal, food and environmental interfaces. One Health approaches to food systems should therefore combine microbial risk control with microbial stewardship: protecting useful microbial diversity and function while preserving food safety. The aim is not to maximise microbial exposure, but to understand which microbial functions matter and how food systems can support gut microbiota resilience across environments, foods and hosts.

Predictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar Region (Algeria)

Publication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.

An accurate estimation of the radiation is a prerequisite for the net radiation balance, evapotranspiration modeling, and optimal scheduling of water extraction by solar-powered irrigation, especially in the water-scarce Saharan zone. In this study, we developed a predictive model to estimate daily terrestrial radiation at the surface of the Adrar region in Algeria. We used a training set of 25 years of data (2000–2025) from 10 stations (Adrar, Tamantit, Sidi Ahmed Timmi, Fenoughil, Zaouiet Kounta, Reggane, Gharmianou, Tittaf, Ikiss, Kassbet Lahrar) with only three features: soil temperature (0–7 cm), air temperature (2 m), and vapor pressure deficit. The robustness of the models was ensured by a time-based split. The random forest (RF), gradient boosting (GB), and extra trees (ET) tree-based models were evaluated on the generalization set. RF and ET exhibited the best performance (R = 0.92, rootean square error—RMSE = 31.85 W/m2, Nash–Sutcliffe efficiency—NSE = 0.84 for RF; R = 0.92, RMSE = 32.33 W/m2, NSE = 0.84 for ET), whereas GB exhibited poor performance (R = 0.90, RMSE = 36.67 W/m2, NSE = 0.79). Finally, we proposed a map for solar-powered irrigation optimization. In particular, we demonstrated that the southern part of the Adrar region (Reggane, Zaouiet Kounta) has high potential for solar-powered irrigation (more than 350 W/m2). This study contributes to hyper-arid agricultural regions in Algeria through water conservation and the utilization of renewable energy.

Spatiotemporal analysis and machine learning prediction of reference evapotranspiration in Khenchela, Algeria: Comparison of MLR, GRNN, and LSTM models

Publication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, Abderahamane

Reference evapotranspiration (ET₀) is a key parameter for water management in semi-arid regions with variable climates. This study analyzed the spatiotemporal dynamics of annual ET₀ in the Khenchela region of north-eastern Algeria (2000–2024). ET₀ was computed using the FAO-56 Penman–Monteith (PM) method. Spatial patterns were mapped using Inverse Distance Weighting (IDW). Meteorological data from 16 stations were used to train three models: Multiple Linear Regression (MLR), Generalized Regression Neural Network (GRNN), and Long Short-Term Memory (LSTM) to predict ET₀. The regional mean annual ET₀ increased by 7.2% from 2010 to 2019 decadal average (1 490 mm/year) to the 2020-2024 period (1597 mm/year), contributing to a cumulative 25-year increase of 7% from 2000 to 2009 baseline with hotspots in Babar 2 reaching ~2194 mm/year. The Mann–Kendall test confirmed significant upward trends (p < 0.05) driven by rising temperatures and declining relative humidity. All models performed well (R² > 0.965, RMSE < 0.49 mm/day, RSR < 0.20), with LSTM showing superior accuracy (R² > 0.987, RMSE < 0.232 mm/day, NSE ≈ 0.991, WI > 0.909). The superior performance of LSTM is attributed to its inherent capability to capture temporal autocorrelation and long-term dependencies in climatic time-series data. These findings support adaptive irrigation and drought mitigation in semi-arid regions of northern Africa.

Neural network approximation based on ANFIS and Geographic Information System mapping for reliable evapotranspiration prediction in Khenchela, Algeria

Publication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, Abderahamane

Accurate estimation of reference evapotranspiration (ET0) is critical for sustainable water resource management, irrigation scheduling, and climate adaptation in heterogeneous semi-arid regions. This study presents a streamlined neural network (NN) approximation inspired by the Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting daily ET0 in Khenchela province, northeastern Algeria. Utilizing meteorological and soil data from 2000 to 2024 at 16 representative stations (Babar (1), Babar (2), Babar (3), Baghai, Bouhmama, Chechar, Djellal, El Hamma, Kais, Khenchela, Khirane, M’sara, Remila, Tamza, Taouzient, and Zaoui), sourced from the Open-Meteo Historical Weather API, the model employs inputs including air temperature, relative humidity, precipitation, wind speed, sunshine duration, terrestrial radiation, soil temperature, and soil moisture. The NN was trained to closely approximate the FAO-56 Penman-Monteith reference ET0 values computed directly by the API. Performance evaluation yielded strong agreement across stations: R2 > 0.96, RMSE 0.22 - 0.46 mm/day, NSE > 0.95, RSR < 0.13, and Willmott’s index 0.88 - 0.93, with peak accuracy (R2 > 0.99, RMSE < 0.24 mm/day) at high-elevation sites. Spatial patterns, mapped via GIS-based inverse distance weighting interpolation, revealed pronounced topographic and aridity-driven variability, confirmed by Emberger and De Martonne indices. This computationally efficient NN offers a scalable surrogate for FAO-56 calculations in data-limited, heterogeneous environments, supporting precision irrigation, drought monitoring, and adaptive strategies in semi-arid North Africa and Mediterranean regions.