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The main objective of this study was to evaluate the potential of olive mill wastewater (OMW) as an organic fertilizer through co-composting with various agricultural by-products. OMW was mixed with agricultural by-products, including maize silage, sugar beet pulp, and sugarcane bagasse, in controlled proportions and conditions. The study was conducted at the National Institute of Agricultural Research in Rabat, Morocco. The composting process was monitored over time, focusing on the evolution of key physicochemical parameters and phenol content of each mixture. The results showed that the performance of the composts varied, with the mixture containing sugar beet pulp (SBPO) exhibiting the most promising results, followed by maize silage (MSO) and sugarcane bagasse (SBO). These results suggest that co-composting OMW with agricultural by-products can produce high-quality organic fertilizers, thus reducing the need for inorganic alternatives and providing a sustainable waste management solution in the olive oil industry. It highlights the potential for reducing phenols characteristic of OMW and promoting sustainable agricultural practices. The application of the composts to crops was not tested, highlighting the need for further research in this regard. Future investigations should focus on evaluating the long-term effects of OMW-derived composts on soil health and crop productivity. This study explored a combination of materials that, to the authors’ knowledge, has not been previously documented in scientific literature. The results underscore the importance of sustainable waste management practices and their potential role in improving soil fertility and reducing the environmental impact associated with olive oil production. [ABSTRACT FROM AUTHOR]

The environmental pollution caused by organic molecules as Tetracyclines and Oxytetracyclines presents a foremost concern, as high levels of these antibiotics when released into the ecosystem are accumulated and are producing overall contamination. Consequently, the need for selective, sensitive, fast, easy to handle, and low-cost early monitoring detection systems is growing. Electrochemical sensors based on modified carbon electrodes involve relevant materials for antibiotics' detection, which allows the early controlling of the spread of these antibiotics into the environment, thus, safeguarding human health against possible contamination. The present review paper has aimed to illustrate examples of newly developed electrochemical bio(chemo)sensors with their real application fields for the sensitive detection of Tetracycline as well as Oxytetracycline in various matrices. Future forecast for the utilisation of these sensors is also discussed. [ABSTRACT FROM AUTHOR]

Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R2) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET0) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R2 = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco. [ABSTRACT FROM AUTHOR]