Jose Maria Carmara-Zapata

Nitrogen fertilization management has a significant impact on the sustainability of agricultural production. To facilitate decision-making for watermelon producers—a key crop in Mediterranean countries—a multi-criteria analysis was employed. This study determines the optimal nitrogen fertilization rate by integrating economic, social, agronomic, and environmental factors. The Analytic Hierarchy Process (AHP) was used to aggregate expert opinions on the importance of each criterion, revealing that economic and agronomic factors were the most critical. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) identified a rate of 200 nitrogen units as the optimal choice. Ultimately, this methodology promotes the sustainability of watermelon production in Mediterranean environments by optimizing nitrogen management.

Enhancing nitrogen fertilization management contributes to the sustainability of agricultural production. A multi-criteria analysis applied to nitrogen fertilization in broccoli crops enables producers to make informed decisions. In 2023, broccoli production was analyzed under different nitrogen fertilization rates using economic, social, agronomic, and environmental criteria to determine the most suitable alternative from a sustainability perspective. Based on expert opinions, the importance of each criterion was quantified using the Analytic Hierarchy Process (AHP). The ranking of the alternatives was established using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Economic and agronomic criteria exhibited a higher weighting than the others. The results established that a rate of 150 units of nitrogen fertilizer is the most suitable under the experimental conditions studied. In conclusion, multi-criteria analysis using a combination of AHP and TOPSIS can support growers' decision-making in broccoli production under Mediterranean conditions.

Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via reinforcement learning (RL) methodologies. The proposed model was tested in an industrial production greenhouse for the cultivation of industrial hemp, applying adapted strategies to the crop, and was guided by an agronomic technician knowledgeable about the plant. The expertise of this technician was crucial in transferring the RL model to a real-world automated greenhouse equipped with IoT technology. The study concludes that the integration of IoT and RL technologies is effective, validating the model’s ability to manage and optimize greenhouse operations efficiently and adapt to different types of crops. Moreover, this integration not only enhances operational efficiency but also reduces the need for constant human intervention, thereby minimizing labor costs and increasing scalability for larger agricultural enterprises. Furthermore, the RL-based control has demonstrated its ability to maintain selected temperatures and achieve energy savings compared to classical control methods.

Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine-tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via Reinforcement Learning (RL) methodologies. The proposed model was tested in an industrial production greenhouse for the cultivation of industrial hemp, applying adapted strategies to the crop, and was guided by an agronomic technician knowledgeable about the plant. The expertise of this technician was crucial in transferring the RL model to a real-world automated greenhouse equipped with IoT technology. The study concludes that the integration of IoT and RL technologies is effective, validating the model's ability to manage and optimize greenhouse operations efficiently and adapt to different types of crops. Moreover, this integration not only enhances operational efficiency but also reduces the need for constant human intervention, thereby minimizing labor costs and increasing scalability for larger agricultural enterprises. Furthermore, the RL-based control has demonstrated its ability to maintain selected temperatures and achieve energy savings compared to classical control methods.

The increase in ambient temperature decreases crop yields. Therefore, greenhouse cooling techniques can be considered adaptation strategies to climate change. To improve the efficiency of crop production, semi-closed greenhouses are utilized, which reduce the mass and energy transfer from the greenhouse. Frequently, these types of structures include an evaporative panel and a distribution system through perforated inflated ducts. To further improve the management of this type of installation, the present work models its behavior. The proper functioning of these installations greatly depends on temperature, as well as the relative humidity of the exterior air. The results show how the exterior climate conditions affect the values of temperature and relative humidity inside the greenhouse due to its effect on the value of evaporative cooling. The cooling capacity of the air per unit mass of evaporated water is reduced when the temperature and/or humidity of the air to be treated in the evaporative panel increases. Thus, when the exterior air is at 40 °C and its relative humidity is 75%, its temperature after passing through the evaporative panel is 15 °C higher than when the initial state of the exterior air is 30 °C and 30%. The effect of the use of frequency drivers in the fans on energy consumption has also been evaluated. A reduction of 8% in the frequency value causes a 22% decrease in the power consumed and a 15% pressure drop in the circuit. Therefore, reducing the frequency of electrical energy can contribute to energy savings without affecting the climate inside the greenhouse.

Biostimulants are substances or microorganisms that are applied to plants, soil, or seeds, to improve the growth, development, performance, and quality of crops. Their application is mainly directed towards improving the resistance of crops against abiotic and biotic stresses. These compounds are formulated from a great variety of compounds: humic substances, complex organic materials (sewage sludge extracts, composts, and manure), chemical elements (Al, Co, Na, Se, and Si), inorganic salts including phosphite, seaweed extracts (brown, red, and green), amino acids, etc. As of today, it has been observed that corn steep liquor (CSL), which is obtained from the industrial process of corn transformation, may be a good ingredient for the formulation of biostimulant products. CSL contains a large amount of different chemical compounds with biological activity for the plants and soil. The use of CSL industrial waste, previously formulated, could have a direct or indirect effect on the physiological processes and metabolic routes of plants related to the adaptation to abiotic and biotic stresses, as their compounds are part of these metabolic pathways, act as elicitor compounds, and/or have their own biological activity in the plants. There is evidence that the application of CSL could protect plants from specific abiotic and biotic stresses, such as an excess of light or temperature, nutritional limitations, salinity, drought, or pathogens. In addition, it can improve the growth of the plant when these are grown in hydroponic systems, and can improve the health of soils. The present article is focused on describing the most relevant scientific aspects of CSL when used as an ingredient to formulate biostimulants for agriculture. It will discuss its chemical composition, the analytical techniques utilized to elucidate and quantify its compounds, its uses in agriculture, and mode of action in the plants.