Sofía Pardo-Pina

This study investigated seed storage behavior and seedling development patterns in the genus Phoenix L. (Arecaceae), addressing the knowledge gap regarding orthodox versus recalcitrant characteristics in these ecologically and economically significant palms. We examined the germination capacity and subsequent growth in 31 seed samples from various Phoenix species stored for up to 10 years at approximately 5 °C, at the Germplasm Bank at the Escuela Politécnica Superior de Orihuela, comprising 465 seeds monitored over a one-year period. The seed germination trials involved planting seeds in pots placed in an open-air greenhouse after ambient temperatures consistently exceeded 20 °C, typically after mid-June. Phoenix dactylifera, P. canariensis, P. theophrasti, the hybrid P. dactylifera × P. canariensis, and P. × “Palmeri” demonstrated orthodox seed storage behavior, maintaining viability for up to nine years. Conversely, P. sylvestris, P. pusilla, P. rupicola, and P. loureiroi consistently failed to germinate despite previous germination success, suggesting potential recalcitrant characteristics. Statistical analyses revealed that species identity and geographic origin exerted greater influence on germination success than seed age. Seedling development exhibited a conserved seasonal pattern across all species, with synchronized leaf emergence in September and March–July, followed by winter dormancy. Significant intraspecific variation was observed, particularly within P. dactylifera varieties, in both leaf production and final leaf length. These findings provide valuable insights into germplasm preservation and cultivation strategies, demonstrating that while some Phoenix species are suitable for long-term seed banking, others may require alternative conservation approaches. The observed species-specific and variety-specific differences offer important selection criteria for horticultural applications and conservation efforts.

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.

Salinity, caused by irrigation with water containing high salt concentrations, excessive fertilization, or the loss of leaching capacity in some soils, is a serious problem on a global scale. Its incidence leads to osmotic and specific effects, as well as an imbalance in nutrient uptake that hinders the growth of most crops. Biostimulants can improve salt tolerance by reducing the uptake and accumulation of toxic ions. Corn steep liquor (CSL) is a byproduct of corn cleaning and maceration. This study investigates whether CSL application induces adaptive responses in pepper (Capsicum annuum L.) plants cultivated under saline conditions. Four treatments were carried out with pepper plants in a culture chamber: irrigation with Hoagland nutrient solution; irrigation with 100 mM NaCl in the Hoagland nutrient solution; irrigation with 100 mM NaCl in the Hoagland nutrient solution and the foliar application of CSL at 5 mL L⁻¹ every 7 days; and irrigation with 100 mM NaCl in the Hoagland nutrient solution and root application of CSL at 5 mL L⁻¹ every 7 days. The beneficial effect of CSL in reducing the phytotoxicity of salt stress was found to be due to an improvement in the photosynthetic efficiency and a reduction in the generation of reactive oxygen species. Thus, the increase in MDA concentration due to saline treatment is less when applying CSL, which is 3.5 times less when it is performed via the foliar route and 4.6 times if the treatment is on the root. The results show that CSL application increased the aerial biomass and leaf area under saline conditions through physiological mechanisms that varied depending on the application method.