Duckweed (Lemna minor L.) has attracted considerable attention in the scientific field due to its nutritional contribution and capacity to phytoremediate waters. Therefore, the objective of the study was to analyze the chemical composition of the macrophyte (Lemna minor) from natural environments. Chemical compounds and Weende composition were determined from the plant, and the fresh weight gain was observed in different types of water (deep well and bovine slurry), waters that were subjected to physical-chemical analysis. The chemical analysis of the macrophyte resulted in the presence of 1.42 mg.g-1 of total chlorophyll; 2.35 mg.kg-1 of ascorbic acid; tannin content less than 2.50 mg.kg-1; 45.34 mg.kg-1 of phenols; also the presence of alkaloids, phenols and reducing sugars in the chemical screening. The Weende analysis indicates a composition of: 89 % of dry matter, 30 % of crude protein, 4 % of gross energy, 3.2 % of ether extract, 15 % of ashes, 32 % of nitrogen-free extract and 10 % of fiber. The fresh weight gain of Lemna minor obtained in water contaminated with bovine slurry increased significantly 13 g.day.m3 and 5 g.day.m3 in deep well water. The physical-chemical properties of the water improve their quality 16 days after treatment with this aquatic plant with respect to the initial analysis. This macrophyte exhibits remarkable phytoremediation properties to absorb, metabolize and stabilize various pollutants effective in the purification of contaminated waters.

This study discusses the optimization of municipal solid waste management through the implementation of automated waste sorting systems, comparing two advanced artificial intelligence methodologies: Vertex AI and convolutional neural network (CNN) architectures, developed using TensorFlow. Automated solid waste classification is presented as an innovative technological approach that leverages advanced algorithms to accurately identify and segregate materials, addressing the inherent limitations of conventional sorting methods, such as high labor dependency, inaccuracies in material separation, and constrained scalability for processing large waste volumes. A system was designed for the classification of paper, plastic, and metal waste, integrating an Arduino Uno microcontroller, a Raspberry Pi, a high-resolution camera, and a robotic manipulator. The system was evaluated based on performance metrics including classification accuracy, response time, scalability, and implementation cost. The findings revealed that Xception achieved a flawless classification accuracy of 100% with an average processing time of 0.25 s, whereas Vertex AI, with an accuracy of 90% and a response time of 2 s, exceled in cloud scalability, making it ideal for resource-constrained environments. The findings highlight Xception’s superiority in high-precision applications and Vertex AI’s adaptability in scenarios demanding flexible deployment, advancing efficient and sustainable waste management solutions.

This research seeks to understand the impact of herbicides on beneficial soil microorganisms, essential for agricultural health, and contribute to more sustainable management practices. The study aimed to evaluate the effect of herbicides commonly used in conventional agriculture, such as glyphosate, paraquat, and MCPA, on the growth of beneficial soil microorganisms. The microorganisms were isolated from the rhizosphere soil of a CCN-51 cacao crop from a production farm in Ecuador, and identified through morphological characterization and molecular analysis. The detected beneficial microorganisms were seeded in culture media with field-used doses of the different herbicides mentioned above, and mycelial growth in fungi (Trichoderma asperellum) and colony extension in bacteria (Bacillus subtilis and Pseudomonas fluorescens) were evaluated. The results showed that the herbicides had a significant inhibitory effect on the growth of these beneficial microorganisms. In the case of T. asperellum, paraquat and MCPA showed greater mycelial inhibition compared to glyphosate. Furthermore, all three herbicides significantly reduced the growth of B. subtilis and P. fluorescens, in contrast to the control groups, which maintained constant growth. These findings suggest that the herbicides studied may have a negative impact on the growth dynamics of beneficial microorganisms, which could affect the balance of the agricultural ecosystem. These results highlight the importance of considering the potential effects of herbicides on the soil microbiota in the management of sustainable agricultural systems.

The excessive and inappropriate use of synthetic fertilizers has the potential to cause significant environmental and economic issues. The utilization of biofertilizers represents a sustainable and environmentally friendly alternative for the enhancement of crop productivity. Accordingly, the present study evaluated the productive and economic behavior of applying biofertilizers on hybrid corn DAS 3383 under a completely random block design, with five treatments and three repetitions. The following treatments were employed: a control treatment devoid of any fertilization (CONTROL), a conventional treatment comprising NPK fertilization (NPK), a treatment incorporating 5 % biofertilizer (5BIO), a treatment incorporating 15 % biofertilizer (15BIO), and a treatment incorporating 25 % biofertilizer (25BIO). At the 90-day mark, the NPK treatment exhibited the greatest plant height (272.81 cm), while at the 108-day interval, the cob insertion height (170.40 cm) was also the highest. However, the 25BIO treatment yielded the most optimal results in terms of cob length (14.29 cm), diameter (6.07 cm), 1,000-grain weight (31.70 g), and yield (9 t.ha-1). This treatment also demonstrated the highest net benefit ($2,019.10) and favorable profitability (72 %) compared to the other treatments. The results of the orthogonal contrast analysis indicate that there are significant linear effects for the majority of variables, with no discernible quadratic effects. This evidence substantiates the potential of biofertilizers in enhancing maize crop productivity. By repurposing organic waste from the agricultural sector and employing it as an alternative to chemical fertilizers, these findings contribute to the advancement of more sustainable and profitable agricultural practices. Keywords: organic fertilizer, genotype, seed, organic waste, profitability.

Necrotrophic fungi are pathogens that cause tissue death in plants, which negatively impacts their growth and productivity. This study focused on identifying the presence of Lasiodiplodia theobromae in CCN-51 cacao plants in the Simón Bolívar canton in Ecuador. We sampled cacao pods exhibiting necrotic lesions and obtained fungal isolates for morphological and molecular characterization. Techniques, such as culturing on selective media, microscopy, and DNA sequencing were used to confirm the fungal identity. We compared our results with international databases and assessed the genetic variability of the isolates. Morphological characterization placed the fungal isolates within the family Botryosphaeriaceae, and molecular analysis using ITS and EF1-α regions confirmed the species as Lasiodiplodia theobromae, with 100% DNA quality for amplicon analysis and 100% sequence similarity in GenBank. We constructed phylogenetic trees using maximum likelihood methods, which revealed high genetic similarity and recent divergence among sequences despite their varied geographic origins. The fungal isolates specifically confirmed the presence of L. theobromae as the causal agent of necrotic lesions in CCN-51 cacao pods from Simón Bolívar. These findings underscore the importance of studying necrotrophic fungi in cacao plants to inform control strategies, improve crop resistance, and support sustainable production, essential to the global cacao trade.

Estimating Photosynthetically Active Radiation (PAR) in the humid tropics is limited by prevailing cloud cover and a sparse network of weather stations. This study develops a five-phase framework to verify, reconstruct, and improve PAR estimates in a coastal region of Ecuador, using the CERES satellite product as a basis. The core is a benchmarking of six configurations (M0–M5), proposing a multivariate physical model (M3) that integrates seven predictors: GHI, zenith angle, relative humidity, temperature, precipitable water, cloud type, and AOD. Using only six months of in situ measurements, it proved feasible to reconstruct the complete annual series. M3 increased the coefficient of determination R² from 0.71 to 0.79 and reduced the overall RMSE to 32%, producing physically more consistent reconstructions than stochastic and quantile mapping methods. A marked satellite underestimation was detected at sunset (RMSE 68%, bias − 45%). Annually, M3 corrected the accumulated energy from 10,705 to 10,981 mol m⁻², approaching the 11,446 mol m⁻² measured in situ, a 37% reduction in annual bias. Because in situ data covered only July–December 2024, skill was assessed by internal cross-validation (leave-one-month-out R² ≈ 0.83), while January–June is reported as a reconstruction. Robustness to severe multicollinearity (VIF > 20) was confirmed with Ridge, LASSO, and PCR.