This review aims to study the applications of sensors for monitoring and controlling the physicochemical parameters of water in aquaculture systems such as Biofloc Technology (BFT), Recirculating Aquaculture Systems (RASs), and aquaponic systems using IoT technology, as well as identify potential knowledge gaps. A bibliometric analysis and systematic review were conducted using the Scopus database between 2020 and 2024. A total of 217 articles were reviewed and analyzed. Our findings indicated a significant increase (74.79%) in research between 2020 and 2024. pH was the most studied physicochemical parameter in aquaculture, analyzed in 98.2% of cases (sensors: SEN0169, HI-98107, pH-4502C, Grove-pH), followed by temperature (92.9%, sensor DS18B20) and dissolved oxygen (62.5%, sensors: SEN0237, MAX30102, OxyGuard DO model 420, ZTWL-SZO2-485, Lutron DO-5509). Overall, water monitoring through the implementation of IoT sensors improved growth rates, reduced culture mortality rates, and enabled the rapid prediction and detection of atypical Total Ammonia Nitrogen (TAN) levels. IoT sensors for water quality monitoring in aquaponics also facilitate the evaluation and prediction of seed and vegetable growth and germination. In conclusion, despite recent advancements, challenges remain in automating parameter control, ensuring effective sensor maintenance, and improving operability in rural areas, which need to be addressed.

Forest fires are the result of poor land management and climate change. Depending on the type of the affected eco-system, they can cause significant biodiversity losses. This study was conducted in the Amazonas department in Peru. Binary data obtained from the MODIS satellite on the occurrence of fires between 2010 and 2022 were used to build the risk models. To avoid multicollinearity, 12 variables that trigger fires were selected (Pearson ≤ 0.90) and grouped into four factors: (i) topographic, (ii) social, (iii) climatic, and (iv) biological. The program Rstudio and three types of machine learning were applied: MaxENT, Support Vector Machine (SVM), and Random Forest (RF). The results show that the RF model has the highest accuracy (AUC = 0.91), followed by MaxENT (AUC = 0.87) and SVM (AUC = 0.84). In the fire risk map elaborated with the RF model, 38.8% of the Amazonas region possesses a very low risk of fire occurrence, and 21.8% represents very high-risk level zones. This research will allow decision-makers to improve forest management in the Amazon region and to prioritize prospective management strategies such as the installation of water reservoirs in areas with a very high-risk level zone. In addition, it can support awareness-raising actions among inhabitants in the areas at greatest risk so that they will be prepared to mitigate and control risk and generate solutions in the event of forest fires occurring under different scenarios.

The objective of this research was to conduct a comprehensive review of rainbow trout (Oncorhynchus mykiss) culture in recirculating aquaculture systems (RAS), identify knowledge gaps, and propose strategies oriented towards intelligent and sustainable aquaculture. A systematic review and bibliometric analysis of 387 articles published between 1941 and 2025 in the Scopus database was carried out. Since 2011, there has been a sustained growth in scientific production, with the United States, Denmark, Finland, and Germany standing out as the main contributors. The journals with the highest number of publications were Aquacultural Engineering, Aquaculture, and Aquaculture Research. The conceptual analysis revealed the following three thematic clusters: experimental studies on physiology and metabolism; research focused on nutrition, growth, and yield; and technological developments for water treatment in RAS. This evolution reflects a transition from basic approaches to applied technologies oriented towards sustainability. There was also evidence of a thematic transition toward molecular tools such as proteomics, transcriptomics, and real-time PCR. However, there is still limited integration of smart technologies such as the IoT. It is recommended to incorporate self-calibrating multi-parametric sensors, machine learning models, and autonomous systems for environmental regulation in real time.

This study examines the use of a spatial multi-criteria approach based on GIS and AHP techniques to model landslide risk in the Utcubamba river basin, Peru. The methodology consisted of selecting twelve triggering variables: slope angle, geology, precipitation, distance to faults, drainage density, TWI, relative relief, profile curve, land use, elevation, distance to roads, and distance to population centers. These variables were then analyzed using the AHP method and then integrated into a GIS environment, where the weighted linear combination (WLC) method was used to map landslide risk. The risk was categorized into five classes, ranging from very low (1) to very high (5). The main results indicate that 32.81% of the area analyzed in the Utcubamba river basin presents a high and very high risk of landslides. The high-risk areas are mainly located in the southern part of the basin and coincide with areas with steep slopes, high rainfall, and proximity to population centers or communication routes. The model generated was highly accurate (AUC of 0.82), confirming that the integration of the AHP method with GIS allows for the precise identification of critical areas, which is useful for territorial planning, the prioritization of interventions, and emergency management, making it a reliable and replicable methodology in other parts of Peru.

Cocoa (Theobroma cacao L.) is one of Peru’s main export products. In the last years, the production of fine aroma cocoa has increased, and the market has preference for them instead of cultivar clones like CCN51. Fine aroma cocoa is a heterogeneous population, but some genotypes has started to be selected. This study aimed to evaluate the chemical properties and volatile organic compound (VOC) profile of different fine aroma cocoa genotypes and CCN51. The chemical composition—including moisture, fat, protein, ash, carbohydrate, pH, fermentation index and total phenolic compounds—was determined using standard analytical procedures. VOC were analyzed using SPME/GC-MS. Protein content varied among genotypes, with ABL01 showing the highest percentage. Fat content also ranged widely, with ABL01 and JBB04 representing the highest and the lowest values, respectively. Significant differences in ash and carbohydrate contents were also observed. The pH values suggested that fermentation was generally adequate, although some genotypes showed potential deficiencies. Fine aroma cocoa (FAC) genotypes exhibited lower phenolic compound content compared to the commercial clone CCN51. VOC analysis revealed compositional differences, with certain compounds contributing more prominently to the variation of specific genetic groups profile. These findings suggest that there exists a huge variability in VOC and other chemical properties of fine aroma cocoa genotypes and it is expected that data generated about roasted and no roasted beans will be useful for future research.

Dengue, a febrile disease that has caused epidemics and deaths in South America, especially Peru, is vectored by the Aedes aegypti mosquito. Despite the seriousness of dengue fever, and the expanding range of Ae. aegypti, future distributions of the vector and disease in the context of climate change have not yet been clearly determined. Expanding on previous findings, our study employed bioclimatic and topographic variables to model both the present and future distribution of the Ae. aegypti mosquito using the Maximum Entropy algorithm (MaxEnt). The results indicate that 10.23% (132,053.96 km²) and 23.65% (305,253.82 km²) of Peru’s surface area possess regions with high and moderate distribution probabilities, respectively, predominantly located in the departments of San Martín, Piura, Loreto, Lambayeque, Cajamarca, Amazonas, and Cusco. Moreover, based on projected future climate scenarios, it is anticipated that areas with a high probability of Ae. aegypti distribution will undergo expansion; specifically, the extent of these areas is estimated to increase by 4.47% and 2.99% by the years 2070 and 2100, respectively, under SSP2-4.5 in the HadGEM-GC31-LL model. Given the increasing dengue epidemic in Peru in recent years, our study seeks to identify tools for effectively addressing this pressing public health concern. Consequently, this research serves as a foundational framework for assessing areas with the highest likelihood of Ae. aegypti distribution in response to projected climate change in the second half of the 21st century.