The Polylepis genus, endemic to the South American Andes, faces significant threats due to environmental variations, which jeopardize its growth and survival. This situation underscores the urgent need to develop conservation strategies. The present research assesses the influence of meteorological variables, such as temperature and humidity, on the growth and adaptation of various Polylepis species in the central Peruvian Andes, aiming to optimize reforestation and sustainable management practices. The study was conducted in experimental plots at the Santa Ana Agricultural Station in Junín, Peru, where Polylepis saplings, obtained from different localities, were planted. Over two years, phenotypic variables (height and diameter) and meteorological variables (precipitation, humidity, temperature, and wind speed) were monitored to evaluate the relationship between environmental condition...

This article calls for reflection on the importance of water as a natural resource increasingly necessary and scarce in the environment, where rainwater harvesting will have to become an important part of our lives if we are to enjoy a sustainable future. The objective of this work was to develop a historical and bibliographical review to defne the ancestral techniques and methods of rainwater harvesting and storage that can be used and applicable in the PeruvianAndes. It was carried out based on an approach to the history of the management of rainwater resources through different civilizations, continents and epochs. It can be concluded that the return and revaluation of these ancestraltechniques and methods is urgent for the populations, which supported by conventional engineering and rainwater harvesting systems can be transformed into a real initiative that allows extending the avail...

Among solar energy technologies, differences exist in terms of costs, performance, and environmental sustainability. Flatplate solar collectors, solar towers, and parabolic dish systems offer high thermal efficiency and versatility, but they may be more costly and bulky compared to other collector models. This study focused on evaluating the efficiency of a cylindrical parabolic collector (CPC) for the production of domestic hot water in a high Andean region of Peru, using the F-Chart method. Its performance was estimated considering the energy demand for hot water in a single-family home with four occupants, in accordance with national regulations and international recommendations. Additionally, the collector area, water temperature, and incident solar radiation were determined based on meteorological data obtained using the PVsyst software. On the other hand, the F-Chart methodology wa...

The biomass that accumulates on the forest floor and its subsequent decomposition play an important role in maintaining the productivity of different terrestrial ecosystems by constituting the main nutrient flow to the soil. The objective of the study focused on analyzing the nutrient contribution to the soil derived from the aboveground biomass of three native forest species in relict forests of the Central Peruvian Sierra with socioeconomic and environmental relevance. Using random delineation methods, soil samples were collected at 20-30 cm depth, which were subjected to physical, chemical, and biological analyses, developing the determination of a Soil Quality Index (SQI). The results highlight that forests of Polylepis racemosa and Alnus acuminata significantly exhibit a higher SQI, with values of 0.66 and 0.58, respectively, compared to Escallonia resinosa, with the forestless syst...

Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of...