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Agricultural and urban expansion has caused considerable degradation of ecosystems. In the case of Peruvian high Andean grasslands, it was reported that between 2000 and 2009, this ecosystem was reduced by 7%. The limited or no protection they receive is partly due to the fact that the benefits of ecosystem services are not widely known. This research aims to establish and predict the influence of high Andean grasslands on the annual occurrence of landslides. To do so, we identified occurrences of landslides, falls, huaycos, avalanches, and alluviums in high Andean grasslands. We also examined urban areas and agricultural zones of Peru for the period from 2003 to 2016. Subsequently, we extracted data on precipitation, temperature, slopes, soil types, and geographical variables. This data was used to train a machine learning model. The results show that 96% of landslides occurred in human-intervened areas, and only 4% in high Andean grasslands. Precipitation and slope thresholds for landslide occurrence are higher in high Andean grasslands compared to agricultural and urban areas. The best-performing machine learning models were linear regression, Gaussian processes, random forest, and support vector machine. They had coefficients of determination of R² = 0.80, 0.80, 0.66, and 0.64, respectively. Predictions show that if agricultural or urban areas are established in wet or dry puna grasslands, the average number of occurrences multiplies. The multiplier factors are 2.1 and 7.08, the number of deaths by 2.8 and 10.49, the number of houses destroyed by 2.4 and 7.51, and the number of roads destroyed by 2.2 and 7.37, respectively. The study demonstrates that conserving high Andean grasslands significantly reduces landslides compared to urban or agricultural areas.

This study analyzed the impact of leachate from a temporary landfill on soil microbial diversity in Tingo María, Huánuco region, Peru. Three treatments were used: untreated soil (S), addition of stream water (T0), leachate (T1), and leachate treated by coagulation and flocculation (T2), with 828.5 ml/week added in three weekly doses. Soil samples were collected from the Reserved Forest of the Universidad Nacional Agraria de la Selva. Twenty-one randomly distributed soil samples were taken and homogenized for analysis. Soil quality parameters measured included sand, clay, silt, texture, pH, organic matter, nitrogen, phosphorus and potassium. As for microorganisms, viable aerobes, lactobacilli, actinomycetes, fungi, nitrogen-fixing bacteria, and Escherichia coli were quantified using specific culture and counting methods for each of them. To evaluate the impact of the leachate on microbial diversity, equity indices (Shannon and inverse Simpson), dominance indices (complementary Simpson and Berger Parker) and the percentage composition of each microorganism per treatment were used. An ANOVA was performed to estimate differences in microbial diversity, with a Tukey test at a significance level of α = 0.05. The study showed that leachates affect soil microbial diversity, reducing equity and increasing the dominance of certain species such as E. coli. They also alter physicochemical parameters, decreasing organic matter and nitrogen but increasing other elements such as phosphorus and potassium. This could have implications for soil health and functionality.