Crecimiento urbano y su influencia en los cambios de cobertura y uso del suelo utilizando autómatas celulares en los distritos de Bagua Grande y Chachapoyas, Perú

En las últimas décadas, el crecimiento urbano se ha incrementado aceleradamente en todas las ciudades del mundo. En esta investigación analizamos el crecimiento urbano y su influencia en los cambios de cobertura y uso del suelo (CCUS) aplicando Autómatas Celulares (AC) en los distritos de Bagua Grande y Chachapoyas (Perú). Utilizamos la plataforma de computación en la nube de Google Earth Engine (GEE) para analizar series temporales anuales de imágenes Landsat 5 (L5) y Landsat 8 (L8) desde 1990 a 2021. Se aplicó una clasificación supervisada Random Forest (RF) para generar mapas de CCUS para 1990, 2000, 2011 y 2021. Posteriormente, se utilizó el complemento MOLUSCE de QGIS integrando cuatro variables predictoras del crecimiento urbano al 2031. Los mapas de cobertura y uso del suelo reportaron una precisión general (OA) superiores al 92%. La superficie de bosque se redujo de 20,807.97 ha en 1990 a 14,629.44 ha en 2021 para el distrito de Bagua Grande. A su vez el distrito de Chachapoyas presentó patrones similares con una superficie de 7,796.08 ha en 1990 a 3,598.19 ha en 2021. Por su parte, las áreas urbanas se incrementaron de 287.49 a 1,128.77 ha para Bagua Grande y de 185.65 a 924.50 ha para Chachapoyas entre 1990 y 2021. Mediante la aplicación de AC se predijo el crecimiento urbano para 2031 con precisiones superiores al 70%, se estimó que el área urbana del distrito de Bagua Grande se incrementará a 1,459.25 ha y 1,138.05 ha el distrito de Chachapoyas. El modelamiento de escenarios futuros del crecimiento urbano a partir de los mapas de CCUS y MOLUSCE demostró un incremento de la superficie urbana y la reducción de superficies de cobertura vegetal al 2031

Medicinal Plants for Rich People vs. Medicinal Plants for Poor People: A Case Study from the Peruvian Andes

Traditional knowledge (TK) of medicinal plants in cities has been poorly studied across different inhabitants’ socioeconomic sectors. We studied the small city of Chachapoyas (~34,000 inhabitants) in the northern Peruvian Andes. We divided the city into three areas according to the socio-economic characteristics of its inhabitants: city center (high), intermediate area (medium), and city periphery (low). We gathered information with 450 participants through semi-structured interviews. Participants of the city periphery showed a higher TK of medicinal plants than participants of the intermediate area, and the latter showed a higher TK than participants of the city center. The acquisition of medicinal plants was mainly through their purchase in markets across the three areas, although it was particularly relevant in the city center (94%). Participants of all socioeconomic levels widely used the same medicinal plants for similar purposes in Chachapoyas, which is likely based on a common Andean culture that unites their TK. However, participants with the lowest socioeconomic level knew and used more plants for different medicinal uses, indicating the necessity of these plants for their livelihoods. City markets with specialized stores that commercialize medicinal plants are key to preserve the good health of poor and rich people living in Andean cities and societies

Evaluación de la calidad hidrogeomorfológica de la cuenca del río Utcubamba, departamento de Amazonas - Perú

Evaluación de la calidad hidrogeomorfológica de la cuenca del río Utcubamba, departamento de Amazonas - Perú.Tesi

Spatial analysis of environmentally sensitive areas to soil degradation using MEDALUS model and GIS in Amazonas (Peru): an alternative for ecological restoration

Land degradation is a permanent global threat that requires an interdisciplinary approach to addressing solutions in a given territory. This study, therefore, analyses environmentally sensitive areas to land degradation using the Mediterranean Desertification and Land Use (MEDALUS) and Geographic Information System (GIS) method through a multi-criteria approach in the district of Florida (Peru). For the method, we considered the main quality indicators such as: Climate Quality Index (CQI), Soil Quality Index (SQI), Vegetation Quality Index (VQI), and Management Quality Index (MQI). There were also identified groups of parameters for each of the quality indicators analyzed. The results showed that 2.96% of the study area is classified as critical; 48.85% of the surface is classified as fragile; 15.48% of the areas are potentially endangered, and 30.46% are not threatened by degradation processes. Furthermore, SQI, VQI, and MQI induced degradation processes in the area. Based on the results, five restoration proposals were made in the study area: (i) organic manure production, (ii) cultivated and improved pastures and livestock improvement, (iii) native forest restoration, (iv) construction of reservoirs in the top hills and (v) uses of new technologies. The findings and proposals can be a basic support and further improved by decision-makers when implemented in situ to mitigate degradation for a sustainable use of the territory

Evaluación de tres tipos de injertos de granadilla sobre maracuyá con púas producidas en medio hidropónico y en sustrato sólido, Chachapoyas

La presente investigación se realizó en la Estación Experimental Chachapoyas del Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva(INDES-CES) con el objetivo de evaluar el efecto de tres tipos de injertos de granadilla (Passiflora ligularis) sobre plantones de maracuyá (Passiflora edulis) a través de púas producidas en medio hidropónico y en sustrato sólido. Se realizó una propagación por semillas tanto de maracuyá como de granadilla. La siembra de maracuyá y una parte de la granadilla fueron realizadas directamente en bolsas con sustrato previamente preparado, y el restante de la granadilla, que fue para las púas, fue almacigado para luego ser repicadas en hidroponía a raíz flotante. Estas fueron repicadas en hidroponía a raíz flotante. Los plantones estuvieron listos 106 días después de la siembra. Se empleó el diseño completo al azar (DCA) con arreglo factorial 2Ax 3B anidado, con los factores A (Sistema de producción de púas) y B (Método de injertación), cuyas combinaciones hicieron un total de cinco tratamientos cada uno, de los cuales resultaron siete unidades de observación y tres repeticiones. Los resultados indicaron que no hubo diferencias significativas, de tal forma que los tratamientos TI, T2 y T4 obtuvieron el 100% de prendimiento 39 días después del injerto. El número de hojas tampoco mostró diferencias significativas, pero el T2 obtuvo el mayor promedio de hojas (3,52); lo mismo ocurrió con el costo por plantón injertado, donde el T2 presentó el menor valor.</p

Modelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru)

Effects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers

Microzonificación agroecológica de sistemas agrosilvopastoriles empleando un modelo de procesamiento basado en SIG en parcelas en la provincia de Bongará, Amazonas (Perú)

La Zonificación Agroecológica (ZAE) es un instrumento de gestión con importancia fundamental en el desarrollo agrario. Permite identificar superficies territoriales con características edafoclimáticas homogéneas para potenciar el desarrollo vegetativo de una especie. En la presente investigación se pretende zonificar agroecológicamente un sistema agrosilvopastoril, para el cual se construirá una herramienta de geoprocesamiento mediante el uso del Model Builder, con el que se evaluarán las variables agroecológicas para identificar zonas con mayor viabilidad productiva. Para el desarrollo de la presente investigación se evaluaron siete especies entre cultivos agrícolas, forestales y pastos, determinando los requerimientos agroecológicos de los mismos. Con esta información es posible seleccionar cultivos que permitan adaptarse a las distintas condiciones agroclimáticas de cada zona o parcela de estudio. Se seleccionaron dos parcelas de estudio, ubicadas en la provincia de Bongará, dentro de las cuales se aperturaron 12 calicatas en cada una, extrayendo muestras de suelo de hasta 1,30 metros de profundidad. Posteriormente las muestras fueron trasladadas al Laboratorio de Investigación en Suelos y Aguas de la UNTRM, para realizar el análisis de caracterización de suelos. Finalmente mediante un Sistema de Información Geográfica (SIG), se superpusieron los mapas agroecológicos de cada cultivo, para lo que se aplicó un lenguaje estructurado de consulta a los atributos del mismo, generando las zonas óptimas para cada cultivo.</p

Updating the distribution of Dicrodon guttulatum Duméril & Bibron, 1839 (Reptilia, Teiidae) with a disjunct population in the eastern slope of the Peruvian Andes

We report a disjunct population of Dicrodon guttulatum Duméril & Bibron, 1839 on the eastern slope of the Cordillera Occidental in the inter-Andean Seasonally Dry Forests of the Marañón River, in the Departments of Cajamarca and Piura in northwestern Peru. We include an updated range distribution map using records from museum specimens, the Global Biodiversity Information Facility, and available photographic records on iNaturalist. In addition, we identify widespread cultivation of rice crops as the main threat to D. guttulatum in the inter-Andean Seasonally Dry Forests of the Marañón

Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru

Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru

Predicting potential distribution and identifying priority areas for conservation of the Yellow-tailed Woolly Monkey (Lagothrix flavicauda) in Peru

Species distribution models (SDMs) provide conservationist with spatial distributions estimations of priority species. Lagothrix flavicauda (Humboldt, 1812), commonly known as the Yellow-tailed Woolly Monkey, is one of the largest primates in the New World. This species is endemic to the montane forests of northern Peru, in the departments of Amazonas, San Martín, Huánuco, Junín, La Libertad, and Loreto at elevation from1,000 to 2,800 m. It is classified as “Critically Endangered” (CR) by the International Union for Conservation of Nature (IUCN) as well as by Peruvian legislation. Furthermore, it is listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Research on precise estimates of its potential distribution are scare. Therefore, in this study we modeled the potential distribution area of this species in Peru, the model was generated using the MaxEnt algorithm, along with 80 georeferenced occurrence records and 28 environmental variables. The total distribution (high, moderate, and low) for L. flavicauda is 29,383.3 km2, having 3,480.7 km2 as high potential distribution. In effect, 22.64 % (6,648.49 km2) of the total distribution area of L. flavicauda is found within Natural Protected Areas (NPAs), with the following categories representing the largest areas of distribution: Protected Forests (1,620.41 km2), Regional Conservation Areas (1,976.79 km2), and Private Conservation Areas (1,166.55 km2). After comparing the predicted distribution with the current NPAs system, we identified new priority areas for the conservation of the species. We, therefore, believe that this study will contribute significantly to the conservation of L. flavicauda in Peru