Examinando por Autor "Salazar Coronal, Wilian"

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Current and future distribution of Shihuahuaco (Dipteryx spp.) under climate change scenarios in the Central-Eastern Amazon of Peru
(MDPI, 2023-05-10) Cárdenas Rengifo, Gloria Patricia; Bravo Morales, Nino Frank; Barboza Castillo, Elgar; Salazar Coronal, Wilian; Ocaña Reyes, Jimmy Alcides; Vásquez Macedo, Miguel; Lobato Gálvez, Roiser Honorio; Injante Silva, Pedro Hugo; Arbizu Berrocal, Carlos Irvin
The consequences of climate change influence the distribution of species, which plays a key role in ecosystems. In this work, the modeling of the current and potential future distribution was carried out under different climate change scenarios of a tree species of high economic and commercial value, Dipteryx spp. This is a hardwood species that plays an important role in carbon sequestration, providing food and nesting for wildlife species, reaching more than 40 m in height with an average diameter of 70 to 150 cm. This species is currently threatened by overexploitation. Thirty-six bioclimatic, topographic and edaphic variables with ~1 km2 spatial resolution obtained from the WorldClim, SoilGrids and SRTM databases where used. Highly correlated variables were identified with the MaxEnt software for forecasting how the species distribution will be affected until the year 2100, according to the climate scenarios SPP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5, representing the periods 2021–2040, 2041–2060, 2061–2080 and 2081–2100, respectively. The AUC accuracy value of 0.88 to 0.89 was found for the distribution models and the highest contributing variables used were Bio 5, precipitation, Bio 2, and Bio 14. In the climate scenario SPP1-2.6 (Bio 5, precipitation and Bio 2) in 2061–2080, suitable and very suitable habitats represented 30.69% of the study area (2616 ha and 586.97 ha, respectively) and those increased by 1.75% under current climate conditions, and the suitable and unsuitable habitats represented 69.31% of the total area. The results of this research provide valuable information on the current and future distribution of the species and identify zones that can be used as the basis for the creation of conservation areas, formulation of restoration projects, reforestation and sustainable management to avoid the extinction of the species in the face of the effects of climate change.
Draft genome sequence resource of Erwinia sp. Strain INIA01, a phytopathogen isolated from a diseased stalk of peruvian maize
(Microbiology resource announcements, 2023-04-13) Estrada Cañari, Richard; Saldaña Serrano, Carla Lizet; Pérez Porras, Wendy Elizabeth; Arteaga, Linda; Martínez Vidal, Gabriel; Injante Silva, Pedro Hugo; Duran Gomez, Moises Rodrigo; Salazar Coronal, Wilian; Cosme de la Cruz, Roberto Carlos; Poemape Tuesta, Carlos Augusto; Arbizu Berrocal, Carlos Irvin
Here, we report the complete genome sequence of Erwinia sp. strain INIA01, a bacterium isolated from lesions of Zea mays from northern Peru. This genome possesses two circular replicons, a 4.2-Mb chromosome, and a 438-kb plasmid.
Microsatellite-based genetic diversity and population structure of Huacaya alpacas (Vicugna pacos) in Southern Peru
(MDPI, 2023-05-05) Figueroa Venegas, Deyanira Antonella; Corredor Arizapana, Flor Anita; Mamani Cato, Ruben; Gallegos Acero, Roberto; Condori Rojas, Nicoll; Estrada Cañari, Richard; Heredia Vilchez, Lizeth Amparo; Salazar Coronal, Wilian; Quilcate Pairazamán, Carlos Enrique; Arbizu Berrocal, Carlos Irvin
The alpaca population mostly consists of the Huacaya phenotype and is widely distributed in Southern Peru. This study aimed to estimate the genetic diversity and population structure of two Huacaya alpaca populations (Ajoyani and Quimsachata) using fourteen and twelve microsatellite markers for each population, respectively. A total of 168 alpaca biological samples were outsourced to Peruvian laboratories for DNA extraction and genotyping. For genetic diversity, observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC), and fixation indices values were estimated. An admixture analysis was performed for the population structure analysis. Different programs were used for these estimations. In total, 133 (Ajoyani) and 129 (Quimsachata) alleles were found, with a range of 4 to 17 by locus. The mean HO, HE, and PIC per marker for Ajoyani were 0.764 ± 0.112, 0.771 ± 0.1, and 0.736; for Quimsachata, they were 0.783 ± 0.087, 0.773 ± 0.095, and 0.738, respectively. The population structure showed no structure with K = 2. This study provides useful indicators for the creation of appropriate alpaca conservation programs.
Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru
(MDPI, 2023-05-19) Saravia Navarro, David; Valqui Valqui, Lamberto; Salazar Coronal, Wilian; Quille Mamani, Javier Alvaro; Barboza Castillo, Elgar; Porras Jorge, Zenaida Rossana; Injante Silva, Pedro Hugo; Arbizu Berrocal, Carlos Irvin
In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.