Examinando por Autor "Salazar Coronel, Wilian"

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Assessment of the genetic diversity and population structure of the peruvian andean legume, tarwi (Lupinus mutabilis), with high quality SNPs
(MDPI, 2023-03-16) Huaringa Joaquin, Amelia Wite; Saldaña Serrano, Carla Lizet; Saravia Navarro, David; García Bendezú, Sady; Rodriguez Grados, Pedro Manuel; Salazar Coronel, Wilian; Camarena Mayta, Felix; Injante Silva, Pedro Hugo; Arbizu Berrocal, Carlos Irvin
Lupinus mutabilis Sweet (Fabaceae), “tarwi” or “chocho”, is an important grain legume in the Andean region. In Peru, studies on tarwi have mainly focused on morphological features; however, they have not been molecularly characterized. Currently, it is possible to explore the genetic parameters of plants with reliable and modern methods such as genotyping by sequencing (GBS). Here, for the first time, we used single nucleotide polymorphism (SNP) markers to infer the genetic diversity and population structure of 89 accessions of tarwi from nine Andean regions of Peru. A total of 5922 SNPs distributed along all chromosomes of tarwi were identified. STRUCTURE analysis revealed that this crop is grouped into two clusters. A dendrogram was generated using the UPGMA clustering algorithm and, like the principal coordinate analysis (PCoA), it showed two groups that correspond to the geographic origin of the tarwi samples. AMOVA showed a reduced variation between clusters (7.59%) and indicated that variability within populations is 92.41%. Population divergence (Fst) between clusters 1 and 2 revealed low genetic difference (0.019). We also detected a negative Fis for both populations, demonstrating that, like other Lupinus species, tarwi also depends on cross-pollination. SNP markers were powerful and effective for the genotyping process in this germplasm. We hope that this information is the beginning of the path towards a modern genetic improvement and conservation strategies of this important Andean legume.
Change of vegetation cover and land use of the Pómac forest historical sanctuary in northern Peru
(Springer Nature, 2024-04-06) Vera Díaz, Elvis; Camila Leandra, Cruz Grimaldo; Barboza Castillo, Elgar; Salazar Coronel, Wilian; Canta Ventura, Jorge Marino; Salazar Hinostroza, Evelin Judith; Vásquez Pérez, Héctor Vladimir; Arbizu Berrocal, Carlos Irvin
The dry forests of northern Peru, in the regions of Piura, Tumbes, Lambayeque, and La Libertad, have experienced significant changes as a result of deforestation and changes in land use, leading to the loss of biodiversity and resources. This work analyzed for the first time the changes in vegetation cover and land use of the Pómac Forest Historical Sanctuary (PFHS), located in the department of Lambayeque (northern Peru). The employed approach was the random forest algorithm and visually interpreted Landsat satellite images for the periods 2000–2002, 2002–2004, and 2004–2008. Gain and loss rates were computed for each period, and the recovery process was assessed using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Results indicate an expansion of agricultural land during each period, resulting in the deforestation of 102.6 hectares of dense dry forest and 739.9 hectares of open dry forest between 2000 and 2008. The degree of reforestation in the cleared areas was measured using the NDVI and EVI indices, revealing an improvement from 0.22 in NDVI in 2009 to 0.36 in 2022, and from 0.14 to 0.21 in EVI over the same period. This study is expected to pave the way for executing land management plans, as well as the use and conservation of natural resources in the PFHS in a sustainable manner.
Cloud computing application for the analysis of land use and land cover changes in dry forests of Peru
(International Information and Engineering Technology Association (IIETA), 2024-09-30) Barboza, Elgar; Salazar Coronel, Wilian; Gálvez Paucar, David; Valqui Valqui, Lamberto; Valqui, Leandro; Zagaceta, Luis H.; Gonzales, Jhony; Vásquez, Héctor V.; Arbizu, Carlos I.
Dry forests are ecosystems of great importance worldwide, but in recent decades they have been affected by climate change and changes in land use. In this study, we evaluated land use and land cover changes (LULC) in dry forests in Peru between 2017 and 2021 using Sentinel-2 images, and cloud processing with Machine Learning (ML) models. The results reported a mapping with accuracies above 85% with an increase in bare soil, urban areas and open dry forest, and reduction in the area of crops and dense dry forest. Protected natural areas lost 2.47% of their conserved surface area and the areas with the greatest degree of land use impact are located in the center and north of the study area. The study provides information that can help in the management of dry forests in northern Peru.
Cover and land use changes in the dry forest of Tumbes (Peru) using sentinel-2 and google earth engine data
(MDPI, 2022-10-21) Barboza Castillo, Elgar; Salazar Coronel, Wilian; Gálvez Paucar, David; Valqui Valqui, Lamberto; Saravia Navarro, David; Gonzales, Jhony; Aldana, Wiliam; Vásquez Pérez, Héctor Vladimir; Arbizu Berrocal, Carlos Irvin
Dry forests are home to large amounts of biodiversity, are providers of ecosystem services, and control the advance of deserts. However, globally, these ecosystems are being threatened by various factors such as climate change, deforestation, and land use and land cover (LULC). The objective of this study was to identify the dynamics of LULC changes and the factors associated with the transformations of the dry forest in the Tumbes region (Peru) using Google Earth Engine (GEE). For this, the annual collection of Sentinel 2 (S2) satellite images of 2017 and 2021 was analyzed. Six types of LULC were identified, namely urban area (AU), agricultural land (AL), land without or with little vegetation (LW), water body (WB), dense dry forest (DDF), and open dry forest (ODF). Subsequently, we applied the Random Forest (RF) method for the classification. LULC maps reported accuracies greater than 89%. In turn, the rates of DDF and ODF between 2017 and 2021 remained unchanged at around 82%. Likewise, the largest net change occurred in the areas of WB, AL, and UA, at 51, 22, and 21%, respectively. Meanwhile, forest cover reported a loss of 4% (165.09 km2 ) of the total area in the analyzed period (2017–2021). The application of GEE allowed for an evaluation of the changes in forest cover and land use in the dry forest, and from this, it provided important information for the sustainable management of this ecosystem
Employing a nondestructive method for the estimation of foliar area of quina (Cinchona officinalis)
(MDPI, 2022-10-15) Sueldo, Andrea; Chumbimune Vivanco, Sheyla Yanett; Mendoza, Erik; Salazar Coronel, Wilian; Minaya, Benjamin; Arbizu Berrocal, Carlos Irvin
Leaf area is related to tree growth, water balance, and mechanical resistance to physical and biotic agents. Given its importance, the purpose of the study was to compare two nondestructive methods of leaf area estimation using the free software ImageJ vs. graph paper in seedlings of quina tree. Three young and mature leaves were evaluated on 18 quina seedlings. Descriptive statistics were obtained, and both methods were compared using the Kruskal–Wallis test, and a regression equation was estimated based on leaf width and length.
First draft genome assembly of the Peruvian creole cattle breed (Bos taurus) and its comparative genomics among the Bovinae subfamily
(MDPI, 2022-08-18) Estrada Cañari, Richard; Corredor Arizapana, Flor Anita; Figueroa Venegas, Deyanira Antonella; Salazar Coronel, Wilian; Quilcate Pairazamán, Carlos Enrique; Vásquez Pérez, Héctor Vladimir; Maicelo Quintana, Jorge Luis; Gonzales, Jhony; Arbizu Berrocal, Carlos Irvin
The Peruvian creole cattle (PCC) is a neglected breed, and is an essential livestock resource in the Andean region of Peru. To develop a modern breeding program and conservation strategies for the PCC, a better understanding of the genetics of this breed is needed. We sequenced the whole genome of the PCC using a paired-end 150 strategy on the Illumina HiSeq 2500 platform, obtaining 320 GB of sequencing data. The obtained genome size of the PCC was 2.77 Gb with a contig N50 of 108Mb and 92.59% complete BUSCOs. Also, we identified 40.22% of repetitive DNA of the genome assembly, of which retroelements occupy 32.39% of the total genome. A total of 19,803 protein-coding genes were annotated in the PCC genome. We downloaded proteomes and genomes of the Bovinae subfamily, and conducted a comparative analysis with our draft genome. Phylogenomic analysis showed that PCC is related to Bos indicus. Also, we identified 7,746 family genes shared among the Bovinae subfamily. This first PCC genome is expected to contribute to a better understanding of its genetics to adapt to the tough conditions of the Andean ecosystem, and evolution.
Genetic diversity and population structure of a Peruvian cattle herd using SNP data
(Frontiers Media S.A., 2023-03-10) Corredor Arizapana, Flor Anita; Figueroa Venegas, Deyanira Antonella; Estrada Cañari, Richard; Salazar Coronel, Wilian; Quilcate Pairazamán, Carlos Enrique; Vásquez Pérez, Héctor Vladimir; Gonzales Malca, Jhony Alberto; Maicelo Quintana, Jorge Luis; Medina Morales, Percy Edilberto; Arbizu Berrocal, Carlos Irvin
New-generation sequencing technologies, among them SNP chips for massive genotyping, are useful for the effective management of genetic resources. To date, molecular studies in Peruvian cattle are still scarce. For the first time, the genetic diversity and population structure of a reproductive nucleus cattle herd of four commercial breeds from a Peruvian institution were determined. This nucleus comprises Brahman (N = 9), Braunvieh (N = 9), Gyr (N = 5), and Simmental (N = 15) breeds. Additionally, samples from a locally adapted creole cattle, the Arequipa Fighting Bull (AFB, N = 9), were incorporated. Female individuals were genotyped with the GGPBovine100K and ma les with the BovineHD. Quality control, and the proportion of polymorphic SNPs, minor allele frequency, expected heterozygosity, observed heterozygosity, and inbreeding coefficient were estimated for the five breeds. Admixture, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were performed. Also, a dendrogram was constructed using the Neighbor-Joining clustering algorithm. The genetic diversity indices in all breeds showed a high proportion of polymorphic SNPs, varying from 51.42% in Gyr to 97.58% in AFB. Also, AFB showed the highest expected heterozygosity estimate (0.41 ± 0.01), while Brahman the lowest (0.33 ± 0.01). Besides, Braunvieh possessed the highest observed heterozygosity (0.43 ± 0.01), while Brahman the lowest (0.37 ± 0.02), indicating that Brahman was less diverse. According to the molecular variance analysis, 75.71% of the variance occurs within individuals, whereas 24.29% occurs among populations. The pairwise genetic differentiation estimates (FST) between breeds showed values that ranged from 0.08 (Braunvieh vs. AFB) to 0.37 (Brahman vs. Braunvieh). Similarly, pairwise Reynold’s distance ranged from 0.09 (Braunvieh vs. AFB) to 0.46 (Brahman vs. Braunvieh). The dendrogram, similar to the PCA, identified two groups, showing a clear separation between Bos indicus (Brahman and Gyr) and B. taurus breeds (Braunvieh, Simmental, and AFB). Simmental and Braunvieh grouped closely with the AFB cattle. Similar results were obtained for the population structure analysis with K = 2. The results from this study would contribute to the appropriate management, avoiding loss of genetic variability in these breeds and for future improvements in this nucleus. Additional work is needed to speed up the breeding process in the Peruvian cattle system.
Genetic diversity and population structure of the Peruvian Andean legume, tarwi (Lupinus mutabilis), with high quality SNPs
(MDPI, 2023-01-19) Huaringa Joaquin, Amelia; Saldaña Serrano, Carla Lizet; Saravia Navarro, David; García Bendezú, Sady; Rodriguez Grados, Pedro; Salazar Coronel, Wilian; Camarena, Felix; Injante Silva, Pedro Hugo; Arbizu Berrocal, Carlos Irvin
Lupinus mutabilis Sweet (Fabaceae), “tarwi” or “chocho”, is an important grain legume in the Andean region. In Peru, studies on tarwi have mainly focused on morphological features; however, they have not been molecularly characterized. Currently, it is possible to explore the genetic parameters of plants with reliable and modern methods such as genotyping by sequencing (GBS). Here, for the first time, we used single nucleotide polymorphism (SNP) markers to infer the genetic diversity and population structure of 89 accessions of tarwi from nine Andean regions of Peru. A total of 5922 SNPs distributed along all chromosomes of tarwi were identified. STRUCTURE analysis revealed that this crop is grouped into two clusters. A dendrogram was generated using the UPGMA clustering algorithm and, like the principal coordinate analysis (PCoA), it showed two groups that correspond to the geographic origin of the tarwi samples. AMOVA showed a reduced variation between clusters (7.59%) and indicated that variability within populations is 92.41%. Population divergence (Fst) between clusters 1 and 2 revealed low genetic difference (0.019). We also detected a negative Fis for both populations, demonstrating that, like other Lupinus species, tarwi also depends on cross-pollination. SNP markers were powerful and effective for the genotyping process in this germplasm. We hope that this information is the beginning of the path towards a modern genetic improvement and conservation strategies of this important Andean legume.
Genome-wide single nucleotide polymorphisms reveal the genetic diversity and population structure of Creole goats from northern Peru
(Elsevier, 2024-04-24) Corredor Arizapana, Flor Anita; Figueroa Venegas, Deyanira Antonella; Estrada Cañari, Richard; Burgos Paz, William; Salazar Coronel, Wilian; Cruz Góngora, Wilder; Lobato Gálvez, Roiser Honorio; Injante Silva, Pedro Hugo; Godoy Padilla, David José; Barrantes Bravo, Christian Alfredo; Ganoza Roncal, Jorge Juan; Cruz Luis, Juancarlos Alejandro; Arbizu Berrocal, Carlos Irvin
Goat farming constitutes a significant source of income for farmers in northern Peru. There is currently an absence of information about the genetics of Peruvian Creole goats that would enable us to understand their origins and genetic spread. The objective of this study was to estimate the genetic diversity of Creole goats from northern Peru using SNP markers. This study involved the collection of 192 male Creole goats from three key goat production geographical departments in northern Peru. These goat samples were genotyped using the GGPGoat70k SNP panel. To explore the genetic influence of other breeds on Peruvian Creole goats, our dataset was combined with previously published SNP genotypes. External data set includes multiple breeds genotypes sampled from Argentina, Brazil, Spain, and Alpine breed from Italy, France, and Switzerland. After quality control 52,832 autosomal SNPs were used to assess genetic diversity in the Peruvian goats. For the population structure analysis of the merged data 20,513 common SNPs were used. Estimations for expected heterozygosity (He), observed heterozygosity (Ho), and inbreeding coefficient (FIS) were computed for the Peruvian groups. AMOVA, principal component analysis and ADMIXTURE were conducted to evaluate the population structure in the two data sets, Peru and merged. The results revealed a considerable genetic diversity, with Ho values ranging from 0.40 to 0.41 for the Peruvian sampling groups, and inbreeding coefficient was notably low for Peruvian goat. The population structure analysis demonstrated a distinction (p < 0.05) from other breeds. These findings suggest a level of genetic differentiation of the Peruvian goat population among other breeds, although further research is needed considering samples from other Peruvian areas. We expect this study will contribute to define genetic management strategies to prevent the loss of genetic diversity in Peruvian goat populations and for upcoming advancements in this field.
Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery
(MDPI, 2023-06-20) Pizarro Carcausto, Samuel Edwin; Pricope, Narcisa G.; Figueroa Venegas, Deyanira Antonella; Carbajal Llosa, Carlos Miguel; Quispe Huincho, Miriam Rocío; Vera Vilchez, Jesús Emilio; Alejandro Méndez, Lidiana Rene; Achallma Mendoza, Lino; González Tovar, Izamar Estrella; Salazar Coronel, Wilian; Loayza, Hildo; Cruz Luis, Juancarlos Alejandro; Arbizu Berrocal, Carlos Irvin
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.
Methodology for avocado (Persea americana Mill.) orchard evaluation using different measurement technologies
(Universidad de Concepción, 2022-12-27) Chumbimune Vivanco, Sheyla Yanett; Cárdenas Rengifo, Gloria Patricia; Saravia Navarro, David; Valqui Valqui, Lamberto; Salazar Coronel, Wilian; Arbizu Berrocal, Carlos Irvin
Avocado crop (Persea americana Mill.) is of great commercial importance due to its high profitability. However, it is being affected by various diseases and pests that affect yield and reduce fruit quality. The aim of this research was to develop methodologies for the evaluation of avocado plantations using different non-destructive technologies for rapid phenotyping and early detection of the incidence of diseases or damage due to stress in the stem. A plot of 0.7 ha. was evaluated, with a total of 44 individuals using Field-Map technology (dasometric and morphological characterization), RGB-multispectral images from Remotely Piloted Aircraft System (RPAS) (rapid phenotyping), while 15 individuals were evaluated using tomography (assessment of the internal state of the stem). The results with tomography indicated that there is a tree with wood rot of 14% with a lower acoustic speed with respect to the other trees evaluated. A high correlation was observed between the dasometric variables (r-Pearson from 0.63 to 0.98) estimated with Field-Map [crown base height, crown projection (m2) and total height] and with RPAS (height, perimeter and area). The vegetation indices do not have a direct correlation with the dasometric variables; five of the indices have a high contribution to variability except for the Normalized Difference Red Edge (NDRE). It can be concluded that the technologies used in this study would help to perform evaluations with a greater number of reliable and precise data with respect to the information obtained in a traditional way, while they can be replicated in commercial plots or research studies of different perennial crops, generating useful information for management decisions and crop evaluation.
Modeling the current and future habitat suitability of Neltuma pallida in the dry forest of northern Peru under climate change scenarios to 2100
(John Wiley & Sons Inc., 2024-08-27) Barboza Castillo, Elgar; Bravo Morales, Nino; Cotrina Sanchez, Alexander; Salazar Coronel, Wilian; Gálvez Paucar, David; Gonzales, Jhony; Saravia Navarro, David; Valqui Valqui, Lamberto; Cárdenas Rengifo, Gloria Patricia; Ocaña Reyes, Jimmy Alcides; Cruz Luis, Juancarlos; Arbizu Berrocal, Carlos Irvin
The development of anthropic activities and climate change effects impact worldwide species' ecosystems and habitats. Habitats' adequate prediction can be an important tool to assess current and future trends. In addition, it allows strategies development for their conservation. The Neltuma pallida of the forest region in northern Peru, although very significant, has experienced a decline in recent years. The objective of this research is to evaluate the current and future distribution and conservation status of N. pallida in the Peruvian dry forest under climate change (Location: Republic of Peru). A total of 132 forest presence records and 10 variables (bioclimatic, topographic, and soil) were processed and selected to obtain the current and future distribution for 2100, using Google Earth Engine (GEE), RStudio, and MaxEnt. The area under the curve values fell within the range of 0.93–0.95, demonstrating a strong predictive capability for both present and future potential habitats. The findings indicated that the likely range of habitats for N. pallida was shaped by factors such as the average temperature of wettest quarter, maximum temperature of warmest month, elevation, rainfall, and precipitation of driest month. The main suitable areas were in the central regions of the geographical departments of Tumbes, Piura, and Lambayeque, as well as in the northern part of La Libertad. It is critical to determine the habitat suitability of plant species for conservation managers since this information stimulates the development of policies that favor sustainable use programs. In addition, these results can contribute significantly to identify new areas for designing strategies for populations conserving and recovering with an ecological restoration approach.
Performance indicators to characterize the water supply to meet the demands of the Lurin River Basin
(IWA Publishing, 2023-11-17) Olortegui Artica, Christiand; Paredes Arquiola, Javier; Ramos Fernández, Lia; Cruz Grimaldo, Camila Leandra; Salazar Coronel, Wilian; Flores del Pino, Lisveth
Water scarcity and the planning of socioeconomic activities are challenges in the management of water resources. Therefore, the objective of this study was to use reliability indicators (RI) to simulate management scenarios in the Lurin River Basin. First, flow rates for the period 1969–2019 were calculated using the EvalHid HBV hydrological model and SIMGES, both from the AQUATOOL decision support system, to simulate demands. The estimation of agricultural demand IRs was made under three conditions: that the deficits for one, two, and 10 years should not exceed 20–40, 40–60, and 80–100% of the annual demand. The goodness-of-fit indices obtained for flow calibration were 0.716, 0.89, and 0.901 for Nash index (NSE), Nash natural logarithm (Ln NSE), and Pearson's correlation coefficient (R), representing the values of satisfactory, very good, and good, respectively. Agricultural demands present annual deficits of 59–96, 92–138, and 333–688% for one, two, and 10 years, so a 50 m3 reservoir is proposed to meet the IR. Thus, the information generated could be used to improve water resource management in the Lurin Basin.
Reference-Guided Draft Genome Assembly, Annotation and SSR Mining Data of the Peruvian Creole Cattle (Bos taurus)
(MDPI, 2022-11-09) Estrada Cañari, Richard; Corredor Arizapana, Flor Anita; Figueroa, Deyanira; Salazar Coronel, Wilian; Quilcate Pairazamán, Carlos Enrique; Vásquez Pérez, Héctor Vladimir; Maicelo Quintana, Jorge Luis; Gonzales, Jhony; Arbizu Berrocal, Carlos Irvin
The Peruvian creole cattle (PCC) is a neglected breed and an essential livestock resource in the Andean region of Peru. To develop a modern breeding program and conservation strategies for the PCC, a better understanding of the genetics of this breed is needed. We sequenced the whole genome of the PCC using a de novo assembly approach with a paired-end 150 strategy on the Illumina HiSeq 2500 platform, obtaining 320 GB of sequencing data. A reference scaffolding was used to improve the draft genome. The obtained genome size of the PCC was 2.81 Gb with a contig N50 of 108 Mb and 92.59% complete BUSCOs. This genome size is similar to the genome references of Bos taurus and B. indicus. In addition, we identified 40.22% of repetitive DNA of the genome assembly, of which retroelements occupy 32.39% of the total genome. A total of 19,803 protein-coding genes were annotated in the PCC genome. For SSR data mining, we detected similar statistics in comparison with other breeds. The PCC genome will contribute to a better understanding of the genetics of this species and its adaptation to tough conditions in the Andean ecosystem.
The presence of tomato brown rugose fruit virus (ToBRV) in tomatoes from the southern peruvian coast
(MDPI, 2024-01-19) Rodriguez Grados, Pedro Manuel; Saldaña Serrano, Carla Lizet; Estrada Cañari, Richard; Salazar Coronel, Wilian; Contreras Liza, Sergio; Arbizu Berrocal, Carlos Irvin
Tomato (Solanum lycopersicum) (Solanaceae) is an important vegetable crop worldwide that contains significant amounts of vitamins A and C. It also possesses a powerful antioxidant, lycopene, which can help prevent the development of many forms of cancer. However, this vegetable is highly susceptible to a number of emerging viruses. Since the first report of ToBRFV in Jordan, this emerging virus has been detected in Germany, Israel, Italy, Mexico, Palestine, and the United States, but its incidence was not reported in Peru. We collected 56 samples of fresh leaves of tomato plants with viral symptoms and 13 without symptoms as control from two regions that comprise more than 50% of tomato production in Peru, Lima and Ica. Mosaic, mottling, plant stunting and brown rugose symptoms were observed in collected leaves that were preserved in liquid nitrogen until processing. We extracted RNA using a commercial Kit. For virus identification, we used the reverse transcription polymerase chain reaction (RT-PCR) technique for the amplification of the capside protein (cp) gene. Specific primers were designed using the NCBI tool by collecting all available cp sequences from Peru Tomato mosaic virus (PToMV) and Tomato Brown Rough Fruit Virus (ToBRV). Results were observed on 1.5 % agarose gels using Gelred(Biotium®, Fremont, CA, USA) and by standard spectrophotometry. We observed the presence of ToBRFV in 24 samples, PToMV in 8 samples and 11 samples presented a mixed infection with ToBRV and PToMV. To the best of our knowledge, this is the first report of ToBRFV in Peru.
Utilización de RPAS en el monitoreo de cultivos
(Instituto Nacional de Innovación Agraria, 2022-06) Salazar Coronel, Wilian
El taller trató sobre la utilización de imágenes RGB multiespectrales y térmicas obtenidas mediante RPAS (Sistema de aeronave pilotada a distancia/vehículo aéreo no tripulado) para el monitoreo del estado de los cultivos, predicción de rendimiento, fenotipado y cálculo de estrés hídrico, además de las herramientas de la teledetección que se utilizan actualmente para el procesamiento y análisis de las imágenes.
Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru
(MDPI, 2022-05-17) Saravia Navarro, David; Salazar Coronel, Wilian; Valqui Valqui, Lamberto; Quille Mamani, Javier Alvaro; Porras Jorge, Rossana; Corredor Arizapana, Flor Anita; Barboza Castillo, Elgar; Vásquez Pérez, Héctor Vladimir; Arbizu Berrocal, Carlos Irvin
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.
Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from UAV in the Coast of Peru
(MDPI, 2022-10-26) Saravia Navarro, David; Salazar Coronel, Wilian; Valqui Valqui, Lamberto; Quille Mamani, Javier Alvaro; Porras Jorge, Zenaida Rossana; Corredor Arizapana, Flor Anita; Barboza Castillo, Elgar; Vásquez Pérez, Héctor Vladimir; Casas Diaz, Andrés V.; Arbizu Berrocal, Carlos Irvin
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 for 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 vegetation indices (VIs). A total of 10 VIs (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. 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 showed 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 estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.