Examinando por Autor "Valqui Valqui, Lamberto"

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Assessment of vegetation índices derived from UAV images for predicting biometric variables in bean during ripening stage
(Universidad de Tarapacá, 2022-03-01) Quille Mamani, Javier Alvaro; Porras Jorge, Rossana; Saravia Navarro, David; Herrera, Jordán; Chávez Galarza, Julio César; Arbizu Berrocal, Carlos Irvin; Valqui Valqui, Lamberto
Here, we report the prediction of vegetative stages variables of canary bean crop employing RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight vegetation indices derived from UAV images from the canary bean, which were evaluated by multiple regression models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations.
Characterization of Coffea arabica L. parent plants and physicochemical properties of associated soils, Peru
(Cell Press, 2022-10-03) Alvarado Chuqui, Wigoberto; Bobadilla Rivera, Leidy Gheraldine; Valqui Valqui, Leandro; Silva Valqui, Gelver; Valqui Valqui, Lamberto; Vigo Mestanza, Carmen Natividad; Vásquez Pérez, Héctor Vladimir
It is important to carry out the morphological characterization of coffee parent plants and the physicochemical properties of the associated soils in the Amazon region, Peru, in order to achieve germplasm conservation. One hundred coffee mother plants were identified and located in five provinces of the region and evaluated according to morphological descriptors such as stipula shape, young leaf color, leaf shape, leaf apex shape, young shoot color, leaf color, fruit color, fruit shape, mature leaf color, and rust incidence percentage. In the plots where the parent plants were located, soil sampling was carried out to determine the physical and chemical properties. The varieties with the greatest presence in the five provinces were Típica and caturra roja, with the greatest number of specimens reported for the province of Bagua. The predominant stipule shape was triangular (91%), lanceolate leaf shape (60%) and red fruit color (90%). Bongará reported the lowest incidence of yellow rust, as well as the Mundo Novo Rojo variety. Soil pH ranged from acidic to neutral values, low electrical conductivity, high organic matter content, low phosphorus content, high potassium levels and medium cation exchange capacity. The predominant textural class was sandy loam. The physical and chemical characterization of the soils under study show favorable ranges to encourage the best development of coffee cultivation.
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
Influence of the arboreal component in the productive and nutritional parameters of Brachiaria mutica grass in northeastern Peru
(MDPI, 2022-10-15) Valqui Valqui, Leandro; Lopez, Edvin L.; Lopez, Cesar A.; Valqui Valqui, Lamberto; Bobadilla Rivera, Leidy Gheraldine; Vigo Mestanza, Carmen Natividad; Vásquez Pérez, Héctor Vladimir
The objective of this study was to evaluate the growth (cm), yield (kg/m2), crude protein (CP %), crude fiber (CF %), ether extract (EE %), NDF (%), ADF (%), gross energy (GE kcal/kg), ELN (%), Ash (%), and in vitro digestibility (IVD %) of Brachiaria mutica grass; under three silvopastoral systems, guava (Inga edulis), poplar (Populus alba), and eucalyptus (Eucalyptus globulus labill) and a treeless system (TS) in the northeast of peru. These were analyzed under a completely randomized design (CRD) with four treatments and four repetitions, and the results were analyzed by analysis of variance (α = 0.05%) and Tukey’s means test (p ≤ 0.05). The SPS of guava showed higher growth at 30 days (59.57 cm), and the there was no difference between systems at 45 (98.43–107.14 cm), 60 (138.86–146.57 cm), and 75 days (159.81–165.67 cm); the highest yield at 30 days was for SPS with guava (0.41 kg/m2), at 45 and 60 days there was no difference (1.01–1.15 and 1.57–1.76 kg/m2), and at 75 days the highest yield was from TS (2.88 kg/m2); the nutritional composition was evaluated in two cut-off frequencies (30 and 75 days); for 30 days, the SPS with guava had a higher value for CP (16.03%), IVD (68.13%), and GE (4502 kcal/kg); the SPS with eucalyptus had a higher percentage for CF (21.08), NDF (33.17), FDA (56.42), and ash (7.74); the highest EE content was in the SPS with poplar (2.46%) and the TS presented the highest percentage of ELN (50.88); for 75 days, the SPS with guava presented a higher value for CP (13.61%), FDA (36.78), and GE (4504.33 kcal/kg), the SPS with eucalyptus had a higher percentage for CF (23.51) and ash (6.42), and the the SPS with poplar had the highest percentage of EE (2.24), ELN (59.18) FDN (62.67), and IVD (56.59).
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.
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.
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.