Artículos científicos

URI permanente para esta colecciónhttps://repositorio.inia.gob.pe/handle/20.500.12955/8

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Multicriteria evaluation and remote sensing approach to identifying degraded soil areas in northwest Peru
(Taylor & Francis Group, 2024-12-23) Arce Inga, Marielita; Atalaya Marin, Nilton; Barboza Castillo, Elgar; Tarrillo Julca, Ever; Chuquibala Checan, Beimer; Tineo Flores, Daniel; Fernandez Zarate, Franklin Hitler; Cruz Luis, Juancarlos Alejandro; Goñas Goñas, Malluri; Gómez Fernández, Darwin
Soil is a vital nonrenewable resource characterized by rapid degradation and slow regeneration processes. In this study, soil degradation in Jaén and San Ignacio was assessed via a multicriteria evaluation approach combined with remote sensing (RS) data. Nine factors were analyzed classified three categories: environmental, topographic, and edaphological factors. The results revealed that the slope (59.07%) was the main influencing factor, followed by land use and land cover (LULC) (56.36%). The degradation map revealed that 83.48% of the area exhibited moderate degradation, 14.49% low degradation, and 1.56% high degradation. The districts of Pomahuaca and San José de Lourdes demonstrated the largest areas of moderate degradation, accounting for 13.71% and 22.54%, respectively. Bellavista and Huarango exhibited the largest areas of very high degradation, accounting for 0.27% and 0.08%, respectively. The (AHP) method and RS data were employed to assess soil degradation, highlighting the need for sustainable soil restoration and conservation strategies.
Correction: analyzing urban expansion and land use dynamics in Bagua Grande and Chachapoyas using cloud computing and predictive modeling
(Springer Nature, 2024-09-26) Barboza, Elgar; Turpo, Efrain Y.; Salas Lopez, Rolando; Silva López, Jhonsy O.; Cruz Luis, Juancarlos Alejandro; Vásquez, Héctor V.; Purohit, Sanju; Aslam, Muhammad; Tariq, Aqil
Urban growth and Land Use/Land Cover (LULC) changes have increased in recent decades due to anthropogenic activities. This study explored past and projected future LULC changes and urban growth patterns in the Bagua Grande and Chachapoyas districts using Landsat imagery, cloud computing, and predictive models for 1990 to 2031. The analysis of satellite images was grouped into four time periods (1990–2000, 2000–2011, 2011–2021 and 2021–2031). The Google Earth Engine (GEE) cloud-based system facilitated the classification of Landsat 5 ETM (1990, 2000, and 2011) and Landsat 8 OLI (2021) images using the Random Forest (RF) model. A simulation model integrating Cellular Automata (CA) and an Artificial Neural Network (ANN) Multilayer Perceptron (MLP) in the MOLUSCE plugin of QGIS was used to forecast urban sprawl to 2031. The resulting maps showed an overall accuracy (OA) of over 92%. A decrease in forested area was observed, from 20,807.97 ha in 1990 to 14,629.44 ha in 2021 in Bagua Grande and from 7,796.08 ha to 3,598.19 ha in Chachapoyas. In contrast, urban areas experienced a significant increase, from 287.49 to 1,128.77 ha in Bagua Grande and from 185.65 to 924.50 ha in Chachapoyas between 1990 and 2021. By 2031, the urban area of Bagua Grande is expected to increase from 1,128.77 to 1,459.25 ha (29%) in a southeast, south, southwest, west, and northwest direction. Chachapoyas expanded from 924.50 to 1138.05 ha (23%) in the southwest, north, northeast, and southeast directions. The study presents an analytical method integrating cloud processing, GIS, and change simulation modeling to evaluate urban growth spatio-temporal patterns and LULC changes. This approach effectively identified the main LULC changes and trends in the study area. In addition, potential urbanization areas are highlighted where there are still opportunities for developing planned and managed urban settlements.
Green manuring and fertilization on rice (Oryza sativa L.): a peruvian Amazon study
(Instituto Nacional de Innovación Agraria, 2024-12-02) Arévalo Aranda, Yuri Gandhi; Rodríguez Toribio, Elmer; Rosillo Cordova, Leodan; Díaz Chuquizuta, Henry; Torres Chávez, Edson Esmith; Cruz Luis, Juancarlos; Siqueira Bahia, Rita de Cássia; Pérez Porras, Wendy Elizabeth
The study was conducted in Juan Guerra district, province and region of San Martin, Peru; it assessed two treatment sets: (1) nitrogen fertilizer dose (FN75, FN100); (2) green manure Crotalaria juncea (CroJ), Canavalia ensiformis (CanE), and without green manure. It was arranged in a split-plot design with four replications. During the experiment, we observed an important fluctuation in soil parameters. Notably, there was a decrease in soil carbon and nitrogen levels, likely attributed to microorganism metabolism. On the other hand, we observed that CanE significantly reduced the diseased tillers through “White Leaf Virus” (RHBV) by 2.82% compared to the control, and significant panicle fertility was achieved by CroJ (91.88%). No significant differences were obtained in yields during this first campaign; however, the highest reported yield was 8.36 t ha-1 with the CanE - FN100 treatment. Additionally, the nutritional quality of the rice was not affected by either green manuring or the application of chemical nitrogen fertilization. These findings allow deeper studies to consider strategic alternatives to reducing dependency on inorganic fertilizers among the poorest communities.
Use of vegetable covers as a strategy to reduce soil erosión and increase the yield of corn (Zea mays L.)
(Universidad de Tarapaca, 2023-12-31) Sanabria Quispe, Samuel; Mendoza Dávalos, Katia; Palomino Paccua, Luz Angélica; Figueroa Venegas, Deyanira Antonella; Pocomucha, Vicente; Cosme de la Cruz, Roberto Carlos
Soil degradation is a problem facing agriculture, with water being the most important erosive agent, affecting, among others, crop yields. The objective of this study was to know the effect of four plant covers on soil erosion and starchy corn yield, in three locations in the Ayacucho region (Peru) during the 2018 - 2019 agricultural season. Five treatments were assigned: control, clover cover, vetch cover, vetch-oat cover, and mulch, in corn plots under a randomized complete block design (RCBD), with four blocks. Combined analysis of variance was used to evaluate the results. It was observed that soil erosion and corn yield were significantly (P < 0.001) influenced by plant cover and locations. Vetch-oat and clover cover significantly reduced soil erosion (–53 and –36%, respectively) due to the higher leaf biomass produced by both (6131 and 6052 kg ha–1, respectively). Clover cover produced the highest corn yield (3749 kg ha–1; +78%); while vetch-oats produced the lowest (1955 kg ha–1), without significant differences with the control. The highest production of biomass, N and C of the foliar coverages was produced in the location with the least slope; while the highest performance occurred in steeper areas. Clover cover turned out to be a better option to reduce soil erosion and increase corn yield.
Characterization and typification of small-scale goat production systems in the highlands of southeast Peru
(Frontiers Media, 2024-11-14) Palomino Guerrera, Walter; Godoy Padilla, David; Huaripaucar Huancahuari, Joseen; Sessarego Dávila, Emmanuel; Trillo Zárate, Fritz; Cruz Luis, Juancarlos
Goat breeding in Peru is one of the main activities of smallholders. Goats are distributed in different agroecological zones and regions of the country, developing under heterogeneity of production systems, making it difficult to understand goat breeders’ socioeconomic, technological, and productive situations. This study aimed to characterize and typify the goat production systems in the highlands of southeast Peru. A survey was conducted with 91 goat farmers from five districts of Ayacucho, Peru, using a structured and individualized questionnaire administered on their farms. The socio-economic, productive, and commercial characteristics of the goat production systems were recorded. A multiple correspondence analysis (MCA) and hierarchical classification analysis (HCA) were performed to establish a typology of the smallholders. The results reveal that the breeding system is extensive, where there is no breeding program, with natural pastures and crop stubble being the source of food for the herds. Only slightly more than half (54%) carry out a deworming program. Farmers were categorized into three different groups, corresponding to three different farming systems: Group 1 farmers raised goats solely for home consumption; Group 2 breeders raised goats for both consumption and marketing of surplus products (cheese, milk, and meat), and Group 3 farmed focused on producing cheese and goat kids and selling to local markets. The study provides valuable insights that will help design effective breeding strategies to develop sustainable goat farming in the region, considering different production systems and their respective socio-economic and trade dynamics. This classification will be essential for tailoring development programs to the specific needs of each group, promoting better use of resources, improving productivity, and enhancing the livelihoods of smallholder goat producers in Peru
Using UAV images and phenotypic traits to predict potato morphology and yield in Peru
(MDPI, 2024-10-24) Ccopi Trucios, Dennis; Ortega Quispe, Kevin; Castañeda Tinco, Italo; Rios Chavarria, Claudia; Enriquez Pinedo, Lucia; Patricio Rosales, Solanch; Ore Aquino, Zoila; Casanova Nuñez-Melgar, David; Agurto Piñarreta, Alex; Zúñiga López, Luz Noemí; Urquizo Barrera, Julio
Precision agriculture aims to improve crop management using advanced analytical tools.In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on phenotypic characteristics of plants and data captured through spectral cameras equipped on UAVs. For this purpose, the experiment was carried out at the Santa Ana Experimental Station in the central Peruvian Andes, where advanced potato clones were planted in December 2023 under three levels of fertilization. Random Forest, XGBoost, and Support Vector Machine models were used to predict yield and quality parameters, such as circularity and the length–width ratio. The results showed that Random Forest and XGBoost achieved high accuracy in yield prediction (R2 > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R2 = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral índices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.
Phenotypic diversity of morphological traits of pitahaya (Hylocereus spp.) and its agronomic potential in the Amazonas region, Peru
(MDPI, 2024-11-02) Santos Pelaez, Julio Cesar; Saravia Navarro, David; Cruz Delgado, Julio H. I.; Del Carpio Salas, Miguel Angel; Barboza, Elgar; Casanova Núñez-Melgar, David Pavel
Pitahaya (Hylocereus spp.) is an economically significant cactus fruit in Peru, renowned for its rich nutritional profile and antioxidant properties while exhibiting wide biological diversity. This study aimed to morphologically characterize seven pitahaya accessions using qualitative and quantitative descriptors related to the cladodes, flowers, and fruits. Univariate and multivariate (FAMD, PCA, MCA, and clustering) analyses were employed to identify and classify the accessions based on their morphological traits. The analyses revealed three distinct groups: one consisting solely of AC.07; another with AC.02, AC.04, and AC.06; and a third including AC.01, AC.03, and AC.05. The first group exhibited superior characteristics, particularly in fruit traits such as the stigma lobe count (23.3), number of bracts (26.5 mm), and length of apical bracts (15.75 mm). The second group recorded the highest spine count (3.21), bract length (16.95 mm), and awn thickness (5.12 mm). The third group had the highest bract count (37) and an average locule number (23.65). These findings highlight the significant morphological diversity among the accessions, indicating the potential for classification and selection in pitahaya cultivation. The potential of AC.07 stands out in terms of its agronomic qualities, such as its fruit weight (451.93 g) and pulp weight (292.5 g), surpassing the other accessions.
Draft genome sequence data of Fusarium verticillioides strain REC01, a phytopathogen isolated from a Peruvian maize
(Elsevier, 2024-09-23) Estrada Cañari, Richard; Aragón, Liliana; Pérez Porras, Wendy E.; Romero Avila, Yolanda; Martínez Vidal, Gabriel; García, Karina; Cruz Luis, Juancarlos; Arbizu Berrocal, Carlos I.
Fusarium verticillioides represents a major phytopathogenic threat to maize crops worldwide. In this study, we present genomic sequence data of a phytopathogen isolated from a maize stem that shows obvious signs of vascular rot. Using rigorous microbiological identification techniques, we correlated the disease symptoms observed in an affected maize region with the presence of the pathogen. Subsequently, the pathogen was cultured in a suitable fungal growth medium and extensive morphological characterization was performed. In addition, a pathogenicity test was carried out in a DCA model with three treatments and seven repetitions. De novo assembly from Illumina Novaseq 60 0 0 sequencing yielded 456 contigs, which together constitute a 42.8 Mb genome assembly with a GC % content of 48.26. Subsequent comparative analyses were performed with other Fusarium genomes available in the NCBI database.
Inoculation with Azospirillum brasilense as a strategy to reduce nitrogen fertilization in cultivating purple maize (Zea mays l.) in the Inter-andean valleys of Peru
(MDPI, 2024-10-21) Condori Ataupillco, Tatiana; Alarcón Romani, Susan; Huasasquiche Sarmiento, Lucero; García Blásquez, Cayo; Padilla Castro, César; Velásquez Mantari, José; Solórzano Acosta, Richard
Purple maize has gained global significance due to its numerous nutraceutical benefits. However, sustaining its production typically requires high doses of nitrogen fertilizers, which, when applied in excess, can contaminate vital resources such as soil and water. Inoculation with nitrogenfixing microorganisms, such as those from the Azospirillum genus, has emerged as an alternative to partially or fully replace nitrogen fertilizers. This study aimed to evaluate the inoculation effect with A. brasilense and varying nitrogen fertilization levels on the yield and quality of purple maize. The experiment was carried out using a randomized complete block design (RCBD) with a 2 × 5 factorial arrangement and five replications. Treatments comprised two inoculation levels (control without inoculation and inoculation with A. brasilense) under five nitrogen doses (0, 30, 60, 90, and 120 kg・ha−1, applied as urea). Inoculation with A. brasilense resulted in a 10.5% increase in plant height, a 16.7% increase in root length, a 21.3% increase in aboveground fresh biomass, a 30.1% increase in root fresh biomass, and a 27.7% increase in leaf nitrogen concentration compared to the no inoculated control. Regarding yield, the inoculated plants surpassed the control in both purple maize yield (kg・ha−1) and cob weight by 21.8% and 11.6%, respectively. Across all fertilization levels and parameters assessed, the inoculated treatments outperformed the control. Furthermore, for parameters, namely plant height, leaf nitrogen content, and cob dimensions (length, diameter, and weight), the A. brasilense inoculation treatment with 90 kg N・ha−1 was statistically equivalent or superior to the non-inoculated control with 120 kg N・ha−1. These results indicate that inoculation with A. brasilense positively impacted purple maize at all nitrogen levels tested and improved nitrogen use efficiency, enabling a reduction of 30 kg N・ha-1 without compromising performance in key parameters.
Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging
(MDPI, 2024-10-06) Urquizo Barrera, Julio Cesar; Ccopi Trucios, Dennis; Ortega Quispe, Kevin; Castañeda Tinco, Italo; Patricio Rosales, Solanch; Passuni Huayta, Jorge; Figueroa Venegas, Deyanira; Enriquez Pinedo, Lucia; Ore Aquino, Zoila; Pizarro Carcausto, Samuel
Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.
Forage yield and nutritive value of plantain and chicory for livestock feed at high altitudes in Peru
(John Wiley & Sons Inc., 2024-10-10) Vallejos Fernández, Luis A.; Guillén, Ricardo; Pinares Patiño, César; García Ticllacuri, Rubén; Muñoz Vilchez, Yudith Yohany; Quilcate, Carlos Enrique; Alvarez García, Wuesley Yusmein
Background: Evaluation of forage resources is vital for the sustainability of livestock farming in the South American Andes, especially under conditions of low water availability for irrigation and acid soils. Methods: We evaluated the productivity and nutritive value of two cultivars of chicory (Cichorium intybus L.) and one of plantain (Plantago lanceolata L.) in three high‐altitude sites (AL) of the northern highlands of Peru: AL‐I: 2300–2800 m.a.s.l, AL‐II: 2801–3300 m.a.s.l. and AL‐III: 3301–3800 m.a.s.l., for 1 year. The parameters evaluated were dry matter yield (DMY), plant height (PH), growth rate (GR) and nutritional value. Results: Plantain achieved the greatest annual DMY (ADMY), PH and GR compared to the two chicory cultivars (9.34, 9.56 and 13.39 Mg ha−1 for Puna II and Sese 100 chicory and Tonic plantain, respectively; p = 0.0019). The greatest ADMY and GR occurred at AL‐I. Regarding nutritional value, differences were observed only for in vitro digestibility of dry matter and metabolisable energy with chicory cultivars higher than plantain. Conclusions: The results indicate that the three cultivars evaluated may be used as a nutritional supplement in cattle feed, associated with grasses because they have high nutritive value suitable for milk production in the mountain regions of Peru.
Molecular identification of the most frequent pathotypes of Escherichia coli in calves with diarrhoea in the Cajamarca region of Peru
(Faculty of Veterinary Medicine, University of Tripoli, 2024-09-30) Cabrera González, Marco; Quilcate Pairazamán, Carlos; Alvarez García, Wuesley Yusmein; Cabrera Hoyos, Héctor; Tayca Saldaña, Antony; Aliaga Tambo, Fernando; Rojas Valdez, Deisy; Cueva Rodríguez, Medali
Background: Colibacillosis caused by Escherichia coli causes significant economic losses in the livestock sector worldwide and is one of the calves’ leading causes of diarrea Aim: This study aimed to identify the most frequent E. coli molecularly pathotypes in calves with diarrhea in six provinces of the Cajamarca region in the northern highlands of Peru. Methods: Twenty-eight herds of dairy cattle under a semi-intensive rearing system were evaluated; 95 samples were isolated from calves with diarrhea up to the first month of life, 62 males and 33 females, during the rainy season. Results: The presence of virulence genes of E. coli strains was more prevalent in males; the astA (89.47%), st (83.15%), and f5 (57.89%) genes were more expressed, and the lt (17.89%) and stx2 (1.05%) genes were less expressed. The eae gene (21.05%) was more present in females. Conclusion: When E. coli strains express virulence genes astA, st, and f5 and their atypical double, triple, and quadruple combination between different observed pathotypes, they give rise to the formation of several pathotypes by the horizontal transfer of virulence genes, which can cause colibacillosis processes in more virulent calves, which is one of the most important causes of diarrhea in calves in the region of Cajamarca, compromising the sanitary viability in the herds.
Responses of Megacyllene andesiana and Oreodera bituberculata (Coleoptera: Cerambycidae) to anti-2,3-hexanediol, fuscumol, and fuscumol acetate in Peru
(Cambridge University Press, 2024-09-25) Aguirre Gil, Oniel Jeremías; Paredes Espinosa, Richard; Egoávil Jump, Giannfranco; Allison, Jeremy Dean
Management of phytosanitary and biosecurity risks associated with the Cerambycidae focuses on prevention and early detection. Semiochemical-baited traps are an important component of these management efforts. Cerambycid pheromones are often screened in field trials to develop inventories of which species can be surveyed with which semiochemicals. We report field trials of two types of intercept traps (four- and three-sided panel traps) baited with known Cerambycidae pheromones aimed to capture Peruvian fauna. Intercept traps were baited with anti-2,3-hexanediol, fuscumol, and fuscumol acetate alone and in binary and ternary blends. The most frequently captured species was Megacyllene andesiana (Casey) (Coleoptera: Cerambycidae) (n = 268), followed by Oreodera bituberculata Bates (Coleoptera: Cerambycidae) (n = 59), Discopus eques Bates (Coleoptera: Cerambycidae) (n = 37), and Aegomorphus longitarsis (Bates) (Coleoptera: Cerambycidae) (n = 31). Trap type did not affect capture rates. Male and female M. andesiana were attracted by anti-2,3-hexanediol. The addition of fuscumol, fuscumol acetate, or the combination of fuscumol and fuscumol acetate reduced male M. andesiana captures, whereas the addition of fuscumol and the combination of fuscumol and fuscumol acetate reduced the response of female M. andesiana. Male O. bituberculata were attracted to traps baited with fuscumol, and this response was reduced by the addition of fuscumol acetate, whether or not anti-2,3-hexanediol was present.
Implementing artificial intelligence to measure meat quality parameters in local market traceability processes
(John Wiley & Sons Inc., 2024-09-20) Alvarez García, Wuesley Yusmein; Mendoza, Laura; Muñoz Vílchez, Yudith Yohany; Casanova Núñez-Melgar, David; Quilcate Pairazaman, Carlos
The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri-food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products’ production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognize that this is an evolving field. This technology offers innovative solutions that enable efficient, cost effective, and consumer-oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real-time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human-machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri-food and agri-industrial sector, highlighting and emphasising the importance of ongoing innovation in the field.
Litter decomposition rates of four species of agroecological importance in the Peruvian coast and Andean highland
(MDPI, 2024-09-13) Samaniego Vivanco, Tomás Daniel; Ramirez Aparicio, Jorge Adrian; Solórzano Acosta, Richard Andi
Crop residue decomposition is fundamental for ecosystems, influencing carbon cycling, organic matter accumulation, and promoting plant development through nutrient release. Therefore, this study aimed to ascertain the rate of decomposition of four commonly cultivated crops (alfalfa, maize, avocado, and eucalyptus) along the northern coast of Lima (Huaral) and in the Ancash Mountain range (Jangas) areas. Decomposition rates were assessed using mass loss from decomposition bags measuring 15 × 10 cm, filled with 10–15 g of material tailored to each species, and buried at a depth of approximately 5 cm. Sampling occurred every three months over a year, totaling four sampling events with three replicates each, resulting in ninety-six experimental units. The findings demonstrate that the decomposition rates and the release of nutrients were markedly greater in Huaral for maize and avocado. In contrast, these rates were notably elevated in Jangas for alfalfa and eucalyptus. The leaf litter of avocado and eucalyptus (tree) had periods of accumulation and release of heavy metals such as Cd. The initial C/N ratio was one of the main factors related to the nutrient decomposition rate; in contrast, there were no significant relationships with soil properties at the study sites.
Mango varietal discrimination using hyperspectral imaging and machine learning
(Springer Nature, 2024-07-29) Castro, Wilson; Tene, Baldemar; Castro, Jorge; Guivin, Alex; Ruesta Campoverde, Nelson Asdrubal; Avila George, Himer
Mango is a highly diverse tropical fruit with numerous varieties that differ in flavor, texture, and chemical composition. Consequently, identifying fraudulent substitutions of mango varieties poses a significant challenge using traditional techniques. Therefore, there is an increasing need for new methods to discriminate between mango varieties. Hyperspectral imaging coupled with machine learning techniques presents a promising approach for varietal discrimination. In this study, mango samples of eleven varieties were collected from a germplasm bank, with four slices obtained from each sample. Hyperspectral images were acquired in the Vis–NIR and NIR ranges for each slice, and spectral profiles were extracted and pretreated. Three discrimination models, linear discriminant analysis, K-nearest neighbor, and artificial neural networks, were implemented and validated using relevant wavelengths selected through a covering array feature selection algorithm. The performance of these models was evaluated using precision, accuracy, and F-score metrics. The average spectral profiles of the studied varieties exhibited a similar behavior with slight differences, which could be used for classification within the evaluated ranges. The optimal number of variables selected to refine the models was 17 for the UV–Vis–NIR range and 21 for the NIR range, with an accuracy ranging between 0.752 and 0.972. This study concludes that hyperspectral imaging combined with machine learning techniques can effectively discriminate between different varieties of mango.
Methodology for the phenotypic evaluation in Guazuma crinita trees in Ucayali, Peru
(Universidade Federal de Santa Maria, 2024-02-20) Revilla Chávez, Jorge Manuel; López Galán, Edinson Eduardo; Gonzales Alvarado, Antony Cristhian; Sáenz Ramírez, Lyanna Hellen; Mori Vásquez, Jorge Arturo; Rojas Mego, Krystel Clarissa; Abanto Rodríguez, Carlos; Sebbenn, Alexandre Magno
The objective of this study was to present a methodological tool for the phenotypic evaluation in progeny tests of Guazuma crinita in producer plots of the Aguaytía river basin, Ucayali, Peru, which allows field technicians to standardize the morphological evaluation criteria of trees in forest plantations. Therefore, the phenotypic traits were evaluated for plant height (m), diameter at the height of the base (cm), number of branches, number of rings, stem form, branch orientation, presence and quantity of leaves. The heritability and genetic and phenotypic correlations between traits were also estimated. Therefore, 32 morphological categories were plotted based on the significant correlations (p≤ 0.05) shown between the place of planting, the stem form, the orientation of the branches and the presence of leaves. For the same reason, the progeny showed low morphological patterns, being a low factor of phenotypic variability. It is concluded that the correlations between the biometric and morphological traits evaluated, allowed to validate the phenotypic evaluation procedures of Guazuma crinita progeny tests at 36 months of age.
Efficiency of a compound parabolic collector for domestic hot water production using the F- chart method
(International Hellenic University School of Science and Technology, 2024-06-01) Ortega Quispe, Kevin Abner; Huari Vila, Oscar Paul; Ccopi Trucios, Dennis; Lozano Povis, Arlitt Amy; Enriquez Pinedo, Lucia Carolina; Cordova Torres, Betty
Among solar energy technologies, differences exist in terms of costs, performance, and environmental sustainability. Flatplate solar collectors, solar towers, and parabolic dish systems offer high thermal efficiency and versatility, but they may be more costly and bulky compared to other collector models. This study focused on evaluating the efficiency of a cylindrical parabolic collector (CPC) for the production of domestic hot water in a high Andean region of Peru, using the F-Chart method. Its performance was estimated considering the energy demand for hot water in a single-family home with four occupants, in accordance with national regulations and international recommendations. Additionally, the collector area, water temperature, and incident solar radiation were determined based on meteorological data obtained using the PVsyst software. On the other hand, the F-Chart methodology was employed to find the dimensionless factors X and Y of the CPC collector, which allowed estimating the solar fraction factor and the monthly useful energy that can be provided by the designed CPC system. The results showed that, during months of maximum solar radiation, the CPC is capable of satisfying between 129% and 144% of the energy demand for hot water. This indicates that there is a surplus of usable solar energy in the collector during the summer, while in autumn and winter, the solar contribution balances and slightly exceeds the demand. CPC can significantly contribute to the development of high Andean areas by improving quality of life, reducing costs, and promoting environmental sustainability compared to other available technologies.
Matrix-assisted laser desorption ionization time-of-flight mass spectrometry combined with chemometrics for protein profiling and classification of boiled and extruded quinoa from conventional and organic crops
(MDPI, 2024-06-17) Galindo Luján, Rocío; Pont, Laura; Quispe Jacobo, Fredy Enrique; Sanz Nebot, Victoria; Benavente, Fernando
Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.
El biofertilizante líquido fermentado de residuos de camal mejora el rendimiento del cultivo de maíz
(Universidad Autónoma de Yucatán, 2024-08-06) Samaniego Vivanco, Tomás Daniel; Pérez Porras, Wendy Elizabeth; Lastra Paúcar, Sphyros; Verme Mustiga, Ezio; Solórzano Acosta, Richard Andi
La aplicación exclusiva de fertilizantes sintéticos u orgánicos sigue generando polémica. La evidencia muestra que su aplicación conjunta puede mejorar la nutrición de los cultivos, evitar el uso excesivo de fertilizantes sintéticos y amortiguar su efecto contaminante en el suelo. Objetivo: Evaluar el uso de dosis de fertilización orgánica y mineral sobre el crecimiento y rendimiento del maíz amarillo duro Megahíbrido 619 INIA empleando un biofertilizante líquido derivado de la fermentación de residuos de camal. Metodología: Mediante un diseño experimental de bloques completos al azar con arreglo factorial 4x2, se ensayaron cuatro dosis de fertilización química NPK y la aplicación del biofertilizante. La fertilización mineral se fraccionó en dos partes, mientras que las aplicaciones del biofertilizante fermentado de residuos de camal se realizaron vía drench durante el crecimiento vegetativo y entre las etapas de panojamiento y llenado de grano a una dosis de 50 L. ha-1 de producto. Resultados: El uso del biofertilizante líquido (K1) tuvo un impacto positivo en el crecimiento, con un efecto equiparable en la altura y área foliar de la planta al aplicar una dosis media de fertilización química (F2_K1). La dosis más baja de fertilización química en combinación con el biofertilizante (F1_K1) obtuvo un índice de cosecha estadísticamente superior (+14%) en comparación con el la fertilización completa (F3_K1). Implicaciones: Si bien las fertilizaciones más altas no produjeron rendimientos superiores, es posible que en otras condiciones y con otros híbridos de maíz sí se observen diferencias significativas. Conclusión: La aplicación del biofertilizante líquido junto con una dosis reducida de fertilización mineral permite obtener un mayor índice de cosecha y rendimientos comparables con el uso de una fertilización mineral completa en el maíz amarillo.

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