Revisión de la literatura científica relacionada con la predicción económico-financiera en Pymes bananeras
Resumen
La revisión de la literatura científica en un área determinada es un indicador del incremento de la investigación y de la generación de conocimientos. El análisis bibliométrico permite realizar un examen retrospectivo sobre el modo que ha sido investigada y dada a conocer la temática, pero también valora el potencial en el conocimiento de las publicaciones más relevantes. El objetivo del presente artículo es evaluar la literatura científica relacionada con predicción económico-financiera a partir del estudio de las tendencias y el estado de la investigación en Pymes bananeras. Para ello se desarrolló un estudio descriptivo transversal que incluyó el análisis de los artículos publicados en SciVerse Scopus, SciELO entre los años 2002-2022, la información fue tabulada por medio del software VOSviwer. Se realizó una búsqueda avanzada empleando el modelo TAK (Title, Abstract, Keywords) y se utilizó la cadena de búsqueda en idioma inglés (Financial, Economic, Predictive, Modeling, Analysis and Agricultural) que estuvieran presentes en los títulos, resumen o palabras claves definidas por el autor. La indagación reflejó el crecimiento de las publicaciones lo que evidencia el desarrollo académico de la temática y a la vez el interés de los investigadores.
Palabras clave:
Análisis bibliométrico, predicción, artículos.
ABSTRACT
The review of the scientific literature in a certain area is an indicator of the increase in research and the generation of knowledge. The bibliometric analysis allows a retrospective examination of the way in which the subject has been investigated and made known, but also assesses the potential in the knowledge of the most relevant publications. The objective of this article is to evaluate the scientific literature related to economic-financial prediction based on the study of trends and the state of research in banana SMEs. For this, a descriptive cross-sectional study was developed that included the analysis of the articles published in SciVerse Scopus, SciELO between the years 2002-2022, the information was tabulated using the VOSviwer software. An advanced search was carried out using the TAK model (Title, Abstract, Keywords) and the search string in English was used (Financial, Economic, Predictive, Modeling, Analysis and Agricultural) that were present in the titles, abstract or keywords. defined by the author. The inquiry reflected the growth of publications, which shows the academic development of the subject and, at the same time, the interest of researchers.
Keywords:
Bibliometric analysis, prediction, articles.
Texto completo:
PDFReferencias
Bandusena, M., Chandrasekera, B., & Edirisinghe, E. (2020). Development of cryopreservation protocol for banana cultivar 'Ambul' (Musa sp.) using vitrification technique. Acta Hortic. 1285, 153-160.
Biney, J. K. M., Vašát, R., Bell, S. M., Kebonye, N. M., Klement, A., John, K., & Borůvka, L. (2022). Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach with multiple pre-treatment combinations. Soil and Tillage Research, 220.
Caicedo Solano, N. E., García Llinás, G. A., & Montoya-Torres, J. R. (2022). Operational model for minimizing costs in agricultural production systems. Computers and Electronics in Agriculture, 197(5).
Calou, V. B. C., dos Santos Teixeira, A., Moreira, L. C. J., Lima, C. S., de Oliveira, J. B., & de Oliveira, M. R. R. (2020). The use of UAVs in monitoring yellow sigatoka in banana. biosystems engineering, 193, 115-125.
Chao, Z., Xiaofei, Y., Kexing, L., & Yongxiang, H. (2021). Effects of potassium application and straw returning on potassium management and benefit of banana. Transactions of the ASABE, 64(5), 1511-1518.
Chen, X., & Du, C. (2021). Advances in research on the phytohormone regulating interactions between plants and Fusarium oxysporum. Chinese Journal of Applied and Environmental Biology, 27(3), 816-822.
Costa, C., Zhang, Y., Howatt, K., Ram, B., Stenger, J., Nowatzki, J., Bajwa, S., & Sun, X. (2022). Palmer amaranth (Amaranthus Palmeri S. Watson) And Soybean (Glycine Max L.) Classification in greenhouse using hyperspectral imaging and chemometrics methods. Journal of the ASABE, 65(1), 179-188.
Diez, F. J., Correa-Guimaraes, A., Chico-Santamarta, L., Martínez-Rodríguez, A., Murcia-Velasco, D. A., Andara, R., & Navas-Gracia, L. M. (2022). Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in an Agrometeorological Station in Castile and León, Spain. Sensors, 22(13).
Efremova, N., Seddik, M. E. A., & Erten, E. (2022). Soil Moisture Estimation Using Sentinel-1/-2 Imagery Coupled with CycleGAN for Time-Series Gap Filing. IEEE Transactions on Geoscience and Remote Sensing, 60.
Gupta, P., Bharadwaj, S., Singh, A., & Saini, D. K. (2023). Predictive Model for Agriculture Using Markov Model (Vol. 421). Springer Science and Business Media Deutschland GmbH.
Hanadé Houmma, I., El Mansouri, L., Gadal, S., Garba, M., & Hadria, R. (2022). Modelling agricultural drought: A review of latest advances in big data technologies. Geomatics, Natural Hazards and Risk, 13(1), 2737-2776.
He, S., Wu, J., Wang, D., & He, X. (2022). Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest. Chemosphere, 290.
Huang, J., Wang, D., Li, H., Tang, Y., Ma, X., Tang, H., Lin, M., & Liu, Z. (2022). Antifungal activity of an artificial peptide aptamer SNP-D4 against Fusarium oxysporum. PeerJ, 10.
Iqbal, B., & Ali, M. (2022). Estimation of spatio-temporal air temperature from satellite based LST under semi-arid to arid environment in Peshawar Basin, Northwest Pakistan. Advances in Space Research, 70(4), 961-975.
Jamei, M., Karbasi, M., Alawi, O. A., Kamar, H. M., Khedher, K. M., Abba, S. I., & Yaseen, Z. M. (2022). Earth skin temperature long-term prediction using novel extended Kalman filter integrated with Artificial Intelligence models and information gain feature selection. Sustainable Computing: Informatics and Systems, 35.
Kabirigi, M., Sekabira, H., Sun, Z., & Hermans, F. (2022). The use of mobile phones and the heterogeneity of banana farmers in Rwanda. Environment, Development and Sustainability, 25(11).
Kassem, Y., Gökçekuş, H., & Alassi, E. (2022). Identifying most influencing input parameters for predicting Cereal production using an artificial neural network model. Modeling Earth Systems and Environment, 8(1), 1157-1170.
Kussul, N., Shelestov, A., Yailymova, H., Shumilo, L., & Drozd, S. (2022). Agriculture Land Appraisal with Use of Remote Sensing and Infrastructure Data. (POnencia). International Geoscience and Remote Sensing Symposium. Kuala, Malasya.
Lamour, J., Le Moguédec, G., Naud, O., Lechaudel, M., Taylor, J., & Tisseyre, B. (2021). Evaluating the drivers of banana flowering cycle duration using a stochastic model and on farm production data. Precision Agriculture, 22(3), 873-896.
Luyckx, M., & Reins, L. (2022). The Future of Farming: The (Non)-Sense of Big Data Predictive Tools for Sustainable EU Agriculture. Sustainability (Switzerland), 14(20).
Ma, W., Zheng, H., & Yuan, P. (2022). Impacts of cooperative membership on banana yield and risk exposure: Insights from China. Journal of Agricultural Economics, 73(2), 564-579.
Magallanes-Quintanar, R., Galván-Tejada, C. E., Galván-Tejada, J. I., Méndez-Gallegos, S. D. J., García-Domínguez, A., & Gamboa-Rosales, H. (2022). Narx Neural Networks Models for Prediction of Standardized Precipitation Index in Central Mexico. Atmosphere, 13(8).
Maino, A., Alberi, M., Anceschi, E., Chiarelli, E., Cicala, L., Colonna, T., De Cesare, M., Guastaldi, E., Lopane, N., Mantovani, F., Marcialis, M., Martini, N., Montuschi, M., Piccioli, S., Raptis, K. G. C., Russo, A., Semenza, F., & Strati, V. (2022). Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture. Remote Sensing, 14(15).
Malerba, M. E., Wright, N., & Macreadie, P. I. (2022). Australian farm dams are becoming less reliable water sources under climate change. Science of the Total Environment, 829.
Martinsen, K. T., & Sand-Jensen, K. (2022). Predicting water quality from geospatial lake, catchment, and buffer zone characteristics in temperate lowland lakes. Science of the Total Environment, 851.
Mathew, D., Kumar, C. S., & Cherian, K. A. (2021). Application of discrete orthonormal Stockwell transform and local neighborhood patterns for leaf disease classification in banana. (Ponencia). International Conference on Communication, Control and Information Sciences. Chongqing, China.
Pesqualoto Canellas, L., Lopes Olivares, F., Canellas, N., Jindo, K., castro Carrielo Rosa, R., & Piccolo, A. (2022). Challenge of transition: The history of a case study involving tropical fruits polyculture stimulated by humic acids and plant-growth promoting bacteria. Chemical and Biological Technologies in Agriculture, 9(1).
Silva-Pérez, C., Marino, A., & Cameron, I. (2022). Learning-based tracking of crop biophysical variables and key dates estimation from fusion of SAR and optical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 7444-7457.
Yang, Q., Zhu, Y., & Wang, F. (2021). Exploring mediating factors between agricultural training and farmers’ adoption of drip fertigation system: Evidence from banana farmers in China. Water (Switzerland), 13(10).
Zhang, D., Wang, P., Cui, R., Yang, H., Li, G., Chen, A., & Wang, H. (2022). Electrical conductivity and dissolved oxygen as predictors of nitrate concentrations in shallow groundwater in Erhai Lake region. Science of the Total Environment, 802.
Enlaces refback
- No hay ningún enlace refback.
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
ISSN on line: 2631-2662
ISSN impreso: 2661-6521