Advanced Multivariate Models for the Optimization of Nutritional Strategies in Commercial Banana Production (Musa AAA)
DOI:
https://doi.org/10.56124/sapientiae.v8i17.018Keywords:
Multivariate models, decision making, nutritional plans, bananasAbstract
This article discusses the optimization of nutritional plans in banana production, a key crop for the economy of countries located in tropical and subtropical regions. The importance of implementing multivariate models to improve efficiency and productivity in commercial plantations is highlighted, facilitating decisions based on accurate data. The research combines foliar analysis and soil testing to gain a comprehensive understanding of the nutritional status of plants. The approach applies specifically to bananas. Principal component analysis (PCA) and cluster analysis were used to identify patterns and reduce the dimensionality of the data. The Ward and K-means methods were used to discriminate groups based on the similarity of nutritional conditions. The results allowed grouping banana lots, identifying more effective management strategies. In addition, a high correlation was observed between the variables studied, which supports the usefulness of Pearson correlations in agricultural research. The implementation of multivariate models not only improves nutrient management, but also encourages sustainable agricultural practices, contributing to the productivity and sustainability of banana cultivation in a modern and dynamic agricultural context.
Downloads
References
Ahmed, N., Zhang, B., Chachar, Z., Li, J., Xiao, G., Wang, Q., Hayat, F., Deng, L., Narejo, M.-U.-N., Bozdar, B., & Tu, P. (2024). Micronutrients and their effects on Horticultural crop quality, productivity and sustainability. Scientia Horticulturae, 323, 112512. https://doi.org/10.1016/j.scienta.2023.112512
Arias, K. L. C., Maldonado, F. J., Olaya, R. M. D., & Saltos, M. B. G. (2021). Strategic cost model: a competitive advantage of sustainability for banana production. Metropolitan Journal of Applied Sciences, 4(3), Article 3. https://doi.org/10.62452/8dw4zk71
Çakmakçı, R., Salık, M. A., & Çakmakçı, S. (2023). Assessment and Principles of Environmentally Sustainable Food and Agriculture Systems. Agriculture, 13(5), Article 5. https://doi.org/10.3390/agriculture13051073
Cornejo-Reyes, J., & Inca, C. (2022). Algorithm to identify the causes of yield gaps in Ecuadorian banana production systems. Polo del Conocimiento, 7, 1198-1219. https://doi.org/10.23857/pc.v7i5.4022
De Deus, J. A. L., Neves, J. C. L., Soares, I., Alvarez V, V. H., de Lima Neto, A. J., de Albuquerque, F. M. R., dos Santos, L. L., & Natale, W. (2020). Multivariate selection and classification of mathematical models to estimate dry matter partitioning in the fertigated Prata banana in the Northeast Brazil. Field Crops Research, 255, 107897. https://doi.org/10.1016/j.fcr.2020.107897
Ding, Y., Wang, L., Li, Y., & Li, D. (2018). Model predictive control and its application in agriculture: A review. Computers and Electronics in Agriculture, 151, 104-117. https://doi.org/10.1016/j.compag.2018.06.004
Garcés-Fiallos, F. R., Olmedo-Zamora, I. M., Garcés-Estrella, R. E., & Díaz-Coronel, T. G. (2015). Agronomic potential of 18 F6 bean lines in Ecuador. Idesia (Arica), 33(2), 107-118. https://doi.org/10.4067/S0718-34292015000200013
Islam, M., Islam, M., Ismail, Z., Islam, A., Khan, R., Hasan, F., Kabir, M., Bede, E., Ibrahim, K., & Idris, A. (2023). Assessment of trace elements in the long-term banana cultivation field’s soil. Frontiers in Environmental Science, 11, 1272840. https://doi.org/10.3389/fenvs.2023.1272840
Jayasinghe, S. L., Ranawana, C. J. K., Liyanage, I. C., & Kaliyadasa, P. E. (2022). Growth and yield estimation of banana through mathematical modelling: A systematic review. The Journal of Agricultural Science, 160(3-4), 152-167. https://doi.org/10.1017/S0021859622000259
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202
Jones, J. W., Antle, J. M., Basso, B., Boote, K. J., Conant, R. T., Foster, I., Godfray, H. C. J., Herrero, M., Howitt, R. E., Janssen, S., Keating, B. A., Munoz-Carpena, R., Porter, C. H., Rosenzweig, C., & Wheeler, T. R. (2017). Brief history of agricultural systems modeling. Agricultural Systems, 155, 240-254. https://doi.org/10.1016/j.agsy.2016.05.014
Khan, Z., Tabasum, A., Muhammad, D., Mussarat, M., & Hassan, J. (2024). Comparative Analysis of Soil Phosphorus Determination Methods and Their Correlation with Plant Phosphorus in Standing Wheat Crops. Turkish Journal of Agriculture - Food Science and Technology, 12, 568-574. https://doi.org/10.24925/turjaf.v12i4.568-574.6393
Kolbe, H. (2022). Comparative Analysis of Soil Fertility, Productivity and Sustainability of Organic Farming in Central Europe—Part 1: Effect of Medium Manifestations on Conversion, Fertilizer Types and Cropping Systems. Agronomy, 12(9), Article 9. https://doi.org/10.3390/agronomy12092001
Kumar, S., Kumar, S., & Mohapatra, T. (2021). Interaction Between Macro‐ and Micro-Nutrients in Plants. Frontiers in Plant Science, 12, 665583. https://doi.org/10.3389/fpls.2021.665583
Lalinde, J. D. H., Castro, J. F. E., Tarazona, M. E. P., Rodriguez, J. E., Rangel, J. G. C., Sierra, C. A. T., Torrado, M. K. A., Sierra, C. A. T., Torrado, M. K. A., Sierra, S. M. C., & Pirela, V. J. B. (2018). On the proper use of Pearson's correlation coefficient: Definition, properties and assumptions. AVFT – Venezuelan Archives of Pharmacology and Therapeutics, 37(5), Article 5. http://saber.ucv.ve/ojs/index.php/rev_aavft/article/view/16165
Lever, J., Krzywinski, M., & Altman, N. (2017). Principal component analysis. Nature Methods, 14(7), 641-642. https://doi.org/10.1038/nmeth.4346
Miranda, I., Varela Nualles, M., Fernández, L., & Chávez Esponda, D. (2010). Use of cluster analysis with mixed variables in the selection of maize genotypes (Zea mays). Operational Research, 31(3), 209-216.
Monzón, J. A. C., Obando, A. R. N., & Montoya, L. L. V. (2022). UNSUPERVISED LEARNING IN THE SEGMENTATION OF AGRONOMIC VARIABLES OF RAPHANUS SATIVUS (RADISH) CROPS. REVISTA DE INVESTIGACIÓN ESTADÍSTICA (ISSN: 2708-1125), 4(1), Article 1. https://revistas.unitru.edu.pe/index.php/REDIES/article/view/4378
Ploetz, R. C., Kema, G. H. J., & Ma, L.-J. (2015). Impact of diseases on export and smallholder production of banana. Annual Review of Phytopathology, 53, 269-288. https://doi.org/10.1146/annurev-phyto-080614-120305
Saed-Moucheshi, A., Fasihfar, E., Hasheminasab, H., Rahmani, A., & Ahmadi, A. (2013). A Review on Applied Multivariate Statistical Techniques in Agriculture and Plant Science. International journal of Agronomy and Plant Production, 4, 127-141.
Shehu, B. M., Garba, I. I., Jibrin, J. M., Kamara, A. Y., Adam, A. M., Craufurd, P., Aliyu, K. T., Rurinda, J., & Merckx, R. (2023). Compositional nutrient diagnosis and associated yield predictions in maize: A case study in the northern Guinea savanna of Nigeria. Soil Science Society of America Journal, 87(1), 63-81. https://doi.org/10.1002/saj2.20472
Tofiño-Rivera, A. P., Pastrana-Vargas, I. J., Melo-Ríos, A. E., Beebe, S., & Tofiño-Rivera, R. (2016). Yield, phenotypic stability and micronutrient content of biofortified beans in the Colombian dry Caribbean. Agricultural Science and Technology, 17(3), Article 3. https://doi.org/10.21930/rcta.vol17_num3_art:511
Vargas Sanchez, A., Delboy Céspedes, M., Vargas Sanchez, A., & Delboy Céspedes, M. (2022). ANALYSIS AND GROUPING OF STOCK MARKET INDICES. Research & Development, 22(2), 5-21. https://doi.org/10.23881/idupbo.022.2-1e
Villamar-Torres, R., Montiel, L. G., Muñoz-Rengifo, J., Montes, S. Z., Jazayeri, S. M., Maddela, N. R., Torres, L. A., Matute, A. M., Pinos, M. H., & Salia, O. I. (2018). Agronomic evaluation and web blight resilience of common bean genotypes in the littoral region of Ecuador. African Journal of Biotechnology, 17(10), 328-336.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Revista Científica Multidisciplinaria SAPIENTIAE. ISSN: 2600-6030.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2.jpg)
