Optimization of banana crop fertilization using GIS tools.

Main Article Content

Pedro Vélez Duque

Abstract

Precision farming tools have been developed for banana cultivation to meet the needs of monitoring and understanding the status of the product from planting to sale. In recent years, tools have emerged from a precision agriculture perspective, the basic definition of which aims to optimize resources through tools that enable their effective and efficient use. Fertilization in banana crops must be applied to meet the high nutrient requirements of the crops and compensate for the loss of nutrients to the open air. Excessive use of chemical fertilizers is a problem for many agricultural production companies in the country because there is no regulatory agency to control the amount of fertilizers used. The use of GIS tools allows us to control the yield of banana crops, since all previous data, such as crop information, nutritional requirements, availability levels of each nutrient and fertilization strategies are executed manually, which generates inaccurate yield reports and causes economic losses to producers, GIS was implemented because this system is accurate, allowing us to accurately identify crop needs and the amount of fertilizer to use, identify specific areas that require nutrients, and see higher yields and less fertilizer loss.

Downloads

Download data is not yet available.

Article Details

How to Cite
Vélez Duque, P. (2022). Optimization of banana crop fertilization using GIS tools. Centrosur Agraria, 1(15). https://doi.org/10.37959/revista.v1i15.220
Section
Articles

References

Agriculture, M. a. (2019). K+S. Retrieved from http://www.ks-minerals-and-agriculture.com/eses/fertiliser/advisory_service/crops/banana.html#anchor2

Cobas, J., Romeu, A., & Macias, Y. (2010). SCIENTIFIC RESEARCH AS A COMPONENT OF THE PROCESS. PODIUM, 10. Retrieved from https://dialnet.unirioja.es/descarga/articulo/6174064.pdf

Espinoza, J., & Mite, F. (2002). Current status and future of banana nutrition and fertilization. Revista Informaciones Agronómicas. , 4-9. Obtenido de http://nla.ipni.net/ipniweb/region/nla.nsf/e0f085ed5f091b1b852579000057902e/02788fd8caeaf69705257a370058dad2/$FILE/Estadobanano.pdf

Gauggel, C. Y. (2010). BANANA FERTILIZATION. SAN SALVADOR: PANAMERICAN AGRICULTURAL SCHOOL.

J.Kuruvilla, G. K. (2010). USE OF GEOGRAPHIC INFORMATION SYSTEMS IN PLANTATION AGRICULTURE. In G. K. J.Kuruvilla, USE OF GEOGRAPHIC INFORMATION SYSTEMS IN PLANTATION AGRICULTURE (pp. 45-50). NEW YORK: THE WAY FORWARD.

Mancilla, G. A. (November 24, 2018). ESCUELA SUPERIOR POLITÉCNICA AGROPECUARIA DE MANABÍ. Retrieved from ESCUELA SUPERIOR POLITÉCNICA POLITÉCNICA AGROPECUARIA DE MANABÍ: http://repositorio.espam.edu.ec/bitstream/42000/849/1/TTMA22.pdf

Maning, A., & Banman, Y. (2010). DECISION SUPPORT SYSTEMS FROM INVENTORY TO MANAGEMENT. CHICAGO: AGRICULTURE WAGENINGEN.

Ponce, V. (2003). Guia para el diseño de Proyectos educativos. Guayaquil: University of Guayaquil.

Puig, A. C. (March 25, 2012). National Institute of Agricultural Sciences. Retrieved from National Institute of Agricultural Sciences: https://www.redalyc.org/pdf/1932/193223840002.pdf

Puig, M. A. (March 2012). SciELO. Retrieved from SciELO: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0258-59362012000100002

Torres, B. J. (2016). National University of Colombia. Retrieved from Universidad Nacional de Colombia: https://repositorio.unal.edu.co/bitstream/handle/unal/56829/jaimetorresbazurto.2016.pdf?sequence=1&isAllowed=y

Villarruel, E. D. (07, 2016). CENTRAL UNIVERSITY OF ECUADOR. Retrieved from UNIVERSIDAD CENTRAL DEL ECUADOR: http://www.dspace.uce.edu.ec/bitstream/25000/8189/1/T-UCE-0004-47.pdf