Application of GIS in the edaphoclimatic characterization of the Cacatachi district - San Martín province, for profitable and sustainable agricultural production purposes

Authors

  • Tedy Castillo-Diaz Universidad Nacional de San Martin - Tarapoto
  • Roaldo Lopez-Fulca Universidad Nacional de San Martin - Tarapoto
  • Williams Ramirez-Navarro Universidad Nacional de San Martin - Tarapoto

DOI:

https://doi.org/10.51252/raa.v1i2.192

Keywords:

Edaphoclimatic characterization, thematic maps, productivity, Quantum GIS software

Abstract

The edaphoclimatic characterization of soils is a process that contributes to the planning of plantings with better profitable prospects, being the starting point of agricultural production activity. The objective was to generate an integrated geospatial database of edaphoclimatic conditions of the Cacatachi district, for dynamic consultation through thematic maps such as physiography, use capacity, physical and chemical characteristics of soils and current land use. For this, GIS software was used using information from public institutions that started the process; Likewise, the work methodology had two stages, at the cabinet level and at the field level. The result was the Quantum GIS database (QGIS), which facilitates the management of edaphoclimatic information, with 104 detailed maps, whose spatial representations expose the characterization of the stratified areas in the project, where the pits were made, of the which were made the chemical, physical and biological analyses of the horizons identified in the profiles. The edaphoclimatic information generated brings us closer to a micro-zoning in detail, which becomes a guiding tool for planting crops, based on the nutritional, physiographic and climatic potential of the soils, segmented into productive sectors.

Downloads

Download data is not yet available.

References

Aguirre-Forero, S. E., Piraneque-Gambasica, N. V., & Vásquez-Polo, J. R. (2018). Características edáficas y su relación con usos del suelo en Santa Marta, Colombia. Entramado, 14(1), 242–250. https://www.redalyc.org/journal/2654/265457559017/html/

Giannini Kurina, F., Hang, S., Cordoba, M. A., Negro, G. J., & Balzarini, M. G. (2018). Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data. Geoderma, 310, 170–177. https://doi.org/10.1016/j.geoderma.2017.09.011

GORESAM. (2009). Las potencialidades y limitaciones del departamento de San Martín. http://siar.regionsanmartin.gob.pe/documentos/potencialidades-limitaciones-departamento-san-martin-zonificacion

GORESAM. (2020). San Martín es la sexta región con mayor crecimiento de sus exportaciones. https://www.regionsanmartin.gob.pe/Noticias?url=noticia&id=6143

Kahveci, M. (2017). Contribution of GNSS in precision agriculture. Proceedings of 8th International Conference on Recent Advances in Space Technologies, RAST 2017, 513–516. https://doi.org/10.1109/RAST.2017.8002939

Laurett, R., Paço, A., & Mainardes, E. W. (2021). Sustainable development in agriculture and its antecedents, barriers and consequences – an exploratory study. Sustainable Production and Consumption, 27, 298–311. https://doi.org/10.1016/j.spc.2020.10.032

Lesmo Duarte, N. D., Ferreira da Silva, A., Schlindwein, M. M., & Solis Mendoza, B. A. (2018). Agricultura Familiar Campesina: un análisis de la producción de cultivos tradicionales en el Departamento de Concepción - Paraguay. Investigación Agraria, 20(2), 136–142. https://doi.org/10.18004/investig.agrar.2018.diciembre.136-142

Longley, P. A., & Frank Goodchild, M. (2020). Geographic Information Science and Systems. In International Encyclopedia of Human Geography (Segunda Ed, pp. 29–36). https://doi.org/10.1016/b978-0-08-102295-5.10557-8

Lyford, J. (2017). Food security in the developing world. Journal of Nutrition Education and Behavior, 49(5), 451. https://doi.org/10.1016/j.jneb.2017.01.010

Mani, P. K., Mandal, A., Biswas, S., Sarkar, B., Mitran, T., & Meena, R. S. (2021). Remote Sensing and Geographic Information System: A Tool for Precision Farming. In Geospatial Technologies for Crops and Soils (pp. 49–111). https://doi.org/10.1007/978-981-15-6864-0_2

Mok, W. K., Tan, Y. X., & Chen, W. N. (2020). Technology innovations for food security in Singapore: A case study of future food systems for an increasingly natural resource-scarce world. Trends in Food Science and Technology, 102, 155–168. https://doi.org/10.1016/j.tifs.2020.06.013

Moysiadis, V., Sarigiannidis, P., Vitsas, V., & Khelifi, A. (2021). Smart Farming in Europe. Computer Science Review, 39, 100345. https://doi.org/10.1016/j.cosrev.2020.100345

Nabati, J., Nezami, A., Neamatollahi, E., & Akbari, M. (2020). GIS-based agro-ecological zoning for crop suitability using fuzzy inference system in semi-arid regions. Ecological Indicators, 117, 106646. https://doi.org/10.1016/j.ecolind.2020.106646

Palomino, W., Morales, G., Huaman, S., & Telles, J. (2018). PETEFA: Geographic Information System for Precision Agriculture. Proceedings of the 2018 IEEE 25th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2018, 1–4. https://doi.org/10.1109/INTERCON.2018.8526414

Perez-Garcia, C. A., Pérez-Atray, J. J., Hernández -Santana, L., Gustabello-Cogle, R., & Becerra-de Armas, E. (2019). Sistema de Información Geográfica para la agricultura cañera en la provincia de Villa Clara. Revista Cubana de Ciencias Informáticas, 13(2), 30–46. https://rcci.uci.cu/?journal=rcci&page=article&op=view&path%5B%5D=1796

Rizaldi, T., Putranto, H. A., Riskiawan, H. Y., Setyohadi, D. P. S., & Riaviandy, J. (2019). Decision support system for land selection to increase crops productivity in Jember Regency Use Learning Vector Quantization (LVQ). Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019, 82–85. https://doi.org/10.1109/ICOMITEE.2019.8921033

Saing, Z., Djainal, H., & Deni, S. (2021). Land use balance determination using satellite imagery and geographic information system: case study in South Sulawesi province, Indonesia. Geodesy and Geodynamics, 12(2), 133–147. https://doi.org/10.1016/j.geog.2020.11.006

Stojanovic, M. (2019). Biomimicry in agriculture: is the Ecological System-Design Model the Future Agricultural Paradigm? Journal of Agricultural and Environmental Ethics, 32, 789–804. https://doi.org/10.1007/s10806-017-9702-7

Sun, Z., Wang, D., & Zhong, G. (2018). Extraction of farmland geographic information using OpenStreetMap data. 2018 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018. https://doi.org/10.1109/Agro-Geoinformatics.2018.8476088

Zhang, F., & Cao, N. (2019). Application and research progress of Geographic Information System (GIS) in agriculture. 2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820476

Published

2021-07-18

How to Cite

Castillo-Diaz, T., Lopez-Fulca, R. ., & Ramirez-Navarro, W. . (2021). Application of GIS in the edaphoclimatic characterization of the Cacatachi district - San Martín province, for profitable and sustainable agricultural production purposes. Revista Agrotecnologica Amazonica, 1(2), 53–67. https://doi.org/10.51252/raa.v1i2.192

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.