GEOSPATIAL DIAGNOSIS OF THE ASSIMILABLE SOIL POTASSIUM IN AREAS OF SUGARCANE

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Yasmany García López
Catheryn Blanco Caballero
Janin Águila Pérez

Abstract

The analysis of spatial variability of the soil must be a premise for site-specific management in precision agriculture in the sugarcane agrosystem. This work compiles the data corresponding to the soil sampling carried out in 2015, among the coordinates 22o 25' 42" N to 22o 29' 39" N and 80o 57' 22" W to 80o 52' 43" W of Ferralsol soil, in Matanzas, Cuba. This soil sampling was adjusted to areas less than or equal to five hectares, for a total of 671 samples collected in 4086.23 hectares. Each sample was georeferenced and its assimilable potassium values ​​were determined, obtained by the Oniani method (H2SO4 0.1 N). The results obtained from the soil samples were the necessary input for the geostatistical analysis and the adjustment of models for a spatial prediction in the evaluated area. Geostatistics it was divided into four stages, exploratory, structural analysis, spatial prediction and cross-validation. An exponential model was fitted to the experimental semivariogram, through which a range of 413.2 m was obtained for the element evaluated. The site-specific associated with categories of the element studied were defined, which do not coincide with the structures for the management of fertilizers in the sugarcane agrosystem, which shows the need to study the soil and management by site-specific within the sugar cane fields, for precision agriculture.

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García López, Y. ., Blanco Caballero , C., & Águila Pérez, J. . (2024). GEOSPATIAL DIAGNOSIS OF THE ASSIMILABLE SOIL POTASSIUM IN AREAS OF SUGARCANE. Cuba Y Caña, 25(1), 23. Retrieved from https://www.cuba-cane.inica.azcuba.cu/index.php/cyc/article/view/45
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References

Beguin, J., Fuglstad, G., Mansuy, N., Paré, D. (2017). Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches. Geoderma, 306, 195–205. http://dx.doi.org/10.1016/j.geoderma.2017.06.016

Bhunia, G., Kumar, P. y Chattopadhyay, R. (2018). Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India). Annals of Agrarian Science, 16, 436–443. https://doi.org/10.1016/j.aasci.2018.06.003

Bivand, R., Pebesma, E. y Gómez-Rubio, V. (2008). Applied Spatial Data Analysis with R. New York, (USA): Springer. doi:10.1007/978-0-387-78171-6.

Bivand, R., Pebesma, E. y Gomez-Rubio, V. (2013). sp: Classes and methods for spatial. R package version 1.3-1. https://CRAN.R-project.org/package=sp

Bivand, R. y Lewin-Koh, N. (2017). maptools: Tools for Reading and Handling Spatial Objects. R package version 0.9-2. https://CRAN.R-project.org/package=maptools

Bivand, R., Keitt, T. y Rowlingson, B. (2018). rgdal: Bindings for the 'Geospatial' Data Abstraction Library. R package version 1.3-3. https://CRAN.R-project.org/package=rgdal

Bivand, R. y Rundel, C. (2018). rgeos: Interface to Geometry Engine - Open Source ('GEOS'). R package version 0.3-28. https://CRAN.R-project.org/package=rgeos

Bogunovic, I., Mesic, M., Zgorelec, Z., Jurisic, A., Bilandzija, D. (2014). Spatial variation of soil nutrients on sandy-loam soil. Soil & Tillage Research, 144, 174–183. http://dx.doi.org/10.1016/j.still.2014.07.020

Cai, L., Wang, Q., Wen, H., Luo, J., Wang, S. (2019). Heavy metals in agricultural soils from a typical township in Guangdong Province, China: Occurrences and spatial distribution. Ecotoxicology and Environmental Safety, 168, 184–191. https://doi.org/10.1016/j.ecoenv.2018.10.092

Castro, M., García, D. y Jiménez, A. (2017). Comparación de técnicas de interpolación espacial de propiedades del suelo en el piedemonte llanero colombiano. Revista Tecnura, 21(53), 78-95. doi:10.14483/22487638.11658

Charlotte, E.L., Neiva, N.B. y Moreira, C. (2014). Creación de mapas de manejo con datos espaciales”. En: Chartuni, E., Magdalena, C. (Eds.). Manual de agricultura de precisión (pp. 76-85). Montevideo (Uruguay): Instituto Interamericano de Cooperación para la Agricultura, PROCISUR. http://www.gisandbeers.com/RRSS/Publicaciones/Manual-Agricultura-Precision.pdf.

De León, M., Pérez, H. y Villegas, R. (2015). Nutrición y Fertilización. En: Pérez et al. (Eds) Manejo Sostenible de Tierras en la Producción de Caña de Azúcar (pp. 25-78). Segunda edición. Machala (Ecuador): Ediciones Universidad Técnica de Machala. http://repositorio.utmachala.edu.ec/bitstream/48000/6649/1/16%20MANEJO%20SOSTENIBLE%20DE%20LA%20TIERRA%20EN%20LA%20PRODUCCION%20DE%20CA%C3%91A%20DE%20AZUCAR%20VOL%20II.pdf.

Fernández, D.E. y Ribes, M.D. (2014). Uso de la geoestadística y los sistemas de información geográfica en agricultura. En: Chartuni, E. y Magdalena, C. (Eds.). Manual de agricultura de precisión (pp. 86-62). Montevideo (Uruguay): Instituto Interamericano de Cooperación para la Agricultura, PROCISUR. http://www.gisandbeers.com/RRSS/Publicaciones/Manual-Agricultura-Precision.pdf.

Ferraro D., Piñeiro G., Laterra P., Nogués A., de Prada J. (2010). Aproximaciones y herramientas para la Evaluación de servicios ecosistémicos. En: Laterra, P, Esteban, G, Paruelo, J. (Eds). Valoración de Servicios ecosistémicos (pp. 673-687). Buenos Aires (Argentina): Instituto Nacional de Tecnología Agropecuaria. http://www.iai.int/files/LaterraJobbagyParueloValorEcosyst.pdf

Fox, J. y Weisberg, S. (2011). car: Companion to Applied Regression. Second Edition. R package version 3.0-2. https://CRAN.R-project.org/package=car

Fu, W., Tunney, H. y Zhang, C. (2010). Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application. Soil and Tillage Research, 106, 185–193. doi:10.1016/j.still.2009.12.001.

Gallardo, A. (2006). Geostadística. Ecosistemas, 15 (3), 48-58. http://www.google.com.cu/url?sa=t&rct=j&q=&esrc=s&source=web&cd=7&cad=rja&uact=8&sqi=2&ved=0CDAQFjAGahUKEwj89I7y1IrIAhUJWx4KHTwEAMA&url=http%3A%2F%2Fwww.revistaecosistemas.net%2Findex.php%2Fecosistemas%2Farticle%2Fdownload%2F161%2F158&usg=AFQjCNEKYP8f_THdP7nqMzjtY7Ou6W07w&bvm=bv.103073922,d.dmo

García, Y. y Hernández, D. (2015). El proceso agroindustrial de la caña de azúcar y los bienes y servicios ecosistémicos. Articulo presentado en el 10mo Congreso Internacional de Educación Superior. Matanzas (Cuba).

García, Y., Sánchez, Y., Orozco, M., Fernández, A., Madan, L. (2019). Manejo de nutriente para caña de azúcar y su relación con los servicios ecosistémicos. Artículo presentado en el Congreso Internacional Sobre Azúcar y Derivados de la Caña. La Habana (Cuba).

García, Y. y Orozco, M. (2021). Análisis geoestadístico como base para contribuir al manejo sostenible del agrosistema azucarero. Ingeniería Industrial, 42(2), 1-14.

García, Y. y Cabrera, J.A. (2023). Procedimiento para valorar la variabilidad espacio-temporal en un proceso ecosistémico de soporte. Ingeniería Industrial, 44(2), 1-17.

González-Corzo, M. (2015). La agroindustria cañera cubana: transformaciones recientes. New York (USA): Bildner Center for Western Hemisphere Studies. http://www.gc.cuny.edu/CUNY_GC/media/365-Images/SugarEbook.pdf

González-Esquivel, C. E., Gavito, M. E., Astier, M., Cadena-Salgado, M., del-Val, E., Villamil-Echeverri, L., Merlín-Uribe, Y., Balvanera, P. (2015). Ecosystem service trade-offs, perceived drivers, and sustainability in contrasting agroecosystems in central Mexico. Ecology and Society, 20 (1), 38. http://dx.doi.org/10.5751/ES-06875-200138

Goovaerts, P. (2018). Flint Drinking Water Crisis: A First Attempt to Model Geostatistically the Space-Time Distribution of Water Lead Levels. In: B. S. Daya et al. (Eds.). Handbook of Mathematical Geosciences (pp. 255-276). Gewerbestrasse (Switzerland): Springer. https://doi.org/10.1007/978-3-319-78999-6_14

Gräler, B., Pebesma, E. y Heuvelink, G. (2016). gstat: Sapatial and spatio-temporal geostatistical modelling, prediction and simulation. R package version 1.1-6. https://CRAN.R-project.org/package=gstat

Gross, J. y Ligges, U. (2015). nortest: Tests for Normality. R package version 1.0-4. https://CRAN.R-project.org/package=nortest

Grunewald, K. y Bastian, O. (2015). Ecosystem Services – Concept, Methods and Case Studies. Berlin (Germany): Springer. doi:10.1007/978-3-662-44143-5.

Guan, F., Xia, M., Tang, X. y Fan, S. (2017). Spatial variability of soil nitrogen, phosphorus and potassium contents in Moso bamboo forests in Yong'an City, China. Catena, 150, 161–172. http://dx.doi.org/10.1016/j.catena.2016.11.017.

Kitchen N. y Clay, S.A. (2019). Understanding and Identifying Variability. In: Shannon et al. (Eds), Precision agriculture basic (pp. 13-24). Madison (USA): Soil Science Society of America. doi:10.2134/precisionagbasics

Kumar, N. y Sinha, N.K. (2018). Geostatistics: Principles and Applications in Spatial Mapping of Soil Properties. In: Reddy, G. P. O., & Singh S. K. (Eds.). Geospatial Technologies in Land Resources Mapping, Monitoring and Management, Geotechnologies and the Environment 21 (pp. 143-159). Gewerbestrasse (Switzerland): Springer. https://doi.org/10.1007/978-3-319-78711-4_8

Logsdon, S.D. y Cole, K.J. (2018). Soil nutrient variability and groundwater nitrate-N in agricultural fields. Science of the Total Environment, 627, 39–45. https://doi.org/10.1016/j.scitotenv.2018.01.182

Mamat, Z., Yimit, H., Ji, R.Z.A. y Eziz, M. (2014). Source identification and hazardous risk delineation of heavy metal contamination in Yanqi basin, northwest China. Science of the Total Environment, 493, 1098-1111. http://dx.doi.org/10.1016/j.scitotenv.2014.03.087

Martínez, J.R., Gálvez, G., de Armas, R., Espinosa, R., Vigoa, R., León, A. (1987). La Caña de Azúcar en Cuba. La Habana (Cuba): Editorial Científico-Técnica.

Oliver, M.A. y Webster, R. (2015). Basic Steps in Geostatistics: The Variogram and Kriging. New York (USA): Springer. doi:10.1007/978-3-319-15865-5_1

Paruelo, J., Herrera, L., Moricz, M., Urrutia, R., Zaccagnini, M., Somma, D., Quispe, C., Giaccio, G., Milano, F., Barreda, M. y Ceballos, D. (2010). Desde la discusión conceptual y metodológica a la acción. El uso del concepto de SE en el proceso de toma de decisiones. En: Laterra, P. et al., (Eds), Valoración de Servicios ecosistémicos (pp. 689-705). Buenos Aires (Argentina): Instituto Nacional de Tecnología Agropecuaria. http://www.iai.int/files/LaterraJobbagyParueloValorEcosyst.pdf

Paterson, S., Minasny, B. y McBratney, A. (2018). Spatial variability of Australian soil texture: A multiscale analysis. Geoderma, 309, 60–74. http://dx.doi.org/10.1016/j.geoderma.2017.09.005

Pérez, H., Santana, I. y Rodríguez, I. (2015). Manejo Sostenible de Tierras en la Producción de Caña de Azúcar. Machala (Ecuador): Ediciones UTMACH. http://repositorio.utmachala.edu.ec/bitstream/48000/6649/1/16%20MANEJO%20SOSTENIBLE%20DE%20LA%20TIERRA%20EN%20LA%20PRODUCCION%20DE%20CA%C3%91A%20DE%20AZUCAR%20VOL%20II.pdf

Piotrowska-Długosz, A., Breza-Boruta, B. y Długosz, J. (2019). Spatial and temporal variability of the soil microbiological properties in two soils with a different pedogenesis cropped to winter rape (Brassica napus L.). Geoderma, 340, 313–324. http://doi.org/10.1016/j.geoderma.2019.01.020

Pomara, L.Y. y Lee, D.C. (2021). The Role of Regional Ecological Assessment in Quantifying Ecosystem Services for Forest Management. Land, 10 (7), 725. https://doi.org/10.3390/land10070725

R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, (Austria). https://www.R-project.org/

Resende, V. y Coelho, M. (2014). Muestreo para mapeo y manejo de la fertilidad del suelo”. En: Chartuni M. y Magdalena, C. (Eds). Manual de Agricultura de precisión (pp. 38-48). Montevideo (Uruguay): Instituto Interamericano de Cooperación para la Agricultura, PROCISUR. http://www.gisandbeers.com/RRSS/Publicaciones/Manual-Agricultura-Precision.pdf.

Rosemary, F., Vitharana, U.W.A., Indraratne, S.P., Weerasooriya, R., Mishra, U. (2017). Exploring the spatial variability of soil properties in an Alfisol soil catena. Catena, 150, 53–61. http://dx.doi.org/10.1016/j.catena.2016.10.017.

Sánchez, P. (1981). Suelos del trópico: Características y manejo. San José (Costa Rica): IICA.

Sarkar, D. (2008). lattice: Trellis graphics for R. R package version 0.20-35. https://CRAN.R-project.org/package=lattice

Servicio de Recomendación de Fertilizantes y Enmiendas. SERFE. (2014). Manual Servicio de Fertilización de la Caña de Azúcar. La Habana (Cuba): Instituto de Investigaciones de la Caña de Azúcar.

Su, C., Liu, H. y Wang, S. (2018). A process-based framework for soil ecosystem services study and management. Science of the Total Environment, 627, 282–289. https://doi.org/10.1016/j.scitotenv.2018.01.244

Tola, E., Al-Gaadi, K.A., Madugundu, R., Zeyada, A.M., Kayad, A.G., Biradar, C.M. (2017). Characterization of spatial variability of soil physicochemical properties and its impact on Rhodes grass productivity. Saudi Journal of Biological Sciences, 24, 421–429. http://dx.doi.org/10.1016/j.sjbs.2016.04.013

Vasu, D., Singh, S.K., Sahu, Nisha, Tiwary, Pramod, Chandran, P., Duraisami, V.P., Ramamurthy, V., Lalitha, M., Kalaiselvi, B. (2017). Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management”. Soil and Tillage Research, 169, 25–34. http://dx.doi.org/10.1016/j.still.2017.01.006

Veronesi, F., Corstanje, R., y Mayr, T. (2014). Landscape scale estimation of soil carbon stock using 3D modelling. Sci. Total Environ, 487, 578–586. http://dx.doi.org/10.1016/j.catena.2016.11.017

Vihervaara, P., Mononen, L., Santos, F., Adamescu, M., Cazacu, C., Luque, S., Geneletti, D., Maes, J. (2017). Biophysical quantification. In: Burkhard & Maes J (Eds.) Mapping Ecosystem Services (pp. 95-146). Sofia (Bulgaria): Pensoft Publishers.

Wang, T., Kang, F., Cheng, X., Han, H., Bai, Y., Ma, J. (2017). Spatial variability of organic carbon and total nitrogen in the soils of a subalpine forested catchment at Mt. Taiyue, China. Catena, 155, 41–52. http://dx.doi.org/10.1016/j.catena.2017.03.004

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York (USA): Springer.

Wu, C., Huang, J., Zhu, H., Zhang, L., Minasny, B., Marchant, B., McBratney, A.B. (2019). Spatial changes in soil chemical properties in an agricultural zone in southeastern China due to land consolidation. Soil and Tillage Research, 187, 152–160. https://doi.org/10.1016/j.still.2018.12.012

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