GEOSPATIAL DIAGNOSIS OF THE ASSIMILABLE SOIL POTASSIUM IN AREAS OF SUGARCANE
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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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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