Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia

Site-|specific management for irrigation purpose remains a great challenge especially within mixed cropping system due to complex correlations and the spatial variability of soil properties. This variability generates crop yield heterogeneity within agricultural fields. This study was conducted in a...

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Autores principales:	Ouazaa, Sofiane, Jaramillo Barrios, Camilo Ignacio, Chaali, Nesrine, Quevedo Amaya, Yeison Mauricio, Montenegro Ramos, Omar, Calderon Carvajal, John Edinson
Formato:	article
Lenguaje:	Inglés
Publicado:	SSRN 2024
Materias:	Agricultura de precisión Propiedades del suelo Riego Aplicación de abono Transversal http://aims.fao.org/aos/agrovoc/c_92363 http://aims.fao.org/aos/agrovoc/c_330883 http://aims.fao.org/aos/agrovoc/c_3954 http://aims.fao.org/aos/agrovoc/c_10795
Acceso en línea:	https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4040572 http://hdl.handle.net/20.500.12324/40155 http://dx.doi.org/10.2139/ssrn.4040572

id	RepoAGROSAVIA40155
record_format	dspace
institution	Corporación Colombiana de Investigación Agropecuaria
collection	Repositorio AGROSAVIA
language	Inglés
topic	Agricultura de precisión Propiedades del suelo Riego Aplicación de abono Transversal http://aims.fao.org/aos/agrovoc/c_92363 http://aims.fao.org/aos/agrovoc/c_330883 http://aims.fao.org/aos/agrovoc/c_3954 http://aims.fao.org/aos/agrovoc/c_10795
spellingShingle	Agricultura de precisión Propiedades del suelo Riego Aplicación de abono Transversal http://aims.fao.org/aos/agrovoc/c_92363 http://aims.fao.org/aos/agrovoc/c_330883 http://aims.fao.org/aos/agrovoc/c_3954 http://aims.fao.org/aos/agrovoc/c_10795 Ouazaa, Sofiane Jaramillo Barrios, Camilo Ignacio Chaali, Nesrine Quevedo Amaya, Yeison Mauricio Montenegro Ramos, Omar Calderon Carvajal, John Edinson Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia
description	Site-\|specific management for irrigation purpose remains a great challenge especially within mixed cropping system due to complex correlations and the spatial variability of soil properties. This variability generates crop yield heterogeneity within agricultural fields. This study was conducted in a 5-ha area cultivated in Inceptisols with rice-corn/cotton rotational cropping system in Tolima region-Colombia. The aim was to delineate field management zones (MZs) using three methods M1, M2 and M3, and evaluate their usefulness for a further site-specific management. Results were validated by comparing soil properties to rice/cotton and corn yield. Georeferenced soil samples were analyzed for soil texture, bulk density (BD), field capacity (FC), soil available water (AW), mesopores percentage (Mes), soil penetration resistance (PR), organic matter (OM), available phosphorus (P), and exchangeable bases content (Ca+Mg/K) ratio. Spatial variability of soil properties was analyzed with geostatistics approach. Further, spatial principal component (sPC), were performed to delineate the MZs using: M1- Fisher-Jenks algorithm with the first sPC; M2- Fuzzy k-means cluster analysis with two sPC; and M3- Fuzzy k-means with soil variables. The obtained MZs were effectively related to the rice-based cropping system yield. Results revealed that the optimum number of MZs depends on the aim of the investigation. Two MZs were recommended using M2 due to its lower fragmentation compared to other methodologies. This delineation could be suggested for fertilization management since Clay+Silt , OM, P, and Ca+Mg/K content differentiate when delineating one and two MZs. M2 provided also differences for AW, Mes, and PR when considering three MZs which can be used as a basis for irrigation site-specific management in precision agriculture.
format	article
author	Ouazaa, Sofiane Jaramillo Barrios, Camilo Ignacio Chaali, Nesrine Quevedo Amaya, Yeison Mauricio Montenegro Ramos, Omar Calderon Carvajal, John Edinson
author_facet	Ouazaa, Sofiane Jaramillo Barrios, Camilo Ignacio Chaali, Nesrine Quevedo Amaya, Yeison Mauricio Montenegro Ramos, Omar Calderon Carvajal, John Edinson
author_sort	Ouazaa, Sofiane
title	Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia
title_short	Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia
title_full	Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia
title_fullStr	Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia
title_full_unstemmed	Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia
title_sort	assessment of spatial variability of soil properties using different methods to identify management zones for rotational cropping systems: a case study from colombia
publisher	SSRN
publishDate	2024
url	https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4040572 http://hdl.handle.net/20.500.12324/40155 http://dx.doi.org/10.2139/ssrn.4040572
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spelling	RepoAGROSAVIA401552024-09-21T03:03:02Z Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia Ouazaa, Sofiane Jaramillo Barrios, Camilo Ignacio Chaali, Nesrine Quevedo Amaya, Yeison Mauricio Montenegro Ramos, Omar Calderon Carvajal, John Edinson Agricultura de precisión Propiedades del suelo Riego Aplicación de abono Transversal http://aims.fao.org/aos/agrovoc/c_92363 http://aims.fao.org/aos/agrovoc/c_330883 http://aims.fao.org/aos/agrovoc/c_3954 http://aims.fao.org/aos/agrovoc/c_10795 Site-\|specific management for irrigation purpose remains a great challenge especially within mixed cropping system due to complex correlations and the spatial variability of soil properties. This variability generates crop yield heterogeneity within agricultural fields. This study was conducted in a 5-ha area cultivated in Inceptisols with rice-corn/cotton rotational cropping system in Tolima region-Colombia. The aim was to delineate field management zones (MZs) using three methods M1, M2 and M3, and evaluate their usefulness for a further site-specific management. Results were validated by comparing soil properties to rice/cotton and corn yield. Georeferenced soil samples were analyzed for soil texture, bulk density (BD), field capacity (FC), soil available water (AW), mesopores percentage (Mes), soil penetration resistance (PR), organic matter (OM), available phosphorus (P), and exchangeable bases content (Ca+Mg/K) ratio. Spatial variability of soil properties was analyzed with geostatistics approach. Further, spatial principal component (sPC), were performed to delineate the MZs using: M1- Fisher-Jenks algorithm with the first sPC; M2- Fuzzy k-means cluster analysis with two sPC; and M3- Fuzzy k-means with soil variables. The obtained MZs were effectively related to the rice-based cropping system yield. Results revealed that the optimum number of MZs depends on the aim of the investigation. Two MZs were recommended using M2 due to its lower fragmentation compared to other methodologies. This delineation could be suggested for fertilization management since Clay+Silt , OM, P, and Ca+Mg/K content differentiate when delineating one and two MZs. M2 provided also differences for AW, Mes, and PR when considering three MZs which can be used as a basis for irrigation site-specific management in precision agriculture. Ministerio de Agricultura y Desarrollo Rural - MADR 2024-09-20T16:02:58Z 2024-09-20T16:02:58Z 2022-02 2022 article Artículo científico http://purl.org/coar/resource_type/c_2df8fbb1 info:eu-repo/semantics/article https://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4040572 http://hdl.handle.net/20.500.12324/40155 http://dx.doi.org/10.2139/ssrn.4040572 reponame:Biblioteca Digital Agropecuaria de Colombia instname:Corporación colombiana de investigación agropecuaria AGROSAVIA eng Elsevier 1 1 38 Abalo, M., Badabate, D., Fousseni, F., Kpérkouma, W., Koffi, A., 2021. Landscape-based analysis of wetlands patterns in the Ogou River basin in Togo (West Africa). Environmental Challenges. 2, 100013. https://doi.org/10.1016/j.envc.2020.100013 Aggelopooulou, K., Castrignan_o, A., Gemtos, T., De Benedetto, D., 2013. Delineation of management zones in an apple orchard in Greece using a multivariate approach. Comput. Electron. Agric. 90, 119e130. https://doi.org/10.1016/j.compag.2012.09.009 Akaike, H., 1976. Canonical correlation analysis of time series and the use of an information criterion. Math. Sci. Eng. 126, 27-96. https://doi.org/10.1016/S0076-5392(08)60869-3 Albornoz, V. M., Cid-García, N. M., Ortega, R., Ríos-Solís, Y. A., 2015. A Hierarchical Planning Scheme Based on Precision Agriculture, in: Plà-Aragonés L. (eds), Handbook of Operations Research in Agriculture and the Agri-Food Industry. International Series in Operations Research & Management Science, vol 224. Springer, New York, NY Albornoz, E. M., Kemerer, A. C., Galarza, R., Mastaglia, N., Melchiori, R., Martínez, C. E., 2018. Development and evaluation of an automatic software for management zone delineation. Prec. Agric., 19(3), 463-476. https://doi.org/10.1007/s11119-017-9530-9 Alvarez, R., Grigera, S., 2005. Analysis of soil fertility and management effects on yields of wheat and corn in the rolling pampa of Argentina. J. Agron. Crop Sci. 191(5), 321- 329. https://doi.org/10.1111/j.1439-037X.2005.00143.x Armindo, R. A., Botrel, T. A., Garzella, T. C., 2011. Flow rate sprinkler development for site- specific irrigation. Irrig. Sci., 29(3), 233-240. https://doi.org/10.1007/s00271-010-0231-7 Armstrong, M. P., Xiao, N., Bennett, D. A., 2003. Using genetic algorithms to create multicriteria class intervals for choropleth maps. Ann. Am. Assoc. Geogr. 93(3), 595-623. https://doi.org/10.1111/1467-8306.9303005 Arrouays, D., Saby, N. P., Thioulouse, J., Jolivet, C., Boulonne, L., Ratié, C., 2011. Large trends in French topsoil characteristics are revealed by spatially constrained multivariate analysis. Geoderma, 161(3-4), 107-114. https://doi.org/10.1016/j.geoderma.2010.12.002 Behera, S. K., Mathur, R. K., Shukla, A. K., Suresh, K., Prakash, C., 2018. Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. Catena. 165, 251-259. https://doi.org/10.1016/j.catena.2018.02.008 Bellvert, J., Marsal, J., Mata, M., Girona, J., 2012. Identifying irrigation zones across a 7.5-ha “Pinot noir” vineyard based on the variability of vine water status and multispectral images. Irrig. Sci. 30, 499–509. https://doi.org/10.1007/s00271-012-0380-y. Bhunia, G. S., Shit, P. K., Chattopadhyay, R., 2018. Assessment of spatial variability of soil 543 properties using geostatistical approach of lateritic soil (West Bengal, India). Ann. Agrar. Sci. 16(4), 436. https://doi.org/10.1016/j.aasci.2018.06.003 Bivand, R., Keitt, T., Rowlingson, B., Pebesma, E., Sumner, M., Hijmans, R., Bivand, M. R., 2015. Package ‘rgdal’. Bindings for the Geospatial Data Abstraction Library. https://cran.R- project.Org/web/packages/rgdal/index.html (accessed on 15 October 2020). Bivand, R., Ono, H., Dunlap, R., Stigler, M., Bivand, M. R., 2019. Package ‘classInt’. https://cran.r- project.org/web/packages/classInt/index.html (accessed on 07 February 2020). Bivand, R., Lewin-Koh, N., 2019. Maptools: tools for handling spatial objects. R package version 0.9, 5. https://rdrr.io/rforge/maptools/ (accessed on 07 February 2020). Bivand, R. S., Wong, D. W. S., 2018. Comparing implementations of global and local indicators of spatial association. Test. 27(3), 716-748. https://doi.org/10.1007/s11749-018-0599-x Bouyoucos, G.J., 1962. Hydrometer method improved for making particle size analysis of soils. Agronomy. 54: 464-465. https://doi.org/10.2134/agronj1962.00021962005400050028x Boydell, B., McBratney, A. B., 2002. Identifying potential withinfield management zones from cotton-yield estimates. Prec. Agric. 3(1), 9e23. https://doi.org/10.1023/A:1013318002609 Bray, R.H., Kurtz, L.T., 1945. Determination of total, organic and available forms of phosphorus in soils. Soil Sci. 59: 39–45. Bullock, D. S., Kitchen, N., Bullock, D. G., 2007. Multidisciplinary teams: A necessity for research in precision agriculture systems. Crop science. 47(5), 1765-1769. https://doi.org/10.2135/cropsci2007.05.0280 Buttafuoco, G., Castrignanò, A., Cucci, G., Lacolla, G., Lucà, F., 2017. Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field. Precis. Agric. 18(1), 37-58. https://doi.org/10.1007/s11119-016-9462-9 Cambardella, C. A., Moorman, T. B., Parkin, T. B., Karlen, D. L., Novak, J. M., Turco, R. F., Konopka, A. E., 1994. Field-scale variability of soil properties in central Iowa soils. SSSAJ. 58(5), 1501-1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x Cameron, K, Hunter, P., 2002. Using Spatial Models and Kriging Techniques to Optimize Long- Term Ground-Water Monitoring Networks: A Case Study. Environmetrics 13:629-59. https://doi.org/10.1002/env.582 Carroll, S. R., Le, K. N., Moreno-García, B., Runkle, B. R., 2020. Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX. Sustainability, 12(17), 6822. https://doi.org/10.3390/su12176822 Chaali, N., Ouazaa, S., Jaramillo-Barrios, C. I., Araujo-Carrillo, G. A., Ávila-Pedraza, E. Á., 2020. Edaphoclimatic characterization and crop water requirement of Arracacha (Arracacia xanthorrhiza Bancroft) roots in upland production areas. Sci. Hortic. 272, 109533. https://doi.org/10.1016/j.scienta.2020.109533 Chung, S. O., Sudduth, K. A., Drummond, S. T., 2002. Determining yield monitoring system delay time with geostatistical and data segmentation approaches. Trans ASAE. 45(4), 915. https://doi.org/10.13031/2013.9938 Cohen, S., Cohen, Y., Alchanatis, V., Levi, O., 2013. Combining spectral and spatial information from aerial hyperspectral images for delineating homogenous management zones. Biosyst. Eng. 114, 435–443. https://doi.org/10.1016/j.biosystemseng.2012.09.003 Córdoba, M., Balzarini, M., Bruno, C., Costa, J. L., 2012. Identificación de zonas de manejo sitio- específico a partir de la combinación de variables de suelo. Corpoica Cienc. y Tecnol. Agropecu. 13(1), 47-54. https://doi.org/10.21930/rcta.vol13_num1_art:239 Córdoba, M., Bruno, C., Costa, J., Balzarini, M., 2013. Subfield management class delineation using cluster analysis from spatial principal components of soil variables. Comput. Electron. Agric. 97, 6-14. https://doi.org/10.1016/j.compag.2013.05.009 Córdoba, M. A., Bruno, C. I., Costa, J. L., Peralta, N. R., Balzarini, M. G., 2016. Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosyst. Eng. 143, 95-107. https://doi.org/10.1016/j.biosystemseng.2015.12.008 Cortés-D, D. L., Camacho-Tamayo, J. H., Giraldo, R., 2018. Spatial prediction of soil infiltration using functional geostatistics. AUC GEOGRAPHICA, 53(2), 149-155. https://doi.org/10.14712/23361980.2018.15 Cressie, N., 1992. Statistics for spatial data. Terra Nova. 4(5), 613-617. https://doi.org/10.1111/j.1365-3121.1992 Davatgar, N., Neishabouri, M. R., Sepaskhah, A. R., 2012. Delineation of site-specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma, 173, 111-118. https://doi.org/10.1016/j.geoderma.2011.12.005 de Oliveira, J. F., Mayi, S., Marchão, R. L., Corazza, E. J., Hurtado, S. C., Malaquias, J. V., Tavares, J., Brossard, M., de Fátima Guimarães, M., 2019. Spatial variability of the physical quality of soil from management zones. Prec. Agric. 20(6), 1251-1273. https://doi.org/10.1007/s11119-019- 09639-9 Delerce, S., Dorado, H., Grillon, A., Rebolledo, M.C., Prager, S.D, Patiño, V.H., Garcés Varón, G., 607 Jiménez, D., 2016. Assessing Weather-Yield Relationships in Rice at Local Scale Using Data 608 Mining Approaches. PLOS ONE. 11(8): e0161620. https://doi.org/10.1371/journal.pone.0161620 Deutsch, C.V., Journel, A.G., 1998. GSLIB Geostatistical Software Library and User's Guide, 2nd 610 Edition, Applied Geostatistics Series, Oxford University Press, Inc. New York, NY. Doane, D. P., Seward, L. E., 2011. Measuring skewness: a forgotten statistic?. J. Stat. Educ. 19(2). https://doi.org/10.1080/10691898.2011.11889611 Doerge, T. A., 2000. Site-specific management guidelines. Norcross: Potash & Phosphate Institute. Dray, S., Saïd, S., Débias, F., 2008. Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation. Appl. Veg. Sci. 19(1), 45- 56. https://doi.org/10.3170/2007-8-18312 Farid, H. U., Bakhsh, A., Ahmad, N., Ahmad, A., Mahmood-Khan, Z., 2016. Delineating site- specific management zones for precision agriculture. J. Agric. Sci. 154(2), 273-286. https://doi.org/10.1017/S0021859615000143 Fisher, W. D., 1958. On grouping for maximum homogeneity. J Am Stat Assoc, 53(284), 789-798. https://doi:10.1080/01621459.1958.10501479 Fleming, K. L., Westfall, D. G., Bausch, W. C., 2000. Evaluating management zone technology and grid soil sampling for variable rate nitrogen application. In Proceedings of the 5th International Conference on Precision Agriculture (pp. 16-19). Madison, WI 53711, USA: ASA-CSSA-SSSA. Fontanet, M., Scudiero, E., Skaggs, T. H., Fernàndez-Garcia, D., Ferrer, F., Rodrigo, G., Bellvert, J., 2020. Dynamic management zones for irrigation scheduling. Agric. Water Manag. 238, 106207. https://doi.org/10.1016/j.agwat.2020.106207 Fraisse, C.W., Sudduth, K.A., Kitchen, N.R., 2001. Delineation of site–specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Int. J. ASABE 44, 155–166. https://doi.org/10.1088/1755-1315/18/1/012046 Fridgen, J. J., Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Wiebold, W. J., Fraisse, C. W., 2004. Management Zone Analyst (MZA) Software for Subfield Management Zone Delineation. Agronomy, 96(1), 100-108. https://doi.org/10.2134/agronj2004.1000 Frogbrook, Z. L., Oliver, M. A., 2007. Identifying management zones in agricultural fields using spatially: constrained classification of soil and ancillary data. Soil. Use. Manage. 23, 40e51. https://doi.org/10.1111/j.1475-2743.2006.00065.x Fu, Q., Wang, Z., Jiang, Q., 2010. Delineating soil nutrient management zones based on fuzzy clustering optimized by PSO. Math. Comput. Model. 51(11-12), 1299-1305. https://doi.org/10.1016/j.mcm.2009.10.034 Gavioli, A., de Souza, E. G., Bazzi, C. L., Schenatto, K., Betzek, N. M., 2019. Identification of management zones in precision agriculture: An evaluation of alternative cluster analysis methods. Biosyst. Eng. 181, 86-102. https://doi.org/10.1016/j.biosystemseng.2019.02.019 Gavioli, A., de Souza, E. G., Bazzi, C. L., Guedes, L. P. C., Schenatto, K., 2016. Optimization of management zone delineation by using spatial principal components. Comput. Electron. Agric. 127, 302-310. http://dx.doi.org/10.1016/j.compag.2016.06.029 Gilbert, RO., 1987. Statistical Methods for Environmental Pollution Monitoring. Van Nostrand Reinhold, New York Gili, A., Álvarez, C., Bagnato, R., Noellemeyer, E., 2017. Comparison of three methods for 649 delineating management zones for site-specific crop management. Comput. Electron. Agric. 139, 213-223. https://doi.org/10.1016/j.compag.2017.05.022 Goovaerts, P., 1998. Geostatistical tools for characterizing the spatial variability of microbiological 652 and physico-chemical soil properties. Biol. Fertil. Soils 27, 315–334. 653 https://doi.org/10.1007/s003740050439 Gonzalez, R., Woods, R., 2008. Image Processing. Digital Image Process, 3rd Edition. 655 IDEAM – UNAL. (2018). Variabilidad Climática y Cambio Climático en Colombia, Bogotá, D.C. ISPA. (2018). International Society of Precision Agriculture, https://ispag.org/about/definition Groeneveld, R. A., Meeden, G., 1984. Measuring skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician), 33(4), 391-399. Guastaferro, F., Castrignan_o, A., Benedetto, D., Sollitto, D., Troccoli, A., Cafarelli, B., 2010. A comparison of different algorithms for the delineation of management zones. Prec. Agric., 11, 600e620. https://doi.org/10.1007/s11119-010-9183-4 Isaaks, E.H., Srivastava, R.M., 1989. An Introduction to Applied Geostatistics. Oxford University Press, New York. Jaramillo, D. F.. 2009. Variabilidad espacial de las propiedades ándicas de un Andisol hidromórfico del Oriente Antioqueño (Colombia). Rev Fac Nac Agron Medellin. 62(1): 4907-4921. Jaramillo-Barrios, C.I., Ortiz-Rico, A.F., Araujo-Carrillo, G.A., Varón-Ramírez, V. M., 2019. Establishment of homogeneous zones in a soil of alluvial origin. Cienc. e Agrotecnologia. 43, e026318. https://doi.org/10.1590/1413-7054201943026318 Kaune, A., Werner, M., Rodríguez, E., Karimi, P., De Fraiture, C., 2017. A novel tool to assess available hydrological information and the occurrence of sub-optimal water allocation decisions in large irrigation districts. Agric. Water. Manag. 191, 229-238. https://doi.org/10.1016/j.agwat.2017.06.013 Kirkham, M.B., 2005. 8 - Field Capacity, Wilting Point, Available Water, and the Non-Limiting Water Range, Editor(s): M.B. Kirkham, Principles of Soil and Plant Water Relations, AcademicPress, Pages 101-115, ISBN 9780124097513. Kirkham, M. B., 2014. Penetrometers. Principles of Soil and Plant Water Relations, 171–183. Kravchenko, A. N., Bullock, D. G., 2002. Spatial variability of soybean quality data as a function of field topography: I. Spatial data analysis. Crop Sci. 42(3), 804-815. https://doi.org/10.2135/cropsci2002.8040 Lee, S. I., 2001. Developing a bivariate spatial association measure: An integration of Pearson's r 681 and Moran's I. J. Geogr. Syst. 3: 369-385. https://doi.org/10.1007/s101090100064 Lee, S. I., 2017. Correlation and Spatial Autocorrelation. In: Shekhar S., Xiong H., Zhou X. (eds) 683 Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1524 Li, X., Pan, Y. C., Zhao, C. J., Wang, J. H., Bao, Y. S., Wang, J. D., 2005a. Delineation and scale 685 effect of precision agriculture management zones using yield monitor data over four years. Sci. Agric. Sin. 38(9), 1825-1833. https://doi.org/10.1016/S1671-2927(07)60033-9 Li, X., Pan, Y., Zhang, C., Liu, L., Wang, J, 2005b. A new algorithm on delineation of management 688 zone. In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium. 689 IGARSS'05. (Vol. 1, pp. 4-pp). IEEE. Li, Y., Shi, Z., Li, F., Li, H. Y., 2007. Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Comput. Electron. Agric. 56(2), 174-186. https://doi.org/10.1016/j.compag.2007.01.013 Li, Y., Shi, Z., Wu, C. F., Li, H. Y., Li, F., 2008. Determination of potential management zones from soil electrical conductivity, yield, and crop data. J. Zhejiang Univ. Sci. B, 9(1), 68-76. https://doi.org/10.1631/jzus.B071379 Li, Y., Shi, Z., Wu, H., Li, F., Li, H., 2013. Definition of management zones for enhancing 697 cultivated land conservation using combined spatial data. Environ. Manage, 52(1), 792e806. htpps://doi.org/10.1007/s00267-013-0124-7 Li, X., Zhao, W., Li, J., Li, Y., 2019. Maximizing water productivity of winter wheat by managing zones of variable rate irrigation at different deficit levels. Agric. Water Manag. 216, 153-163. https://doi.org/10.1016/j.agwat.2019.02.002 Liu, H., Whiting, M.L., Ustin, S.L., Zarco-Tejada, P.J., Huffman, T., Zhang, X., 2018. Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images. Precis. Agric. 19, 348–364. https://doi.org/10.1007/s11119-017-9521-x Longchamps, L., Khosla, R., 2015. Improving N use efficiency by integrating soil and crop properties for variable rate N management. In Precision agriculture'15 (pp. 43-55). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-814-8 López de Sabando, M. J., Diaz-Zorita, M., 2022. Field methods for making productivity classes for site-specific management of wheat. Prec. Agric., 1-21. https://doi.org/10.1007/s11119-022-09878-3 Martínez-Reina, A.M., Hernández. M. J., 2015. La competitividad del algodón colombiano frente a los principales países productores mediante el enfoque de costos de producción. Corpoica Cienc. y Tecnol. Agropecu. 16:189-215. https://doi.org/10.21930/rcta.vol16_num2_art:368 Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C. C., Meyer, M. D.. 714 2019. Package ‘e1071’. R J. https://cran.r-project.org/web/packages/e1071/index.html Mendes, W. R., Araújo, F. M. U., Dutta, R., Heeren, D. M., 2019. Fuzzy control system for variable rate irrigation using remote sensing. Expert Syst. Appl. 124, 13-24. https://doi.org/10.1016/j.eswa.2019.01.043 Mirás-Avalos, J. M., Fandiño, M., Rey, B. J., Dafonte, J., Cancela, J. J., 2020. Zoning of a newly- planted vineyard: spatial variability of physico-chemical soil properties. Soil syst. 4(4), 62. https://doi.org/10.3390/soilsystems4040062 Moharana, P. C., Jena, R. K., Pradhan, U. K., Nogiya, M., Tailor, B. L., Singh, R. S., Singh, S. K., 2020. Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India. Prec. Agric. 21(2), 426- 448. https://doi.org/10.1007/s11119-019-09671-9 Molin, J. P., Castro, C. N. D., 2008. Establishing management zones using soil electrical 726 conductivity and other soil properties by the fuzzy clustering technique. Sci. Agric. 65(6), 567-573. https://doi.org/10.1590/S0103-90162008000600001 Moral, F.J., Terrón, J.M., Silva, J.R.M., 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil Tillage Res. 106, 335–343. https://doi.org/10.1016/j.still.2009.12.002 Moran, P., 1948. “The interpretation of statistical maps”, J R Stat Soc Series. 10, 243-251. 732 https://doi.org/10.1111/j.2517-6161.1948.tb00012.x Mulla, D., 1989. Soil spatial variability and methods of analysis. Proc. International Workshop on Soil, Crop, and Water Management Systems for Rainfed Agriculture in the Sudano-Sahelian Zone, Niamey (Niger), 11-16. Nawar, S., Corstanje, R., Halcro, G., Mulla, D., Mouazen, A. M., 2017. Delineation of soil management zones for variable-rate fertilization: A review. Adv. Agron. 143, 175-245. https://doi.org/10.1016/bs.agron.2017.01.003 Nelson, D.W., Sommers, L. E., 1996. Total carbon, organic carbon and organic matter D.L. Sparks, A.L. Page, P.A. Helmke, R.H. Loeppert, P.N. Soltanpour, M.A. Tabatabai, C.T. Johnston, M.E. 741 Sumner (Eds.), Methods of Soil Analysis, Chemical Methods, Soil Science Society of America, Madison, WI, pp. 961-1010 Part III Nielsen, D. R. J. W., Biggar, J. W., Erh, K. T., 1973. Spatial variability of field-measured soil-water 744 properties. Hilgardia. 42(7), 215-259. https://doi.org/10.3733/hilg.v42n07p215 Odeh, I. O. A., Chittleborough, D. J., McBratney, A. B., 1992. Soil pattern recognition with fuzzy- c-means: application to classification and soil-landform interrelationships. SSSAJ. 56(2), 505-516. https://doi.org/10.2136/sssaj1992.03615995005600020027x Ohana-Levi, N., Bahat, I., Peeters, A., Shtein, A., Netzer, Y., Cohen, Y., Ben-Gal, A., 2019. A weighted multivariate spatial clustering model to determine irrigation management zones. Comput. Electron. Agric. 162, 719-731. https://doi.org/10.1016/j.compag.2019.05.012 Ohana-Levi, N., Knipper, K., Kustas, W. P., Anderson, M. C., Netzer, Y., Gao, F., Karnieli, A., 2020. Using Satellite Thermal-Based Evapotranspiration Time Series for Defining Management Zones and Spatial Association to Local Attributes in a Vineyard. Remote Sens. 12(15), 2436. https://doi.org/10.3390/rs12152436 Okada, K., Lopez-Galvis, L., 2018. Improving resource utilization efficiency in rice production systems with contour-levee irrigation in Colombia. In Crop production under stressful conditions (pp. 71-86). Springer, Singapore Ortega, R. A., Santibáñez, O. A., 2007. Determination of management zones in corn (Zea mays L.) based on soil fertility. Comput. Electron. Agric. 58(1), 49-59. https://doi.org/10.1016/j.compag.2006.12.011 Ortuani, B., Chiaradia, E. A., Priori, S., L'Abate, G., Canone, D., Comunian, A., Facchi, A., 2016. 762 Mapping Soil Water Capacity Through EMI Survey to Delineate Site-Specific Management Units 763 Within an Irrigated Field. Soil Sci., 181(6), 252-263. https://doi.org/10.1097/ss.0000000000000159 Ortuani, B., Sona, G., Ronchetti, G., Mayer, A., Facchi, A., 2019. Integrating geophysical and 765 multispectral data to delineate homogeneous management zones within a vineyard in Northern 766 Italy. Sensors, 19(18), 3974. https://doi.org/10.3390/s19183974 O’Shaughnessy, S. A., Evett, S. R., Colaizzi, P. D., 2015. Dynamic prescription maps for site- specific variable rate irrigation of cotton. Agric. Water Manag. 159, 123-138. https://doi.org/10.1016/j.agwat.2015.06.001 O'Shaughnessy, S. A., Evett, S. R., 2010. Canopy temperature-based system effectively schedules and controls center pivot irrigation of cotton Agric. Water Manag. 97(9), 1310-1316. Osemwota, I. O., Omueti, J. A. I., Ogboghodo, A. I., 2007. Effect of calcium/magnesium ratio in soil on magnesium availability, yield, and yield components of maize. Commun. Soil. Sci. Plant. 38(19-20), 2849-2860. https://doi.org/10.1080/00103620701663081 Paccioretti, P., Córdoba, M., Balzarini, M., 2020. FastMapping: Software to create field maps and 776 identify management zones in precision agriculture. Comput. Electron. Agric. 175, 105556. https://doi.org/10.1016/j.compag.2020.105556 Pedroso, M., Taylor, J., Tisseyre, B., Charnomordic, B., Guillaume, S., 2010. A segmentation 779 algorithm for the delineation of agricultural management zones. Comput. Electron. Agric. 70(1), 199-208. https://doi.org/10.1016/j.compag.2009.10.007 Peralta, N.R., Costa, J.L., Balzarini, M., Franco, M.C., Córdoba, M., Bullock, D., 2015. Delineation 782 of management zones to improve nitrogen management of wheat. Comput. Electron. Agric. 110, 103–113. https://doi.org/10.1016/j.compag.2014.10.017 Pinheiro, J, Bates, D, DebRoy, S, Sarkar, D., 2019. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-140, https://CRAN.R-project.org/package=nlme. (accessed on 18 October 2020) Ping, J. L., Dobermann, A., 2003. Creating spatially contiguous yield classes for site specific management. Agronomy J. 95, 1121e1131. Rai, R. K., Singh, V. P., Upadhyay, A., 2017. Chapter 17 - Soil Analysis, In: Planning and Evaluation of Irrigation Projects, Academic Press, 505-523. R, Core Team., 2019. R: A language and environment for statistical computing. R Foundation for Statistical. Computing, Vienna, Austria. URL https://www.R-project.org/. (accessed on 08 May 2020) Reich, R.M., 2008. Spatial Statistical Modeling of Natural Resources. Colorado State University, Fort Collins Reyes, J., Wendroth, O., Matocha, C., Zhu. J., 2019. Delineating Site-Specific Management Zones and Evaluating Soil Water Temporal Dynamics in a Farmer’s Field in Kentucky. Vadose Zone J. 18, 1-19. https://doi.org/10.2136/vzj2018.07.0143 Rodrigues, M. S., Corá, J. E., 2015. Management zones using fuzzy clustering based on spatial- temporal variability of soil and corn yield. Eng. Agricola. 35(3), 470- 483. https://doi.org/10.1590/1809-4430-Eng.Agric.v35n3p470-483/2015 Roel, A., Plant, R. E., 2004. Spatiotemporal analysis of rice yield variability in two California fields. Agronomy J. 96(1), 77e90. https://doi.org/10.2134/agronj20 Saby, N. P. A., Thioulouse, J., Jolivet, C. C., Ratié, C., Boulonne, L., Bispo, A., Arrouays, D., 2009. Multivariate analysis of the spatial patterns of 8 trace elements using the French soil monitoring network data. Sci. Total Environ. 407(21), 5644–5652. https://doi.org/10.1016/j.scitotenv.2009.07.002 Srinivasan, A., 2006. Handbook of Precision Agriculture: Principles and Applications. Food Products Press, New York, NY. 708 p. Santra, P., Kumar, M., Panwar, N., 2017. Digital soil mapping of sand content in arid western India through geostatistical approaches. Geoderma Reg. 9, 56-72. https://doi.org/10.1016/j.geodrs.2017.03.003 Schenatto, K., de Souza, E.G., Bazzi, C.L., Betzek, N.M., Gavioli, A., Beneduzzi, H.M., 2017. Use of the farmer’s experience variable in the generation of management zones. Semina: Ciênc. Agrár. 38 (4), 2305-2321. http://dx.doi.org/10.5433/1679-0359.2017v38n4Supl1p2305 Schepers, A.R., Shanahan, F.J., Liebig, M.A., Schepers, J.S., Johnson, S.H., Luchiari, J.A., 2004. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy J. 96, 195- 203. htpps://doi.org/10.2134/agronj2004.0195 Scudiero, E., Teatini, P., Manoli, G., Braga, F., Skaggs, T., Morari, F., 2018. Workflow to Establish Time-Specific Zones in Precision Agriculture by Spatiotemporal Integration of Plant and Soil Sensing Data. Agronomy 8, 253. https://doi.org/10.3390/agronomy8110253 Shukla, A.K., Sinha, N.K., Tiwari, P.K., Prakash, C., Behera, S.K., Lenka, N.K., Singh, V.K., Dwivedi, B.S., Majumdar, K., Kumar, A., Srivastava, P.C., Pachauri, S.P., Meena, M.C., Lakaria, B.L., Siddiqui, S., 2017. Spatial distribution and management zones for sulfur and micronutrients in Shiwalik Himalayan region of India. Land Degrad. Dev. 28, 959–969. http://dx.doi.org/10.1002/ldr.2673 Siddiqui, A. U., Jain, M. K., 2021. Change analysis in land use land cover due to surface mining in Jharia coalfield through Landsat time series data. Materials Today: Proceedings. Slocum, T.A., McMaster, R.B., Kessler, F.C., Howard, H.H., 2005. Thematic Cartography and Geographic Visualization, Prentice Hall, Upper Saddle River NJ. Sui, R.X., Yan, H.J., 2017. Field study of variable rate irrigation management in humid climates. Irrig. Drain. 66 (3), 327–339. https://doi.org/10.1002/ird.2111 Taylor, J. A., McBratney, A. B., Whelan, B. M., 2007. Establishing management classes for broadacre agricultural production. Agronomy J. 99, 1366e1376. https://doi.org/10.2134/agronj2007.0070 Tripathi, R., Nayak, A. K., Shahid, M., Lal, B., Gautam, P., Raja, R., Sahoo, R. N., 2015. Delineation of soil management zones for a rice cultivated area in eastern India using fuzzy clustering. Catena, 133, 128-136. htpps://doi.org/10.1016/j.catena.2015.05.009 USDA, 2014. Keys to Soil Taxonomy, twelfth edition. United States Department of Agriculture. Soil Conservation Service, Washington D.C, pp. 362. Varón-Ramírez, V. M., Camacho-Tamayo, J. H., González-Nivia, J., 2018. Management zones determination based on physical properties of the soil. Cienc. e Agrotecnologia. 42(3), 248-260. https://doi.org/10.1590/1413-70542018423002318 Verma, R.R., Manjunath, B.L., Singh, N.P., Kumar, A., Asolkar, T., Chavan, V., Kumar, T., Singh, P., 2018. Soil mapping and delineation of management zones in the Western Ghats of coastal India. Land. Degrad. Dev. 29: 4313– 4322. https://doi.org/10.1002/ldr.3183 Wadoux, A. M. C., Brus, D. J., Heuvelink, G. B., 2018. Accounting for non-stationary variance in geostatistical mapping of soil properties. Geoderma, 324, 138-147. https://doi.org/10.1016/j.geoderma.2018.03.010 Warrick, A. W., Nielsen, D. R., 1980. Spatial variability of soil physical properties in the field. Application of soil physics, 319-344. https://doi.org/10.1590/S0100-69162012000100007 West, T. B., Welch, K. B., Galecki, A. T., 2015. Linear mixed models: A practical guide using statistical software (2nd ed.). Boca Raton: Chapman & Hall/CRC, 440 pp. Xin-Zhang, W., Guo-Shun, L., Hong-Chao, H., Zhen-Hai, W., Qing-Hua, L., Xu-Feng, L., Wei-Hong, H., Yan-Tao, L., 2009. Determination of management zones for a tobacco field based on soil fertility. Comput. Electron. Agric. 65, 168–175. Xu, Y., Dong, D., Duan, G., Yu, X., Yu, Z., Huang, W., 2013. Geostatistical analysis of soil nutrients based on GIS and geostatistics in the typical plain and hilly-ground area of Zhongxiang, Hubei Province. Open J. Soil Sci. 3(5), 1-7. https://doi.org/10.4236/ojss.2013.35026 Zebarth, B. J., Bélanger, G., Cambouris, A. N., Ziadi, N., 2012. Nitrogen fertilization strategies in relation to potato tuber yield, quality, and crop N recovery165–186. In Z. He, Z., Larkin, R., Honeycutt, W., eds. Sustainable Potato Production: Global Case Studies. Springer. Zhang, X. Y., Yue-Yu, S. U. I., Zhang, X. D., Kai, M. E. N. G., Herbert, S. J., 2007. Spatial variability of nutrient properties in black soil of northeast China. Pedosphere, 17(1), 19-29. https://doi.org/10.1016/S1002-0160(07)60003-4 Zhao, G., Miao, Y., Wang, H., Su, M., Fan, M., Zhang, F., Ma, D., 2013. A preliminary precision rice management system for increasing both grain yield and nitrogen use efficiency. Field Crops Res.154, 23-30. https://doi.org/10.1016/j.fcr.2013.07.019 Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf application/pdf Colombia SSRN Elsevier; (2022): Elsevier (Feb.);p. 1 -38.

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