Use of two relative depths of the soil apparent electrical conductivity to define experimental blocks with spatial regression models

Keywords: precision agriculture, large-scale blocking, management zone analyst

Abstract

Aim of study: Our main objective was to take advantage of the ECa information that the EM38-MK2 sensor records simultaneously at two relative depths for modeling using spatial regression and the subsequent blocking of the conductivity estimate values, incorporating elevation.

Area of study: A 23.1-ha field located in the municipality of Puerto López (Meta, Colombia).

Material and methods: A series of georeferenced data (15438) was collected from the EM38-MK2 sensor, through which the ECa was obtained at two depths, a spatial aggregation was performed using a grid of 40 m ´ 40 m (167 grid cells), to provide data in Lattice form, the centroid of the cells was determined as the new representative spatial coordinates, to adjust a Spatial Autoregression Model (SAC), and then define the blocks from the predictions of the adjusted model.

Main results: The adjusted model has a comparative purpose with the usual proposals for delimiting management zones separately, so it was convenient to incorporate in the model a 3D weighting matrix relating the two relative depths recorded by the EM38MK2 sensor. By mapping the surface layer with the predictions of the SAC model, two distinguishable blocks were delimited in its ECa and management zone analyst (MZA), which can be suitable for experimentation or agricultural management.

Research highlights: These results can be adopted to define the shape and dimension of the blocks in the context of experimental design so that with adequate blocking, the effect of spatial dependence associated with the physicochemical properties of soils related to ECa can be mitigated or suppressed.

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References

Arbia G, 2014. A primer for spatial econometrics with applications in R. Palgrave Macmillan, UK. https://doi.org/10.1057/9781137317940

Badewa E, Unc A, Cheema M, Kavanagh V, Galagedara L, 2018. Soil moisture mapping using multi-frequency and multi-coil electromagnetic induction sensors on managed podzols. Agronomy 8(10): 224-239. https://doi.org/10.3390/agronomy8100224

Bivand R, Wong D, 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

Callegary J, Ferré T, Groom R, 2007. Vertical spatial sensitivity and exploration depth of low-induction-number electromagnetic-induction instruments. Vadose Zone J 6(1): 158-167. https://doi.org/10.2136/vzj2006.0120

Chartuni E, De Assis F, Marcal D, Ruz E, 2007. Precision agriculture: new tools to improve technology management in agricultural enterprises. Com Mag 16: 24-31.

Christensen R, 2018. Analysis of variance, design, and regression: Linear modeling for unbalanced data. CRC Press, USA. https://doi.org/10.1201/9781315370095

Córdoba M, Bruno C, Aguate F, Tablada M, Balzarini M, 2014. Análisis de la variabilidad espacial en lotes agrícolas. Manual de buenas prácticas agrícolas. Eudecor, Argentina.

Corwin D, Lesch S, 2005. Apparent soil electrical conductivity measurements in agriculture. Comput Electron Agr 46(1-3): 11-43. https://doi.org/10.1016/j.compag.2004.10.005

Corwin D, Scudiero E, 2019. Mapping soil spatial variability with apparent soil electrical conductivity (ECa) directed soil sampling. Soil Sci Soc Am J 83(1): 3-4. https://doi.org/10.2136/sssaj2018.06.0228

Deakin R, Bird S, Grenfell R, 2002. The centroid? Where would you like it to be be? Cartography 31(2): 153-167. https://doi.org/10.1080/00690805.2002.9714213

Doolittle J, Brevik E, 2014. The use of electromagnetic induction techniques in soils studies. Geoderma (223): 33-45. https://doi.org/10.1016/j.geoderma.2014.01.027

Elhorst J, 2014. Spatial econometrics from cross-sectional data to spatial panels. Springer, USA. https://doi.org/10.1007/978-3-642-40340-8

El-Naggar A, Hedley C, Roudier P, Horne D, Clothier B, 2021. Imaging the electrical conductivity of the soil profile and its relationships to soil water patterns and drainage characteristics. Precis Agr 22: 1045-1066. https://doi.org/10.1007/s11119-020-09763-x

ESRI, 2017. ArcGIS desktop Release 10.5.1. Env Syst Res Inst (844): 845. Redlands, CA, USA.

Fan J, McConkey B, Wang H, Janzen H, 2016. Root distribution by depth for temperate agricultural crops. Field Crops Res 189: 68-74. https://doi.org/10.1016/j.fcr.2016.02.013

Fridgen J, Kitchen N, Sudduth K, Drummond S, Wiebold W, Fraisse C, 2004. Management Zone Analyst (MZA) software for subfield management zone delineation. Agron J 96(1): 100-108. https://doi.org/10.2134/agronj2004.1000

Fukuyama Y, 1989. A new method of choosing the number of clusters for the fuzzy c-mean method. Proc Fuzzy Syst Symp 5: 247-250.

Gavrilov I, Pusev R, 2014. Normtest: Tests for Normality. R package CRAN 1.1: 1-14.

GEONICS, 2012. Ground conductivity meter operating manual EM38MK2. Geonics Ltd. Lead Electr, p: 57. Ontario, Canada.

Gotway C, Cressie N, 1990. A spatial analysis of variance applied to soil‐water infiltration. Water Resour Res 26(11): 2695-2703. https://doi.org/10.1029/WR026i011p02695

Heil K, Schmidhalter U, 2015. Comparison of the EM38 and EM38-MK2 electromagnetic induction-based sensors for spatial soil analysis at field scale. Comput Electron Agr 110: 267-280. https://doi.org/10.1016/j.compag.2014.11.014

IGAC, 2005. Estudio general de suelos y zonificación de tierras del Departamento de Meta. Inst Geog Agus Coda, Bogotá, Colombia. 159 pp.

Joschko M, Gebbers R, Barkusky D, Timmer J, 2010. The apparent electrical conductivity as a surrogate variable for predicting earthworm abundances in tilled soils. J Plant Nut Soil Sci 173(4): 584-590. https://doi.org/10.1002/jpln.200800071

Li H, Calder C, Cressie N, 2007. Beyond Moran's I: testing for spatial dependence based on the spatial autoregressive model. Geog Analy 39(4): 357-375. https://doi.org/10.1111/j.1538-4632.2007.00708.x

Longley P, Goodchild M, Maguire D, Rhind D, 2015. Geographic information science and systems. Wiley, USA.

Martinez G, Vanderlinden K, Ordóñez R, Muriel J, 2009. Can apparent electrical conductivity improve the spatial characterization of soil organic carbon? Vad Zone J 8(3): 586-593. https://doi.org/10.2136/vzj2008.0123

Medeiros W, Valente D, Queiroz D, Pinto F, Assis I, 2018. Apparent soil electrical conductivity in two different soil types1. Rev Ciên Agron 49: 43-52. https://doi.org/10.5935/1806-6690.20180005

Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F, Chang C, Lin C, 2021. e1071: Misc Functions of the Department of Statistics, Probability Theory Group. R package CRAN (1.7): 1-66.

Millán S, Moral F, Prieto M, Pérez-Rodriguez J, Campillo C, 2019. Mapping soil properties and delineating management zones based on electrical conductivity in a hedgerow olive grove. T ASABE 62(3): 749-760. https://doi.org/10.13031/trans.13149

Moral F, Terrón J, Da Silva J, 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil Till Res 106(2): 335-343. https://doi.org/10.1016/j.still.2009.12.002

Pedrera A, Pachepsky Y, Taguas E, Martos S, Giráldez J, Vanderlinden K, 2017. Concurrent temporal stability of the apparent electrical conductivity and soil water content. J Hydr 544: 319-326. https://doi.org/10.1016/j.jhydrol.2016.10.017

Peralta N, Costa J, 2013. Delineation of management zones with soil apparent electrical conductivity to improve nutrient management. Comput Electron Agr 99: 218-226. https://doi.org/10.1016/j.compag.2013.09.014

Rezaei M, Saey T, Seuntjens P, Joris I, Boënne W, Van Meirvenne M, Cornelis W, 2016. Predicting saturated hydraulic conductivity in a sandy grassland using proximally sensed apparent electrical conductivity. J Appl Geoph 126: 35-41. https://doi.org/10.1016/j.jappgeo.2016.01.010

Robinet J, von Hebel C, Govers G, van der Kruk J, Minella J, Schlesner A, et al., 2018. Spatial variability of soil water content and soil electrical conductivity across scales derived from electromagnetic induction and time domain reflectometry. Geoderma 314: 160-174. https://doi.org/10.1016/j.geoderma.2017.10.045

Rodríguez H, Darghan A, Henao M, 2019. Spatial regression modeling of soils with high cadmium content in a cocoa producing area of Central Colombia. Geoderma 16: 214-226. https://doi.org/10.1016/j.geodrs.2019.e00214

Santos P, Roa H, Contreras A, Parra J, 2018. Modelado espacial del carbono orgánico del suelo y su relación con otras propiedades químicas en el cultivo de arroz del distrito de riego del Norte de Santander Colombiano. Gest Amb 21(2): 252-262. https://doi.org/10.15446/ga.v21n2.73004

Saey T, Meirvenne M, Vermeersch H, Ameloot N, Cockx L, 2009. A pedotransfer function to evaluate the soil profile textural heterogeneity using proximally sensed apparent electrical conductivity. Geoderma 150: 389-395. https://doi.org/10.1016/j.geoderma.2009.02.024

Shapiro S, Wilk M, Chen H, 1968. A comparative study of various tests for normality. J Am Stat Assoc 63(324): 1343-1372. https://doi.org/10.1080/01621459.1968.10480932

Sudduth K, Kitchen N, Bollero G, Bullock D, Wiebold W, 2003. Comparison of electromagnetic induction and direct sensing of soil electrical conductivity. Agron J 95(3): 472-482. https://doi.org/10.2134/agronj2003.4720

Sudduth K, Myers D, Kitchen N, Drummond S, 2013. Modeling soil electrical conductivity-depth relationships with data from proximal and penetrating ECa sensors. Geoderma 199: 12-21. https://doi.org/10.1016/j.geoderma.2012.10.006

Webster R, Oliver M, 2007. Geostatistics for environmental scientists. Wiley, UK. https://doi.org/10.1002/9780470517277

Xie X, Beni G, 1991. A validity measure for fuzzy clustering: IEEE Trans Pat Anal Mach Intel 13(8): 1-7. https://doi.org/10.1109/34.85677

Xie W, Yang J, Yao R, Wang X, 2021. Spatial and temporal variability of soil salinity in the Yangtze River estuary using electromagnetic induction. Rem Sens 13(10): 1-16. https://doi.org/10.3390/rs13101875

Yrigoyen C, 2003. Econometría espacial aplicada a la predicción-extrapolación de datos microterritoriales. Dirección General de Economía y Planificación. Com Mad 132: 79-94.

Zhang H, Ding F, 2013. On the Kronecker products and their applications. J Appl Math 13: 1-8. https://doi.org/10.1155/2013/296185

Zhang Y, Han K, Jung K, Cho H, Seo M, Sonn Y, 2017. Study on the standards of proper effective rooting depth for upland crops. Kor J Soil Sci Fert 50: 21-30. https://doi.org/10.7745/KJSSF.2017.50.1.021

Zhang T, Gaffrey M, Monroe M, Thomas D, Weitz K, Piehowski P, et al., 2020. Block design with common reference samples enables robust large-scale label-free quantitative proteome profiling. J Proteom Res 19(7): 2863-2872. https://doi.org/10.1021/acs.jproteome.0c00310

Published
2022-02-07
How to Cite
GrisalesE. F., DarghanA. E., & Rivera C. A. (2022). Use of two relative depths of the soil apparent electrical conductivity to define experimental blocks with spatial regression models. Spanish Journal of Agricultural Research, 20(1), e1102. https://doi.org/10.5424/sjar/2022201-18631
Section
Soil science