Calibration and validation of Veris MSP3 on two soils in Guanacaste, Costa Rica
DOI:
https://doi.org/10.15517/am.v30i2.33579Keywords:
precision agriculture, electrical conductivity, pH, reflectanceAbstract
Introduction. It is necessary to implement and validate precision agriculture (PA) technologies in tropical regions. Objective. The objective of this study was to calibrate and validate Veris MSP3 equipment in two different fields located in Guanacaste, Costa Rica. Materials and methods. The data obtained by the sensors integrated into the Veris MSP3 equipment was correlated with laboratory analysis of soil samples with the intent to develop simple or multiple linear regressions to predict soil texture, organic matter, nitrogen in the soil, total cation exchange capacity, and pH, in order to obtain a model that best suits each of the parameters. Results. The regressions that resulted with the best models were: i) apparent electrical conductivity (CEa) with soil texture, ii) optic ratio and slope with organic matter, iii) optic ratio with N, iv) CEa with total cation exchange capacity (total CIC), and v) Veris pH with pH in water and KCl. The higher determination coefficient was obtained between CEa and sand percentage with r2 of 0,82. In addition, the r2 for the rest of parameters ranged from 0,28 to 0,82. Conclusion. The calibration method used gave reasonably precise correlations (r2≥0,55) for soil texture at depth from 0 to 30 cm and organic matter variables. However, from 30 to 90 cm soil texture, N, total CIC, and pH anoter calibration methodology should be considered because of imprecise correlations (r2≤0,55).
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Adamchuk, V.I., E.D. Lund, T.M. Reed, and R.B. Ferguson. 2007. Evaluation of an on-the-go technology for soil pH mapping. Precision Agric. 8(3):139-149. doi:10.1007/s11119-007-9034-0 DOI: https://doi.org/10.1007/s11119-007-9034-0
Adamchuk, V.I., M. Morgan, and D.R Ess. 1999. An automated sampling system for measuring soil pH. Trans. ASABE 42:885-892. doi:10.13031/2013.13268 DOI: https://doi.org/10.13031/2013.13268
Bianchini, A.A., and Mallarino A.P. 2002. Soil sampling alternatives and variable-rate liming for a soybean-corn rotation. Agro J. 94(6):1355-1366 doi:10.2134/agronj2002.1355 DOI: https://doi.org/10.2134/agronj2002.1355
Cho, Y., A.H. Sheridan, K.A. Sudduth, and K.S. Veum. 2017. Comparison of field and laboratory VNIR spectroscopy for profile soil property estimation. Trans. ASABE. 60:1503-1510. doi:10.13031/trans.12299 DOI: https://doi.org/10.13031/trans.12299
CIA (Centro de Investigaciones Agronómicas). 2016. Mapa digital de suelos de Costa Rica CIA, San José, CRI. http://www.cia.ucr.ac.cr/?page_id=139 (consultado 6 feb. 2017).
Corwin, D.L. 2005. Geospatial measurement of apparent soil electrical conductivity for characterizing soil spatial variability. In: J. Álvarez, and R. Munoz, editors, Soil-water-solute process characterization: an integrated approach. CRC Press, FL, USA. p. 639-664. DOI: https://doi.org/10.1201/9781420032086.ch18
Corwin, D.L., and S.M. Lesch. 2005. Characterizing soil spatial variability with apparent soil electrical conductivity. Part I. Survey protocols. Comput. Electron. Agric. 46:135-152. doi:10.1016/j.compag.2004.11.002 DOI: https://doi.org/10.1016/j.compag.2004.11.003
Di-Rienzo, J.A., F. Casanoves, M.G. Balzarini, L. González, M. Tablada, y C.W. Robledo. 2017. InfoStat: software estadístico 2017. Universidad Nacional de Córdoba, ARG.
Google Earth. 2017. Versión 7.1.8.3036. Google, Mountain View, CA, USA.
Grisso, R.B., M. Alley, D. Holshouser, and W. Thomason. 2009. Precision farming tools: Soil electrical conductivity. DOCPLAYER. https://docplayer.net/10831185-Precision-farming-tools-soil-electrical-conductivity.html (accessed May 6, 2017).
Heil, K., and U. Schmidhalter. 2010. Characterization of soil texture variability using the apparent soil electrical conductivity at a highly variable site. Comput. Geosci. 39:98-110. doi: 10.1016/j.cageo.2011.06.017 DOI: https://doi.org/10.1016/j.cageo.2011.06.017
Hernández, C.A., W.A. Barrera, E. Spaans, y R. Vaquero. 2006. Agricultura de precisión en la empresa agrocomercial EARTH: fertilización dinámica. Tierra Trop. 2(2):177-185.
IMN (Instituto Meteorológico Nacional). 2017. Datos climáticos. IMN, CRI. https://www.imn.ac.cr/web/imn/inicio (consultado 7 feb. 2017).
Johnson, C.K., J.W. Doran, H.R. Duke, B.L. Weinhold., K.M. Eskridge, and J.F. Shanahan. 2001. Field-scale electrical conductivity mapping for delineating soil condition. Soil Sci. Am. J. 65:1829-1837. DOI: https://doi.org/10.2136/sssaj2001.1829
Knadel, M., A. Thomsen, K. Schelde, and M.H. Greve. 2015. Soil organic carbon and particle sizes mapping using vis–NIR, EC and temperature mobile sensor platform. Comp. Electron. Agric. 114:134-144. doi:10.1016/j.compag.2015.03.013 DOI: https://doi.org/10.1016/j.compag.2015.03.013
Kweon, G., E. Lund, and C. Maxton. 2013. Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors. Geoderma 199:80-89. doi:10.1016/j.geoderma.2012.11.001 DOI: https://doi.org/10.1016/j.geoderma.2012.11.001
Lesch, S.M., D.L. Corwin, and D.A Robinson. 2005. Apparent soil electrical conductivity mapping as an agricultural management tool in arid zone soils. Comp. Electron. Agric. 46:351-378. doi: 10.1016/j.compag.2004.11.007 DOI: https://doi.org/10.1016/j.compag.2004.11.007
Machado, F.C., R. Montanari, L.S Shozo, L.H. Lovera, and E.D Lima. 2015. Spatial dependence of electrical conductivity and chemical properties of the soil by electromagnetic induction. Rev. Bras. Ciênc. Solo 39:1112-1120. doi:10.1590/01000683rbcs20140794 DOI: https://doi.org/10.1590/01000683rbcs20140794
Maxton, C., P. Drummond, E. Lund, and K. Jensen. 2017. Mobile soil optical mapping system. Patent 9,743,574. Veris Technologies Inc., USA.
Melo, J.A., J.L. Loratte, E.R. Alves, R. Negrão, and J.L. Morelli. 2014. Precision agriculture for sugarcane management: a strategy applied for brazilian conditions. Acta Sci. Agron. 36:111-117. doi:10.4025/actasciagron. v36i1.17664. DOI: https://doi.org/10.4025/actasciagron.v36i1.17664
Montgomery, D.C., E.A. Peck, y G.G. Vining. 2002. Introducción al análisis de regresión lineal. 3ra ed. Compañía Editorial Continental, México, DF. MEX.
Morellos, A., X.E. Pantazi, D. Moshou, T. Alexandridis, R. Whetton, G. Tziotzios, and A.M. Mouazen. 2016. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosyst. Eng. 152:104-116. doi:10.1016/j.biosystemseng.2016.04.018 DOI: https://doi.org/10.1016/j.biosystemseng.2016.04.018
Mulla, D.J., and McBratney A.B 2000. Soil spatial variability. In: M.E. Summer, editor, Handbook of soil science. CRC Press, FL, USA, p. A321-A352.
Rodriguez, JR., R.E. Plant, J.J Lambert, and D.R. Smart. 2011. Using apparent soil electrical conductivity (ECa) to characterize vineyard soils of high clay content. Precision Agric. 12:775-794. doi:10.1007/s11119-011-9220-y DOI: https://doi.org/10.1007/s11119-011-9220-y
Schirrmann, M., R. Gebbers, E. Kramer, and J. Seidel. 2011. Soil pH mapping with an on-the-go sensor. Sensors (Basel) 11:573-598. doi:10.3390/s110100573 DOI: https://doi.org/10.3390/s110100573
Sethuramasamyraja, B., V.I. Adamchuk, A. Dobermann, D.B Marx, D.D. Jones, and G.E. Meyer. 2008. Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents. Comp. Electron. Agric. 60:212-225. doi:10.1016/j.compag.2007.08.003 DOI: https://doi.org/10.1016/j.compag.2007.08.003
Sudduth, K.A., N.R. Kitchen, G.A. Bollero, D.G. Bullock, and W.J. Wiebold. 2003. Comparison of electromagnetic induction and direct sensing of soil electrical conductivity. Agron. J. 95:472-482. doi:10.2134/agronj2003.4720 DOI: https://doi.org/10.2134/agronj2003.4720
Sudduth, KA., N.R. Kitchen, W.J. Wiebold, W.D. Batchelor, G.A. Bollero, D.G. Bullock, and K.D. Thelen. 2005. Relating apparent electrical conductivity to soil properties across the north-central USA. Comput. Electron. Agric. 46:263-283. doi:10.1016/j.compag.2004.11.010 DOI: https://doi.org/10.1016/j.compag.2004.11.010
Surfer. 2017. Golden software. 13. Golden Surfer, Golden, CO, USA.
Veris Technologies. 2017a. Veris MSP3 EC/pH/OM Parts Manual. Veris Technologies, Salina, KS, USA. https://www.veristech.com/more/support/manuals (consultado 6 nov 2017).
Veris Technologies. 2017b. Veris Soil Viewer 2.87. Veris Technologies, Salina, KS, USA.
Weller, U., M. Zipprich, M. Sommer, W.Z. Castell, and M. Wehrhan. 2007. Mapping clay content across boundaries at the landscape scale with electromagnetic induction. Soil Sci. Soc. Am. J. 71:1740-1747. doi:10.2136/sssaj2006.0177 DOI: https://doi.org/10.2136/sssaj2006.0177
Wetterlind, J., K. Piikki, B. Stenberg, and M. Söderström. 2015. Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool. Eur. J. Soil Sci. 66:631-638. doi:10.1111/ejss.12228 DOI: https://doi.org/10.1111/ejss.12228
Williams, B.G., and D. Hoey. 1987. The use of electromagnetic induction to detect the spatial variability of the salt and clay contents of soils. Soil Res. 25:21-27. doi:10.1071/SR9870021 DOI: https://doi.org/10.1071/SR9870021
Yao, R., J. Yang, D. Wu, W. Xie, P. Gao, and W. Jin. 2016. Digital mapping of soil salinity and crop yield across a coastal agricultural landscape using repeated electromagnetic induction (EMI) surveys. PloS One 11(5):e0153377. doi:10.1371/journal.pone.0153377 DOI: https://doi.org/10.1371/journal.pone.0153377
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