Assessing the temporal stability of spatial patterns of soil apparent electrical conductivity using geophysical methods

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Cocoa remains in the same field for decades, resulting in plantations dominated with aging trees growing on variable and depleted soils. We determined the spatio-temporal variability of key soil properties in a (5.81 ha) field from the International Cocoa Genebank, Trinidad using geophysical methods. Multi-year (2008-2009) measurements of apparent electrical conductivity at 0-0.75 m (shallow) and 0.75-1.5 m (deep) were conducted. Apparent electrical conductivity at deep and shallow gave the strongest linear correlation with clay-silt content (R = 0.67 and R = 0.78, respectively) and soil solution electrical conductivity (R = 0.76 and R = 0.60, respectively). Spearman rank correlation coefficients ranged between 0.89-0.97 and 0.81- 0.95 for apparent electrical conductivity at deep and shallow, respectively, signifying a strong linear dependence between measurement days. Thus, in the humid tropics, cocoa fields with thick organic litter layer and relatively dense understory cover, experience minimal fluctuations in transient properties of soil water and temperature at the topsoil resulting in similarly stable apparent electrical conductivity at shallow and deep. Therefore, apparent electrical conductivity at shallow, which covers the depth where cocoa feeder roots concentrate, can be used as a fertility indicator and to develop soil zones for efficient application of inputs and management of cocoa fields.

Abdu H., Robinson D.A., and Jones S.B., 2007. Comparing bulk soil electrical conductivity determination using the DUALEM-1S and EM38-DD electromagnetic induction instruments. Soil Sci. Soc. Amer. J., 71, 189-196.

Atwell M., Wuddivira M., Gobin J., and Robinson D., 2013. Edaphic controls on sedge invasion in a tropical wetland assessed with electromagnetic induction. Soil Sci. Soc.Amer. J., 77, 1865-1874.

Bohling G., 2005. Introduction to geostatistics and variogram analysis. Available at:

Bréchet L., Oatham M., Wuddivira M., and Robinson D.A., 2012. Determining spatial variation in soil properties in teak and native tropical forest plots using electromagnetic induction. Vadose Zone J., 11, DOI:10.2136/vzj2011.0102.

Burrough P.A. and McDonnell R.A., 1998. Principles of Geographical Information Systems. Oxford University Press, Oxford, UK.

Doerge T.A., 1999. Management zone concepts. Available at:$FILE/SSMG-02.pdf.

Eneje R.C., Asawalam D.O., and Ezemobi C., 2012.Variability in physicochemical properties of some selected cocoa growing soils in Umuahia north local government area of Abia state. Res. J. Engr. Applied Sci., 1, 235-239.

Farahani H.J. and Buchleiter G.W., 2004. Temporal stability of soil electrical conductivity in irrigated sandy fields in Colorado. Trans. Amer. Soc. Agric. Eng., 47, 79-90.

Gockowski J., 2007. Cocoa production strategies and the conservation of globally significant rainforest remnants in Ghana. In: Production, markets, and the future of smallholders: the role of cocoa in Ghana. Inter. Inst. Trop. Agri., Accra, Ghana.

Goovaerts P., 2010. Geostatistical software. In: Handbook of applied spatial analysis. Springer Berlin Heidelberg, Germany.

Granger O.E., 1983. The hydroclimatology of a developing tropical island: a water resources perspective. Ann. Assoc. Amer. Geogra., 73, 183-205.

Haimelin R., 2008. Mapping soil water content on agricultural fields using electromagnetic induction. Report Helsinki Universty of Technology, Helsinki.

King J.A., Dampney P.M.R., Lark M., Mayr T.R., and Bradley R.I., 2001. Sensing soil spatial variability by electro-magnetic induction (EMI): its potential in precision farming. In: Third European Conference on Precision Agriculture (Eds G. Grenier, S. Blackmore), Montpellier, France.

Lesch S.M., Strauss D.J., and Rhoades J.D., 1995. Spatial prediction of soil salinity using electromagnetic induction techniques 1. Satistical prediction models: Acomparison of multiple linear regression and cokriging. Water Resour. Res., 31, 373-386.

Mann K.K., Schumann A.W., and Obreza T.A., 2011. Delineating productivity zones in a citrus grove using citrus production, tree growth and temporally stable soil data. Prec. Agri., 12, 457-472.

Martinez G., Vanderlinden K., Ordóñez R., and Muriel J.L., 2009. Can apparent electrical conductivity improve the spatial characterization of soil organic carbon? Vadose Zone J., 8, 586-593.

Moral F.J., Terrón J.M., and Silva J.R., 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil Till. Res., 106, 335-343.

Nehmdahl H. and Greve M.H., 2001. Using soil electrical conductivity measurements for delineating management zones on highly variable soils in Denmark. In: 3rd Eur. Conf. Precision Agriculture, Montpellier, France.

Obalum S.E., Oppong J., Igwe C.A., Watanabe Y., and Obi M.E., 2013. Spatial variability of uncultivated soils in derived savanna. Int. Agrophys., 27, 57-67.

Remy N., 2005. S-GeMS: The Stanford Geostatistical Modeling Software: A Tool for New Algorithms Development. In: Geostatistics Banff 2004: 7th Int. Geostatistics Conf., Quantitative Geology and Geostatistics, September 26 - October 1, Alberta, Canada, Springer, the Netherlands.

Rhoades J.D. and Chanduvi F., 1999. Soil salinity assessment: Methods and interpretation of electrical conductivity measurements. FAO, 57, 1-150.

Robinson D.A., Lebron I., Kocar B., Phan K., Sampson M., Crook N., and Fendorf S., 2009. Time-lapse geophysical imaging of soil moisture dynamics in tropical deltaic soils: An aid to interpreting hydrological and geochemical processes. Water Resour. Res., 45, DOI: 2008WR006984.

Rossi R., Amato M., Bitella G., and Bochicchio R., 2013. Electrical resistivity tomography to delineate greenhouse soil variability. Int. Agrophys., 27, 211-218

Schumann A.W., Fares A., Alva A.K., and Paramasivam S., 2003. Response of ‘Hamlin’ orange to fertilizer source, rate and irrigated area. In: Proceedings of Florida State Horticultural Society, 116, 256-260.

Snoeck D., Abolo D., and Jagoret P., 2010. Temporal changes in VAM fungi in the cocoa agroforestry systems of central Cameroon. Agro. Sys., 78, 323-328.

Triantafilis J. and Lesch S.M., 2005. Mapping clay content variation using electromagnetic induction techniques. Comp. Elec. Agri., 46, 203-237.

Vachaud G., Passerat De Silans A., Balabanis P., and Vauclin M., 1985. Temporal stability of spatially measured soil water probability density function. Soil Sci. Soc. Amer. J., 49, 822-828.

Walter C., McBratney A.B., Douaoui A., and Minasny B., 2001. Spatial prediction of topsoil salinity in the Chelif Valley, Algeria, using local ordinary kriging with local variograms versus whole-area variogram. Aust. J. Soil Res., 39, 259-272.

Wood G.A.R. and Lass R.A., 1985. Cocoa. Longman Group Limited, London, UK.

Wuddivira M.N., Robinson D. A., Lebron I., Bréchet L., Atwell M., De Caires S., Oatham M., Jones S.B., Abdu H. Verma A.K., and Tuller M., 2012. Estimation of soil clay content from hygroscopic water content measurements. Soil Sci. Soc. Amer. J., 76, 1529-1535.

International Agrophysics

The Journal of Institute of Agrophysics of Polish Academy of Sciences

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