Eurasian Journal of Soil Science

Volume 3, Issue 2, Oct 2014, Pages 123 - 130
DOI: 10.18393/ejss.24169
Stable URL: http://ejss.fess.org/10.18393/ejss.24169
Copyright © 2014 The authors and Federation of Eurasian Soil Science Societies



Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation

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Eren,E., Öksüz,Y., Karadağ,S., Özen,S., Gemici,Z., ,., 2014. Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation. Eurasian J Soil Sci 3(2):123 - 130. DOI : 10.18393/ejss.24169
Eren,E.,Öksüz,Y.Karadağ,S.Özen,S.Gemici,Z.,& ,. Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation Eurasian Journal of Soil Science, DOI : 10.18393/ejss.24169
Eren,E.,Öksüz,Y.Karadağ,S.Özen,S.Gemici,Z., and ,,."Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation" Eurasian Journal of Soil Science, DOI : 10.18393/ejss.24169
Eren,E.,Öksüz,Y.Karadağ,S.Özen,S.Gemici,Z., and ,,. "Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation" Eurasian Journal of Soil Science, DOI : 10.18393/ejss.24169
E,Eren.Y,Öksüz.S,Karadağ.S,Özen.Z,Gemici., "Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation" Eurasian J. Soil Sci, vol., no., pp., DOI : 10.18393/ejss.24169
Eren,Emel ;Öksüz,Yalcın ;Karadağ,Sevinç ;Özen,Selin ;Gemici,Zafer ;, Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation. Eurasian Journal of Soil Science,. DOI : 10.18393/ejss.24169

How to cite

Eren, E., Öksüz, Y., Karadağ, S., Özen, S., Gemici, Z., , ., 2014. Investigation of a novel soil analysis method in agricultural areas of Çarşamba plain for fertilizer recommendation. Eurasian J. Soil Sci. 3(2): 123 - 130. DOI : 10.18393/ejss.24169

Author information

Emel Eren , Mir Arastirma ve Gelistirme A.S., Istanbul, Turkey
Yalcın Öksüz , Mir Arastirma ve Gelistirme A.S., Istanbul, Turkey
Sevinç Karadağ , Mir Arastirma ve Gelistirme A.S., Istanbul, Turkey
Selin Özen , Mir Arastirma ve Gelistirme A.S., Istanbul, Turkey
Zafer Gemici , Mir Arastirma ve Gelistirme A.S., Istanbul, Turkey
,

Publication information

Issue published online: 30 Oct 2014
Article first published online : 19 Oct 2014
Manuscript Accepted : 14 Oct 2014
Manuscript Received: 01 Aug 2014
DOI: 10.18393/ejss.24169
Stable URL: http://ejss.fesss.org/10.18393/ejss.24169

Abstract

In this study, a novel soil analysis method for fertilization recommendation was developed and validated with 161 soil samples taken from Turkey - Çarşamba plain for determination of potassium as a plant nutrient. In conventional soil analysis methods, available potassium (K) nutrient was determined by ammonium acetate extraction with flame photometer. In this study an alternative to existing method was proposed by developing extraction solutions suitable for interference dynamics of ion selective electrodes in a flow injection setup. Flow injection analysis system was optimized and K ion concentration of 161 soil samples taken from Turkey –Çarşamba plain was determined with potentiometrically. For the same soil samples, K+ ion concentration was determined with ammonium acetate extraction using flame photometer in parallel. Fertilization recommendations for potassium was calibrated on ammonium acetate extraction based measurements. In order to evaluate available potassium nutrient analysis results from new generation soil analysis method in fertilization recommendation process, a correlation model is required for relating new generation method results to conventional method results. An artificial neural network based soft sensor system was developed for this task. Potentiometric K+ ion measurement of soil sample in flow injection analysis system was presented as input to soft sensor system. Soft sensor predicted available K in soil sample based on artificial neural network model which can be used in fertilizer recommendation. Prediction performance of soft sensor was validated with experimental data and fitted with high correlation coefficient (R2= 0.902). Experimental studies have shown that K determined by potentiometric measurements can be used in fertilization recommendations in Çarşamba plain by using soft sensor approach.

Keywords

Soil analysis, fertilization recommendation, soft sensor, artificial neural network

Corresponding author

References

Adamchuk, V.I., Lund, E.D., Sethuramasamyraja, B., Morgan, M.T., Dobermann, A., Marx, D.B., 2005. Direct measurement of soil chemical properties on-the-go using ion-selective electrodes. Computers and Electronics in Agriculture. 48,272–294

Adamchuk, V.I., Hummel, J.W., Morgan, M.T., Upadhyaya, S.K., 2004. On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture. 44, 71–91

Birrell, S.J., Hummel, J.W., 2001. Real-time multi ISFET/FIA soil analysis system with automatic sample extraction. Computers and Electronics in Agriculture 32, 45–67

Bishop, C.M., 1995. Neural Network for Pattern Recognition, Oxford University Press, 116p..

Bortolon, L., Gıanello, C., Welter, S., Almeıda, R.G.O., Gıasson, E., 2011. Simultaneous extraction of phosphorus, potassium, calcium and magnesium from soils and potassium recommendations for crops in southern brazil. Pedosphere 21(3), 365–372

Cieśla, J., Ryżak, M., Bieganowski, A., Tkaczyk, P., Walczak, R.T., 2007. Use of ion-selective electrodes for determination of content of potassium in egner-rhiem soil extracts. Research in Agricultural Engineering 53(1): 29–33

Dufour, P., Bhartiya, S., Dhurjati P. S., Francis J. Doyle III. 2005. Neural network-based software sensor: training set design and application to a continuous pulp digester. Control Engineering Practice 13: 135–143.

Eddy, S. and Johnston, S. R., 2009. Comparison of Palintest Soil Analysis to External Laboratory Analysis. Palintest Ltd

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten I. H. 2009. The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.

Hastie, T., Tibshirani, R., 2011. Element of statistical Learning: Data Mining, Inference and Prediction 2nd edition, page 38

Kim,H.J., Hummel, J.W., Sudduth, K.A., Motavalli, P.P., 2007. simultaneous analysis of soil macronutrients using ıon-selective electrodes. Soil Science Society of America Journal 71: 1867–1877.

Lin, B., Recke, B., Knudsen J. K. H., Jørgense S. B., 2007. A systematic approach for soft sensor development. Computers and Chemical Engineering 31: 419–425

Lobsey, C., Rossel, R. V., McBratney, A., 2010. An automated system for rapid in-field soil nutrient testing. 19th World Congress of Soil Science, Soil Solutions for a Changing World, 1 – 6 August 2010, Brisbane, Australia.

Sethuramasamyraja, B., Adamchuk, V.I., Dobermann, A., Marx, D.B., Jones, D.D., Meyer, G.E., 2008. Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents. Computers and Electronics in Agriculture 60: 212–225

Wang, J., Scott, A.D., 2001. Determination of exchangeable potassium in soil using ion-selective electrodes in soil suspensions. European Journal of Soil Science 52: 143-150.

Yue, X.L., Li, F., Hu, Y.C., Zhang, H.Z., Ji, H.J., Zhang, W.L., Schmidhalter, U., 2012. Evaluating The Validity Of A Nitrate Quick Test İn Different Chinese Soils. Pedosphere 22(5): 623–630

Abstract

In this study, a novel soil analysis method for fertilization recommendation was developed and validated with 161 soil samples taken from Turkey - Çarşamba plain for determination of potassium as a plant nutrient. In conventional soil analysis methods, available potassium (K) nutrient was determined by ammonium acetate extraction with flame photometer. In this study an alternative to existing method was proposed by developing extraction solutions suitable for interference dynamics of ion selective electrodes in a flow injection setup. Flow injection analysis system was optimized and K ion concentration of 161 soil samples taken from Turkey –Çarşamba plain was determined with potentiometrically. For the same soil samples, K+ ion concentration was determined with ammonium acetate extraction using flame photometer in parallel. Fertilization recommendations for potassium was calibrated on ammonium acetate extraction based measurements. In order to evaluate available potassium nutrient analysis results from new generation soil analysis method in fertilization recommendation process, a correlation model is required for relating new generation method results to conventional method results. An artificial neural network based soft sensor system was developed for this task. Potentiometric K+ ion measurement of soil sample in flow injection analysis system was presented as input to soft sensor system. Soft sensor predicted available K in soil sample based on artificial neural network model which can be used in fertilizer recommendation. Prediction performance of soft sensor was validated with experimental data and fitted with high correlation coefficient (R2= 0.902). Experimental studies have shown that K determined by potentiometric measurements can be used in fertilization recommendations in Çarşamba plain by using soft sensor approach.

Keywords: Soil analysis, fertilization recommendation, soft sensor, artificial neural network

References

Adamchuk, V.I., Lund, E.D., Sethuramasamyraja, B., Morgan, M.T., Dobermann, A., Marx, D.B., 2005. Direct measurement of soil chemical properties on-the-go using ion-selective electrodes. Computers and Electronics in Agriculture. 48,272–294

Adamchuk, V.I., Hummel, J.W., Morgan, M.T., Upadhyaya, S.K., 2004. On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture. 44, 71–91

Birrell, S.J., Hummel, J.W., 2001. Real-time multi ISFET/FIA soil analysis system with automatic sample extraction. Computers and Electronics in Agriculture 32, 45–67

Bishop, C.M., 1995. Neural Network for Pattern Recognition, Oxford University Press, 116p..

Bortolon, L., Gıanello, C., Welter, S., Almeıda, R.G.O., Gıasson, E., 2011. Simultaneous extraction of phosphorus, potassium, calcium and magnesium from soils and potassium recommendations for crops in southern brazil. Pedosphere 21(3), 365–372

Cieśla, J., Ryżak, M., Bieganowski, A., Tkaczyk, P., Walczak, R.T., 2007. Use of ion-selective electrodes for determination of content of potassium in egner-rhiem soil extracts. Research in Agricultural Engineering 53(1): 29–33

Dufour, P., Bhartiya, S., Dhurjati P. S., Francis J. Doyle III. 2005. Neural network-based software sensor: training set design and application to a continuous pulp digester. Control Engineering Practice 13: 135–143.

Eddy, S. and Johnston, S. R., 2009. Comparison of Palintest Soil Analysis to External Laboratory Analysis. Palintest Ltd

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten I. H. 2009. The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.

Hastie, T., Tibshirani, R., 2011. Element of statistical Learning: Data Mining, Inference and Prediction 2nd edition, page 38

Kim,H.J., Hummel, J.W., Sudduth, K.A., Motavalli, P.P., 2007. simultaneous analysis of soil macronutrients using ıon-selective electrodes. Soil Science Society of America Journal 71: 1867–1877.

Lin, B., Recke, B., Knudsen J. K. H., Jørgense S. B., 2007. A systematic approach for soft sensor development. Computers and Chemical Engineering 31: 419–425

Lobsey, C., Rossel, R. V., McBratney, A., 2010. An automated system for rapid in-field soil nutrient testing. 19th World Congress of Soil Science, Soil Solutions for a Changing World, 1 – 6 August 2010, Brisbane, Australia.

Sethuramasamyraja, B., Adamchuk, V.I., Dobermann, A., Marx, D.B., Jones, D.D., Meyer, G.E., 2008. Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents. Computers and Electronics in Agriculture 60: 212–225

Wang, J., Scott, A.D., 2001. Determination of exchangeable potassium in soil using ion-selective electrodes in soil suspensions. European Journal of Soil Science 52: 143-150.

Yue, X.L., Li, F., Hu, Y.C., Zhang, H.Z., Ji, H.J., Zhang, W.L., Schmidhalter, U., 2012. Evaluating The Validity Of A Nitrate Quick Test İn Different Chinese Soils. Pedosphere 22(5): 623–630



Eurasian Journal of Soil Science