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Improving spatial prediction of soil organic matter using soil auxiliary data as secondary information | ||
| Environmental Resources Research | ||
| دوره 11، شماره 1، مهر 2023، صفحه 141-154 اصل مقاله (1.14 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2023.21220.1401 | ||
| نویسندگان | ||
| Nikou Hamzehpour* 1؛ Fereshte Alizadeh Motaghi2 | ||
| 1Associate Professor, Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Iran | ||
| 2M.Sc Student, Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Iran | ||
| چکیده | ||
| The aim of this research was to predict soil organic matter (SOM) using kriging and cokriging methods using soil auxiliary data. Soil samples were gathered from an area of 63 km2 in Bonab plain in Iran and overall of 78 samples from depth 0-20 cm were collected. SOM and ten other soil physicochemical properties were measured. Later correlation between SOM and soil properties was determined and those properties with high correlation in 1% probability level with SOM were used to develop cross-semivariograms. Later SOM prediction was done on a grid of 100 m with kriging and cokriging methods using BMElib package developed for MATLAB software. Results showed that among studied soil properties, CCE, silt, sand and wet aggregate stability (WAS) had the highest correlations with SOM and therefore they were chosen as auxiliary data in cokriging of SOM. Spatial prediction of SOM with kriging method resulted in MSE and RMSE of 0.055 % and 0.234 % respectively. However, SOM prediction with developed cross-semivarigrams by using auxiliary data revealed that CCE and silt could improve SOM prediction with MSE and RMSE of 0.047%, 0.032% and 0.216%, 0.178 % respectively. Selecting appropriate soil parameters with high correlation with SOM and high spatial dependency can improve spatial prediction of SOM and thus, a step forward in sustainable management of SOM as a key soil quality index, especially in areas with salinization and desertification danger. | ||
| کلیدواژهها | ||
| auxiliary data؛ Cokriging؛ CCE؛ Kriging؛ silt | ||
| مراجع | ||
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Adhikari, K., and Hartemink, A.E. 2017. Soil organic carbon increases under intensive agriculture in the Central Sands, Wisconsin, USA. Geoderma Regional. 10, 115-125.
Allen, D.E. Pringle, M.J., Page, K.L., and Dalal, R.C. 2010. A review of sampling designs for the measurement of soil organic carbon in Australian grazing lands. The Rangeland Journal. 32(2), 227-246.
Amezketa, E. 1999. Soil aggregate stability: A Review. Journal of sustainable agriculture. 14(2-3), 83-151.
Bradford, M.A., Wieder, W.R., Bonan, G.B., Fierer, N., Raymond, P.A., and Crowther, T.W. 2016. Managing uncertainty in soil carbon feedbacks to climate change. Nature Climate Change. 6(8), 751.
Bouyoucos, G.J. 1962. Hydrometer method improved for making particle size analyses of soils 1. Agronomy journal. 54(5), 464-465.
Bruun, T.B., Elberling, B., de Neergaard, A., and Magid, J. 2015. Organic carbon dynamics in different soil types after conversion of forest to agriculture. Land Degradation and Development. 26(3), 272-283.
Cañasveras, J.C., Barrón, V., Del Campillo, M.C., Torrent, J., and Gómez, J.A. 2010. Estimation of aggregate stability indices in Mediterranean soils by diffuse reflectance spectroscopy. Geoderma. 158(1-2), 78-84.
Cantón, Y., Solé-Benet, A., Asensio, C., Chamizo, S., and Puigdefábregas, J. 2009. Aggregate stability in range sandy loam soils relationships with runoff and erosion. Catena. 77(3), 192-199.
Chabala, L.M., Mulolwa, A., and Lungu, O. 2017. Application of ordinary kriging in mapping soil organic carbon in Zambia. Pedosphere. 27(2), 338-343.
Christakos, G. 2002. On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. Advances in Water Resources. 25(8-12), 1257-1274.
Dai, F., Zhou, Q., Lv, Z., Wang, X., and Liu, G. 2014. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators. 45, 184-194.
Dono, G., Cortignani R., Dell'Unto, D., Deligios, P., Doro, L., Lacetera, N., and Roggero, P.P. 2016. Winners and losers from climate change in agriculture: Insights from a case study in the Mediterranean basin. Agricultural Systems. 147, 65-75.
Filippi, P., Minasny, B., Cattle., S.R. and Bishop, T.F.A. 2016. Monitoring and Modeling Soil Change: The Influence of Human Activity and Climatic Shifts on Aspects of Soil Spatiotemporally. In Advances in Agronomy.139, 153-214.
Elbasiouny, H., Abowaly, M., Abu_Alkheir, A., and Gad, A. 2014. Spatial variation of soil carbon and nitrogen pools by using ordinary Kriging method in an area of north Nile Delta, Egypt. Catena. 113, 70-78.
Forth, H. 1990. Fundamentals of soil science. 8th Ed. New York: Wiley. ISBN: 0-471-52279-1.
Hamzehpour, N., and Rahmati, M. 2016. Investigation of soil salinity to distinguish boundary line between saline and agricultural lands in Bonab Plain, southeast Urmia Lake, Iran. Journal of Applied Sciences and Environmental Management. 20(4), 1037-1042.
Heimann, M., and Reichstein, M. 2008. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature. 451, 289.
Herrick, J.E., and Wander, M.M. 1997. Relationships between soil organic carbon and soil quality in cropped and rangeland soils: the importance of distribution, composition, and soil biological activity (pp. 405-425). Boca Raton, CRC Press.
Hoffmann, U., Hoffmann, T., Jurasinski, G., Glatzel, S., and Kuhn, N.J. 2014. Assessing the spatial variability of soil organic carbon stocks in an alpine setting (Grindelwald, Swiss Alps). Geoderma 232: 270-283.
Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, M.F., Bampa, F., van Wesemael. B., and Lorenz, K. 2014. Current status, uncertainty and future needs in soil organic carbon monitoring. Science of the total environment. 468, 376-383.
Jegajeevagan, K., Sleutel, S., Ameloot, N., Kader, M.A., and Neve, S. De. 2013. Organic matter fractions and N mineralization in vegetable‐cropped sandy soils. Soil Use and Management, 29(3), 333-343.
John, R.N. and Kim, S.P. 2002. Aggregate stability and size distribution. (pp. 201-414), In: Jacob, H.D., and Clarke Topp, G (Ed.), Methods of Soil Analysis. Part 4. Physical Methods. Soil Science Socienty of America, Madison, WI., USA.
Kennedy, M.J., Pevear, D.R., and Hill, R.J. 2002. Mineral surface control of organic carbon in black shale. Science 295(5555), 657-660.
Kumar, S., Lal, R., and Liu, D. 2012. A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma. 189,627-634.
Lal, R. 2004. Soil carbon sequestration impacts on global climate change and food security. Science. 304(5677), 1623-1627.
Li, J., and Heap, A.D. 2011. A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecological Informatics. 6(3-4), 228-241.
Liao, K.H., Xu, S.H., Wu, J.C., Ji, S.H., and Qing, L.I.N. 2011. Cokriging of soil cation exchange capacity using the first principal component derived from soil physico-chemical properties. Agricultural sciences in China. 10(8), 1246-1253.
Magdoff, F., and Weil, R.R., (Eds.). 2004. Soil organic matter in sustainable agriculture. CRC press.
Marchant, B.P., Villanneau, E.J., Arrouays, D., Saby, N.P.A., and Rawlins, B.G. 2015. Quantifying and mapping topsoil inorganic carbon concentrations and stocks: approaches tested in France. Soil Use and Management. 31(1), 29-38.
Martínez-Cob, A. 1996. Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain.
Math Works Inc. 1999. MatLab, the language of technical computing, using MATLAB version 5. the Mathwork Inc. http://www.mathworks.com, Natick.
Middleton, N.J. 2017. Desert dust hazards: A global review. Aeolian research. 24, 53-63.
Miklos, M., Short, M.G., McBratney, A.B., and Minasny, B. 2010. Mapping and comparing the distribution of soil carbon under cropping and grazing management practices in Narrabri, north-west New South Wales. Soil Research. 48(3), 248-257.
Mirzaee, S., Ghorbani-Dashtaki, S., Mohammadi, J., Asadi, H. and Asadzadeh, F. 2016. Spatial variability of soil organic matter using remote sensing data. Catena. 145, 118-127.
Mulder, V.L., De Bruin, S., Schaepman, M.E., and T.R. Mayr. 2011. The use of remote sensing in soil and terrain mapping—A review. Geoderma. 162(1-2), 1-19.
Novara, A., Gristina, L., Sala, G., Galati, A., Crescimanno, M., Cerdà, A., and Mantia, T. La. 2017. Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Science of the Total Environment. 576, 420-429.
Page, A.L., Miller, R.H. and Kenney, D.R. 1992. Methods of Soil Analysis Part II, Chemical and Mineralogical Properties, 2nd ed. SSSA Pub, Madison (1159 pp).
Page, A.L., Miller R.H., and Keeney, D.R. 1982. Methods of Soil Analysis: Part 2. Chemical and Microbiological Properties, 2nd edition. Agronomy, vol. 9. ASA, SSSA Publishing, Madison, WI, p. 1159.
Parras-Alcántara, L., Lozano-García, B., Keesstra, S., Cerdà, A., and Brevik, E.C. 2016. Long-term effects of soil management on ecosystem services and soil loss estimation in olive grove top soils. Science of the Total Environment. 571, 498-506.
Piccini, C., Marchetti, A., and Francaviglia, R. 2014. Estimation of soil organic matter by geostatistical methods: Use of auxiliary information in agricultural and environmental assessment. Ecological indicators. 36, 301-314.
Qi-yong, Y., Zhong-cheng, J., Wen-jun, L., and Hui, L. 2014. Prediction of soil organic matter in peak-cluster depression region using kriging and terrain indices. Soil and Tillage Research. 144,126-132.
Rhoades, J.D. 1982. Soluble salts. In: A.L. Page (ed.) Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties. Agronomy monograph no. 9. 2nd ed. SSSA and ASA,Madison,WI, 167-179.
Saia, S., Benítez, E., García-Garrido, J.M., Settanni, L., Amato, G., and Giambalvo, D. 2014. The effect of arbuscular mycorrhizal fungi on total plant nitrogen uptake and nitrogen recovery from soil organic material. The Journal of Agricultural Science. 152(3), 370-378.
Singh, A., Santra, P., Kumar, M., Panwar, N., and Meghwal, P.R. 2016. Spatial assessment of soil organic carbon and physicochemical properties in a horticultural orchard at arid zone of India using geostatistical approaches. Environmental Monitoring and Assessment. 188(9), 529.
Song, Y.Q., Yang, L.A., Li, B., Hu, Y., Wang, M., Zhou, A.L. and Liu, Y.L. 2017. Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging. Sustainability. 9(5), 754.
Stein, A., and Corsten, L.C.A. 1991. Universal kriging and cokriging as a regression procedure. Biometrics. 575-587.
Stevens, F., Bogaert, P., and Van Wesemael, B. 2015. Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery. Geoderma. 259, 93-103.
Su, P.A.N.G., Li, T.X., Wang, Y.D., Yu, H.Y., and Xi, L.I. 2009. Spatial interpolation and sample size optimization for soil copper (Cu) investigation in cropland soil at county scale using cokriging. Agricultural Sciences in China. 8(11), 1369-1377.
Sullivan, D.G., Shaw, J.N., and Rickman, D. 2005. IKONOS imagery to estimate surface soil property variability in two Alabama physiographies. Soil Science Society of America Journal. 69(6), 1789-1798.
Taxonomy, S. 2014. Key to soil taxonomy. Soil Survey staff. AID. USDA. SMSS. Technical Monograph, 19.
Triantafilis, J., Odeh, I.O.A. and McBratney, A.B. 2001. Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Science Society of America Journal. 65(3), 869-878.
Van Wesemael, B., Paustian, K., Andrén, O., Cerri, C.E., Dodd, M., Etchevers, J., and S. Ogle. 2011. How can soil monitoring networks be used to improve predictions of organic carbon pool dynamics and CO 2 fluxes in agricultural soils? Plant and Soil. 338(1-2), 247-259.
Viaud, V., Angers, D.A., and Walter, C. 2010. Toward landscape-scale modeling of soil organic matter dynamics in agroecosystems. Soil Science Society of America Journal. 74(6), 1847-1860.
Wang, T., Kang, F., Cheng, X., Han, H., Bai, Y., and Ma, J. 2017. Spatial variability of organic carbon and total nitrogen in the soils of a subalpine forested catchment at Mt. Taiyue, China. Catena. 155, 41-52.
Wang, K., Zhang, C., and Li, W. 2013. Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging. Applied Geography. 42, 73-85.
Wu, C., Wu, J., Luo Y., Zhang, L., and DeGloria, S.D. 2009. Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society of America Journal. 73(4), 1202-1208.
Wu, J., Norvell, W.A., Hopkins, D.G., Smith, D.B., Ulmer, M.G., and Welch, R.M. 2003. Improved prediction and mapping of soil copper by kriging with auxiliary data for cation-exchange capacity. Soil Science Society of America Journal. 67(3), 919-927.
Yates, S.R. and Warrick, A.W. 1987. Estimating Soil Water Content Using Cokriging 1. Soil Science Society of America Journal. 51(1), 23-30.
Ye, H., Huang, W., Huang, S., Huang, Y., Zhang, S., Dong, Y., and Chen, P. 2017. Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon. Spatial statistics. 20, 76-91.
Zaouche, M., Bel, L., and Vaudour, E. 2017. Geostatistical mapping of topsoil organic carbon and uncertainty assessment in Western Paris croplands (France). Geoderma Regional. 10, 126-137.
Zeng, C., Yang, L., Zhu, A.X., Rossiter, D.G., Liu, J., and Wang, D. 2016. Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method. Geoderma. 281, 69-82.
Zhang, S., Huang, Y., Shen, C., Ye, H., and Du, Y. 2012. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma. 171, 35-43.
Zhang, R., Shouse, P., and Yates, S. 1997. Use of pseudo-crossvariograms and cokriging to improve estimates of soil solute concentrations. Soil Science Society of America Journal. 61(5), 1342-1347. | ||
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