Abstract
Field capacity (FC) and permanent wilting point (PWP) are the two vital hydraulic properties that determine the availability and retention of the water for plant growth. In the present study, profile FC and PWP were mapped in Karnataka using 5352 soil profile datasets. Class pedotransfer function was developed based on soil texture classes, and the total profile FC and PWP were computed for 1 m soil depth. Primary and secondary terrain attributes, vegetation indices, lithology and climatic datasets were used as environmental covariates for the prediction of hydraulic properties. Random forest regression kriging model was calibrated using 80% of total datasets and validated using 20% of datasets. Root mean square error (RMSE), mean error (ME) and the coefficient of determination (R2) were calculated to assess the performance of models. The model recorded RMSE of 127 mm for total profile field capacity (TFC) and RMSE of 87 mm for total profile permanent wilting point (TPWP). The predicted TFC and TPWP in Karnataka are ranged from 58 to 521 mm and 35 to 342 mm, respectively. Available water content (AWC) was calculated as the difference between TFC and TPWP, and the results showed that northern Karnataka has lower AWC (< 50 mm) compared to the south and western part of Karnataka. The present high-resolution (250 m) maps of TFC and TPWP are useful for irrigation scheduling, land use planning and different crop modeling studies.
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References
Al Majou, H., Bruand, A., Duval, O., & Cousin, L. (2007). Variation of the water-retention properties of soils: Validity of class-pedotransfer functions. Comptes Rendus Geoscience, 339(9), 632–639.
Arnold, J. G., & Fohrer, N. (2005). SWAT. Current capabilities and research opportunities in applied watershed modeling. Hydrological Processes, 19(3), 563–572.
Breiman, L. (2001). Random forests. Machine Learning. https://doi.org/10.1023/A:1010933404324
Dharumarajan, S., Hegde, R., Lalitha, M., Kalaiselvi, B., & Singh, S. K. (2019a). Pedotransfer functions for predicting soil hydraulic properties in semi-arid regions of Karnataka Plateau, India. Current Science, 116(7), 1237–1246.
Dharumarajan, S., Hegde, R., Janani, N., & Singh, S. K. (2019b). The need for digital soil mapping in India. Geoderma Regional. https://doi.org/10.1016/j.geodrs.2019.e00204
Dharumarajan, S., Vasundhara, R., Suputhra, A., Lalitha, M., & Hegde, R. (2020). Prediction of soil depth in Karnataka using digital soil mapping approach. Journal of the Indian Society of Remote Sensing, 48, 1593–1600.
Dharumarajan, S., Kalaiselvi, B., Suputhra, A., Lalitha, M., Hegde, R., Singh, S. K., & Lagacherie, P. (2020). Digital soil mapping of key GlobalSoilMap properties in northern Karnataka plateau. Geoderma Regional. https://doi.org/10.1016/j.geodrs.2019.e00250
Dharumarajan, S., & Hegde, R. (2020). Digital mapping of soil texture classes using Random Forest classification algorithm. Soil Use Management. https://doi.org/10.1111/sum.12668
Dobarco Román, M., Bourennane, H., Arrouays, D., Saby, N., Cousin, I., & Martin, M. (2019). Uncertainty assessment of GlobalSoilMap soil available water capacity products: a French case study. Geoderma, 2019(344), 14–30.
Gooley, L., Huang, J., Pagé, D., & Triantafilis, J. (2014). Digital soil mapping of available water content using proximal and remotely sensed data. Soil Use Management, 30, 139–151. https://doi.org/10.1111/sum.12094
Hong, S. Y., Minasny, B., Han, K. H., Kim, Y., & Lee, K. (2013). Predicting and mapping soil available water capacity in Korea. Peer J, 1, e71. https://doi.org/10.7717/peerj.71
Lagacherie, P. (2008). Digital soil mapping: A state of the Art. In A. E. Hartemink, A. McBratney, & M. Mendonça-Santos (Eds.), Digital soil mapping with Limited Data. Dordrecht: Springer. https://doi.org/10.1007/978-1-4020-8592-5_1
Leenaars, J. G. B., Claessens, L., Heuvelink, G. B. M., Hengl, T., Ruiperez González, M., van Bussel, L. G. J., Guilpart, N., Yang, H., & Cassman, K. G. (2018). Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa. Geoderma, 324, 18–36. https://doi.org/10.1016/j.geoderma.2018.02.046
Liaw, A., & Wiener, M. (2002). Classification and regression by Random Forest. R News, 2, 18–21.
Malone, B. P., McBratney, A. B., Minasny, B., & Laslett, G. M. (2009). Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154, 138–152.
Malone, B. P., Luo, Z., He, D., Viscarra Rossel, R. A., & Wang, E. (2020). Bioclimatic variables as important spatial predictors of soil hydraulic properties across Australia’s agricultural region. Geoderma Regional. https://doi.org/10.1016/j.geodrs.2020.e00344
McBratney, A. B., Mendonça-Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117, 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4
Minasny, B., McBratney, A. B., & Bristow, K. L. (1999). Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma, 93(3–4), 225–253. https://doi.org/10.1016/S0016-7061(99)00061-0
Minasny, B., & McBratney, A. B. (2002). Uncertainty analysis for pedotransfer functions. European Journal of Soil Science, 53(3), 417–429.
Nemes, A., Schaap, M. G., & Wösten, J. H. M. (2003). Functional evaluation of pedotransfer functions derived from different scales of data collection. Soil Science Society of America Journal, 67(4), 1093–1102. https://doi.org/10.2136/sssaj2003.1093
Nijssen, B., O’Donnell, G. M., Lettenmaier, D. P., Lohmann, D., & Wood, E. F. (2001). Predicting the discharge of global rivers. Journal of Climate, 14, 3307–3323. https://doi.org/10.1175/15200442(2001)014%3c3307:PTDOGR%3e2.0.CO;2
O’Leary, G. J., Li Liu, D., Ma, Y., Li, F. Y., McCaskill, M., Conyers, M., et al. (2016). Modelling soil organic carbon 1. Performance of APSIM crop and pasture modules against long term experimental data. Geoderma, 264, 227–237. https://doi.org/10.1016/j.geoderma.2015.11.004
Parton, W. J., Schimel, D. S., Cole, C. V., & Ojima, D. S. (1987). Analysis of factors controlling soil organic matter levels in Great Plains Grasslands. Soil Science Society of America Journal, 51(5), 1173–1179. https://doi.org/10.2136/sssaj1987.03615995005100050015x
Piedallu, C., Gegout, J. C., Bruand, A., & Seynave, I. (2011). Mapping soil water holding capacity over large areas to predict potential production of forest stands. Geoderma, 160(3–4), 355–366. https://doi.org/10.1016/j.geoderma.2010.10.004
Poggio, L., Gimona, A., Brown, I., & Castellazzi, M. (2010). Soil available water capacity interpolation and spatial uncertainty modelling at multiple geographical extents. Geoderma, 160(2), 175–188. https://doi.org/10.1016/j.geoderma.2010.09.015
Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., Ruget, F., Nicoullaud, B., et al. (1998). STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomy, 18(5–6), 311–346.
Romano, N., & Palladino, M. (2002). Prediction of soil water retention using soil physical data and terrain attributes. Journal of Hydrology, 265, 56–75.
Santra, P., Kumar, M., Kumawat, R. N., Painuli, D. K., Hati, K. M., Heuvelink, G. B. M., et al. (2018). Pedotransfer functions to estimate soil water content at field capacity and permanent wilting point in hot Arid Western India. Journal of Earth System Science, 127, 35. https://doi.org/10.1007/s12040-018-0937-0.
Styc, Q., & Lagacherie, P. (2019). What is the best inference trajectory for mapping soil functions: An example of mapping soil available water capacity over Languedoc Roussillon (France). Soil Systems, 3, 34.
Tao, F., Yokozawa, M., Hayashi, Y., & Lin, E. (2003). Terrestrial water cycle and the impact of climate change. Ambio, 32, 295–301. https://doi.org/10.1579/0044-7447-32.4.295
Tetegan, M., Richer de Forges, A. C., Verbeque, B., Nicoullaud, B., Desbourdes, C., Bouthier, A., Arrouays, D., & Cousin, I. (2015). The effect of soil stoniness on the estimation of water retention properties of soils: A case study from central France. CATENA, 129, 95–102.
Ugbaje, S. U., & Reuter, H. I. (2013). Functional digital soil mapping for the prediction of available water capacity in Nigeria using legacy data. Vadose Zone Journal. https://doi.org/10.2136/vzj2013.07.0140
Viscarra Rossel, R. A., Chen, C., Grundy, M. J., Searle, R., Clifford, D., & Campbell, P. H. (2015). The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research, 53, 845–864. https://doi.org/10.1071/SR14366
Wösten, J. H. M., Lilly, A., Nemes, A., & Le Bas, C. (1999). Development and use of a database of hydraulic properties of European soils. Geoderma, 90(3–4), 169–185. https://doi.org/10.1016/S00167061(98)00132-3
Acknowledgements
This work is part of IndianSoilGrid project supported by ICAR-National Bureau of Soil Survey and Land Use Planning. The authors thank C.R. Shiva Prasad, R.S. Reddy, S. Thayalan, Prabhakara, P. Krishnan, K.V. Niranjana, B.A. Dhanorkar, Sujatha, Arthi Koyel and other scientists and technical officers of ICAR-NBSS&LUP who have involved in Soil Resource mapping project of Karnataka and Sujala-III project. The authors also thank Director, ICAR-NBSS&LUP for supporting IndianSoilGrid project.
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Dharumarajan, S., Hegde, R., Lalitha, M. et al. Predicting and Mapping of Soil Hydraulic Properties in Karnataka. J Indian Soc Remote Sens 49, 1623–1631 (2021). https://doi.org/10.1007/s12524-021-01336-3
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DOI: https://doi.org/10.1007/s12524-021-01336-3