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Information on soil hydraulic properties is a prerequisite in irrigation management decisions and crop planning. Such information on soils of the black soil region (BSR) occupying of India is sparse. Soil profile information for 49 representative sites (244 samples) was collected and used for analysis. Ten different functions were evaluated for their efficacy to describe soil water retention characteristics (SWRC) of the BSR soils. Campbell model fitted to measured SWRC data with relatively lower root mean square error (), higher degree of agreement ), and lower absolute error on average (). The next best description was by van Genuchten (VG) function with RMSE (), , and MAE (). Pedotransfer functions (PTF) were developed to predict field capacity (FC) and permanent wilting point (PWP) using nearest neighbor (kNN) algorithm and artificial neural networks (ANN). Four levels of input information used for point PTF development include (1) textural data (data on sand, silt, and clay fraction-SSC), (2) level 1+bulk density data (SSCBD), (3) level 2+organic matter (SSCBDOM), and (4) level 1+organic matter (SSCOM). The RMSE of predictions by kNN PTFs ranged from 0.0346 to with an average of . The ANN PTFs performed with an average RMSE of and a range of 0.0367 to . Relatively better estimates of were obtained using SSCBD-based PTF. Accuracy of FC and PWP estimates obtained by using analytical functions was relatively greater than the estimates by kNN and ANN PTFs. Campbell and VG functions were relatively more accurate. The study demonstrated the efficacy of kNN technique vis-a-vis neural regression with the additional benefit of appending the development data as and when desired. The proposed PTFs could be useful in making irrigation management decisions for BSR soils of India. Identification of the most suitable SWRC function for the study soils will help in crop modeling/water balance studies of the region. [ABSTRACT FROM AUTHOR]

Irrigation management in vertisols is one of the major challenges to increase agricultural productivity in India and many developing countries. Unfortunately, information on hydraulic properties of these soils is very sparse. In an attempt to understand these soils for better management, 10 different functions were evaluated for their efficacy to describe soil-water retention characteristics (SWRC) of vertisols of India, and point pedotransfer functions (PTFs) were developed by using a nearest neighbor (k-NN) algorithm as an alternative to widely used artificial neural networks (ANN) for prediction of available water capacity (AWC). Soil profile information of 26 representative sites comprising 157 soil samples was used for analysis. The Campbell model fit to measured SWRC data better than any other model, with relatively lower root mean square error (RMSE) (0.0199), higher degree of agreement (0.9867), and lower absolute error on an average (0.0134). Three other functions, namely, modified Cass-Hutson, Brooks-Corey, and van Genuchten, also described the SWRC data with acceptable accuracy. Four levels of input information were used for point pedotransfer function (PTF) development: (1) textural data [data on sand, silt, and clay fraction (SSC)]; (2) Level 1+bulk density data (SSCBD); (3) Level 2+organic matter (SSCBDOM); and (4) Level 1+organic matter (SSCOM). The RMSE in predictions by k-NN PTFs ranged from 0.0339 to 0.0450 m3 m-3 with an average of 0.0403 m3 m-3. The ANN PTFs performed with average RMSE 0.0426 m3 m-3 and a range of 0.0395 to 0.0474 m3 m-3. The k-NN algorithm provided a viable alternative to neural regression with marginally better performance and the benefit of flexibility in the appending reference database. The results are significant because SWRC data are still in the development stage in India, and k-NN PTFs would have a greater value because of the flexibility. [ABSTRACT FROM AUTHOR]