Modelling and mapping of soil moisture characteristics of the Punjab soils

Abstract

Soil moisture retention (SMR) is the most important soil physical property that directly capture the soil’s capacity to store plant available water in hydrological analysis and for determining irrigation water requirements for agricultural crops, Characterisation of SMR at Field Capacity (FC) and Permanent Wilting Point (PWP) is important for agricultural water management, but due to the lack of technical expertise and high cost of pressure plate apparatus, this information is not readily available. So, in this study we adopted alternate method “modelling approach”. Literature based nine pedotransfer functions (PTFs) were identified/selected for predicting FC and PWP with minimum available dataset of biophysical variables. About 200 soil samples were collected from different agroclimatic zones of Punjab to calibrate ( n = 78) and validate ( n = 40) the selected PTFs. The models were evaluated using statistical indicators like Root Mean Square Error (RMSE), Index of Agreement (d) and mean absolute error (MAE). The PTF model developed by Pidgeon [ 45 ] and by Gupta and Larson [ 27 ] for PWP performed best, prediction capability of resp. models on validation showed RMSE-0.05, d-0.72. MAE-0.049 at FC and RMSE-0.048, d-0.770. MAE-0.041 at PWP. With the best PTFs, database of biophysical variables such as of sand, silt, clay, and SOC extracted from 520 pedons of Punjab soils was used to predict FC and PWP. The predictions were then mapped using the QGIS software. Maps revealed that in Punjab soils FC, PWP, and AW ranged from 0.131 to 0.387, 0.009–0.228, 0.113–0.183 (cm 3 cm − 3 ), respectively. The findings highlight that validated PTF regression models can be used to predict soil moisture characteristics. Mapping the predicted information revealed the spatial variability of soil moisture content across the region that would help to configure the efficient utilisation of available water resources.