Modeling of Water Holding Capacity Using Readily Available Soil Characteristics

Soil water holding capacity (WHC) and its spatial variability is heavily affected by soil organic matter and texture and had significant influence for varied application such as regulating plant growth, soil drainage and soil functional attributes. The present study was conducted in central region of the state of Arunachal Pradesh with the aim of modeling the WHC using readily available soil characteristics. Soil parameters were analyzed using standard methodologies, and WHC modeling was done by developed predictive model using partial least squares regression (PLSR) technique. Water holding capacity (WHC) and bulk density decrease with the increase in altitude. Soil was acidic in nature, and acidity decreases with increasing altitude. Soil texture ranges from sandy loam to sandy clay loam. There was a significant increase in porosity with the increase in altitude and decrease in soil organic matter. The developed models using PLSR technique showed good predictivity based on different statistical performances and error indices ranged R-2 = 0.73-0.77; root-mean-square error (RMSE) = 5.69-6.08%; and mean squared error (MSE) = 32.45-37.02%). Variable importance in prediction analysis reflects the relative importance of each soil variables in the developed prediction models. It showed that clay was among the most important influencing variable of WHC followed by soil moisture, altitude and silt. Hence, clay percent can be considered among the most important variable in the model for WHC prediction. Correlation analysis of the variables also showed that water holding capacity was found to be strongly positively correlated (r(2) = 0.88) with clay content followed by bulk density (r(2) = 0.62) and organic matter (r(2) = 0.43). However, a negative relationship was observed with other soil parameters. Based on the analysis of the results, the PLSR developed model results good fit with the selected variables. Hence, it may be concluded that the developed model could be successfully used to predict WHC using identified predictors under limiting data conditions. The findings showed altitude as one of the suitable predictors along with RASCs, as inclusion of this resulted in improvement in predictive accuracy of developed models of WHC. Hence, inclusion of altitude as predictor is being recommended for further studies.