Modeling tomato root water uptake influenced by soil salinity under drip irrigation with an inverse method

The influence of soil salinity on plant root water uptake (RWU) must be considered in order to promote efficient irrigation management. However, due to challenges in parameterization, modeling RWU response to salinity is problematic. We set out to characterize the influence of soil salinity on tomato RWU under conditions of irrigation with high salinity water and to improve methods for optimizing parameters of RWU model. Experiments were conducted in a greenhouse over two seasons (2016 and 2017). Three irrigation water salinity treatments [0.4 (S1), 3.4 (S2) and 6.4 (S3) mg cm−3] were evaluated. The results showed that soil water content and salinity increased with the irrigation water salinity, especially in the top soil layers, and that RWU was reduced by the increased root zone salinity. An inverse method based on a one-dimensional equation of soil water movement was used to estimate the RWU rate distribution under drip irrigation and determined to be reliable. The parameter value of the normalized root length distribution (NRLD) function was fitted through the estimated RWU rate distributions for treatment S1 without the effect of salinity stress. The fitted NRLD function was used to optimize the parameters of the salinity stress reduction factor (β) through the measured soil moisture and salinity of treatments S2 and S3 in 2016. With the optimized parameters of β, the RWU rates of treatments S2 and S3 in 2017 were simulated. The simulated RWU rates based on the RWU model agreed well with the estimated RWU rates using the inverse method, with root mean squared error (RMSE) and normalized root mean squared error (NRMSE) less than 0.00067 cm3 cm−3 d−1 and 12.36%, and index of agreement (IA) higher than 0.94. These findings can be used to estimate the NRLD distribution and determine actual crop water requirements under saline conditions with no root distribution data required. This should contribute to efficient and sustainable irrigation scheduling.