Modifying water absorption process to enhance model performance on biomass accumulation under soil water and salt stresses

• The canopy development and biomass accumulation of alfalfa was well simulated by APSIM-Lucerne model. • Two modification methods of plant water extraction under water and salt stress were proposed. • The modified APSIM performance on biomass was improved under water and salt stresses. • The power model indicated a higher accuracy under severe salt stress. • The modification method could be used to chart irrigation strategies under varying soil conditions. Irrigation is expected to playing a pivotal role against climate change and cropping systems intensification, whilst the secondary soil salinization caused by imprecise irrigation is posing a serious challenge to crop production. Despite increasing attention has been paid to food crops, a more profound understanding of water deficit and soil salinity constraints on forage production is greatly desired, and the response of forage growth to enhanced water stress raised by salinity needs to be considered in crop models. For this purpose, the water absorption module in the APSIM-Lucerne model was extended with two modules that calculate the reduction of the water extraction coefficient ( KL ) from the chloride concentration ( Cl ) in soil to enable the simulation of inhibited plant growth under enhanced water stress due to soil salinity. Both modules assume that KL decreases with Cl above a threshold Cl . In the first module, the decrease is exponential (exponential KL modifier), whilst in the second module, KL decreases according to a power law (power KL modifier) until it reaches zero at another higher threshold Cl . In field experiments, soil water content, leaf area index and biomass were measured for alfalfa grown under different combinations of irrigation amounts and salinity levels. The performance of the modified models (exponential and power KL modifiers) and the original model (no KL modifier) to reproduce these data were compared. Results reveal that both modified models showed improved prediction of canopy development and biomass accumulation, while the modified model with the power KL modifier exhibited a comparatively higher predictability under high salinity level, with a relative root mean square error of 23%-27% for biomass, better than 24%-31% of the exponential model and 43%-45% of the original model. The soil water dynamics were not well predicted by the modified models due to an underestimation of soil evaporation which requires further investigation. The study improved the predictability of crop models for forage crop development and production under coupling soil water and salt stresses via the optimization of the dynamic plant water extraction process, thus can be used to chart more reliable irrigation strategies under various pedoclimatic conditions.