Sensitivity analysis and evaluation of Aquacrop model in simulating water productivity and quinoa yield under different irrigation water amount and salinity management and Biochar and NanoBiochar application

Background and purpose: Plant models are a suitable tool for simulating important agricultural parameters. Due to the existence of environmental stresses in each region, plant models should be evaluated and approved in each region. In recent years, many models have been used to investigate the relationship between water, soil and plants. One of these models is AquaCrop model. The mentioned model should be measured and evaluated for each product and in each specific region. The basis of this model is the reaction of the yield to water productivity, and it simulates the yield by using climate, plant, soil and management variables. Sensitivity analysis helps researchers to have enough information about the effect of each parameter and the amount of its changes in the calibration stage.
Materials and methods: The research was carried out in the form of a completely randomized factorial design in the second half of November 1400 and 1401 in the greenhouse. The treatments include three irrigation water treatments (60, 80, and %100 of irrigation water, I1, I2, and I3, respectively), three salinity levels (1, 4, and 7 dS/m, S1, S2, and S3, respectively). There were two types of amendment materials (Biochar (B) and Nanobiochar (NB)) and three levels of Biochar and Nanobiochar (0, 2 and 4% ). At the end of the harvest season, the yield of the product was measured and the water productivity was calculated.Crop data of the one year were used for model calibration and crop data of the second year were used for model validation. The root mean square error (RMSE), mean bias error (MBE), R2 and relative error percentage (RE) were used to test the accuracy and effectiveness of the model. Also, in the research, the sensitivity of the model to the humidity parameters in crop capacity, wilting and in saturated state, plant coefficient for transpiration, effective root depth, upper and lower limit of soil water discharge coefficient for plant development, maximum canopy growth, growth coefficient and reduction of cover and Normalized water productivity was investigated.
Findings: According to the comparison of the measured and predicted values of the yield and water productivity of quinoa and the calculation of statistical evaluation indices in both calibration and validation stages, it can be stated that the AquaCrop model has been able to simulate the yield and water productivity in the conditions of using water with different amounts and qualities and Biochar and nanBiochar modifiers. Relative error percentage (RE), root mean square error (RMSE), mean bias error (MBE) and coefficient of determination (R2) for the yield in the validation stage with Biochar amendment material are 0.66, 33.38, respectively. 24.12 and 0.98 and 0.29, 0.33, 0.11 and 0.96 were obtained for water productivity in this stage, respectively. Relative error percentage (RE), root mean square error (RMSE), mean bias error (MBE) and R2 for the yield in validation stage with NanoBiochar amendment material are 0.12, 22.08, respectively. 5.61 and 0.98 and for water productivity at this stage, 0.17, 0.29, 0.05 and 0.96 were calculated respectively. Considering the lower values of the error statistics in the conditions of using the NanoBiochar amendment, it can be said that the model has been able to simulate the yield and water productivity better in these conditions.
Conclusion: According to the obtained results, it can be stated that the Aquacrop model is an acceptable reliable level using the simulation of the yield and water productivity of quinoa plant under different quantitative and qualitative treatments of irrigation water and soil amendment are used and help farmers, designers, experts and agricultural managers as a powerful and efficient tool to choose optimal irrigation management.