The effect of ırrigation water management and fertilizer amount on aquacrop accuracy and efficiency for tomato yield and water use efficiency simulation. Ebrahimipak NA, Egdernezhad A, Tafteh A and Ansari MA (2022).Agricultural Water Management, 274 (July), 107949. Evaluation of AquaCrop model for greenhouse cherry tomato with plastic film mulch under various water and nitrogen supplies. Cheng M, Wang H, Fan J, Xiang Y, Liu X, Liao Z, Elsayed A, Zhang F and Li Z (2022).International Journal of Innovative Research in Science, Engineering and Technology, 3(4): 1-8. Validation of the aquacrop model for full and deficit ırrigated potato production in environmental condition of Korça Zone, South-Eastern Albania. Bitri M, Grazhdani S and Ahmeti A (2014).Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review. Ara I, Turner L, Harrison MT, Monjardino M, deVoil P and Rodriguez D (2021).Agricultural Water Management, 201 (January), 46-57. Surface irrigation simulation-optimization model based on meta-heuristic algorithms. Akbari M, Gheysari M, Mostafazadeh-Fard B and Shayannejad M (2018).Assessing the impacts of population growth and climate change on performance of water use systems and water allocation in Kano River basin, Nigeria. Agricultural Water Management, 271 (December 2021), 107741. A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR. Abdollah S, Ali A, Ritzema H, Dam J Van and Hellegers P (2022).Thus, the study ascertained that crop simulation models such as AquaCrop and optimization algorithms can be used to identify optimal parameters that if maintained on the field could improve the yield of crops such as tomato. The GA revealed that the yield and biomass of tomato can be increased by 57% and 23% respectively if the optimized parameters were either attained on the field experiment or used during simulation. The optimization algorithm terminated when the optimal values of yield and biomass were 4.496 〖ton ha〗^(-1) and 4.90 〖ton ha〗^(-1) respectively. The CRM values of -0.11, -0.06 and -0.20 were obtained for the yield, biomass and water productivity of tomato which indicated a very slight over-estimation of the observed results by the AquaCrop model. All the statistical indices except CRM used in comparing the simulated and observed values indicated good agreement. The governing equation of AquaCrop simulation software was then optimized using the evolutionary optimization method of GA with MATLAB programming software. The Root Mean Square Error (RMSE), Coefficient of Residual Mass (CRM) Normalized Root Mean Square Error (NRMSE) and Modelling efficiency (EF) were used to compare the observed and simulated values. The AquaCrop model was firstly calibrated using the data obtained from the field and was later used to simulate the observed yield, water productivity and biomass of tomato. This study simulate and optimize the yield and yield parameters of tomato using AquaCrop model and genetic algorthm (GA) respectively.
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