Aquacrop Model Calibration and Validation for Processing Tomato Crop in a Sub-Humid Climate

Battilani, A.; Letterio, Tommaso; Chiari, Giacomo

doi:10.17660/actahortic.2015.1081.19

Cited by 8 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2009) regarding maize simulation using AquaCrop. This may result from accumulated systematic errors in the simulation of crop evapotranspiration, either related to transpiration or evaporation terms (Battilani et al, 2015). We did not calibrate parameters directly related to evapotranspiration, which could have eliminated this error.…”

Section: Discussionmentioning

confidence: 99%

“…However, the simulation performance was worse than that of CC and biomass which were similar to the results of many studies using AquaCrop (Iqbal et al, 2014), and the model predicted a slightly higher soil water storage than the measured data in the late part of the crop growing season, which is similar to the results of Iqbal et al (2014) regarding wheat simulation and Steduto et al (2009) regarding maize simulation using AquaCrop. This may result from accumulated systematic errors in the simulation of crop evapotranspiration, either related to transpiration or evaporation terms (Battilani et al, 2015). We did not calibrate parameters directly related to evapotranspiration, which could have eliminated this error.…”

Section: Model Validationmentioning

confidence: 99%

See 1 more Smart Citation

Calibrating AquaCrop model using genetic algorithm with multi‐objective functions applying different weight factors

et al. 2021

View full text Add to dashboard Cite

Fast and efficient calibration is essential for the effective application of crop models. However, many formulas, parameters, and nonlinear responses in crop models make calibration difficult and time consuming. Using an intelligent optimization algorithm to calibrate the model has advantages in global search ability, optimization speed, and automatic calibration compared to the manual trial and error method, although performance may depend strongly on the objective function used. This study evaluated the use of an improved genetic algorithm, namely elite genetic algorithm (EGA), for calibration of a water-driven crop model (AquaCrop) using three different objective functions separately, which comprise observed variables from harvest and in-season data and differ in calculating the weight factors of these variables. Observations of maize (Zea mays L.) and wheat (Triticum aestivum L.) under different irrigation treatments were used for model calibration and validation. The results showed satisfactory calibration performances for the EGA applying the three objective functions, that is, the coefficient of determination and index of agreement were all >0.97 for canopy cover (CC) and biomass of both maize and wheat, and also showed good agreement between simulated and observed soil water storage. The three objective functions differed in calibration speed and performance, since they differ in error source and calculation, moreover, they performed similar or better than manual calibration. The validation results showed that the AquaCrop model calibrated by the EGA can predict CC, biomass, yield, and soil water storage of maize and wheat. In general, calibration of the AquaCrop model using EGA greatly improves the model application efficiency for irrigation management. INTRODUCTIONCrop models are important tools for designing and assessing agricultural production systems, and they have been widely

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

Calibrating AquaCrop model using genetic algorithm with multi‐objective functions applying different weight factors

et al. 2021

View full text Add to dashboard Cite

show abstract

The probabilistic behavior of AquaCrop parameters: a Monte-Carlo study

Etedali

Adabi

Gorgin

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Crop growth models are multi-outputs and can be valuable tools for the quanti cation of crop growth and production. However, these models usually require several input data, which are costly, time-consuming, and sometimes impossible to measure. These input parameters are mostly estimated by calibration and inverse solving. In this study, ve output variables of the AquaCrop model, including soil evaporation, crop transpiration, evapotranspiration, crop biomass at maturity, and grain yield, were investigated to study 47 genotypic input parameters on the output time series of the model for wheat in the Qazvin Synoptic Station. The SAFE toolbox in the Matlab environment was used to study the GSA and uncertainty of inputs and their impact on outputs. Applying the Monte-Carlo-based methods on closed-source models is not as easy as open-source models. The uncertainty in the outputs of the validated AquaCrop model in simulating wheat yield in the Qazvin Synoptic Station over 36 years was analyzed using the Generalized Likelihood Uncertainty Estimation (GLUE) method.The uncertainty in the outputs of the validated AquaCrop model in simulating wheat yield in the Qazvin station over 36 years was analyzed using the GLUE method. Using RMSE<0.9 as the threshold in a 95% con dence level, the best parameter sets included all the observations. Results showed that evaporation and yield are the least reliable outputs of the AquaCrop model that have not been calibrated, while others consider them reliable. After that, the new domain of each output was determined based on the two indexes. Then we modi ed the domain to reduce its size. Finally, the probabilistic behavior of each input on outputs were introduced by the Easy Fit software. The signi cant result of this study is that the probabilistic nature of the input parameter that is calibrated for a particular output variable can differ from other output variables. Also, when we trust a speci c run of the model (calibrated run) as observed data, the uncertainty bounds covering are very high. Finally, we utilized the GLUE to optimize multi-output models by introducing one unique, optimized PDF for each input parameter for all outputs estimated by collecting all accepted output series of all target outputs.

show abstract

Optimal model-based deficit irrigation scheduling using AquaCrop: A simulation study with cotton, potato and tomato

Linker

Ioslovich

Sylaïos

et al. 2016

Agricultural Water Management

105

View full text Add to dashboard Cite

Aquacrop Model Calibration and Validation for Processing Tomato Crop in a Sub-Humid Climate

Cited by 8 publications

References 0 publications

Calibrating AquaCrop model using genetic algorithm with multi‐objective functions applying different weight factors

Calibrating AquaCrop model using genetic algorithm with multi‐objective functions applying different weight factors

The probabilistic behavior of AquaCrop parameters: a Monte-Carlo study

Optimal model-based deficit irrigation scheduling using AquaCrop: A simulation study with cotton, potato and tomato

Contact Info

Product

Resources

About