Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization

Paredes, Paula; Melo-Abreu, J.P. de; Alves, Isabel; Pereira, Luís S.

doi:10.1016/j.agwat.2014.06.002

Cited by 115 publications

(96 citation statements)

References 54 publications

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“…Nevertheless, Paredes et al (2014) reported insufficient accuracy of AquaCrop in simulating the SWC of maize and concluded that AquaCrop was not suitable for irrigation scheduling purposes. They identified the problem to daily evapotranspiration calculation and its partitioning of AquaCrop and advised to revise the evapotranspiration partitioning based on the approaches proposed in FAO56.…”

Section: Soil Water Contentmentioning

confidence: 95%

“…Katerji et al (2013) also reported that the canopy expansion equations of AquaCrop did not react to water rehydration in terms of increased rate of canopy expansion and biomass after an imposed severe water stress, which is likely in real field practices. Several researchers have also reported that AquaCrop performance was not good under severe water stress conditions of maize (Paredes et al 2014;Katerji et al 2013;Hsiao et al 2009;Heng et al 2009), sunflower (Todorovic et al 2009), quinoa (Geerts et al 2009), cotton (Farahani et al 2009), and canola (Zeleke et al 2011).…”

Section: In-season Biomass Developmentmentioning

confidence: 95%

“…Another important reason is choosing non-adjusted crop coefficient (K c ). Choosing low K c would potentially overestimate SWC and vice versa regarding the soil evaporation and crop transpiration partitioning (Paredes et al 2014). Therefore, in addition to precise modeling of SWC, choosing a precise K c improves the modeling performance of other components of AquaCrop.…”

Section: Soil Water Contentmentioning

confidence: 97%

“…Nevertheless, in the literature there is no consistent tendency to conclude if AquaCrop overestimates or underestimates biomass of maize. Discrepancy in the tendency to overestimate or underestimate strongly depends on the quality of measured data ), accuracy of calibrating the model parameters Steduto et al 2009), rainfed conditions (Mebane et al 2013;Heng et al 2009) or irrigation managements (Abedinpour et al 2012), water stress level and timing (Paredes et al 2014;Heng et al 2009;Hsiao et al 2009), and the environmental growing conditions (Wellens et al 2013). In a sensitivity analysis, Geerts et al (2009) reported that soil water depletion thresholds (P upper and P lower ) of canopy expansion and senescence were the key parameters for precise growth and biomass simulations.…”

Section: In-season Biomass Developmentmentioning

confidence: 98%

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Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Ahmadi

Mosallaeepour

Kamgar-Haghighi

et al. 2015

Water Resour Manage

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The AquaCrop model was used to simulate maize growth and soil water content under full and deficit irrigation managements as 1.2, 1, 0.8, and 0.6 of the potential crop water requirement. Generally, the RMSEs in simulating soil water content in calibration and validation were 0.01-0.039 and 0.012-0.037 m 3 m −3 , respectively, that overall corresponds to 3-14 % error. For the in-season biomass development, the RMSEs in calibration varied between 2.16 and 2.73 Mg ha −1 , while they varied between 1.97 and 5.19 Mg ha −1 in validation for the four irrigation managements. The model showed poor performance for simulating biomass late in the season under deficit irrigation managements. The RMSEs of final grain yield simulation were 0.71 and 1.77 Mg ha −1 that corresponded to 7 and 18 % error in calibration and validation, respectively. Likewise, the RMSEs for simulating the final biomass in calibration and validation were 1.29 and 2.21 Mg ha −1 that equals to 6 and 10 % error, respectively. Results demonstrated that AquaCrop is a useful decision-making tool for investigating deficit irrigations and maize growth in the region. However, in agreement with the findings in earlier studies on AquaCrop, the model showed insufficient accuracy in simulating final grain yield and biomass under moderate to severe water stresses. It is suggested that AquaCrop would benefit of including some calibrating parameters about the root distribution pattern in the soil because it is a water-driven model and highly depends on the accurately simulated water uptake from the soil profile.

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Section: Soil Water Contentmentioning

confidence: 95%

Section: In-season Biomass Developmentmentioning

confidence: 95%

Section: Soil Water Contentmentioning

confidence: 97%

Section: In-season Biomass Developmentmentioning

confidence: 98%

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Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Ahmadi

Mosallaeepour

Kamgar-Haghighi

et al. 2015

Water Resour Manage

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“…RMSE expresses the variance of the error; and EF is a normalized statistic that determines the relative magnitude of the residual variance compared with variance of the measured data [51,52].…”

Section: Statistical Analysis and Criteria Of Model Evaluationmentioning

confidence: 99%

Root Development of Transplanted Cotton and Simulation of Soil Water Movement under Different Irrigation Methods

Zhang

Liu

Sun

et al. 2017

Water

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Abstract:Winter wheat and cotton are the main crops grown on the North China Plain (NCP). Cotton is often transplanted after the winter wheat harvest to solve the competition for cultivated land between winter wheat and cotton, and to ensure that both crops can be harvested on the NCP. However, the root system of transplanted cotton is distorted due to the restrictions of the seedling aperture disk before transplanting. Therefore, the investigation of the deformed root distribution and water uptake in transplanted cotton is essential for simulating soil water movement under different irrigation methods. Thus, a field experiment and a simulation study were conducted during 2013-2015 to explore the deformed roots of transplanted cotton and soil water movement using border irrigation (BI) and surface drip irrigation (SDI). The results showed that SDI was conducive to root growth in the shallow root zone (0-30 cm), and that BI was conducive to root growth in the deeper root zone (below 30 cm). SDI is well suited for producing the optimal soil water distribution pattern for the deformed root system of transplanted cotton, and the root system was more developed under SDI than under BI. Comparisons between experimental data and model simulations showed that the HYDRUS-2D model described the soil water content (SWC) under different irrigation methods well, with root mean square errors (RMSEs) of 0.023 and 0.029 cm 3 cm −3 and model efficiencies (EFs) of 0.68 and 0.59 for BI and SDI, respectively. Our findings will be very useful for designing an optimal irrigation plan for BI and SDI in transplanted cotton fields, and for promoting the wider use of this planting pattern for cotton transplantation.

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Multiple model averaging methods for predicting regional rice yield

Shi²,

Zhan

2023

Agronomy Journal

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Using multiple crop models in an ensemble can generate more accurate crop growth predictions than only using an individual crop model. However, few studies have investigated the performances of individual crop models and multiple model averaging (MMA) methods for predicting regional rice (Oryza sativa L.) yield with limited inputs and observations. This study assessed the performances of three individual crop models, that is, AquaCrop, WOFOST, and Oryza version 3 (OryzaV3), and five MMA methods for predicting regional rice yield. Results showed that the AquaCrop model achieved better performances than the WOFOST and OryzaV3 models for predicting regional rice yield, especially for the early‐season rice yield, which implied that a crop model with a simple structure was powerful for making accurate regional rice yield predictions. The MMA methods achieved better performances than the AquaCrop model for predicting the late‐season rice yield; however, the AquaCrop model and three information criterion (IC) methods, that is, Akaike IC (AIC), Bayesian IC (BIC), and AICc (bias‐corrected), achieved more acceptable prediction accuracies of the early‐season rice yield than the other MMA methods. Overall, not all the MMA methods could necessarily produce more accurate regional rice yield prediction than individual crop models. This study suggested using the simple‐structure crop models and the AIC, AICc, and BIC methods for making accurate regional crop yield prediction when limited data are available for model parameters calibration.

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Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization

Cited by 115 publications

References 54 publications

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Root Development of Transplanted Cotton and Simulation of Soil Water Movement under Different Irrigation Methods

Multiple model averaging methods for predicting regional rice yield

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