Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

Sima, Matthew; Fang, Qiannan; Qi, Zhiming; Yu, Qiang

doi:10.1002/agj2.20088

Cited by 9 publications

(8 citation statements)

References 49 publications

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“…In the current case, RZWQM2-S daily T was generally in good agreement with T M with the Flow32-1K equipment. Similarly, Qi et al [34] and Sima et al [35] demonstrated that the RZWQM2 S the crop ET well under full and deficit irrigation.…”

Section: Discussionmentioning

confidence: 81%

“…The RZWQM2, a hybrid model between RZWQM and DSSAT4.0, has been applied to optimize field management practices for crop growth under different climate conditions [29][30][31]. This model also performed well in simulating crop growth and soil water content under different deficit irrigation conditions [32][33][34][35]. Li et al [36] and Fang et al [37] reported that the RZWQM2 was appropriate for simulating crop growth and yield under deficit strategies.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

Chen

Feng

et al. 2022

Agriculture

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Irrigated cotton (Gossypium hirsutum L.) is produced mainly in Northwest China, where groundwater is heavily used. To alleviate water scarcity and increase regional economic benefits, a four-year (2016–2019) field experiment was conducted in Qira Oasis, Xingjiang Province, to evaluate irrigation water use efficiency (IWUE) in cotton production using the Root Zone Water Quality Model (RZWQM2), that was calibrated and validated using volumetric soil water content (θ), soil temperature (Tsoil°) and plant transpiration (T), along with cotton growth and yield data collected from full and deficit irrigation experimental plots managed with a newly developed Decision Support System for Irrigation Scheduling (DSSIS). In the validation phase, RZWQM2 adequately simulated (S) topsoil θ and Tsoil°, as well as cotton growth (average index of agreement (IOA) > 0.76). Relative root mean squared error (RRMSE) and percent bias (PBIAS) of cotton seed yield were 8% and 2.5%, respectively, during calibration, and 20% and −10.3% during validation. The cotton crop’s (M) T was well S (−18% < PBIAS < 14% and IOA > 0.95) for both full and deficit irrigation fields. The validated RZWQM2 model was subsequently run with seven irrigation scenarios with 850 to 350 mm water (Irr850, Irr750, Irr700, Irr650, Irr550, Irr450, and Irr350) and long-term (1990–2019) weather data to determine the best IWUE. Simulation results showed that the Irr650 treatment generated the greatest cotton seed yield (4.09 Mg ha−1) and net income (US $3165 ha−1), while the Irr550 treatment achieved the greatest IWUE (6.53 kg ha−1 mm−1) and net water production (0.94 $ m−3). These results provided farmers guidelines to adopt deficit irrigation strategies.

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Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

Chen

Feng

et al. 2022

Agriculture

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“…However, calibrating only plant‐related parameters in crop models does not necessarily improve the simulation of soil water flux‐related key variables. The existence of compensating effect during parameter calibration in crop models (Sima et al., 2020) leading to nonuniqueness, as defined already for hydrological models in the early 1990s (Beven, 1993) is still a challenging aspect in the calibration approach. The problem that models are often right for the wrong reason (Kirchner, 2006), when only a single criterion is considered in model calibration (Houska et al., 2021), is added here as a challenge to calibration.…”

Section: Discussionmentioning

confidence: 99%

“…However, calibrating only plant-related parameters in crop models does not necessarily improve the simulation of soil water flux-related key variables. The existence of compensating effect during parameter calibration in crop models (Sima et al, 2020) leading to nonuniqueness, as defined already for hydrological models in the early 1990s (Beven, 1993) is still a challenging aspect in the calibration approach.…”

Section: Modeling Agroecosystemsmentioning

confidence: 99%

Same soil, different climate: Crop model intercomparison on translocated lysimeters

Groh

Diamantopoulos

Duan

et al. 2022

Vadose Zone Journal

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Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux‐related performance of a set of crop models. The aim was to predict weighing lysimeter‐based crop (i.e., agronomic) and water‐related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014–2018) were from the Dedelow (Dd), Bad Lauchstädt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop‐ and soil‐related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi‐model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site‐specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil‐related data (i.e., water fluxes and system states) when simulating soil–crop–climate interrelations in changing climatic conditions.

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“…The altered weather conditions significantly influence SWC and cropland growth components. Sima et al [27] and Hu et al [2] suggested that higher supplementary water increased the simulation errors for SWC and crop biomass, and the simulation errors were influenced by the background soil water condition. Therefore, it is necessary to monitor and simulate the response of SWC, and crop growth and grain yield to climate change.…”

Section: Introductionmentioning

confidence: 99%

Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2

Qiao

Zhang

et al. 2021

Agronomy

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Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions.

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Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

Cited by 9 publications

References 49 publications

Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

Same soil, different climate: Crop model intercomparison on translocated lysimeters

Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2

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