Analysis of Parameter Sensitivity and Identifiability of Root Zone Water Quality Model (RZWQM) for Dryland Sugarbeet Modeling

Anar, Mohammad J.; Lin, Zhulu; Ma, Liwang; Bartling, P.N.S.; Teboh, Jasper M.; Ostlie, Michael

doi:10.13031/trans.12313

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…The crop modeling category includes studies simulating crop growth or yield, either as stand-alone models or as submodels within hydrologic and water quality models. McDaniel et al, 2017aMcDaniel et al, , 2017bMcDaniel et al, , 2017cMittelstet et al, 2017;Muenich et al, 2017;Renkenberger et al, 2017;Wallace et al, 2017 USLE andWEPP Lang et al, 2107;Svendsen et al, 2017;Wu et al, 2017 WEPP andUSLE Grace III, 2017;Lang et al, 2107;Lisenbee et al, 2017;Srivastava et al, 2017;Wang, L. et al, 2017;Wu et al, 2017Crop modeling APSIM Araya et al, 2017Whitbread et al, 2017AquaCrop Espadafor et al, 2017Linker and Kisekka, 2017;Wibowo et al, 2017DSSAT-CSM-CERES-Beet Anar et al, 2017DSSAT-CSM-CERES-Maize DeJonge and Thorp, 2017Fang et al, 2017aFang et al, , 2017bJoshi et al, 2017;Linker and Kisekka, 2017;Marek et al, 2017;Sharda et Anar et al, 2017;Fang et al, 2017aFang et al, , 2017bLinker and Kisekka, 2017;Liu et al, 2017SWAT Feng et al, 2017 feed value but not crude protein.…”

Section: Crop Yields and Water Usementioning

confidence: 99%

Current Research in Land, Water, and Agroecosystems: ASABE Journals 2017 Year in Review

Douglas-Mankin¹

2018

Transactions of the ASABE

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Abstract. This article highlights current research into land and water resources, agroecosystems, and agricultural production systems published by the Natural Resources and Environmental Systems (NRES) community of ASABE journals ( and ) in 2017. Context, scope, and key results of the published articles are reviewed and perhaps more importantly areas for increased research attention are recommended. This article describes experimental and modeling advances in hydrology, agroecosystems, climate change effects, soil erosion, agricultural irrigation, agricultural drainage, forest resource management, livestock systems, natural treatment systems, international water issues, and water quality topic areas. Three special collections were published (International Watershed Technology, Crop Modeling and Decision Support for Optimizing Use of Limited Water, and Advances in Drainage). Other focal areas included 14 articles relating to livestock waste management, 13 concerning irrigated agricultural systems, 8 addressing climate change effects on land, water, and agroecosystems, and 16 on various aspects of soil erosion measurement and modeling. Building on the articles reviewed from 2017 and toward a vision of future agroecosystems research, the NRES community of ASABE journals strives to expand its role in making new knowledge accessible to sustain agricultural and natural systems in a changing world. With this goal in mind, recommendations for future research needs are proposed with an emphasis on increased application of remote sensing data to agroecosystems research, improved assessment of agroecosystem resiliency and vulnerability to land and climate change, development of integrated models of agroecosystem services, meeting stubborn water management challenges in agricultural production systems, and focusing on publishing fully reproducible model results. Keywords: Agriculture, Climate change, Composting, Crop models, Crop water use, Crop yield, Drainage, Drought, Environment, Field experimentation, Food, Forest, Greenhouse gas emissions, Hydrologic modeling, Hydrology, Irrigation, Livestock, Manure management, Mode

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Section: Crop Yields and Water Usementioning

confidence: 99%

Current Research in Land, Water, and Agroecosystems: ASABE Journals 2017 Year in Review

Douglas-Mankin¹

2018

Transactions of the ASABE

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“…Two articles in the collection discuss the importance of model parameterization and the need for high-quality experimental data in model calibration and validation: Espadafor et al (2017) and Anar et al (2017). Even for deterministic or process-based models, calibration and validation using high-quality experimental datasets is a basic requirement before the models should be used to assess management strategies.…”

Section: Model Development and Parameterizationmentioning

confidence: 99%

“…Espadafor et al (2017) concluded that their calibrated and validated AquaCrop model can be used for simulation of dry beans in different environments. Anar et al (2017) parameterized, calibrated, and validated the CERES sugarbeet model to simulate the effects of sugarbeet production on soil and water quality in North Dakota. They emphasized the need for sensitivity analysis to identify and determine parameters to which model output was most sensitive.…”

Section: Model Development and Parameterizationmentioning

confidence: 99%

Crop Modeling Applications in Agricultural Water Management

Kisekka¹,

DeJonge²,

Ma³

et al. 2017

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

Sima

Fang

et al. 2020

Agronomy Journal

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Correct soil water simulation is critical for water balance and plant growth in agricultural systems. Crop production simulation errors have often been attributed to a lack of accuracy in soil water content (SWC) estimates. However, only a few studies have quantified the effects of SWC estimate errors on crop production and evapotranspiration (ET), especially under different irrigation treatments. The objective of this study was to investigate the impacts of direct assimilation of measured SWC during model calibration for deficit irrigated maize (Zea mays L.) on simulated ET, leaf area index (LAI), biomass, and yield. The CERES-Maize model within the Root Zone Water Quality Model (RZWQM) was calibrated using the automatic parameter estimation (PEST) software. Simulation results showed that, using PEST-optimized crop parameters, RZWQM was able to adequately predict crop yield (relative root mean squared error, rRMSE, of 4.8%) and biomass (rRMSE of 7.1%) in response to irrigation levels, in spite of the bias in SWC and ET simulation. However, with the same crop parameters but replacing simulated SWC with measured data, simulations of crop yield and biomass became worse, with higher rRMSE values (14.5% for yield and 21.5% for biomass). This unexpected model performance with SWC assimilation was mainly associated with the water addition and removal from the soil, which was improved only by recalibration of both soil and crop parameters. This study suggested compensating effects between soil and crop parameters during model calibration. Caution should be applied when using measured SWC as model inputs, especially under water stress conditions. Abbreviations: ET, evapotranspiration; LAI, leaf area index; PEST, parameter estimation software; RMSE, root mean squared error; rRMSE, relative root mean squared error; RZWQM, root zone water quality model; SWC, soil water content.

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Analysis of Parameter Sensitivity and Identifiability of Root Zone Water Quality Model (RZWQM) for Dryland Sugarbeet Modeling

Abstract: ABSTRACT. Sugarbeet is being considered as one of the most viable feedstock alternatives to corn for biofuel production

Cited by 6 publications

References 44 publications

Current Research in Land, Water, and Agroecosystems: ASABE Journals 2017 Year in Review

Current Research in Land, Water, and Agroecosystems: ASABE Journals 2017 Year in Review

Crop Modeling Applications in Agricultural Water Management

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

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