Evaluating four nitrous oxide emission algorithms in response to N rate on an irrigated corn field

Fang, Qiannan; Li, Ma; Halvorson, Ardell D.; Malone, Robert W.; Ahuja, Lajpat R.; Grosso, Stephen J. Del; Hatfield, Jerry L.

doi:10.1016/j.envsoft.2015.06.005

Cited by 38 publications

(22 citation statements)

References 79 publications

(108 reference statements)

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“…However, our evaluation of model bias with regard to b 1 in a linear regression model demonstrates significant bias toward the underestimation of high magnitude cumulative flux (Table 2). Fang et al (2015) also showed that four different models (including DayCent) underestimated the four highest (out of eight) cumulative N 2 O fluxes from irrigated corn systems in Colorado. DNDC has been shown to underestimate cumulative flux from corn fields in Canada (Uzoma et al 2015) but overestimate cumulative flux from pasture in Ireland (Abdalla et al 2010).…”

Section: Underestimation Of Daily N 2 O Fluxmentioning

confidence: 83%

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Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Gaillard

Jones

Ingraham

et al. 2018

Ecological Applications

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Process-based models are increasingly used to study agroecosystem interactions and N O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi-model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross-model bias to underestimate high magnitude daily and cumulative N O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH , and soil NO , which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.

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Section: Underestimation Of Daily N 2 O Fluxmentioning

confidence: 83%

“…Fang et al. () also showed that four different models (including DayCent) underestimated the four highest (out of eight) cumulative N 2 O fluxes from irrigated corn systems in Colorado. DNDC has been shown to underestimate cumulative flux from corn fields in Canada (Uzoma et al.…”

Section: Discussionmentioning

confidence: 97%

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Gaillard

Jones

Ingraham

et al. 2018

Ecological Applications

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“…The RZWQM is a field‐scale hydrological model that simulates soil water, soil temperature, plant growth, pesticide fate, and soil C and N dynamics as influenced by various agricultural management practices (Ahuja et al., 2000). Ma, Hoogenboom, Ahuja, Ascough II, and Saseendran (2006) linked the process‐based DSSAT‐CERES crop growth module with RZWQM, which has been used to evaluate agricultural management effects on crop growth, water quality, and environmental consequences in different regions worldwide (Fang et al., 2013, 2015; Ma et al., 2012).…”

Section: Methodsmentioning

confidence: 99%

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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“…The issue remains challenging, especially at the within-field scale. N 2 O emissions show high variability in space and time due to the complex interaction of several environmental and management factors, such as site specific pedo-climatic conditions, crop type, irrigation systems and type, amount and timing of fertilizers supply, which drive microbial processes and gas diffusivity in soils [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Denitrification Rate and Its Potential to Predict Biogenic N2O Field Emissions in a Mediterranean Maize-Cropped Soil in Southern Italy

Forte

Fierro

2019

Land

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The denitrification rate in C2H2-amended intact soil cores and soil N2O fluxes in closed static chambers were monitored in a Mediterranean irrigated maize-cropped field. The measurements were carried out during: (i) a standard fertilization management (SFM) activity and (ii) a manipulation experimental (ME) test on the effects of increased and reduced application rates of urea at the late fertilization. In the course of the SFM, the irrigations following early and late nitrogen fertilization led to pulses of denitrification rates (up to 1300 μg N2O-N m−2 h−1) and N2O fluxes (up to 320 μg N2O-N m−2 h−1), thanks to the combined action of high soil temperatures and not limiting nitrates and water filled pore space (WFPS). During the ME, high soil nitrates were noted in all the treatments in the first one month after the late fertilization, which promoted marked N-losses by microbial denitrification (from 500 to 1800 μg N2O-N m−2 h−1) every time the soil WFPS was not limiting. At similar maize yield responses to fertilizer treatments, this result suggested no competition for N between plant roots and soil microbial community and indicated a probable surplus of nitrogen fertilizer input at the investigated farm. Correlation and regression analyses (CRA) on the whole set of data showed significant relations between both the denitrification rates and the N2O fluxes with three soil physical-chemical parameters: nitrate concentration, WFPS and temperature. Specifically, the response functions of denitrification rate to soil nitrates, WFPS and temperature could be satisfactorily modelled according to simple Michaelis-Menten kinetic, exponential and linear functions, respectively. Furthermore, the CRA demonstrated a significant exponential relationship between N2O fluxes and denitrification and simple empirical functions to predict N2O emissions from the denitrification rate appeared more fitting (higher concordance correlation coefficient) than the predictive empirical algorithm based on soil nitrates, WFPS and temperature. In this regard, the empirically established relationships between the denitrification rate on intact soil cores under field conditions and the soil variables provided local-specific threshold values and coefficients which may effectively work to calibrate and adapt existing N2O process-based simulation models to the local pedo-climatic conditions.

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Evaluating four nitrous oxide emission algorithms in response to N rate on an irrigated corn field

Cited by 38 publications

References 79 publications

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

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

Denitrification Rate and Its Potential to Predict Biogenic N2O Field Emissions in a Mediterranean Maize-Cropped Soil in Southern Italy

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Evaluating four nitrous oxide emission algorithms in response to N rate on an irrigated corn field

Cited by 38 publications

References 79 publications

Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models

Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models

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

Denitrification Rate and Its Potential to Predict Biogenic N2O Field Emissions in a Mediterranean Maize-Cropped Soil in Southern Italy

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Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models