JEQ Grow Your Career With ASA
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published online 23 June 2008
Published in J Environ Qual 37:1383-1389 (2008)
DOI: 10.2134/jeq2007.0292
© 2008 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Del Grosso, S. J.
Right arrow Articles by Parton, W. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Del Grosso, S. J.
Right arrow Articles by Parton, W. J.
Agricola
Right arrow Articles by Del Grosso, S. J.
Right arrow Articles by Parton, W. J.
Related Collections
Right arrow Nitrogen
Right arrow Biogeochemical Processes
Right arrow Global Change
Right arrow Other Models

Testing DAYCENT Model Simulations of Corn Yields and Nitrous Oxide Emissions in Irrigated Tillage Systems in Colorado

S. J. Del Grossoa,*, A. D. Halvorsona and W. J. Partonb

a USDA, Agricultural Research Service, 2150 Centre Ave, Bldg. D, Ste. 100, Fort Collins, CO 80526
b Natural Resource Ecology Lab., Colorado State Univ., Fort Collins, CO 80523

* Corresponding author email (steve.delgrosso{at}ars.usda.gov).

Received for publication June 1, 2007.

   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 Materials and Methods
 Results
 Discussion
 Summary
 REFERENCES
 
Agricultural soils are responsible for the majority of nitrous oxide (N2O) emissions in the USA. Irrigated cropping, particularly in the western USA, is an important source of N2O emissions. However, the impacts of tillage intensity and N fertilizer amount and type have not been extensively studied for irrigated systems. The DAYCENT biogeochemical model was tested using N2O, crop yield, soil N and C, and other data collected from irrigated cropping systems in northeastern Colorado during 2002 to 2006. DAYCENT uses daily weather, soil texture, and land management information to simulate C and N fluxes between the atmosphere, soil, and vegetation. The model properly represented the impacts of tillage intensity and N fertilizer amount on crop yields, soil organic C (SOC), and soil water content. DAYCENT N2O emissions matched the measured data in that simulated emissions increased as N fertilization rates increased and emissions from no-till (NT) tended to be lower on average than conventional-till (CT). However, the model overestimated N2O emissions. Lowering the amount of N2O emitted per unit of N nitrified from 2 to 1% helped improve model fit but the treatments receiving no N fertilizer were still overestimated by more than a factor of 2. Both the model and measurements showed that soil NO3– levels increase with N fertilizer addition and with tillage intensity, but DAYCENT underestimated NO3– levels, particularly for the treatments receiving no N fertilizer. We suggest that DAYCENT could be improved by reducing the background nitrification rate and by accounting for the impact of changes in microbial community structure on denitrification rates.

Abbreviations: CT, conventional till • GHG, greenhouse gas • GWP, global warming potential • NT, no-till • SOC, soil organic carbon


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
Materials and Methods
 Results
 Discussion
 Summary
 REFERENCES
 
NITROUS oxide (N2O) and carbon dioxide (CO2) concentrations in the Earth's atmosphere have increased since the mid 1700's (IPCC, 2001). Although CO2 is the primary long-lived anthropogenic greenhouse gas (GHG), N2O is responsible for the majority of emissions from agricultural soils. This is of concern because N2O is a potent greenhouse gas (GHG) with a global warming potential (GWP) that is approximately 296 times the GWP of CO2 on a mass basis (IPCC, 2001). Nitrous oxide also influences ozone chemistry (Crutzen and Ehhalt, 1977; Crutzen, 1981). Agricultural soils can be a source or sink of CO2. Much research has been devoted to improving estimates of soil GHG fluxes and finding ways to reduce N2O emissions and enhance C storage in soils.

Because it is not feasible to measure N2O emissions and changes in soil C levels at large scales, process-based models have been developed to estimate regional and national soil GHG fluxes (Li et al., 1992; Del Grosso et al., 2006; Gabrielle et al., 2006) and to compare the impacts of different land management practices on emissions (Del Grosso et al., 2005; Adler et al., 2007). However, the models often yield N2O emission estimates that substantially differ from measured values. Thus, it is essential to continually compare model outputs with observed data to identify model weaknesses and spur model development. As models are gradually improved, the confidence intervals in model generated emission estimates narrow and uncertainty decreases.

Agricultural soils usually emit more N2O than non-cultivated soils because common practices (synthetic and organic nitrogen (N) fertilizer additions, legume cropping), increase N availability in soil. Higher N availability provides substrate for the microbial processes of nitrification and denitrification. Nitrification is the aerobic oxidation of ammonium (NH4+) to nitrate (NO3–) and denitrification is the anaerobic reduction of NO3– to N2O and N2 (Conrad, 1996; Khalil et al., 2004). Agricultural practices that alter the oxygen status of the soil, e.g., irrigation, also tend to increase N2O emissions above background levels. Under conventional tillage cultivation, agricultural soils usually lose SOC or maintain relatively constant levels of SOC. Alternatively, reduced tillage intensity can lead to increased SOC in soils and this can be used as a GHG mitigation strategy (Lal, 2004). Minimal tillage has other benefits as well such as reduced erosion and improved water quality (Uri, 2000).

Models used to estimate soil GHG fluxes lie on a spectrum between simple empirical models and complex mechanistic models (Roelandt et al., 2005). A well known example of the former is IPCC (2006) emission factor methodology, which assumes that 1% of annual N inputs from fertilizer and crop residue is emitted as N2O-N and that 2% of annual manure N inputs from grazing animals is emitted as N2O-N. An example of a highly mechanistic model is ecosys, which has been used to model C and N fluxes for various native and managed systems (Wang et al., 2001; Grant et al., 2006). Simple models use easy to obtain inputs and can readily be implemented in spreadsheet calculations but estimates are often not reliable because many of the factors that control emissions (e.g., competition between plants and microbes for soil N) are not represented. Highly mechanistic models simulate most of the processes that control emissions and usually give better results when compared with field observations than simple models, but they require much more detailed inputs and are difficult to run. However, when aggregated at sufficiently large spatial scales and averaged over time, simple and complex models can give similar results (e.g., Del Grosso et al., 2005). Of course, it is not possible to measure variables such as N2O emissions at large scales so one cannot say which methodology is better but process-based models tend to perform better where data are available (e.g., Del Grosso et al., 2005). One disadvantage of complex models is that as the degrees of freedom increase so does the possibility of over-parameterization. This risk can be reduced by showing that parameter values selected a priori give favorable results when comparing model outputs with different types of data from different sites. DAYCENT is a biogeochemical model of intermediate complexity; many of the processes controlling emission are represented, but model inputs are relatively easy to acquire and the model is not too difficult to run.

The ability of DAYCENT to simulate plant growth, SOC, N2O emissions, NO3– leaching, and CH4 oxidation has been tested with data from various native and agricultural systems (Del Grosso et al., 2000b, 2001a, 2002, 2005). Simulated and observed grain yields for major cropping systems in North America generally agree well with data at both the site (r2 = 0.90) and regional (r2 = 0.66) levels (Del Grosso et al., 2005). Nitrous oxide emission data from eight cropped sites and NO3– leaching data from three cropped sites showed reasonable agreement with DAYCENT simulations with r2 values of 0.74 and 0.96 for N2O and NO3–, respectively (Del Grosso et al., 2005). Sensitivity analyses showed that simulated N2O emissions increase with N inputs, tend to increase as soils become finer textured, and vary in response to changes in precipitation (Del Grosso et al., 2006). DAYCENT has been used to estimate N2O emission from cropped soils (rainfed and irrigated) for the U.S. national GHG inventory (USEPA, 2006; Del Grosso et al., 2006). However, DAYCENT has not been extensively tested in irrigated systems. In this paper, we test the ability of DAYCENT to simulate the effects of management practices on key N and C fluxes for irrigated corn (Zea mays L.) cropping. Our objectives were to evaluate the ability of DAYCENT to simulate N2O emissions, crop yields, mineral N levels, soil water content, and changes in SOC for irrigated cropping systems in northern Colorado and suggest how the model may be improved. It is important to compare a variety of model outputs with field measurements because model parameters can often be adjusted to properly represent one or two factors, but this can lead to unreasonable model outputs for other factors.


   Materials and Methods
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 Summary
 REFERENCES
 
DAYCENT Model Overview
DAYCENT is a biogeochemical model that simulates fluxes of carbon (C) and N among the atmosphere, vegetation, and soil (Parton et al., 1998; Del Grosso et al., 2001b). Flows of C and N between the different pools are controlled by the size of the pools, C/N ratio and lignin content of material, and abiotic water/temperature controls (Parton et al., 1994). Soil water content and temperature are simulated for each horizon throughout the defined depth of the soil profile (Parton et al., 1998). The model simulates saturated water flow on days that receive rainfall, irrigation, or snow melt and unsaturated flow every day on a sub-daily time step. Plant production is a function of genetic potential, phenology, nutrient availability, water/temperature stress, and solar radiation. Net primary productivity (NPP) is allocated to plant components (e.g., roots vs. shoots) based on vegetation type, phenology, and water/nutrient stress. Nutrient concentrations of plant components vary within specified limits depending on vegetation type and nutrient availability relative to plant demand. Crop germination date can be specified or made a function of soil temperature and harvest date can be specified or made a function of accumulated growing degree days since germination. Decomposition of litter and soil organic matter and nutrient mineralization are functions of substrate availability, substrate quality (lignin content, C/N ratio), water/temperature stress, and tillage intensity.

Primary model inputs are: daily maximum/minimum air temperature and precipitation, soil texture by horizon, and land cover/use data (e.g., vegetation type, cultivation/planting schedules, amount and timing of nutrient amendments). Outputs include: daily N-gas flux (N2O, NOx, N2), CH4 uptake, soil organic matter (top 20 cm layer), CO2 flux from heterotrophic soil respiration, actual evapotranspiration (AET), soil NO3, water content, and temperature by horizon, soil NH4+ in top 15 cm, NO3 leaching, weekly live biomass, monthly plant growth, surface litter, standing dead litter, and other ecosystem parameters. The nominal spatial scale for DAYCENT simulations is one square meter, but in practice, the model is typically used to simulate plots, fields, and regions of arbitrary area. The relevant caveat is that whatever spatial scale the model is run for, DAYCENT assumes that all model inputs are uniform within that scale.

Nitrous oxide gas fluxes from nitrification and denitrification are driven by soil NH4+ and NO3– concentrations, water content, temperature, texture, and labile C availability (Del Grosso et al., 2000a; Parton et al., 2001). In DAYCENT, NO3– is distributed throughout the soil profile and can be leached into the subsoil. Nitrate movement and leaching are largely controlled by soil water flow and plant N uptake. Ammonium, on the other hand, is assumed to be immobile and is distributed entirely in the top 15-cm layer of soil. Simulated heterotrophic respiration is used as a proxy for labile C availability in the denitrification submodel. Heterotrophic respiration rates are a function of organic matter quantity, quality, abiotic factors, and tillage intensity as described above. On a daily basis, denitrification rates are calculated for each soil layer based on the driving variables (NO3– concentration, heterotrophic respiration, water content, texture, and temperature) while nitrification rates are calculated based on NH4+ concentration, water content, texture, and temperature in the top 15 cm layer. The current version of DAYCENT allows the user to vary the portion of nitrified N that is emitted as N2O whereas previous versions assumed a fixed portion of 2%.

Experimental Data
The DAYCENT model was tested with data collected from the Agricultural Research Development and Education Center (ARDEC) in northeastern Colorado near Fort Collins, USA (40°39' N; 104°59' W). The region has a semiarid temperate climate and the soil is a clay loam. We used data collected from continuous irrigated corn cropping under conventional-till (CT) and no-till (NT) production systems at high (225 kg N ha–1) and 0 N fertilization levels. The amount of irrigation water added to the CT and NT systems was identical for the plots considered here except that an additional 2.5 cm was added to CT plots before emergence in 2005 due to very dry soil conditions. Plots ranged in size from 163 to 231 m2.

Nitrogen fertilizer was applied as urea-ammonium nitrate (UAN) in a single application near planting time from 1999–2004. In 2005, half the N fertilizer rate was applied as UAN at corn planting and the second half of the N rate was applied as polymer-coated urea in mid-June. In 2006, half the N rate was applied as polymer-coated urea at corn emergence and the second half of the N rate as dry granular urea fertilizer in June. Soil carbon was sampled once per year beginning in 1999 and soil NO3– and NH4+ were sampled twice per year (before planting in spring and after harvest in autumn) beginning in 2000. Beginning in 2002, N2O emissions and soil water content were sampled one to three times per week during the growing season. Estimates of N2O emissions between sampling days were made using a linear estimate between the previous sampling date and the current sampling date so that total growing season fluxes could be estimated. For details on the experimental design and data collection, see Mosier et al. (2006) and Halvorson et al. (2008a).

DAYCENT Simulations
Model outputs during any given year are influenced by SOC and mineral N levels, which, in turn, are influenced by previous land use. Consequently, DAYCENT simulations typically begin at least 100 yr before the time period of interest so that soil C and N pools are properly initialized. For the fields used in this exercise, we simulated shortgrass steppe until 1949, irrigated, CT rotations of corn, dry bean, sugarbeet, and barley from 1950 to 1992, and irrigated, CT corn from 1993 to 1998. Model outputs from 1998 were saved and used as initial conditions for the four sets of simulations to represent four of the experimental treatments: high N CT, high N NT, 0 N CT, and 0 N NT. The treatments were simulated from 1999 to 2006. The DAYCENT files that schedule management events (day of year when fertilizer was applied and amount, day of year irrigation water was added and amount, and day of year fields were tilled and intensity of tillage) were based on records of when these events were actually implemented. Model outputs for C and N in corn grain yields, daily N2O emissions, SOC, soil water content, and soil mineral N (NH4+, NO3–) were saved and compared with measured values of these variables. Simulations were conducted assuming that 2% of nitrified N was emitted as N2O and that 1% of nitrified N was emitted as N2O.


   Results
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
Discussion
 Summary
 REFERENCES
 
DAYCENT correctly simulated substantially higher C and N in grain yields for the high N compared to the 0 N treatments and slightly higher C and N yields for CT production system compared to a NT production system (Fig. 1 ). The model also showed higher C storage in the NT compared to the CT treatment in accordance with measured data (Fig. 2 ). Varying the amount of N lost as N2O during nitrification did not appreciably affect yields or C storage. However, assuming that 2% of nitrified N is emitted as N2O overestimated seasonal N2O emissions, particularly from the 0 N treatments (Fig. 3 ), although the proper trends were evident (higher emissions with high N and higher emissions from CT). Lowering the amount of N assumed to be lost as N2O during nitrification resulted in much better agreement with the data, but the model still overestimated emissions substantially for the 0 N treatments and moderately for the CT high N treatment (Fig. 3). IPCC (2006) overestimated emissions to a greater extent than DAYCENT for the high N treatments but came closer to the measured values for the 0 N treatments (Fig. 3). Analysis of time series data showed that DAYCENT (assuming 1% loss of nitrified N as N2O) tended to overestimate emissions during the second half of the growing season for the high N treatments (Fig. 4a,b ). For the 0 N treatments, DAYCENT tended to overestimate emissions throughout the growing season, particularly for the CT system (Fig. 4c,d). DAYCENT properly simulated the observed general trends in soil water content (Fig. 5 ) and agreed with the data in that the NT system had slightly higher water content on average during the growing season (~2% volumetric higher at 5-cm depth for both the model and the observed data). However, the model severely underestimated soil NO3– in the profile, but showed the observed treatment effect of NO3– increasing in response to N fertilizer addition (Fig. 6a ). For NH4+ in the top 15 cm, the model tended to overestimate for the treatments with N addition, but came close to the observed data for the 0 N treatments (Fig. 6b). Growing season dynamics for soil mineral N show that NH4+ and NO3– spike after fertilizer application, NH4+ gradually decreases due to plant uptake and nitrification, while NO3– drops more abruptly and then gradually increases as NH4+ is nitrified to NO3– (Fig. 6).


Figure 1
View larger version (19K):
[in this window]
[in a new window]

  		Fig. 1. Measured and DAYCENT model simulated mean (1999–2006) carbon (a) and nitrogen (b) in grain yields for irrigated corn cropping in Colorado under conventional tillage (CT) and no-till (NT) production systems and different N addition rates. Impact of fertilizer level is statistically significant for measured values but tillage is only significant for grain C (p < 0.05).

 

Figure 2
View larger version (11K):
[in this window]
[in a new window]

  		Fig. 2. Measured and DAYCENT model simulated soil organic carbon for irrigated corn cropping in Colorado under conventional tillage (CT) and no-till (NT) production systems. The measured increase in soil C is statistically significant only for the NT system (p < 0.05).

 

Figure 3
View larger version (19K):
[in this window]
[in a new window]

  		Fig. 3. Measured, DAYCENT model simulated assuming that 2 and 1% of nitrified N is released as N2O, and IPCC (2006) methodology mean (2002–2006) growing season nitrous oxide emissions for irrigated corn cropping in Colorado under conventional tillage (CT) and no-till (NT) production systems and different N addition rates. Impact of tillage on measured values is not statistically significant but fertilizer level is (p < 0.05).

 

Figure 4
View larger version (23K):
[in this window]
[in a new window]

  		Fig. 4. Measured and DAYCENT model simulated daily nitrous oxide emissions for irrigated corn cropping in Colorado under conventional (CT) at (a) high N (225 kg N ha–1), and (b) 0 N fertilizer addition rates and no-till (NT) production systems at (c) high N (225 kg N ha–1), and (d) 0 N fertilizer addition rates. Daily correlation coefficients (r2) are less than 2% in all cases.

 

Figure 5
View larger version (28K):
[in this window]
[in a new window]

  		Fig. 5. Measured and DAYCENT model simulated daily volumetric soil water content for irrigated corn cropping in Colorado under (a) conventional tillage (r2 = 0.47) and (b) no-till production systems (r2 = 0.48).

 

Figure 6
View larger version (15K):
[in this window]
[in a new window]

  		Fig. 6. Measured and DAYCENT model simulated non-growing season mean (2000–2006) soil nitrate in the 0–150 cm layer (a) and ammonium in the 0–15 cm layer (b) for irrigated corn cropping in Colorado under conventional tillage (CT) and no-till (NT) production systems and different N addition rates.

 

   Discussion
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
 Discussion
Summary
 REFERENCES
 
Comparisons of DAYCENT outputs with measured data for irrigated corn fields in northern Colorado showed that the model correctly simulated a strong effect of N fertilizer on crop yields (Fig. 1), N2O emissions (Fig. 3), and soil NO3– levels (Fig. 6a), although the model exaggerated the N effect for N2O and soil NO3–. Average yields simulated by the model were very close to measured values but the model was biased in that N2O emissions were overestimated on average while soil NO3– levels were underestimated on average. Reducing the portion of nitrified N emitted as N2O improved model fit but N2O emissions from the 0 N treatments were still substantially overestimated (Fig. 3). This implies that DAYCENT simulated N2O emissions associated with background nitrification rates are too high. Underestimation of soil NO3– levels suggests that denitrification rates are overestimated in this soil. IPCC (2006) also showed the proper trends (higher emissions with fertilizer additions and with tillage) but overestimated emissions substantially for the high N treatments (Fig. 3).

DAYCENT and observations show relatively stable SOC levels under CT and sequestration of SOC in the top 15-cm layer under NT (Fig. 2). Decomposition rates are usually lower for NT because SOC is more likely to be physically protected in stable aggregates (Rhoton, 2000; Grandy et al., 2006) and the surface residue insulates the soil and impacts soil temperature. DAYCENT accounts for the insulating effects of surface residue on soil temperatures and assumes that decomposition rates increase with tillage intensity (i.e., soil aggregation is not modeled explicitly). Measured and simulated crop yields were slightly lower for the NT system, likely a result of lower soil temperatures in the spring under NT which slows crop development (Halvorson et al., 2006). Both the field data and model showed lower N2O under NT, but DAYCENT exaggerated this effect compared to the data (Fig. 3). Lower N2O under NT is related to increasing SOC for this system; as SOC increases, more N is tied up in the organic form, so less mineral N is available for the microbial processes that result in N2O emissions (Halvorson et al., 2008b). This is consistent with data showing lower N2O emissions from cropped soils that were afforested compared to continuously cropped soils even though the afforested soils were storing C (Merino et al., 2004). However, other data show a positive correlation between soil C levels and N2O emissions (Xu-Ri et al., 2003). Whether soil C is at disequilibrium due to land management change likely influences the impact of soil C levels on N2O emissions. Building of soil organic matter also explains lower NO3– for both the model and the measurements in the NT system (Fig. 6a). The observed data showed no consistent effect of N addition on soil NH4+ levels, but DAYCENT showed higher NH4+ for the fertilized treatments in the top 15-cm layer (Fig. 6b).

On a daily basis, DAYCENT outputs compared favorably with data for soil water content (Fig. 5) but not for N2O emissions (Fig. 4). In particular, the model underestimates the large peak in emissions observed for the high N treatments in 2003 (Fig. 5). The large emissions observed in the spring of 2003 are likely due to very wet soil conditions from a major snowfall event in March of that year. The model also tended to show more peaks than the data. The peaks exhibited by the model during the growing season are largely driven by fluctuations in soil water content because simulated NH4+ levels remain relatively high until the end of July (Fig. 7 ).


Figure 7
View larger version (9K):
[in this window]
[in a new window]

  		Fig. 7. Time series of DAYCENT model simulated soil mineral N in 2003 for the conventional tillage (CT) production system with high N fertilizer.

 
This exercise suggests that the submodels in DAYCENT responsible for N transformations need to be improved. The model properly simulated uptake of N by plants (Fig. 1b) but underestimated soil NO3– levels (Fig. 6a) and overestimated N2O emissions (Fig. 3). Underestimating soil NO3– implies that nitrification rates are too low but overestimating N2O implies that nitrification rates are too high. This contradiction can be partially explained by pointing out that N2O emissions from nitrification are not 100% correlated with nitrification rates. In previous versions of DAYCENT, the portion of NH4+-N that is emitted from the soil surface as N2O is assumed to be a fixed portion (2%) of the NH4+-N that is converted to NO3– during the nitrification process. However, the amount of N2O emitted per unit of N nitrified can vary by almost a factor of 10 based on O2 availability (Khalil et al., 2004). Reducing the amount of N2O emitted per unit of N nitrified from 2 to 1% improved model performance in this soil. The model needs to be tested with data from other sites to determine if the 1% assumption can be applied generally. If not, a relationship would need to be derived to predict this portion based on O2 availability or other variables. That is, it would not be proper to arbitrarily vary the amount of N2O emitted per unit of N nitrified for different sites or treatments to improve model performance. Nitrous oxide produced from nitrification also varies with microbial community (Conrad, 1996) and by soil depth (Paul, 2006). Underestimating soil NO3– also implies that simulated denitrification rates are too high in this soil.

We suggest that background nitrification rates in DAYCENT should be reduced because N2O emissions for the 0 N treatments were overestimated by large amounts (Fig. 3). This could be accomplished by raising the minimum concentration of NH4+ in soil that is required for nitrification. We also suggest that denitrification rates be reduced because soil NO3– levels were underestimated by DAYCENT (Fig. 6a). Currently, the model estimates denitrification for each soil layer in the profile based on NO3– concentration, labile C availability, soil water content, and texture. However, there is evidence that the denitrifier community varies with soil depth and tillage practice (Venterea et al., 2005). Thus, DAYCENT could be overestimating denitrification in deeper soil depths.


   Summary
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
 Discussion
 Summary
REFERENCES
 
The DAYCENT model correctly simulated the impacts of N addition and tillage on crop yields, SOC changes, and soil water content. DAYCENT also correctly simulated higher soil NO3– concentrations and higher N2O emissions in response to N addition and tillage. However, mean simulated soil NO3– was much lower than the measured data and mean N2O emissions were substantially higher than the measurements for the 0 N treatments. We suggest that DAYCENT could be improved by raising the minimum threshold of soil NH4+ required for nitrification to occur and by accounting for the impacts of soil depth-dependent changes in microbial community on denitrification rates.


   ACKNOWLEDGMENTS
 
We thank M. Smith, S. Skiles, R. Strong, W. Morgan, D. Jensen, C. Reule, P. Norris, and B. Floyd for their technical assistance. This publication is based on work supported by the Agricultural Research Service under the ARS GRACEnet Project


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
 Discussion
 Summary
 REFERENCES
 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
 Discussion
 Summary
 REFERENCES
 




This article has been cited by other articles:


Home page
 J. Environ. Qual.Home page
M. K. Jarecki, T. B. Parkin, A. S. K. Chan, J. L. Hatfield, and R. Jones
Comparison of DAYCENT-Simulated and Measured Nitrous Oxide Emissions from a Corn Field
J. Environ. Qual., August 8, 2008; 37(5): 1685 - 1690.
[Abstract] [Full Text] [PDF]

Home page
 J. Environ. Qual.Home page
A. D. Halvorson, S. J. Del Grosso, and C. A. Reule
Nitrogen, Tillage, and Crop Rotation Effects on Nitrous Oxide Emissions from Irrigated Cropping Systems
J. Environ. Qual., June 23, 2008; 37(4): 1337 - 1344.
[Abstract] [Full Text] [PDF]

Home page
 J. Environ. Qual.Home page
R. T. Venterea and A. J. Stanenas
Profile Analysis and Modeling of Reduced Tillage Effects on Soil Nitrous Oxide Flux
J. Environ. Qual., June 23, 2008; 37(4): 1360 - 1367.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Del Grosso, S. J.
Right arrow Articles by Parton, W. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Del Grosso, S. J.
Right arrow Articles by Parton, W. J.
Agricola
Right arrow Articles by Del Grosso, S. J.
Right arrow Articles by Parton, W. J.
Related Collections
Right arrow Nitrogen
Right arrow Biogeochemical Processes
Right arrow Global Change
Right arrow Other Models

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Agronomy Journal Crop Science
Journal of Natural Resources
and Life Sciences Education		 Vadose Zone Journal
Soil Science Society of America Journal Journal of Plant Registrations The Plant Genome