Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg · N · ha - 1 · yr - 1 and crop residues at 3 t · ha - 1 · yr - 1 . For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R 2 = 0.31 - 0.55, p < 0.05). Only the ECOSSE-simulated N 2 O fluxes showed a significant relationship (R 2 = 0.33, p < 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N 2 O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09 % , 0.31 % and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance da ta (R 2 = 0.34 - 0.41, p < 0.05). Compared to the measured value (3624 kg · C · ha - 1 · yr - 1 ), th e ECOSSE unde restimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50% ) and DailyDayCent (24%) estimates. All models simulated CH 4 uptake we
Agricultural activity is estimated to be responsible for approximately 14% of global anthropogenic greenhouse gas (GHG) emissions [
Most of the Annex-I countries are using IPCC Tier 1 methodologies [
In line with commitments under the UNFCCC, the ROI is committed to improving the estimation of GHG budgets by developing Tier 3 approaches. There has been much progress in recent years in developing models to simulate GHG emissions. Modelling is considered a low-cost method of estimating GHG emissions from agricultural soils at different scales and for exploring potential mitigation strategies [
Several process-based models are currently used to predict a variety of variables related to different ecosystems. DNDC (DeNitrification-DeComposition) is a process-based model that simulates carbon (C) and nitrogen (N) biogeochemistry in agro-ecosystems and has been used for predicting GHG emissions, soil C dynamics, crop growth and other relevant data [
Based on the different characteristics and performances, three process-based models (DNDC v9.4, DailyDayCent and ECOSSE v5+) were chosen to evaluate GHG emissions associated with the major Irish cropland type. The goal was to establish the basis of an emission inventory system using process-based models with the minimum number of commonly available input parameters that reflect the site-specific diversity of management practices that influence GHG emissions. Barley, with dominancy of spring barley, is the major cereal crop in Ireland, comprising 71% of the total cereals in 2014 [
Data on inputs and management practices were collected from plot-scale field experiments conducted at the Teagasc Oak Park Research Centre, Carlow (52°86' N and 6°54' W). The soil (0 - 10 cm depth) at Oak Park site is classified as a sandy loam (overlying loam) in texture, free draining, Euteric Cambisol (Grey Brown Podzolics). Detailed site characteristics, which may differ from other published information [
The larger plots used (2.5 ha) for field-scale studies were part of an experiment comparing the effects of conventional and minimum tillage practices [
Following the harvesting of the crop (July or August), crop residues were chopped and left on the field over the autumn and winter period (
Site characteristics | ||
---|---|---|
Location | Oak Park, Carlow | |
Latitude-longitude | 52˚86'N - 6˚54'W | |
Mean annual air temperature (˚C) | 9.8 | |
Mean annual precipitation (mm) | 870.5 | |
Land use history | Cereals (15 years), croplands (50 years), received 140 - 160 kg∙N∙ha−1 in 2003 and the year before. Spring barley since 2000. | |
Soil type (FAO/Irish GSG) | Euteric Cambisol/Grey Brown Podzolics | |
Soil texture: 0 - 10/0 - 25 cm | Sandy loam | |
Clay (%): 0 - 10/0 - 25 cm | 15.13/14.73 | |
Silt (%): 0 - 10/0 - 25 cm | 25.63/33.73 | |
Sand (%): 0 - 10/0 - 25 cm | 59.24/51.55 | |
Bulk density (g∙m−3): 0 - 10/0 - 25 cm | 1.42/1.46 | |
Total soil organic carbon (kg∙ha−1): 0 - 10/0 - 25 cm | 19.912/42.888 | |
Total inert soil organic carbon (kg∙ha−1): 0 - 10/0 - 25 cm | 3.863/8.163 | |
Soil pH: 0 - 10/0 - 25 cm | 7.24/7.35 | |
Available water (AW) at field capacity (mm): 0 - 10/0 - 25 cm | 22.69/55.13 | |
Water content at saturation (%): 0 - 10/0 - 25 cm | 47.21 (AW = 29.51 mm)/45.56 = 113.87 mm (AW = 71.17) | |
Water content at field capacity (%): 0 - 10/0 - 25 cm | 40.39 (AW = 22.69 mm)/38.97 = 97.43 mm (AW = 54.73 mm) | |
Water content at wilting point (%): 0 - 10/0 - 25 cm | 17.70 (=17.70 mm)/17.08 = 42.7 mm | |
Initial NH4 and NO3− (kg∙N∙ha−1): 0 - 10/0 - 25 cm | 2.8/6.9 and 9.5/23.17 | |
Annual atmospheric N deposition (kg∙ha−1) | 11 | |
Slope (%) and water table depth (cm) | 0.004% from vertical and 240 | |
Depth of impermeable layer (cm) and drainage class | >150 and High | |
Inputs and management practices | ||
Land use | Spring barley (var. Tavern or Quench) | |
Date of previous crop harvested | 17/08/03 | |
Type and depth of tillage practices | Conventional (22 - 25 cm) | |
Date of tillage practices (ploughed and light till) | 19/02/04 and 25/03/04; 09/03/05 and 14/03/05; 10/03/06 and 19/03/06; 24/02/07 and 18/03/07; 22/02/08 and 19/03/08; 18/02/09 and 18/03/09; 02/03/10 and 08/03/10; 02/03/11 and 08/03/11 | |
Date of sowing | 26/03/04; 16/03/05; 20/03/06; 21/03/07; 20/03/08; 19/03/09; 09/03/10; 09/03/11 | |
Residue incorporation | 3.0 t∙DM∙ha−1(1.32 t∙C∙ha−1), chopped and left on the field; incorporated during tillage operation only | |
Type of N fertilizer | Calcium Ammonium Nitrate (CAN) | |
Number of fertilizer application | 2003-04: 1; 2005-11: 2 | |
Fertilizer N rates (kg∙N∙ha−1) | 2003: 140; 2004: 0 and 140; 2005: 0 and 159 (106 + 53); 2006: 0 and 140 (90 + 50); 2007-2011: 0 and 135 (67.5 + 67.5) | |
Date of fertilizer application | 27/04/04; 12/04/05 and 10/05/05; 12/04/06 and 11/05/06; 20/04/07 and 10/05/07; 16/04/08 and 15/05/08; 21/04/09 and 22/05/09; 13/04/10 and 07/05/10; 04/04/11 and 10/05/11 | |
Date of harvest | 17/08/03; 17/08/04; 09/08/05; 09/08/06; 17/07/07; 22/08/08; 12/08/09; 06/08/10; 14/08/11 | |
Measurements of GHGs from the experimental plots were either made seasonally or annually and for three years commencing from 2009 to 2011, at daily or fortnightly intervals. N2O emissions were measured using the static closed chamber method. Gas was sampled at 0, 30 and 60 min intervals between 9 and 11 am every week and more intensively (twice weekly) following fertilizer application. The gas samples were stored in exetainers (Labco, High Wycombe, UK) prior to the analyses. The gas analyses were performed using a gas chromatography (Varian CP 3800 GC, Varian, USA) fitted with a 63Ni electron capture detector (ECD) for N2O analysis and a Flame Ionisation Detector (FID) for CH4 analysis with high purity helium as a carrier gas. Samples were returned to ambient pressure prior to analysis and fed into the system by a Combi-Pal automatic sampler (CTC Analysis, Switzerland). Following a two-year gap, gas samples for the measurement of both N2O and CH4 were collected from September 2008 to September 2010 and from April 2009 to September 2010, respectively. Gas sampling was carried out weekly during the crop growth period and less frequently (2 - 3 weeks) during the fallow period using static chambers, with 18 replicates.
Eddy Covariance (EC) systems installed in the large plots, consisted of Gill R3 sonic anemometer (Gill Instruments, USA) and Li-7000 infra-red gas analyser (Licor Inc., USA), for net ecosystem exchange (NEE) and ecosystem respiration (Reco) measurements. Estimates of Reco (2003-2007) were based on half-hourly measurements and expressed on a daily basis.
Soils were sampled during the gas sampling periods and soil nitrate concentrations were determined on 2M KCl extracts using an auto-analyzer (Bran and Luebbe, Norderstedt, Germany) [
Three dynamic models (ECOSSE v5 updated in 2012, DNDC v9.4 and DailyDayCent) were selected for this comparative study. Input requirements for each model differ as indicated previously. However, the site characteristics and crop management practices used were the same for all the models which were run for 8 years. Other inputs were either defaults or module-based. A brief description of the models is given below. ECOSSE was mainly calibrated under UK conditions [
The ECOSSE model was developed to simulate SOC in highly organic soils from algorithms originally derived for mineral soils in the RothC and SUNDIAL models [
The DNDC is a widely used process-based model [
DailyDayCent is a biogeochemical model based on the Century soil C model and, for the most part, the parameter files used are identical to the ones used by Century 4.5 and DayCent 4.5 [
The datasets were collated and compiled to prepare a list of common input parameters with respect to site characteristics and managements to initialize the models (
The ECOSSE model can predict soil heterotrophic respiration (RH) only whereas the EC provides Reco (soil autotrophic and heterotrophic respiration + crop respiration). For comparison and validation of ECOSSE-simu- lated RH with measured ones, daily Reco measured by EC from the large fertilized plot was transformed to daily RH using DailyDayCent fractions (RH/Reco) obtained from this study. Calculation of the total/cumulative N2O, RH and Reco through integration of the measurement values and the sum of simulated values were performed. Seasonal and annual emission factors (EFs) for N2O over the 8 years were calculated by subtracting cumulative measured and model outputs of the unfertilized control from that of the fertilized treatments and dividing by the respective N inputs.
The outputs were collated and converted into standard units for comparison with measured datasets. The simulated values of GHGs were compared and validated quantitatively with measured values using MS Excel speadsheet (MODEVAL v 2.0) [
The measured surface soil
and the DNDC (R2 = 0.31) model estimates correlated significantly (p < 0.05) with the measured values (
The maximum N2O flux measured across all years was observed in 2004 (56.0 g∙N∙ha−1∙d−1) (
For the fertilized fields, both DNDC (87%) and DailyDayCent (81%) underestimated the total N2O fluxes
Statistical parameters | Fertilized | Unfertilized (Control) | ||||
---|---|---|---|---|---|---|
DNDC | DailyDayCent | ECOSSE | DNDC | DailyDayCent | ECOSSE | |
Soil NO3− concentration | ||||||
R2 | 0.31* | 0.14 | 0.55* | −0.07 | 0.00 | 0.13 |
RMSE (%) | 925* | 2847* | 115* | 837* | 684* | 169* |
RMSE95% (%) | 103 | 103 | 103 | 157 | 157 | 157 |
RE (%) | −610* | −1807* | −46* | −419* | −497* | −86* |
RE95% (%) | 66 | 66 | 66 | 65 | 65 | 65 |
MD (%) | −68 | −203 | −5 | −14 | −16 | −3 |
N2O fluxes | ||||||
R2 | −0.02 | 0.19 | 0.33* | −0.02 | −0.03 | −0.04 |
RMSE (%) | 189 | 367 | 154 | 186 | 183 | 197 |
RMSE95% (%) | 372 | 372 | 372 | 305 | 305 | 305 |
RE (%) | 87 | 74 | −59 | 94 | 87 | −43 |
RE95% (%) | 267 | 267 | 267 | 305 | 305 | 305 |
MD (%) | 5* | 4* | −3 | 2* | 2* | −1 |
*Significant at 5% level of probability. R2 = Coefficient of Determination; RMSE = Root Mean Square Error; RE = Relative Error (Mean); MD = Mean Difference; n = 130.
(seasonal/annual), whilst the ECOSSE model overestimated these by 59% (
The Reco measured using EC from the large fertilized plot reached a maximum flux of 75.6 kg∙C∙ha−1∙d−1 during crop growth that decreased to 0.59 kg∙C∙ha−1∙d−1 during the non-crop period, corresponding to RH (
The annual total Reco measured using the EC was on average 6771 kg∙C∙ha−1, which is closer to the DailyDayCent value (6736) but higher than the DNDC estimate (4455;
The measured CH4 fluxes (emission and oxidation) were small and differed significantly between the fertilized (−040 to 0.36 g∙C∙ha−1∙d−1) and unfertilized (−0.09 to 0.12) plots (
Total N2O fluxes | Fertilized | Unfertilized (Control) | ||||||
---|---|---|---|---|---|---|---|---|
Measured | DNDC | DailyDayCent | ECOSSE | Measured | DNDC | DailyDayCent | ECOSSE | |
Seasonal (04) | 522 | 137 | 94 | 1091 | −20 | 18 | 83 | 816 |
Seasonal (05) | 1145 | 33 | 74 | 1066 | 194 | 2 | 64 | 342 |
Annual (08 - 09) | 1168 | 88 | 380 | 2049 | 689 | 61 | 119 | 1423 |
Annual (8 yrs Av) | - | 207 | 644 | 2037 | - | 81 | 218 | 1319 |
N2O EFs | ||||||||
Seasonal (04) | 0.39 | 0.09 | 0.01 | 0.20 | ||||
Seasonal (05) | 0.60 | 0.02 | 0.01 | 0.46 | ||||
Annual (08 - 09)* | 0.34 | 0.02 | 0.19 | 0.46 | ||||
Annual (08 - 09)** | - | 0.06 | 0.34 | 0.48 | ||||
Annual (8 yrs Av) | - | 0.09 | 0.31 | 0.52 |
*Integrated (harvest to harvest); **Sum of daily simulated values (harvest to harvest); EF = Emission factor.
Statistical parameters | Reco | RH | ||||||
---|---|---|---|---|---|---|---|---|
Measured | DNDC | DailyDayCent | ECOSSE | Measured! | DNDC | DailyDayCent | ECOSSEf | |
R2 | 0.34* | 0.41* | - | - | 0.58* | 0.62* | 0.44* | |
RMSE (%) | 85 | 91 | - | - | 85 | 68 | 87 | |
RE (%) | 34 | 1 | - | - | 50 | 24 | 7 | |
MD (%) | 6* | 0 | - | - | 5* | 2* | 1* | |
Total CO2 fluxes kg∙C∙ha−1 | ||||||||
Annual total Reco | 6771 | 4455 | 6736 | - | - | - | - | - |
Annual total RH | - | 1826 | 2668 | 3218 | ||||
Annual total RH (4 yrs average) | 3624 | 1794 | 2744 | 3387 |
*Significant at 5% level of probability. ! = estimated using DailyDayCent derived ratio; f = Reco estimated using a conversion ratio derived from DNDC outputs for ECOSSE and DailyDayCent. R2 = Coefficient of determination; RMSE = Root Mean Square Error; RE = Relative Error (Mean); MD = Mean Difference.
large bias and error differences between them (
Annual budgets based on the measured data showed the arable land to be a small CH4 source, with an emission of 2.35 g∙C∙ha−1 from the unfertilized plot, increasing to 3.50 g∙C∙ha−1 for the fertilized plot (
Compared to the unfertilized field, the measured soil
Statistical parameters | Fertilized | Unfertilized (Control) | ||||||
---|---|---|---|---|---|---|---|---|
DNDC | DailyDayCent | ECOSSE | DNDC | DailyDayCent | ECOSSE | |||
R2 | 0.02 | 0.02 | 0.34 | 0.02 | - | 0.07 | ||
RMSE (%) | 18,926* | 183,761* | 401 | 38,037* | - | 2286* | ||
RMSE95% (%) | 14,821 | 14,821 | 14,821 | 2071 | - | 2071 | ||
RE (%) | 17,564* | 16,786* | -65 | 35,238* | - | 1670* | ||
RE95% (%) | 101,499 | 101,499 | 101,499 | 1318 | - | 1318 | ||
MD (%) | 2* | 2* | 4* | 2* | - | 0* | ||
Total annual fluxes (g∙C∙ha−1) | Measured | DNDC | DailyDayCent | ECOSSE | Measured | DNDC | DailyDayCent | ECOSSE |
Integrated | 3.50 | −646 | −612 | −25 | 2.35 | −729 | - | −31.1 |
Sum of daily flux | −682 | −657 | −28 | −712 | - | −31.4 | ||
8 years average | −666 | −704 | −28 | −667 | - | −30.3 |
*Significant at 5% level of probability. R2 = Coefficient of determination; RMSE = Root Mean Square Error; RE = Relative Error (Mean); MD = Mean Difference.
soil
The ECOSSE simulated values are closer to the amount of NO3-N applied, in line with the results of other researchers [
Simulated N2O emissions using the three models are reasonably consistent over the different years. However, all models are unable to predict N2O fluxes less than zero. This contrasts with the measured values where a sink of N2O under conditions of low oxygen and/or mineral N was observed [
For the fertilized fields, both DNDC and DailyDayCent underestimated, and the ECOSSE model overestimated the total N2O fluxes (seasonal/annual). Based on an 8-year average, DNDC simulated total fluxes are 2 - 15 times lower than the DailyDayCent and the ECOSSE estimates. The variations among the model estimates and their relationship with key driving forces such as soil water and
Similarly, a large underestimations of N2O EFs by DNDC as well as by DailyDayCent, compared to the measured data, are evident. Estimation of EFs using simulated values is constrained by total flux differences between the fertilized and unfertilized plots. Replacement of unfertilized values by using background annual N2O emissions of 1 kg∙N∙ha−1 [
There is a discrepancy between the integration approach and the corresponding sum of daily fluxes in calculating the total/cumulative N2O fluxes, which may under or overestimate the values, depending on the corresponding peak sizes, and thereby influence the EFs. Nitrous oxide emissions show large temporal and/or spatial variability [
The simulated values for Reco from both DailyDayCent and DNDC demonstrated good correlation with the measured values (R2 = 0.41 versus 0.34, p < 0.05), with relatively small total bias and error differences. Likewise, DNDC simulates well the cumulative CO2 fluxes of cropland sites in Europe, except for some overestimation of net CO2 uptake [
Accordingly, the annual estimates for the total Reco measured using EC (6771 kg∙C∙ha−1) is closer to the global cropland average (5440 ± 800 kg∙C∙ha−1) [
The measured data demonstrated both CH4 emissions and oxidation though the magnitude of the fluxes was relatively small. This might be linked to the contribution of RH with simultaneous influence of mainly soil water contents/precipitation events creating aerobic and anaerobic conditions [
The measured data show that cropland is a CH4 source that increases with the application of N fertilizer, indicating fertilizer-induced limitation for CH4 oxidation to occur. Arable soils are mainly considered as a sink rather than a source of CH4 [
Compared to the measured values, ECOSSE could simulate nitrate concentration more robustly than DNDC and DailyDayCent. Both DNDC and DailyDayCent underestimated daily and total N2O fluxes compared to ECOSSE, providing an improved prediction of fertilizer-induced N2O fluxes and EFs. All models could simulate soil and/or heterotrophic respiration adequately, except for an underestimation with DNDC that may be related to the greater impact of variations in soil properties compared to other model predictions. Only the ECOSSE model was able to predict field CH4 emissions/oxidation that were closer to the measured ones, and demonstrate the overall dominance of oxidation processes. There are constraints in terms of processes and driving forces in all the models for predicting coupled C and N emissions, leading to the underestimation of GHGs. Thus, refinement and further validation of the models using country-specific activity data are required to better predict GHG emissions. In addition, to avoid a dependency on default inputs that may lead to significant errors in the model outputs more measurements are required that account for temporal and spatial variability. Furthermore, validations and sensitivity tests need to focus more on site-related characteristics, land use differences, management interventions, and climatic factors for providing national GHG estimates and for identification of mitigation options.
The senior author gratefully acknowledges the funding by the Science, Technology, Research and Innovation for the Environment (STRIVE) Programme of the Irish Government under the National Development Plan 2007-2013 and the Department of the Environment, Heritage and Local Government. The authors would like to thanks Phillip O’Brien (EPA) for extending technical and relevant support; Mike Williams, Mike Jones and Matt Saunders (TCD), Komsan Rueangritsarakul and Mohamed Helmy (UCD) for supplying experimental data for modelling work; as well as Tom Bolger and Tommy Gallagher (UCD) for providing administrative support.
Mohammad I. Khalil,Mohamed Abdalla,Gary Lanigan,Bruce Osborne,Christoph Müller,1 1, (2016) Evaluation of Parametric Limitations in Simulating Greenhouse Gas Fluxes from Irish Arable Soils Using Three Process-Based Models. Agricultural Sciences,07,503-520. doi: 10.4236/as.2016.78051