DNDC Model Research Articles

Effects of Long-Term Organic–Inorganic Nitrogen Application on Maize Yield and Nitrogen-Containing Gas Emission

A sustainable model of combined organic–inorganic fertilizer application for high maize yields and environmental health is important for food security. The short-term combined application of organic and inorganic fertilizers can improve crop yields; however, the effect of different proportions of organic and inorganic fertilizers on the maize yield and nitrogen gas emissions in a long time series has not been reported. In this study, field experiments and DeNitrification-DeComposition (DNDC) model simulations were used to study the long-term effects of substituting inorganic fertilizers with organic fertilizers on crop yields and nitrogen-containing gas emissions. Six treatments were included: no nitrogen (CK); urea (U1); and 25%, 50%, 75%, and 100% of the urea N substituted by organic fertilizers (U3O1, U1O1, U1O3, and O1, respectively). The DNDC model was calibrated using the field data from the U1 treatment from 2018 to 2020 and was validated for the other treatments. The results showed that this model could effectively simulate crop yields (e.g., nRMSE < 5%), soil NH3 volatilization, and N2O emissions (nRMSE < 25%). In addition, long-term (26 years) simulation studies found that the U1O1 treatment could considerably increase maize yields and ensure yield stability, which was 15.69–55.31% higher than that of the U1 treatment. The N2O, NH3, and NO emissions were in the descending order of U1 > U3O1 > O1 > U1O3 > U1O1, and the total nitrogen-containing gas emissions from the U1O1 treatment decreased by 53.72% compared with the U1 treatment (26 years). Overall, substituting 50% of inorganic nitrogen with organic nitrogen could maintain the high yield of maize and reduce emissions of nitrogen-containing gases, constituting a good mode for the combined application of organic–inorganic nitrogen in this area.

Modeling the Effects of Rice-Vegetable Cropping System Conversion and Fertilization on GHG Emissions Using the DNDC Model

The cropping system conversion, from rice to vegetable, showed various influences on the greenhouse gases (GHG) emission with conversion time and fertilizer/irrigation management. In this study, we evaluated the DeNitrification-DeComposition (DNDC) model for predicting carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) emissions and crop yields as rice converted to vegetable cropping system under conventional or no fertilization from 2012 to 2014. Then, we quantified the long-term (40 years) impacts of rice-vegetable cropping system conversions and fertilization levels (0, 50, 100 and 150% conventional fertilization rate) on GHGs emissions and global warming potentials (GWP) using the calibrated model. The DNDC model-simulated daily GHG emission dynamics were generally consistent with the measured data and showed good predictions of the seasonal CH4 emissions (coefficient of determination (R2) = 0.96), CO2 emissions (R2 = 0.75), N2O emissions (R2 = 0.75) and crop yields (R2 = 0.89) in response to the different cropping systems and fertilization levels across the two years. The overall model performance was better for rice than for vegetable cropping systems. Both simulated and measured two-year data showed higher CH4 and CO2 emissions and lower N2O emissions for rice than for vegetable cropping systems and showed positive responses of the CO2 and N2O emissions to fertilizations. The lowest GWP for vegetable without fertilization and highest the GWP for rice with fertilization were obtained. These results were consistent with the long-term simulation results. In contrast to the two-year experimental data, the simulated long-term CH4 emissions increased with fertilization for the rice-dominant cropping systems. The reasonable cropping systems and fertilization levels were recommended for the region.

Modelling the impacts of inhibitors and fertilizer placement on maize yield and ammonia, nitrous oxide and nitrate leaching losses in southwestern Ontario, Canada

Application of urease and nitrification inhibitors is an effective strategy for decreasing nitrogen (N) losses without negatively impacting yields, but the long-term impacts of inhibitors and N placement on N dynamics remains unclear. The objectives of this study were to 1) use a 3-year maize experiment in southwestern Ontario to evaluate the Denitrification-DeComposition (DNDC) model fitted with default and enhanced urea hydrolysis mechanisms, 2) determine the impacts of climate variability (30-year) on inhibitor performance on maize yield and N losses under a range of N treatments, and 3) estimate the optimal N application rates for maintaining high yield and minimizing N losses. The experiment treatments included a factorial arrangement of two N sources (urea, urea ammonium nitrate [UAN]), three N placement methods (broadcast urea [B-Urea], broadcast incorporated urea [BInc-Urea], injected UAN [Inj-UAN]), and three inhibitors (no inhibitor, urease inhibitor [UI], urease plus nitrification inhibitor [UI&NI]). Although both the default and improved model provided “fair” to “good” simulations of maize yield, N uptake, soil temperature, soil moisture, soil inorganic N content and nitrous oxide (N2O) emissions across treatments, more accurate simulations were obtained using the improved model. The default DNDC model indicated “poor” simulations of ammonia (NH3) volatilization according to the Nash-Sutcliffe efficiency (NSE <0) and index of agreement (d ≤ 0.38), whereas the improved DNDC produced “good” simulations (NSE = 0.22–0.39, d = 0.65–0.84) because of improved estimates of urea diffusion, hydrolysis and inhibitor efficiency. The 30-year simulation based upon 95% of the maximum yield resulted in annual NH3 losses of 35.4 kg N ha−1 for B-Urea and these losses were reduced by 40.1% with BInc-Urea and by 92.7% with Inj-UAN. When UI was added to either B-Urea or BInc-Urea, NH3 losses were reduced by 61.8–66.4%, whereas when UI&NI was added, losses were reduced by 41.0–49.4%. The Inj-UAN reduced annual N2O losses by 18.8% compared to B-Urea whereas two inhibitor treatments of B-Urea + UI&NI and BInc-Urea + UI&NI reduced N2O losses by 11.5–11.8% compared to B-Urea and BInc-Urea. Simulated annual N losses from Inj-UAN + UI&NI were reduced relative to Inj-UAN + UI because of decreased NO3− leaching. Simulated long-term average percentage of annual total N losses at the optimal N rate were 36.8, 30.8 and 22.0% from B-Urea, BInc-Urea and Inj-UAN, respectively, which reduced to 25.9–28.1, 24.9–25.5 and 21.2–21.9%, when inhibitors were added. Our study suggests that the DNDC model can be used to investigate the impacts of climate variability on inhibitor performance and determine the optimum range of N application rates for different inhibitor treatments.

Assessment of Greenhouse Gas Emissions into the Atmosphere from the Northern Peatlands Using the Wetland-DNDC Simulation Model: A Case Study of the Great Vasyugan Mire, Western Siberia

The peatlands of Western Siberia occupy an area of about 1 million km2 and act as important regulator of carbon exchange between the earth and the atmosphere. Extrapolation of the results of discrete field measurements of CO2 fluxes in bog ecosystems to such a territory is a difficult task, and one of the ways to overcome it is to use a simulation model such as DNDC. However, using this model with a specific territory requires ground verification to confirm its effectiveness. Here, we tested the DNDC model on the largest pristine bog ecosystem of the world, the Great Vasyugan Mire (GVM). The GVM of western Siberia is virtually undisturbed by anthropogenic activity and is the largest bog of Northern Eurasia (53,000 km2). Based on various ground-based observations, the performance of the Wetland-DNDC model was demonstrated (Thale coefficient 0.085 and R2 = 0.675 for CO2). Model input parameters specific to the GVM were constrained and model sensitivity to a wide range of input parameters was analyzed. The estimated annual terrestrial carbon fluxes in 2019 from the GVM test site are mainly controlled by plant respiration (61%) and forest floor degradation (38%). The net CO2 emission flux was 8600 kg C ha−1 year−1, which is in line with estimates from other independent studies.

Predicted Soil Greenhouse Gas Emissions from Climate × Management Interactions in Temperate Grassland

Grassland management practices and their interactions with climatic variables have significant impacts on soil greenhouse gas (GHG) emissions. Mathematical models can be used to simulate the impacts of management and potential changes in climate beyond the temporal extent of short-term field experiments. In this study, field measurements of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) emissions from grassland soils were used to test and validate the DNDC (DeNitrification-DeComposition) model. The model was then applied to predict changes in GHG emissions due to interactions between climate warming and grassland management in a 30-year simulation. Sensitivity analysis showed that the DNDC model was susceptible to changes in temperature, rainfall, soil carbon and N-fertiliser rate for predicting N2O and CO2 emissions, but not for net CH4 emissions. Validation of the model suggests that N2O emissions were well described by N-fertilised treatments (relative variation of 2%), while non-fertilised treatments showed higher variations between measured and simulated values (relative variation of 26%). CO2 emissions (plant and soil respiration) were well described by the model prior to hay meadow cutting but afterwards measured emissions were higher than those simulated. Emissions of CH4 were on average negative and largely negligible for both simulated and measured values. Long-term scenario projections suggest that net GHG emissions would increase over time under all treatments and interactions. Overall, this study confirms that GHG emissions from intensively managed, fertilised grasslands are at greater risk of being amplified through climate warming, and represent a greater risk of climate feedbacks.

Sown alfalfa pasture decreases grazing intensity while increasing soil carbon: Experimental observations and DNDC model predictions.

Grasslands are the most important land use in China and have experienced extensive degradation in the past few decades due to overgrazing. However, regionally viable solutions to grazing intensity alleviation remained elusive to date. Here, we evaluated the grazing intensity effects of sown alfalfa pastures in northern China using an experiment-modeling combined approach that involved six sites in field experiments and five provinces in DNDC modeling of sown alfalfa pasture's forage production and carbon sequestration potentials in marginal lands. Our results showed that the sown alfalfa pasture's dry-matter yield varied between 4.5 and 9.0 Mg ha-1 under rainfed and irrigated conditions, respectively, from 2025 to 2035. If half of the available marginal lands were mobilized for alfalfa forage production, these yield levels meant that livestock grazing intensity on natural grasslands may drop 8-13% under rainfed and 20-33% under irrigated conditions. Our results also showed that marginal land's soil organic carbon contents were systematically higher under sown alfalfa pasture than under fallow management by a big margin of 8.5 and 9.9 g kg-1 (i.e., +79 and +95%), under rainfed and irrigated conditions, respectively, during 2025-2035. Overall, these results demonstrated that sown alfalfa pasture on marginal lands represents an effective grassland conservation pathway over the short- to medium-term time horizon based on current technologies.

Simulating Soil-Plant-Climate Interactions and Greenhouse Gas Exchange in Boreal Grasslands Using the DNDC Model

With global warming, arable land in boreal regions is tending to expand into high latitude regions in the northern hemisphere. This entails certain risks; such that inappropriate management could result in previously stable carbon sinks becoming sources. Agroecological models are an important tool for assessing the sustainability of long-term management, yet applications of such models in boreal zones are scarce. We collated eddy-covariance, soil climate and biomass data to evaluate the simulation of GHG emissions from grassland in eastern Finland using the process-based model DNDC. We simulated gross primary production (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco) with fair performance. Soil climate, soil temperature and soil moisture at 5 cm were excellent, and soil moisture at 20 cm was good. However, the model overestimated NEE and Reco following crop termination and tillage events. These results indicate that DNDC can satisfactorily simulate GHG fluxes in a boreal grassland setting, but further work is needed, particularly in simulated second biomass cuts, the (&gt;20 cm) soil layers and model response to management transitions between crop types, cultivation, and land use change.

Optimizing strategies to reduce the future carbon footprint of maize under changing climate

Many countries have set ambitious goals to achieve carbon neutrality around 2050. In this context food production systems are of interest, as they contribute one-third of global greenhouse gas emissions. This study assessed the carbon footprint (CF) of maize production under different agricultural management practices and scenarios based on a cradle-to-farm gate life cycle analysis and DNDC model, considering future climate impacts and clean energy use. Maintaining the current energy use strategy, under the management of residue removal, future climate change will lead to a 3.0% increase in the CF of maize, while in the residue returning system, the CF of maize will decrease by 40.9%. In the highly mechanized maize production system, the CF of maize was shown to be mainly determined by the CO2 equivalent emissions (CE) from upstream production and in-field portions, including soil emissions and the use of agricultural machinery. Residue return plays a critical role in establishing maize's CF, making maize fields a carbon sink. The use of clean energy can reduce the CF of maize by 482.9±8.6 kg CO2 eq ha−1. Our study serves as a reference for determining the potential of mitigating the total carbon emissions from maize production by 2050.

Estimation of greenhouse gas emission flux from agricultural lands of Khuzestan province in Iran.

Greenhouse gas emissions and their effects on global warming are one of the serious challenges of developed and developing countries. Investigating the amount of greenhouse gas emissions of different countries makes it possible to determine the share of countries in the production of greenhouse gases. The purpose of this study is to use DAYCENT and DNDC models to estimate the emission rate of methane, nitrous oxide, and carbon dioxide greenhouse gases as well as to estimate the global warming potential in Khuzestan agricultural lands in Iran. For this purpose, the gas sampling was done in rice, wheat, and sugarcane fields using a static chamber, and then the concentration of methane, nitrous oxide, and carbon dioxide was determined by using gas chromatography. In the following, DAYCENT and DNDC models were used to estimate gas emissions and the global warming potential of these gases was estimated. Finally, TOPSIS method was used to prioritize gas emissions. In order to evaluate the modeling accuracy, the statistical indicators of maximum error, root mean square error, determination coefficient, model efficiency, and residual mass coefficient were used. According to the results, the highest measured gas flux was obtained for rice fields at Baghmalek and the lowest for sugarcane in Abadan. The results of DAYCENT model estimation showed that the highest emissions were obtained for methane gas and rice cultivation, and lowest gas emissions were obtained for sugarcane cultivation. The results of DNDC model estimation also showed that the highest flux was determined for nitrous oxide gas in rice cultivation. The results of the estimation of global warming potential also showed that it was the highest in sugarcane cultivation (Shushtar station) and the DAYCENT model, and the lowest was also in wheat cultivation and the DNDC model. The statistical results of the estimation of DAYCENT and DNDC models showed that the DAYCENT model in sugarcane cultivation (Shushtar station) was the most accurate in estimating carbon dioxide gas, and the lowest accuracy was related to the DNDC model and sugarcane cultivation (Shushtar station) in estimating nitrous oxide gas. According to the results of agricultural activities in Khuzestan province, they have made a major contribution to the production of greenhouse gases, which, or the lack of attention to this issue, will have an effect on the future climate of this region.

Estimation of nitrous oxide emissions from rice paddy fields using the DNDC model: a case study of South Korea.

The Denitrification-Decomposition (DNDC)-Rice is a mechanistic model which is widely used for the simulation and estimation of greenhouse gas emissions [nitrous oxide (N2O)] from soils under rice cultivation. N2O emissions from paddy fields in South Korea are of high importance for their cumulative effect on climate. The objective of this study was to estimate the N2O emissions and biogeochemical factors involved in N2O emissions such as ammonium (NH4+) and nitrate (NO3-) using the DNDC model in the rice-growing regions of South Korea. N2O emission was observed at every application of fertilizer and during end-season drainage at different rice-growing regions in South Korea. Maximum NH4+ and NO3- were observed at 0-10 cm depth of soil. NH4+ increased at each fertilizer application and no change in NO3- was observed during flooding. NH4+ decreased and NO3- increased simultaneously at end-season drainage. Minimum and maximum cumulative N2O emissions were observed at Chungcheongbuk-do and Jeju-do regions of South Korea, respectively. The simulated average cumulative N2O emission in rice paddies of South Korea was 1.37 kg N2O-N ha-1 season-1. This study will help in calculating the total nitrogen emissions from agriculture land of South Korea and the World.

Impact of planting Lycium barbarum L. on the carbon–land–water nexus of a Haloxylon ammodendron nature reserve in the Qaidam basin, China

AbstractDesert ecosystems are an important component of terrestrial ecosystems and land use change directly affects their composition and function. However, there have been few studies on how land use change impacts the carbon–land–water nexus in desert ecosystems. We studied the effects of replacing sacsaoul (Haloxylon ammodendron), a typical natural vegetation in the Qaidam basin, China, with artificially planted goji berry (Lycium barbarum L.) using the denitrification‐decomposition (DNDC) model. Our results showed that DNDC model coefficient of determination () and Nash‐Sutcliffe modelling efficiency (NSE) both reached 0.92 for sacsaoul and goji berry, and the root mean square error (RMSE) of the simulation was 495 kg ha−1 and 116 kg ha−1 for sacsaoul and goji berry, respectively. The land use change from sacsaoul to goji berry induced a significant increase in global warming potential (GWP) and the water footprint, but decreased the water user efficiency (WUE). The WUE decreased by 1.2, 2.9, and 3.5 kg m−3 for the core zone, buffer zone, and experimental zone when the land cover changed from sacsaoul to goji berry, respectively. Meanwhile, the total water footprint and the GWP increased 2214 m3 t−1 and 4744 CO2 eq. ha−1, 2295 m3 t−1 and 5297 CO2 eq. ha−1, and 2316 m3 t−1 and 5224 CO2 eq. ha−1 for the core zone, buffer zone, and experimental zone, respectively. In conclusion, we suggest that the maintenance of natural vegetation (sacsaoul) in desert ecosystems may be the optimal choice to cope with climate change and mitigate land desertification.

How can process-based modeling improve peat CO2 and N2O emission factors for oil palm plantations?

Oil palm plantations on peat and associated drainage generate sizeable GHG emissions. Current IPCC default emission factors (EF) for oil palm on organic soil are based on a very limited number of observations from young plantations, thereby resulting in large uncertainties in emissions estimates. To explore the potential of process-based modeling to refine oil palm peat CO2 and N2O EFs, we simulated peat GHG emissions and biogeophysical variables over 30 years in plantations of Central Kalimantan, Indonesia. The DNDC model simulated well the magnitude of C inputs (litterfall and root mortality) and dynamics of annual heterotrophic respiration and peat decomposition N2O fluxes. The modeled peat onsite CO2-C EF was lower than the IPCC default (11 Mg C ha−1 yr−1) and decreased from 7.7 ± 0.4 Mg C ha−1 yr−1 in the first decade to 3.0 ± 0.2 and 1.8 ± 0.3 Mg C ha−1 yr−1 in the second and third decades of the rotation. The modeled N2O-N EF from peat decomposition was higher than the IPCC default (1.2 kg N ha−1 yr−1) and increased from 3.5 ± 0.3 kg N ha−1 yr−1 in the first decade to 4.7–4.6 ± 0.5 kg N ha−1 yr−1 in the following ones. Modeled fertilizer-induced N2O emissions were minimal and much less than 1.6% of N inputs recommended by the IPCC in wet climates regardless of soil type. Temporal variations in EFs were strongly linked to soil C:N ratio and soil mineral N content for CO2 and fertilizer-induced N2O emissions, and to precipitation, water table level and soil NH4+ content for peat decomposition N2O emissions. These results suggest that current IPCC EFs for oil palm on organic soil could over-estimate peat onsite CO2 emissions and underestimate peat decomposition N2O emissions and that temporal variation in emissions should be considered for further improvement of EFs.

Analysis of Reactive Nitrogen Emissions from Maize Ethanol Production Based on the DNDC Model

Agricultural system is an important source of reactive nitrogen (Nr) emissions. In this study, DNDC model was established for analyzing Nr emissions in maize planting and maize ethanol production under climate change scenarios (i.e., RCPs 4.5 and 8.5). The DNDC model was applied in maize fields of Shandong Province. The Nr emissions in 2025 and 2030 under the RCPs 4.5 and 8.5 scenarios would range from 2869.24 to 2969.18 kg N/ha. An inventory of Nr emissions in maize ethanol production was obtained in this study. The results showed that compared with maize planting in other cities, maize fields in Linyi would release the biggest amount of N2O and NO, as well as the smallest amount of NH3. The study can support decision making for Nr emissions reduction in agricultural systems.

Soil carbon and nitrous oxide dynamics in corn (Zea mays L.) production under different nitrogen, tillage and residue management practices

Reducing tillage frequency and intensity and returning crop residues to the soil are two agricultural management practices to increase soil organic carbon (SOC). Timing and rates of nitrogen fertilizer, coupled with tillage methods and residue incorporation, can also influence nitrous oxide emissions. In this study, we used measured data from a long term (23 years) tillage and residue management experiment, under intensive grain corn production, together with the DNDC (DeNitrification-DeComposition) process-based model, to ascertain the most effective fertilizer and tillage management practices to reduce nitrous oxide emissions and nitrate leaching, and increase soil carbon stocks. The field experiment consisted of no-till [NT], reduced [RT], and conventional tillage [CT], with and without corn residue return. After calibration and validation, using field observed data, the DNDC model was run with 108 management scenarios (six N application rates, spring single N fertilization, spring split N fertilization and fall fertilization, three tillage practices and two residue-return options). N application rate was predicted to be a significant driver for corn yield, SOC sequestration, N2O emissions and nitrate leaching, indicating the need to identify a critical N rate to both optimize production and achieve sustainability goals. Split (vs. single) N application did not significantly affect corn yield, nitrate leaching, N2O emissions or SOC, while fall fertilization should be avoided due to greater nitrate leaching and higher N2O emissions as well as lower crop yield. Although stimulating N2O emissions, both NT and residue return are recommended for their ability to enhance SOC stocks. Results of this study can be used for policy development regarding carbon sequestration and GHG emissions for intensive grain cropping systems in the humid regions of North America.

Control-released urea improved agricultural production efficiency and reduced the ecological and environmental impact in rice-wheat rotation system: A life-cycle perspective

Application of control-released urea (CRU) is considered as an attractive means to improve nitrogen use efficiency (NUE) while maintaining and even improving the grain yield. However, life-cycle impacts, including economic benefit, energy flow, and environmental and ecological benefits, from CRU production to crop harvesting at various application strategies remain unclear. To fully assess the life-cycle impacts of CRU application, a 2-year field experiment covering an area of 3.78 ha was conducted in Yangtze River Delta, China during 2019−2020 with a rice-wheat rotation. The grain yield, NUE, net profits, energy flow, life-cycle N and C footprints, ecosystem economic benefits from the application of bulk blending urea (BBU) consisting of CRU and farmer’s conventional urea were evaluated and compared to farmer’s conventional urea fertilizing practice. Results suggested that twice-split fertilization of BBU improved the total grain yield and NUE, while single-dose fertilization of BBU had no impacts on total grain yield but reduced the NUE. By optimizing fertilization rate and frequency, the total net profit was improved from 3016 $ ha −1 to 3300 $ ha −1 across the rice-wheat-rice season from 2019 to 2020. Compared to the farmer’s conventional urea fertilizing practice, application of BBU not only significantly decreased the life-cycle reactive N (Nr) losses and greenhouse gas (GHG) emissions by 35.6%−54.5% and 34.1%−44.7% respectively, but also reduced the N and C footprints by 41.1%−60.8%, and 41.8%−42.3% respectively, which was mainly profited by decreasing the on-farm emissions of CH 4 , N 2 O, NH 3 , and NO 3 − . The ecosystem economic benefits were improved by 18.1%−59.8% because of the reductions in labor, ecosystem, and human health costs. Energy productivities also were enhanced by 3.9%−12.4% except for single-dose fertilization of BBU at the rate of 300 kg N ha −1 . Comprehensive analysis indicated that twice-split application of BBU at the rate of 300 kg N ha −1 obtained relatively high performances for grain yield, net profit, energy productivity, reductions in N and C footprints, and ecosystem economic benefit, which demonstrated that twice-split application of BBU was an effective fertilization strategy to balance the economic benefit and ecological and environmental impacts in the rice-wheat rotation system, thereby promoting agricultural sustainability. • LCA and energy flow analysis revealed the life-cycle N, C, and energy footprints in a rice-wheat rotation system. • DNDC model simulated the reductions of Nr losses and GHG emissions by application of BBU. • Appropriate BBU fertilization increased the grain yield and net profits. • CRU was more sustainable than conventional urea from the life-cycle perspective.

The effects of plastic film mulching and straw mulching on licorice root yield and soil organic carbon content in a dryland farming

The increasing worldwide demand for traditional herbs has been met by growing cultivated herbs. It is undoubtedly very important to seek a reasonable cultivation mode for the yield, quality and long-term production stability of traditional herbs. In this study, licorice (Glycyrrhiza uralensis Fisch.) was investigated using a field experiment and a process-based model (Denitrification-Decomposition (DNDC) model) to study the effects of mulching methods on root yield and soil organic carbon (SOC) long-term changes. The field experiment contained four treatments: plat planting without mulching (CK), ridge-furrow maize straw mulching (SM), ridge-furrow plastic film mulching (RP), and plat planting with plastic film mulching (FP). Licorice root yield was significantly higher in the SM, RP, and FP than in the CK. SM, RP and FP treatments increased the accumulation of liquiritin and glycyrrhizin in licorice roots. The SM significantly increased SOC content, SOC stocks, SOC sequestration rate, dissolved organic carbon (DOC) content and microbial biomass carbon (MBC) compared to CK, but there was no significant difference in SOC and DOC among CK, RP and FP. The DNDC model was calibrated based on the field test data and showed that under the four CMIP6 SSPs scenarios, the predicted root yield of each treatment was increasing obviously. The production and stability of RP and FP were greater than CK and SM. The SOC under SM showed an increasing trend, whereas it continuously decreased under CK, RP, and FP in the future. The SOC of simulated RS treatment of straw incorporation plus a plastic film mulch was always at the highest value in all the treatments, and its root yield was slightly lower than that of RP and FP, the latter both were very close. Therefore, it is suggested that RS should be adopted to achieve sustained high yield while maintaining a high SOC level.

Modeling Soil Organic Carbon Changes under Alternative Climatic Scenarios and Soil Properties Using DNDC Model at a Semi-Arid Mediterranean Environment

Soil organic carbon (SOC) is one of the central issues in dealing with soil fertility as well as environmental and food safety. Due to the lack of relevant data sources and methodologies, analyzing SOC dynamics has been a challenge in Morocco. During the last two decades, process-based models have been adopted as alternative and powerful tools for modeling SOC dynamics; whereas, information and knowledge on the most sensitive model inputs under different climate, and soil conditions are still very limited. For this purpose, a sensitivity analysis was conducted in the present work, using the DeNitrification-DeComposition (DNDC) model based on the data collected at a semi-arid region (Merchouch station, Morocco). The objective is to identify the most influential factors affecting the DNDC-modeled SOC dynamics in a semi-arid region across different climatic and soil conditions. The results of sensitivity analysis highlighted air temperature as the main determinant of SOC. A decrease in air temperature of 4 °C results in an almost 161 kg C ha−1 yr−1 increase in C sequestration rate. Initial SOC was also confirmed to be one of the most sensitive parameters for SOC. There was a 96 kg C ha−1 yr−1 increase in C sequestration rate under low initial SOC (0.005 kg C ha−1). In the DNDC, air temperature in climatic factors and initial SOC in variable soil properties had the largest impacts on SOC accumulation in Merchouch station. We can conclude that the sensitivity analysis conducted in this study within the DNDC can contribute to provide a scientific evidence of uncertainties of the selected inputs variables who can lead to uncertainties on the SOC in the study site. The information in this paper can be helpful for scientists and policy makers, who are dealing with regions of similar environmental conditions as Merchouch Station, by identifying alternative scenarios of soil carbon sequestration.

Exploring wheat-based management strategies to balance agricultural production and environmental sustainability in a wheat−maize cropping system using the DNDC model

Faced with the great challenge of food demand and environmental pollution, optimizing agricultural practices can potentially balance food security and environmental protection. In this study, the DeNitrification-DeComposition (DNDC) model was applied to explore the effect of wheat-based management strategies on crop productivity and greenhouse gas emissions in the wheat−maize system. The DNDC model was tested against crop yield, daily nitrous oxide (N2O) fluxes, and cumulative N2O emissions determined from field measurements in a typical winter wheat−summer maize cropping system. Model evaluations demonstrated a good agreement between the observations and simulated crop yield (4.4%≤NRMSE≤8.0%), daily N2O fluxes (0.68 ≤ d ≤ 0.88), and cumulative N2O emissions (4.9%≤NRMSE≤11.9%). By adopting sensitivity analysis, the DNDC model then assessed the impacts on crop yield and cumulative N2O emissions of multiple management practices from the winter wheat season. Delaying the sowing date from October 7 to November 4 reduced annual yield by 1.9%, while cumulative N2O emissions were increased by 10.4%. Furthermore, postponing the supplementary irrigation date from April 1 to May 20 decreased annual yield by 2.4% without affecting cumulative N2O emissions. An N fertilizer rate of 120–150 kg N ha−1 was able to reduce N usage and cumulative N2O emissions without sacrificing annual yield. Despite an improvement in the annual yield at the 0–30 cm tillage depth by 2.9%, cumulative N2O emissions increased by 11.6%. The results suggest that sowing in early October, applying supplementary irrigation in early April, an N fertilizer rate of 120–150 kg N ha−1, and no-tillage from the winter wheat season can improve crop yield and mitigate N2O emissions. This is conducive to the synergism of agricultural production and environmental sustainability.

Evaluation of the DNDC Model to Estimate Soil Parameters, Crop Yield and Nitrous Oxide Emissions for Alternative Long-Term Multi-Cropping Systems in the North China Plain

Optimizing crop rotations is one of the proposed sustainable management strategies for increasing carbon sequestration. The main aim of this study was to evaluate the DeNitrification-DeComposition (DNDC) model for estimating soil parameters (temperature, moisture and exchangeable NO3− and NH4+), crop yield and nitrous oxide (N2O) emissions for long-term multi-cropping systems in Hebei, China. The model was validated using five years of data of soil parameters, crop yields and N2O emissions. The DNDC model effectively simulated daily soil temperature, cumulative soil nitrogen and crop yields of all crops. It predicted the trends of observed daily N2O emissions and their cumulative values well but overestimated the magnitude of some peaks. However, the model underestimated daily water filled pore space, especially in dry seasons, and had difficulties in correctly estimating daily exchangeable NO3− and NH4+. Both observed and simulated cumulative N2O results showed that optimized and alternative cropping systems used less nitrogen fertiliser, increased grain yield and decreased N2O emissions compared to the conventional cropping system. Our study shows that although the DNDC model (v. 9.5) is not perfect in estimating daily N2O emissions for these long-term multi-cropping systems, it could still be an effective tool for predicting cumulative emissions.

Simulation of Nitrous Oxide Emissions in Zoysia Turfgrass Using DAYCENT and DNDC Ecosystem Models

Nitrous oxide (N2O) is an important greenhouse gas (GHG) implicated in global climate change. Process-based ecosystem models, such as DAYCENT and DNDC, have been widely used to predict GHG fluxes in agricultural systems. However, neither model has yet been applied to warm-season turfgrasses such as zoysiagrass. This study parameterized, calibrated, and validated the DAYCENT and DNDC models for N2O emissions from Meyer zoysiagrass (Zoysia japonica Steud.) using Bayes’ theorem and field data from Braun and Bremer (2018a, 2019) and Lewis and Bremer (2013). Results indicated DAYCENT, but not DNDC, reasonably simulated the impacts of irrigation and N-fertilization practices on biweekly and annual N2O emissions in zoysia turfgrass. When assuming no further climate change, the validated DAYCENT model predicted that typical recommendations for N-fertilization and irrigation in zoysiagrass (a low-input turfgrass) would reduce its cumulative global warming potential (GWP) for the first 45 years after establishment by encouraging soil carbon sequestration. Thereafter, soils would become saturated with carbon and hence, reductions of N inputs would be beneficial for mitigating further increases in N2O emissions and GWP.