DNDC Model Research Articles

Effect of mulched drip irrigation on crop biomass and carbon fluxes in maize field

The agroecosystem, a critical component of the terrestrial carbon cycle, plays an indispensable role in maintaining the balance of carbon pools and ensuring global food security. Investigating the interplay between crop yield and carbon cycle is essential for the advancement of sustainable agricultural practices. In the quest for water-efficient agricultural solutions, mulched drip irrigation emerges as a promising technique to mitigate water scarcity in agriculture. In the arid regions of northwest China, where water is a precious resource, understanding the impact of irrigation methods on the productivity and carbon dynamics of crops like spring maize is critical. Our study aimed to evaluate the influence of two irrigation methods—mulched drip irrigation (DI) and mulched border irrigation (BI)—on the biomass accumulation and carbon fluxes of spring maize by field observation and DNDC model simulation for comparative analysis. The findings based on field and flux observation data, underscore the transformative potential of shifting from BI to DI. DI was found to significantly enhance the soil's hydrothermal environment, which is crucial for fostering optimal conditions for crop growth. This improvement is instrumental in promoting the allocation of photosynthetic products to the aboveground biomass, ultimately leading to a substantial increase in grain yield. Our results indicated that the DI treatment not only bolstered the gross primary productivity (GPP) but also elevated the ecosystem respiration (RE) compared to the BI treatment. Finally, DI can increase the net ecosystem productivity (NEP) of maize fields by 6.08 %. The Denitrification-Decomposition (DNDC) model, after calibration and validation, proved to be a reliable tool for estimating ecosystem respiration under the two irrigation systems. A thorough analysis of the simulated data revealed that DI's enhancement of the soil's hydrothermal environment also led to an increase in soil heterotrophic respiration. This insight is vital as it sheds light on the complex interactions between irrigation practices and soil microbial processes, which are integral to the carbon cycle. These findings contribute to the growing body of knowledge on sustainable agricultural practices and provide a theoretical foundation for strategies aimed at achieving carbon neutrality. By adopting data-driven approaches and leveraging advanced models, we can pave the way for a more sustainable and resilient agricultural future that harmoniously balances productivity and environmental stewardship.

An integral assessment of the impact of diet and manure management on whole-farm greenhouse gas and nitrogen emissions in dairy cattle production systems using process-based models

Feed management decisions are crucial in mitigating greenhouse gas (GHG) and nitrogen (N) emissions from ruminant farming systems. However, assessing the downstream impact of diet on emissions in dairy production systems is complex, due to the multifunctional relationships between a variety of distinct but interconnected sources such as animals, housing, manure storage, and soil. Therefore, there is a need for an integral assessment of the direct and indirect GHG and N emissions that considers the underlying processes of carbon (C), N and their drivers within the system. Here we show the relevance of using a cascade of process-based (PB) models, such as Dutch Tier 3 and (Manure)-DNDC (Denitrification-Decomposition) models, for capturing the downstream influence of diet on whole-farm emissions in two contrasting case study dairy farms: a confinement system in Germany and a pasture-based system in New Zealand. Considerable variation was found in emissions on a per hectare and per head basis, and across different farm components and categories of animals. Moreover, the confinement system had a farm C emission of 1.01 kg CO2-eq kg−1 fat and protein corrected milk (FPCM), and a farm N emission of 0.0300 kg N kg−1 FPCM. In contrast, the pasture-based system had a lower farm C and N emission averaging 0.82 kg CO2-eq kg−1 FPCM and 0.006 kg N kg−1 FPCM, respectively over the 4-year period. The results demonstrate how inputs and outputs could be made compatible and exchangeable across the PB models for quantifying dietary effects on whole-farm GHG and N emissions.

Assessing the impact of climate and crop diversity on regional greenhouse gas emissions and water demand of cropland

To tackle the challenges of climate warming and freshwater scarcity, evaluating greenhouse gas (GHG) emissions and water demand in agricultural production can contribute positively to sustainable agricultural practices. This study aims to quantify the GHG emissions and water requirements of three major grains in diverse environmental regions in China, with a specific focus on exploring their spatial heterogeneity due to regional differences. The parameterized DNDC model was used to quantify crop GHG emissions, while the Penman-Monteith equation was utilized to assess crop water demand. The result showed that the total GHG emission of major grain in China was 372.43 Tg/yr (CO2), 11.68 Tg/yr (CH4), 475.56 Gg/yr (N2O) respectively; and the water demand was 473.60 Gm3, in 2015. The Middle-lower Yangtze Plain (MLYP) had the highest emission and water demand. High-temperature and high-precipitation regions had greater GHG emission intensity; the intensity of different GHG emissions was significantly influenced by the type of crops. Late rice had the highest emission intensity. The geospatial distribution of GHG emissions and water demand displayed opposite patterns, with meteorological conditions and crop types as the main reasons. Soil bulk density was the most significant soil element influencing regional emissions. These results will help to assess the spatial heterogeneity of agricultural “water and carbon”, and the quantitative results can be used as evaluation indicators to establish resource- and environment- friendly agricultural cropping patterns suitable for different cropping regions.

Exploring management strategies to improve yields and reduce reactive nitrogen emissions in a summer maize‐winter wheat cropping system under long‐term climate variability

AbstractAchieving high stable crop yields and minimal environmental damage is crucial to enhance the sustainability of agriculture in China. Process‐based models are indispensable tools to develop agronomy management practices to achieve sustainable agriculture by simulating crop production and emissions of reactive nitrogen (N), particularly in complex climate scenarios. In this study, a long‐term field experiment with an intensive summer maize‐winter wheat rotation system in north‐central China was simulated using the DeNitrification‐DeComposition (DNDC) model. The DNDC model validation and calibration was done by using two‐year monitoring data of crop yields and nitrous oxide emission fluxes and ammonia volatilization. Moreover, the optimal management practices to promote crop production and reduce the reactive N loss under 22 years of climate variability were explored using the calibrated DNDC model in this region. The results showed that the DNDC model effectively simulated wheat and maize yields, N uptake, ammonia volatilization, and nitrous oxide emissions. Sensitivity analyses demonstrated that the agronomic management practices (N rates and ratio of base to topdressing, planting time, and tillage depth) substantially affected crop yields and reactive N losses under long‐term climate variability. Compared with current farming practices, optimal Nutrient Expert (NE) management achieved an increase in high yields and environmental pollution radiation by altering the rate of N application and ratio of base to topdressing. Moreover, the optimal management strategies developed by the DNDC model, such as adjusting the planting date and tillage depth, further increased the average grain yield by 2.9% and reduced the average reactive N losses by 10.5% compared with the NE management implemented in the annual rotation cropping with a 22‐year simulation. This study suggests that the modeling method facilitates the development of most effective agronomic management practices to promote crop production and alleviate the negative impact on environment.

Climate mitigation potential and economic costs of natural climate solutions for main cropping systems across China

CONTEXTNatural climate solutions (NCS) offer immediate and cost-effective ways to tackle the climate crisis by transforming atmospheric carbon to soil carbon or avoiding greenhouse gas emissions. However, the climate mitigation potential and economic costs of NCS for cropping systems in China remain inconclusive. OBJECTIVEThe major objectives of this study are (i) to estimate the NGHG reduction potential across Chinese main cropping systems caused by NCS, (ii) to quantify the economic costs of adopting optimal management with different abatement potentials. METHODSThe Denitrification-Decomposition (DNDC) model was used to estimate the NGHG, including SOC sequestration, N2O emissions, and CH4 emissions. After calibration, we ran the DNDC model with derived data at municipality-level. We estimated the NGHG under current management, adopting optimal management individually, and integrated optimal management to project the maximum NGHG reduction potential caused by NCS. We used the marginal abatement cost method to estimate the economic cost. RESULTS AND CONCLUSIONSThe four natural climate solutions, N fertilization management (NFM), straw returning management (SRM), tillage management (TM), and rice irrigation management (RIM) can reduce national NGHG in main cropping systems by approximately 118.8 Tg CO2-eq, 116.3 Tg CO2-eq, 35.2 Tg CO2-eq, and 74.4 Tg CO2-eq, respectively. The integrated optimal management may potentially reduce NGHG by 256.9 Tg CO2-eq, equivalent to 90.2% of current NGHG, and 80% of which can be realized at the price of CN¥ 285 (Mg CO2-eq)−1. We further identify the priority NCS for different crops and regions and conclude that nitrogen fertilization management should be the priority NCS at the national level. SIGNIFICANCEOur findings highlight the great potential of NGHG reduction by adopting NCS precisely and the importance of optimizing agricultural management as a whole, and they will help policy makers to develop smart-agriculture in China.

Assessing the Impacts of Climate Change-induced Variations in Air Temperature and Precipitation on Plant Physiological and Soil Microbial Processes with DNDC Model

Abstract The DNDC (DeNitrification-DeComposition) model (version 9.5) was applied to predict the differences in transpiration and photosynthesis rates of perennial grasses (red clover and timothy), and autotrophic respiration of a sandy Spodosol. The input parameters for two growing seasons (from 1st of May to 31st of August in 2010 and 2015) contrasting in meteorological conditions were used in the modeling experiment. In 2010, the mean air temperature of the period was 14.1 ±3.3 °C and the total precipitation – 0.1796 m, while in 2015 the mean air temperature was 16.8 ±5.5 °C and the total precipitation – 0.538 m. These meteorological parameters were unfavorable for plants in 2010 and favorable in 2015. The results have shown that the DNDC model adequately predicted the weather-induced differences in total and mean transpiration rates of perennial grasses: 0.12204 m. and 0.00099 ±0.00040 m.day−1, respectively, under favorable meteorological conditions of 2015 and 0.05969 m. and 0.00049 ±0.00035 m.day−1, respectively, under unfavorable meteorological conditions of 2010. Dynamics of daily transpiration rates of plants was significantly (r = 0.34 p <0.001) correlated with soil water content only under unfavorable meteorological conditions. Mean values of simulated photosynthesis rates were equal to 84.4 ±27.9 kg.C.ha−1.day−1 in 2015 and 52.3 ±23.4 kg.C.ha-1.day−1 in 2010. There were significant differences (p <0.001) in the mean values of photosynthesis rates between the two weather scenarios. The results of one-way analysis of variance (ANOVA) have shown that the rates of autotrophic respiration were significantly (p <0.001) higher under favorable (8.14 ±2.25 kg.C.ha−1.day−1) than under unfavorable (5.17 ±2.19 kg.C.ha−1.day−1) meteorological conditions.

Soil respiration and its response to climate change and anthropogenic factors in a karst plateau wetland, southwest China.

It is important to investigate the responses of greenhouse gases to climate change (temperature, precipitation) and anthropogenic factors in plateau wetland. Based on the DNDC model, we used meteorological, soil, and land cover data to simulate the soil CO2 emission pattern and its responses to climate change and anthropogenic factors in Guizhou, China. The results showed that the mean soil CO2 emission flux in the Caohai Karst Plateau Wetland was 5.89 ± 0.17 t·C·ha-1·yr-1 from 2000 to 2019, and the annual variation showed an increasing trend with the rate of 23.02 kg·C·ha-1·yr-1. The soil total annual mean CO2 emissions were 70.62 ± 2.04 Gg·C·yr-1 (annual growth rate was 0.28 Gg·C·yr-1). Caohai wetland has great spatial heterogeneity. The emissions around Caohai Lake were high (the areas with high, middle, and low values accounted for 3.07%, 70.96%, and 25.97%, respectively), and the emission pattern was characterized by a decrease in radiation from Caohai Lake to the periphery. In addition, the cropland and forest areas exhibited high intensities (7.21 ± 0.15 t·C·ha-1·yr-1 and 6.73 ± 0.58 t·C·ha-1·yr-1, respectively) and high total emissions (54.97 ± 1.16 Gg·C·yr-1 and 10.24 ± 0.88 Gg·C·yr-1, respectively). Croplands and forests were the major land cover types controlling soil CO2 emissions in the Caohai wetland, while anthropogenic factors (cultivation) significantly increased soil CO2 emissions. Results showed that the soil CO2 emissions were positively correlated with temperature and precipitation; and the temperature change had a greater impact on soil respiration than the change in precipitation. Our results indicated that future climate change (increased temperature and precipitation) may promote an increase in soil CO2 emissions in karst plateau wetlands, and reasonable control measures (e.g. returning cropland to lakes and reducing anthropogenic factors) are the keys to controlling CO2 emissions.

Toward Low-Carbon Rice Production in China: Historical Changes, Driving Factors, and Mitigation Potential.

Under the "Double Carbon" target, the development of low-carbon agriculture requires a holistic comprehension of spatially and temporally explicit greenhouse gas (GHG) emissions associated with agricultural products. However, the lack of systematic evaluation at a fine scale presents considerable challenges in guiding localized strategies for mitigating GHG emissions from crop production. Here, we analyzed the county-level carbon footprint (CF) of China's rice production from 2007 to 2018 by coupling life cycle assessment and the DNDC model. Results revealed a significant annual increase of 74.3 kg CO2-eq ha-1 in the average farm-based CF (FCF), while it remained stable for the product-based CF (PCF). The CF exhibited considerable variations among counties, ranging from 2324 to 20,768 kg CO2-eq ha-1 for FCF and from 0.36 to 3.81 kg CO2-eq kg-1 for PCF in 2018. The spatiotemporal heterogeneities of FCF were predominantly influenced by field CH4 emissions, followed by diesel consumption and soil organic carbon sequestration. Scenario analysis elucidates that the national total GHG emissions from rice production could be significantly reduced through optimized irrigation (48.5%) and straw-based biogas production (18.0%). Moreover, integrating additional strategies (e.g., advanced crop management, optimized fertilization, and biodiesel application) could amplify the overall emission reduction to 76.7% while concurrently boosting the rice yield by 11.8%. Our county-level research provides valuable insights for the formulation of targeted GHG mitigation policies in rice production, thereby advancing the pursuit of carbon-neutral agricultural practices.

Plastic film mulching increases crop yields and reduces global warming potential under future climate change

Identifying suitable cropping systems and planting areas with high yields and low greenhouse gas (GHG) emissions to adapt to climate change is essential for sustainable agricultural development. The DeNitrification-DeComposition (DNDC) model and the Coupled Model Inter-comparison Project Phase 6 (CMIP6) dataset were used to evaluate the impact of climate scenarios on the crop yields and global warming potential (GWP) of maize, winter wheat, and potato under plastic film mulching (PM) and without mulching (CK) systems on the Loess Plateau for 2020–2100. The result showed that the DNDC model could simulate crop yields and carbon dioxide, nitrous oxide, and methane emissions (adjusted R2 > 0.55, normalized root mean square error (nRMSE) < 0.32). Under multiple emission scenarios, the yields of maize and winter wheat increased linearly with time (p < 0.05), the GWP decreased linearly with time (p < 0.05), and the coefficients of variation of both were lower for the PM than CK, indicating that the impact of future climate change on PM system was small and can achieve synergy between food security and carbon reduction. The results of regional divergence characteristics suggested that areas with precipitation below 400 mm were suitable for maize cultivation, especially the PM system, while areas with precipitation above 400 mm were suitable for winter wheat cultivation – both of which could achieve the goals of high yield and low GHG emissions. This study presents an overview of spatiotemporal changes in production and ecological effects, providing a basis for developing agricultural management practices and cultivation areas to address climate change, and could promote the development of dryland agroecosystems in China in a green and sustainable direction.

Assessing the Impact of Organic-Inorganic Nitrogen Application on Maize Yield and Nitrogen Leaching Using the DNDC Model

In order to optimize maize yield and minimize nitrogen leaching, field experiments and DNDC model simulations assessed various organic-inorganic nitrogen application treatments. Trials were conducted in the Hetao irrigation area of Inner Mongolia, comparing six treatments: no nitrogen (CK), solely inorganic nitrogen (U1), and combinations where 25%, 50%, 75%, and 100% of inorganic nitrogen was replaced with organic nitrogen (U3O1, U1O1, U1O3, O1). The DNDC model was calibrated using the U1 treatment data from 2018-2020 and validated with other treatments. Results indicated the model accurately simulated maize yield (NRMSE &lt; 5°%) and nitrate nitrogen leaching (NRMSE &lt; 15%). Simulation outcomes demonstrated that increasing inorganic nitrogen application reduced crop yield and significantly increased nitrate leaching. Conversely, higher proportions of organic nitrogen, along with adjusted irrigation and frequency of inorganic nitrogen fertilization, increased maize yield while reducing nitrate leaching. At a fixed nitrogen application rate of 240 kg hm−2, maize yield initially rose and then declined with increasing organic nitrogen ratios, while nitrate nitrogen leaching decreased consistently. Optimal results were achieved with a 3:2 ratio of organic to inorganic nitrogen, yielding 12578 kg hm−2 with nitrate nitrogen leaching at 15.7 kg NO-3-N hm−2, indicating this ratio as the preferred method for combined nitrogen application in the region.

Modelled Climate Change Impacts on Spring Canola Production Across British Columbia, Canada

This paper investigates the impact of projected near future climate trends on the production of spring planted canola (Brassica napus, Brassica rapa, and Brassica juncea of canola classification) across British Columbia, Canada. Analysis of historic climate trends from 2001-2020, informed by the Global Historical Climatology Network daily (GHCNd) database, establishes patterns of warming temperatures and increasing precipitation values across the province over the early 21st century. Near future climate trends were modelled using CMIP6 climate models, from 2021-2040 under SSP1-2.6, SSP2-4.5 and SSP5-8.5, with the projections downscaled using ClimateBC. The projected trends mirrored those of the observed historic record, while an observable relationship between rising levels of climate change and increasing projected annual precipitation and temperature is recorded. The subsequent crop modelling using the CSM-CROPGRO-Canola and DNDC models, fed with the modelled climate trends, highlighted the expectation for near future climate change to cause significant decreases in spring canola production across British Columbia.

Plausible impacts on crop production under climate change in Bangladesh: An analysis of the Denitrification-Decomposition (DNDC) model

This paper ffrst analyzes the changing pattern of temperature and precipitation and then constructs a Denitriffcation-Decomposition (DNDC) model to better understand the climate change impact on crop yields in Bangladesh. In the DNDC model, historical daily precipitation and temperature data used for baseline scenario and projected data have been taken from general circulation model (GCM). Different general circulation models (GCM) have been employed to analyze and estimate future temperatures and precipitations. The result of the general circulation model (GCM) study ffnds that the overall temperature in Bangladesh tends to increase by 1.5 0C and 2.8 0 C in the years 2030 and 2050. Precipitation patterns are also projected to increase in 2030 and 2050. The result from the Denitriffcation-Decomposition (DNDC) model ffnds that overall rice, corn, winter wheat, potato, vegetable, and pulses yields decrease both in 2030 and 2050, and decrease more rapidly in 2050. In the year 2050, the output of rice, potatoes, and pulses falls by -33%, -35%, and -54%, respectively, while the production of corn and wheat falls by -22% collectively. Since rice is the main food consumed in Bangladesh, a decline in rice output will pose a serious threat to the country's ability to feed itself.

Modelling adaptation measures to improve maize production and reduce soil N2O emissions under climate change in Northeast China

Optimizing management practices is essential to guarantee crop yield and reduce greenhouse gas emissions for the sustainable agricultural production under climate change. The objectives of this study were to i) calibrate and evaluate the Denitrification-Decomposition (DNDC) model using the measurements of maize yield, nitrogen (N) uptake, soil temperature, soil moisture, soil inorganic N and nitrous oxide (N2O) emissions from a 5-year maize field experiment in Northeast China (NEC) and ii) simulate the impacts of climate change on maize yield and N2O emissions and explore adaptation strategies based on DNDC modelling. For N-fertilized treatments (farmers’ practice, FP-N; current optimized management, OPT-N), the DNDC model overall showed “good” performance in simulating maize yield, aboveground biomass, N uptake and soil inorganic N based on the average statistical values. The model also performed well in simulating soil moisture, soil temperature and N2O emissions. For N-unfertilized treatments, however, the DNDC model overestimated crop yield and N uptake mainly due to the overestimation of crop growth under low-temperature stress conditions, and the underestimation of soil inorganic N resulted from the underestimated N mineralization and N availability from the dissolved inorganic N pools under continuous N deficiency. The DNDC model showed that compared to the baseline scenario, future climate change would improve the maize yield (1.4–8.8%) while increasing the N2O emissions (37.6–83.4%) without adaptation measures in NEC. Compared to OPT-N management, solely adjusting N fertilization rate (200–220 kg N ha−1) would concurrently increase maize yield (5.0–8.7%) and N2O emissions (0.9–18.6%), while the adoption of optimized planting dates (April 27th to May 18th), new cultivars (with thermal degree days ranging from 2550 to 2700 °C), and the implementation of no-tillage and maize-soybean rotation would increase the maize yield by 1.8–8.9% and reduce the N2O emissions by 0.7–26.5% under future climate change scenarios. This study provided insights into the adaptive management strategies for the sustainable maize production under climate change in NEC.

Optimization of a nitrogen fertilizer application scheme for spring maize in full-film double-ridge furrow in Longzhong, China

Full-film double-ridge furrow sowing technology (FDRFST) and nitrogen (N) management are crucial for sustainable agricultural development. To investigate the N fertilizer application scheme for spring maize with high yield, precipitation water productivity (PWP), and N use efficiency (NUE) under the FDRFST, a two-year field experiment was carried out in a rainfed area of Longzhong (LZ). Treatments included two types of N fertilization (common urea [U] and controlled release urea [CU]), three application rates (180, 225, and 270 kg hm−2), and the conditions of no N fertilizer application and no plastic film mulching. The results showed that the temperature-increase effect of PM on the soil between 2021 and 2022 was concentrated for 0–90 days, with average values of the 5 cm soil temperature increasing by 4.15 and 3.58 ºC, respectively. However, the N fertilizer application rate had negligible effects on soil temperature. Plastic film mulching (PM) required temperature-increase compensation, particularly during the sowing–emergence stage, with a 1.68 compensation coefficient. Based on the simulation results of the modified DNDC model, the recommended application rate of CU (180 kg hm−2) for spring maize in LZ increased the average yield, PWP, and NUE by 15.8%, 16.0%, and 36.4% from 1981 to 2020, respectively, compared to U treatments. Our results provide a theoretical basis for N management in spring maize production in LZ and offer critical insights for improving the DNDC model under PM conditions.

Soil organic carbon changes in China's croplands: A newly estimation based on DNDC model

Soil Organic Carbon (SOC) in cropland represents a significant facet of the terrestrial ecosystem's carbon reservoirs, playing a pivotal role in global climate change mitigation efforts. Within the specific context of China, cropland SOC not only extends its implications beyond environmental impact but also serves as a critical factor in ensuring the stability and security of the nation's food supply. However, there is an ongoing argument about the changes in SOC and their spatial and temporal distribution patterns within China's croplands. In this study, we constructed a new county-level DNDC database for 2020, building upon 2003 research that quantified SOC stock in China's cropland using the DNDC model. Our aim was to assess the SOC storage and temporal changes of China's cropland in 2020 using same methodology to enhance estimation accuracy. The simulation results of the validated DNDC model revealed that the average SOC storage of China's croplands (0–30 cm) in 2020 was 6.02 Pg C, with the Northeast region contributing 23 % (1.37 Pg C). The SOC density in China varied from 18.55 to 152.57 t C ha−1, averaging at 49.65 t C ha−1. In 2020, China's cropland transitioned from a net loss of SOC in 2003 to a carbon sink, with cropland SOC density and SOC storage increased by 18.2 % and 21.6 % respectively. Notably, despite experiencing a loss of SOC compared to 2003, the Northeast region had the highest average SOC density in China. This study highlights that despite the increase in SOC density and storage in China's croplands over the last 17 years, there remains substantial potential for carbon sequestration given the current spatial distribution of SOC density's significant heterogeneity within China. The findings of this study offer data support for China's strategy to achieve food security and carbon neutrality.

Simulation Study of CH4 and N2O Emission Fluxes from Rice Fields in Northeast China under Different Straw-Returning and Irrigation Methods Based on the DNDC Model

In order to explore the long-term variation law of methane (CH4) and nitrous oxide (N2O) emissions from rice fields in cold regions under different straw-returning and irrigation methods, this study set up two irrigation methods, namely, conventional flooding and controlled irrigation, and two straw-returning quantities (0 t·hm−2 and 6 t·hm−2). Based on the field in situ test data, a sensitivity analysis of the main factors of the DNDC model affecting the emissions of CH4 and N2O from rice fields was conducted, and the emission fluxes of CH4 and N2O were calibrated and validated. Under different future climate scenarios (RCP4.5 and RCP8.5), greenhouse gas emissions from rice fields were simulated on a 60-year scale under different straw-returning and irrigation methods using the DNDC model. The results indicate that the DNDC model can effectively simulate the seasonal emission laws of CH4 and N2O from rice fields in cold regions under different straw-returning and irrigation methods. The simulated values have a significant correlation with the measured values (R2 ≥ 0.794, p &lt; 0.05), and the consistency is controlled within 30%. The soil texture, soil organic carbon (SOC) content, annual average temperature, and straw-returning amount are sensitive factors for CH4 emissions from rice fields. The total nitrogen fertilizer application amount and SOC content are sensitive factors for N2O emissions from rice fields. Over the next 60 years, under the two different emission scenarios of RCP4.5 and RCP8.5, straw returning combined with control irrigation has a good coupling effect on the GWP of rice fields, and compared with conventional flooding without straw returning, the GWP of rice fields is reduced by 31.41% and 34.13%, respectively, and the SOC content in 0–20 cm soil layer is increased by 54.69% and 52.80%, respectively. Thus, it can be used as a long-term carbon sequestration and emission reduction tillage model for rice fields in Northeast China. The results of this study can provide a reference for a further regional estimation of greenhouse gas emissions from rice fields using models.

SIMULATION OF NITROGEN BALANCE UNDER SUB-SURFACE DRAINAGE CONDITIONS AT THEHRI MUKTSAR PUNJAB, USING THE DNDC MODEL V. 9.5

Aim of the study: The aim of the study is DNDC model simulation for nitrogen balance in rice-wheat cropping system. Material and methods: The DeNitrification-DeComposition (DNDC) model is a computer simulation model for the biogeochemistry of carbon and nitrogen in agro ecosystems that takes a process-oriented approach. The DNDC model version 9.5 (http:// www.dndc.sr.unh.edu) was selected for estimating nitrogen balance. The model consists of two modules. The first component simulates moisture, soil temperature, pH, and substrate concentration, which are determined by ecological parameters such as soil, climate, anthropogenic activities, and vegetation. It consists of sub-models for plant growth, decomposition, and soil climate. The second module predicts the emission of gasses from plant-soil systems such as methane (CH4), nitrous oxide (N2O), nitrogen oxide (NO), dinitrogen (N2), ammonia (NH3), and carbon dioxide (CO2). The model includes empirical equations developed from laboratory studies and is based on the classical laws of chemistry, physics, and biology. The empirical equations included parameterizing specific biochemical or geochemical reactions. The entire model bridges the primary ecological drivers with the biogeochemical cycles of C and N (see: Figures 1 and 2). Results and conclusions: This study used the DNDC model to estimate nitrogen balance in the study area. A calibrated and validated DNDC model was used to simulate NO3-N loss in runoff and leachate from a rice–wheat cropping system from 2018 to 2020. The total nitrogen balance estimated by the DNDC model was negative (–99.44 kg N ha–1 yr–1) and positive (69.1 kg N ha–1 yr–1) for rice and wheat cropping systems, respectively, in the study area.

Modeling ammonia emissions and abatement potential from the rice-wheat rotation fields using the calibrated DNDC model: A case study in Shanghai, China

Abating ammonia (NH3) volatilization from intensive cropping system is of great importance for preventing the pollution of atmospheric environment and sustainable agriculture. Urea, a common nitrogen fertilizer, can convert to an unstable carbamate and further releases NH3 from the fields. However, few investigations of NH3 volatilization loss following fertilizer application to agricultural soils have been done using process-based models. In this study, a two-year field study was conducted to calibrate and evaluate the DeNitrification-DeComposition (DNDC) model for simulating NH3 volatilizations and crop production for the rice-wheat rotation system under four fertilizer regimes: control group without N fertilizer (CK), chemical fertilizer (CF), mixed application of inorganic and organic fertilizer (MF), and organic fertilizer (OF) treatments. The calibrated and modified DNDC performed effectively in simulating cumulative NH3 volatilization (coefficient of determination (R2) = 0.96 for rice and 0.92 for wheat field, relative deviation (RD) = −6.3% - 2.1% for rice and −22.5 to −4.6% for wheat) and grain yields for both crops (R2 = 0.94 for rice and 0.91 for wheat, RD of rice: 4.94%–9.59%; wheat: −1.84%–11.51%). The sensitivity analysis demonstrated that NH3 volatilization from rice-wheat rotation field was most sensitive to N application rate. Compared to CF and OF, MF treatments significantly mitigated NH3 volatilization and improved crop yield. Therefore, scenario simulation was conducted to confirm the optimal N application rate under MF. The results revealed that 250 kg N ha−1 and 112.5 kg N ha−1 in MF were recommended for rice- and wheat-growing, respectively. Compared to the current N fertilizer rate, the recommended rate saved 50 kg N ha−1 and mitigated NH3 volatilization loss by 28.31% from rice field and saved 37.5 kg N ha−1 and mitigated NH3 volatilization loss by 19.29% from wheat field. Overall, the calibrated DNDC model proved to be an effective tool to simulate NH3 volatilization from the rice-wheat rotation system and provides a promising fertilizer management strategy to decrease N input and mitigate NH3 volatilization.

Measured and modeled nitrogen balances in lowland rice-pasture rotations in temperate South America

Rotational rice systems, involving pastures, other crops and/or livestock, are common in temperate South America, exemplified by the rice-pasture-livestock system of Uruguay which combines very high rice yields with tight nitrogen (N) balances. The generally good nutrient use efficiency in these systems provides a template for nutrient management in other mixed farming systems, if the underlying processes can be sufficiently well quantified and understood. Here, we studied N balances in rice–non-rice rotations in a long-term experiment in Uruguay, with the aim of parameterizing and testing the DNDC model of N dynamics for such systems for use in future work. The experiment includes three rotations: continuous rice (RI-CONT), rice-soybean (RI-SOY) and rice-pasture (RI-PAST). We considered 9 years of data on N balances (NBAL), defined as all N inputs minus all N outputs; N surplus (NSURP), defined as all N inputs minus only N outputs in food products; and N use efficiency (NUE), defined as the fraction of N inputs removed in food products. We parameterized DNDC against measured yield and input and output data, with missing data on N losses inferred from the N balance and compared with literature values. The model performance was assessed using standard indices of mean error, agreement and efficiency. The model simulated crop yields and rice cumulative N uptake very well, and soil N reasonably well. The values of NBAL were +45 and−20 kg N ha−1 yr−1 in RI-CONT and RI-SOY, respectively, and close to zero in RI-PAST (−6 kg N ha−1 yr−1). Values of NSURP decreased in the order RI-CONT &amp;gt;&amp;gt; RI-SOY &amp;gt; RI-PAST (+115, +25 and +13 kg N ha−1 yr−1, respectively). Values of NUE (84, 54, and 48% for RI-SOY, RI-PAST, and RI-CONT, respectively) decreased as NBAL increased. The sensitivity of DNDC's predictions to the agronomic characteristics of the different crops, rotations and water regimes agreed with expectations. We conclude that the DNDC model as parameterized here is suitable for exploring how to optimize N management in these systems.

Development of DNDC-BC model to estimate greenhouse gas emissions from rice paddy fields under combination of biochar and controlled irrigation management

Simulating the impacts of biochar and controlled irrigation (CI, a water-saving irrigation technology without surface ponding of water) combination on greenhouse gas (GHG) emissions from paddy fields is crucial, however, the original DNDC model is not capable of capturing these management impacts on emissions. In this study, we have created a new modelling approach called DNDC-Biochar-Controlled Irrigation (DNDC-BC) by adding two modules to the original DNDC: the water balance principle, and the two-pool biochar model. The performance of the DNDC-BC was tested and validated using a two-year field experiment, designed with two irrigation treatments (flood irrigation (FI), and CI) and three biochar application rates (0, 20, and 40 t ha−1), carried in the Lake Taihu region, China. Results revealed that the DNDC-BC model performed satisfactorily in simulating impacts of biochar-CI combination on emissions of CH4, N2O, SOC, and grain yield. The corresponding R2 values of DNDC-BC increased by 22.63%-35.66%, 43.00%-71.26%, 1.06%-60.87% and 15.38%-34.78% compared with those of the original model. Sensitivity analysis for the DNDC-BC showed that CH4 emissions were most sensitive to rainfall and irrigation; N2O emissions were sensitive to rainfall, irrigation and N fertilizer application rate, and SOC was sensitive to biochar application rates. Our results suggest that the combination of biochar- and CI can save water resources and mitigate GHG emissions without detrimental impacts on rice yield. The DNDC-BC can be used to estimate the long-term impacts of biochar-CI combination on GHG emission, crop productivity and climate change.