Denitrification Decomposition Research Articles

Assessing the long‐term effects of conservation agriculture on cotton production in Northeast Louisiana using the denitrification–decomposition model

AbstractConservation agriculture (CA) aims to sustain agricultural production, soil, and environmental health in agroecosystems and has been promoted throughout the United States. The adoption of CA in cotton (Gossypium hirsutum) systems provides both agronomic and environmental benefits. Yet, there is limited information on the long‐term effects of CA practices on crop yield and adaptation strategies. An integrated CA system, that is, cover crops with no‐tillage (NT) instead of conventional agriculture, was implemented in the long‐term field experiments and assessed with an integrated biogeochemical model. Using the denitrification–decomposition model, this study estimated the effects of four different cover crops, for example, native grass (NG), hairy vetch (Vicia villosa), winter wheat (Triticum aestivum L.), and crimson clover (Trifolium incarnatum), on cotton yield under four different nitrogen (N) levels (e.g., 0, 50, 100, and 150 kg N/ha) and estimated responses on carbon (C) sequestration, and ecosystem functionality over a 10‐year study. The NT‐NG 50 N was used as a calibration dataset to accurately estimate the cotton lint yield with a normalized root mean square error (NRMSE) of 21% and model efficiency of 0.3. The calibration data validated the effects of hairy vetch, winter wheat, and crimson clover under the NT‐50 N with NRMSE of 24%, 21%, and 25%, respectively. According to the scenario analysis, the 50 kg N/ha application with a single‐irrigation event (10‐cm depth) was most beneficial for maximizing the cotton yield with cover crop incorporation at the NT system over the long term. The effects of increasing cover crop biomass (i.e., double seed rate) on C content, regardless of N application rates, varied based on the relationship between the main and cover crop species. Besides, the furrow plow tillage system provided efficient C sequestration. The proposed approach stands to provide agricultural and environmental sustainability with the implementation of cover crop or crop residue incorporation instead of increased N application, seed rates, and irrigation events under NT practices.

Interactive effects of environmental factors and fertilization practices on soil nitrate leaching and tea productivity in Tianmu Lake Basin, China

Although the effects of fertilization practices and environmental factors on tea yield and NO3−–N leaching have been acknowledged, the interactive effects of these influencing factors remain unknown. These effects were investigated in this study using the denitrification–decomposition (DNDC) model, which was validated using field data collected from a typical tea garden hillslope in the Tianmu Lake Basin, China. Environmental factors such as temperature, precipitation intensity, slope, saturated hydraulic conductivity (Ksat), and fertilization practices (fertilizer amount and type) were combined to construct 1080 scenarios. The results revealed that DNDC achieved acceptable performance in simulating NO3−–N leaching under the application of a compound fertilizer (CF) and a slow-release fertilizer (SF). During the calibration period, the Nash–Sutcliffe efficiency (NSE) values were 0.48 and 0.24. During the validation period, the NSE values were 0.71 and 0.67. Scenario analysis indicated that precipitation intensity (p < 0.001), saturated soil hydraulic conductivity (Ksat) (p < 0.001), fertilizer amount (p < 0.001), and fertilizer type (p < 0.001) significantly influenced the leaching flux of NO3−–N and tea yield. The interactive effects of different factors also varied across the different types of fertilizers. The amount of fertilizer had the most significant impact on NO3−–N leaching, with relative contribution rates of 70% and 53% for compound and SFs, respectively. As CF application increased, there was a significant increase (93%) in the average flux, whereas SF application resulted in an increase of only 76%. The highest contribution rate in terms of tea yield was observed for the amount of fertilizer under CF application at 44%. However, the highest contribution rate under SF application was attributed to Ksat at 51%. The application of SFs resulted in a higher yield improvement rate than that of CFs as the fertilizer amount increased, with respective rates of 15% and 8%. Additionally, environmental factors, excluding temperature, exerted opposite effects on tea production and NO3−–N leaching flux. These findings contribute to optimizing fertilization management in tea plantations by integrating multiple environmental factors to control nonpoint nitrogen loss. Future research should conduct long-term monitoring of tea garden yield and nitrogen loss to accurately assess the impact of SF application on the ecological and economic benefits of tea gardens.

Estimation of greenhouse gas emissions from rice and annual upland crops in Red River Delta of Vietnam using the denitrification–decomposition model

Abstract This study used the denitrification–decomposition (DNDC) model and ArcGIS 10.1 to calculate and quantify the greenhouse gas (GHG) potential from rice and annual upland crops in the Red River Delta of Vietnam. GHG emissions were monitored, analyzed, and calculated at experimental sites. The operating mechanism, sensitivity analysis of the parameters, calibration, and verification of the DNDC model for the GHG emission calculation were studied and performed, and a set of parameters was built. A good correlation between actual and simulated values was shown. From the data set of meteorological stations in and around the Red River Delta, the current land-use map, the topographic and soil map, a complex map of meteorology–soil–land use was built. Each unit of this map contains complete information about climate, soil, and crops as input data for modeling GHG emissions from crop production. From the spatial analysis and collected input data, GHG emissions were measured and calculated for the cultivated field of the Red River Delta (annual rice and upland crops) using the DNDC model. The model's outputs were used to build thematic maps on the distribution of global warming potential (CH4, N2O) for each unit of the complex map of climate, soil, and crops.

A multi-objective optimization approach to simultaneously halve water consumption, CH4, and N2O emissions while maintaining rice yield

Sustainable rice farming practices are urgently needed to meet increasing food demand, cope with water scarcity, and mitigate climate change. Traditional farming methods that prioritize a single objective, such as increasing crop yield, reducing irrigation water consumption, or minimizing greenhouse gas (GHG) emissions, have proven to be insufficient. Simultaneously optimizing multiple competing rice farming objectives remains less explored. The present study aimed to increase current rice yield, reduce irrigation water consumption, and tackle the dilemma to reduce GHG (CH4 and N2O) emissions at once. Using a heuristic and holistic method, we optimized farm management, focusing on irrigation regimes, sowing window, fertilization rate, tillage depth, and their interactions. We calibrated and validated the process-based DeNitrification–DeComposition (DNDC) model with 5 years of eddy covariance observations in a rice paddy site. The DNDC model was integrated with a Pareto-based non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. Results show that current farm management practices have failed to achieve the potential yield and imposed high environmental costs in the form of water use (604–810 mm/yr) and GHG emissions (CH4: 186–220 kg C/ha/yr; N2O: 0.3–1.6 kg C/ha/yr). By contrast, our new approach suggested that the optimized management could maintain or even increase current crop yield to its potential (∼10 t/ha), while reducing irrigation demand and GHG emissions by >50 %. Our results suggest that earlier sowing, combined with improved irrigation practices, may play a crucial role in maximizing crop yield and potentially providing environmental benefits. We found that the optimal proportion of non-flooded days was around 54 % of the growing season length, and the optimal stage for non-flooding days was the vegetative stage. Our approach could further be applied to evaluate the environmental sustainability of farming systems under various climate and local conditions, and to guide policymaking and farming practices.

Modified DNDC model to improve performance of soil temperature simulation under plastic film mulching and snow cover

Soil temperature plays a fundamental role in climatological and hydrological modeling as well as in simulating environmental processes and climate change. There are many models to simulate soil temperature, and the denitrification decomposition (DNDC) model is a widely used process-based biogeochemical model among them. However, this model does not simulate soil temperatures well under seasonal snow cover and plastic film mulching in arid and semi-arid regions. In this study, the DNDC model was calibrated and validated using a 9 years (2012–2020) soil temperature dataset from a typical semi-arid region of Gansu province, China (Yuzhong). Then, the model was tested in two other experimental sites in semi-arid regions (Dingxi and Tongwei in Gansu province, China). Model sensitivity analysis demonstrated that the simulated soil temperature was mainly related to air temperature. Model validation results indicated that the measured and simulated soil temperatures were in agreement with each other. Compared with the original model, the average RMSE of the soil temperature in the modified model decreased by 125.3 % and the average R2 increased by 52.1 % in winter under conditions of plastic film mulching and snow cover. For the test dataset, the R, R2, and RMSE of the modified DNDC model were 0.96–0.97, 0.89–0.94, and 2.01–2.76 in the Dingxi experiment site, and 0.94–0.95, 0.88–0.93, and 1.86–3.16, in the Tongwei experiment site, respectively. The performance of the modified model simulation was better than that of the original model. The modified DNDC model can be used as an effective simulation tool to predict soil temperature. The modified model also had good expandability and applicability in different experimental sites in different semi-arid regions. It can be used as an alternative scheme for future soil temperature predictions, and it can provide theoretical input for the formulation of local agricultural strategies.

Conceptual framework addressing timescale mismatch uncertainty: Nitrous-oxide (N2O) modeled and measured, Kansas, USA

The uncertainty that arises from the differences in time scales between modeled and measured variables during sensitivity analysis, calibration, and validation in process-based models are often not addressed in the literature. A conceptual framework was developed to represent the uncertainty arising due to this mismatch in timescales. Modeling N2O fluxes from agricultural lands in Manhattan, Kansas using Denitrification–Decomposition (DNDC) model, and with measurements available at biweekly time scale is chosen in the demonstration. A conceptual framework was developed to represent the known-unknown uncertainty using integration methods, management practices, sensitivity analysis methods, calibration and validation performance measures. The known-known and known-unknown uncertainty were represented for combinations of three integration methods (mean, median and cumulative sum), four management practice combinations (till-urea, no-till-urea, till-compost, no-till-compost), three sensitivity analysis methods (two graphical approaches and an index based method), and two calibration and validation performance measures (ME, R2). In the framework, the unknown uncertainty was represented but not quantified. The various assumptions and some of the implications were also discussed. The framework followed in this exercise and insights gained can be applicable to other process-based models.

Effects of Future Climate Change on Spring Maize Yield and Water Use Efficiency under Film Mulching with Different Materials in the LOESS Plateau Region of China

Background: Traditional polyethylene film mulching is widely used in the Loess Plateau region of China to improve crop yields. However, whether long-term polyethylene film mulching can continue to ensure crop yield under future climate change conditions is questionable. First, we conducted a four-year field experiment to calibrate and validate the biogeochemical DeNitrification–DeComposition (DNDC) model. Then, based on the calibrated and validated model, we evaluated the spring maize yield and water use efficiency under different film mulching methods (no mulching, traditional polyethylene film mulching, and biodegradable film mulching) in the Loess Plateau region. Results: The temperature and rainfall in the Loess Plateau region are predicted to increase in the future (2021–2100) under four scenarios due to higher CO2 concentrations. Through 252 simulation results, we found that future climate change will have positive impacts under no mulching, traditional polyethylene film mulching, and degradable film mulching conditions. The yield increase will be greater with no mulching, but in the future, film mulching will continue to reduce crop yields. Additionally, the crop yield reduction under traditional polyethylene film mulching is greater. A sensitivity analysis indicated that rainfall will have a major effect on yield, and polyethylene film mulching will reduce the sensitivity of the yield to rainfall. As the rainfall increases, the differences between the yield and water use efficiency under ordinary plastic film and degradable film will become smaller. In the later period with a warmer and wetter climate under the SSP585 scenario, the water use efficiency will be higher under degradable film than traditional polyethylene film mulching. Conclusion: It can be seen that degradable film is more adaptable to the warmer and wetter climate in the future.

Optimizing rice in-season nitrogen topdressing by coupling experimental and modeling data with machine learning algorithms

The modern in-season crop N recommendation approaches should have high reliability in promoting agricultural sustainability. These approaches are relevant to soil properties, meteorological conditions, management practices, and crop in-season growing status. This study aims to use machine learning (ML) algorithms to incorporate the above variables as well as the field reactive nitrogen (N) losses (i.e., N damage cost) simulated by a DeNitrification–DeComposition (DNDC) model to develop a new strategy for optimizing rice in-season topdressing N (TN) usage. Rice field experiments with multiple N treatments and rice varieties were carried out during 2015–2021 at four study sites in eastern China. Four ML algorithms, namely random forest regression (RFR), support vector regression (SVR), lasso regression (LSR), and partial least square regression (PLSR) were used to develop in-season prediction models of yield and reactive N losses by combining soil, meteorological, and management data with crop remote sensing data. The observed in-season agronomic optimum N rates (AONR) that can maximize rice yield at different sites were in the range of 116.5 to 177.4 kg N ha−1, while the in-season economic optimum N rates (EONR) that can maximize marginal revenue (i.e., yield income minus N fertilizer costs and N damage costs) were in the range of 97.4 to 163.6 kg N ha−1. The developed ML models were further used to simulate yield and marginal revenue responses to a series of assumed TN rates (0–300 kg N ha−1, gradient = 20 kg N ha−1). Comparably, RFR model and SVR model were more suitable for determining optimum TN rates, because their simulated response curves of yield and marginal revenue fit the normal regulation (linear plus plateau or single-peak shapes). Independent validation results showed that the in-season AONR and EONR predicted by RFR and SVR well accorded with the observed values (R2 ≥ 0.64, RRMSE ≤ 18.3 %), and the accuracy of ML models containing both historical and in-season meteorological information is superior to ML models that contain in-season meteorological information only. The proposed ML-based strategy is expected to help the regional rice production systems precisely manage N use, improve net profits, and reduce environmental footprints.

Simulated effects of cover crops with no-tillage on soil and crop productivity in rainfed semi-arid cotton production systems

Cover cropping and no-tillage (NT) practices are promoted for soil health and environmental benefits. However, producing enough quantity of biomass to meet expected benefits can be a challenge in semi-arid cropping systems where water resources are limited, and producers have lingering questions regarding cover crop selection, termination timings, water use, and their subsequent effect on cash crop. The objective of this study was to assess the impacts of cover crops with NT on soil and crop productivity in a rainfed, continuous cotton (Gossypium hirsutum L.) system using the Denitrification Decomposition (DNDC) model. Treatments included: (a) conventional till (CT); (b) NT, and NT with the following cover crops: (c) Austrian winter pea (Pisum sativum L.) (Pea); (d) crimson clover (Trifolium incarnatum L.) (Clover); (e) hairy vetch (Vicia villosa L.) (Vetch); (f) a multi-species mixture (Mixture); and (g) winter wheat (Triticum aestivum L.) (Wheat). The DNDC model adequately simulated the seed cotton yield and cover crop biomass during calibration (root mean square error (RMSE) = 200 and 398 kg ha−1, respectively) and validation (RMSE = 277 and 270 kg ha−1, respectively). Stocks of soil organic carbon (SOC) and total nitrogen (TN) were simulated with Nash–Sutcliffe efficiency (NSE) of 0.91 and 0.78 during calibration, and with NSE of 0.88 and 0.75, respectively during validation. Long-term simulations revealed: (1) Vetch and Pea produced greater biomass and hence highest SOC and TN contents, which was followed by Mixture, Wheat, and Clover; (2) cotton yield in cover crop treatments was no different to that in non-cover crop treatments when cover crop was terminated about six weeks prior to cotton planting; (3) later termination of cover crops can result in greater cotton yield loss due to soil water depletion by cover crops. Overall, this study highlights the potential of conservation practices, such as cover crops and NT to improve soil health without significant loss in cotton yields.

Utilization of the DNDC model to estimate yield and CO2 and CH4 emissions in a cotton-wheat rotation under the influence of various tillage treatments

Tillage practices have a substantial impact on the production and consumption of greenhouse gasses (GHG), which are the primary contributors to global warming. CO2 and CH4 emissions were stimulated in cotton-wheat rotations using the DeNitrification DeComposition (DNDC) model under various tillage techniques. The DNDC model was parameterized using experimental data collected at Darab Agricultural Research Station over the course of five years (2013–2017). Wheat and cotton were grown with zero-tillage (direct seeding), minimum tillage, and conventional tillage. The model was then utilized to simulate CO2 and CH4 emissions from cotton fields in rotation with wheat in the fourth and fifth years. The model validation results indicate that the model was effective at simulating soil factors and emitting gasses. The simulation results indicated that the average annual CO2 emissions for conventional tillage, minimum tillage, and zero-tillage treatments were 1858.0, 2116.0, and 2359.50 kg/ha, respectively, and the average annual CH4 emissions were 4.75, 4.00, and 3.45 kg/ha, respectively, in the fourth and fifth years of the experiment. Based on the results of this study, it can be recommended that zero-tillage is preferable to other cultivation treatments in wheat-cotton rotations under similar conditions to those used in this study.

Agroecological practices in organic fennel cultivation to improve environmental sustainability

ABSTRACT The simplification and intensification of agricultural systems have often caused environmental harm, whereas implementing agroecological practices may improve the sustainability of farms. Therefore, a four-year experiment was carried out on an organic fennel crop comparing a system with organic management and a cover crop (ORG+CC) with both a traditional organic system (ORG) and a system without living mulch and fertilizer (CONTR). The systems were evaluated for agronomic performance, energy analysis, and carbon footprint. The carbon footprint was calculated considering the impacts of agricultural operations, and those by the production and biogeochemical processes of the soil through the DNDC (DeNitrification DeComposition) model. The ORG+CC system showed the most stable production recording the lowest losses over the years under adverse climatic conditions. The ORG and CONTR systems had an average marketable yield reduction of −29.58% and −43.66%, respectively, when extreme rainfall events occurred, compared to ORG+CC. The ORG+CC was the most sustainable system, showing the best Net Energy (output-input) value and the lowest total CO2 eq. emissions, despite the highest energetic requirements and CO2 emissions during cultivation. The study showed the effects of the practices used and quantified the adaptation and mitigation potential over the medium term that included extreme weather events.

Modelling the impact of climate change and tillage practices on soil CO2 emissions from dry farmland in the Loess Plateau of China

Conservation tillage to agricultural fields has been widely used in the Loess Plateau of China. However, uncertainties remain regarding the long-term effects of this approach on soil carbon dioxide (CO2) emissions under future climate scenarios. We used the pre-validated DeNitrification DeComposition (DNDC) model to investigate the effects of no-tillage and straw mulch implementation on soil CO2 emissions from dry farmland in the Loess Plateau of China under different future climate scenarios and their dynamic changes. Based on the baseline period (1971–2019) and climate data (2022–2070) under RCP4.5 and RCP8.5 scenarios for two climate models, soil CO2 emissions from dry farmland were simulated under four tillage practices (CT: conventional tillage, CTS: conventional tillage with straw mulch, NT: no-tillage, NTS: no-tillage with straw mulch). The results showed that future climate change would affect soil CO2 emissions of each treatment. Compared with the baseline period, the annual mean CO2 emissions of four treatments increased under the RCP4.5 and RCP8.4 scenarios for the two climate models, with increases ranging from 5.81% to 56.73% under the RCP4.5 scenario and 24.48% to 68.75% under the RCP8.5 scenario. In addition, compared with CT and NT treatments, the CTS and NTS treatments with straw mulch had higher annual mean CO2 emissions under both climate scenarios, while the NT treatment had the lowest. Further analysis showed that the changes in soil CO2 emissions of each treatment under different future climate scenarios were related to the constant changes in annual mean temperature, annual precipitation, and atmospheric CO2 concentration. Simulation of the long-term effects on soil CO2 emissions under different tillage practices based on future climate scenarios led to the conclusion that conservation tillage practices, together with future changes in temperature, precipitation, and atmospheric CO2 concentrations, affect soil CO2 emissions, while no-tillage has potential to mitigate soil CO2 emissions from dry farmland in the Loess Plateau of China under future climate change.

Suitable Tillage Depth Promotes Maize Yields by Changing Soil Physical and Chemical Properties in A 3-Year Experiment in the North China Plain

Rotary tillage is a common farming method because of its ease of operation and low cost in the North China Plain. However, the rotary tillage depth is generally no more than 20 cm, and successive years of rotary tillage harden the root soil layers, which reduces maize’s ability to take root into the deep layer and decreases maize yields. The impact of the different rotary tillage depths and different plow pan thicknesses on maize yields was unclear and needs further study. In this study, a 3-year experiment was conducted, and three rotary tillage depths were designed: 20 cm tillage depth (D20), 25 cm tillage depth (D25), and 30 cm tillage depth (D30). The effects of different rotary tillage depths on soil’s physical and chemical properties, water use efficiency, photosynthetic rate, and maize yields were investigated. The results showed that soil bulk density significantly decreased and field capacity significantly increased in 10–30 cm soil layers by increasing the rotary tillage depths; soil water consumption, photosynthetic rate, and maize yields of D25 significantly increased in comparison to those of D20 and D30; soil bulk density, plow pan thickness, total nitrogen, total phosphorus, and total potassium had an obvious negative correlation with tillage depth and field capacity; the Denitrification–Decomposition (DNDC) model predicted maize yields well; structural equation models (SEM) revealed that rotary tillage depths and soil water consumption played an important role on maize yields; and D25 could increase maize yields by improving maize water use efficiency and photosynthetic rate. The tillage depth of 25 cm is a suitable rotary tillage depth for the increase in maize yields in the North China Plain.

Modeling the impacts of cover crops and no-tillage on soil health and cotton yield in an irrigated cropping system of the Texas Rolling Plains

An interest to grow cover crops is increasing in the semi-arid Texas Rolling Plains (TRP) region, where cotton ( Gossypium hirsutum L.) is a major cultivated crop. Under conservation tillage systems, cover crops are expected to enhance ecosystem services, including improved soil health and erosion reduction. However, there is a concern that cover crops could deplete soil water content and affect the subsequent cotton production. This study evaluated the efficacy of growing winter cover crops in a continuous cotton production system under irrigated conditions at Chillicothe, TX with the Denitrification Decomposition (DNDC) model. Treatments included: conventional tillage (CT), no-till (NT), no-till with mixed-species cover crop (Mixture), and no-till with winter wheat ( Triticum aestivum ) cover crop (Wheat) . The DNDC model was calibrated and validated against observed data from 2012 to 2020. Seed cotton yield prediction during model calibration had a percent bias (PBIAS) of 4.4 %, root mean square error (RMSE) of 385 kg ha −1 , r 2 of 0.55, d-index of 0.84, and a Nash–Sutcliffe model efficiency (NSE) of 0.92. Seed cotton yield during model validation had PBIAS of − 2.0 %, RMSE of 279 kg ha −1 , r 2 of 0.75, d-index of 0.93, and a NSE of 0.72. Simulated soil organic carbon (SOC) stock had a PBIAS of 8.6 % and 3 %, RMSE of 1.8 and 0.74 Mg ha −1 , r 2 of 0.92 and 0.98, d-index of 0.97 and 0.99, a NSE of 0.89 and 0.98 during model calibration and validation, respectively. Daily trends of simulated soil water content at 0–100 cm depth were within the acceptable range. Long-term simulations (25 years) revealed: (1) planting cover crops could accrue 11–19 % more SOC and 4–14 % more TN compared to no-cover crops, (2) cotton yield under cover crops was not significantly different from that under no-cover crops, (3) cover crop biomass increased immoblisation of nitrogen (N), which led to a decline in available N and hence, N-stress was simulated during the initial cotton growth period. In nutshell, results showed the potential of conservation management systems such as no-till and cover crops in improving soil health indicators without affecting cotton yields. • Simulated effect of mixture and winter wheat cover crops on seed cotton yield was not significant. • Cover crops sequestered 11–19 % more soil organic carbon (SOC) compared to no-cover crops. • Cover crops accrued 4–14 % more total nitrogen (TN) compared to no-cover crops. • Rate of SOC and TN accrual was highest under mixed species cover crop followed by winter wheat. • Soil N availability was declined in early stages of cotton due to increased N immobilization by cover crop biomass.

Soil Organic Carbon Storage in Australian Wheat Cropping Systems in Response to Climate Change from 1990 to 2060

It is important to examine the effects of climate change on temporal variations in SOC storage, in order to optimize management practices for sustainable grain production. Using the denitrification–decomposition (DNDC) model to simulate biogeochemical processes in agro-ecosystems, SOC variability was evaluated in the Australian wheat cropping system from 1990 to 2060, under the Representative Concentration Pathway 85 (RCP85) climate change scenario. We analyzed the impacts of temperature and precipitation on SOC variability and further simulated six management scenarios for wheat cultivation over 71 years, which included wheat cropping under common nitrogen fertilizer (N-fertilizer) application rate (12 kg N/ha), adequate N-fertilizer application rate (50 kg N/ha), and legume–wheat rotation with N fertilizer application rates at 0, 12, and 50 kg N/ha. The results indicated that the DNDC model provided a good simulation of biogeochemical processes associated with wheat growth; the normalized root mean square error (NRMSE) of wheat yield was 15.16%, and the NRMSE of SOC was 13.21%. The SOC (0–30 cm) decreased from 3994.1 kg C/ha in 1990 to 2848.0 kg C/ha in 2060, an average decrease of 0.4% per year. Temperature and precipitation were the important factors affecting SOC storage, with contributions of 13% and 12%, respectively. Furthermore, adding a legume phase increased SOC and wheat yield in the low N-fertilizer scenario. In contrast, adding a legume phase in the adequate N-fertilizer scenario decreased SOC and wheat yield.

Spatially resolved inventory and emissions modelling for pea and lentil life cycle assessment

Pulses are an increasingly popular source of plant-based protein among food manufacturers, food service providers, and retailers globally. Canada is currently a global leader in pulse production and exports. Given the large geographical scope of their production within Canada, pulses are produced under a variety of conditions reflecting differences in soil, climate, and characteristic management practices. In order to understand the influence of regional factors on the impacts of pulse production, high-resolution, regionalized life cycle assessments (LCAs) were carried out using both region-specific input/output data and emissions modelling. Six hundred Canadian pea and lentil farmers were surveyed to collect detailed data regarding characteristic farm inputs, yields and management practices at the reconciliation unit level of spatial resolution, which reconciles Canadian provincial borders with terrestrial ecozones based on soil and climate factors. The process-based model DeNitrification DeComposition (DNDC) was used to estimate regionally specific N emissions. This was compared to emissions estimates generated using the IPCC Tier II empirical models, which are typically used to estimate greenhouse gas emissions for national inventory reports. The main contributors to the life cycle environmental impacts of pea and lentil production were fertilizer and fuel use. This was consistent across all levels of regional aggregation including the total Prairie province average, as well as ecozone and provincial levels. There was variation in the magnitude of the impacts in each region assessed, which was mainly attributable to differences in yield, as well as reported fertilizer application rates and related emissions and fuel use for field operations. Significant differences were found in N emissions estimates between the DNDC and IPCC models, the magnitude of which varied by region and by the N emissions model employed. DNDC was found to provide better-resolved emissions estimates at the ecozone scale. This demonstrates the relevance of regionally specific emissions modelling since local soil and climate conditions had a large impact on the emissions estimates. In addition, the levels of uncertainty in the models were generally higher at the provincial and prairie province scale than at the ecozone scale. This may indicate that farming practices and associated resource/environmental impacts are more strongly influenced by soil and climate conditions than by provincial standards and guidelines. These results underscore the necessity of spatially resolved data collection and modelling to provide accurate estimates of the environmental impacts of crop production and to support more sustainable management of arable crop production.

Development of a calibration approach using DNDC and PEST for improving estimates of management impacts on water and nutrient dynamics in an agricultural system

Calibration and validation are standardized practices to establish the credibility of biogeochemical models for understanding agroecosystem nutrient dynamics. We evaluated three automatic calibration approaches, including simultaneous, sequential, and separate, for the calibration of model parameters of the biogeochemical DeNitrification DeComposition (DNDC) model through inverse modeling using PEST, open-source parameter estimation, and uncertainty analysis software. While manual calibration by experts performed the best during calibration period, followed by simultaneous calibration, sequential calibration had the best model performance during the validation period. Model sensitivity analyses demonstrated water leaching to be sensitive to curve number and drain spacing, nitrate leaching to be sensitive to porosity and clay content, and corn yield to be sensitive to accumulative temperature and grain C/N ratio. While some level of expertise is required to inform the automated calibration procedure, it represents a more efficient and robust approach toward increasing the performance of biogeochemical models.

Modeling Adaptive Strategies on Maintaining Wheat-Corn Production and Reducing Net Greenhouse Gas Emissions under Climate Change

Climate change has posed serious challenges to food production and sustainable development. We evaluated crop yields, N2O emissions, and soil organic carbon (SOC) in a typical wheat–corn rotation system field on the North China Plain on a 50-year scale using the Denitrification–Decomposition (DNDC) model and proposed adaptive strategies for each climate scenarios. The study showed a good consistency between observations and simulations (R2 &gt; 0.95 and nRMSE &lt; 30%). Among the twelve climate scenarios, we explored ten management practices under four climate scenarios (3 °C temperature change: P/T−3 and P/T+3; 30% precipitation change: 0.7P/T and 1.3P/T), which have a significant impact on crop yields and the net greenhouse effect. The results revealed that changing the crop planting time (CP) and using cold-resistant (CR) varieties could reduce the net greenhouse effect by more than 1/4 without sacrificing crop yields under P/T−3. Straw return (SR) minimized the negative impact on yields and the environment under P/T+3. Fertigation (FG) and Drought-Resistant (DR) varieties reduced the net greenhouse effect by more than 8.34% and maintained yields under 0.7P/T. SR was most beneficial to carbon sequestration, and yields were increased by 3.87% under 1.3P/T. Multiple adaptive strategies should be implemented to balance yields and reduce the environmental burden under future climate change.

Update of a biogeochemical model with process-based algorithms to predict ammonia volatilization from fertilized cultivated uplands and rice paddy fields

Abstract. Accurate simulation of ammonia (NH3) volatilization from fertilized croplands is crucial to enhancing fertilizer-use efficiency and alleviating environmental pollution. In this study, a process-oriented model, CNMM–DNDC (Catchment Nutrient Management Model–DeNitrification–DeComposition), was evaluated and modified using NH3 volatilization observations from 44 and 19 fertilizer application events in cultivated uplands and paddy rice fields in China, respectively. The major modifications for simulating NH3 volatilization from cultivated uplands were primarily derived from a peer-reviewed and published study. NH3 volatilization from cultivated uplands was jointly regulated by wind speed, soil depth, clay fraction, soil temperature, soil moisture, vegetation canopy, and rainfall-induced canopy wetting. Moreover, three principle modifications were made to simulate NH3 volatilization from paddy rice fields. First, the simulation of the floodwater layer and its pH were added. Second, the effect of algal growth on the diurnal fluctuation in floodwater pH was introduced. Finally, the Jayaweera–Mikkelsen model was introduced to simulate NH3 volatilization. The results indicated that the original CNMM–DNDC not only performed poorly in simulating NH3 volatilization from cultivated uplands but also failed to simulate NH3 volatilization from paddy rice fields. The modified model showed remarkable performances in simulating the cumulative NH3 volatilization of the calibrated and validated cases, with drastically significant zero-intercept linear regression of slopes of 0.94 (R2 = 0.76, n = 40) and 0.98 (R2 = 0.71, n = 23), respectively. The simulated NH3 volatilization from cultivated uplands was primarily regulated by the dose and type of the nitrogen fertilizer and the irrigation implementation, while the simulated NH3 volatilization from rice paddy fields was sensitive to soil pH; the dose and depth of nitrogen fertilizer application; and flooding management strategies, such as floodwater pH and depth. The modified model is acceptable to compile regional or national NH3 emission inventories and develop strategies to alleviate environmental pollution.

Estimating the impact of climate change on the carbon exchange of a temperate meadow steppe in China

Temperate meadow steppe is a representative ecosystem in northeast China. A process-oriented biogeochemistry model, denitrification–decomposition (DNDC) biogeochemistry model was employed and adapted to interpret and integrate field observations to determine whether temperate meadow steppe ecosystem was a carbon dioxide (CO2) sink during the growing seasons from 2010 to 2011. Then, we applied the model to predict the long-term impacts of climate change on the carbon dynamics in the ecosystem. Daily weather data for 2010–2011 in conjunction with the soil properties data and management practices data for this site were utilized as inputs to simulate the grass growth and soil carbon dynamics. The modeled carbon fluxes were compared with eddy tower data. The observed and modeled CO2 fluxes data were consistent, with both showing that the temperate meadow steppe is a stable carbon sink. Prediction tests were conducted with a baseline and eight alternative climate scenarios for the period 2017–2050. Simulations for the years 2017–2050 found that (1) the temperate meadow steppe will be a carbon sink under the baseline climate conditions; (2) the decelerated warming and drier climate scenario produced the lowest grass production and lowest carbon sequestration; and (3) the accelerated warming and wetter climate scenario produced the highest biomass and highest carbon sequestration. By using DNDC model we can understand carbon dynamics in temperate meadow steppe. The ecosystem production is limited by precipitation, a wetter future climate would substantially elevate the carbon sequestration capacity of the ecosystem. However, the carbon sequestration potential could significantly decrease if the climate is more arid from 2017 to 2050.