DeNitrification-DeComposition Model 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.

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.

Assessing perennial alfalfa yield by incorporating physiologically-lucid function into DeNitrification-DeComposition model

Reliable, rapid and timely assessments of forage crop biomass are crucial for managing feed supply and animal husbandry. While process-based models are regularly promulgated in modeling crop and pasture growth and development, the reliability and functionality of such models in simulating alfalfa (Medicago sativa L.) growth remains limited. The purpose of this study was to address deficits associated with simulation of long-term growth dynamics in perennial alfalfa; to assess potential alfalfa yield across the Loess Plateau and dissect key abiotic factors constraining alfalfa yields. We undertook an extensive review of the literature to collate and analyze existing alfalfa biomass data across the entire Loess Plateau in China. We then developed an alfalfa multi-year growth curve using a Gauss-amplitude function; when enacted this algorithm reliably reproduced the rise, peaks and declines in biomass with crop development. To assess the performance of our new function as part of an integrated system, we incorporated a multi-year growth curve into the DeNitrification-DeComposition (DNDC) model to assess longitudinal yields under rain-fed and irrigation conditions. Our adapted model reproduced aboveground trends in biomass more accurately than the original model. The regional simulation results show that alfalfa yield was less than 8000 kg ha–1 in most areas with an annual precipitation lower than 500 mm under rain-fed condition, while the alfalfa yield in most areas was above 8000 kg ha–1 under irrigation condition. Compared with rain-fed alfalfa, there is an increased 69% of yield after irrigation in irrigated areas of the Loess Plateau. The rain-fed alfalfa yield was severely constrained by drought when the aridity index fell below 0.31. Our new model development paves the way for additional refinement of alfalfa crops in process-based models, and provides opportunities for adoption of alfalfa crops in new regions by adjusting the parameters in the Gauss-amplitude function.

Greenhouse gases fluxes and carbon cycle in agroecosystems under humid continental climate conditions

Few complex studies of greenhouse gas (GHG: carbon dioxide, CO2; methane, CH4; nitrous oxide, N2O) fluxes and carbon cycle components were carried out on the geographical territory of Eastern Europe and represented in world databases. Most of the investigations had focused on particular problems of carbon and nitrogen pools and fluxes, or had covered a narrow range of ecosystems. Therefore, the idea of this research was to carry out a multi-disciplinary study of carbon fluxes and pools in the sector of agriculture and land use – one of the main sources of GHG – in regions with humid continental climate that are the most affected by current climate change. Our work was aimed at solving the problem of quantifying GHG fluxes and carbon pools in crop and livestock production, analyzing the reasons for their formation and possible absorption pathways at three locations in the European part of Russia in the cold continental climate zone. The study examined both agricultural and natural ecosystems; field measurements, laboratory analyses, and simulation modelling techniques were applied. The sites included seven types of ecosystems: croplands, pastures, hayfields, fallows, forests, stockyards, and compost piles. Livestock facilities are potent sources of three main biogenic GHG (0.42 − 9.27 g C-CO2 m-2 h-1; 2.43 − 783.51 mg C-CH4 m-2 h-1; 0.01 − 1.49 mg N-N2O m-2 h-1); whereas croplands, hayfields and forests can absorb methane (from −1.10 to −118.2 μg C-CH4 m-2 h-1), as well as pastures, hayfields, fallows and forests are weak sinks of nitrous oxide (from −0.10 to −4.57 μg N-N2O m-2 h-1). Ecosystems were ranked using a non-parametric test by increasing rate of CO2 emission from soil in the following order: croplands (0.06 − 0.24 g C-CO2 m-2 h-1) ≤ hayfields (0.06 − 0.25 g C-CO2 m-2 h-1) ≤ pastures (0.06 − 0.22 g C-CO2 m-2 h-1) = fallows (0.13 − 0.26 g C-CO2 m-2 h-1) = forests (0.16 − 0.30 g CO2 m-2 h-1) ≤ stockyards (0.42 − 1.57 g CO2 m-2 h-1) < compost piles (1.95 − 9.27 g CO2 m-2 h-1). According to the correlation analysis, the most important dynamic environmental factor on which the intensity of soil respiration depends is the soil temperature at the depth of 10 cm (r = 0.55 − 0.97, p < 0.05). As the regression analysis shows, hydrothermal and agrochemical controls explain the 46 − 89% variance of CO2 emission from soil. The content of total carbon (1.31−7.95%) as well as of total nitrogen (0.12 − 0.62%) in soil follows a latitudinal pattern and increases from north to south regardless of the type of land use and farming intensity. The amount of carbon incorporated into the arable soil layer by root residues decreases in the following sequence: barley > winter wheat > spring wheat > oats > soybean from 402 to 198 kg C ha-1 yr-1. Field crop stubble is an additional source of soil carbon at 118 − 368 kg C ha-1 yr-1 in the following sequence: spring wheat > winter wheat > oats > soybean > barley. According to simulation modelling (DeNitrification-DeComposition model, DNDC; and Rothamsted Carbon model, RothC), accumulation of soil organic carbon is possible under winter wheat (270 kg C ha-1 yr-1), while under other crops its stocks decrease (from −190 to −423 kg C ha-1 yr-1). According to DNDC modelling, agrolandscapes are net sinks of carbon to a varying degree (at least 500 kg C ha-1 yr-1), with phytomass being the main pool of carbon sink, most of which is removed with yields.

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.

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.

Modeling impacts of drought-induced salinity intrusion on carbon dynamics in tidal freshwater forested wetlands.

Tidal freshwater forested wetlands (TFFW) provide critical ecosystem services including an essential habitat for a variety of wildlife species and significant carbon sinks for atmospheric carbon dioxide. However, large uncertainties remain concerning the impacts of climate change on the magnitude and variability of carbon fluxes and storage across a range of TFFW. In this study, we developed a process-driven Tidal Freshwater Wetlands DeNitrification-DeComposition model (TFW-DNDC) that has integrated new features, such as soil salinity effects on plant productivity and soil organic matter decomposition to explore carbon dynamics in the TFFW in response to drought-induced saltwater intrusion. Eight sites along the floodplains of the Waccamaw River (USA) and the Savannah River (USA) were selected to represent the TFFW transition from healthy to moderately and highly salt-impacted forests, and eventually to oligohaline marshes. The TFW-DNDC was calibrated and validated using field observed annual litterfall, stem growth, root growth, soil heterotrophic respiration, and soil organic carbon storage. Analyses indicate that plant productivity and soil carbon sequestration in TFFW could change substantially in response to increased soil pore water salinity and reduced soil water table due to drought, but in interactive ways dependent on the river simulated. These responses are variable due to nonlinear relationships between carbon cycling processes and environmental drivers. Plant productivity, plant respiration, soil organic carbon sequestration rate, and storage in the highly salt-impacted forest sites decreased significantly under drought conditions compared with normal conditions. Considering the high likelihood of healthy and moderately salt-impacted forests becoming highly salt-impacted forests under future climate change and sea-level rise, it is very likely that the TFFW will lose their capacity as carbon sinks without up-slope migration.

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.

Spatiotemporal changes in greenhouse gas emissions and soil organic carbon sequestration for major cropping systems across China and their drivers over the past two decades

Chinese agricultural systems have experienced dramatic changes in crop planting area, cropping system, irrigation and fertilization managements, and crop yields in recent decades. These changes can substantially affect greenhouse gases (GHG) emissions and soil organic carbon (SOC) sequestration in croplands. However, the spatiotemporal patterns, as well as their driving factors and mechanisms, have not been well understood. Here, the Denitrification-Decomposition model is calibrated and validated to estimate nitrous oxide (N2O) and methane (CH4) emissions and SOC sequestration for seven major cropping systems in China during 2001–2020. The Logarithmic Mean Divisia Index method is further applied to attribute the net GHG emissions (NGEs) trend to various drivers. The results show that the total N2O emissions, CH4 emissions, and SOC sequestration were approximately 23.7, 182.0, and 177.6 Tg CO2-eq/year in the croplands across China. The national average NGEs per unit area ranged from −8705 to 8431 kg CO2-eq ha−1 year−1 across the major cropping systems. During 2001–2020, the trend in national annual NGEs was 0.66 kg CO2-eq ha−1 year−2, ranging from −78.9 to 82.2 kg CO2-eq ha−1 year−2 across the major cropping systems. The paddy lands were mainly a carbon source due to the large amount of CH4 emissions while the uplands could be a carbon sink owing to SOC sequestration. As a whole, the cropland in China was a carbon source with the NGEs equal to 28.4 Tg CO2-eq/year, and the NGEs increased by 0.047 Tg CO2-eq/year2 in the past 20 years. Nationally, changes in crop planting area and yields reduced the NGEs whereas changes in nitrogen use efficiency and cropping systems increased them, although the major factors and their impacts varied greatly among regions. Optimizing cropping systems and nitrogen fertilization based on the local genotype, environment and management should be the most effective method to reduce the NGEs in croplands.

Effects of climate and grazing on the soil organic carbon dynamics of the grasslands in Northern Xinjiang during the past twenty years

The soil organic carbon (SOC) of grasslands is a vital component of the global carbon cycle. The SOC in grassland ecosystems in arid and semi-arid regions is sensitive to global climate change, but the dynamic drivers of the SOC content are still controversial. Grazing is the main factor affecting SOC contents in grasslands; however, the response of different types of grasslands to the grazing intensity remains unclear. Based on the Denitrification-Decomposition model and field investigations, in this study, the spatial and temporal dynamics of SOC and its drivers in the grasslands in Northern Xinjiang were investigated, and the response of the SOC in the different types of grasslands to various grazing intensities at the regional scale. The results reveal that the SOC content of the grasslands in Northern Xinjiang increased at a rate of 161.32 kg C ha −1 yr −1 from 2001 to 2020, and the spatial distribution of the SOC dynamics varied greatly. There was an increasing trend at higher elevations and a decreasing trend at lower elevations. Grazing contributed less to the SOC dynamics, and temperature and precipitation were the main drivers of the SOC dynamics. The overall effect of the grazing intensity on SOC content was negative. The responses of the different types of grassland to the grazing intensity varied greatly, with the least effect in alpine meadows and the greatest effect in lowland meadows and temperate desert grasslands.

Optimum fertilizer application rate to ensure yield and decrease greenhouse gas emissions in rain-fed agriculture system of the Loess Plateau

Application of nitrogen (N) can increase the supply of N in soil and, in turn, can lead to higher yield—but also to large increase in emissions of greenhouse gases (GHGs) if applied in excess. To determine the optimum dose of N for maize planting system, we analysed the relationship between yield and emissions of GHGs at seven levels of N, namely 50, 100, 150, 200, 250, 300, and 350 kg ha−1, using the DNDC (denitrification decomposition) model and maize grown with and without mulching. The model simulated the following variables: maize production; emissions of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4); global warming potential (GWP); and GHG intensity (GHGI). We used data from 1980 to 2013 for a rain-fed region of the Loess Plateau in north-western China and validated the DNDC model against data from field experiments. The model performed well in simulating yield and GHG emissions (Adj.R2 > 0.61). Under mulching, the average yield of maize was 3.6–12.2 t ha−1 and the partial factor productivity was 73.1–35.0 kg kg−1; and both of these were significantly higher 78%–236% than those in the crop without mulching. The emissions of CO2, N2O, and the GWP increased with the increase in the dose of N whereas CH4 emissions remained unaffected by the dose. Mulching increased yields significantly in the north-western region, and the GWP and GHGI were higher mainly in the central and north-western regions. The optimum dose of N for maize grown with mulching ranged between 150 kg ha−1 and 200 kg ha−1 and offers the best balance between higher yield and lower emissions. The optimum dose may promote the development of mulched maize and provide a reference standard for dryland agriculture in zones with similar climates elsewhere in the world.

Estimates of methane and nitrous oxide emission from a rice field in Central Java, Indonesia, based on the DeNitrification DeComposition model

&lt;p&gt;Indonesia is the world’s third largest rice producer, with most rice being cultivated (estimated 3.1 million ha) in Central Java. However, one of the environmental challenges in producing rice is greenhouse gas (GHG) emissions from rice fields. Therefore, understanding the GHG emissions (methane and nitrous oxide) from the rice farming system is important for better management practices. The objective of this study is to estimate the GHG emissions supported by a satellite database, namely, the DeNitrification DeComposition (DNDC) model, at three regencies at Central Java, Indonesia, Cilacap, Karanganyar, and Pati, as well as the factors determining the emissions. The DNDC model was obtained from &lt;a href="https://www.dndc.sr.unh.edu/"&gt;https://www.dndc.sr.unh.edu&lt;/a&gt;, which consists of three main submodels that worked together in simulating N&lt;sub&gt;2&lt;/sub&gt;O and N&lt;sub&gt;2&lt;/sub&gt; emissions: (1) the soil-climate/thermal-hydraulic flux submodel, (2) the decomposition submodel, and (3) the denitrification submodel. The results showed that the N&lt;sub&gt;2&lt;/sub&gt;O emissions from rice farming in Karanganyar, Cilacap, and Pati were 19.0, 18.8, and 12.8 kg N ha&lt;sup&gt;−1&lt;/sup&gt; yr&lt;sup&gt;−1&lt;/sup&gt;, respectively, while they were 213.7, 270.6, and 360.6 kg C ha&lt;sup&gt;−1&lt;/sup&gt; yr&lt;sup&gt;−1&lt;/sup&gt; for CH&lt;sub&gt;4&lt;/sub&gt; emissions, respectively. Consecutive dry or high precipitation, which resulted in cumulative depleted or elevated soil moisture, respectively, along with warmer temperature likely promoted higher methane and nitrous oxide. Experimental fields for validating the model in accordance with various agricultural practices are suggested for further study. Overall, the DNDC model has successfully estimated the CH&lt;sub&gt;4&lt;/sub&gt; and N&lt;sub&gt;2&lt;/sub&gt;O emissions in Central Java when incorporated with various secondary climatic and land management big data resources.&lt;/p&gt;

On the prediction of methane fluxes from pristine tropical peatland in Sarawak: application of a denitrification-decomposition (DNDC) model.

Tropical peatlands have high potential function as a major source of atmospheric methane (CH4) and can contribute to global warming due to their large soil carbon stock, high groundwater level (GWL), high humidity and high temperature. In this study, a process-based denitrification-decomposition (DNDC) model was used to simulate CH4 fluxes in a pristine tropical peatland in Sarawak. To test the accuracy of the model, eddy covariance tower datasets were compared. The model was validated for the year 2014, which showed the good performance of the model for simulating CH4 emissions. The monthly predictive ability of the model was better than the daily predictive ability, with a determination coefficient (R2) of 0.67, model error (ME) of 2.47, root mean square error (RMSE) of 3.33, mean absolute error (MAE) of 2.92 and mean square error (MSE) of 11.08. The simulated years of 2015 and 2016 showed the good performance of the DNDC model, although under- and overestimations were found during the drier and rainy months. Similarly, the monthly simulations for the year were better than the daily simulations for the year, showing good correlations at R2 at 0.84 (2015) and 0.87 (2016). Better statistical performance in terms of monthly ME, RMSE, MAE and MSE at - 0.11, 3.38, 3.05 and 11.45 for 2015 and - 1.14, 5.28, 4.93 and 27.83 for 2016, respectively, was also observed. Although the statistical performance of the model simulation for daily average CH4 fluxes was lower than that of the monthly average, we found that the results for total fluxes agreed well between the observed and the simulated values (E = 6.79% and difference = 3.3%). Principal component analysis (PCA) showed that CH4, GWL and rainfall were correlated with each other and explained 41.7% of the total variation. GWL was found to be relatively important in determining the CH4 fluxes in the naturally inundated pristine tropical peatland. These results suggest that GWL is an essential input variable for the DNDC model for predicting CH4 fluxes from the pristine tropical peatland in Sarawak on a monthly basis.

Modelling soil salinity effects on salt water uptake and crop growth using a modified denitrification-decomposition model: A phytoremediation approach

Soil salinization is a widespread problem affecting global food production. Phytoremediation is emerging as a viable and cost-effective technology to reclaim salt-affected soil. However, its efficiency is not clear due to the uncertainty of plant responses in saline soils. The main objective of this paper is to propose a phytoremediation dynamic model (PDM) for salt-affected soil within the process-based biogeochemical denitrification-decomposition (DNDC) model. The PDM represents two salinity processes of phytoremediation: plant salt uptake and salt-affected biomass growth. The salt-soil-plant interaction is simulated as a coupled mass balance equation of water and salt plant uptake. The salt extraction ability by plant is a combination of salt uptake efficiency (F) and transpiration rate. For water filled pore space (WFPS), the statistical measures RMSE, MAE, and R2 during the calibration period are 2.57, 2.14, and 0.49, and they are 2.67, 2.34, and 0.56 during the validation period, respectively. For soil salinity, RMSE, MAE, and R2 during the calibration period are 0.02, 0.02, and 0.92, and 0.06, 0.04, and 0.68 during the validation period, respectively, which are reasonably good for further scenario analysis. Over the four years, cumulative salt uptake varied based on weather conditions. At the optimal salt uptake efficiency (F = 20), cumulative salt uptake from soil was 16–90% for alfalfa, 11–70% for barley, and 10–80% for spring wheat. While at the lowest salt uptake efficiency (F = 40), cumulative salt uptake was nearly zero for all crops. Although barley has the highest peak transpiration flux, alfalfa and spring wheat have greater cumulative salt uptake because their peak transpiration fluxes occurred more frequently than in barley. For salt-tolerant crops biomass growth depends on their threshold soil salinity which determines their ability to take up salt without affecting biomass growth. In order to phytoremediate salt-affected soil, salt-tolerant crops having longer duration of crop physiological stages should be used, but their phytoremediation effectiveness will depend on weather conditions and the soil environment.

Precipitation and nitrogen application stimulate soil nitrous oxide emission

Precipitation and nitrogen (N) fertilization are the two most important drivers for soil nitrous oxide (N2O) emission. However, the effects of changes in N fertilization and precipitation patterns (i.e., precipitation intensity and frequency) on N2O emissions in agricultural fields are still unclear. In this study, we simulated soil N2O emission under different precipitation patterns (6 precipitation intensities, and 12 precipitation frequencies by either merging or splitting precipitation events) and N fertilization rates (low, typical, and high N fertilization) in a cornfield using the DeNitrification-DeComposition model. The model was parameterized and validated using meteorological data and N experimental measurements in Nashville, Tennessee, USA. Results showed that soil water filled pore space (WFPS) and simulated soil N2O emission increased as precipitation intensity increased. Less frequent but high intensity precipitation treatments reduced the soil WFPS by 25.2% and stimulated soil N2O emission by 45.3%, while more frequent but low intensity precipitation treatments increased soil WFPS by 9.0% and reduced soil N2O emission by 23.9%. Compared to typical N fertilization, the sensitivity of soil N2O emission to precipitation was higher under high N than low N fertilization treatments, and the response ratios were 50.0% and 40.1%, respectively. There was significant interactive effect of precipitation intensity and N fertilization on soil N2O emission. These findings improved our understanding of precipitation and N impacts on soil N2O emissions and provided useful knowledge for irrigation and N fertilizer management in agriculture to mitigate greenhouse gas emissions.