Search Results (23)

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article

Carbon fluxes are valuable indicators of soil and ecosystem health, particularly in the context of climate change, where reducing carbon emissions from anthropogenic activities, such as forest fires, is a global priority. This study aimed to evaluate the impact of prescribed burns on soil respiration in semi-arid grasslands. Two treatments were applied: a prescribed burn on a 12.29 ha paddock of an introduced grass (Eragostis curvula) with 11.6 t ha⁻¹ of available fuel, and a simulation of three fire intensities, over 28 circular plots (80 cm in diameter) of natural grasslands (Bouteloua gracilis). Fire intensities were simulated by burning with butane gas inside an iron barrel, which represented three amounts of fuel biomass and an unburned treatment. Soil respiration was measured with a soil respiration chamber over two months, with readings collected in the morning and afternoon. Moreover, CO₂ emissions by combustion and productivity after fire treatment were quantified. The prescribed burns significantly reduced soil respiration: all fire intensities resulted in a decrease in soil respiration when compared with the unburned area. Changes in albedo increased the soil temperature; however, there was no relationship between changes in temperature and soil respiration; in contrast, precipitation highly stimulated it. These findings suggest that fire, under certain conditions, may not lead to more CO₂ being emitted into the atmosphere by stimulating soil respiration, whereas aboveground biomass was reduced by 60%. However, considering the effects of fire in the long-term on changes in nutrient deposition, aboveground and belowground biomass, and soil properties is crucial to effectively quantify its impact on the global carbon cycle. Full article

Determining the influencing factors of winter wheat field carbon emissions and their dynamic trends is of great significance to study the carbon emission mechanism of winter wheat, reduce greenhouse gas emissions from agricultural fields, and promote the sustainable development of agriculture. The aim of this study is to analyze the relationship between different influencing factors and CO₂ emission fluxes in winter wheat fields and to construct a dynamic simulation model of field carbon emission so as to provide a basis for accurate and convenient calculation of CO₂ emission from wheat fields in the Henan region. This study comprehensively considered the effects of the dynamic changes in meteorological, soil, hydrological, and other factors over time on the field carbon emission during the growth process of the crop and carried out a dynamic simulation study of the field carbon emission in the experimental field with six sets of experiments, using the multiple regression method. Six groups of experiments were set up, and a multi-parameter field carbon emission dynamic model was constructed by the multiple regression method to simulate the optimal calculation model. The results showed that the simulated values of field CO₂ emissions were consistent with the trend of the measured values, and the total cumulative CO₂ emissions in fields A1, A2, and A3 were 8624.2 kg/hm², 7924.3 kg/hm², and 7531.4 kg/hm², respectively, while the model-simulated values were 9399.2 kg/hm², 8935.2 kg/hm², and 8371.1 kg/hm². The errors between the simulated and actual emissions were 7.9%, 12.8%, and 11.1%, respectively, indicating a high accuracy in the simulation results. The model developed in this study comprehensively accounts for the dynamic impacts of meteorological, soil, and hydraulic factors on CO₂ emissions, effectively reflecting the dynamic changes in field carbon emissions and achieving high calculation accuracy. Full article

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 < 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. Full article

Globally, croplands represent a significant contributor to climate change, through both greenhouse gas emissions and land use changes associated with cropland expansion. They also represent locations with significant potential to contribute to mitigating climate change through alternative land use management practices that lead to increased soil carbon sequestration. In spite of their global importance, there is a relative paucity of tools available to support field- or farm-level crop land decision making that could inform more effective climate mitigation practices. In recognition of this shortcoming, the Simple Algorithm for Yield Estimate (SAFY) model was developed to estimate crop growth, biomass, and yield at a range of scales from field to region. While the original SAFY model was developed and evaluated for winter wheat in Morocco, a key advantage to utilizing SAFY is that it presents a modular architecture which can be readily adapted. This has led to numerous modifications and alterations of specific modules which enable the model to be refined for new crops and locations. Here, we adapted the SAFY model for use with spring barley, winter wheat and winter oilseed rape at selected sites in Ireland. These crops were chosen as they represent the dominant crop types grown in Ireland. We modified the soil–water balance and carbon modules in SAFY to simulate components of water and carbon budgets in addition to crop growth and production. Results from the modified model were evaluated against available in situ data collected from previous studies. Spring barley biomass was estimated with high accuracy (R² = 0.97, RMSE = 95.8 g·m⁻², RRMSE = 11.7%) in comparison to GAI (R² = 0.73, RMSE = 0.44 m²·m⁻², RRMSE = 10.6%), across the three years for which the in situ data was available (2011–2013). The winter wheat module was evaluated against measured biomass and yield data obtained for the period 2013–2015 and from three sites located across Ireland. While the model was found to be capable of simulating winter wheat biomass (R² = 0.71, RMSE = 1.81 t·ha⁻¹, RRMSE = 8.0%), the model was found to be less capable of reproducing the associated yields (R² = 0.09, RMSE = 2.3 t·ha⁻¹, RRMSE = 18.6%). In spite of the low R² obtained for yield, the simulated crop growth stage 61 (GS61) closely matched those observed in field data. Finally, winter oilseed rape (WOSR) was evaluated against a single growing season for which in situ data was available. WOSR biomass was also simulated with high accuracy (R² = 0.99 and RMSE = 0.52 t·ha⁻¹) in comparison to GAI (R² = 0.3 and RMSE = 0.98 m²·m⁻²). In terms of the carbon fluxes, the model was found to be capable of estimating heterotrophic respiration (R² = 0.52 and RMSE = 0.28 g·C·m⁻²·day⁻¹), but less so the ecosystem respiration (R² = 0.18 and RMSE = 1.01 g·C·m⁻²·day⁻¹). Overall, the results indicate that the modified model can simulate GAI and biomass, for the chosen crops for which data were available, and yield, for winter wheat. However, the simulations of the carbon budgets and water budgets need to be further evaluated—a key limitation here was the lack of available in situ data. Another challenge is how to address the issue of parameter specification; in spite of the fact that the model has only six variable crop-related parameters, these need to be calibrated prior to application (e.g., date of emergence, effective light use efficiency etc.). While existing published values can be readily employed in the model, the availability of regionally derived values would likely lead to model improvements. This limitation could be overcome through the integration of available remote sensing data using a data assimilation procedure within the model to update the initial parameter values and adjust model estimates during the simulation. Full article

The detection of low gas concentrations from the soil surface demands expensive high-precision devices to estimate nitrous oxide (N₂O) flux. As the prevalence of N₂O concentration in the soil atmosphere is higher than its surface, the present study aimed to simulate N₂O surface flux (CF) from soil gas measured in a soil-interred silicone diffusion cell using a low-cost device. The methodological steps included the determination of the diffusion coefficient of silicone membrane (D_slcn), the measurement of the temporal variations in the N₂O gas in the soil (C_si) and on the surface (MF), and the development of a simulation process for predicting CF. Two experiments varying the procedure and periods of soil moisture saturation in each fertilized soil sample were conducted to detect C_si and MF. Using D_slcn and C_si, the variations in the soil gas (C_soil) were predicted by solving the diffusion equation using the implicit finite difference analysis method. Similarly, using six soil gas diffusivity models, the CF values were simulated from C_soil. For both experiments, statistical tests confirmed the good agreement of CF with MF for soil gas diffusivity models 4 and 5. We suggest that the tested simulation method is appropriate for predicting N₂O surface emissions. Full article

Knowledge of the characteristics of N₂O emissions and the influential mechanism is of great significance to mitigate greenhouse gas emissions in semi-arid areas. In the present study, a three-year water-control study was conducted; three simulated rainfall amounts (heavy, normal, and light rainfall = 275, 200, and 125 mm, respectively), two wheat (Triticum aestivum L.) planting modes (RF (ridge–furrow mulching system) and TF (traditional flat planting)) and four supplementary irrigation amounts (150, 75, 37.5, and 0 mm) were set up. The effects of different cultivation methods and irrigation amounts on soil N₂O emissions, the soil water content, available nitrogen content, and denitrifying enzyme activity were investigated to clarify the N₂O emission mechanism in winter wheat fields (Triticum aestivum L.). The results obtained after three years showed that compared with TF, the N₂O emissions under RF decreased by 21.62–30.72% (p < 0.001), whereas the soil water content increased by 6.26–8.82%, the available nitrogen content decreased by 1.71–16.24%, and the denitrifying enzyme activities increased by 0.2–24.16% under heavy rainfall conditions. Under conditions with normal and light rainfall, the N₂O emission fluxes under RF increased by 3.66–12.46% and 6.08–15.57% (p > 0.05), while the soil water contents increased by 6.13–11.49% and 8.05–13.88%, the soil available nitrogen contents decreased by 11.0–21.42% and 19.93–34.44%, and the denitrifying enzyme activities increased by 0.01–24.08% and 0.03–20.79% compared with TF. Principal component analysis showed that the main factors related to N₂O emissions under RF were the soil moisture content and available nitrogen content; these factors combined explained 94.37% the variation of the N₂O emissions. However, the main factors under TF were the soil moisture content and denitrifying enzyme activity; these factors combined explained 85.81%. In the heavy and normal rainfall years, compared with TF, using RF and 75 mm irrigation achieved the goal of reducing water usage as well as decreasing the N₂O emissions (or N₂O increase was not significant). In light rainfall years, RF with 150 mm irrigation obtained significant reductions in water usage compared with TF but it also increased the N₂O emission flux. Under different rainfall years, the yield of RF increased by 2.89–50.44% compared with the TF system, and the increase in wheat grain yield increased with decreasing rainfall. Full article

With the growing number of datasets to describe greenhouse gas (GHG) emissions, there is an opportunity to develop novel predictive models that require neither the expense nor time required to make direct field measurements. This study evaluates the potential for machine learning (ML) approaches to predict soil GHG emissions without the biogeochemical expertise that is required to use many current models for simulating soil GHGs. There are ample data from field measurements now publicly available to test new modeling approaches. The objective of this paper was to develop and evaluate machine learning (ML) models using field data (soil temperature, soil moisture, soil classification, crop type, fertilization type, and air temperature) available in the Greenhouse gas Reduction through Agricultural Carbon Enhancement network (GRACEnet) database to simulate soil CO₂ fluxes with different fertilization methods. Four machine learning algorithms—K nearest neighbor regression (KNN), support vector regression (SVR), random forest (RF) regression, and gradient boosted (GB) regression—were used to develop the models. The GB regression model outperformed all the other models on the training dataset with R² = 0.88, MAE = 2177.89 g C ha⁻¹ day⁻¹, and RMSE 4405.43 g C ha⁻¹ day⁻¹. However, the RF and GB regression models both performed optimally on the unseen test dataset with R² = 0.82. Machine learning tools were useful for developing predictors based on soil classification, soil temperature and air temperature when a large database like GRACEnet is available, but these were not highly predictive variables in correlation analysis. This study demonstrates the suitability of using tree-based ML algorithms for predictive modeling of CO₂ fluxes, but no biogeochemical processes can be described with such models. Full article

Nitrous oxide (N₂O) is a potent greenhouse gas stemming mainly from nitrogen (N)-fertilizer application. It is challenging to quantify N₂O emissions from agroecosystems because of the dearth of measured data and high spatial variability of the emissions. The eco-hydrological model SWAT (Soil and Water Assessment Tool) simulates hydrological processes and N fluxes in a catchment. However, the routine for simulating N₂O emissions is still missing in the SWAT model. A submodule was developed based on the outputs of the SWAT model to partition N₂O from the simulated nitrification by applying a coefficient (K₂) and also to isolate N₂O from the simulated denitrification (N₂O + N₂) with a modified semi-empirical equation. The submodule was applied to quantify N₂O emissions and N₂O emission factors from selected crops in two agricultural catchments by using NH₄NO₃ fertilizer and the combination of organic N and NO₃⁻ fertilizer as N input data. The setup with the combination of organic N and NO₃⁻ fertilizer simulated lower N₂O emissions than the setup with NH₄NO₃ fertilizer. When the water balance was simulated well (absolute percentage error <11%), the impact of N fertilizer application on the simulated N₂O emissions was captured. More research to test the submodule with measured data is needed. Full article

Process-based biogeochemical models are valuable tools to evaluate impacts of environmental or management changes on the greenhouse gas (GHG) balance of forest ecosystems. We evaluated LandscapeDNDC, a process-based model developed to simulate carbon (C), nitrogen (N) and water cycling at ecosystem and regional scales, against eddy covariance and soil chamber measurements of CO₂ and N₂O fluxes in an 80-year-old deciduous oak forest. We compared two LandscapeDNDC vegetation modules: PSIM (Physiological Simulation Model), which includes the understorey explicitly, and PnET (Photosynthesis–Evapotranspiration Model), which does not. Species parameters for both modules were adjusted to match local measurements. LandscapeDNDC was able to reproduce daily micro-climatic conditions, which serve as input for the vegetation modules. The PSIM and PnET modules reproduced mean annual net CO₂ uptake to within 1% and 15% of the measured values by balancing gains and losses in seasonal patterns with respect to measurements, although inter-annual variations were not well reproduced. The PSIM module indicated that the understorey contributed up to 21% to CO₂ fluxes. Mean annual soil CO₂ fluxes were underestimated by 32% using PnET and overestimated by 26% with PSIM; both modules simulated annual soil N₂O fluxes within the measured range but with less interannual variation. Including stand structure information improved the model, but further improvements are required for the model to predict forest GHG balances and their inter-annual variability following climatic or management changes. Full article

Pastures require year-round access to water and in some locations rely on irrigation during dry periods. Currently, there is a dearth of knowledge about the potential for using irrigation to mitigate N₂O emissions. This study aimed to mitigate N₂O losses from intensely managed pastures by adjusting irrigation frequency using soil gas diffusivity (D_p/D_o) thresholds. Two irrigation regimes were compared; a standard irrigation treatment based on farmer practice (15 mm applied every 3 days) versus an optimised irrigation treatment where irrigation was applied when soil D_p/D_o was ≈0.033 (equivalent to 50% of plant available water). Cow urine was applied at a rate of 700 kg N ha⁻¹ to simulate a ruminant urine deposition event. In addition to N₂O fluxes, soil moisture content was monitored hourly, D_p/D_o was modelled, and pasture dry matter production was measured. Standard irrigation practices resulted in higher (p = 0.09) cumulative N₂O emissions than the optimised irrigation treatment. Pasture growth rates under treatments did not differ. Denitrification during re-wetting events (irrigation and rain) contributed to soil N₂O emissions. These results warrant further modelling of irrigation management as a mitigation option for N₂O emissions from pasture soils, based on D_p/D_o thresholds, rainfall, plant water demands and evapotranspiration. Full article

Swamp forests have been insufficiently studied yet in comparison with thoroughly examined carbon pools and greenhouse gas fluxes of peat bogs. This is primarily since the GHGs in swamp forests have huge spatial (due to the developed microrelief) and temporal variations (due to strong fluctuations in the groundwater level (GWL)). This significantly complicates their study, producing ambiguous results, especially in short-term field research. From June to October 2013–2016, we measured soil respiration (R_soil) in an alder swamp using the static chamber method at five microsites: depression (DEP), flat surface (FL), elevations (EL), tussocks (TUS), and near-stem tussocks (STUS). We carried out a computer simulation of the total R_soil for the season based on R_soil measurements, monitoring of GWL, and soil temperature. In 2013–2016, the average R_soil values (mgC m⁻² h⁻¹ ± σ) on DEP, FL, EL, TUS and STUS comprised 54 ± 50, 94 ± 72, 146 ± 89, 193 ± 96, and 326 ± 183, respectively, whereas the total R_soil values for the season (tC ha⁻¹ season⁻¹ ± σ) comprised 2.0 ± 0.5, 3.5 ± 0.5, 5.3 ± 1.6, 5.4 ± 2.7, and 12.6 ± 3.2. According to the results of observations, GWL was at the level of several cm below the soil surface for most of the season. In 2014 and 2015, there were extra dry periods that led to a drop in GWL to a mark of 30–40 cm below the soil surface. Despite their short duration (2–3 weeks), these dry periods can lead to an increase in the total R_soil for the season from 9 to 45% in the TUS–EL–STUS–FL–DEP sequence. Full article

Clean energy for all, as listed in the United Nation’s SDG7, is a key component for sustainable environmental development. Therefore, it is imperative to uncover the environmental implications of alternative energy technologies. SustainableGAS project simulates different process chains for the substitution of natural gas with renewable energies in the German gas market. The project follows an interdisciplinary approach, taking into account techno-social and environmental variabilities. However, this research highlights the project results from the environmental perspective. So far, a detailed assessment of the environmental costs of alternative gas technologies with a focus on the process of energy transition has remained rare. Although such data constitute key inputs for decision-making, this study helps to bridge a substantial knowledge gap. Competing land-use systems are examined to secure central ecosystem services. To fulfill this obligation, an Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) serves as the modelling tool. InVEST assesses ecosystem services (ES) that are or may be affected by alternative bioenergy technologies. Spatially explicit model results include the water provisioning from the Water Yield Model (WYM), soil erosion and sedimentation described by the Sediment Delivery Ratio (SDR), and nutrient fluxes (N) in response to changing land use are obtained through the Nutrient Delivery Ratio (NDR). The detailed model results are finally extrapolated, which provides a comprehensive image of the environmental impacts associated with bioenergy expansion in Germany from our combination of unique Renewable Gas Plants (RGPs). The final result shows that nutrient load will reduce in southern Germany by the year 2050 compared to the reference state, and biomass use reduced by 46% crops. Full article

Rivers of the Arctic will become ever more important for the global climate, since they carry a majority of continental dissolved organic carbon flux into the rapidly changing polar ocean. Aqueous organics comprise a wide array of functional groups, several of which are likely to impact coastal and open water biophysical properties. Light attenuation, interfacial films, aerosol formation, gas release and momentum exchange can all be cited. We performed Lagrangian kinetic modeling for the evolution of riverine organic chemistry as the molecules in question make their way from the highlands to Arctic outlets. Classes as diverse as the proteins, sugars, lipids, re-condensates, humics, bio-tracers and small volatiles are all included. Our reduced framework constitutes an idealized northward flow driving a major hydrological discharge rate and primarily representing the Russian Lena. Mountainous, high solute and tundra sources are all simulated, and they meet up at several points between soil and delta process reactors. Turnover rates are parameterized beginning with extrapolated coastal values imposed along a limited tributary network, with connections between different terrestrial sub-ecologies. Temporal variation of our total dissolved matter most closely resembles the observations when we focus on the restricted removal and low initial carbon loads, suggesting relatively slow transformation along the water course. Thus, channel combinations and mixing must play a dominant role. Nevertheless, microbial and photochemical losses help determine the final concentrations for most species. Chemical evolution is distinct for the various functionalities, with special contributions from pre- and post-reactivity in soil and delta waters. Several functions are combined linearly to represent the collective chromophoric dissolved matter, characterized here by its absorption. Tributaries carry the signature of lignin phenols to segregate tundra versus taiga sources, and special attention is paid to the early then marine behaviors of low molecular weight volatiles. Heteropolycondensates comprise the largest percentage of reactive carbon in our simulations due to recombination/accumulation, and they tend to be preeminent at the mouth. Outlet concentrations of individual structures such as amino acids and absorbers lie above threshold values for biophysical influence, on the monolayer and light attenuation. The extent of coastal spreading is examined through targeted regional box modeling, relying on salinity and color for calibration. In some cases, plumes reach the scale of peripheral arctic seas, and amplification is expected during upcoming decades. Conclusions are mapped from the Lena to other boreal discharges, and future research questions are outlined regarding the bonding type versus mass release as permafrost degrades. Dynamic aqueous organic coupling is recommended for polar system models, from headwaters to coastal diluent. Full article

The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO₂, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO₂ fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO₂ flux (root mean square error (RMSE) = 0.97 gC·m⁻²·d⁻¹ and determination coefficient (R²) = 0.83) and water flux (RMSE = 0.68 mm·d⁻¹ and R² = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m⁻², respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets. Full article