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A spatially referenced water and nitrogen management model (WNMM) for (irrigated) intensive cropping systems in the North China Plain
Abstract
A spatially referenced biophysical model, the water and nitrogen management model (WNMM), was developed and shown to simulate dynamic soil water movement and soil–crop carbon (C) and nitrogen (N) cycling under a given agricultural management, for the purpose of identifying optimal strategies for managing water and fertiliser N under intensive cropping systems (mainly wheat–maize) in the North China Plain and other regions in the world. A uniform data structure, ARC GRID ASCII format, was used both in GIS and WNMM for achieving a close Model-GIS coupling. A significant part of WNMM adopts and modifies concepts and components from widely used models, with a focus on soil N transformations. WNMM simulates the key processes of water dynamics in the surface and subsurface of soils: including evapotranspiration, canopy interception, water movement and groundwater fluctuations; heat transfer and solute transport; crop growth; C and N cycling in the soil–crop system; and agricultural management practices (crop rotation, irrigation, fertiliser application, harvest and tillage). The model runs on a daily time step at any desired scale and is driven by lumped variables (meteorological and crop biological data) in text data format, and spatial variables (soil and agricultural management) in ARC GRID ASCII format. In particular, WNMM simulates all key N transformations in agricultural fields, including mineralisation of fresh crop residue N and soil organic N, formation of soil organic N, immobilisation in biomass, nitrification, ammonia (NH3) volatilisation, denitrification and nitrous oxide (N2O) emissions.
... Given that none of the models reviewed include microsites within soil aggregates, this was not considered in this review. N 2 O emissions estimates from agricultural soils range from a simple emission factor (Bouwman 1996;IPCC 2014;Del Grosso et al. 2020 and reference within) to more complex processbased models such as APSIM (Keating et al. 2003;Holzworth et al. 2014), DayCent (Parton et al. 1996(Parton et al. , 2001Del Grosso et al. 2000), WNMM (Li et al. 2007) or DNDC (Li et al. 1992Li 2000). Although emission factors are generally used for regional-scale inventories, process-based models capable of predicting the effect of environmental conditions and management practices play an important role in modifying emission factors (Xing et al. 2013;Shen et al. 2018), and may possibly replace them. ...
... The most commonly used process-based models for predicting N 2 O production from soils include APSIM (Thorburn et al. 2010;Xing et al. 2011), DayCent (Del Grosso et al. 2000Parton et al. 2001), DNDC (Li 2000;Li et al. 2000), FASSET (Chatskikh et al. 2005), NOE (Hénault et al. 2005) and WNMM (Li et al. 2007). Chen et al. (2008) reviewed and summarised the strengths, limitations and applications of commonly used field scale N 2 O emissions models (i.e. ...
... More recently, Giltrap et al. (2020) reviewed the concepts of APSIM, DayCent and DNDC. Other studies have compared the performance of process-based models at predicting N 2 O emissions (e. g., Li et al. 2007;Xing et al. 2011;Ehrhardt et al. 2018). There are two ways to compare model performance: (1) extracting the specific algorithms from the different models and recoding them in the same platform (model), then comparing N 2 O emissions prediction performance after calibrating and validating against measurements (e.g. ...
Agricultural soils are the most important anthropogenic source of nitrous oxide (N2O) emissions. This occurs via two main pathways: (1) from microbial-mediated oxidation of ammonium to nitrite and nitrate; and (2) denitrification. Most agro-ecological models explicitly deal with these two pathways albeit with different degrees of process understanding and empiricism. Models that integrate the impact of multiple environmental factors on N2O emissions can provide estimates of N2O fluxes from complex agricultural systems. However, uncertainties in model predictions arise from differences in the algorithms, imperfect quantification of the nitrification and denitrification response to edaphic conditions, and the spatial and temporal variability of N2O fluxes resulting from variable soil conditions. This study compared N2O responses to environmental factors in six agro-ecological models. The comparisons showed that environmental factors impact nitrification and denitrification differently in each model. Reasons include the inability to apportion the total N2O flux to the specific N transformation rates used to validate and calibrate the simplifications represented in the model algorithms, and incomplete understanding of the multiple interactions between processes and modifying factors as these are generally not quantified in field experiments. Rather, N2O flux data is reported as total or net N2O emissions without attributing emissions to gross and/or net rates for specific N processes, or considering changes that occur between production and emissions. Additional measurements that quantify all processes understand the multiple interactions that affect N2O emissions are needed to improve model algorithms and reduce the error associated with predicted emissions.
... Abundant regional precipitation leads to intense soil erosion and severe N leaching on red soil sloping uplands [67] . Application of controlled-release urea reduced N surface runoff losses [121] . ...
... During irrigation, NO 3 --N tends to move downward with irrigation water and becomes the main form of N leaching [120,121] . The concentration of NO 3 --N in the leaching solution became lower as irrigation proceeded, probably because of the continuous decrease in soil redox potential due to continuous saturation of the irrigated soil [121] . ...
... During irrigation, NO 3 --N tends to move downward with irrigation water and becomes the main form of N leaching [120,121] . The concentration of NO 3 --N in the leaching solution became lower as irrigation proceeded, probably because of the continuous decrease in soil redox potential due to continuous saturation of the irrigated soil [121] . Studies under fertilizer application and clear water irrigation conditions have concluded that the higher the irrigation intensity the higher the NH 4 + -N, NO 3 --N and total N leaching from the leaching solution and the lower the NH 4 + -N and NO 3 --N contents in the soil, increasing the risk of N leaching [120] . ...
The subtropical hilly region of China is a region with intensive crop and livestock production, which has resulted in serious N pollution in soil, water and air. This review summarizes the major soil N cycling processes and their influencing factors in rice paddies and uplands in the subtropical hilly region of China. The major N cycling processes include the N fertilizer application in croplands, atmospheric N deposition, biological N fixation, crop N uptake, ammonia volatilization, N2O/NO emissions, nitrogen runoff and leaching losses. The catchment nutrients management model for N cycle modeling and its case studies in the subtropical hilly region were also introduced. Finally, N management practices for improving N use efficiency in cropland, as well as catchment scales, are summarized.
... Previous research studies have mainly focused on comparing results regarding the fate of water and N, as well as crop growth, under different field management practices in the same cropping system [2,8,17]. However, few studies have considered the matching degree between water and heat resources required by crop growing development and weather conditions. ...
... However, few studies have considered the matching degree between water and heat resources required by crop growing development and weather conditions. Thus, in this study, a process-based water and nitrogen management model (WNMM), which was developed by Li et al. [17], was used to quantify the WUE and NUE of different cropping systems. The model is able to simulate water, carbon, and N dynamics, as well as plant growth under various agricultural management practices. ...
... The model is able to simulate water, carbon, and N dynamics, as well as plant growth under various agricultural management practices. It has been well calibrated and validated for the Chinese agroecosystems [17][18][19][20], specifically for the intensive cropping (wheat-maize) systems in the NCP, and these scholars studied water drainage, N leaching, and crop growth for different cropping systems based on this model. Therefore, considering water and N input, drainage, N leaching, yields, WUE, and NUE, this study aimed to (i) quantify the WUE and NUE of different cropping systems under optimized water and N inputs with the WNMM and (ii) provide suggestions for improving WUE and NUE, and developing sustainable agriculture in the NCP using an alternative cropping system. ...
The North China Plain (NCP) is one of the most important grain production regions in China. However, it currently experiences water shortage, severe nonpoint source pollution, and low water and N use efficiencies (WUE and NUE). To explore sustainable agricultural development in this region, a field experiment with different cropping systems was conducted in suburban Beijing. These cropping systems included a winter wheat and summer maize rotation system for one year (WM), three harvests (winter wheat-summer maize-spring maize) in two years (HT), and continuous spring maize monoculture (CS). Novel ways were explored to improve WUE and NUE and to reduce N loss via the alternative cropping system based on the simulation results of a soil-crop system model. Results showed that the annual average yields were ranked as follows: WM > HT > CS. The N leaching of WM was much larger than that of HT and CS. WUE and NUE were ranked as follows: WM < HT < CS. Comprehensive evaluation indices based on agronomic and environmental effects indicated that CS or HT have significant potential for approaches characterized by water-saving, fertilizer-saving, high-WUE, and high-NUE properties. Once spring maize yield reached an ideal level HT and CS became a high-yield, water-saving, and fertilizer-saving cropping systems. Therefore, this method would be beneficial to sustainable agricultural development in the NCP.
... Optimizing N fertilizer use is necessary to avoid the over-application of N fertilizer, high N loss, or decreased NUE. Li et al. (2007) proposed that reducing fertilizer N input can reduce N loss and improve NUE [59]. It is also possible to achieve greater NUE by adjusting the amount and effective period of fertilization for different soil textures to synchronize crop demand [60]. ...
... Optimizing N fertilizer use is necessary to avoid the over-application of N fertilizer, high N loss, or decreased NUE. Li et al. (2007) proposed that reducing fertilizer N input can reduce N loss and improve NUE [59]. It is also possible to achieve greater NUE by adjusting the amount and effective period of fertilization for different soil textures to synchronize crop demand [60]. ...
Determining the best management practices (BMPs) for farmland under different soil textures can provide technical support for improving maize yield, water- and nitrogen-use efficiencies (WUE and NUE), and reducing environmental N losses. In this study, a two-year (2013–2014) maize cultivation experiment was conducted on two pieces of farmland with different textural soils (loamy clay and sandy loam) in the Phaeozems zone of Northeast China. Three N fertilizer treatments were designed for each farmland: N168, N240, and N312, with N rates of 168, 240, and 312 kg ha−1, respectively. The WHCNS (soil Water Heat Carbon Nitrogen Simulator) model was calibrated and validated using the observed soil water content, soil nitrate concentration, and crop biological indicators. Then, the effects of soil texture combined with different N rates on maize yield, water consumption, and N fates were simulated. The integrated index considering the agronomic, economic, and environmental impacts was used to determine the BMPs for two textural soils. Results indicated that simulated soil water content and nitrate concentration at different soil depths, leaf area index, dry matter, and grain yield all agreed well with the measured values. Both soil texture and N rates significantly affected maize yield, N fates, WUE, and NUE. The annual average grain yield, WUE, and NUE under three N rates in sandy loam soil were 8257 kg ha−1, 1.9 kg m−3, and 41.2 kg kg−1, respectively, which were lower than those of loam clay, 11440 kg ha−1, 2.7 kg m−3, and 46.7 kg kg−1. The order of annual average yield and WUE under two textural soils was N240 > N312 > N168. The average evapotranspiration of sandy loam (447.3 mm) was higher than that of loamy clay (404.9 mm). The annual average N-leaching amount of different N treatments for sandy loam ranged from 5.1 to 13.2 kg ha−1, which was higher than that of loamy clay soil, with a range of 1.8–5.0 kg ha−1. The gaseous N loss in sandy loam soil accounted for 14.7% of the fertilizer N application rate, while it was 11.1%in loamy clay soil. The order of the NUEs of two textural soils was: N168 > N240 > N312. The recommended N fertilizer rates for sandy loam and loamy clay soils determined by the integrated index were 180 and 200 kg ha−1, respectively.
... Biogeochemical models, such as DNDC, LandscapeD-NDC, WNMM, MOMOS, CENTURY and DayCent, are effective tools for simulating the cycling of nitrogen and carbon and quantifying the effects of climate change and anthropogenic activities on ecosystems (e.g., Foereid et al., 2007;Haas et al., 2012;Li, 2007;Li et al., 2007;Pansu et al., 2010;Cheng et al., 2014;Pansu et al., 2014). In recent years, some new conceptual approaches have been applied in the biogeochemical models, such as centering on the functional role of the soil microbial biomass (Pansu et al., 2010(Pansu et al., , 2014 and detailing the lateral transport of water and nutrients (Haas et al., 2012;. ...
... In recent years, some new conceptual approaches have been applied in the biogeochemical models, such as centering on the functional role of the soil microbial biomass (Pansu et al., 2010(Pansu et al., , 2014 and detailing the lateral transport of water and nutrients (Haas et al., 2012;. Generally, comprehensive hydrological processes, especially for the lateral transport of water and nutrients, are simplified or ignored in most models due to specific questions that must be addressed (e.g., Li, 2007;Li et al., 2007;Chen et al., 2008;Deng et al., 2014). For the land surface or hydrological models at large scales, they are designed with explicit mechanisms of hydrology and generally focus on vertical and lateral nutrient transport, such as nitrate loads into rivers (e.g., Liu et al., 2019). ...
The hydro-biogeochemical model Catchment Nutrient
Management Model – DeNitrification-DeComposition (CNMM-DNDC) was established
to simultaneously quantify ecosystem productivity and losses of nitrogen and
carbon at the site or catchment scale. As a process-oriented model, this
model is expected to be universally applied to different climate zones,
soils, land uses and field management practices. This study is one of many
efforts to fulfill such an expectation, which was performed to improve the
CNMM-DNDC by incorporating a physically based soil thermal module to simulate
the soil thermal regime in the presence of freeze–thaw cycles. The modified
model was validated with simultaneous field observations in three typical
alpine ecosystems (wetlands, meadows and forests) within a catchment located
in seasonally frozen regions of the eastern Tibetan Plateau, including
observations of soil profile temperature, topsoil moisture, and fluxes of methane (CH4)
and nitrous oxide (N2O). The validation showed that the modified
CNMM-DNDC was able to simulate the observed seasonal dynamics and magnitudes
of the variables in the three typical alpine ecosystems, with index-of-agreement values of 0.91–1.00, 0.49–0.83, 0.57–0.88 and 0.26–0.47,
respectively. Consistent with the emissions determined from the field
observations, the simulated aggregate emissions of CH4 and N2O
were highest for the wetland among three alpine ecosystems, which were
dominated by the CH4 emissions. This study indicates the possibility
for utilizing the process-oriented model CNMM-DNDC to predict
hydro-biogeochemical processes, as well as related gas emissions, in
seasonally frozen regions. As the original CNMM-DNDC was previously
validated in some unfrozen regions, the modified CNMM-DNDC could be
potentially applied to estimate the emissions of CH4 and N2O from
various ecosystems under different climate zones at the site or catchment
scale.
... Biogeochemical models, such DNDC, WNMM, CENTURY and DayCent, are effective tools for simulating the 60 cycling of nitrogen and carbon and quantifying the effects of climate change and human anthropogenic activities on 61 ecosystems (e.g., Foereid et al., 2007;Li, 2007;Li et al., 2007;Cheng et al., 2014). However, comprehensive hydrological 62 processes, especially for the lateral transport of water and nutrients, are generally simplified or ignored in these models due 63 to specific questions that must be addressed (e.g., Li, 2007;Li et al., 2007;Chen et al., 2008;Deng et al., 2014). ...
... Biogeochemical models, such DNDC, WNMM, CENTURY and DayCent, are effective tools for simulating the 60 cycling of nitrogen and carbon and quantifying the effects of climate change and human anthropogenic activities on 61 ecosystems (e.g., Foereid et al., 2007;Li, 2007;Li et al., 2007;Cheng et al., 2014). However, comprehensive hydrological 62 processes, especially for the lateral transport of water and nutrients, are generally simplified or ignored in these models due 63 to specific questions that must be addressed (e.g., Li, 2007;Li et al., 2007;Chen et al., 2008;Deng et al., 2014). On the 64 other hand, land surface or hydrological models at large scales, which are designed with explicit mechanisms of hydrology, 65 https://doi.org/10.5194/bg-2020-433 ...
To evaluate the sustainability of terrestrial ecosystems, the hydro-biogeochemical model Catchment Nutrient Management Model – DeNitrification-DeComposition (CNMM-DNDC) was established to simultaneously quantify ecosystem productivity and losses of nitrogen and carbon at the site or catchment scale. As a process-oriented model, this model is expected to be universally applied to different climate zones, soils, land uses and field management practices. This study, as one of many efforts to fulfil such an expectation, was performed to improve the CNMM-DNDC by incorporating a physical-based soil thermal module to simulate the soil thermal regime in the presence of freeze-thaw cycles. The modified model was validated with simultaneous field observations in three typical alpine ecosystems (wetlands, meadows and forests) within a catchment located in the seasonally frozen region of the eastern Tibetan Plateau. Then, the model was further applied to evaluate its performance in simulating the effects of alpine wetland degradation on methane (CH4) and nitrous oxide (N2O) fluxes. The validation showed that the modified CNMM-DNDC was able to simulate the observed seasonal dynamics of soil temperature, moisture, and fluxes of CH4 and N2O in the three typical alpine ecosystems, with index of agreement values of 0.91–1.00, 0.49–0.83, 0.57–0.88 and 0.26–0.47, respectively. Consistent with the emissions determined from the field observations, the simulated aggregate emissions of CH4 and N2O were significantly reduced due to wetland degradation and were dominated by a reduction in CH4 emissions. This study indicates the potential for utilizing the process-oriented model CNMM-DNDC to predict hydro-biogeochemical processes, as well as related gas emissions, in seasonally frozen regions. As the original CNMM-DNDC was previously validated in some unfrozen regions, the modified CNMM-DNDC could be applied to evaluate the sustainability of various ecosystems under different climates, soils and field management practices at the site or catchment scale.
... APSIM simulates N 2 O emissions from both nitrification and denitrification processes. N 2 O emissions during denitrification is predicted by combining the rate of denitrification with the ratio of N 2 to N 2 O emitted during denitrification [38,39], and N 2 O emission during nitrification is calculated as a proportion (0.002) of nitrified N [40,41]. Further details are described by Thorburn et al. [39]. ...
... Further details are described by Thorburn et al. [39]. Based on N 2 O emissions measurements from a two-year field experiment conducted in Huantai County of North China Plain, APSIM was directly validated for N 2 O simulation with the parameters mentioned in [40,41], and the results showed that it was successful in simulating N 2 O emissions [42]. Thus, we used the same parameters for N 2 O simulation in this study. ...
Reducing the use of nitrogen fertilizers and returning straw to field are being promoted in northeast China (NEC). In this paper, the agricultural production system model (APSIM) was applied to assess the long-term variations of crop yield and soil GHG emissions in a maize mono-cropping system of NEC, and the simulation results were combined with lifecycle assessment to estimate annual GHG emissions (GHGL) and GHG emission intensity (GHGI, GHG emissions per unit yield) of different agricultural practices. Under current farmers’ practice, emissions due to machinery input (including production, transportation, repair, and maintenance) and soil organic carbon (SOC) decline accounted for 15% of GHGL, while emissions from nitrogen fertilizer input (production and transportation) and direct N2O emissions from soil accounted for the majority (~60% of GHGL). Current farmers’ practice in terms of N application and residue management are nearly optimal for crop production but not for climate change mitigation. Reducing N input by 13% and increasing straw retention by 20% can maintain crop yield and SOC, and also reduce GHGL and GHGI by 13% and 11%, respectively. However, it is not feasible to incorporate the straw used as household fuel into soil, which could incur substantial fossil CO2 emissions of 3.98 Mg CO2-eq ha−1 resulting from the substitution of coal for straw. APSIM was successful in simulating crop yield, N2O emissions, and SOC change in NEC, and our results highlight opportunities to further optimize management strategies (especially for the nitrogen and straw management) to reduce GHG emissions while maintaining crop yield.
... APSIM assumes, by default, that 60% of decomposed carbon (C) is lost to the atmosphere. N 2 O emission during nitrification is calculated as a proportion of nitrified N (Li et al. 2007). N 2 O emission during denitrification is calculated by combining predictions of denitrification with the ratio of N 2 to N 2 O emitted during denitrification (Grosso et al. 2000). ...
... Numerous approaches have been developed to predict N 2 O emissions from agricultural soils (Bessou et al., 2010;Chatskikh et al., 2005;Del Grosso et al., 2001;Grant et al., 2020;Jacobsen et al., 1998;Li et al., 1992;Li et al., 2007;Parton et al., 2001;Parton et al., 1996;Venterea et al., 2020). Typically, N 2 O emissions are estimated as fractions of nitrified and denitrified N that controlled by various soil-related factors Here, we incorporated several new N 2 O estimation algorithms into the SWAT-C model, including six N 2 O emission methods for the nitrification process and seven N 2 O emission methods for the denitrification process, respectively ( Table 1). ...
... where, K max is the maximum nitrification rate and K NH 4 is the NH4 concentration for half the maximum reaction velocity. N2O emissions during nitrification are calculated as a proportion of nitrified N (0.2%) (Li et al., 2007). A detailed description of the method used in APSIM to simulate N2O emissions from soil is given by Thorburn et al. (2010). ...
CONTEXT: Farming systems face the dual pressures of reducing greenhouse gas (GHG) emissions to mitigate climate change and safeguarding food security to adapt to climate change. Building soil organic carbon (SOC) is a key strategy for climate change mitigation and adaptation. However, practices that increase SOC may also increase nitrous oxide (N2O) emissions, and impact crop yields and on-farm income. A comprehensive assessment of the effects of different management practices on trade-offs between GHG emissions and crop profitability under climate change is needed.
OBJECTIVE: In this study, we aimed to: (1) analyze the trends of SOC and N2O emissions, and ascertain whether the croplands of the study region are net GHG sources or sinks under climate change; (2) quantify the GHG abatement on the gross margin basis; (3) identify the most effective management practices that could achieve a win-win strategy; and (4) investigate sources of uncertainty in estimates of GHG emissions and gross margins under climate change.
METHODS: A biophysical model (APSIM) was used to simulate the effects of three crop residue retention rates (10%, 50% and 100%), and six representative crop rotations (wheat-canola, wheat-field pea-wheat-canola, wheat-field pea-wheat-oats, wheat-wheat-barley, wheat-wheat-canola, and wheat-wheat-oats) under two Shared Socio-economic Pathways scenarios (SSP245 and SSP585) with climate projections from 27 global climate models. GHG emissions and gross margins from 1961 to 2092 were assessed across 204 study sites in southeastern Australia.
RESULTS AND CONCLUSIONS: Our results showed that residue retention can turn the soil from a carbon source (10% retention, 304~450 kg CO2-eq ha-1 yr-1) to a carbon sink (100% retention, -269~-57 kg CO2-eq ha-1 yr-1), and the potential of carbon sequestration was partly offset by concomitantly increased N2O emissions. Wheat-wheat-canola rotation with full residue retention can be a win-win solution with both large potential of GHG abatement and high gross margin compared to other rotations. Spatial analysis showed that the eastern part of the study region had higher gross margins while the western part had greater GHG emission reduction potentials, with different environmental and economic outcomes in this study region. Although the climate change led to increased GHG emissions and decreased yields for some crops, these adverse effects can be overweighed by the advantages from full residue retention.
SIGNIFICANCE: This study emphasizes the significant potential for agronomic management to maximize gross margin and remove GHG emissions under climate change in southeast Australia. Results from this study could be used by farmers and policymakers to mitigate climate change without compromising agroecosystem economic benefits.
... Considering the parabolic relationship between yield and planting density as well as the influence of planting density on water sensitivity coefficient, the modified Rao model underestimated yield by 3%, but performed well in predicting yield over a wide range (119-794 g m -2 ). Compared with other yield models such as DSSAT (Tsuji et al., 1994), APSIM (McCown et al., 1996),WNMM (Li et al., 2007) and AquaCrop (Steduto et al., 2009), the modified Rao model employs less number of parameters with clear physiological meaning. In the kernel weight model, the maximum relative kernel weight was limited to 1.06. ...
Improvement in seed vigor and yield of hybrid maize is required to ensure food security. Optimal planting density and irrigation depth are critical for hybrid maize seed production in arid areas. Using data from 2012 to 2017, a new integrated model optimized planting density and border irrigation depth for achieving high yield and seed vigor of hybrid maize under limited water availability. For field experiments, a planting density of 9.75 plants m⁻² under full irrigation was in the control treatment. The results showed that water deficit decreased yield, evapotranspiration, and aboveground biomass per plant. The highest yield was obtained for the planting density of 12.75 plants m⁻² under full irrigation. The single crop coefficient and crop water production function models were modified to simulate evapotranspiration and yield, respectively. Using kernel number per plant and plant growth rate during the flowering stage, a kernel weight model was established. The maximum yield and minimum irrigation depth were weighted and kernel weight was constrained. Three minimum relative kernel weight (RKWmin) options were considered, option 1 with RKWmin= 1.00 (control), option 2 with RKWmin= 0 (unconstrained), and option 3 with RKWmin ranged from 0.60 to 0.90. In option 1, the optimal irrigation depth during the growing season under control planting density decreased by 24.3–39.4% compared to the conventional practices. In option 2, yield increased but average kernel weight decreased by 25% than control. In the option 3, average yield and water use efficiency increased by 9.2% and 6.5% compared to option 1, respectively. The average planting density decreased by 10.2%, yield reduced by 2.0%, and water use efficiency reduced by 1.7%, but kernel weight increased by 9% compared to option 2. Thus option 3 can be recommended for hybrid maize seed production with limited water availability in arid regions of Northwest China. Our research provided a new theoretical method to improve yield, and ensure seed vigor of hybrid maize in arid regions.
... Therefore, many studies used the soil-crop-atmosphere system models to simulate soil physical, chemical and biological processes, as well as crop growth, but only a few models can be used to simulate the daily dynamics of N 2 O emissions for the crop growing season and fallow periods. Commonly used models for simulating N 2 O emissions include DNDC (Li et al., 1992), DAYCENT (Parton et al., 1994), ECOSYS (Grant et al., 1993), FASSAT (Parton et al., 1996) and WNMM (Li et al., 2007). Those models had been widely used to simulate N 2 O emissions under various field management practices (irrigation, fertilization, tillage, etc.) or different land use types (agricultural fields, forests, grasslands and other plant types) at the field or regional scales. ...
Quantifying greenhouse gas emissions from greenhouse vegetable production systems (GVPS) is helpful to direct carbon sequestration and greenhouse gas emissions reduction. The objectives of this study were to (i) evaluate the performance of the improved WHCNS-Veg model (soil Water Heat Carbon Nitrogen Simulation for Vegetable) in simulating nitrous oxide (N2O) emissions under different GVPS and (ii) analyze the impact of different management systems on N2O emissions from the GVPS. Greenhouse vegetable experiments were carried out under different management systems, including conventional (CON), integrated (INT) and organic (ORG), in Quzhou County, Hebei province from 2013 to 2015. Field observations of soil water content, soil nitrate concentration and N2O emissions were used to calibrate and validate the WHCNS-Veg model. The sensitivity analysis results showed that the effects of soil hydraulic parameters on N2O emissions were higher than those of N transformation and vegetable genetic parameters. The improved WHCNS-Veg model simulated soil water content, soil nitrate concentration and N2O emissions well under different GVPS. The average N2O emissions in the spring-summer season (16.3 kg N ha⁻¹) were about 2.5 times higher than the autumn-winter season (6.4 kg N ha⁻¹). Among the four seasons, the average N2O emissions of CON, ORG and INT were 13.5, 11.3 and 9.3 kg N ha⁻¹, respectively, accounting for 1.4–1.7% of the total fertilizer input. The N2O emissions of the INT and ORG systems were 31.1% and 16.3% lower than the CON treatment, respectively. The results indicated that the improved WHCNS-Veg model can be used to simulate and analyze N2O emissions under different management systems and substituting organic manure for chemical fertilizer could effectively reduce the N2O emissions in the GVPS.
... The N 2 O/ (N 2 O+N 2 ) ratio in denitrification was used to estimate N 2 emissions in process-based models. These models generally assume the ratio is a fixed value (Hénault et al., 2005) or determined by a function based on soil properties such as soil water condition and soil NO 3 content (Del Grosso et al., 2000;Stehfest and Muller, 2004;Li et al., 2007). In this regard, the wide range of N 2 O/(N 2 O+N 2 ) ratio observed in this study suggests that the estimation of N 2 emissions in process-based models is highly uncertain. ...
Dinitrogen (N2) and nitrous oxide (N2O) produced via denitrification may represent major nitrogen (N) loss in terrestrial ecosystems. A global assessment of soil denitrification rate, N2O/(N2O+N2) ratio, and their driving factors and mitigation strategies is lacking. We conducted a global synthesis using 225 studies (3367 observations) to fill this knowledge gap. We found that daily N loss through soil denitrification varied with ecosystems and averaged 0.25 kg N ha⁻¹. The average emission factor of denitrification (EFD) was 4.8%. The average N2O/(N2O+N2) ratio from soil denitrification was 0.33. Soil denitrification rate was positively related to soil water-filled pore space (WFPS) (p < 0.01), nitrate (NO3⁻) content (p < 0.05) and soil temperature (p < 0.01), and decreased with higher soil oxygen content (p < 0.01). N2 emissions increased with latitude (p < 0.05), WFPS (p < 0.01) and soil mineral N (p < 0.05) but decreased with soil oxygen content (p < 0.05). The N2O/(N2O+N2) ratio increased with soil oxygen content (p < 0.01) but decreased with organic carbon (C) (p < 0.05), C/N ratio (p < 0.01), soil pH (p < 0.05) and WFPS (p < 0.01). We also found that optimizing N application rates, using ammonium-based fertilizers compared to nitrate-based fertilizers, biochar amendment, and application of nitrification inhibitors could effectively reduce soil denitrification rate and associated N2 emissions. These findings highlight that N loss via soil denitrification and N2 emissions cannot be neglected, and that mitigation strategies should be adopted to reduce N loss and improve N use efficiency. Our study presents a comprehensive data synthesis for large-scale estimations of denitrification and the refinement of relevant parameters used in the submodels of denitrification in process-based models.
... Also, these models were utilized for better understanding of water and N simulation, crop growth, and organic matter turnover. In this context, models such as WOFOST, EPIC, HYDRUS-1D, WNMM, DNDC, SPACSYS, RZWQM, DAISY, HERMES, DSSAT, and APSIM were already used (Penning de Vries et al. 1989;Ahuja et al. 2000;Li et al. 2007;Šimůnek et al. 2008). Thus, these soil-crop models are ecologically designed and justify specific questions and simulate different processes to acquire their relevant objectives (Kersebaum et al. 2015). ...
Soil health and quality are key aspects upon which various ecosystem processes depend. Ongoing series of land degradations, deforestation, intensive agricultural practices, etc. affects the soil health. These deleterious unsustainable practices deprive soil fertility and affect overall ecosystem services (ES). Depleting nature of soil affects tree-crop productivity that is not fruitful for satisfying global hunger populations. Healthy soil promises food-income-climate security and maintains overall environmental sustainability and ecological stability. Human and livestock's health are entirely dependent upon soil quality. Therefore, the query "how does soil maintain plant-human-animal health and productivity?" arises. This indicates toward synergistic concept between soil and living
organisms. However, adopting eco-model in varying land use (agriculture, forestry,
agroforestry, and other farming practices) helps to minimize the soil
degradation and ensures higher productivity. But the main problem is that “how
does eco-designing of varying land use systems ensure healthy and quality soil?”.
Climate-smart agriculture, conservation agriculture, zero-tillage practices, use of
cover crop, mulching, and soil water conservation practices are intrinsic parts of
eco-designing or eco-models. These practices ensure healthy and productive
ecosystem that makes a pathway for sustainable development (SD).
Eco-designing for sustainable soil management practices promotes the storage
and sequestration of carbon (C) as soil organic C pools which leads to C balance.
Above- and belowground biomass productions, rhizosphere biology, microbial
populations, earthworm and other organisms, etc. modify soil health and productivity.
Higher nutrient use efficiency, C cycling, water regulation and purification,
erosion control, higher biomass and C stocks, food and nutritional security, and
higher economy of farmers can be ensured through healthy eco-models. Therefore,
eco-designing of different land use systems ensures a healthy ecosystem and
environment. Eco-modeling modifies ES in more sustainable ways without
disturbing our environment. Thus, adopting eco-designing models in soils
promises higher productivity and profitability and ensures SD of the world. In
this context, a government and public policy will strengthen the ecosystem health
by adopting a sustainable soil-based eco-model. A scientific-based research and
design add another effort to drive these eco-design practices in more efficient and
productive way to ensure the global SD.
... In the absence of simulation modelling, the crop coefficient method is usually used to calculate ET c (Jiang et al., 2014), and the weighing lysimetric method is usually used to calculate DP (Domínguez et al., 2016), both of which are time and labor consuming and hard to measure in a field with high soil spatial variability. Given these limitations, agricultural system models can be useful tools for estimating data that are hard to measure (e.g., ET c , DP, and nitrogen leaching) in field-or regional-scale applications (Li et al., 2007;Wen et al., 2020;Chen et al., 2020b). The use of agricultural system models in a spatial way needs further development to improve robustness and prediction accuracy. ...
Estimating water balance is the foundation of improving water productivity (WP) and managing crop production efficiently in fields with significant spatial variability of soil properties. Agricultural system models are useful tools to simulate crop yield, WP, and water balance; however, they are rarely focused on geostatistical characteristics on the spatial scale. If agricultural system models are used spatially, it is important to consider whether the geostatistical characteristics of the simulations are similar to those based on measured data. In this study, two process-based models, the widely-used Agricultural Production Systems sIMulator (APSIM) and the newly-developed soil Water Heat Carbon Nitrogen Simulator (WHCNS), were used to simulate crop yield, water balance, and WP in a 54-ha field with spatially variable soil properties. Performance of the simulations was good for both models; however, the simulation accuracy of WHCNS was higher than for APSIM. Geostatistical characteristics of measured maize yield and final soil water storage (SWS f) were different from the simulated data. The fitted semivariogram models of simulated yield and SWS f had a higher semivariogram range and lower random variation than that of measured data. The fitted semivariogram models and geostatistical characteristics of simulated water balance also varied between the two models. Although the agricultural system models simulated the spatial distribution of variables efficiently, their spatial structure was changed in comparison with the spatial structure of measured data. This would affect the interpolation precision of spatial distribution maps. More work is required on the robustness and prediction accuracy of both models for their implementation in a spatial way.
... The process model approach represents the mechanisms that control N 2 O emissions; these can capture the general patterns and magnitudes that have been widely used for predicting the N 2 O emission, such as APSIM (Keating et al., 2003) and WNMM (Li et al., 2007) as well as the other 10 models based on various processing initiated by global N 2 O model intercomparison project (Tian et al., 2018). However, process-based models may not perform well when extrapolating from site specific to a larger scale, owing to their deficiency in capturing the key processes in response to driving factors and their complex interacting factors, and detailed parameterization (Pan et al., 2021;Saha et al., 2021). ...
Robust global simulation of soil background N2O emissions (BNE) is a challenge due to the lack of a comprehensive system for quantification of the variations in their magnitude and location. We mapped global BNE based on 1,353 field observations from globally distributed sites and high‐resolution climate and soil data. We then calculated global and national total BNE budgets and compared them to the IPCC estimated values. The average BNE was 1.10, 0.92, and 0.84 kg N ha‐1 yr‐1 with variations from 0.18 to 3.47 (5–95th percentile, hereafter), 0.20 to 3.44, and ‐1.16 to 3.70 kg N ha‐1 yr‐1 for cropland, forestland, and grassland, respectively. Soil pH, soil N mineralization, atmospheric N deposition, soil volumetric water content, and soil temperature were the principle significant drivers of BNE. The total BNE of three land use types was lower than IPCC estimated total BNE by 0.83 Tg (10^12g) N yr‐1, ranging from ‐47% to 94% across countries. The estimated BNE with cropland values were slightly higher than the IPCC estimates by 0.11 Tg N yr‐1, and forestland and grassland lower than the IPCC estimates by 0.4 and 0.54 Tg N yr‐1, respectively. Our study underlined the necessity for detailed estimation of the spatial distribution of BNE to improve estimates of global N2O emissions and enable the establishment of more realistic and effective mitigation measures.
... Another aspect is its integrated structure: high order mechanisms (i.e., soil water balance, crop production and soil N dynamics) as distinct modules. Several processbased models are in use or are being developed, for example, APSIM 23 , DAISY 24 , DNDC 24 , and WNMM 25 . These models present the ability to examine the role of particular process towards system outputs. ...
The tremendous increase in industrial development and urbanization has become a severe threat to the Chinese climate and food security. The Agricultural Production System Simulator model was used to simulate soil nitrogen in black soil in Yangling Jilin Province for 20 years. The observed values are consistent with the simulated values. The predicted values of total soil NO 3 ⁻ –N and NH 4 ⁺ –N nitrogen are 10 kg ha ⁻¹ and 5 kg ha ⁻¹ higher than the observed values. The total soil NO 3 ⁻ –N loss has the same trend as the rainfall, and it increases with the number of rainfall days over the years. The average 20 years losses of NO 3 ⁻ –N and NH 4 ⁺ –N observed were 1375.91 kg ha ⁻¹ , and 9.24 kg ha ⁻¹ , while in the simulation increase was 1387.01 kg ha ⁻¹ and 9.28 kg ha ⁻¹ , respectively. The difference between the observed and simulated values of NO 3 ⁻ –N and NH 4 ⁺ –N of mean loss was 11.15 kg ha ⁻¹ and 0.04 kg ha ⁻¹ respectively. Moreover, our findings highlight the opportunity further to improve management policies (especially for nitrogen) to maintain crop yield.
... Para determinar los flujos gaseosos de áreas más amplias, considerando las emisiones de gases a nivel de ecosistema (cientos de m 2 a km 2 ), se usan técnicas micrometeorológicas como el método Eddy covariance, que mide las variaciones en la velocidad vertical del viento y la concentración de N2O con alta resolución temporal (Baldocchi, 2003). También se han desarrollado modelos computacionales que simulan los ciclos biogeoquímicos en distintas escalas espaciales y temporales, que permiten estimar las emisiones integrando las principales variables, los procesos y las interacciones involucradas Li et al. 2007;Parton et al. 1998). Finalmente, para estimar las emisiones anuales acumuladas a escala regional o nacional, el método más difundido es el de Factores de emisión (IPCC, 2006). ...
... , and is the function to determine the Figure S3). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t 8 ...
Nitrification is a major pathway of N2O production in aerobic soils. Measurements and model simulations of nitrification and associated N2O emission are challenging. Here we innovatively integrated data mining and machine learning to predict nitrification rate (Rnit) and the fraction of nitrification as N2O emissions (fN2ONit). Using our global database on Rnit and fN2ONit, we found that the machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating Rnit and N2O emission from nitrification. We then applied the SGB technique for global prediction. The potential Rnit was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas fN2ONit by mean annual precipitation, soil clay content, soil pH, soil total N. The global fN2ONit varied by over 200 times (0.006%-1.2%), which challenges the common practice of using a constant value in process-based models. This study provides insights into advancing process-based models for projecting N dynamics and greenhouse gas emissions using a machine learning approach.
... Such large-scale calculations often investigate only one output variable such as ammonia (NH 3 ) volatilization (e.g., NARSES; Zhang et al., 2011) or nitrous oxide (N 2 O) emissions (DNDC; Li et al., 2001). Li et al. (2007) used the water and nitrogen management model (WNMM) at regional scale in Fengqiu (Henan Province) and ...
Background: Intensive winter wheat–summer maize (Triticum aestivum L.–Zea mays L.) double‐cropping systems in the North China Plain often show high nitrogen (N) losses and water use causing harmful threats to the environment.
Methods: Continuous multiple‐year simulations (31 years) with the HERMES model were used to spatially quantify N and water losses at county scale in order to identify best‐practice management applications.
Results: Results show simulated annual long‐term N losses for the investigated Quzhou County, Hebei Province of 297 kg N ha⁻¹ under common farmers practice treatment (FP) and 102 kg N ha⁻¹ under optimized treatment including model derived N fertilizer recommendation and automated irrigation (OPTai). Total losses by N leaching, volatilization and denitrification were 57% (FP) and 40% (OPTai) of the applied fertilizer N, respectively. Spatial differences in N losses were found due to survey‐specific differences of average N inputs among the townships. More than 260 kg N ha⁻¹ y⁻¹ of fertilizer input, N losses of 190 kg N ha⁻¹ y⁻¹, and around 116 mm y⁻¹ of irrigation water could be saved on average by optimized treatments compared to farmers practice.
Conclusion: On clay loam soil only OPTai could maintain crop yield without drought stress. The optimized treatments had the lowest N inputs and N losses but they did not seem to be able to sustain the organic N pools.
... Using a range of state-of-the-art analytical techniques available at the UM, the ACJRC quantified nutrients loss and efficiencies of new products with field trials in collaboration with commercial service providers and other end-users, in landscapes of varying degrees of soil degradation. These trials include measurements of (1) growth and nitrogen contents of biomass at key growth stages including the final yield, (2) nitrogen loss pathways including ammonia (NH 3 ) volatilization (measured using micrometeorological techniques, e.g., open-path lasers, open-path FTIR [26] ), nitrous oxide (N 2 O) emissions (measured by static chambers, open-path FTIR and quantum cascade laser [27] ), and nitrate leaching (based on mineral nitrogen dynamics and process-based modeling, e.g., water and nitrogen management model (WNMM) [28] , and (3) fertilizer nitrogen recovery using 15 N techniques. ...
... Other inputs include: 1) the fertilization information including types of fertilizer, method (broadcasting or flushing), schedule and quantity; 2) irrigation quantity; and 3) the physical and chemical properties of each soil type. Initial value of parameters are either by default or from local agriculture studies (Li et al., 2017;Wang et al., 2011) and were adjusted during calibration. ...
Regional nitrate contamination in groundwater is a management challenge involving multi-sector benefits. There is always conflict between restricting anthropogenic activities to protect groundwater quality and prioritizing economic development, especially in productive agriculture dominated areas. To mitigate the nitrate contamination in groundwater, it is necessary to develop management alternatives that simultaneously support environmental protection and sustainable economic development. A regional transport modeling framework is applied to evaluate nitrate fate and transport in the Dagu Aquifer, a shallow sandy aquifer that supplies drinking water and irrigation water for a thriving agricultural economy in Shandong Province in east coastal China. The aquifer supports intensive high-value vegetable farms and nitrate contamination is extensive. Detailed land use information and fertilizer use data were compiled and statistical approaches were employed to analyze nitrogen source loadings and the spatio-temporal distribution of nitrate in groundwater to support model construction and calibration. The evaluations reveal that the spatial distribution and temporal trends of nitrate contamination in the Dagu Aquifer are driven by intensive fertilization and vertical water exchange, the dominant flow pattern derived from intensive agricultural pumping and irrigation. The modeling framework is employed to assess the effectiveness of potentially applicable management alternatives. The predictive results provide quantitative comparisons for the trend and extent of groundwater quality mitigation under each scenario. Recommendations are made for measures that can both improve groundwater quality and sustain productive agricultural development.
... Modeling approaches based on sufficient validation have been proposed to overcome the limits of field measurements (e.g., Chen et al., 2017). Because process-oriented biogeochemical models such as DNDC (e.g., Li, 2000), LandscapeDNDC (e.g., Haas et al., 2012), WNMM (e.g., Li et al., 2007) and CNMM-DNDC (Zhang et al., 2018) are generally designed following the basic theories of physics, chemistry, physiology and biology, they are expected to be widely applicable under various climates, soils, land uses and field management practices. These models, in principle, can facilitate the understanding of the interactions among various processes, identify gaps in current knowledge, and temporally and spatially extrapolate the results from experiments (Chen et al., 2008). ...
To meet increasing demands, tea plantations are rapidly
expanding in China. Although the emissions of nitrous oxide (N2O) and
nitric oxide (NO) from tea plantations may be substantially influenced by
soil pH reduction and intensive nitrogen fertilization, process model-based
studies on this issue are still rare. In this study, the process-oriented
biogeochemical model, Catchment Nutrient Management Model –
DeNitrification-DeComposition (CNMM-DNDC), was modified by adding tea-growth-related processes that may induce a soil pH reduction. Using a
dataset for intensively managed tea plantations at a subtropical site, the
performances of the original and modified models for simulating the
emissions of both gases subject to different fertilization alternatives and
stand ages were evaluated. Compared with the observations in the early stage of
a tea plantation, the original and modified models showed comparable
performances for simulating the daily gas fluxes (with a Nash–Sutcliffe index
(NSI) of 0.10 versus 0.18 for N2O and 0.32 versus 0.33 for NO), annual
emissions (with an NSI of 0.81 versus 0.94 for N2O and 0.92 versus 0.94
for NO) and annual direct emission factors (EFds). For the modified
model, the observations and simulations demonstrated that the short-term
replacement of urea with oil cake stimulated N2O emissions by
∼62 % and ∼36 % and mitigated NO emissions
by ∼25 % and ∼14 %, respectively. The
model simulations resulted in a positive dependence of EFds of either
gas on nitrogen doses, implicating the importance of model-based
quantification of this key parameter for inventory purposes. In addition, the
modified model with pH-related scientific processes showed overall
inhibitory effects on the gases' emissions in the middle to late stages during a
full tea plant lifetime. In conclusion, the modified CNMM-DNDC exhibits the
potential for quantifying N2O and NO emissions from tea plantations
under various conditions. Nevertheless, wider validation is still required
for simulation of long-term soil pH variations and emissions of both gases
from tea plantations.
... Measuring GHG emissions at the field scale is costly, laborious, and time-consuming; consequently, there have been efforts to develop and validate biophysical models for simulating GHG emissions temporally and spatially under diverse cropping systems to evaluate the impacts of beneficial management practices (Brilli et al., 2017). Process-based models have been developed and tested for estimating GHG emissions, such as DAYCENT , Denitrification-Decomposition (DNDC; Li et al., 1992), and the Water and Nitrogen Management Model (WNMM; Li et al., 2007). These models have enriched our understanding of the complicated physical, microbial, and chemical processes in the soil profile through comparisons with field observations, and they provide estimates of soil responses to climate change and agricultural management practices (Necpálová et al., 2015). ...
Highlights
RZWQM2 was compared with DNDC to predict greenhouse gas emissions.
RZWQM2 was applied to simulate the greenhouse gas emissions under manure application.
RZWQM2 performed better than DNDC in simulating soil water content and CO 2 emissions.
Abstract. N management has the potential to mitigate greenhouse gas (GHG) emissions. Process-based models are promising tools for evaluating and developing management practices that may optimize sustainability goals as well as promote crop productivity. In this study, the GHG emission component of the Root Zone Water Quality Model (RZWQM2) was tested under two different types of N management and subsequently compared with the Denitrification-Decomposition (DNDC) model using measured data from a subsurface-drained field with a corn-soybean rotation in southern Ontario, Canada. Field-measured data included N 2 O and CO 2 fluxes, soil temperature, and soil moisture content from a four-year field experiment (2012 to 2015). The experiment was composed of two N treatments: inorganic fertilizer (IF), and inorganic fertilizer combined with solid cattle manure (SCM). Both models were calibrated using the data from IF and validated with SCM. Statistical results indicated that both models predicted well the soil temperature, but RZWQM2 performed better than DNDC in simulating soil water content (SWC) because DNDC lacked a heterogeneous soil profile, had shallow simulation depth, and lacked crop root density functions. Both RZWQM2 and DNDC predicted the cumulative N 2 O and CO 2 emissions within 15% error under all treatments, while the timing of daily CO 2 emissions was more accurately predicted by RZWQM2 (RMSE = 0.43 to 0.54) than by DNDC (RMSE = 0.60 to 0.67). Modeling results for N management effects on GHG emissions showed consistency with the field measurements, indicating higher CO 2 emissions under SCM than IF, higher N 2 O emissions under IF in corn years, but lower N 2 O emissions in soybean years. Overall, RZWQM2 required more experienced and intensive calibration and validation, but it provided more accurate predictions of soil hydrology and better timing of CO 2 emissions than DNDC. Keywords: CO2 emission, Corn-soybean rotation, Inorganic fertilization, Manure application, N2O emission, Process-based modeling.
... NUE values for maize and wheat decreased as the amount of applied N fertilizer increased Zhang et al., 2015b). Reducing the amount of N fertilizer applied could reduce N loss and improve NUE (Li et al., 2007). In our study, we found that NUE became a relatively stable lower value once N application rate exceeded the critical amount. ...
Appropriate irrigation and nitrogen (N) management practices should be implemented to obtain high grain yields while considering the influence of water and N leaching on groundwater in the intensively cropped winter wheat (Triticum aestivum)-maize (Zea mays L.) rotation system. The calibrated RZWQM2 model was used to explore long-term (1984–2017) effects of irrigation and N fertilization on crop yield, water and nitrogen use efficiency, deep percolation water (DPW), N leaching loss (NLL), and their effects on deep soil layers (2−30 m) in this rotation system in the Jinghui Canal irrigation area of the Guanzhong Plain in China. Results showed that crop yields increased with increasing N rate. The critical N application rates of 140 and 240 kg N ha−1 coupled with 75 and 90 mm of irrigation for maize and wheat, respectively, resulted in high yields (7535 and 8977 kg ha−1), water use efficiency (2.22, 2.07 kg m-3), and nitrogen use efficiency (44.56, 40.78 kg kg−1). The simulated DPW values were 69 and 110 mm for maize and wheat, respectively, and NLL values were 25.36 and 25.47 kg ha−1. Crop yield and NLL for wheat were more sensitive to N application timing than maize yield and NLL, indicating that split N applications of two application times and three application times for maize and wheat, respectively, could be a more effective way of applying N fertilizer. DPW fluxes increased from 0.021-0.024 (varied over the 2−30 m depth) to 0.107-0.110 mm d−1 for irrigation ranging from 60 to 135 mm. The response times for DPW to be observed at the groundwater depth of 30 m could range from one year to more than two years, with DPW velocities of 0.034 and 0.077 m d−1 for 60 and 135 mm irrigation application amounts, respectively. NLL flux increased with added irrigation and N application rate, while decreasing exponentially with soil depth. Annual recharge of NO3-N to the groundwater (0.15–6.2 kg N ha−1) with lower irrigation amounts (60−90 mm) could be neglected, but higher irrigations (≥105 mm) even with lower N application rates could cause groundwater contamination. Therefore, comprehensively considering the effects of irrigation and fertilization practices on grain yields and groundwater could improve the sustainability of the agro-hydrological environment and agricultural production.
... The DNDC is a process-based ecological model of C and N biogeochemistry for agricultural ecosystem (Li et al., 2002). The classical laws of physics, chemistry and biology, as well as empirical equations generated from laboratory studies, are incorporated in this model to parameterize each specific geochemical or biochemical process, and numerous changes have been made to the DNDC model to develop country or need-specific models (Li et al., 1992;Frolking et al., 1999;Salas et al., 2006;Li et al., 2007;Giltrap et al., 2010). ...
... In the past few decades, various simulation approaches integrating process-based knowledge of the N cycle have been developed to explore the soil N dynamics and quantify N fluxes (Negm et al., 2014). Examples of these models include the Water and Nitrogen Management Model (WNMM) (Li et al., 2007), Root Zone Water Quality Model (RZWQM) (Cameira et al., 2014), Decision Support System for Agrotechnology Transfer (DSSAT) (Yakoub et al., 2017), Agricultural Production Systems Simulator (APSIM) (Vogeler et al., 2019), Denitrification Decomposition (DNDC) (Zhang et al., 2018a,b,c,d), TOUGHREACT-N (Maggie et al., 2008), and DAYCENT ( Alvaro-Fuentes et al., 2017). Ten specific N processes were considered in these models: nitrification, denitrification, volatilization, leaching, symbiotic fixation, assimilation, mineralization, immobilization, plant uptake, and clay fixation. ...
Intensive agriculture and urbanization have led to the excessive and repeated input of nitrogen (N) into soil and further increased the amount of nitrate (NO3-) leaching into groundwater, which has become an environmental problem of widespread concern. This review critically examines both the recent advances and remaining knowledge gaps with respect to the N cycle in the vadose zone-groundwater system. The key aspects regarding the N distribution, transformation, and budget in this system are summarized. Three major missing N pieces (N in dissolved organic form, N in the deep vadose zone, and N in the nonagricultural system), which are crucial for closing the N cycle yet has been previously assumed to be insignificant, are put forward and discussed. More work is anticipated to obtain accurate information on the chemical composition, transformation mechanism, and leaching flux of these missing N pieces in the vadose zone-groundwater system. These are essential to support the assessment of global N stocks and management of N contamination risks.
... Model improvements have also been attempted by revising the soil water module in N biogeochemistry models, revising the N biogeochemistry module in soil hydrology models, or constructing new models that integrate sophisticate hydrological and biogeochemical processes. For example, Li et al. (2007) built the water and N management model based on the processes of water dynamics, soil temperature, C and N cycles in soils and crops, crop growth, and agricultural management practices. Zhang et al. (2016) the extended the distributed time variant gain model by integrating detailed processes of soil biogeochemistry ecology. ...
Interactions among soil water processes and the nitrogen (N) cycle govern biological productivity and environmental outcomes in the earth's critical zone. Soil water influences the N cycle in two distinct but interactive modes. First, the spatio-temporal variation of soil water content (SWC) controls redox coupling among oxidized and reduced compounds, and thus N mineralization, nitrification, and denitrification. Secondly, subsurface flow controls the movement of water and dissolved N. These two processes interact such that subsurface flow dynamics control the occurrence of relatively static, isolated soil solution environments that span a range of reduced to oxidized conditions. However, the soil water-N cycle is usually treated as a black box. Models focused on N cycling simplify soil water parameters, while models focused on soil water processes simplify N cycling parameters. In addition, effective ways to deal with upscaling are lacking. New techniques will allow comprehensive coupling of the soil water-N cycle across time and space: 1) using hydrogeophysical tools to detect soil water processes and then linked to electrochemical N sensors to reveal the soil N cycle, (2) upscaling small-scale observations and simulations by constructing functions between soil water-N cycle and ancillary soil, topography and vegetation variables in the hydropedological functional units, and (3) integrating soil hydrology models with N cycling models to minimize the over-simplification of N biogeochemistry and soil hydrology mechanisms in these models. These suggestions will enhance our understanding of interactions among soil water dynamics and the N cycle, thus improving modeling of N losses as important sources of greenhouse gas emissions and water pollution.
... Measuring GHG emissions at the field scale is costly, laborious, and time-consuming; consequently, there have been efforts to develop and validate biophysical models for simulating GHG emissions temporally and spatially under diverse cropping systems to evaluate the impacts of beneficial management practices (Brilli et al., 2017). Process-based models have been developed and tested for estimating GHG emissions, such as DAYCENT , Denitrification-Decomposition (DNDC; Li et al., 1992), and the Water and Nitrogen Management Model (WNMM; Li et al., 2007). These models have enriched our understanding of the complicated physical, microbial, and chemical processes in the soil profile through comparisons with field observations, and they provide estimates of soil responses to climate change and agricultural management practices (Necpálová et al., 2015). ...
... N 2 O is mainly produced during nitrification (Wrage et al. 2001;Cheng et al. 2004) and denitrification (Firestone and Davidson 1989). Due to its biogenic nature, soil emissions of N 2 O are affected by a set of environmental factors, such as the availability of soil mineral nitrogen soil temperature, and soil moisture as well as soil pH (Zou et al. 2005;Li et al. 2007). A large source of N 2 O from soil denitrification process was reported in acid soil (Cheng et al. 2015). ...
Tea (Camellia sinensis L.), a perennial leaf-harvested crop, favors warm/humid climate and acidic/well-drained soils, and demands high nitrogen (N) fertilizer inputs which lead to significant emissions of N2O. Potential mitigation options should be adopted to improve N use efficiency (NUE) and reduce environmental pollution in tea field system. A 3-year field experiment was carried out in a tea field in southern China from January 2014 to December 2016 to investigate the effect of controlled-release fertilizer (CRF) application on N2O emissions in tea field system. Three practices, namely conventional treatment (CON, 105 kg N-oilcake ha-1 year-1 + 345 kg N-urea ha-1 year-1), treatment with a half amount of the N fertilizer (CRF50%, 105 kg N-oilcake ha-1 year-1 + 120 kg N CRF ha-1 year-1) and full amount of N fertilizer (CRF100%, 105 kg N-oilcake ha-1 year-1 + 345 kg N CRF ha-1 year-1) were used. Compared with the CON, our results showed that CRF50% reduced the N2O emissions by 26.2% (p > 0.05) and increased the tea yield by 31.3% (p > 0.05), while CRF100% significantly increased the N2O emissions by 96.7% (p < 0.05) and decreased the tea yield by 6.77% (p > 0.05). Overall, yield-scaled N2O emissions of tea were reduced by 44.5% (p > 0.05) under CRF50% and significantly increased by 100% (p < 0.05) under CRF100%, compared with CON. Based on the gross margin analysis, CRF50% obtained the highest net economic profit. Our findings suggest that reducing N input of CRF (CRF50%) is necessary and feasible for adoption in the current tea plantation system.
... Nitrification is determined by Michaelis-Menten kinetics and modified by soil pH, soil moisture and temperature and N 2 O emissions via nitrification is estimated as a (Liu et al., 2003). fraction of the nitrified N ( = 0.002) (Li et al., 2007). More details on nitrification and denitrification processes simulated by the APSIM model are described by Thorburn et al. (2010). ...
Agroecosystems face double pressures of producing more food to feed growing global population and reducing greenhouse gas (GHG) emissions to mitigate climate change. The Huang-Huai-Hai (HHH) plain produces ∼1/3 wheat and maize of China with very high resource inputs, particularly synthetic nitrogen (N) fertilizers since the 1980 s. Although fertilizer input has substantially increased crop yield and enhanced biomass carbon (C) input to the soil and thus stimulating soil C sequestration, GHG emissions (e.g., nitrous oxide (N2O)) relating to the fertilizers have been also dramatically increased. Yet, a systematic regional assessment on the trade-offs between crop yield, soil C sequestration and N2O emissions as impacted by management practices and environmental conditions is lacking. Here we calibrated a farming system model to conduct comprehensive assessment on crop yield and GHG emissions (soil CO2 and N2O emissions) during the period 1981–2010 across the HHH plain at the resolution of 10 km. We found that soil in HHH plain was a C sink with an annual C sequestration rate of 1.53 CO2-eq ha⁻¹ yr–1 (0–30 cm soil layer) during the period under current typical agricultural practices, but this sink could only offset about 68% of global warming potential from contemporary N2O emissions. By reducing the annual N input rate (from current more than 300 to ∼250 kg N ha⁻¹ yr⁻¹) and enhancing crop residue retention rate (from current 30% to 100%), the HHH plain could act as a net sink of GHG without sacrificing grain yield. Apart from management, the effects of three key environmental factors, i.e., mean annual rainfall and temperature and initial soil organic carbon stock on dynamics of crop yield, soil CO2 and N2O emissions were also studied. The results will have important implications for the development of management strategies to maintain yield while reducing GHG emissions.
Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha−1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha−1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha−1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha−1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.
Essential soil functions such as plant productivity, C storage, nutrient cycling and the storage and purification of water all depend on soil biological processes. Given this insight, it is remarkable that in modeling of these soil functions, the various biological actors usually do not play an explicit role. In this review and perspective paper we analyze the state of the art in modeling these soil functions and how biological processes could more adequately be accounted for. We do this for six different biologically driven processes clusters that are key for understanding soil functions, namely i) turnover of soil organic matter, ii) N cycling, iii) P dynamics, iv) biodegradation of contaminants v) plant disease control and vi) soil structure formation. A major conclusion is that the development of models to predict changes in soil functions at the scale of soil profiles (i.e. pedons) should be better rooted in the underlying biological processes that are known to a large extent. This is prerequisite to arrive at the predictive models that we urgently need under current conditions of Global Change.
Bioretention systems have the potential of simultaneous runoff volume reduction and nitrogen removal. Internal water storage (IWS) layers and real-time control (RTC) strategies may further improve performance of bioretention systems. However, optimizing the design of these systems is limited by the lack of effective models to simulate nitrogen transformations under the influences of IWS design and environment conditions including soil moisture and temperature. In this study, nitrogen removal models (NRMs) are developed with two complexity levels of nitrogen cycling: the Single Nitrogen Pool (SP) models and the more complex 3 Nitrogen Pool (3P) models. The 0-order kinetics, 1st order kinetics, and the Michaelis-Menten equations are applied to both SP and 3P models, creating six different NRMs. The Storm Water Management Model (SWMM), in combination with each NRM, is calibrated and validated with a lab dataset. Results show that 0-order kinetics are not suitable in simulating nitrogen removal or transformations in bioretention systems, while 1st order kinetics and Michaelis-Menten equation models have similar performances. The best performing NRM (referred to as 3P-m) can accurately predict nitrogen event mean concentrations in bioretention effluent for 20% more events when compared to SWMM. When only calibrated with soil moisture conditions in bioretention systems without internal storage layers, 3P-m was sufficiently adaptable to predict cumulative nitrogen mass removal rates from systems with IWS or RTC rules with less than ±7% absolute error, while the absolute error from SWMM prediction can reach -23%. In general, 3P models provide higher prediction accuracy and improved time series of biochemical reaction rates, while SP models improve prediction accuracy with less required user input for initial conditions.
Ammonia volatilization, a main pathway of nitrogen (N) loss in paddy fields, can easily lead to environmental problems, such as haze and water eutrophication. Field measurement of ammonia volatilization is time-consuming and expensive. Soil–crop system models can simulate the effect of different fertilizer management practices on NH3 volatilization and compensate for the limitations of field measurement. Therefore, the WHCNS (soil Water Heat Carbon Nitrogen Simulator) model was used to evaluate the feasibility of simulating NH3 volatilization in paddy fields under different N fertilizer types and application methods. The field experiment, including four treatments of farmer’s practice (FP), basal application of urea in one dose (SBN), split application of urea in three doses (SPN), and basal application of controlled-release urea in one dose (CRU), was conducted from 2013 to 2014. All treatments were conducted with a fertilizer rate of 165 kg N ha⁻¹, except for FP with a rate of 210 kg N ha⁻¹. The measured dry matter weight, grain yield, N uptake, and NH3 flux were used to test the WHCNS model. The model evaluation indices of Nash–Sutcliffe efficiency (NSE) and index of agreement (d) were close to 1 for the measured variables, except for NSE of yield (0.16). The normalized root mean square error (NRMSE) of NH3 flux was slightly over 30%, but lower than 30% for the other items. Results showed that the model can successfully simulate rice yield and NH3 flux of urea and controlled-release urea under different application methods. The simulated annual average cumulative NH3 losses of FP, SBN, SPN, and CRU accounted for 18.9%, 17.8%, 20.6%, and 10.1% of their N inputs, respectively. Compared with the FP treatment, the ratios of NH3 losses of the SBN and CRU treatments were decreased by 1.2%-8.8%. Compared with the SBN treatment (the same rate of 165 kg N ha⁻¹), SPN reduced the peak of NH3 flux but increased the cumulated NH3 losses and CRU significantly reduced the NH3 flux and cumulative NH3 losses. The application rate of controlled-release urea could be further optimized by refined model calculations to reduce NH3 volatilization and total N loss while maintaining yield.
A process-based nitrogen model suitable for urban stormwater Best Management Practices (BMPs), when used as an extension to current stormwater models, can assist decision-making by predicting nitrogen removal performances. While such a model has not been created, process-based nitrogen modules for agricultural fields have been developed and applied. In this paper, the nitrogen modules in six agricultural models are reviewed in the perspective of assisting the development of a process-based nitrogen module for stormwater BMPs. The agricultural nitrogen modules have consistent structures and mathematical descriptions of biochemical reactions, but show more variety in incorporating environmental factors. The operational challenges, strengths, and weaknesses of simulating stormwater BMPs’ nitrogen removal performances with current models are illustrated with a case study. Finally, suggestions on developing the process-based nitrogen module for stormwater BMPs are offered based on the review, the case study results, and the differences between the stormwater BMPs and agricultural fields.
Process-based agroecosystem model provides a powerful tool to evaluate the effects of various field management practices on soil water balance, nitrogen (N) fates, and crop growth for cropland ecosystems. However, parameter calibration of those models is usually time-consuming and uncertain due to the involvement of complex soil and plant growth processes in models. In this study, the Different Evolution Adaptive Metropolis (DREAM) algorithm was successfully used to estimate model parameters and quantify uncertainties of the soil Water Heat Carbon Nitrogen Simulator (WHCNS) model. The calibration efficiency was further improved by identifying the sensitive parameters using global sensitivity analysis. The simulated soil water content, crop N uptake, and yield were in agreement with the measured values, with indices of NRMSE, IA, and NSE ranging from 6.4% to 22.1%, from 0.59 to 0.98, and from 0.43 to 0.92, respectively. The simulation of soil nitrate concentration showed a relatively large NRMSE that varied from 35.9% to 50.2% but was still within the acceptance range. This study suggested that the two-step method of global sensitivity analysis and DREAM algorithm is a robust way to estimate model parameters and quantify uncertainties of agroecosystem models.
CONTEXT
Modelling approaches have already been used to quantitatively assess the trade-offs between crop yield and N2O emissions as impacted by management practices. However, the model's performance in terms of predicting N2O emissions was mainly assessed against total emissions per growing season or year, which may reduce accuracy in modelling due to the uncertainties in total emissions estimated using the manual (static) chamber method.
OBJECTIVE
Here, a comparison between optimizations for Agricultural Production Systems sIMulator (APSIM) with total N2O emissions and daily N2O emissions was conducted. We further used the validated model to develop simple surrogate models for estimating total N2O emissions in different years and target potential opportunities to reduce N2O emissions while still maintaining the grain yield under long-term climatic conditions.
METHODS
Five parameters relating to denitrification and nitrification were optimized using differential evolution algorithm for global optimization based on two-year field experimental data at Huantai site in North China Plain, and comprehensive simulation experiments were further conducted under long-term climate variability in an irrigated wheat–maize rotation system. The method of Levenberg-Marquardt was implemented to fit simple surrogate models for estimating total N2O emissions in different years, and Analysis of Variance was used for model comparison.
RESULTS AND CONCLUSIONS
APSIM model optimized with daily N2O emissions could better simulate soil N2O and nitrate dynamics than that optimized with total N2O emissions. We obtained the posterior distributions of five key parameters to which N2O emissions are sensitive, and demonstrated that original model using default parameters could underestimate the rate of nitrification and denitrification and the subsequent N2O emissions. Total N2O emissions increased exponentially with nitrogen application rate and mean temperature, and IPCC (1% emission factor) could underestimate whole-year N2O emissions when N rate was higher than the optimized nitrogen rate for crop production. We also found that there was potential to optimize nitrogen fertiliser rate to reduce N2O emissions while still maintaining crop yield in the irrigated wheat–maize rotation system.
SIGNIFICANCE
This study demonstrated the necessity of optimization with daily N2O emissions in improving model accuracy, and the posterior distributions of five parameters relating to N2O emissions offered reference range for future model improvement and applications.
Soil nitrogen (N) loss is a part of N biogeochemical processes, which plays an important role in the agricultural, ecological and environmental management. Because it is difficult to assess the temporal and spatial changes of different N forms in leachates by field measurement methods, conceptual and physical models are usually used to describe soil N loss. However, soil N models are often associated with multiple sources of uncertainty (e.g., model parameter and structure), which may largely influence the reliability and accuracy of the models. The multimodel ensemble prediction (MEP) is specifically designed to reduce the parameter and structural uncertainty in N biogeochemical modelling by representing a set of candidate models. However, the existing MEP methods still need to be improved by integrating various kinds of prior knowledge and quantifying each part of predictive uncertainty. In addition, published studies mainly focused on the regional scale MEP of the land carbon balance. However, the regional scale MEP of soil N losses is lacking. This paper firstly proposed the MEP methods of soil N losses at different spatial scales: 1) using the Monte-Carlo sampling to randomly alter the soil and crop parameters governing the N cycle and driving multiple soil N models at plot scale; and 2) generating an ensemble of TIGGE (THORPEX Interactive Grand Global Ensemble) weather forecasts and an ensemble of random soil and crop parameters and driving multiple soil N models at regional scale. This study also discussed different methods used for realizing MEP. It is found that the ensemble mean produced a large bias when simulating soil N losses. By using the bias correction technique, the RMSEs of the ensemble mean decreased by 57.5%~86.0%. Overall, the MEP can enhance our understanding of soil N cycle. In addition, this study is also helpful to accurately estimate the response of soil N loss to global change and provide support for agricultural production and environmental protection.
Accurately simulating the soil nitrogen (N) cycle is crucial for assessing food security and resource utilization efficiency. The accuracy of model predictions relies heavily on model parameterization. The sensitivity and uncertainty of the simulations of soil N cycle of winter wheat-summer maize rotation system in the North China Plain (NCP) to the parameters were analyzed. First, the N module in the Vegetation Interface Processes (VIP) model was expanded to capture the dynamics of soil N cycle calibrated with field measurements in three ecological stations from 2000 to 2015. Second, the Morris and Sobol′ algorithms were adopted to identify the sensitive parameters that impact soil nitrate stock, denitrification rate, and ammonia volatilization rate. Finally, the shuffled complex evolution developed at the University of Arizona (SCE-UA) algorithm was used to optimize the selected sensitive parameters to improve prediction accuracy. The results showed that the sensitive parameters related to soil nitrate stock included the potential nitrification rate, Michaelis constant, microbial C/N ratio, and slow humus C/N ratio, the sensitive parameters related to denitrification rate were the potential denitrification rate, Michaelis constant, and N2 O production rate, and the sensitive parameters related to ammonia volatilization rate included the coefficient of ammonia volatilization exchange and potential nitrification rate. Based on the optimized parameters, prediction efficiency was notably increased with the highest coefficient of determination being approximately 0.8. Moreover, the average relative interval length at the 95% confidence level for soil nitrate stock, denitrification rate, and ammonia volatilization rate were 11.92, 0.008, and 4.26, respectively, and the percentages of coverage of the measured values in the 95% confidence interval were 68%, 86%, and 92%, respectively. By identifying sensitive parameters related to soil N, the expanded VIP model optimized by the SCE-UA algorithm can effectively simulate the dynamics of soil nitrate stock, denitrification rate, and ammonia volatilization rate in the NCP.
To be able to compare many agricultural models, a general framework for model comparison when field data may limit direct comparison of models is proposed, developed, and also demonstrated. The framework first calibrates the benchmark model against the field data, and next it calibrates the test model against the data generated by the calibrated benchmark model. The framework is validated for the modeling of the soil nutrient nitrogen (N), a critical component in the overall agriculture system modeling effort. The nitrogen dynamics and related carbon (C) dynamics, as captured in advanced agricultural modeling such as RZWQM, are highly complex, involving numerous states (pools) and parameters. Calibrating many parameters requires more time and data to avoid underfitting. The execution time of a complex model is higher as well. A study of tradeoff among modeling complexities vs. speed-up, and the corresponding impact on modeling accuracy, is desirable. This paper surveys soil nitrogen models and lists those by their complexity in terms of the number of parameters, and C-N pools. This paper also examines a lean soil N and C dynamics model and compares it with an advanced model, RZWQM. Since nitrate and ammonia are not directly measured in this study, we first calibrate RZWQM using the available data from an experimental field in Greeley, CO, and next use the daily nitrate and ammonia data generated from RZWQM as ground truth, against which the lean model’s N dynamics parameters are calibrated. In both cases, the crop growth was removed to zero out the plant uptake, to compare only the soil N-dynamics. The comparison results showed good accuracy with a coefficient of determination (R2) match of 0.99 and 0.62 for nitrate and ammonia, respectively, while affording significant speed-up in simulation time. The lean model is also hosted in MyGeoHub cyberinfrastructure for universal online access.
The aim of this study is to determine the optimal soil bulk density in newly added cultivated land for maintaining soil moisture and crops growing. In this work, a field experiment was conducted to study the effect of 5 different soil’s bulk density (BD) on soil moisture and crop yield in newly created land on gravel: T23 (BD = 1.3 g/cm³), T24 (BD = 1.4 g/cm³), T25 (BD = 1.5 g/cm³), T26 (BD = 1.6 g/cm³), T27 (BD = 1.7 g/cm³). The root zone water quality model (RZWQM) was calibrated and verified with experimental data, and then applied to simulate the soil moisture and crop yield under different bulk density from 2004 to 2016. Simulation prediction shows that the average yield of the soil bulk density of T25 treatment is highest with 7551 kg/ha, and the CV of multi-year crop yield is the minimum with 31.9%, meaning that the yield of crops of this bulk density is the most stable. The relationship between soil water storage and growth period yield is approximately linear, with R² of 0.46. Comprehensive analysis indicates that the soil bulk density of that 0–40 cm – soil bulk density = 1.2 g/cm³; 40–50 cm soil bulk density = 1.5 g/cm³ is more conducive to water conservation and crop growth.
A wide variety of models have been developed and used in studies of land-atmosphere interactions and the carbon cycle, with aims of data integration, sensitivity analysis, interpolation, and extrapolation. This review summarizes the achievements of model studies conducted in Asia, a focal region in the changing Earth system, especially collaborative works with the regional flux measurement network, AsiaFlux. Process-based biogeochemical models have been developed to simulate the carbon cycle, and their accuracy has been verified by comparing with carbon dioxide flux data. The development and use of data-driven (statistical and machine learning) models has further enhanced the utilization of field survey and satellite remote sensing data. Model intercomparison studies were also conducted by using the AsiaFlux dataset for uncertainty analyses and benchmarking. Other types of models, such as cropland models and trace gas emission models, are also briefly reviewed here. Finally, we discuss the present status and remaining issues in data-model integration, regional synthesis, and future projection with the models.
In the present study, the sensitivity analysis of reference evapotranspiration to major climatic variables in east district of Sikkim was carried out using the daily time series data of major climatic parameters namely temperature, relative humidity, wind speed and sunshine hour. Daily ET 0 was estimated from FAO-56 Penman-Monteith equation. The results indicated that annual ET 0 were most sensitive to maximum temperature followed by sunshine hours. However, wind speed, minimum temperature and relative humidity had varying effect on annual ET 0. Annual ET O increased by about 2% (920mm to 938mm) for 20% increase in mean minimum temperature, whereas, ETo decreased by 2.9% for decrease in minimum temperature by 20%. Increase in maximum temperature by 20%, increased the annual ET O by 8.0% (916mm to 989mm). Decrease in mean maximum temperature by 20%, decreased the ET O by 7.4%. Annual ET O increased from 919.80 to 963.60 mm for 20% increase in duration of sunshine hours. However, when duration of sunshine hours is decreased by 20%, ET O decreased to 905.20 mm.The study suggest that the climate variability would affect reference ET 0 ; however, its impact on ET 0 would be different for different parameters.
The North China Plain (NCP) is one of the most productive and intensive agricultural regions in China. High doses of mineral nitrogen (N) fertiliser, often combined with flood irrigation, are applied, resulting in N surplus, groundwater depletion and environmental pollution. The objectives of this thesis were to use the HERMES model to simulate the N cycle in winter wheat (Triticum aestivum L.)–summer maize (Zea mays L.) double crop rotations and show the performance of the HERMES model, of the new ammonia volatilisation sub-module and of the new nitrification inhibition tool in the NCP. Further objectives were to assess the models potential to save N and water on plot and county scale, as well as on short and long-term. Additionally, improved management strategies with the help of a model-based nitrogen fertiliser recommendation (NFR) and adapted irrigation, should be found. Results showed that the HERMES model performed well under growing conditions of the NCP and was able to describe the relevant processes related to soil–plant interactions concerning N and water during a 2.5 year field experiment. No differences in grain yield between the real-time model-based NFR and the other treatments of the experiments on plot scale in Quzhou County could be found. Simulations with increasing amounts of irrigation resulted in significantly higher N leaching, higher N requirements of the NFR and reduced yields. Thus, conventional flood irrigation as currently practised by the farmers bears great uncertainties and exact irrigation amounts should be known for future simulation studies. In the best-practice scenario simulation on plot-scale, N input and N leaching, but also irrigation water could be reduced strongly within 2 years. Thus, the model-based NFR in combination with adapted irrigation had the highest potential to reduce nitrate leaching, compared to farmers practice and mineral N (Nmin)-reduced treatments. Also the calibrated and validated ammonia volatilisation sub-module of the HERMES model worked well under the climatic and soil conditions of northern China. Simple ammonia volatilisation approaches gave also satisfying results compared to process-oriented approaches. During the simulation with Ammonium sulphate Nitrate with nitrification inhibitor (ASNDMPP) ammonia volatilisation was higher than in the simulation without nitrification inhibitor, while the result for nitrate leaching was the opposite. Although nitrification worked well in the model, nitrification-born nitrous oxide emissions should be considered in future. Results of the simulated annual long-term (31 years) N losses in whole Quzhou County in Hebei Province were 296.8 kg N ha−1 under common farmers practice treatment and 101.7 kg N ha−1 under optimised treatment including NFR and automated irrigation (OPTai). Spatial differences in simulated N losses throughout Quzhou County, could only be found due to different N inputs. Simulations of an optimised treatment, could save on average more than 260 kg N ha−1a−1 from fertiliser input and 190 kg N ha−1a−1 from N losses and around 115.7 mm a−1 of water, compared to farmers practice. These long-term simulation results showed lower N and water saving potential, compared to short-term simulations and underline the necessity of long-term simulations to overcome the effect of high initial N stocks in soil. Additionally, the OPTai worked best on clay loam soil except for a high simulated denitrification loss, while the simulations using farmers practice irrigation could not match the actual water needs resulting in yield decline, especially for winter wheat. Thus, a precise adaption of management to actual weather conditions and plant growth needs is necessary for future simulations. However, the optimised treatments did not seem to be able to maintain the soil organic matter pools, even with full crop residue input. Extra organic inputs seem to be required to maintain soil quality in the optimised treatments. HERMES is a relatively simple model, with regard to data input requirements, to simulate the N cycle. It can offer interpretation of management options on plot, on county and regional scale for extension and research staff. Also in combination with other N and water saving methods the model promises to be a useful tool.
Straw retention is an important practice increasing crop production worldwide. It also can reduce nitrous oxide (N2O) gas emissions from agricultural soils. But it is very difficult to predict N2O gas discharge amounts when straw is incorporated at different soil depths. This research combined wheat growing season data from the Agricultural Meteorological Experiment Station of Nanjing University of Information Science and Technology, China, with the denitrification-decomposition model (DNDC model) to determine whether the DNDC could simulate the N2O emission rate, annual discharge, and effect of incorporation depth on N2O emissions. Straw was retained on the soil surface, and incorporated at 10, 20, and 30 cm depths. The sensitivity of N2O emissions to several site-specific and environmental factors was also examined. The results were as follows: (1) the DNDC model was able to simulate the N2O emission rate and annual emission rate from different straw incorporation depths; (2) N2O gas emissions are sensitive to annual mean temperature, soil pH, and amounts of soil organic carbon, fertilization, and retained straw, which have different impacts under different straw incorporation depths; and (3) the direct influence of soil internal factors was more important than environmental factors in the surface and 10-cm treatments; however, the influence of straw retention decreased and the influence of environmental factors increased in the 20- and 30-cm treatments.
Process-based models capture our understanding of key processes that interact to determine productivity and environmental outcomes. Combining measurements and modelling together help assess the consequences of these interactions, identify knowledge gaps and improve understanding of these processes. Here, we present a dataset (collected in a two-month fallow period) and list potential issues related to use of the APSIM model in predicting fluxes of soil water, heat, nitrogen (N) and carbon (C). Within the APSIM framework, two soil water modules (SoilWat and SWIM3) were used to predict soil evaporation and soil moisture content. SWIM3 tended to overestimate soil evaporation immediately after rainfall events, and SoilWat provided better predictions of evaporation. Our results highlight the need for testing the modules using data that includes wetting and drying cycles. Two soil temperature modules were also evaluated. Predictions of soil temperature were better for SoilTemp than the default module. APSIM configured with different combinations of soil water and temperature modules predicted nitrate dynamics well, but poorly predicted ammonium-N dynamics. The predicted ammonium-N pool empties several weeks after fertilisation, which was not observed, indicating that the processes of mineralisation and nitrification in APSIM require improvements. The fluxes of soil respiration and nitrous oxide, measured by chamber and micrometeorological methods, were roughly captured by APSIM. Discrepancies between the fluxes measured with chamber and micrometeorological techniques highlight difficulties in obtaining accurate measurements for evaluating performance of APSIM to predict gaseous fluxes. There was uncertainty associated with soil depth, which contributed to surface emissions. Our results showed that APSIM performance in simulating N2O fluxes should be considered in relation to data precision and uncertainty, especially the soil depths included in simulations. Finally, there was a major disconnection between the predicted N loss from denitrification (N2 + N2O) and that measured using the 15N balance technique.
Reducing nitrous oxide (N2O) emissions from agriculture soils is crucial, as it accounts for 5.6-6.8% of global anthropogenic emissions. This study aims to understand the interaction among climate, soil nitrogen (N) and applied N on N2O emissions from the irrigated cotton farming system and its implications on farm economics. We conducted simulations for 116 years (1900-2015) and assessed the effect of different N-fertiliser application rates, initial soil nitrate (NO3) N levels and rainfall conditions on N2O emissions, N2O emission factors (EFs) and financial returns (with and without N2O costs). Results showed the following. 1) The proportional impact of higher N fertiliser rates on soil N2O emissions was greater when initial soil N level was lower (5 mg NO3 kg-1) than higher (35 mg NO3 kg-1). However, the volume of impact was greater under higher initial soil N levels. 2) The relationship between N fertiliser rates and the EFs (range 0.03-7.2%) was not linear but bell-shaped. 3) Fertiliser N requirements increased with rainfall and decreased with initial soil N. Accordingly, the cotton returns for the driest rainfall condition (<10th percentile) were maximum at 300, 250 and 150 kg N ha-1 for initial soil N of 5, 20 and 35 mg NO3 kg-1. For the wettest rainfall condition (>90th percentile), these rates were 50 kg ha-1 higher across the initial soil N conditions. Any additional application of N-fertiliser above these rates was counterproductive. 4) Inclusion of N2O cost into farm economics reduced the annual returns by up to $39 ha-1, but the optimal fertiliser application rates remain the same. 5) Optimising N fertiliser rates to soil N and rainfall conditions increased the annual returns by up to $303 ha-1, with a further increase of $15 ha-1 from fertiliser use efficiency when the Australian Government incentives under the $2.55 billion dollar Emission Reduction Fund program was considered. These findings suggest that N-fertiliser application rates and N2O emission mitigation strategies need further refinements specific to prevailing soil and climate variabilities.
This chapter highlights types of models and modelling strategies with a focus on environment and human health applications that link environmental triggers and subsequent disease exposures and risks. It reviews bio-geophysical modelling applications generally employed by environmental and health scientists, and by policy and decision making authorities REFERENCES
While it has been recognised for many years that gaseous transfer is an important pathway in the terrestrial N cycle, it is only in recent times that direct field measurements have been made of the exchanges of nitrogenous gases between soils, plants and the atmosphere. Methods of three general kinds have been employed: those using diffusion theory to calculate gas transport in the soil profile; enclosure methods in which the flux density of the gas at the soil or water surface is calculated from changes in gas concentration in an enclosure placed over the surface; and micrometeorological techniques in which the vertical flux density of the gas is measured in the free air above the surface.
A computer model to analyze nonpoint-source pollution and to prioritize potential water quality problems in rural areas is described. The event-based model uses geographic cells of data units at resolutions of 0.4 to 16 ha to represent upland and channel conditions. Within the framework of the cells, runoff characteristics and transport processes of sediment, nutrients, and chemical oxygen demand are simulated for each cell and routed to the outlet. This permits the flow at any point in the watershed to be examined. Upland sources contributing to a potential problem can be identified and prioritized where remedial measures could be initiated to improve water quality most efficiently. -Authors
In recent years, worldwide attention has shifted from point source to non-point source (NPS) pollutants, particularly with regard to the pollution of surface and subsurface sources of drinking water. This is due to the widespread occurrence and potential chronic health effects of NPS pollutants. The ubiquitous nature of NPS pollutants poses a complex technical problem. The area1 extent of their contamination increases the complexity and sheer volume of data required for assessment far beyond that of typical point source pol- lutants. The spatial nature of the NPS pollution problem necessitates the use of a geo- graphic information system (GIS) to manipulate, retrieve, and display the large volumes of spatial data. This chapter provides an overview of the components (i.e., spatial vari- ability, scale dependency, parameter-data estimation and measurement, uncertainty analy- sis, and others) required to successfully model NPS pollutants with GIS and a review of recent applications of GIS to the modeling of non-point source pollutants in the vadose zone with deterministic solute transport models. The compatibility, strengths, and weak- nesses of coupling a GIS to deterministic one-dimensional transport models are discussed.
Changes in soil hydraulic properties with depth are common in field soils and need to be accounted for in simulations of field soil-water balances. We show that this is possible using the Kirchhoff transform (K transform) when solving Richards' equation numerically, even though the K transform is not continuous across boundaries between layers with different hydraulic properties. The solution is achieved by dividing the soil into depth elements, using the K transform to compute flow within each element, and then using matric potential (which is continuous across boundaries) to couple the elements. Combinations of the K transform with iterative, implicit, mass-conserving methods of solving the flow equations results in efficient solutions that take no more than a few seconds on a personal computer to simulate infiltration. -from Authors
NCSOIL is a submodel of a larger program NTRM (nitrogen‐tillage‐residue management). NCSOIL computes short‐term dynamics of carbon and nitrogen organics, ammonium, and nitrate which result from the processes of residue decomposition, mineralization, immobilization, nitrification, and denitrification. Both total and isotopic nitrogen are considered. NCSOIL is built on the concept of catenary sequence of heterogenous substrates. The active soil organic phase is divided in two pools which are dynamic, defined by their kinetic rate constants and their position in the model structure. Residues are defined in terms of their chemical or morphological nature. A double feedback loop in the carbon flow adjusts the rate of residue decomposition and the efficiency factor to the availability of inorganic nitrogen. NCSOIL was calibrated with, and its behavior contrasted against published and unpublished data from an experiment reported by Chichester et al. in Soil Science (see p. 455, vol. 120): “Relative Mineralization Rates of Indigenous and Recently Incorporated 15‐N labeled Nitrogen.” Experimental results of the Chichester et al. experiment were discussed in view of computer‐simulated flow rates and substrate concentrations.
A new and relatively simple equation for the soil-water content-pressure head curve is described. The particular form of the equation enables one to derive closed-form analytical expressions for the relative hydraulic conductivity, when substituted in the predictive conductivity models of N. T. Burdine or Y. Mualem. The resulting expressions contain three independent parameters which may be obtained by fitting the proposed soil-water retention model to experimental data. Results obtained with the closed-form analytical expressions based on the Mualem theory are compared with observed hydraulic conductivity data for five soils with a wide range of hydraulic properties.
Over one million hectares of the wheatbelt of Western Australia (WA) are affected by secondary salinisation and this area is expected to increase to between 3 and 5 million hectares if current trends continue. Deep open drains, as an engineering solution to dryland salinity, have been promoted over the past few decades; however, the results of initial experiments were variable and no thorough analysis has been done. This research quantifies the effects of deep open drains on shallow and deep groundwater at farm and subcatchment level. Analysis of rainfall data showed that the only dry year (below average rainfall) after the construction of drainage in the Narembeen area of WA (in 1998 and 1999) was 2002. The dry year caused some decline in groundwater levels in the undrained areas but had no significant impact in the drained areas. The study found that the effect of drains on the groundwater levels was particularly significant if the initial water levels were well above the drain bed level, permeable materials were encountered, and drain depth was adequate (2.0-3.0 m). Visual observations and evidence derived from this study area suggested that if the drain depth cut through more permeable, macropore-dominated siliceous and ferruginous hardpans, which exist 1.5-3m from the soil surface, its efficiency exceeded that predicted by simple drainage theory based on bulk soil texture. The effect of drains often extended to distances away (>200 m) from the drain. Immediately following construction, drains had a high discharge rate until a new hydrologic equilibrium was reached. After equilibrium, flow largely comprised regional groundwater discharge and was supplemented by quick responses driven by rainfall recharge. Comparison between the hydrology of the drained and undrained areas in the Wakeman subcatchment showed that, in the valley floors of the drained areas, the water levels fluctuated mainly between 1.5 and 2.5 m of the soil surface during most of the year. In the valley floors of the undrained areas, they fluctuated between 0 and 1 m of the soil surface. The impact of an extreme rainfall event (or unusual wet season) on drain performance was predicted to vary with distance from the drain. Within 100 m from the drain, water levels declined relatively quickly, whereas it took a year before the water levels at 200-300 m away from the drain responded. The main guidelines that can be recommended based on the results from this study are the drain depth and importance of ferricrete layer. In order to be effective, a drain should be more than 2 m deep and it should cut through the ferricrete layer that exists in many landscapes in the wheatbelt.
We describe a submodel to simulate NOx and N20 emissions from soils and present comparisons of simulated NOx and N20 fluxes from the DAYCENT ecosystem model with observations from different soils. The N gas flux submodel assumes that nitrification and denitrification both contribute to N20 and NOemissions but that NO emissions are due mainly to nitrification. N20 emissions from nitrification are calculated as a function of modeled soil NH4 + concentration, water-filled pore space (WFPS), temperature, pH, and texture. N20 emissions from denitrification are a function of soil NO3- concentration, WFPS, heterotrophic respiration, and texture. NOemissions are calculated by multiplying total N20 emissions by a NO:N20 equation which is calculated as a function of soil parameters (bulk density, field capacity, and WFPS) that influence gas diffusivity. The NOx submodel also simulates NOemission pulses initiated by rain events onto dry soils. The DAYCENT model was tested by comparing observed and simulated parameters in grassland soils across a range of soil textures and fertility levels. Simulated values of soil temperature, WFPS (during thenon-winter months), and NOgas flux agreed reasonably well with 2 ß measured values (r = 0.79, 0.64, and 0.43, respectively). Winter seasonFPS was poorly simulated (r 2 = 0.27). Altho_ugh the correlation between simulated and observed N20 flux ß . 2 was poor on a dally basis (r =0.02), DAYCENT was able to reproduce soil textural and treatment differences and the observed seasonal patterns of gas flux emissions with r 2 values of 0.26 and 0.27, for monthly and NOflux rates, respectively.
Observations of N gas loss from incubations of intact and disturbed soil cores were used to model N2O and N-2 emissions from soil as a result of denitrification. The model assumes that denitrification rates are controlled by the availability in soil of NO3 (e(-) acceptor), labile C compounds (e(-) donor), and O-2 (competing e(-) acceptor). Heterotrophic soil respiration is used as a proxy for labile C availability while O-2 availability is a function of soil physical properties that influence gas diffusivity, soil WFPS, and O-2 demand. The potential for O-2 demand, as indicated by respiration rates, to contribute to soil anoxia varies inversely with a soil gas diffusivity coefficient which is regulated by soil porosity and pore size distribution. Model inputs include soil heterotrophic respiration rate, texture, NO3 concentration, and WFPS. The model selects the minimum of the NO3 and CO2 functions to establish a maximum potential denitrification rate for particular levels of e(-) acceptor and C substrate and accounts for limitation of O-2 availability to estimate daily N-2+N2O flux rates. The ratio of soil NO3 concentration to CO2 emission was found to reliably (r(2)=0.5) model the ratio of N-2 to N2O gases emitted from the intact cores after accounting for differences in gas diffusivity among the soils. The output of the ratio function is combined with the estimate of total N gas flux rate to infer N2O emission. The model performed well when comparing observed and simulated values of N2O flux rates with the data used for model building (r(2)=0.50) and when comparing observed and simulated N2O+N-2 gas emission rates from irrigated field soils used for model testing (r(2)=0.47).
Models of spatial processes such as the movement of groundwater, the erosion of soil or the colonization of bare surfaces by organisms have been developed by specialists whose primary aim is often to represent a physical process as accurately as possible. The success of the process models is often directly proportional to the amount of data available about the areas in which they are used. Application of these models to whole landscapes requires many supporting data which may be present in one or other form in a GIS. The problems and dangers associated with the ad hoc linkages of simulation models and GIS are discussed. Particular attention needs to be paid to the problems of error propagation and of costs and benefits when using models. -from Authors
Recommends the use of GIS as a means to overcome the data collection and manipulation constraints of many transportation models. The paper outlines a variety of methodologies, particularly the land use transportation system approach, and considers aspects of data inventory. The author concludes that although GIS is still a relatively new technology and can be expensive and difficult to set up, ultimately it is a very cost-effective planning tool. -P.Hardiman
Locational referencing of highways and other events related to highways requires a metric along the highway, as well as geometry embedded in a coordinate system. Route-mile-point, control-section, and chain/node referencing are three schemes that should satisfy most applications. Highway segmentation based on attribute coding is an important consideration for analysis and display. Locational referencing and highway segmentation are fundamental design issues in a geographic information system for transportation (GIS-T), but they are not the only important issues. One technical issue in need of further clarification is how to represent and process multi-level transportation networks for micro-macro spatial modeling. Although a number of research issues exist for GIS-T, GIS concepts and technology in general can be useful to transportation organizations at this time. Much of the technology currently exists in commercial systems for solving transportation information problems.
Biometrical data of the leaf area density and spatial distribution of the leaf area in corn crop fields were used to calculate penetration of the direct solar radiation into the canopy, the effective leaf area function, sunlit leaf area index and the intensity of the direct solar radiation falling on leaf surtace. The leaf orientation function was obtained with a silhouette method described in a previous paper (UDAGAWA et al., 1968). The relation proposed by Ross and NILSON (1965) was used to determine the penetration of the direct solar radiation from the data of the leaf orientation and the direction of the sun. For simplicity, the following relation was used to obtain the effective leaf area function: where gL(w, θLj, φLk) is the leaf normal distribution function, |cos r0rL|jk cosine of the angle between the leaf normal rL and the direction of the sun r0, and w the depth in the canopy. The mean extinction coefficient (kd) for attenuation of the direct solar radiation within the canopy was determined from kd=cosech0GL, where GL is the mean effective leaf area function and h0 the sun altitude. The analysis indicates that the profile of the effective leaf area function changes with sun altitude. When the canopy was relatively sparse, the profile at the low sun altitude was found to be concave against z-axis, indicating that both the upper and lowest layers of the canopy were more penetrative compared with middle layers. On the other hand, the profile was convex when the sun altitude was higher than 45° This means that the direct solar radiation is strongly diminished in both the upper and lowest layers. When the sun altitude was between 30° and 40°, the profile became approximately constant and the value was about 0.5, implying that the canopy behaved to the direct solar radiation like a random orientation canopy in which the spatial distribution of leaves is non preferential as to both the inclination angle and azimuth angle. The diurnal change in the profile GL(w) fade gradually away with development of the canopy as can be seen in Fig. 2. © 1968, The Society of Agricultural Meteorology of Japan. All rights reserved.
A mathematical model called EPIC (Erosion-Productivity Impact Calculator) continuously simulates the processes involved simultaneously and realistically, using a daily time step and readily available inputs. EPIC is composed of physically based components for simulating erosion, plant growth, and related processes and economic components for assessing the cost of erosion, determining optimal management strategies, etc. The EPIC components include weather simulation, hydrology, erosion-sedimentation, nutrient cycling, plant growth, tillage, soil temperature, economics, and plant environment control.-after Authors English
A simple conceptual model was developed, based on a state-of-the-art approach, to describe NH3 volatilization losses from land areas receiving animal manures. Environmental and soil factors influencing NH3 volatilization were also included in the model. Rate constants for first order relationship were estimated from the existing literature data. The sub-model described assumes that NH3 volatilization follows first-order kinetics. The rate of NH3 loss was shown by the model to occur in one or two stages. In the first stage, losses were relatively rapid with approximately 80% of applied NH3 being volatilized during the first week after application. Manures incorporated into soils with low CEC resulted in greater losses of NH3 compared to the manures incorporated into soils with high CEC. Losses of NH3 increased with increase in temperature and air flow rate above the soil surface. Correction factors for temperature, CEC, and wind velocity were presented to adapt the sub-model under varying soil and environmental conditions.
This is a book describing and illustrating a simulation model for water resources in rural basins (SWRRB). The model is to be used to predict hydrologic and sediment yield impacts of land use management decisions in ungauged rural basins. After a very brief overview of the model, and how it addresses some of the deficiencies of an earlier developed CREAMS model, the model is described and all the as-sumptions built into the code are presented. Following this, the operation of SWRRB is discussed and the program sub-routines are listed and their functions defined. Model inputs are then briefly discussed, and an interactive data entry sys-tem program, written to prompt the user for these input data and arrange them in the correct format for SWRRB, is pre-sented. A major section of the book is devoted to three example applications. These applications present the input and out-put data. The input and output are discussed in some detail. Sensitivity analyses were performed to assess the relative change in model output resulting from a change in model inputs, and the results were discussed. The final section, prior to the 108 pages of appendixes, addresses the model's ac-curacy based on 11 watershed studies throughout the USA. The 10 appendices include symbol definitions and addi-tional information on input data such as weather, hydrology, erosion, soils, topographic maps, crops, and the definitions of each input and output variable. The book also includes the diskettes containing all the needed executable files, sample data sets, and the FOR-TRAN source code should anyone wish to modify that code. The book provides a quite detailed description of what is in the code and what data are needed as well as what the output data will be. The code is considerably less documented and structured, but still very readable. This reviewer ran the code and did his best to mess it up, but was unsuccessful. In other words, the model seems to work very reliably, but as ex-pected the user needs to know or have available a complete set of input data. Little or no guidance is offered by the program in case some of the required data are unknown. For example, some users may not know the hydraulic con-ductivity of the sediments on lake or pond bottoms, but they might know or guess the type of sediments. The program could be made a little friendlier if it was modified to accept and then convert general classes of various inputs, such as soil types, to their expected or approximate numerical val-ues, such as hydraulic conductivities. Also missing is the use of computer graphics to aid in data input, editing, and dis-play of model results. Map background and graphic displays are especially helpful when model results vary over space and time, as do those from SWRRB. To summarize, the authors have produced a thorough and well-written documentation and description of the SWRRB simulation model. Users of this model will find this book essential to the understanding of the model's operation and output.--DANIEL P. LOUCKS, This book is a compilation of papers about various com-ponents of the nitrogen cycle as related to agricultural man-agement of nitrogen and subsequent impacts on ground-water. The authors of the chapters are well known and have recognized expertise in the areas they present in this book. Each chapter includes tables and figures to enhance the dis-cussion and is supported by numerous literature citations. Chapter 1. Groundwater quality concerns about nitrogen. This chapter introduces the main areas of concern for ni-trogen impacting groundwater quality as it discusses health concerns, economic concerns, and resource conservation concerns.
Two efficient finite difference methods for solving Richards' equation
in one dimension are presented, and their use in a range of soils and
conditions is investigated. Large time steps are possible when the
mass-conserving mixed form of Richards' equation is combined with an
implicit iterative scheme, while a hyperbolic sine transform for the
matric potential allows large spatial increments even in dry,
inhomogeneous soil. Infiltration in a range of soils can be simulated in
a few seconds on a personal computer with errors of only a few percent
in the amount and distribution of soil water. One of the methods adds
points to the space grid as an infiltration or redistribution front
advances, thus gaining considerably in efficiency over the other fixed
grid method for infiltration problems. In 17-s computing, this advancing
front method simulated infiltration, redistribution, and drainage for 50
days in an inhomogeneous soil with nonuniform initial conditions. Only
16 space and 21 time steps were needed for the simulation, which
included early ponding with the development and dissipation of a perched
water table.
This paper focuses on the conceptual basis for recent changes to the CENTURY soil organic matter (SOM) model. Model predictions are compared on the effect of soil texture on total soil C levels and turnover rates of C in different pools, short-term (1-2 yr) dynamics of added plant residue, and loss of soil C due to cultivation with observation. Modeling studies of the long-term impact of different crop management practices on soil C and N stabilization were also summarized for sites at Pendleton, Oregon, Sidney, Nebraska and in Sweden. The model was used to interpret observed long-term soil C data sets, to highlight uncertainties in understanding about SOM dynamics and to suggest research topics that are critical for advancing knowledge about SOM. -from Authors
This paper describes a rain-event driven, process-oriented simulation model, DNDC, for the evolution of nitrous oxide (N2O), carbon dioxide (CO2), and dinitrogen (N2) from agricultural soils. The model consists of three submodels: thermal-hydraulic, decomposition, and denitrification. Basic climate data drive the model to produce dynamic soil temperature and moisture profiles and shifts of aerobic-anaerobic conditions. Additional input data include soil texture and biochemical properties as well as agricultural practices. Between rainfall events the decomposition of organic matter and other oxidation reactions (including nitrification) dominate, and the levels of total organic carbon, soluble carbon, and nitrate change continuously. During rainfall events, denitrification dominates and produces N2O and N2. Daily emissions of N2O and N2 are computed during each rainfall event and cumulative emissions of the gases are determined by including nitrification N2O emissions as well. Sensitivity analyses reveal that rainfall patterns strongly influence N2O emissions from soils but that soluble carbon and nitrite can be limiting factors for N2O evolution during denitrification. During a year sensitivity simulation, variations in temperature, precipitation, organic C, clay content, and pH had significant effects on denitrification rates and N2O emissions. The responses of DNDC to changes of external parameters are consistent with field and experimental results reported in the literature.
Simulations of nitrous oxide (N2O) and carbon dioxide (CO2) emissions from soils were carried out with a rain-event model of nitrogen and carbon cycling processes in soils (Li et al., this issue). Model simulations were compared with five field studies: a 1-month denitrification study of a fertilized grassland in England; a 2-month study of N2O emissions from a native and fertilized grassland in Colorado; a 1-year study of N2O emissions from agricultural fields on drained, organic soils in Florida; a 1-year study of CO2 emissions from a grassland in Germany; and a 1-year study of CO2 emissions from a cultivated agricultural site in Missouri. The trends and magnitude of simulated N2O (or N2O+N2) and CO2 emissions were consistent with the results obtained in field experiments. The successful simulation of nitrous oxide and carbon dioxide emissions from the wide range of soil types studies indicates that the model, DNDC, will be a useful tool for studying linkages among climate, land use, soil-atmosphere interactions, and trace gas fluxes.
We describe a model of N2 and N2O gas fluxes from nitrification and denitrification. The model was developed using laboratory denitrification gas flux data and field-observed N2O gas fluxes from different sites. Controls over nitrification N2O gas fluxes are soil texture, soil NH4, soil water-filled pore space, soil N turnover rate, soil pH, and soil temperature. Observed data suggest that nitrification N2O gas fluxes are proportional to soil N turnover and that soil NH4 levels only impact N2O gas fluxes with high levels of soil NH4 (>3 μg N g−1). Total denitrification (N2 plus N2O) gas fluxes are a function of soil heterotrophic respiration rates, soil NO3, soil water content, and soil texture. N2:N2O ratio is a function of soil water content, soil NO3, and soil heterotrophic respiration rates. The denitrification model was developed using laboratory data [Weier et al, 1993] where soil water content, soil NO3, and soil C availability were varied using a full factorial design. The Weier's model simulated observed N2 and N2O gas fluxes for different soils quite well with r2 equal to 0.62 and 0.75, respectively. Comparison of simulated model results with field N2O data for several validation sites shows that the model results compare well with the observed data (r2 = 0.62). Winter denitrification events were poorly simulated by the model. This problem could have been caused by spatial and temporal variations in the observed soil water data and N2O fluxes. The model results and observed data suggest that approximately 14% of the N2O fluxes for a shortgrass steppe are a result of denitrification and that this percentage ranged from 0% to 59% for different sites.
Effective evaluation of alternative management strategies to control global warming requires tools for simulating emissions of N2O from soils across a range of soil properties, weather, and management inputs. We hypothesized that with modification to the nitrification and denitrification submodels of the Nitrate Leaching and Economic Analysis Package (NLEAP) model, we could simulate daily N2O emissions as a function of soil moisture, temperature, N content, and other factors. Field parameterization was conducted on an Ulm clay loam soil (a fine, montmorillonitic, mesic Ustollic Haplargid) and validation experiments for N2O gas emissions were performed on an on-farm swine effluent study site on a Valent sandy soil (a mixed, mesic Ustic Torripsamment). The unitless model parameters reflecting the maximum fraction of selected N transformations emitted as N2O for nitrification (αN), wet-period denitrification (αw), and dry-period denitrification (αd) were calibrated as 0.065, 0.050, and 0.520 separately and then used in the validation study. The trends and magnitudes of simulated N2O emissions were statistically consistent with the results obtained from the field experiments (r = 0.78). Experimental results showed that the decline of N2O emission rates from 70 to 2 g N ha-1 d-1 during the growing season was related to soil N content decline from 33 to 4 mg kg-1. Simulated effects of field management on annual N2O emissions indicated that plowing decreased N2O relative to no-tillage corn (Zea mays L.), irrigation increased N2O 14% relative to dry-land corn, and doubling fertilization N rates from 100 to 200 kg ha-1 increased N2O emissions 60%.
Mulched soil slots may be used to increase infiltration and reduce runoff and erosion on land where infiltration is restricted by soil freezing or low soil permeability. To establish optimum slot dimensions and spacing, the effect of slot width, slot depth, and saturated hydraulic conductivity on infiltration must be determined. A numerical solution to the soil water flow equation in two dimensions was used to simulate slot mulch infiltration. The Kirchhoff integral transform was used to reduce the nonlinearity of the coefficients, and the finite difference equations were solved using a Newton‐Raphson procedure. Simulations were in reasonable agreement with field measurements. A sensitivity analysis was used to show the effect of simulated changes in saturated conductivity, slot dimensions, and slope on water infiltration into the slots. The effect of slope on infiltration was negligible. A function was derived which predicts steady infiltration rate, given values for saturated conductivity, slot depth, and slot width.
Synopsis
Synopsis
Planting dates had small effects on the numbers, sizes, rate of emergence, and longevity of corn leaves. There were differences among hybrids in the numbers, sizes, rates of emergence, and longevity of leaves. Longer-season (later) hybrids developed and maintained larger leaf areas per plant than the shorter-season hybrids. Increasing the availability of nutrients, particularly of N, early in the season generally increased the numbers of leaves formed per plant, increased the sizes of leaves more consistently than the numbers, and increased the rates of leaf emergence and leaf area expansion.
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The temperatures of the roots, the apical meristem, and the shoots of Zea mays plants were varied independently of each other and the rates of leaf extension were measured. When the temperature of the
apical meristem and region of cell expansion at the base of the leaf was kept at 25 °C, changes of leaf extension in response
to changes of root and shoot temperatures were less pronounced. When the temperature of the meristematic region was changed
by increments of 5 or 10 °C from 0 to 40 °C, and the root and shoot temperatures were kept at 25 °C, rapid changes in leaf
extension occurred.
It was concluded that the rates of leaf extension were controlled at root-zone temperatures of 5 to 35 °C by heating or cooling
of the meristematic region. Changes in rates of leaf extension in response to changes in air temperature were attributed to
direct effects on the temperature of the meristematic region and on the physiology of the leaf.
The theoretical basis of the simulation model CANDY (CArbon-Nitrogen-DYnamics) is briefly described along with results of its application to a test data set. The model contains modules for calculating soil temperature, moisture content and the processes of the soil carbon-nitrogen cycle. The model inputs are weather data (air temperature, precipitation and global radiation on a daily basis), plant development characteristics (seeding, harvesting, crop height and root depth), soil texture data and agricultural management (irrigation, fertilization, manuring, tillage).The test data material from two catchments in north Germany, one with loam soil (Neuenkirchen) and one with sand (Nienwohlde) included data from several nitrogen fertilizer treatments. The results show that the model outputs for temperature, moisture and nitrogen fit the observations quite well despite some deviations. Summarizing for all soil layers the square root of the mean quadratic differences of simulated and observed data are: for soil temperature ≤ 2.3 K for the loamy soil over a five-year period and ≤2.5 K for the sandy soil over a five-year period; for soil moisture ≤4.1 Vol.% for the loam and ≤4.4 Vol.% for the sand both over a three-year period.The results of soil nitrogen in 0–90 cm depth depend on the particular site under consideration. In most cases 23 of the whole data was contained within the deviation class less than or equal to 40 kg ha−1.The differences between observed and simulated data are within the usual range for applications on farm fields.
The efficiency of crop production is defined in thermodynamic terms as
the ratio of energy output (carbohydrate) to energy input (solar
radiation). Temperature and water supply are the main climatic
constraints on efficiency. Over most of Britain, the radiation and
thermal climates are uniform and rainfall is the main discriminant of
yield between regions. Total production of dry matter by barley,
potatoes, sugar beet, and apples is strongly correlated with intercepted
radiation and these crops form carbohydrate at about 1.4 g per MJ solar
energy, equivalent to 2.4% efficiency. Crop growth in Britain may
therefore be analysed in terms of (a) the amount of light intercepted
during the growing season and (b) the efficiency with which intercepted
light is used. The amount intercepted depends on the seasonal
distribution of leaf area which, in turn, depends on temperature and
soil water supply. These variables are discussed in terms of the rate
and duration of development phases. A factorial analysis of efficiency
shows that the major arable crops in Britain intercept only about 40% of
annual solar radiation and their efficiency for supplying energy through
economic yield is only about 0.3%. Some of the factors responsible for
this figure are well understood and some are immutable. More work is
needed to identify the factors responsible for the large differences
between average commercial and record yields.
An expression has been derived to describe both saturated and unsaturated permeability of porous media in terms of the pore size distribution as obtained from mercury-injection data or water-desorption isotherms. An interaction model has been adopted wherein both pore radius and effective area available for flow have been considered. The permeability values obtained using this expression have been compared with water and gas permeabilities of a variety of porous media. Satisfactory agreement is found between experimental and calculated values over a wide range of permeability.
A non-point source pollution management model, ANSWERS-2000, was developed to simulate long-term average annual runoff and sediment yield from agricultural watersheds. The model is based on the event-based ANSWERS model and is intended for use without calibration. The physically based Green-Ampt infiltration equation was incorporated into ANSWERS-2000 to improve estimates of infiltration. An evapotranspiration submodel was added to permit long-term, continuous simulation. The model was validated without calibration using data from the field-sized P2 and P4 watersheds in Watkinsville, Ga. Additional validation with limited calibration was done on the Owl Run watershed in Virginia. Model predictions of cumulative sediment yield were within 12% and 68% of observed values. Predicted cumulative runoff volumes ranged from 3% to 35% of observed values. Predictions of sediment yield and runoff volume for individual storms were less accurate, but generally within 200% of observed values. In a practical application, the use of the model in agricultural non-point-source pollution control planning was demonstrated.