DAYCENT Model Research Articles

Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks

AbstractThere is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands across the European Union. For each soil function, indicators were developed to evaluate their performance. To calculate the indicators and assess the interdependencies between the soil functions, data from continental long‐term simulation with the DayCent model were used to build crop‐specific Bayesian networks. These Bayesian Networks were then used to calculate the soil functions' performance and trade‐offs between the soil functions under current conditions. For each soil function the maximum potential was estimated across the European Union and changes in trade‐offs were assessed. By deriving current and potential soil function delivery from Bayesian networks a better understanding is gained of how different soil functions and their interdependencies can differ depending on soil, climate and management.Highlights When increasing a soil function, how do trade‐offs affect the other functions under different conditions? Bayesian networks evaluate trade‐offs between soil functions and estimate their maximal delivery. Maximizing a soil function has varied effects on other functions depending on soil, climate and management. Differences in trade‐offs make some locations more suitable for increasing a soil function then others.

N2O emission and mineral N leaching from contrasting land‐use hillslopes as jointly affected by climate and rock fragment factors

AbstractBackground and aims: The effects of climate factors and rock fragment content (RFC) on soil nitrous oxide (N2O) emission and mineral N leaching can be important in reducing N losses in different agroecosystems. This study aimed to analyze and quantify the intertwined effects of precipitation intensity, temperature, RFC, and land use on these two kinds of soil N losses.Methods: The DayCent models were calibrated and validated using observed data for the representative tea garden (TG), bamboo forest (BF), and mixed forest (MF) hillslopes in the southeastern hilly region of China. The N2O emissions and mineral N leaching were then simulated under a total of 882 scenarios with the combinations of three land‐use types (TG, BF, and MF), seven precipitation intensities (PI, from 8 to 99.2 mm d−1 but the total precipitation amount maintained), seven temperature conditions (from baseline –1°C to baseline +2°C with 0.5°C increment), and six RFCs (from 0 to 50% in volume with 10% increment).Results: The TG had the greatest soil N2O emission (1.8 and 3.3 times of BF and MF, respectively) and mineral N leaching (5.5 and 36.8 times of BF and MF, respectively). On three land‐use hillslopes, the greatest soil N2O emissions were observed in summer, while great mineral N leaching were generally observed in spring and winter. Peaks of N2O emissions were observed as the PI reached 59.8 mm d−1, while peaks of mineral N leaching were always observed when the PI reached 8.0 and 59.8 mm d−1. With temperature increasing and RFC decreasing, both types of soil N losses increased. The land use and RFC had the primary influences on both types of soil N losses compared to climate factors. Under the same RFCs, the PI had higher influence on soil N2O emission compared to temperature, while those on mineral N leaching were just opposite.Conclusion: Findings of this study would provide scientific bases for designing strategies to reduce soil N losses on the stony‐soil hillslopes.

The importance of management information and soil moisture representation for simulating tillage effects on N<sub>2</sub>O emissions in LPJmL5.0-tillage

Abstract. No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund–Potsdam–Jena managed Land) and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions and the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to overestimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water and N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions.

Simulated Biomass Sorghum GHG Reduction Potential is Similar to Maize

Policy support for cellulosic biofuels is contingent on their achieving much greater reductions in life-cycle greenhouse gas emissions than corn starch ethanol. Biomass sorghum has been suggested as a genetically and agronomically tractable feedstock species to augment near-term cellulosic feedstock production. This study used DayCent modeling to investigate biomass sorghum production emissions relative to corn with and without stover utilization at 3,265 across the rainfed United States. Sorghum produced greater average feedstock dry matter (15.6 ± 1.4 vs 14.8 ± 2.2 Mg ha-1 yr-1) and slightly lower estimated ethanol energy yields (10.6 ± 1.0 vs 11.8 ± 2.9 MJ m-2 yr-1) as corn grain with 75% stover collection. The high biomass removals in both the sorghum and corn stover scenarios led to soil organic carbon losses on 90 and 100% of sites, respectively. Average feedstock production emissions intensities were similar between sorghum and corn with 75% stover removal (17.6 ± 2.8 vs 18.8 ± 3.0 g CO2e MJ-1), but were notably lower under sorghum for sites in the southwestern study region (13.6 ± 3.0 vs 22.5 ± 3.1 g CO2e MJ-1). These results suggest that biomass sorghum produces cellulosic feedstock with similar emissions to corn grain and at current yield levels is unlikely to meet the Renewable Fuel Standard emissions reduction threshold for cellulosic biofuels.

Assessing life cycle impacts from changes in agricultural practices of crop production

PurposeThis paper presents an improved methodological approach for studying life cycle impacts (especially global warming) from changes in crop production practices. The paper seeks to improve the quantitative assessment via better tools and it seeks to break down results in categories that are logically separate and thereby easy to explain to farmers and other relevant stakeholder groups. The methodological framework is illustrated by a concrete study of a phosphate inoculant introduced in US corn production.MethodsThe framework considers a shift from an initial agricultural practice (reference system) to an alternative practice (alternative system) on an area of cropland A. To ensure system equivalence (same functional output), the alternative system is expanded with displaced or induced crop production elsewhere to level out potential changes in crop output from the area A. Upstream effects are analyzed in terms of changes in agricultural inputs to the area A. The yield effect is quantified by assessing the impacts from changes in crop production elsewhere. The field effect from potential changes in direct emissions from the field is quantified via biogeochemical modeling. Downstream effects are assessed as impacts from potential changes in post-harvest treatment, e.g., changes in drying requirements (if crop moisture changes).Results and discussionAn inoculant with the soil fungus Penicillium bilaiae has been shown to increase corn yields in Minnesota by 0.44 Mg ha−1 (~ 4%). For global warming, the upstream effect (inoculant production) was 0.4 kg CO2e per hectare treated. The field effect (estimated via the biogeochemical model DayCent) was − 250 kg CO2e ha−1 (increased soil carbon and reduced N2O emissions) and the yield effect (estimated by simple system expansion) was − 140 kg CO2e ha−1 (corn production displaced elsewhere). There were no downstream effects. The total change per Mg dried corn produced was − 36 kg CO2e corresponding to a 14% decrease in global warming impacts. Combining more advanced methods indicates that results may vary from − 27 to − 40 kg CO2e per Mg corn.Conclusion and recommendationsThe present paper illustrates how environmental impacts from changes in agricultural practices can be logically categorized according to where in the life cycle they occur. The paper also illustrates how changes in emissions directly from the field (the field effect) can be assessed by biogeochemical modeling, thereby improving life cycle inventory modeling and addressing concerns in the literature. It is recommended to use the presented approach in any LCA of changes in agricultural practices.

Bayesian calibration of the DayCent ecosystem model to simulate soil organic carbon dynamics and reduce model uncertainty

Benefits of carbon sequestration in agricultural soils are well recognized, and process-based models have been developed to better understand sequestration potential. However, most studies ignore the uncertainty arising during model prediction—a critical requirement for scientific understanding, policy implementation and carbon emission trading. Furthermore, the dependencies created in process-based models due to many parameters and a relatively small set of empirical data hinder parameterization. We have implemented a Bayesian approach using the sampling importance resampling (SIR) method to calibrate the DayCent ecosystem model for estimating soil organic carbon (SOC) stocks, and to quantify uncertainty in model predictions. A SOC dataset compiled from 19 long-term field experiments, representing 117 combinations of management treatments, with 491 measurements of SOC, was split into independent datasets for model calibration and evaluation. The most important DayCent model parameters were identified through a global sensitivity analysis (GSA) for parameterization and SIR was used to calibrate the model and produce posterior distributions for the most sensitive parameters. On average, the Bayesian calibration reduced the model uncertainty by a factor of 6.6 relative to the uncertainty associated with the prior. The Bayesian model analysis framework will allow for ongoing updates to the model as new datasets and model structural improvements are made in future research, and overall provide a stronger basis for models to support policy and management decisions associated with GHG mitigation through C sequestration in agricultural soils.

DayCent Model Predictions of NPP and Grain Yields for Agricultural Lands in the Contiguous U.S.

AbstractAccurate estimation of crop net primary production (NPP) and yields is fundamental for regional analyses of agroecosystem dynamics using process‐based models. In this study, we simulated croplands in the contiguous U.S. using the DayCent ecosystem model with new production algorithms. Crops were divided into crop variety groups based on regional varieties of three major crops (corn, soybeans, and winter wheat) and generic parameter values that were generated for each group. These varieties have been developed through crop breeding programs and enhance production of major crop types in different temperature and precipitation regimes. NPP and yields for the three major crops were evaluated at the county level with reported yields from the National Agricultural Statistics Service (NASS). The predictions of the multiyear average yields in all counties were more accurate than most other published results using process‐based models. DayCent predictions of yields produced an overall R2 of 0.54, 0.54, and 0.38 for corn, soybean, and winter wheat, respectively, with predictions for most counties within ±20% of the NASS reported yields. Our estimations of the total annual NPP for the three crops in the contiguous U.S. are 0.24, 0.09, and 0.06 Pg C yr−1 for corn, soybean, and winter wheat, respectively. Together, they contribute 7.3% to 14.8% of the total NPP for all vegetation in the contiguous U.S. We conclude that crop variety groups capture heterogeneity in NPP for major crop types and can improve biogeochemical model predictions of NPP for croplands.

Simulation of a regional soil nitrogen balance in Swiss croplands

Nitrogen (N) management in cropping systems needs adjustments because constraints to crop production are mostly related to high inputs and low recovery of N fertilizers. We used the DayCent model to predict regional N inputs, outputs, and balances of Swiss soils under (1) a range of N fertilizer input levels (0–300% of recommended crop-specific rates) and (2) organic fertilization, reduced tillage or cover cropping at the recommended input of N. The crops included wheat, maize, and root/tuber crops and legumes. Decreasing N inputs reduced the environmental impact by 13.5–51.3 kg N ha $$^{-1}$$ , but it also reduced the yield by 6.8–44.8 kg N ha $$^{-1}$$ . Increasing N inputs led to an increase in yield by 5.6–29.5 kg N ha $$^{-1}$$ , but with additional losses of 14.9–181.8 kg N ha $$^{-1}$$ into the environment. Harvested crop N and $$\hbox {NO}_3^{-}$$ leaching accounted for 30–59% and 27–62% of fertilizer-derived changes in total N output, respectively. Converting conventional to organic cropping led to a mean increase in soil N balance by 0.3–62.6 kg N ha $$^{-1}$$ . The soil N balance increased the most by using partially decomposed organic fertilizer in combination with cover cropping and reduced tillage. However, this positive N balance was mainly due to a reduction in N removal with harvest and $$\hbox {NO}_3^{-}$$ leaching. The use of highly decomposable organic matter and cover cropping did not lead to any N yield penalty while decreasing $$\hbox {NO}_3^{-}$$ leaching. These results highlight that organic practices combined with reduced tillage and cover cropping can optimize the use and recovery of N resources.

Biophysical potential of organic cropping practices as a sustainable alternative in Switzerland

Little is known about the potential of adopting organic farming at regional scales for mitigating agricultural greenhouse gas (GHG) emissions. We simulated the effect of organic matter addition (manure and compost) on all cropland soils in Switzerland, with reduced tillage and winter cover cropping in combination. The DayCent model was run for the period 1991–2013 by accounting for factors such as soils, land use, and climate to evaluate alternative practices. Converting conventional cropping with only chemical fertilizers to organic cropping led to a regional mitigation in soil GHG emissions by 0.34–1.10 Mg CO2-eq ha−1 yr−1. Soil organic C (SOC) stocks increased by 104–259 kg C ha−1 yr−1, accounting for 86–100% of the GHG mitigation. Adopting organic practices with reduced tillage or cover cropping increased SOC stocks up to 433 kg C ha−1 yr−1. Our results suggest that the alternative practices could reverse C decline in conventional soils, predicted to be −241 kg C ha−1 yr−1. Organic practices combined with reduced tillage or cover cropping had varying effects on N2O emissions, ranging from a mean decrease of −0.60 kg N ha−1 yr−1 to an increase of 0.29 kg N ha−1 yr−1. Model results suggest the need to deliver mitigation that is more permanent with the careful combination of alternative practices. Also, risks such as increased N2O emission, especially when using organic amendments with high N decomposition potential, and yield penalty must be managed.

Predicting plant–soil N cycling and soil N2O emissions in a Chinese old-growth temperate forest under global changes: uncertainty and implications

Soil-emitted N2O contributes to two-thirds of global N2O emissions, and is sensitive to global change. We used DayCent model to simulate major plant–soil N cycling processes under different global change scenarios in a typical temperate mixed forest in north-eastern China. Simulated scenarios included warming (T), elevated atmospheric CO2 concentration ([CO2]) (C), increased N deposition (N) and precipitation (P), and their full factorial combinations. The responses of plant–soil nitrogen cycling processes including net N mineralization, plant N uptake, gross nitrification, denitrification and soil N2O emission were examined. Concurrent increase of elevated [CO2] and N deposition displayed most strong interactive effects on most fluxes. Using the results from experimental studies for evaluation, simulation uncertainty was highest under elevated [CO2] and increased precipitation among the four global change factors. N deposition had a fundamental impact on soil N cycle and N2O emission in our studied forest. Despite forest soil acting as a N sink for added N, scenarios which included increased N deposition showed higher cumulative soil N2O emissions (summed up from 2001 to 2100). In particular, the scenario which included T, P, and N had the largest cumulative soil N2O emission, which was a 24.4% increase over that under ambient conditions. Our study points to the importance of the interactive effects of global change factors on plant–soil N cycling and the necessity of multi-factor manipulation experiments.

Evaluation of the Impacts of Regional Climate Factors and Crop Management on Corn Yields in Different Climate Regimes of China Using the DayCent Model

Corn is one of most important agricultural products in China. Understanding impacts of regional climate change, as well as agricultural management practices, on corn yields is critical for maintaining stable corn production. Using the DayCent model and observed climatic data in Sichuan province (a humid and hot environment) and Hebei province (a cold and dry environment) in China, corn yields in 1948-2010 were simulated. The spatial variations of simulated corn yields and the relationship between regional climate variability and warming with corn yields in these two environments were analyzed. The results demonstrated that: (1) corn yields in Zhangjiakou of Hebei and most regions of Sichuan decreased significantly after 2000 compared to other regions; (2) relative humidity and precipitation exhibit a significant negative correlation with observed crop yields in the growing season in Hebei province; (3) air temperature from 23.33 °C to 29 °C constitutes the ideal range influencing the increase of corn yields in Sichuan; (4) the planting of the large amount of silage maize in Sichuan compensated the negative impact of the rising air temperature on corn yields; (5) sensitivity tests for different fertilization levels and OMAD suggest that an increasing fertilization level significantly affects corn yields in Hebei province, a cold and dry environment, while a decreasing fertilization level has a significant negative effect in Sichuan province, a hot and humid environment. The overarching goal of these analyses is to provide the theoretical basic for maintaining stable corn production under regional climate warming and different agricultural management practices.

Carbon Balance in Sugarcane Areas Under Different Tillage Systems

Soil tillage for sugarcane production has a significant impact on greenhouse gas (GHG) emissions and soil carbon (C) stocks. However, the short- and long-term effects of different soil tillage methods in sugarcane systems on C footprints are still poorly studied. We conducted a field experiment in a sugarcane system to evaluate the effects of conventional tillage (CT), chiseling (CHI), no tillage (NT), and compacted no-till (NTC) on nitrous oxide (N2O) and methane (CH4) emissions and stalk yields in the cane plant cycle and on soil C changes in the short term (3 years) in a Brazilian subtropical climate. Furthermore, we used the DayCent model to predict soil C dynamics for the next 30 years under green harvest management in scenarios with 0, 50 and 75% straw removal and to estimate the C savings of different soil tillage methods. Soil C stocks were determined before the treatments were applied and 1, 2 and 3 years after planting sugarcane. GHG sampling was performed only 1 year during the sugarcane plant cycle. Soil tillage did not significantly affect sugarcane stalk yield. The CH4 fluxes ranged from − 58.2 to − 3.6 μg CH4 m−2 h−1, and annual emissions did not differ between soil tillage systems. The N2O fluxes after soil tillage for sugarcane ranged from 3.8 to 2279 μg N2O m−2 h−1, and the highest annual emissions were observed in NTC. The conversion from grain production system to sugarcane production resulted in an initial decrease in soil C stocks, with C losses in the 0–20-cm layer over 3 years varying from 0.26 to 2.12 Mg C ha−1. The simulated data for the period 2016–2046 indicated recovery of C stocks in all soil tillage systems, with accumulation rates varying from 0.01 to 0.10 Mg C ha−1 year−1. Regardless of the soil tillage system, straw removal decreased the soil C stocks. The time interval for recovery of the initial C stocks ranged from 10 to 20 years, depending on the intensity of straw removal and soil tillage. Soil C losses and GHG emissions reduced the potential C savings of sugarcane ethanol. The CO2eq emissions observed during soil tillage and the sugarcane growing cycle should be quantified and included in the C savings estimates of biofuel production systems.

Climate change impacts on Canadian yields of spring wheat, canola and maize for global warming levels of 1.5 °C, 2.0 °C, 2.5 °C and 3.0 °C

Science-based assessments of climate change impacts on cropping systems under different levels of global warming are essential for informing stakeholders which global climate targets and potential adaptation strategies may be effective. A comprehensive evaluation of climate change impacts on Canada’s crop production under different levels of global warming is currently lacking. The DayCent, DNDC and DSSAT models were employed to estimate changes in crop yield and production for three prominent crops including spring wheat, canola and maize in current agricultural regions of Canada. Four warming scenarios with global mean temperature changes of 1.5 °C, 2.0 °C, 2.5 °C and 3.0 °C above the pre-industrial level were investigated. Climate scenarios from 20 Global Climate Models, included in the Coupled Model Intercomparison Project Phase 5 and downscaled with a multivariate quantile mapping bias correction method, were used to drive the crop simulation models. Simulated yield changes demonstrate a potentially positive impact on spring wheat and canola yields at all four temperature levels, particularly when shifting planting date is considered in the simulations. There was less consensus for the currently utilized short-season maize cultivars, as yields were only projected to increase by DNDC compared to a slight decrease by DayCent and a slight increase up to 2.5 °C followed by a decrease at 3.0 °C by DSSAT. These findings indicate that climate at the global warming levels up to 3.0 °C above the pre-industrial level could be beneficial for crop production of small grains in Canada. However, these benefits declined after warming reached 2.5 °C.

Metrics of biomass, live-weight gain and nitrogen loss of ryegrass sheep pasture in the 21st century

This study argues that several metrics are necessary to build up a picture of yield gain and nitrogen losses for ryegrass sheep pastures. Metrics of resource use efficiency, nitrous oxide emission factor, leached and emitted nitrogen per unit product are used to encompass yield gain and losses relating to nitrogen. These metrics are calculated from field system simulations using the DAYCENT model, validated from field sensor measurements and observations relating to crop yield, fertilizer applied, ammonium in soil and nitrate in soil and water, nitrous oxide and soil moisture. Three ryegrass pastures with traditional management for sheep grazing and silage are studied. As expected, the metrics between long-term ryegrass swards in this study are not very dissimilar. Slight differences between simulations of different field systems likely result from varying soil bulk density, as revealed by a sensitivity analysis applied to DAYCENT. The field with the highest resource use efficiency was also the field with the lowest leached inorganic nitrogen per unit product, and vice versa. Field system simulation using climate projections indicates an increase in nitrogen loss to water and air, with a corresponding increase in biomass. If we simulate both nitrogen loss by leaching and by gaseous emission, we obtain a fuller picture. Under climate projections, the field with the lowest determined nitrous oxide emissions factor, had a relatively high leached nitrogen per product amongst the three fields. When management differences were investigated, the amount of nitrous oxide per unit biomass was found to be significantly higher for an annual management of grazing only, than a silage harvest plus grazing, likely relating to the increased period of livestock on pasture. This work emphasizes how several metrics validated by auto-sampled data provide a measure of nitrogen loss, efficiency and best management practise.

Surrogate-based multi-objective optimization of management options for agricultural landscapes using artificial neural networks

We demonstrate the use of a surrogate-based optimization framework for large-scale and high-resolution landscape management optimization, using irrigated corn production systems in eastern Colorado, USA as a case study. An artificial neural network was employed to create a surrogate of the DayCent biogeochemical simulation model. Our optimization considered trade-offs among seven different objectives at different scales, including farm profits, irrigation water use, corn grain, corn stover, soil organic carbon (SOC), greenhouse gas (GHG) emissions, and nitrogen leaching. The results show that the surrogate captured greater than 99% of the variations in the DayCent’s simulated outputs and was 6.2 million times faster than the DayCent model for our analysis. Farm-level optimization increased farm profits by 83%–150%, SOC by 16%–53%, grain yield by 10.1–11.3%, and reduced GHG emissions by 19%–55% compared to the ‘business-as-usual’ scenario.

Sensitivity of Predicted Agro-Ecosystem Variables to Errors in Weather Input Data

Abstract. Statistically interpolated weather station data, outputs from climate reanalyses, and results from downscaled general circulation model (GCM) simulations are widely used to drive a variety of agro-ecosystem model applications, including regional- and national-scale crop modeling. In this study, we compared these three types of gridded weather datasets (total of nine datasets) with actual point-level weather station observations and analyzed the biases in predicted ecosystem variables of evapotranspiration (ET), crop grain yield, soil organic carbon (SOC) change, and soil N2O emissions using the process-based DayCent ecosystem model. As a reference system, we defined continuous corn cropping systems for three different regions in the U.S. Our results suggested that the predicted ecosystem variables can be highly sensitive to the sources of weather input data. Interpolated weather data from the PRISM and Daymet data products provided relatively accurate estimations of important ecosystem variables. Compared with the bias-corrected constructed analogs (BCCA) method, GCM results downscaled with the multivariate adaptive constructed analogs (MACA) method performed better for agro-ecosystem simulations under climate change; for datasets using BCCA, the rainfall frequency was positively biased and likely caused models to significantly underestimate solar radiation. For regional climate change studies that use a baseline simulation (historical period) for comparison, we suggest including the uncertainty of the baseline due to biases in the weather data in addition to the uncertainty in the projected weather data. Keywords: Agro-ecosystem model, DayCent model, Prediction bias, Weather data.

Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland

Process-based models are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N2O emissions from soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N2O emissions under data-limited conditions. A study was carried out to compare the capability of four models to estimate seasonal cumulative N2O fluxes from 419 field measurements representing 65 studies across China's croplands. The models were 1) the DAYCENT model, 2) the DNDC model, 3) the linear regression model (YLRM) of Yue et al. (2018), and 4) IPCC Tier 1 emission factors. The DAYCENT and DNDC models estimated crop yields with R2 values of 0.60 and 0.66 respectively, but both models showed significant underestimation for all measurements. The estimated seasonal N2O emissions with R2 of 0.31, 0.30, 0.21 and 0.17 for DAYCENT, DNDC, YLRM, and IPCC, respectively. Based on RMSE, modelling efficiency and bias analysis, YLRM performed well on N2O emission prediction under no fertilization though bias still existed, while IPCC performed well for cotton and rapeseed and DNDC for soybean. The DAYCENT model accurately predicted the emissions with no bias across other crop and fertilization types whereas the DNDC model underestimated seasonal N2O emissions by 0.42 kg N2O-N ha−1 for all observed values. Model evaluation indicated that the DAYCENT and DNDC models simulated temporal patterns of daily N2O emissions effectively, but both models had difficulty in simulating the timing of the N2O fluxes following some events such as fertilization and water regime. According to this evaluation, algorithms for crop production and N2O emission should be improved to increase the accuracy in the prediction of unfertilized fields both for DAYCENT and DNDC. The effects of crop types and management modes such as fertilizations should also be further refined for YLRM.

Soil heterotrophic respiration: Measuring and modeling seasonal variation and silvicultural impacts

To determine the effectiveness of forests in sequestering atmospheric carbon (C), we must know the amount of fixed carbon dioxide (CO2) that is subsequently lost due to heterotrophic microbial activity in the soil. Furthermore, the heterotrophic proportion of total soil respiration (Rs) must be quantified as it changes between different physiographic regions, seasons, and silvicultural treatments. This research quantified heterotrophic contributions to Rs in loblolly pine (Pinus taeda) plantations in the Piedmont (n = 3) and Upper Coastal Plain (n = 3) of the Southeastern USA under control, fertilized, and herbicide treatments over an annual cycle. Heterotrophic respiration (Rh) was separated in the field from autotrophic root respiration (Ra) using metal root-excluding collars. The Rh proportion of Rs was not significantly different between regions or treatments, but demonstrated some seasonal variance. The average Rh proportion across the study was found to be ∼73 ± 2% but ranged from ∼70% in winter, spring, and summer to 82% in the fall. Statistical models using microbial biomass, temperature, moisture, and other soil characteristics explained 82% and 75% of Rs and Rh variability, respectively. In contrast, the process based DAYCENT model, parameterized for each site to model Rs, Rh, and Rh proportion compared poorly to field measurements. Model predicted mean seasonal Rh proportions also extended beyond the range of those measured (65–88%) from 61 ± 1.3% to 94 ± 0.4%. DAYCENT performed slightly better (i.e., lower root mean square error) for Piedmont than Coastal Plain sites. DAYCENT does not simulate CO2 fluxes below 20 cm and may be missing substantial fluxes from deeper roots and microbial activity. The results from this study suggest that statistical models such as multiple regression may provide more accurate estimates of Rh proportion for regional extrapolation than the current formulation of the process based DAYCENT model. It is unclear, however, if either approach captures seasonal variation in Rh or how strongly Rh varies with season. Finally, the empirical field data suggest the use of fertilizer and herbicides in these ecosystems increases ecosystem productivity without increasing Rh, which results in an increase in net ecosystem productivity that may lead to greater rates of C sequestration.

Modeling Deficit Irrigation of Maize with the DayCent Model

Core Ideas A new version of the DayCent model was capable of simulating the responses in maize‐dominated agroecosystems under water‐deficit conditions. The accuracy of DayCent‐simulated GLAI, biomass, SWC, and yield was similar to that observed with more complex models. There was no significant difference in the major output variables when the stress coefficient, Ks, was calculated by the FAO method or the original DayCent method. A promising solution to help close the gap between water demand and supply in semiarid regions is deficit irrigation strategies that use less water but aim to increase crop yield per unit of water applied. Dynamic models can be used to assess the impacts of deficit irrigation and to support decision‐making. In this study, we evaluated the capability of a new version of the DayCent agroecosystem model to model crop growth, yield, and water use in response to water‐deficit conditions resulting from different deficit irrigation strategies employed in three field experiments with maize (Zea mays L.) in eastern Colorado. Overall, the DayCent model with improved algorithms for plant canopy development provided accurate estimates of green leaf area index (GLAI) for full and deficit irrigation treatments, though the method tended to overpredict the GLAI during the late vegetative growth period in the deficit irrigation treatments. Simulated soil water content (SWC), biomass, and grain yields compared well with the field measurements. We tested the drought stress coefficient (Ks) method in the FAO Irrigation and Drainage Paper No. 56 as an alternative to the original DayCent method for estimating drought stress. There were only marginal differences between the two Ks methods in predicting the major output variables in DayCent, but the FAO method is simpler and easier to parameterize. Overall, the DayCent model can simulate the responses in agroecosystems with maize under water deficit conditions and could be used as a guide for applying deficit irrigation strategies for water savings.

Soil organic carbon sequestration and mitigation potential in a rice cropland in Bangladesh – a modelling approach

An increase in the storage of carbon (C) in soil by changing management practices can help to mitigate climate change and increase soil quality. The objective of this study was to evaluate the best management options for reducing greenhouse gas (GHG) emissions. An ecosystem model DayCent was tested for two rice (Oryza sativa L.) experimental sites in Bangladesh. The sites are under different management practices and we first tested the models ability to simulate SOC turnover, and then estimated the potential for SOC sequestration by comparing change in SOC for each management scenario with baseline management (current farmers’ practices) including conventional tillage, 5% residue incorporation and nitrogen (N) fertilizer. Predicted yield was also compared at both sites to ensure that yield was not compromised by mitigation measures. A control treatment was tested at both sites. At site 1, two other treatments of mineral N fertilizer, and combination of farmyard manure (FYM) and N were tested in a double rice based crop rotation. At site 2, a treatment receiving cowdung (CD) application, and a combination of CD and N were tested in a wheat (Triticum aestivum L.)-rice based crop rotation. The DayCent model was able to simulate SOC increase from the double rice test sites under unfertilized conditions, considering additional N and C sources in the simulations. Assuming N fertilizer (180 kg N ha−1 yr−1) application for site 1, and CD application (25 t ha−1 yr−1) for site 2, respectively, as the baseline, four single, and one integrated, scenarios were implemented in the model to predict SOC and yield at both sites. Two additional scenarios with alternate wet and drying (AWD) as a single treatment, and as part of an integrated approach, were also tested for their mitigation potential at site 1. The highest simulated positive impact on SOC development (60% higher than that of the baseline) was observed at site 1 when FYM was used in place of mineral N fertilizer. As there is a yield penalty associated with the use of only FYM, integrated approaches might show more promise, such as inclusion of 15% residue return, reduced tillage, less mineral N fertilizer, FYM addition, with or without AWD. This approach increases SOC by up to 23% while keeping the yield stable (nearly 3.5 t ha−1). The application of CD only as determined for baseline of site 2, gives a yield of about 1.8 t ha−1 yr−1. In contrast nearly two times more yield was obtained under the scenario associated with integrated management which also increases SOC by 30% relative to the baseline at the second site. Net GHG emissions, including nitrous oxide and methane emissions were estimated using the Intergovernmental Panel on Climate Change (IPCC) tier 1 methods, and country specific emission factors (where available), suggests that the integrated management scenario can reduce the net GHG emissions from 0.58-0.82 t carbon dioxide (CO2)-eq. ha−1 yr−1 (equivalent to 0.16-0.24 t Ceq. ha−1 yr−1) at site 2, while a net reduction in GHGs of nearly 1.00 t CO2-eq. ha−1 yr−1 (equivalent to 0.27 t Ceq. ha−1 yr−1) at site 1 was only achieved if AWD was also implemented with the integrated management scenario. Future studies could attempt to model non-CO2 GHGs with a dynamic model.