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Abstract Background Achieving a net zero greenhouse gas United States (US) economy is likely to require both deep sectoral mitigation and additional carbon dioxide removals to offset hard-to-abate emissions. Enhancing the terrestrial carbon sink, through practices such as the adoption of no-till and cover cropping agricultural management, could provide a portion of these required offsets. Changing domestic agricultural practices to optimize carbon content, however, might reduce or shift US agricultural commodity outputs and exports, with potential implications on respective global markets and land use patterns. Here, we use an integrated energy-economy-land-climate model to comprehensively assess the global land, trade, and emissions impacts of an adoption of domestic no-till farming and cover cropping practices based on carbon pricing. Results We find that the adoption of these practices varies depending on which aspects of terrestrial carbon are valued. Valuation of all terrestrial carbon resulted in afforestation at the expense of domestic agricultural production. In contrast, a policy valuing soil carbon in agricultural systems specifically indicates strong adoption of no-till and cover cropping for key crops. Conclusions We conclude that under targeted terrestrial carbon incentives, adoption of no-till and cover cropping practices in the US could increase the terrestrial carbon sink with limited effects on crop availability for food and fodder markets. Future work should consider integrated assessment modeling of non-CO2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations.

Background: Achieving a net zero greenhouse gas United States (US) economy is likely to require both deep sectoral mitigation and additional carbon dioxide removals to offset hard-to-abate emissions. Enhancing the terrestrial carbon sink, through practices such as the adoption of no-till and cover cropping agricultural management, could provide a portion of these required offsets. Changing domestic agricultural practices to optimize carbon content, however, might reduce or shift US agricultural commodity outputs and exports, with potential implications on respective global markets and land use patterns. Here, we use an integrated energy-economy-land-climate model to comprehensively assess the global land, trade, and emissions impacts of an adoption of domestic no-till farming and cover cropping practices based on carbon pricing. Results: We find that the adoption of these practices varies depending on which aspects of terrestrial carbon are valued. Valuation of all terrestrial carbon resulted in afforestation at the expense of domestic agricultural production. In contrast, a policy valuing soil carbon in agricultural systems specifically indicates strong adoption of no-till and cover cropping for key crops. Conclusions: We conclude that under targeted terrestrial carbon incentives, adoption of no-till and cover cropping practices in the US could increase the terrestrial carbon sink with limited effects on crop availability for food and fodder markets. Future work should consider integrated assessment modeling of non-CO2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations. [ABSTRACT FROM AUTHOR]

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

National scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process-based Daily Century (DAYCENT) model with site-specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States. The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2 Mg ha−1 year−1, with a spatiotemporal average of (Formula presented.) Mg ha−1 year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as (Formula presented.) Mg CO2e ha−1 year−1 and (Formula presented.) Mg CO2e ha−1 year−1, respectively. Compared to field-observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6 Mg ha−1 year−1. In comparison to the multi State (n = 21) NASS database, our results showed RMSE of 5.5 Mg ha−1 year−1. Model projections of baseline SOC showed RMSE of 1.9 kg/m2 in comparison to a recently available continental SOC stock dataset. The model-predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern United States will produce >10 Mg ha−1 year−1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan, and North Dakota showed lower sorghum biomass productivity (average: 6.9 Mg ha−1 year−1) with net sequestration (average: 0.13 Mg CO2e ha−1 year−1). Our national-scale spatially explicit results are critical inputs for robust life cycle assessment of bioenergy production systems and land use-based climate change mitigation strategies.

Carbon net emission is a critical aspect of the environmental footprint in agricultural systems. However, the alternatives to describe soil organic carbon (SOC) changes associated with different agricultural management practices/land uses are limited. Here we provide an overview of carbon (C) stocks of non-forested areas of Uruguay to estimate SOC changes for different soil units affected by accumulated effects of crop and livestock production systems in the last decades. For this, we defined levels based on SOC losses relative to the original (reference) SOC stocks: 25% or less, between 25% and 50%, and 50% or more. We characterized the reference SOC stocks using three approaches: (1) an equation to derive the potential SOC capacity based on the clay and fine silt soil content, (2) the DayCent model to estimate the SOC stocks based on climate, soil texture and C inputs from the natural grasslands of the area, (3) an estimate of SOC using a proxy derived from remote sensing data (i.e., the Ecosystem Services Supply Index) that accounts for differences in C inputs. Depending on the used reference SOC, the soil units had different distributions of SOC losses within the zones defined by the thresholds. As expected, the magnitude of SOC changes observed for the different soil units was related to the relative frequency of annual crops, however, the high variability observed along the gradient of land uses suggests a wide space for increasing SOC with agricultural management practices. The assessment of the C stock preserved (CSP) belowground and the potential for increasing C accumulation or sequestration (CAP) are critical components of the C footprint of a given system. Thus, we propose a methodological road map to derive indicators of CSP and CAP at the farm level combining both, biogeochemical simulation models and conceptual models based on remote sensing data. We recognize at least three critical issues that require scientific and political consensus to implement the use of this propose: (1) how to define reference C stocks, (2) how to estimate current C stocks over large areas and in heterogeneous agricultural landscapes, and (3) what is a reasonable/acceptable threshold of C stocks reduction.

Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process‐based biogeochemical model (DayCent) at a daily time step to explore the long‐term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long‐term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site‐specific daily climate data from two Earth system models (CanESM2 and HadGEM‐ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one‐time amendment of compost to rangeland acted as a slow‐release organic fertilizer and increased NPP by up to 390–814 kg C ha−1 year−1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha−1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N2O) emissions, the cumulative net emissions (in CO2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO2e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal‐scale climate change mitigation goals alongside emissions reductions. [ABSTRACT FROM AUTHOR]

Abstract National scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process‐based Daily Century (DAYCENT) model with site‐specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States. The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2 Mg ha−1 year−1, with a spatiotemporal average of 9.7‐2.4+2.1 Mg ha−1 year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as 0.79‐0.45+0.38 Mg CO2e ha−1 year−1 and 0.38‐0.06+0.04 Mg CO2e ha−1 year−1, respectively. Compared to field‐observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6 Mg ha−1 year−1. In comparison to the multi State (n = 21) NASS database, our results showed RMSE of 5.5 Mg ha−1 year−1. Model projections of baseline SOC showed RMSE of 1.9 kg/m2 in comparison to a recently available continental SOC stock dataset. The model‐predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern United States will produce >10 Mg ha−1 year−1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan, and North Dakota showed lower sorghum biomass productivity (average: 6.9 Mg ha−1 year−1) with net sequestration (average: 0.13 Mg CO2e ha−1 year−1). Our national‐scale spatially explicit results are critical inputs for robust life cycle assessment of bioenergy production systems and land use‐based climate change mitigation strategies.

Livestock manure is applied to rangelands as an organic fertilizer to stimulate forage production, but the long-term impacts of this practice on soil carbon (C) and greenhouse gas (GHG) dynamics are poorly known. We collected soil samples from manured and nonmanured fields on commercial dairies and found that manure amendments increased soil C stocks by 19.0 ± 7.3 Mg C ha(-1) and N stocks by 1.94 ± 0.63 Mg N ha(-1) compared to nonmanured fields (0-20 cm depth). Long-term historical (1700-present) and future (present-2100) impacts of management on soil C and N dynamics, net primary productivity (NPP), and GHG emissions were modeled with DayCent. Modeled total soil C and N stocks increased with the onset of dairying. Nitrous oxide (N2 O) emissions also increased by ~2 kg N2 O-N ha(-1)  yr(-1) . These emissions were proportional to total N additions and offset 75-100% of soil C sequestration. All fields were small net methane (CH4 ) sinks, averaging -4.7 ± 1.2 kg CH4 -C ha(-1)  yr(-1) . Overall, manured fields were net GHG sinks between 1954 and 2011 (-0.74 ± 0.73 Mg CO2 e ha(-1)  yr(-1) , CO2 e are carbon dioxide equivalents), whereas nonmanured fields varied around zero. Future soil C pools stabilized 40-60 years faster in manured fields than nonmanured fields, at which point manured fields were significantly larger sources than nonmanured fields (1.45 ± 0.52 Mg CO2 e ha(-1)  yr(-1) and 0.51 ± 0.60 Mg CO2 e ha(-1)  yr(-1) , respectively). Modeling also revealed a large background loss of soil C from the passive soil pool associated with the shift from perennial to annual grasses, equivalent to 29.4 ± 1.47 Tg CO2 e in California between 1820 and 2011. Manure applications increased NPP and soil C storage, but plant community changes and GHG emissions decreased, and eventually eliminated, the net climate benefit of this practice.

There 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. [ABSTRACT FROM AUTHOR]

Nitrogen (N) loss through ammonia (NH 3) volatilization in agricultural soils is a significant source of atmospheric NH 3 , contributing to low N use efficiency in crops, risk to human health, environmental pollution, and is an indirect source of nitrous oxide (N 2 O) emissions. Our objective was to develop an ammonia volatilization method within the DayCent ecosystem model that incorporates key 4R N management practices (right type, right rate, right placement, and right timing) that influence NH 3 volatilization associated with application of urea-based nitrogen fertilizers to agricultural soils. The NH 3 volatilization method was developed with Bayesian calibration using sampling importance resampling methods and Bayes factors to select the level of complexity in the model that best represents NH 3 volatilization given the observed data. The final model included urea hydrolysis and the influence of urease inhibitors; short-term soil pH changes following fertilization; fertilizer incorporation into the soil (mechanically and through irrigation/precipitation); and specification of the fertilizer placement method (i.e. broadcast vs. banding and surface vs incorporated). DayCent predicts NH 3 volatilization with a root-mean-squared error of 158 (95% interval ranging from 133 to 192), bias of 7 (95% interval ranging from − 106 to 102) g NH3-N ha−1 day−1, and with a Bayesian R2 value of 0.39 (95% interval ranging from 0.17 to 0.62). Furthermore, the model incorporates key management options influencing NH 3 volatilization related to placement method and fertilizer type with and without urease inhibitors that can be used to evaluate management and policy options for reducing losses of NH3 from urea fertilization. [ABSTRACT FROM AUTHOR]

• DayCent now includes microbial-explicit decomposition and soil carbon stabilization. • Refined perennial plant traits better represent litter chemistry. • Microbe model projects less soil carbon under future climate than first-order model. Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Unlabelled Image • Provides yield-nitrogen metrics for ryegrass sheep pastures • Combination of climate, sensor and sampling data applied to agri-system modelling • Highest resource use efficiency coincides with lowest leaching per unit product. • N 2 O per unit biomass significantly lower for silage & grazing than grazing only. • Several related metrics reveal info about the system, hidden by using one metric. [ABSTRACT FROM AUTHOR]

The deployment of dedicated energy crops and the related land-use change are topical issues, particularly in relation to carbon storage and climate change mitigation effects. In order to maximize their mitigation potential and to fully supply new biorefineries, perennial energy crops may be established, not only on former idle and grazing lands, but also on the least remunerative cropland, as indirect land use change effects are still very uncertain. Possibly becoming a future land-use change option, the carbon flows of the most common crop rotation in Europe (maize-wheat) and the perennial grass switchgrass were measured, and later included in a biogeochemical model to build possible scenarios. Yearly mean soil respiration did not statistically differ between switchgrass and the annual cereals (2.9 and 2.5 Mg CO 2 ha−1 month−1, respectively), but in switchgrass the peak flux was reached during crop growth (6.1 Mg CO 2 ha−1 month−1), while in the cereal system it occurred in bare soil (after harvest and soil tillage) (4.5 Mg CO 2 ha−1 month−1). Harvest residues contributing to soil organic matter were highest in maize (12.4 Mg ha−1 y−1) and decreased in switchgrass (−79%) and wheat (−87%). Root biomass was much higher in switchgrass (10.0 Mg ha−1 y−1) than maize (−81%) or wheat (−94%). Model projections showed how continuous switchgrass cycles of 15 years following annual crops cultivation were capable to keep building up SOC inventories (0.24 or 0.32 Mg ha−1 y−1) up to the year 2100. On the opposite, maintaining the land under maize-wheat cultivation, depending on maize stover management, would either produce a SOC loss (−3.6 Mg ha−1) or could help the soil increasing SOC (+9.4 Mg ha−1) towards a new equilibrium after two decades. • Organic C inputs to the soil were higher in switchgrass than in maize-wheat. • Soil heterotrophic respiration was likely substantial in the maize-wheat system. • DAYCENT was calibrated to simulate land-use change from annual crops to switchgrass. • Simulated land-use change from annuals to switchgrass increased SOC for many decades. • Land-use change to switchgrass may generate higher GHG benefits than using residues. [ABSTRACT FROM AUTHOR]

The need to reduce the expected impact of climate change, finding sustainable ways to maintain or increase the carbon (C) sequestration capacity and productivity of agricultural systems, is one of the most important challenges of the twenty-first century. Olive (Olea europaea L.) groves can play a fundamental role due to their potential to sequester C in soil and woody compartments, associated with widespread cultivation in the Mediterranean basin. The implementation of field experiments to assess olive grove responses under different conditions, complemented by simulation models, can be a powerful approach to explore future land-atmosphere C feedbacks. The DayCent biogeochemical model was calibrated and validated against observed net ecosystem exchange, net primary productivity, aboveground biomass, leaf area index, and yield in two Italian olive groves. In addition, potential changes in C-sequestration capacity and productivity were assessed under two types of management (extensive and intensive), 35 climate change scenarios (ΔT-temperature from + 0 °C to + 3 °C; ΔP-precipitation from 0.0 to − 20%), and six areas across the Mediterranean basin (Brindisi, Coimbra, Crete, Cordoba, Florence, and Montpellier). The results indicated that (i) the DayCent model, properly calibrated, can be used to quantify olive grove daily net ecosystem exchange and net primary production dynamics; (ii) a decrease in net ecosystem exchange and net primary production is predicted under both types of management by approaching the most extreme climate conditions (ΔT = + 3 °C; ΔP = − 20%), especially in dry and warm areas; (iii) irrigation can compensate for net ecosystem exchange and net primary production losses in almost all areas, while ecophysiological air temperature thresholds determine the magnitude and sign of C-uptake; (iv) future warming is expected to modify the seasonal net ecosystem exchange and net primary production pattern, with higher photosynthetic activity in winter and a prolonged period of photosynthesis inhibition during summer compared to the baseline; (v) a substantial decrease in mitigation capacity and productivity of extensively managed olive groves is expected to accelerate between + 1.5 and + 2 °C warming compared to the current period, across all Mediterranean areas; (vi) adaptation measures aimed at increasing soil water content or evapotranspiration reduction should be considered the mostly suitable for limiting the decrease of both production and mitigation capacity in the next decades. [ABSTRACT FROM AUTHOR]

Abstract Process-based models are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N 2 O emissions from soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N 2 O emissions under data-limited conditions. A study was carried out to compare the capability of four models to estimate seasonal cumulative N 2 O 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 N 2 O 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 N 2 O 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 N 2 O emissions by 0.42 kg N 2 O-N ha−1 for all observed values. Model evaluation indicated that the DAYCENT and DNDC models simulated temporal patterns of daily N 2 O emissions effectively, but both models had difficulty in simulating the timing of the N 2 O fluxes following some events such as fertilization and water regime. According to this evaluation, algorithms for crop production and N 2 O 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. Graphical abstract Unlabelled Image Highlights • DAYCENT, DNDC, linear regression model and IPCC tier 1 approach were evaluated to estimate N 2 O emissions in China's cropland. • DAYCENT emerged as the best performing model for application under Chinese conditions. • Algorithms should be improved to increase the accuracy in the prediction of unfertilized fields. [ABSTRACT FROM AUTHOR]

Improving the prediction of crop production is critical for strategy development associated with global food security, particularly as the climate continues to change. Process-based ecosystem models are increasingly used for simulating global agricultural production. However, such simulations often use a single crop variety in global assessments, implying that major crops are identical across all regions of the world. To address this limitation, we applied a Bayesian approach to calibrate regional types of maize ( Zea mays L), capturing the aggregated traits of local varieties, for DayCent ecosystem model simulations, using global crop production data from 2001 to 2013. We selected major cropping regions from the FAO Global Agro-Environmental Stratification as a basis for the regionalization and identified the most important model parameters through a global sensitivity analysis. We calibrated DayCent using the sampling importance resampling algorithm and found significant improvement in DayCent simulations of maize yields with the calibrated regional varieties. Compared to a single type of maize for the world, the regionalization of maize leads to reductions in root mean squared error of 11%, 31%, 27%, 30%, 19%, and 27% and reductions in bias of 59%, 59%, 50%, 81%, 32%, and 56% for Africa, East Asia, Europe, North America, South America, and South and Southeast Asia, respectively. We also found the optimum parameter values of radiation use efficiency are positively correlated with the income level of different regions, which indicates that breeding has enhanced the photosynthetic efficiency of maize in developed countries. There may also be opportunities for expanding crop breeding programs in developing countries to enhance photosynthesis efficiency and reduce the yield gap in these regions. This study highlights the importance of representing regional variation in crop types for achieving accurate predictions of crop yields.

Effective project implementation and quantification of emissions reduction in climate-smart agriculture initiatives face challenges in measurement, monitoring, and verification. To address these challenges, predictive models are regularly used to estimate the emissions reduction potential of land management changes and to prioritize funding for projects. Despite their growing utility, few studies have evaluated the performance of publicly available model tools using site specific data. This study evaluated the performance and utility of four common model tools that represent the three Intergovernmental Panel on Climate Change model tiers to predict soil organic carbon storage and estimate greenhouse gas emissions on working lands under organic matter amendment. Field data from two long-term, compost application experiments in Washington State formed the basis for model simulations using DayCent, COMET-Farm, Cool Farm, and the Washington State Climate Smart Estimator (WaCSE). Soil carbon sequestration and emissions estimates varied among the evaluated models, which was expected given their differential data requirements and input capabilities. COMET-Farm, although easier to use, exhibited a higher level of bias compared to DayCent, which was expected as a mixed tier model. The DayCent model, the model engine for the COMET-Farm tool, demonstrated the ability to explain ∼50% more of the variation in the observed values compared to COMET-Farm when initiated using the same parameters. Cool Farm was unsuitable for estimating SOC sequestration benefits from compost application primarily because it did not add carbon to the soil pool following amendment. The differences in emissions estimates derived from WaCSE compared with other tools could be attributed solely to its highly constrained input parameters and basis in tier 1 emissions factors. We conclude that online tools can provide rapid estimates of greenhouse gas emissions reduction potential over larger areas or groups of farms but should be used with caution for site-specific estimates. Hence, it is crucial to clarify the intended purpose of an assessment and the designed function of model tools when evaluating their suitability for prioritizing funding for climate-smart agriculture initiatives at the individual farm level. [ABSTRACT FROM AUTHOR]

Although the Soil and Water Assessment Tool (SWAT) model has been widely used to assess the environmental impacts of growing perennial grasses for bioenergy production, its utility is limited by not explicitly accounting for shoot and root biomass development. In this study, we integrated the DAYCENT model's grass growth algorithms into SWAT (SWAT–GRASS D) and further modified it by considering the impact of leaf area index (LAI) on potential biomass production (SWAT–GRASS M). Based on testing at eight sites in the US Midwest, we found that SWAT–GRASS M generally outperformed SWAT and SWAT–GRASS D in simulating switchgrass biomass yield and the seasonal development of LAI. Additionally, SWAT–GRASS M can more realistically represent root development, which is key for the allocation of accumulated biomass and nutrients between aboveground and belowground biomass pools. These improvements are critical for credible assessment of agronomic and environmental impacts of growing perennial grasses for biomass production. • We developed the SWAT-GRASS model by integrating the grass growth module from the DAYCENT model into SWAT. • We further enhanced SWAT-GRASS to better represent the effects of LAI on biomass accumulation. • The new model achieved better simulation of switchgrass yield across multiple sites. • The new model can explicitly and reasonably represent root and shoot development of switchgrass. • A new tool for comprehensive assessment of agronomic and environmental impacts of growing switchgrass. [ABSTRACT FROM AUTHOR]

Shaped by the hydrology of the Kissimmee‐Okeechobee‐Everglades watershed, the Florida Everglades is composed of a conglomerate of wetland ecosystems that have varying capacities to sequester and store carbon. Hydrology, which is a product of the region's precipitation and temperature patterns combined with water management policy, drives community composition and productivity. As shifts in both precipitation and air temperature are expected over the next 100 years as a consequence of climate change, CO2 dynamics in the greater Everglades are expected to change. To reduce uncertainties associated with climate change and to explore how projected changes in atmospheric CO2 concentration and climate can alter current CO2 exchange rates in Everglades freshwater marsh ecosystems, we simulated fluxes of carbon among the atmosphere, vegetation, and soil using the DAYCENT model. We explored the effects of low, moderate, and high scenarios for atmospheric CO2 (550, 850, and 950 ppm), mean annual air temperature (+1, +2.5, and +4.2°C) and precipitation (−2, +7, and +14%), as predicted by the IPCC for the year 2100 for the region, on CO2 exchange rates in short‐ and long‐hydroperiod wetland ecosystems. Under 100 years of current climate and atmospheric CO2 concentration, Everglades freshwater marsh ecosystems were estimated to be CO2‐neutral. As atmospheric CO2 concentration increased and under climate change projections, there were slight shifts in the start and length of the wet season (−1 to +7 days) and a small enhancement in the sink capacity (by −169 to −573 g C m−2 century−1) occurred at both short‐ and long‐ hydroperiod ecosystems compared to CO2 dynamics under the current climate regime. Over 100 years, rising temperatures increased net CO2 exchange rates (+1 to 13 g C m−2 century−1) and shifts in precipitation patterns altered cumulative net carbon uptake by +13 to −46 g C m−2 century−1. While changes in ecosystem structure, species composition, and disturbance regimes were beyond the scope of this research, results do indicate that climate change will produce small changes in CO2 dynamics in Everglades freshwater marsh ecosystems and suggest that the hydrologic regime and oligotrophic conditions of Everglades freshwater marshes lowers the ecosystem sensitivity to climate change.

Graphical abstract Highlights • Fertilizer and herbicides increase productivity but did not increase R h Proportion. • R s , R h , and R h Proportion showed strong seasonal variation. • Linear regression estimated R s and R h better than the current formulation of DAYCENT. Abstract To determine the effectiveness of forests in sequestering atmospheric carbon (C), we must know the amount of fixed carbon dioxide (CO 2) that is subsequently lost due to heterotrophic microbial activity in the soil. Furthermore, the heterotrophic proportion of total soil respiration (R s) must be quantified as it changes between different physiographic regions, seasons, and silvicultural treatments. This research quantified heterotrophic contributions to R s 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 (R h) was separated in the field from autotrophic root respiration (R a) using metal root-excluding collars. The R h proportion of R s was not significantly different between regions or treatments, but demonstrated some seasonal variance. The average R h 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 R s and R h variability, respectively. In contrast, the process based DAYCENT model, parameterized for each site to model R s , R h , and R h proportion compared poorly to field measurements. Model predicted mean seasonal R h 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 CO 2 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 R h 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 R h or how strongly R h varies with season. Finally, the empirical field data suggest the use of fertilizer and herbicides in these ecosystems increases ecosystem productivity without increasing R h , which results in an increase in net ecosystem productivity that may lead to greater rates of C sequestration. [ABSTRACT FROM AUTHOR]

There is an urgent need to identify and evaluate management practices for their biophysical potential to maintain productivity under climate change while mitigating greenhouse gas (GHG) emissions from individual cropping systems under specific pedo-climatic conditions. Here, we examined, through DayCent modeling, the long-term impact of soil management practices and their interactions on soil GHG emissions and GHG intensity from Swiss cropping systems. Based on experimental data from four long-term experimental sites in Switzerland (Therwil, Frick, Changins, and Reckenholz), we robustly parameterized and evaluated the model for simulating crop productivity, soil C dynamics and soil N 2 O emissions across a range of management practices and pedo-climatic conditions. Net soil GHG emissions (NSGHGE) were derived from changes in soil C, N 2 O emissions and CH 4 oxidation. Soils under conventional management acted as a net source of soil GHG emissions (1361–1792 kg CO 2 eq ha −1 yr −1 ) and NSGHGE were dominated by N 2 O (50–63%). Reduced tillage and no-tillage reduced long-term NSGHGE by up to 31 and 58%, respectively. Organic farming, represented by organic fertilization, reduced NSGHGE by up to 31% compared to systems based solely on mineral fertilization. Replacement of slurries with a composted FYM led to an additional reduction in NSGHGE by 46%, although our approach considered only soil GHG emissions and thus did not take into account GHG emissions from the composting process. Cover cropping did not significantly influence NSGHGE, however vetch tended to reduce NSGHGE (-19%). The highest mitigation potential was associated with organic farming plus reduced tillage management, it reduced long-term NSGHGE by up to 128%. Soil C sequestration accounted, on average, for 89% of GHG mitigation potentials, consequently N 2 O dominated NSGHGE across all treatments and sites (60 − 80%). This indicates that these mitigation potentials are time limited and reversible, if the management is not maintained, in contrast to the reduction in N 2 O emissions, which is considered permanent. Not all the management practices sustained crop yields. Nevertheless, composting of organic manures, reduced tillage and no-tillage effectively reduced NSGHGE and GHG intensity without a noticeable yield reduction. Our results suggest that implementation of the above soil management practices in Swiss cropping systems have a considerable potential for climate change mitigation, although time-limited. [ABSTRACT FROM AUTHOR]

Photodegradation accelerates litter decay in arid grasslands where plant growth and litter decay are strongly controlled by precipitation and evapotranspiration. However, the effects of photodegradation on ecosystem C and N dynamics are not well understood. We examined the effects using an ecosystem biogeochemical model DayCent-UV with photodegradation explicitly represented and validated. The model was parameterized for a California grassland where photodegradation was documented to release CO2 from litter. The model was parameterized with an inverse modeling approach using an extensive data set of six years of daily observed carbon and water gas exchange (gross primary production, ecosystem respiration, and evapotranspiration), soil temperature, and soil moisture. DayCent-UV correctly simulated the seasonal patterns of the observed gas exchange and closely simulated the inter-annual variation in the gas exchange and biomass production rates. The simulations suggested that the inter-annual variation is driven more by actual evapotranspiration than by precipitation because a large portion of precipitation is lost as runoff during wet years. Photodegradation in DayCent-UV accelerated C and N cycling, decreasing system C and N by 9.2% and 9.5% and C and N residence times by 9.4% and 18.2%. Accelerated N cycling made a greater fraction of system N available for plants, increasing net N mineralization and plant production for a given amount of system N. Increased net N mineralization was due to decreased immobilization by microbes in the aboveground organic matter. Photodegradation did not alter the control on plant production by evapotranspiration. These results suggest that at the ecosystem level, the central effect of photodegradation is to suppress microbial activity. We conclude that photodegradation accelerates N cycling at the expense of microbes in this grassland, making it more efficient in supporting plant growth for a given amount of N in the system. [ABSTRACT FROM AUTHOR]

Abstract: Process‐based models are increasingly used to study agroecosystem interactions and N2O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N2O flux from three process‐based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi‐model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross‐model bias to underestimate high magnitude daily and cumulative N2O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH4+, and soil NO3−, which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N2O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development. [ABSTRACT FROM AUTHOR]

Abstract: United States mandated the production of biofuel from lignocellulosic feedstocks. Nonetheless, the cultivation of these feedstocks may produce debates, as agricultural land is scarce and it is primarily needed for food production and grazing. Thus, it is thought that biofuel production should be placed on land with low economical value (i.e., marginal land). At the same time, depending on what land is considered marginal and therefore available for lignocellulosic crops, different greenhouse gas impacts will be generated upon land use change. Here, we attempted to estimate the biomass production and soil greenhouse gas emissions of the cultivation of switchgrass (Panicum virgatum L.) and giant reed (Arundo donax L.) in the U.S. Southeast, when converting distinct former land uses. We employed the NLCD and the SSURGO databases to select grasslands, shrublands, and marginal croplands and to then allocate switchgrass and giant reed on this land basing on biophysical parameters included in the Land Capability Classification. After calibration, the DAYCENT model was employed to simulate 15‐year cultivation of both crops in the U.S. Southeast. Florida, Georgia, Mississippi and South Carolina were the States with the highest availability of land, thus the highest potential for biofuel production. Among scenarios, the one converting poor grazing land and marginal croplands yielded the greatest benefits: converting 3.6 Mha of land, 44 Mt/year of dry biomass could be produced, storing 0.05 Mt/year of soil organic C at the same time. In this scenario, considering 80‐km supply areas, nineteen biorefineries could deliver 7,124 Ml/year of advanced ethanol across the region. When minimizing giant reed invasion risks through reallocating giant reed outside flooded areas, 4,695 Ml/year of advanced ethanol could be still delivered from thirteen biorefineries, but the scenario turned in a biogenic greenhouse gas source (3.2 Mt CO2eq/year). [ABSTRACT FROM AUTHOR]

Process-based models are useful tools for estimating the complex interactions between plant, soil and climate systems, assessments which are necessary for improving nutrient cycling and reducing trace gas emissions. Incorporation of knowledge gained through new research is ongoing, thus there is a need for evaluation of model processes and process interactions. In this study, three sites in eastern Canada (St. Bruno, St. Jean, and Ottawa) planted with spring wheat during the years 1993-2007 were used to evaluate and compare the water and N process simulations of the models DayCent, DNDC, and STICS. The simulated soil moisture by all models was generally well represented with low ARE (< 8%) and an EF > 0.1. The unsaturated flow mechanism included in DayCent further improved soil moisture estimates compared to the other models. When sufficient replicate data was available measurement variability was considered, resulting in soil nitrogen being only slightly underestimated (ARE of −10, −1, and −22%, for DayCent, DNDC, and STICS, respectively). On average across the three sites, considering all statistics, the DNDC model proved to be most accurate for simulating mineral N, followed by DayCent and then STICS. Continued process model development is reliant on measurement datasets that can accurately represent carbon and nitrogen dynamics. Frequently, site specific biases convolute model mechanism evaluation and thus assessments have to be conducted across numerous sites to better benchmark model performance. On this premise a comprehensive multi-site inter-model mechanism evaluation was conducted and future model development needs were identified. [ABSTRACT FROM AUTHOR]

Warming temperatures and increasing CO2 are likely to have large effects on the amount of carbon stored in soil, but predictions of these effects are poorly constrained. We elevated temperature (canopy: +2.8 °C; soil growing season: +1.8 °C; soil fallow: +2.3 °C) for 3 years within the 9th-11th years of an elevated CO2 (+200 ppm) experiment on a maize-soybean agroecosystem, measured respiration by roots and soil microbes, and then used a process-based ecosystem model (DayCent) to simulate the decadal effects of warming and CO2 enrichment on soil C. Both heating and elevated CO2 increased respiration from soil microbes by ~20%, but heating reduced respiration from roots and rhizosphere by ~25%. The effects were additive, with no heat × CO2 interactions. Particulate organic matter and total soil C declined over time in all treatments and were lower in elevated CO2 plots than in ambient plots, but did not differ between heat treatments. We speculate that these declines indicate a priming effect, with increased C inputs under elevated CO2 fueling a loss of old soil carbon. Model simulations of heated plots agreed with our observations and predicted loss of ~15% of soil organic C after 100 years of heating, but simulations of elevated CO2 failed to predict the observed C losses and instead predicted a ~4% gain in soil organic C under any heating conditions. Despite model uncertainty, our empirical results suggest that combined, elevated CO2 and temperature will lead to long-term declines in the amount of carbon stored in agricultural soils. [ABSTRACT FROM AUTHOR]

Abstract Protection of lands threatened with conversion to agriculture can reduce carbon emissions. Until recently, most climate change mitigation incentive programs for avoided conversion have focused on forested ecosystems. We applied the Avoided Conversion of Grasslands and Shrublands v.1.0 (ACoGS) methodology now available through the American Carbon Registry to a threatened region of grasslands in the northern Great Plains. For all soil types across 14 counties in North and South Dakota, we used the DAYCENT model calibrated to the study area to quantify the difference in CO2 and N2O emissions under a cropping and a protection scenario, and we used formulas in the ACoGS methodology to calculate CH4 emissions from enteric fermentation under the protection scenario. We mapped the resulting GHG emissions across the entire project area. Emissions averaged 51.6 tCO2e/ha over 20 years, and with a 31% reduction for leakage and uncertainty from the ACoGS methodology, carbon offsets averaged 35.6 tCO2e/ha over 20 years. Protection of 10% of the 2.1 million unprotected ha in the project area with the highest emissions would reduce emissions by 11.7 million tCO2e over 20 years (11% of the total emissions from all unprotected grassland) and avoid a social cost of $430 million worth of CO2 emissions. These results suggest that carbon offsets generated from avoided conversion of grasslands can meaningfully contribute to climate mitigation and grassland conservation objectives.

Rice (Oryza sativa L.) is cultivated as a major crop in most Asian countries and its production is expected to increase to meet the demands of a growing population. This is expected to increase greenhouse gas (GHG) emissions from paddy rice ecosystems, unless mitigation measures are in place. It is therefore important to assess GHG mitigation potential whilst maintaining yield. Using the process-based ecosystem model DayCent, a spatial analysis was carried out in a rice harvested area in Bangladesh for the period 1996 to 2015, considering the impacts on soil organic carbon (SOC) sequestration, GHG emissions and yield under various mitigation options. An integrated management (IM, a best management practice) considering reduced water, tillage with residue management, reduced mineral nitrogen fertilizer and manure, led to a net offset by, on average, −2.43 t carbon dioxide equivalent (CO2-eq.) ha−1 year−1 (GHG removal) and a reduction in yield-scaled emissions intensity by −0.55 to −0.65 t CO2-eq. t−1 yield. Under integrated management, it is possible to increase SOC stocks on average by 1.7% per year in rice paddies in Bangladesh, which is nearly 4 times the rate of change targeted by the “4 per mille” initiative arising from the Paris Climate Agreement.

Nitrous oxide (N 2 O) is an ozone-depleting gas and important greenhouse gas (GHG) that has been implicated in global climate change. Irrigated and N-fertilized turfgrass systems cover large areas of land and emit N 2 O. DAYCENT and Denitrification-Decomposition (DNDC) are two widely used process-based models that predict GHG fluxes in agricultural lands. Despite many experiments applying these models to other cropping systems, they have not been applied to warm-season (C 4) turfgrass systems. Our objectives were to 1) calibrate DAYCENT and DNDC for N 2 O emissions from 'Meyer' zoysiagrass (Zoysia japonica Steud., referred to as zoysia); 2) validate both models and compare their prediction accuracies; and 3) predict long-term impacts of different N fertilization and irrigation management. A combination of global sensitivity analysis and a Bayesian method was used to calibrate DAYCENT and DNDC, followed by validation, using measurements from zoysia field studies. Validation results indicated DAYCENT (R2 = 0.30–0.90; relative RMSE = 30–163%) outperformed DNDC (R2 = 0.01–0.38; relative RMSE = 119–183%) in biweekly N 2 O fluxes. Annual N 2 O emissions from DAYCENT were validated within − 54 to + 14% of estimates interpolated from measurements of different N fertilization and irrigation management, whereas DNDC simulations generally underestimated emissions by − 24 to − 85%. DAYCENT, but not DNDC, adequately simulated the impacts of irrigation and N-fertilization practices on N 2 O emissions in zoysia, a C 4 turfgrass. DAYCENT predicted that typical N-fertilization and irrigation practices in fairway zoysia turf would reduce net global warming potential (GWP) better than no N fertilization by encouraging soil carbon sequestration in the first 30–45 years of establishment. Instead of fixed-amount or no N inputs, gradually reducing N fertilization to maintain turfgrass growth over decades would mitigate the increases of net GWP and better alleviate climate challenges in this century. [Display omitted] • First calibration & validation of DAYCENT and DNDC to warm-season turfgrass. • DAYCENT outperformed DNDC in simulating N 2 O emissions after Bayesian calibration. • DAYCENT predicted the impacts of various irrigation and N-fertilization practices. • N-inputs initially reduced global warming potential (GWP) via more C sequestration. • Gradually reducing N may mitigate increases in N 2 O emissions and GWP as turf ages. [ABSTRACT FROM AUTHOR]

Process-based numerical models in environmental science can help understand and quantify terrestrial material cycles in nature. However, the existing models usually focus on the cycles of one or more elements (e.g., water, carbon, or nitrogen). For example, hydrological models such as Soil and Water Assessment Tool (SWAT) focus on the water cycle and nutrient loadings at watershed scale, whereas biogeochemical models such as DayCent (i.e., daily CENTURY) emphasize carbon/nitrogen storage and fluxes of ecosystems at landscape scale. Therefore, using either one of the two categories of models is not enough for understanding/solving the current complex environmental issues that involve multiple aspects. Although use of both models (SWAT and DayCent) could be an expedient way toward treating the problem, creating separate model projects for a single area could be challenging and time consuming, and integration/analyses of model results have some limitations due to the non-uniformity of input spatial data between models. To overcome this issue, we developed an integrated model implementation coupler that aims to drive SWAT and DayCent—the two representative models in hydrology and biogeochemistry, respectively—just using a user's SWAT project without the need of any extra efforts such as developing a framework or preparing input data for DayCent modeling. This software is easy to use and would be promising for conducting comprehensive environmental impact assessment involving hydrological and biogeochemical cycles at watershed scale. [ABSTRACT FROM AUTHOR]

Protection of lands threatened with conversion to agriculture can reduce carbon emissions. Until recently, most climate change mitigation incentive programs for avoided conversion have focused on forested ecosystems. We applied the Avoided Conversion of Grasslands and Shrublands v.1.0 ( ACo GS) methodology now available through the American Carbon Registry to a threatened region of grasslands in the northern Great Plains. For all soil types across 14 counties in North and South Dakota, we used the DAYCENT model calibrated to the study area to quantify the difference in CO2 and N2O emissions under a cropping and a protection scenario, and we used formulas in the ACo GS methodology to calculate CH4 emissions from enteric fermentation under the protection scenario. We mapped the resulting GHG emissions across the entire project area. Emissions averaged 51.6 tCO2e/ha over 20 years, and with a 31% reduction for leakage and uncertainty from the ACo GS methodology, carbon offsets averaged 35.6 tCO2e/ha over 20 years. Protection of 10% of the 2.1 million unprotected ha in the project area with the highest emissions would reduce emissions by 11.7 million tCO2e over 20 years (11% of the total emissions from all unprotected grassland) and avoid a social cost of $430 million worth of CO2 emissions. These results suggest that carbon offsets generated from avoided conversion of grasslands can meaningfully contribute to climate mitigation and grassland conservation objectives. [ABSTRACT FROM AUTHOR]

Switchgrass ( Panicum virgatum L.) is a perennial C 4 grass native to North America and successfully adapted to diverse environmental conditions. It offers the potential to reduce soil surface carbon dioxide (CO 2 ) fluxes and mitigate climate change. However, information on how these CO 2 fluxes respond to changing climate is still lacking. In this study, CO 2 fluxes were monitored continuously from 2011 through 2014 using high frequency measurements from Switchgrass land seeded in 2008 on an experimental site that has been previously used for soybean ( Glycine max L.) in South Dakota, USA. DAYCENT, a process-based model, was used to simulate CO 2 fluxes. An improved methodology CPTE [Combining Parameter estimation (PEST) with “Trial and Error” method] was used to calibrate DAYCENT. The calibrated DAYCENT model was used for simulating future CO 2 emissions based on different climate change scenarios. This study showed that: (i) the measured soil CO 2 fluxes from Switchgrass land were higher for 2012 which was a drought year, and these fluxes when simulated using DAYCENT for long-term (2015–2070) provided a pattern of polynomial curve; (ii) the simulated CO 2 fluxes provided different patterns with temperature and precipitation changes in a long-term, (iii) the future CO 2 fluxes from Switchgrass land under different changing climate scenarios were not significantly different, therefore, it can be concluded that Switchgrass grown for longer durations could reduce changes in CO 2 fluxes from soil as a result of temperature and precipitation changes to some extent. [ABSTRACT FROM AUTHOR]