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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-CO 2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations., (© 2024. Battelle Memorial Institute and Alliance for Sustainable Energy, LLC.)

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 CO 2 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 (N 2 O) emissions, the cumulative net emissions (in CO 2 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 CO 2 e 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., (© 2022 The Ecological Society of America.)

Agri-environmental measures (AEMs) are meant to foster environmentally-friendly farming techniques. The use of AEMs to enhance agroecosystem quality is still under debate due to site-specific spatial mismatches that often occur between adopted AEMs and delivered ecosystem services. Here, a site-specific approach was employed to assess the advantages and disadvantages of AEMs adopted from the Rural Development Programme and applied in the Veneto Region (NE Italy) during 2014-2020. Specifically, a DayCent model-GIS platform compared business-as-usual (BAU) and AEM scenarios. The effect of AEMs on ecosystem services was assessed by integrating high-resolution spatial data from multiple pedo-climates and land managements and combined agronomic and environmental outcomes. Results showed that AEM adoption generally improved ecosystem service delivery, especially by reducing water pollution and increasing soil fertility. Among simulated practices, permanent soil cover and minimum soil disturbance (i.e., conservation agriculture, pasture and meadow maintenance) produced the best results across the Veneto Region, despite compromises in agronomic performance due to AEM-specific commitments (e.g., narrow crop rotation in conservation agriculture, fertilizer use restrictions in pastures and meadows). Other AEMs (e.g., organic farming) appeared highly dependent on their spatial distribution and were influenced by a strong interaction between pedo-climatic characteristics (e.g., soil properties) and management techniques (e.g., type and quantity of nutrients input). The spatial-target approach is highly recommended to identify AEMs that achieve environmental quality objectives and develop indications as to where they should be encouraged to maximize ecosystem services delivery., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

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 R 2 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 R 2 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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Process-based models are increasingly used to study agroecosystem interactions and N 2 O 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 N 2 O 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 N 2 O 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 NH 4 + , and soil NO 3 - , 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 N 2 O 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., (© 2017 by the Ecological Society of America.)

Warming temperatures and increasing CO 2 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 CO 2 (+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 CO 2 enrichment on soil C. Both heating and elevated CO 2 increased respiration from soil microbes by ~20%, but heating reduced respiration from roots and rhizosphere by ~25%. The effects were additive, with no heat × CO 2 interactions. Particulate organic matter and total soil C declined over time in all treatments and were lower in elevated CO 2 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 CO 2 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 CO 2 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 CO 2 and temperature will lead to long-term declines in the amount of carbon stored in agricultural soils., (© 2016 John Wiley & Sons Ltd.)

Switchgrass (Panicum virgatum L.) is a perennial C4 grass native to North America and successfully adapted to diverse environmental conditions. It offers the potential to reduce soil surface carbon dioxide (CO2) fluxes and mitigate climate change. However, information on how these CO2 fluxes respond to changing climate is still lacking. In this study, CO2 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 CO2 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 CO2 emissions based on different climate change scenarios. This study showed that: (i) the measured soil CO2 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 CO2 fluxes provided different patterns with temperature and precipitation changes in a long-term, (iii) the future CO2 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 CO2 fluxes from soil as a result of temperature and precipitation changes to some extent., (Copyright © 2015. Published by Elsevier B.V.)

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., (© 2015 John Wiley & Sons Ltd.)

Understanding the potential for greenhouse gas (GHG) mitigation in agricultural lands is a critical challenge for climate change policy. This study uses the DAYCENT ecosystem model to predict GHG mitigation potentials associated with soil management in Chinese cropland systems. Application of ecosystem models, such as DAYCENT, requires the evaluation of model performance with data sets from experiments relevant to the climate and management of the study region. DAYCENT was evaluated with data from 350 cropland experiments in China, including measurements of nitrous oxide emissions (N2 O), methane emissions (CH4 ), and soil organic carbon (SOC) stock changes. In general, the model was reasonably accurate with R(2) values for model predictions vs. measurements ranging from 0.71 to 0.85. Modeling efficiency varied from 0.65 for SOC stock changes to 0.83 for crop yields. Mitigation potentials were estimated on a yield basis (Mg CO2 -equivalent Mg(-1) Yield). The results demonstrate that the largest decrease in GHG emissions in rainfed systems are associated with combined effect of reducing mineral N fertilization, organic matter amendments and reduced-till coupled with straw return, estimated at 0.31 to 0.83 Mg CO2 -equivalent Mg(-1) Yield. A mitigation potential of 0.08 to 0.36 Mg CO2 -equivalent Mg(-1) Yield is possible by reducing N chemical fertilizer rates, along with intermittent flooding in paddy rice cropping systems., (© 2013 John Wiley & Sons Ltd.)