Search

The potential of large‐scale deployment of basalt to reduce N2O emissions from cultivated soils may contribute to climate stabilization beyond the CO2‐removal effect from enhanced weathering. We used 3 years of field observations from maize (Zea mays) and miscanthus (Miscanthus × giganteus) to improve the nitrogen (N) module of the DayCent model and evaluate the potential of basalt amendments to reduce N losses and increase yields from two bioenergy crops. We found 20%–60% improvement in our N2O flux estimates over previous model descriptions. Model results predict that the application of basalt would reduce N2O emissions by 16% in maize and 9% in miscanthus. Lower N2O emissions responded to increases in the N2:N2O ratio of denitrification with basalt‐induced increases in soil pH, with minor contributions from the impact of P additions (a minor component of some basalts) on N immobilization. The larger reduction of N2O emissions in maize than in miscanthus was likely explained by a synergistic effect between soil pH and N content, leading to a higher sensitivity of the N2:N2O ratio to changes in pH in heavily fertilized maize. Basalt amendments led to modest increases in modeled yields and the nitrogen use efficiency (i.e., fertilizer‐N recover in crop production) of maize but did not affect the productivity of miscanthus. However, enhanced soil P availability maintained the long‐term productivity of crops with high nutrient requirements. The alleviation of plant P limitation led to enhanced plant N uptake, thereby contributing to lower microbial N availability and N2O emissions from crops with high nutrient requirements. Our results from the improved model suggest that the large‐scale deployment of basalt, by reducing N2O fluxes of cropping systems, could contribute to the sustainable intensification of agriculture and enhance the climate mitigation potential of bioenergy with carbon capture and storage strategies.

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 19.2Mgha−1year−1, with a spatiotemporal average of (Formula presented.) Mgha−1year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as (Formula presented.) MgCO2eha−1year−1 and (Formula presented.) MgCO2eha−1year−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 5.6Mgha−1year−1. In comparison to the multi State (n=21) NASS database, our results showed RMSE of 5.5Mgha−1year−1. Model projections of baseline SOC showed RMSE of 1.9kg/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 >10Mgha−1year−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: 6.9Mgha−1year−1) with net sequestration (average: 0.13MgCO2eha−1year−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.

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.

Explicit consideration of microbial physiology in soil biogeochemical models that represent coupled carbon–nitrogen dynamics presents opportunities to deepen understanding of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physicochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS with coupled C and nitrogen (N) cycling through litter, microbial, and soil organic matter (SOM) pools. The model was parameterized and validated against C and N data from the Long-Term Inter-site Decomposition Experiment Team (LIDET; six litter types, 10 years of observations, and 13 sites across North America). The model simulates C and N losses from litterbags in the LIDET study with reasonable accuracy (C: R2=0.63; N: R2=0.29), which is comparable with simulations from the DAYCENT model that implicitly represents microbial activity (C: R2=0.67; N: R2=0.30). Subsequently, we evaluated equilibrium values of stocks (total soil C and N, microbial biomass C and N, inorganic N) and microbial process rates (soil heterotrophic respiration, N mineralization) simulated by MIMICS-CN across the 13 simulated LIDET sites against published observations from other continent-wide datasets. We found that MIMICS-CN produces equilibrium values in line with measured values, showing that the model generates plausible estimates of ecosystem soil biogeochemical dynamics across continental-scale gradients. MIMICS-CN provides a platform for coupling C and N projections in a microbially explicit model, but experiments still need to identify the physiological and stoichiometric characteristics of soil microbes, especially under environmental change scenarios.

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.

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.

The conservation of natural ecosystems is an essential component for sustainable land use (LU). One of the challenges facing society worldwide is climate change, where reduce emissions, and sequest...

Aeolian processes in temperate grasslands (TGs) are unique because the plant growth–decay cycle, soil water, and land-use interactions affect the seasonal and inter-annual changes in dust events. Land-use types in Inner Mongolian TGs are unique (settled grazing and grass mowing) compared with those in Mongolian TGs. Since 2003, land use has been controlled by grassland protection legislation, which is intended to prevent desertification and dust storms. In this study, we used process-based ecosystem (DAYCENT) and statistical modeling, along with dust event observations from March to June of 1981–2015, to (1) identify critical land surface factors controlling dust emissions (vegetation components, live grass, standing dead grass, litter, and soil moisture) at typical and desert steppe sites in Inner Mongolia and (2) estimate the impact of controlled land-use legislation on dust events. The DAYCENT model realistically simulated the dynamics of the observed vegetation components and soil moisture in 2005–2015. At both sites, similar significant correlations were obtained between spring dust events and wind speed or a combination of all surface factors that retained anomalies (memory) from the preceding year. Among the surface factors, vegetation was a critical factor that suppressed dust in Inner Mongolian TGs, similar to that in Mongolian TGs. In the desert steppe, standing dead grass had the strongest memory and was significantly correlated with dust events, whereas no significant correlations were observed in the typical steppe. This suggests that, in a typical steppe region, heavy grazing and mowing result in few dead grasses, thereby inhibiting the prevention of dust events. Moreover, the simulations of dust events under controlled (light grazing) and uncontrolled (heavy grazing) land-use conditions demonstrated that the grassland protection legislation reduced the occurrence of dust events in typical and desert steppe sites by 25 and 40%, respectively, since 2003.

PurposeTo estimate life cycle impacts from introducing the yield-enhancing inoculant containing the nitrogen-fixing bacteriumBradyrhizobium japonicumand the signal molecule lipochitooligosaccharide (LCO) in Argentinian soybean production. The study focuses on soybeans grown in rotation with corn in the Buenos Aires province. We also provide the life cycle impact assessment for the inoculant production. The study represents a novel scope in terms of the studied crop, inoculant type, and location.MethodsConsequential LCA is used to assess the cradle-to-gate soybean production systems with and without inoculant use. Stepwise is used for quantification of 16 impacts at mid-point level. Also, the LCA-based guidance of Kløverpris et al. (2020) is followed, and we divide the change in impacts caused by the inoculant’s use into four effects. The field effect accounts for changes in field emissions. The yield effect accounts for additional soybean production in the inoculant system that displaces soybean production elsewhere (system expansion). The upstream effect covers the inoculant production and the downstream effect covers post-harvest changes such as soybean transport and drying. Small plot field-trials data is applied in the biogeochemical model DayCent to estimate field emissions, among others.Results and discussionThe use of this inoculant reduces environmental impacts from soybean production in all studied impact categories. The main contributing factor is the yield effect, i.e., reduced impacts via avoided soybean production elsewhere including reduced pressure on land and thereby avoided impacts in the form of indirect land-use-change (iLUC). The field effect is the second-largest contributor to the overall impact reduction. Upstream and downstream effects only had minor influence on results. The yield and field effects are closely tied to the yield change from the inoculant use, which was not fully captured in the DayCent modeling. Thereby, a potential underestimation of the environmental benefits of roughly 10% can be expected, corresponding to the difference of empiric yield data and the modeled yield data in DayCent.Conclusion and recommendationsThe use of this inoculant shows environmental benefits and no trade-offs for the 16 impacts assessed. Results depend primarily on avoided soybean production (the yield effect) which entails iLUC impacts in Brazil and USA, and to a lesser degree on field emissions modelled with DayCent. Better data and parametrization of DayCent, to better capture the change in yields and estimate field emissions, economic modelling for the system expansion assumptions, and accounting for uncertainty in iLUC modelling could improve the assessment.

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.

Grasslands comprised of grass-legume mixtures could become a substitute for nitrogen fertiliser through biological nitrogen fixation (BNF) which in turn can reduce nitrous oxide emissions directly from soils without negative impacts on productivity. Models can test how legumes can be used to meet environmental and production goals, but many models used to simulate greenhouse gas (GHG) emissions from grasslands have either a poor representation of grass-legume mixtures and BNF, or poor validation of these features. Our objective is to examine how such systems are currently represented in two process-based biogeochemical models, APSIM and DayCent, when compared against an experimental dataset with different grass-legume mixtures at three nitrogen (N) fertiliser rates. Here, we propose a novel approach for coupling DayCent, a single species model to APSIM, a multi-species model, to increase the capability of DayCent when representing a range of grass-legume fractions. While dependent on specific assumptions, both models can capture the key aspects of the grass-legume growth, including biomass production and BNF and to correctly simulate the interactions between changing legume and grass fractions, particularly mixtures with a high clover fraction. Our work suggests that single species models should not be used for grass-legume mixtures beyond about 30% legume content, unless using a similar approach to that adopted here.

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.

Emissions of greenhouse gases (GHG) from paddy rice are significant, so reducing these emissions has significant potential for climate change mitigation. We investigated alternate wetting and drying (AWD) as part of an integrated management approach to enhance mitigation, together with combinations of mineral nitrogen (N), reduced tillage, a suitable combination of plant residues and well decomposed manure. To quantify GHG emissions, and the potential for mitigation without yield decline, a process-based model, DayCent was used to simulate methane (CH4) and nitrous oxide (N2O) emissions from paddy rice (Oryza sativa L.) in Bangladesh. The four test sites selected were amended with mineral N fertilizer or an organic amendment (rice straw). A good agreement (p < 0.05) was observed between model simulated and measured daily CH4 flux at most of these test sites with no significant bias. The seasonal CH4 emission from a site receiving mineral N fertilizer at a rate of 110 kg N ha−1 was predicted by the model to be 210 and 150 kg ha−1 for the water management scenarios of continuous flood (CF) and AWD, respectively. These values compare well with estimates of CH4 emissions using Intergovernmental Panel on Climate Change tier 1 methods for the different water regimes. Our model results suggest emission factors for N2O of 0.4% and 0.6% of applied fertilizer under CF and AWD water regimes, respectively. Based on modelling studies, AWD was found to be an important strategy not only with respect to reducing GHG emissions, but also in terms of cost effectiveness. We also found that integrated management is a promising option for farmers and policy makers interested in either yield increase, GHG mitigation or both. Yield scaled emissions intensity under AWD was found to be about 24% lower than under CF, followed by integrated management.

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.

Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environme...

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.

The densely farmed U.S. Midwest is a prominent source of nitrous oxide (N2O) but top‐down and bottom‐up N2O emission estimates differ significantly. We quantify Midwest N2O emissions by combining observations from the Atmospheric Carbon and Transport‐America campaign with model simulations to scale the Emissions Database for Global Atmospheric Research (EDGAR). In October 2017, we scaled agricultural EDGAR v4.3.2 and v5.0 emissions by factors of 6.3 and 3.5, respectively, resulting in 0.42 nmol m−2 s−1 Midwest N2O emissions. In June/July 2019, a period when extreme flooding was occurring in the Midwest, agricultural scaling factors were 11.4 (v4.3.2) and 9.9 (v5.0), resulting in 1.06 nmol m−2 s−1 Midwest emissions. Uncertainties are on the order of 50 %. Agricultural emissions estimated with the process‐based model DayCent (Daily version of the CENTURY ecosystem model) were larger than in EDGAR but still substantially smaller than our estimates. The complexity of N2O emissions demands further studies to fully characterize Midwest emissions., Plain Language Summary: Nitrous oxide (N2O) is the third most important anthropogenic greenhouse gas contributing to the warming of the planet and the dominant man‐made ozone‐depleting substance in the stratosphere. Its atmospheric concentrations have been rising since industrialization mainly due to an increase in anthropogenic sources, with agriculture being the dominant source. The densely farmed U.S. Midwest plays an important role in the global N2O budget. However, previous studies that have collected observations of N2O indicate that estimates of surface emissions in the Midwest are substantially underestimating the truth. In this study, we combine unique aircraft‐based N2O measurements and model simulations to quantify Midwest emissions in October 2017 and June/July 2019. Agricultural inventory estimates had to be increased by factors up to 20 to match observations, revealing a large underestimation in current inventories. An extreme flooding event in 2019 when the summer observations occurred may be responsible for some of this discrepancy. Estimations of soil N2O emissions calculated with a state‐of‐the‐art biogeochemical model show less underestimation but are still too low compared to the fluxes derived from the aircraft observational data., Key Points: Within the ACT‐America project, we gathered a unique airborne in‐situ N2O data set over the U.S. Midwest with enhancements up to 9 ppb. N2O emissions in the U.S. Midwest were on average 0.42 ± 0.28 nmol m−2 s−1 in October 2017 and 1.06 ± 0.57 nmol m−2 s−1 in June to July 2019. Bottom‐up estimates from EDGAR and DayCent underestimate U.S. Midwest N2O emissions by factors up to 20.

Nitrous oxide (N2O) is a long-lived greenhouse gas that contributes to global warming. Emissions of N2O mainly stem from agricultural soils. This review highlights the principal factors from peer-reviewed literature affecting N2O emissions from agricultural soils, by grouping the factors into three categories: environmental, management and measurement. Within these categories, each impact factor is explained in detail and its influence on N2O emissions from the soil is summarized. It is also shown how each impact factor influences other impact factors. Process-based simulation models used for estimating N2O emissions are reviewed regarding their ability to consider the impact factors in simulating N2O. The model strengths and weaknesses in simulating N2O emissions from managed soils are summarized. Finally, three selected process-based simulation models (Daily Century (DAYCENT), DeNitrification-DeComposition (DNDC), and Soil and Water Assessment Tool (SWAT)) are discussed that are widely used to simulate N2O emissions from cropping systems. Their ability to simulate N2O emissions is evaluated by describing the model components that are relevant to N2O processes and their representation in the model.

Warm season perennial C4 grasses (WSGs), switchgrass (Panicum virgatum L.) and miscanthus species (Miscanthus spp.), have been reported to positively influence short-term (15&ndash, 20 years) soil organic carbon (SOC). In this study, the DayCent model was used to predict changes in long-term SOC stocks under WSGs for moderate (Representative Concentration Pathway (RCP) 4.5) and high (RCP 8.5) warming climate change scenarios in southern Ontario, Canada, and to determine how long the enhanced SOC stock will last when WSGs are converted back to annual crop rotation. The model predicted that a consistent corn&ndash, corn&ndash, soybean&ndash, winter wheat (CCSW) rotation prevented SOC from depletion over the 21st century. Under WSGs, the model predicted high rates of SOC sequestration during the first 20&ndash, 30 years which then tended to stabilize after 50&ndash, 60 years. However, the rate of SOC sequestration over 90 years for RCP 4.5 was 0.26 and 0.94 Mg C ha&minus, 1 yr&minus, 1 for switchgrass and miscanthus, respectively. If 40-year stands of WSGs are converted back to CCSW, the model predicted SOC decline to the previous level in 40&ndash, 50 years. DayCent predicted that under RCP 8.5 scenario in the second half of the 21st century and in the future, there will be a reduction in SOC stocks, especially under miscanthus stands.

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.

Purpose This 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. Methods The 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 discussion An 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 recommendations The 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.

Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.

Crops can uptake only a fraction of nitrogen from nitrogenous fertilizer, while losing the remainder through volatilization, leaching, immobilization and emissions from soils. The emissions of nitrogen in the form of nitrous oxide (N2O) have a strong potency for global warming and depletion of stratospheric ozone. N2O gets released due to nitrification and denitrification processes, which are aided by different environmental, management and soil variables. In recent years, researchers have focused on understanding and simulating the N2O emission processes from agricultural farms and/or watersheds by using process-based models like Daily CENTURY (DAYCENT), Denitrification-Decomposition (DNDC) and Soil and Water Assessment Tool (SWAT). While the former two have been predominantly used in understanding the science of N2O emission and its execution within the model structure, as visible from a multitude of research articles summarizing their strengths and limitations, the later one is relatively unexplored. The SWAT is a promising candidate for modeling N2O emission, as it includes variables and processes that are widely reported in the literature as controlling N2O fluxes from soil, including nitrification and denitrification. SWAT also includes three-dimensional lateral movement of water within the soil, like in real-world conditions, unlike the two-dimensional biogeochemical models mentioned above. This article aims to summarize the N2O emission processes, variables affecting N2O emission and recent advances in N2O emission modeling techniques in SWAT, while discussing their applications, strengths, limitations and further recommendations.

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 (?T-temperature from + 0 °C to + 3 °C; ?P-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 (?T = + 3 °C; ?P = - 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.

Dryland agroecosystems could be a sizable sink for atmospheric carbon (C) due to their spatial extent and level of degradation, providing climate change mitigation. We examined productivity and soil C dynamics under two climate change scenarios (moderate warming, representative concentration pathway [RCP] 4.5; and high warming, RCP 8.5), using long-term experimental data and the DayCent process-based model for three sites with varying climates and soil conditions in the US High Plains. Each site included a no-till cropping intensity gradient introduced in 1985, with treatments ranging from wheat-fallow ( L.) to continuous annual cropping and perennial grass. Simulations were extended to 2100 using data from 16 global circulation models to estimate uncertainty. Simulated yields declined for all crops (up to 50% for wheat), with small changes after 2050 under RCP 4.5 and continued losses to 2100 under RCP 8.5. Of the cropped systems, continuous cropping had the highest average productivity and soil C sequestration rates (78.1 kg C ha yr from 2015 to 2045 under RCP 4.5). Any increase in soil C for cropped rotations was realized by 2050, but grassland treatments increased soil C (up to 69%) through 2100, even under RCP 8.5. Our simulations indicate that reduced frequency of summer fallow can both increase annualized yields and store more soil C. As evapotranspiration is likely to increase, reducing fallow periods without live vegetation from dryland agricultural rotations may enhance the resilience of these systems to climate change while also increasing soil C storage and mitigating carbon dioxide emissions.