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Recent studies have found that solar ultraviolet ( UV ) radiation significantly shifts the mass loss and nitrogen dynamics of plant litter decomposition in semi‐arid and arid ecosystems. In this study, we examined the role of photodegradation in litter decomposition by using the DayCent‐ UV biogeochemical model. DayCent‐ UV incorporated the following mechanisms related to UV radiation: (1) direct photolysis, (2) facilitation of microbial decomposition via production of labile materials, and (3) microbial inhibition effects. We also allowed maximum photodecay rate of the structural litter pool to vary with litter's initial lignin fraction in the model. We calibrated DayCent‐ UV with observed ecosystem variables (e.g., volumetric soil water content, live biomass, actual evapotranspiration, and net ecosystem exchange), and validated the optimized model with Long‐Term Intersite Decomposition Experiment ( LIDET ) observations of remaining carbon and nitrogen at three semi‐arid sites in Western United States. DayCent‐ UV better simulated the observed linear carbon loss patterns and the persistent net nitrogen mineralization in the 10‐year LIDET experiment at the three sites than the model without UV decomposition. In the DayCent‐ UV equilibrium model runs, UV decomposition increased aboveground and belowground plant production, surface net nitrogen mineralization, and surface litter nitrogen pool, but decreased surface litter carbon, soil net nitrogen mineralization, and mineral soil carbon and nitrogen. In addition, UV decomposition had minimal impacts on trace gas emissions and biotic decomposition rates. The model results suggest that the most important ecological impact of photodecay of surface litter in dry grasslands is to increase N mineralization from the surface litter (25%), and decay rates of the surface litter (15%) and decrease the organic soil carbon and nitrogen (5%).

Compost amendments to grasslands have been proposed as a strategy to mitigate climate change through carbon (C) sequestration, yet little research exists exploring the net mitigation potential or the long-term impacts of this strategy. We used field data and the DAYCENT biogeochemical model to investigate the climate change mitigation potential of compost amendments to grasslands in California, USA. The model was used to test ecosystem C and greenhouse gas responses to a range of compost qualities (carbon to nitrogen [C:N] ratios of 11.1, 20, or 30) and application rates (single addition of 14 Mg C/ha or 10 annual additions of 1.4 Mg C·ha −1 ·yr −1 ). The model was parameterized using site-specific weather, vegetation, and edaphic characteristics and was validated by comparing simulated soil C, nitrous oxide (N 2 O), methane (CH 4 ), and carbon dioxide (CO 2 ) fluxes, and net primary production (NPP) with three years of field data. All compost amendment scenarios led to net greenhouse gas sinks that persisted for several decades. Rates of climate change mitigation potential ranged from 130 ± 3 g to 158 ± 8 g CO 2 -eq·m −2 ·yr −1 (where \"eq\" stands for \"equivalents\") when assessed over a 10-year time period and 63 ± 2 g to 84 ± 10 g CO 2 -eq·m −2 ·yr −1 over a 30-year time period. Both C storage and greenhouse gas emissions increased rapidly following amendments. Compost amendments with lower C:N led to higher C sequestration rates over time. However, these soils also experienced greater N 2 O fluxes. Multiple smaller compost additions resulted in similar cumulative C sequestration rates, albeit with a time lag, and lower cumulative N 2 O emissions. These results identify a trade-off between maximizing C sequestration and minimizing N 2 O emissions following amendments, and suggest that compost additions to grassland soils can have a long-term impact on C and greenhouse gas dynamics that contributes to climate change mitigation.

Many yield predictions in perennial bioenergy species have been made based on data collected during the establishment phase of growth or a limited number of long‐term studies. Few studies compare multiple perennial crops with the dominant agricultural vegetation of the landscape over long time periods. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean (Zea mays L., Glycine max L.) production. We examined the long‐term productivity and drought susceptibility of Miscanthus x giganteus Greef et. Deu. ex. Hodkinson et Renvoize (miscanthus), Panicum virgatum L., Cave‐in‐Rock cultivar (switchgrass), and a native prairie mix, in contrast to annual maize/soybean agriculture. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Harvest timing and differences in yields from hand and machine harvests in perennial crops likely contribute to overestimates of potential yields. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean production. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response.

Understanding drivers of nutrient balances in cropping systems, and possible strategies to replenish nutrient and carbon losses from soils, is crucial for maintaining and improving soil health. We determined nitrogen, phosphorus, and potassium balances, likely trajectories of soil organic carbon (SOC) and implications of alternative management scenarios on long-term (10 to 50 year) nutrient and SOC balances for smallholder farms. Farmer interviews and modeling was used to estimate nutrient and carbon flows into and out of 35 fields from three representative farm types within three agroecological zones in western Kenya, over two growing seasons (within one year). Management scenarios representing different strategies to balance nutrients and stabilize SOC stocks were simulated and compared against current farming practices. Our results indicate that net nutrient balances were highly variable across all farms but were on average negative for nitrogen (− 25.5 kg ha −1  yr −1 ) and potassium (− 20.8 kg ha −1  yr −1 ) and slightly positive for phosphorus (2.3 kg ha −1  yr −1 ) across all fields. Agroecological zone and farm type did not significantly affect the overall nutrient balances, but the relative magnitude of inputs and outputs (e.g., mineral N input) was significantly higher in Vihiga (65.8 kg N ha −1  yr −1 ) than Busia (22 kg N ha −1  yr −1 ) and Nandi (34.4 kg N ha −1  yr −1 ); p = 0.01. Management scenarios focusing on erosion control reduced nitrogen losses by half in all zones. A residue retention scenario had the greatest impact on potassium balances (i.e., average − 49.2 kg K ha −1  yr −1 when residues were removed vs − 11.74 kg K ha −1  yr −1 when all residues were retained), while complete residue export to feed livestock (and applying the manure produced) had minimal effects on all three nutrient balances across zones. When simulating SOC stocks with the DayCent model, scenarios with erosion control, residue retention, and incorporation of a high biomass legume (lablab) resulted in at least 50% more SOC than current trends or the residue export scenario after 50 modeled years across regions. Combining these three management interventions showed promise for stabilizing SOC trajectories over a 50-year horizon. Our findings suggest that researchers, policymakers, and extension agents should emphasize erosion control, residue retention, and high biomass legumes in rotation as strategies for improving nutrient balances and SOC stocks in smallholder farming systems.

Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production.

Bioenergy cropping systems could help offset greenhouse gas emissions, but quantifying that offset is complex. Bioenergy crops offset carbon dioxide emissions by converting atmospheric CO2 to organic C in crop biomass and soil, but they also emit nitrous oxide and vary in their effects on soil oxidation of methane. Growing the crops requires energy (e.g., to operate farm machinery, produce inputs such as fertilizer) and so does converting the harvested product to usable fuels (feedstock conversion efficiency). The objective of this study was to quantify all these factors to determine the net effect of several bioenergy cropping systems on greenhouse-gas (GHG) emissions. We used the DAYCENT biogeochemistry model to assess soil GHG fluxes and biomass yields for corn, soybean, alfalfa, hybrid poplar, reed canarygrass, and switchgrass as bioenergy crops in Pennsylvania, USA. DAYCENT results were combined with estimates of fossil fuels used to provide farm inputs and operate agricultural machinery and fossil-fuel offsets from biomass yields to calculate net GHG fluxes for each cropping system considered. Displaced fossil fuel was the largest GHG sink, followed by soil carbon sequestration. N2O emissions were the largest GHG source. All cropping systems considered provided net GHG sinks, even when soil C was assumed to reach a new steady state and C sequestration in soil was not counted. Hybrid poplar and switchgrass provided the largest net GHG sinks, >200 g CO2e-C·m-2·yr-1 for biomass conversion to ethanol, and >400 g CO2e-C·m-2·yr-1 for biomass gasification for electricity generation. Compared with the life cycle of gasoline and diesel, ethanol and biodiesel from corn rotations reduced GHG emissions by 40%, reed canarygrass by 85%, and switchgrass and hybrid poplar by 115%.

Recently, many countries have introduced policies that promote sustainable agricultural practices, such as reducing synthetic nitrogen fertiliser and promoting diversified crop rotation. While such management changes might represent an opportunity for the agricultural sector to mitigate the impacts of climate change through carbon (C) sequestration in soils, there are still uncertainties due to the scarcity of reliable long-term data to prove this assumption. In this study, we applied the DayCent model using empirical data from a farm-scale study and an experimental trial study at Nafferton farm in the UK, to assess the long-term effects of contrasting agricultural systems (conventional vs. organic), grazing regimes (non-grazed vs. grazed), arable systems with ley phases, mineral vs. compost fertility sources and conventional vs. organic crop rotation on soil C stocks (0–0.20 m depth). The simulations showed that grazing and higher ley time proportions can increase soil C stocks for a period of at least 30 years, regardless of the agricultural system used (average increase in rates of 0.25 ± 0.02 Mg ha−1 yr−1). Compost fertiliser promoted soil C accumulation for the same period (average increase in rates of 0.3 Mg ha−1 yr−1), but its magnitude was dependent on the choice of crops in the rotation. However, ley time proportions higher than 40% of the full crop rotation did not improve soil C accumulation further. We conclude that the DayCent model can be used to identify the quantity of and the effective period for which management practices can be used to target mitigation efforts, but the balance between sustainability and productivity aspects warrants further research.

Above ground litter decomposition is the result of three interlinked processes: leaching, fragmentation and catabolism. Litter decomposition estimates are most commonly based on measurements of mass loss from litter residues, confined in mesh bags. This method provides a rough estimate of leaching and catabolism, while preventing fragmentation from occurring. Alternatively, litter decomposition is studied in the laboratory as microbial respiration of litter residue. In this case, generally only catabolism is measured. While those limits are often discussed, their careful assessment has never been attempted. We present here results from a study where the decomposition rate of Arbutus unedo leaf litter, at a throughfall manipulation experiment, was investigated using: 1) litterbags; 2) turnover based on litter input/standing litter pool; 3) 14C-bomb spike; 4) laboratory incubation; 5) DayCent modeling. Aims of this study were: 1) to quantitatively assess the hypothesis that the litterbags and the laboratory incubation methods, by preventing fragmentation, overestimate above ground litter mean residence time; 2) to evaluate the ability of the above methods to capture the effects of changes in precipitation on litter decay rates. Results confirmed our hypothesis and demonstrated that the litterbag and the laboratory incubation methods do capture the effects of the water manipulation treatment on litter decay rates.

Despite the importance of agriculture in California's Central Valley, the potential of alternative management practices to reduce soil greenhouse gas (GHG) emissions has been poorly studied in California. This study aims at (1) calibrating and validating DAYCENT, an ecosystem model, for conventional and alternative cropping systems in California's Central Valley, (2) estimating CO 2 , N 2 O, and CH 4 soil fluxes from these systems, and (3) quantifying the uncertainty around model predictions induced by variability in the input data. The alternative practices considered were cover cropping, organic practices, and conservation tillage. These practices were compared with conventional agricultural management. The crops considered were beans, corn, cotton, safflower, sunflower, tomato, and wheat. Four field sites, for which at least five years of measured data were available, were used to calibrate and validate the DAYCENT model. The model was able to predict 86-94% of the measured variation in crop yields and 69-87% of the measured variation in soil organic carbon (SOC) contents. A Monte Carlo analysis showed that the predicted variability of SOC contents, crop yields, and N 2 O fluxes was generally smaller than the measured variability of these parameters, in particular for N 2 O fluxes. Conservation tillage had the smallest potential to reduce GHG emissions among the alternative practices evaluated, with a significant reduction of the net soil GHG fluxes in two of the three sites of 336 ± 47 and 550 ± 123 kg CO 2 -eq·ha −1 ·yr −1 (mean ± SE). Cover cropping had a larger potential, with net soil GHG flux reductions of 752 ± 10, 1072 ± 272, and 2201 ± 82 kg CO 2 -eq·ha −1 ·yr −1 . Organic practices had the greatest potential for soil GHG flux reduction, with 4577 ± 272 kg CO 2 -eq·ha −1 ·yr −1 . Annual differences in weather or management conditions contributed more to the variance in annual GHG emissions than soil variability did. We concluded that the DAYCENT model was successful at predicting GHG emissions of different alternative management systems in California, but that a sound error analysis must accompany the predictions to understand the risks and potentials of GHG mitigation through adoption of alternative practices.

Public policies are promoting biofuels as an alternative to fossil fuel consumption in order to mitigate greenhouse gas (GHG) emissions. However, the mitigation benefit can be at least partially compromised by emissions occurring during feedstock production. One of the key sources of GHG emissions from biofuel feedstock production, as well as conventional crops, is soil nitrous oxide (N 2 O), which is largely driven by nitrogen (N) management. Our objective was to determine how much GHG emissions could be reduced by encouraging alternative N management practices through application of nitrification inhibitors and a cap on N fertilization. We used the US Renewable Fuel Standards (RFS2) as the basis for a case study to evaluate technical and economic drivers influencing the N management mitigation strategies. We estimated soil N 2 O emissions using the DayCent ecosystem model and applied the US Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG) to project GHG emissions for the agricultural sector, as influenced by biofuel scenarios and N management options. Relative to the current RSF2 policy with no N management interventions, results show decreases in N 2 O emissions ranging from 3 to 4 % for the agricultural sector (5.5–6.5 million metric tonnes CO 2  eq. year −1 ; 1 million metric tonnes is equivalent to a Teragram) in response to a cap that reduces N fertilizer application and even larger reductions with application of nitrification inhibitors, ranging from 9 to 10 % (15.5–16.6 million tonnes CO 2  eq. year −1 ). The results demonstrate that climate and energy policies promoting biofuel production could consider options to manage the N cycle with alternative fertilization practices for the agricultural sector and likely enhance the mitigation of GHG emissions associated with biofuels.