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Soil organic matter is a large global carbon pool. Isotopic labelling of litter in the lab and the field reveals that soil organic matter forms from labile organic compounds and litter fragments early and late in decomposition, respectively. Soil organic matter is the largest terrestrial carbon pool 1 . The pool size depends on the balance between formation of soil organic matter from decomposition of plant litter and its mineralization to inorganic carbon. Knowledge of soil organic matter formation remains limited 2 and current C numerical models assume that stable soil organic matter is formed primarily from recalcitrant plant litter 3 . However, labile components of plant litter could also form mineral-stabilized soil organic matter 4 . Here we followed the decomposition of isotopically labelled above-ground litter and its incorporation into soil organic matter over three years in a grassland in Kansas, USA, and used laboratory incubations to determine the decay rates and pool structure of litter-derived organic matter. Early in decomposition, soil organic matter formed when non-structural compounds were lost from litter. Soil organic matter also formed at the end of decomposition, when both non-structural and structural compounds were lost at similar rates. We conclude that two pathways yield soil organic matter efficiently. A dissolved organic matter–microbial path occurs early in decomposition when litter loses mostly non-structural compounds, which are incorporated into microbial biomass at high rates, resulting in efficient soil organic matter formation. An equally efficient physical-transfer path occurs when litter fragments move into soil.

Elevated atmospheric CO₂ concentration (eCO₂) has the potential to increase vegetation carbon storage if increased net primary production causes increased long‐lived biomass. Model predictions of eCO₂ effects on vegetation carbon storage depend on how allocation and turnover processes are represented. We used data from two temperate forest free‐air CO₂ enrichment (FACE) experiments to evaluate representations of allocation and turnover in 11 ecosystem models. Observed eCO₂ effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO₂ effects on vegetation carbon storage. Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions; careful testing of allocation schemes; and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets.

The rhizosphere priming effect (RPE) is a mechanism by which plants interact with soil functions. The large impact of the RPE on soil organic matter decomposition rates (from 50% reduction to 380% increase) warrants similar attention to that being paid to climatic controls on ecosystem functions. Furthermore, global increases in atmospheric CO2 concentration and surface temperature can significantly alter the RPE. Our analysis using a game theoretic model suggests that the RPE may have resulted from an evolutionarily stable mutualistic association between plants and rhizosphere microbes. Through model simulations based on microbial physiology, we demonstrate that a shift in microbial metabolic response to different substrate inputs from plants is a plausible mechanism leading to positive or negative RPEs. In a case study of the Duke Free-Air CO2 Enrichment experiment, performance of the PhotoCent model was significantly improved by including an RPE-induced 40% increase in soil organic matter decomposition rate for the elevated CO2 treatment – demonstrating the value of incorporating the RPE into future ecosystem models. Overall, the RPE is emerging as a crucial mechanism in terrestrial ecosystems, which awaits substantial research and model development.

Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon. Temperature sensitivity of bulk soil carbon stocks is controlled by the compositional distribution between mineral-associated and particulate carbon, according to analyses of global soil carbon pools.

We simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-UV ecosystem model. For this study we developed new methods for informing DayCent-UV of growing season length and validating its plant productivity estimates for grasslands by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model’s phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model results from each simulated grid cell were compared to a remote-sensing estimate of grassland plant productivity offered by the Rangeland Analysis Platform (RAP). We determined that a modified RAP ANPP calculation that incorporated total annual precipitation instead of mean annual temperature to estimate the fraction of total productivity allocated to roots improved temporal correlations between RAP and field measurements and between RAP and DayCent-UV, We found that RAP provides a valuable data set for evaluating grassland ANPP predictions from ecosystem and other types of models because it provides estimates of grassland plant productivity over large spatial regions and a long historical period and captures temporal variablilty in plant production. This work provides the foundation for using the DayCent-UV model to predict climate change impacts on grassland cecosystem dynamics in the contiguous US.

Nitrous oxide (N₂O) is an important greenhouse gas (GHG) that also contributes to depletion of ozone in the stratosphere. Agricultural soils account for about 60% of anthropogenic N₂O emissions. Most national GHG reporting to the United Nations Framework Convention on Climate Change assumes nitrogen (N) additions drive emissions during the growing season, but soil freezing and thawing during spring is also an important driver in cold climates. We show that both atmospheric inversions and newly implemented bottom-up modeling approaches exhibit large N₂O pulses in the northcentral region of the United States during early spring and this increases annual N₂O emissions from croplands and grasslands reported in the national GHG inventory by 6 to 16%. Considering this, emission accounting in cold climate regions is very likely underestimated in most national reporting frameworks. Current commitments related to the Paris Agreement and COP26 emphasize reductions of carbon compounds. Assuming these targets are met, the importance of accurately accounting and mitigating N₂O increases once CO₂ and CH₄ are phased out. Hence, the N₂O emission underestimate introduces additional risks into meeting long-term climate goals.

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%.

21st‐century modeling of greenhouse gas (GHG) emissions from bioenergy crops is necessary to quantify the extent to which bioenergy production can mitigate climate change. For over 30 years, the Century‐based biogeochemical models have provided the preeminent framework for belowground carbon and nitrogen cycling in ecosystem and earth system models. While monthly Century and the daily time‐step version of Century (DayCent) have advanced our ability to predict the sustainability of bioenergy crop production, new advances in feedstock generation, and our empirical understanding of sources and sinks of GHGs in soils call for a re‐visitation of DayCent's core model structures. Here, we evaluate current challenges with modeling soil carbon dynamics, trace gas fluxes, and drought and age‐related impacts on bioenergy crop productivity. We propose coupling a microbial process‐based soil organic carbon and nitrogen model with DayCent to improve soil carbon dynamics. We describe recent improvements to DayCent for simulating unique plant structural and physiological attributes of perennial bioenergy grasses. Finally, we propose a method for using machine learning to identify key parameters for simulating N2O emissions. Our efforts are focused on meeting the needs for modeling bioenergy crops; however, many updates reviewed and suggested to DayCent will be broadly applicable to other systems. This review evaluates current challenges with biogeochemical modeling of soil carbon dynamics, trace gas fluxes, and drought and age‐related impacts on bioenergy crop productivity. We propose coupling a microbial process‐based soil organic carbon and nitrogen model with DayCent, or other Century‐based biogeochemical models, to improve representation of soil carbon dynamics. We describe recent improvements to DayCent for simulating unique plant structural and physiological attributes of perennial bioenergy grasses. Finally, we propose a method for using machine learning to identify key parameters for simulating N2O emissions.

In recent years, litter decomposition studies have begun to move beyond the concept of mass loss to consider the fate of fresh and pyrolized decomposing plant material in the ecosystem. However, these concepts have yet to be incorporated into conceptual models of litter decomposition. Understanding how fresh and pyrolized plant litter chemical traits control the partitioning of mass loss to dissolved organic carbon (DOC) leaching and respiration to CO₂ would help to inform models of litter-soil-atmosphere carbon (C) cycling. To test these controls, we incubated five fresh and one pyrolized leaf litters with differing chemistry and measured DOC and CO₂ fluxes as well as changes in substrate and dissolved organic matter (DOM) chemistry over time using Fourier transformed infrared spectroscopy and wet chemistry. We found that the amount of hot water extractable C was a strong predictor of initial DOC leaching, while the lignocellulose index [Lignin/(Lignin + α-Cellulose)] was a strong inverse predictor of later stage DOC:CO₂ partitioning. Changes in substrate and DOM chemistry indicated a progression of substrate availability for leaching: from soluble plant components, to partially decomposed cellulose and lignin, to microbial products. Based on these results we developed a new conceptual model that demonstrates how chemical traits of fresh and pyrolyzed plant litter can be used to predict the fate of aboveground organic matter decomposition and form a better linkage between aboveground decomposition and terrestrial ecosystem C cycling.

Nitrogen (N) and phosphorus (P) availability regulate plant productivity throughout the terrestrial biosphere, influencing the patterns and magnitude of net primary production (NPP) by land plants both now and into the future. These nutrients enter ecosystems via geologic and atmospheric pathways and are recycled to varying degrees through the plant–soil–microbe system via organic matter decay processes. However, the proportion of global NPP that can be attributed to new nutrient inputs versus recycled nutrients is unresolved, as are the large-scale patterns of variation across terrestrial ecosystems. Here, we combined satellite imagery, biogeochemical modeling, and empirical observations to identify previously unrecognized patterns of new versus recycled nutrient (N and P) productivity on land. Our analysis points to tropical forests as a hotspot of new NPP fueled by new N (accounting for 45% of total new NPP globally), much higher than previous estimates from temperate and high-latitude regions. The large fraction of tropical forest NPP resulting from new N is driven by the high capacity for N fixation, although this varies considerably within this diverse biome; N deposition explains a much smaller proportion of new NPP. By contrast, the contribution of new N to primary productivity is lower outside the tropics, and worldwide, new P inputs are uniformly low relative to plant demands. These results imply that new N inputs have the greatest capacity to fuel additional NPP by terrestrial plants, whereas low P availability may ultimately constrain NPP across much of the terrestrial biosphere.