William Parton’s research while affiliated with Colorado State University and other places

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks in our ability to process information have reduced our capacity to fully exploit the growing volume and variety of data. Here, we take a critical look at the information infrastructure that connects modeling and measurement efforts, and propose a roadmap that accelerates production of new knowledge. We propose that community cyberinfrastructure tools can help mend the divisions between empirical research and modeling, and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, not just a clique of ‘modelers’. This roadmap focuses on five key opportunities for community tools: the underlying backbone to community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is key to meeting the pressing needs of science and society in the 21st century.

Highly variable precipitation in western U.S. rangelands makes it challenging for ranchers to match animal demand to forage supply. Flexible stocking can enhance matching, thereby reducing losses during drought and increasing profit during wet years. Yet the benefits of flexible stocking depend on the availability of highly accurate and applicable seasonal climate outlooks. The availability and skill of seasonal climate outlooks is summarized, revealing shortcomings that make flexible stocking less practical and less beneficial. A new grassland productivity forecast, Grass-Cast, can facilitate flexible stocking by translating climate outlooks into more applicable summer-forage outlooks, and its strengths and limitations are described.

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

A current national research priority in Brazil is the assessment of potential climate change impacts in agriculture. In this study, the DayCent model was used to predict changes in crop biomass, soil C stocks, and N2O fluxes of a major agricultural area in Brazil. The model was calibrated and validated using datasets from 30-yr-old experiments. Simulations of current and alternative management practices to 2100 using IPCC climate scenarios A2 and B1 were conducted. Predicted crop biomass increases ranged from 5% (sorghum [B1]) to 65% (soybean [A2]). DayCent simulated higher soil organic carbon stocks in the wheat–soybean system (VAL1) than in the wheat–soybean–vetch–sorghum system (VAL2) in both climate scenarios. Soil organic carbon accumulation by 2100 (±16 Mg C ha⁻¹ above current 46 Mg C ha⁻¹) was forecast in cropping systems with pastures (Cynodon sp.), regardless of the climate scenario. Daily N2O fluxes were underestimated by ±41% (0.85 g N–N2O ha⁻¹ d⁻¹) in VAL1 and overestimated by 17.5% (0.28 g N–N2O ha⁻¹ d⁻¹) in VAL2. Cumulative N2O fluxes produced mixed results, which were 29% lower than observed in VAL1 and 5% higher in VAL2 but within the range of values reported in other greenhouse gas studies in southern Brazil. Simulations of N2O fluxes to 2100 with IPCC climate change scenarios B1 and A2 in southern Brazilian indicated higher annual fluxes across the alternative treatments tested in comparison to current fluxes. According to model predictions, climate change would lead to larger relative increases in N2O emissions from no-tillage (27% in B1 to 41% in A2), but these enhanced fluxes would still be lower than those from tillage by approximately 25%. © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA. All Rights reserved.

Determining whether the terrestrial biosphere will be a source or sink of carbon

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

Understanding the influence of grazing management and environmental drivers on net ecosystem exchange of CO2 (NEE) is essential for optimizing carbon (C) uptake in rangelands. Herein, using 15 treatment-years (two 3-yr experiments, one with three grazing treatments, the other two) and Bowen ratio flux towers, we evaluated the influence of grazing intensity, soil water content (SWC), and plant cover (Normalized Difference Vegetation Index, or NDVI) on NEE in Colorado shortgrass steppe. Among several soil water and plant cover traits evaluated over 6-yr, early season (April, DOY 91–120) SWC and early season (DOY 130) NDVI were most highly correlated with NEE (− 0.96 and − 0.98, respectively) during the second quarter (April to June) of the year and also over the entire growing season (April to September; − 0.97 and − 0.96). Due to the strong effect of early-season SWC, an average of 166 g m− 2 CO2 were lost in 2 yr with dry spring weather, compared with an average annual uptake of 218 g m− 2 CO2 in 4 yr with more abundant early-season precipitation and plant cover. Grazing effects on NEE were also apparent. In one experiment, moderate grazing resulted in annual CO2 uptake of 267 g m− 2 CO2 over 3 yr compared with essentially zero NEE in heavily grazed pasture. However, that treatment difference in annual NEE was only half that experienced between dry and wet years. Similar trends were observed in a second experiment, although results were insignificant. Results suggest that the recommended moderate grazing intensity for the Colorado shortgrass steppe is near optimal for CO2 uptake under season-long continuous grazing, with annual climatic variability sometimes being more influential. To enhance C sequestration in the western Great Plains of North America, grazing management strategies should emphasize flexible and adaptive practices that consider early-season SWC and promote vegetation cover during the key early spring growth period.

Soil carbon (C) is a critical component of Earth system models (ESMs) and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the 3rd to 5th assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. Firstly, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by 1st-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic SOC dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Secondly, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based datasets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Thirdly, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable datasets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.