Regional and global impact assessment tools are increasingly used to explore and evaluate the impact of climate change and extreme events on crop yield and environmental externalities. However, the large uncertainties associated with the inputs or the parameters in crop models within these tools, limits their predictive ability, exceeding the spatiotemporal variability of observed yields. The objective of this study is to explore and quantify different sources of uncertainties and assumptions made behind initial conditions (IC), soil input, meteorological forcing, management practices and model cultivar parameters by running regional simulations for the time period between 2009 and 2019. Simulations were performed for maize and soybean using the pSIMS platform across the U.S Midwest by incrementally accounting for five sources of uncertainty with a 7 k m × 7 k m resolution using the APSIM and DSSAT crop growth models. First, the relative contribution of different sources of uncertainty was estimated over time and space. Then, a series of nitrate leaching hotpots were identified and a regional maize yield productivity index was estimated by decomposing the uncertainty in the same scenario using a hierarchical Bayesian random-effect model. All factors showed a strong spatial pattern in their contribution to the total uncertainty and their contribution was found to be partially dependent on location. However, across the whole region, it was found that the uncertainty around management is larger than IC, soil and meteorological forcing while showing a strong correlation with each of these factors. Given the high spatial correlation, we hypothesize that constraining soil inputs and management uncertainty could allow for the largest reduction in predictive uncertainty for crop yield. Our results showed vast areas over northern IA, IL and IN with high potential for NO₃ leaching and southern IA, IL and east NE with lower maize productivity index compared to the regional average.

Biochar application to soils is a promising practice with agronomic and environmental benefits. Our analysis is based on an experiment in which soil columns were incubated with three biochar application rates (0%, 3% and 6% w/w), two application methods and three replications. Soil water retention curves (SWRCs) were determined at three sampling times, 15, 29 and 91 days, after the start of the incubation. The van Genuchten (VG) model was fitted to all SWRCs and then used to estimate the pore-size distribution (PSD). Standard deviation (SD), skewness and mode (D) were calculated to interpret the geometry of PSDs. The Dexter “S-index” and saturated hydraulic conductivity (K_s) were also estimated using the VG model parameters. Statistical analysis was performed for all the parameters using a linear mixed model. Relative to controls, all biochar treatments showed increased porosity, water content at both saturation and field capacity and improved soil physical quality. Biochar applications lowered , K_s, bulk density and D, indicative of a shift in PSDs toward smaller pore sizes.

The heating of biomass under low-oxygen conditions generates three co-products, bio-oil, biogas, and biochar. Bio-oil can be stabilized and used as fuel oil or be further refined for various applications and biogas can be used as an energy source during the low-oxygen heating process. Biochar can be used to sequester carbon in soil and has the potential to increase crop yields when it is used to improve yield-limiting soil properties. Complex bio-physical interactions have made it challenging to answer the question of where biochar should be applied for the maximum agronomic and economic benefits. We address this challenge by developing an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. We use a probabilistic graphical model to study the relationships between soil and biochar variables and predict the probability and magnitude of crop yield response to biochar application. Our results show an average increase in crop yields ranging from 4.7% to 6.4% depending on the biochar feedstock and application rate. Expected yield increases of at least 6.1% and 8.8% are necessary to cover 25% and 10% of US cropland with biochar. We find that biochar application to crop area with an expected yield increase of at least 5.3%-5.9% would result in carbon sequestration offsetting 0.57%-0.67% of US greenhouse gas emissions. Applying biochar to corn area is the most profitable from a revenue perspective when compared to soybeans and wheat because additional revenues accrued by farmers are not enough to cover the costs of biochar applications in many regions of the United States.

The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.