The promise of biochar: From lab experiment to national scale impacts

Biochar is a carbon rich soil amendment produced from biomass by a thermochemical process, pyrolysis or gasication. Soil biochar applications have generated a great deal of interest as a strategy for mitigating climate change by sequestering carbon in soils, and simultaneously as a strategy for enhancing global food security by increasing crop yields especially on degraded and poor quality soils. In this study we evaluated the eect of biochars presence on soil and crop in various spatial scales ranging from lab experiments to regional scale simulations. In the rst chapter, we used an incubated experiment with 3 biochar application rates (0%, 3% and 6%), two application methods and three replications. Soil water retention curves (SWRC) were determined at three sampling times. The Van-Genuchten (VG) model was tted to all SWRCs and then used to estimate the pore size distribution (PSD). Standard deviation (SD), skewness and mode (D) were calculated in order to interpret the geometry of PSDs. The Dexter S-index and saturated hydraulic conductivity (Ks) were also estimated. Statistical analysis was performed for all parameters using a linear mixed model. Relative to controls, all biochar treatments increased porosity, water content at both saturation and eld capacity and improved soil physical quality. Biochar applications lowered Ks, bulk density and D indicative of a shift in pore size distributions toward smaller pore sizes. The second chapter was focused on evaluating the impacts of biochar on soil hydraulic properties at the eld scale by combining a modeling approach with soil water content measurements. Soil water measurements were collected from a corn-corn cropping system over two years. The eect of biochar was expected to be the difference between the physical soil properties of the biochar and no-biochar treatments. An inverse modeling was performed after a global sensitivity analysis to estimate the parameters for the soil physical properties of the APSIM (The Agricultural ProductionSystems sIMulator ) model . Results of the sensitivity analysis showed that the drainage upper limit (DUL) was the most sensitive soil property followed by saturated hydraulic conductivity (KS), saturated water content (SAT), maximum rate of plant water uptake (KL), maximum depth of surface storage (MAXPOND), lower limit volumetric water content (LL15) and lower limit for plant water uptake (LL). The dierence between the posterior distributions (with and without biochar) showed an increase in DUL of approximately 10%. No considerable change was noted in LL15, MAXPOND and KS whereas SAT and LL showed a slight increase and decrease in biochar treatment, respectively, compared to no-biochar. In the third chapter, we tried to ans r the question: Where should we apply biochar? For this task, we developed an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. we used a probabilistic graphical model to study the relationships between soil and biochar variables and predict the probability of crop yield response to biochar application. Our Bayesian network model was trained using the data collected from 103 published studies reporting yield response to biochar. Our results showed an average 12% increase in crop yield from all the studies with a large variability ranging from -24.4% to 98%. Soil clay content, pH, cation exchange capacity and organic carbon appeared to be strong predictors of crop yield response to biochar. we also found that biochar carbon, nitrogen content and highest pyrolysis temperature signicantly inuenced the yield response to biochar. Our large spatial-scale modeling revealed that 8.4% to 30% of all U.S. cropland can be targeted and is expected to show a positive yield response to biochar application. It was found that biochar application to areas with high probability of crop yield response in the U.S could ofset a maximum of 2% of the current global anthropogenic carbon emissions per year. In the last chapter, we made regional scale simulations of biochar effects on crop yield and nitrate leaching using APSIM for parts of Iowa and California. Three main pieces of work were integrated in this study. The suitable areas found for biochar application in the previous chapter in both states, the biochar module in the APSIM model and a new developed algorithm for speeding up the large spatial scale simulations. This allowed us to simulate 30 years of biochar effects on soil and crop for corn-corn cropping system in Iowa and alfalfa in California starting in1980 until 2016. Model outputs were then aggregated at a climate division level and the eect of biochar was estimated as the percent change relative to no biochar. In this study, the APSIM model suggested an insignicant change in crop yield/biomass following biochar application with a more substantial eect on nitrate leaching depending on weather conditions. It was found that in wet years (PDSI\u3e3) there is a reduction in nitrate leaching along with an increase in crop yield, suggesting more mineral nitrogen being available for the crop. As one of the significant findings of this study, it was found that the biochar effect lasted almost for the entire 30 years of simulation period while biochar application allowed for sustainable harvest of the crop residue without losing yield or increasing nitrate leaching. During the simulation period, biochar acted as a source of carbon which consistently helped with increasing the mineral nitrogen pool through carbon mineralization and relieving nitrogen stress

Where should we apply biochar?

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

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements

Development of a data-assimilation system to forecast agricultural systems: A case study of constraining soil water and soil nitrogen dynamics in the APSIM model

As we face today\u27s large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model\u27s soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system

Regional techno‐economic and life‐cycle analysis of the pyrolysis‐bioenergy‐biochar platform for carbon‐negative energy

This study investigates the sensitivity of greenhouse gas (GHG) emissions and the minimum fuel selling price for a 2000 metric ton day−1 integrated pyrolysis‐bioenergy‐biochar platform with respect to the biorefinery location and biomass types. The regional techno‐economic and life‐cycle analysis is evaluated in three US counties using representative crops: rice in Glenn County (California), corn in Hamilton County (Iowa), and peanuts in Jackson County (Florida). We evaluate the biochar selling price considering crop yield increases of 0.6%, 2.9%, and 10% after biochar application over 20 years in Glenn County, Hamilton County, and Jackson County, respectively. The biochar prices are calculated under low and high commodity prices to determine upper and lower bounds. Jackson County has the most economically beneficial scenario with an average minimum fuel selling price (MFSP) of $1.55 gal−1 of biofuel produced whereas Hamilton County has the highest average MFSP of$3.82 gal−1. The life‐cycle analysis shows that woody biomass has a strong potential to produce carbon‐negative energy compared to grass and straw. Of the 304 cases scenarios considered for this platform, 64% could produce biofuel with negative GHG emissions, which would meet the Renewable Fuel Standard (RFS) target for cellulosic biofuels

The promise of biochar: From lab experiment to national scale impacts

Biochar is a carbon rich soil amendment produced from biomass by a thermochemical process, pyrolysis or gasication. Soil biochar applications have generated a great deal of interest as a strategy for mitigating climate change by sequestering carbon in soils, and simultaneously as a strategy for enhancing global food security by increasing crop yields especially on degraded and poor quality soils. In this study we evaluated the eect of biochars presence on soil and crop in various spatial scales ranging from lab experiments to regional scale simulations. In the rst chapter, we used an incubated experiment with 3 biochar application rates (0%, 3% and 6%), two application methods and three replications. Soil water retention curves (SWRC) were determined at three sampling times. The Van-Genuchten (VG) model was tted to all SWRCs and then used to estimate the pore size distribution (PSD). Standard deviation (SD), skewness and mode (D) were calculated in order to interpret the geometry of PSDs. The Dexter S-index and saturated hydraulic conductivity (Ks) were also estimated. Statistical analysis was performed for all parameters using a linear mixed model. Relative to controls, all biochar treatments increased porosity, water content at both saturation and eld capacity and improved soil physical quality. Biochar applications lowered Ks, bulk density and D indicative of a shift in pore size distributions toward smaller pore sizes. The second chapter was focused on evaluating the impacts of biochar on soil hydraulic properties at the eld scale by combining a modeling approach with soil water content measurements. Soil water measurements were collected from a corn-corn cropping system over two years. The eect of biochar was expected to be the difference between the physical soil properties of the biochar and no-biochar treatments. An inverse modeling was performed after a global sensitivity analysis to estimate the parameters for the soil physical properties of the APSIM (The Agricultural ProductionSystems sIMulator ) model . Results of the sensitivity analysis showed that the drainage upper limit (DUL) was the most sensitive soil property followed by saturated hydraulic conductivity (KS), saturated water content (SAT), maximum rate of plant water uptake (KL), maximum depth of surface storage (MAXPOND), lower limit volumetric water content (LL15) and lower limit for plant water uptake (LL). The dierence between the posterior distributions (with and without biochar) showed an increase in DUL of approximately 10%. No considerable change was noted in LL15, MAXPOND and KS whereas SAT and LL showed a slight increase and decrease in biochar treatment, respectively, compared to no-biochar. In the third chapter, we tried to ans r the question: Where should we apply biochar? For this task, we developed an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. we used a probabilistic graphical model to study the relationships between soil and biochar variables and predict the probability of crop yield response to biochar application. Our Bayesian network model was trained using the data collected from 103 published studies reporting yield response to biochar. Our results showed an average 12% increase in crop yield from all the studies with a large variability ranging from -24.4% to 98%. Soil clay content, pH, cation exchange capacity and organic carbon appeared to be strong predictors of crop yield response to biochar. we also found that biochar carbon, nitrogen content and highest pyrolysis temperature signicantly inuenced the yield response to biochar. Our large spatial-scale modeling revealed that 8.4% to 30% of all U.S. cropland can be targeted and is expected to show a positive yield response to biochar application. It was found that biochar application to areas with high probability of crop yield response in the U.S could ofset a maximum of 2% of the current global anthropogenic carbon emissions per year. In the last chapter, we made regional scale simulations of biochar effects on crop yield and nitrate leaching using APSIM for parts of Iowa and California. Three main pieces of work were integrated in this study. The suitable areas found for biochar application in the previous chapter in both states, the biochar module in the APSIM model and a new developed algorithm for speeding up the large spatial scale simulations. This allowed us to simulate 30 years of biochar effects on soil and crop for corn-corn cropping system in Iowa and alfalfa in California starting in1980 until 2016. Model outputs were then aggregated at a climate division level and the eect of biochar was estimated as the percent change relative to no biochar. In this study, the APSIM model suggested an insignicant change in crop yield/biomass following biochar application with a more substantial eect on nitrate leaching depending on weather conditions. It was found that in wet years (PDSI>3) there is a reduction in nitrate leaching along with an increase in crop yield, suggesting more mineral nitrogen being available for the crop. As one of the significant findings of this study, it was found that the biochar effect lasted almost for the entire 30 years of simulation period while biochar application allowed for sustainable harvest of the crop residue without losing yield or increasing nitrate leaching. During the simulation period, biochar acted as a source of carbon which consistently helped with increasing the mineral nitrogen pool through carbon mineralization and relieving nitrogen stress.</p

Where should we apply biochar?

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