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.