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.