Biochar: Rediscovering the Wisdom of Indigenous Soils

Figure  6.  Impact  of  each  experimental  variable  on  Plant  Height,  derived  from  Stochastic  Gradient  Descent  (SGD)   Machine  Learning  Algorithm,  run  in  Dataiku Python  Environment.  R2  Score  (explained  variance)  of  0.847,  Mean   Absolute  Error  3.73,  and  Pearson  Coefficient  (correlation  coefficient)  of  0.92,  emphasizing  the  predictive  value  of   this  model,  while  acknowledging  the  potential  for  error.  The  greatest  negative  impact  on  plant  growth  was  found   to  be  sterile  soil,  while  the  greatest  positive  impact  was  20%  biochar.  

BIOCHAR:  Rediscovering  the  Wisdom  of  Indigenous  Soils

Paige Whitehead, BSc. Microbiology and Environmental Studies Double Major Candidate

Dr. Darcy Mathews, PhD. School of Environmental Studies

German  L.  (2003)  Ethnoscientific Understandings  of  Amazonian  Dark  Earths.  In:  Lehmann  J.,  Kern  D.C.,  Glaser  B.,  Wodos W.I.  (eds)  Amazonian  Dark  Earths.  Springer,  Dordrecht  /  Pietikainen,  J.,  Kiikkila,  O.,  &  Fritze,  H.  (2000).  Charcoal  as  a  habitat  for  microbes  and  its  effect  on  the  microbial  community  of  the  underlying  humus.  Oikos,  89(2),  231–242.  /  Manyà,  J.  J.  (2012).  Pyrolysis  for  Biochar  Purposes:  A  Review  to   Establish  Current  Knowledge  Gaps  and  Research  Needs.  Environmental  Science  &  Technology,  46(15),  7939–7954.  /Chen,  S.,  Rotaru,  A.-­‐E.,  Shrestha,  P.  M.,  Malvankar,  N.  S.,  Liu,  F.,  Fan,  W.,  …  Lovley,  D.  R.  (2015).  Promoting  Interspecies  Electron  Transfer  with  Biochar.  Scientific  Reports,  4(1),  5019.  

R e f e r e n c e s

Indigenous burning and soil building practices throughout time created productive, resilient, and biodiverse soils which are studied today for applications in carbon sequestration, soil remediation, and designing resilient

ecosystems in preparation for climate change. In this experiment we emulated these indigenous anthrosols in

order to better understand and characterize their unique features, specifically focusing on biochar structure and its role in supporting the microbiome of the soil. ‘DIY Biochar’, two types of commercially available biochar

(RoughChar and FineChar), and a charcoal control were compared in a plant growth study, analyzed using Scanning Electron Microscopy (SEM) pre and post inoculation, and characterized using ATR-FTIR Spectroscopy. Based on

ATR-FTIR data, the ‘DIY BioChar’ and Charcoal Control both showed strong peaks in the 1620 cm-1 region,

indicative of the formation of aromatic rings, suggesting successful formation of recalcitrant carbon and potentially suitable for carbon sequestration applications. The plant growth study suggests that 20% biochar/soil ratio is

preferred, and microbially active soil is critical for plant growth (full model: predictive value 0.79, F score 0.0005). Based on the SEM images, the ‘DIY Biochar’ was most suitable for microbial habitat; this sample showed a well-preserved cellular structure, a variety of pore sizes, had the best volume to total internal surface area ratio, and showed successful microbial colonization after 6 month growth. This poster represents the seed of further

research into the long-term impacts of indigenous biochars on soil structure, metagenome, and biodiversity.

A b s t r a c t

I n t r o d u c t i o n

A literature review was conducted to develop experimental protocol, compare biochar research for various end-uses, and catalogue biochar’s archeological history worldwide. ‘Do-It-Yourself’ biochar from dried Douglas-fir (pyrolized in homemade ‘BioCharlie’) was compared to commercial biochar samples, non-biochar charcoal, and woody debris in the following categories; chemical properties, structural properties, effect on plant growth, and microbial colonization.

DIY, RoughChar, FineChar, and Charcoal samples were each scanned using the Agilent 4500 Series Portable FTIR Spectrophotometer and imaged using a SEM (Scanning Electron Microscope). Varying concentrations (0%, 5%, 20%, 20%) were mixed with different soil types (Sterile, Regular, Mycoactive) and plant growth was monitored for 6

months as a baseline model of biochar-inclusive anthrosols.

Samples from each blend were collected from 1inch below soil surface, fixed using paraformaldehyde, stepwise ethanol dehydration, critical point dried, then scanned again with the SEM to characterize microbial colonization. ANOVA Linear Multivariable Regression statistical analysis methods and Dataiku Stochastic Gradient Descent

Machine Learning Algorithms were used to predict and validate weightings of each experimental variable.

M e t h o d s a n d M a t e r i a l s

The  particle  size  range  elucidates  the  importance  of  pore  structure  on  the  total  internal  surface  area,  which  is  key   for  increasing  density  of  microbial  habitation  (Fig.  1).  Note  that  these  are  approximations  based  on  SEM  images.   Biochar  samples  showed  clear  microbial  colonization  post  6  month  soil  incubation  period,  with  the  additional   formation  of  EPS  visualized  on  selected  samples  (Fig.  2  and  Fig.  3,  image  G).  Unfortunately,  the  SEM  imaged   samples  formed  too  small  a  sample  population  to  use  for  quantitative  evaluation.  Future  research  via  

metagenome  sequencing  is  suggested  to  quantify  and  identify  microbial  populations.

Via  the  FTIR  Data  a  greater  conversion  to  recalcitrant  carbon  was  discovered  in  the  DIY  biochar  and  Control  

Charcoal  sample  compared  to  the  RoughChar and  Fine  Char,  based  on  greater  signal  strength  in  the  aromatic  ring   region  of  the  spectrum   (1620 cm-1 )(Fig.  4)  .  

Multiple  methods  of  statistical  analysis  were  used;  Stochastic  Gradient  Descent  Machine  Learning  algorithm  was   utilized  on  a  binomial  distribution  of  experimental  variables  to  weigh  impact  on  plant  height  (Fig.  6).  The  ANOVA   Multivariable  Linear  Regression  Model  of  the  plant  growth  data  resulted  in  the  following  predictive  formula  for   determining  plant  height  based  on  soil  conditions,  biochar  concentration,  and  biochar  type  (using  sterile  soil,   RoughChar,  and  0%  Biochar  as  the  baseline):

Plant Height

 =  6  +  8.3(DIYChar)  +  16.7(Regular Soil)  +  16.3(MycoActive)

+  

1(5%)  +  1.67(10%)  +  11(20%)

Predictive  value:  0.76  (R  square)

ANOVA  Linear  Regression  Significance  F:  0.00059

Further  comparison  was  performed  using  XGBoost Machine  Learning  modeling  which  also  supported  that  the   greatest  influencing  factor  on  plant  growth  was  sterile  soil.  

D i s c u s s i o n

Based on the results of this preliminary experiment, the small-scale ‘DIY Biocharlie’ approach with Douglas-fir as the starting material resulted in biochar with the most well-preserved internal pore structure, strongest peak in the aromatic region (indicating a high conversion to recalcitrant carbon), greatest plant growth, and

successful microbial biofilm formation. I contend that highly-stable porous biochar, a common soil amendment to Terra Preta indigenous soils, serves as a matrix for microbial inoculation, likely serving as a stable long-term structure for continued microbiome reproduction throughout time. Significantly, the greatest predictor of plant growth was not biochar concentration, but living soil, highlighting the extreme importance of the soil

microbiome.

Future goals:

1. Sequence various effective microorganism mixes, conduct a mercury porosity assay to accurately determine total internal surface area of biochar, and continue to assay microorganism film formation impact on

conductivity.

2. Continue to attempt to replicate biochar-inclusive indigenous anthrosols in order to build a model of healthy, biodiverse, and resilient soils impact on micro and macrobiodiversity.

C o n c l u s i o n s

Biochars are occur globally in indigenous soils including here on the Pacific Coast of North America. The term

Biochar (biological+charcoal) refers to organic biomass which has undergone pyrolysis —the process of combustion in a low-oxygen environment. With woody biomass pyrolization transforms the cellulose, lignin, and hemicellulose into a pure carbon aromatic ring formation, along with the production of valuable bio-oils and gases.

C

H

O

(biomass) + heat → Σ

C

H

O

+ Σ

C

H

O

+ H

O + C

This results in temporally and environmentally stable carbon and forms the base of biochar’s most beneficial

features; long-term water retention, high cation exchange capacity, and stable porous structure. Effective biochars designed for agricultural use are ‘primed’ (soaked in water) and inoculated with microbial and fungal species in order to boost beneficial effects, mimicking indigenous anthrosols worldwide. There is evidence of stable, carbon-rich substrates (ie. charcoal), in anthrosols around the globe, such as the Terra Preta of the Amazon (Lehmann, 2003) - it was the addition of charcoal via indigenous burning which enabled these forest gardens to thrive

(Watkins 2000). Based on literature review, it was expected that biochar content would have a positive impact on plant growth, microbial content and microbial diversity. In particular, a solid pore structure is likely of great

importance to microbial colonization. The impact on the soil microbiome over time has not been well characterized.

R e s u l t s

Biochar Type Biochar % Substrate June July August September October November December Rough 0 sterile soil 1 1 1 1 1 1 1 Rough 0 regular soil 1 2 8 16 20 30 30.2 Rough 0 mycoactive soil 1 2 12 24 30 34 35.4 Rough 5 sterile soil 1 1 1 1 1 1 1 Rough 5 regular soil 1 2 16 24 33 37 41.6 Rough 5 mycoactive soil 1 2 15 25 28 30 32.8 Rough 20 sterile soil 1 2 8 19 27 30 32 Rough 20 regular soil 1 2 12 22 30 35 38.6 Rough 20 mycoactive soil 1 2 15 22 31 35 38.2 Rough 10 sterile soil 1 1 1 1 1 1 1 Rough 10 regular soil 1 1 1 1 1 1 1 Rough 10 mycoactive soil 1 2 13 22 35 40 41.8 DIY 10 sterile soil 1 2 10 16 24 27 29.6 DIY 10 regular soil 1 2 17 28 40 45 48.8 DIY 10 mycoactive soil 1 2 14 25 38 42 45 DIY 20 sterile soil 1 2 13 21 25 32.5 39.2 DIY 20 regular soil 1 2 15 26 37 40 46 DIY 20 mycoactive soil 1 2 18 35 45 49 51.2

Figure  4.  Plant  Growth  over  time.  Light  Blue  indicates;  0%  Biochar;  Green:  5%  Rough  Char;  Purple:  10%  RoughChar; Orange:  20%  RoughChar;  Yellow:  10%  DIY  Char;  Pink:  20%  Biochar.  All  samples  were  subject  to  the  same  

temperature  and  light  conditions.  Biochar-­‐mixed  soils  maintained  water,  so  were  watered  slightly  less  than  non-­‐ biochar  soils  to  preserve  plant-­‐life.  Note  the  three  sterile  soil  lines  that  remained  at  0  due  to  no  plant  growth.  

Figure  3.  Scanning  electron  microscopy  of  Samples  after  6  month  inoculation  period.  (from  left  to  right)  A.  rod   shaped  bacteria  B.  spirochete  and  mycelium  C.  yeast  colonies  D.  yeast  colonies  E.  diversity  of  bacteria  F.  zoom  of   diversity  G.  bacteria  forming  EPS  network  H.  SEM  of  wood  fragment  (not  pyrolized).  

Sample Photo Mag Total Pieces Piece Height (mm) Length (mm) Width (mm) Avg Height Avg Length Avg Width Volume (mm3) Sorted volumes Notes

Control N3_04 x50 ~1460 A 0.23 6.02 1.53 7.78 7.55 0.98 B 0.23 2.48 0.75 3.46 3.23 1.31 C 0.32 0.55 0.48 1.35 1.03 1.35 D 0.28 2.59 1.59 4.46 4.18 3.46 E 0.3 2.93 0.95 4.18 3.88 4.18 F 0.13 0.61 0.24 0.98 0.85 4.46 G 0.3 0.11 0.9 1.31 1.01 7.78

N2_09 x50 1 A 10 20.8 8 38.8 28.8 min size to maintain some pore structure ~ 300nm 38.8

N2_01 x50 1 A 4.42 24.18 10.39 38.99 34.57 38.99

N2_04 Significant cracking seen (ie. the charcoal piece is crumbling) as seen in N1_04 and N1_05 AVERAGE 1.801111111 6.696666667 2.758888889 11.25666667

MEAN 0.3 2.59 0.95 3.84

ST DEV 3.369419254 9.16566146 3.722917016 Internal Surface Area (hexagonal)

DIY D3_06 x50 >200 A 1 12 10.69 23.69 0 B 0.2 5 0.5 5.7 0.03 C 0.36 1.9 0.18 2.44 0.06 D 0.22 0.8 0.48 1.5 0.23 E 0.15 0.37 0.1 0.62 0.62 F 0.05 0.48 0.14 0.67 0.67 D3_05 x250 - A 1.17 1.084 2.41 4.664 1.21 B 3.48 1.574 3.6 8.654 1.5 C 2.3 5.3 4.5 12.1 1.65

D3_04 x200 ~1620 ` ` ` ` 0 2.44 Just used this sample for counting particles

D3_02 x300 - A 0.06 0.9 0.69 1.65 2.7

B 0.26 0.83 0.12 1.21 4.664

C 0.01 0.17 0.05 0.23 5.35

D 0.01 0.01 0.01 0.03 5.7

E 0.03 1.4 1.27 2.7 8.654

F 0.01 0.03 0.02 0.06 12.1 no pores at all! for all of these (too small) D3_01 x35 - A 1.08 16.71 12.54 30.33 17.21 B 1.49 10.66 5.06 17.21 23.69 C 0.46 4.14 0.75 5.35 30.33 D2_07 x90 1 A 4.84 - -AVERAGE 0.9042105263 3.519888889 2.395 6.819099415 MEAN 0.26 0.26 0.595 1.115 STD DEV 1.324429923 4.825391248 3.726788951

Fine F1_01 x35 1 A 9.06 3.129 2.392 14.581 difficult to measure pores, piece is very crumbly 0.024

F3_01 X80 A 0.107 1.236 0.332 1.675 0.261 B 0.004 0.294 0.206 0.504 0.299 C 0.046 0.099 0.116 0.261 0.504 D 0.447 3.097 1.047 4.591 0.802 E 0.159 1.142 0.388 1.689 1.675 F 0.304 0.491 0.007 0.802 1.689 G 0.006 0.219 0.074 0.299 4.591

H 0.007 0.014 0.003 0.024 added in to symbolize the fine dust particles 6.522

F2_05 x40 1 A 1.175 4.279 1.068 6.522 14.581

AVERAGE 1.1315 1.4 0.5633 3.0948

MEAN 0.133 0.8165 0.269 1.2185

STD DEV 2.808567735 1.538730573 0.7533349631 Rough unfortunately this sample is too crumbly to get meaningful data from

Control FineChar DIYChar 0.85 0.024 0 1.01 0.261 0.03 1.03 0.299 0.23 3.23 0.504 0.67 3.88 0.802 1.21 4.18 1.2185 1.65 7.55 1.675 2.7 12 1.689 4.664 17 3.0948 5.7 28.2 4.591 12.1 32.2 6.522 23.69 34.57 14.581 30.33

Sample Photo Mag Scale Pore Depth (um) Length (um) Width (um) Depth (mm) Particle Volume range Vol Internal Surface Area (um)ISA (mm) AVG LENGTH 270.6363636 mm Control N2_12 x200 200um A 1888 2024 1452 5.548545024 0 5548545.024 13527000 13527 AVG WIDTH 196.3327273

B - 155 86 #VALUE! 0.000012864 AVG DEPTH 1324.333333 C - 78 50.66 #VALUE! 0.009664 AVG ISA 2120224.571 2120.224571

D - 120 82 #VALUE! 0.010296 avg vol 70368154.18 70368.15418 Sample AVG VOL AVG ISA E - 26 27 #VALUE! 0.017578 Control 7.036815418 3.212022457 F 268 8 6 0.000012864 0.03056 4932 4.932 DIY 6.268927883 13.18368854 G - 126 26 #VALUE! 0.1 Fine 4.858756252 1.380438741

0 1.2 0

N2_09 x50 1mm A 1430 90 80 0.010296 2.9 274820 274.82 *only area where we were able to directly observe length, width, and depth. Sample Total Internal Surface Area to Volume Ratio B 940 170 110 0.017578 3.7 359100 359.1 Control 0.4564596719

C 1510 80 80 0.009664 4.5 257580 257.58 DIY 2.103021247 D 1910 100 160 0.03056 5.548545024 418140 418.14 FineChar 0.2841136022

0 8

0 12 DIY D2_12 x300 100um A - 717 383 #VALUE!

B - 416 177 #VALUE! AVG length 277.46875 C - 197 248 #VALUE! AVG width 185.4 D - 125 170 #VALUE! AVG depth 14130.14286

E - 355 206 #VALUE! AVG VOL 726892788.3 726892.7883 *noted that these pore types are largest in sample* F - 602 285 #VALUE! AVG ISA 13183688.54 131836.8854

G - 642 356 #VALUE! H - 194 141 #VALUE! I - 102 94 #VALUE! D2_11 x350 100um A - 547 266 #VALUE! B - 317 531 #VALUE! C - 293 308 #VALUE! D - 171 160 #VALUE! E - 249 207 #VALUE! F - 137 168 #VALUE! G - 116 63 #VALUE! H - 22 18 #VALUE! I - 89 58 #VALUE! J - 33 29 #VALUE! D1_02 x90 500um A - 81 43 #VALUE! B - 136 84 #VALUE! C - 97 30 #VALUE! D - 388 217 #VALUE! E - 413 332 #VALUE! F - 273 61 #VALUE!

D2_03 x35 1mm A 4420 244 - #VALUE! These measurements are for the larger pore type in this sample, and are likely misleading. The pore when whole extrends throughout the entire sample - htes are the surface pores I can see, some are broken and I cannot follow the entire length B 10436 268 - #VALUE! C 11771 358 - #VALUE! D 23828 199 - #VALUE! E 19319 396 - #VALUE! F 3247 375 - #VALUE! G 25890 327 - #VALUE! 0 0

Fine F2_08 x500 100um A 321 228 0 Super crumbly, pore structure not well maintained :( (see circled areas)

B 211 289 0 C 213 264 0 F2_05 x40 1mm A - 2510 1340 #VALUE! B - 540 350 #VALUE! C - 200 170 #VALUE! D - 130 70 #VALUE! E - 280 160 #VALUE! F - 120 110 #VALUE!

F2_01 x50 1mm A 1620 130 - #VALUE! Not the best measures again, estimates of length of interior pores based on chipping away of surface structure of biochar piece B 13140 270 - #VALUE! Note: size of biochar piece itself: 2619um x 5560 um

C 11130 100 - #VALUE! D 23350 200 - #VALUE! E 5720 190 - #VALUE! F 2400 250 - #VALUE!

in mm Rough unfortunately this sample is too crumbly to get meaningful data from AVG Depth 9560

AVG Length 377.6666667 AVG Height 331.2222222 AVG Volume 1195875625 1195875.625 AVG ISA 13804138.74 13804.13874

Figure  2.  Left:  SEM  image  of  DIYBiochar pre-­‐inoculation,  displaying  varied  and  stable  pore  structure.  Right:  SEM   image  of  DIYBiochar covered  in  microbiota  (putative  yeast  colonies),  demonstrating  microbial  colonization.

A c k n o w l e d g e m e n t s

I would first like to thank Dr. Darcy Mathews for his incredible support and mentorship, along with the School of Environmental Studies for forever inspiring my studies and selecting me for the honour of receiving this JCURA. Additional gratitude to Brent McGowan of the UVic Advanced Microscopy Facility for training me in Scanning Electron Microscopy and my friend Rory Hills for letting me use the FTIR-Spectrophotometer in his lab! Finally, with great respect I thank the Lkwungen and Songhees Peoples on whose traditional territory I live and work, and Indigenous peoples of the world whose relationships with the land continue to this day.

Δ

Figure  5.  Left:  represents  the  range  of  particle  volumes  in  Control,  DIY,  and  Fine  Char.    Right:  represents  the  

average  total  volume/internal  surface  area  (ISA).  ISA  provides  habitat  for  soil  microorganisms;  high  ISA  =  potential   for  greater  microbes  per  mm3  of  soil.  (Note  that  RoughChar was  too  ‘crumbly’  to  measure.)  Data  was  observed   from  SEM  images  of  ~200  biochar  fragments.  

Figure  2.  FTIR-­‐ATR  Spectroscopy  readings  of  (clockwise  from  top  left)  1.  Control  Charcoal,  2.  RoughChar,  3.  

FineChar,  and  4.  DIY  Biochar.  Samples  were  dried,  crushed,  then  analyzed  on  benchtop  FTIR-­‐ATR.  Greater  peaks  at   1620 cm-1 region indicate conversion of cellulose/lignin to stable, aromatic carbon.