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Impacts of biochar application to a Douglas-fir forest soil on greenhouse gas fluxes and water quality Hawthorne, Iain 2017

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Impacts of Biochar Application to a Douglas-fir Forest Soil on Greenhouse Gas Fluxes and Water Quality by  Iain Hawthorne  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMEcleNTS FOR THE DEGREE OF   Doctor of Philosophy  in  The Faculty of Graduate and Postdoctoral Studies  (Geological Sciences)  THE UNIVERSITY OF BRITISH COLIMBIA (Vancouver)  April 2017 © Iain Hawthorne, 2017  ii Abstract Forest management for carbon sequestration is a valuable tool to combat rising greenhouse gas (GHG) concentrations in the Earth’s atmosphere. This thesis examined the use of biochar, a product of the thermal decomposition of waste organic matter in a reduced oxygen environment (i.e. pyrolysis) that is applied to soil, as an option for increasing carbon sequestration in a Coastal Douglas-fir forest soil in British Columbia when applied with and without urea fertilizer at 200 kg N ha-1. Biochar produced from Douglas-fir forestry slash materials was used in this study to address this from a systems-based perspective.  A soil incubation study showed that biochar application at high rates (10% oven dry soil basis) significantly increased CO2 and N2O emissions when applied without fertilizer and at both low (1%) and high rates (10%) decreased CH4 consumption without fertilization. In terms of carbon dioxide equivalent emissions (CO2e), it was shown that CO2 accounted for >98% from all treatments. In a field study, GHG fluxes were measured after application of 5 t ha-1 of biochar to a Douglas-fir forest soil in the first year followed by urea-N fertilization in the second year. The results showed that 5 t ha-1 of biochar had little effect on GHG fluxes and their total CO2e fluxes. Applying biochar prior to fertilizer application following industry-standard practices did not significantly change treatment CO2e fluxes. It was concluded that low rates of biochar application to this forest soil would improve soil C sequestration with or without fertilization.  In the field and laboratory experiments, soil pore water was extracted and analyzed for C and N concentrations and dissolved organic carbon using spectral indices. The results showed that low biochar application rates could be beneficial for both increasing C-sequestration and N-retention. Changes in spectral indices measured in the laboratory suggested that alterations in the dissolved organic matter pool could lead to changes in GHG emissions due to changing substrate  iii supply for microbes as application rates increased. There is a recognized need for further studies prior to large-scale industrial applications; however, as result of this work it is possible to provide recommendations for large-scale pilot studies.      iv Preface Chapter 2 is based on work conducted in UBC laboratories. The author designed and conducted an experiment utilizing a state-of-the-art measurement system capable of quantifying difficult-to-detect greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) using a novel soil incubation system. This development facilitated a first of its kind comparison of treatment effects on soil GHG fluxes after treatment of a Douglas-fir forest soil with 0%, 1% and 10% Douglas-fir derived biochar with and without a 200 kg ha-1 urea-N fertilizer application. This work was presented at the 2016 Canadian Society of Soil Science and Pacific Society of Soil Science Joint Annual General Meeting 16th May 2016. The author collected and analyzed the data, and wrote a paper, to which Drs. M.S. Johnson, T.A. Black, R. S. Jassal, S. M. Smukler and N. J. Grant made editorial contributions that is published in the Journal of Environmental Management: Hawthorne, I., Johnson, M. S., Jassal, R. S., Black, T. A., Grant, N. J., and Smukler, S. M. (2017), Application of biochar and nitrogen influences fluxes of CO2, CH4 and N2O in a forest soil, Journal of Environmental Management, 192, 203-214, doi:http://dx.doi.org/10.1016/j.jenvman.2016.12.066.  Chapter 3 is based on work conducted near a well-established micrometeorology and stream water quality research site, jointly operated by the UBC Biometeorology and Soil Physics Research Group and UBC Ecohydrology Research Group, in a Coastal Douglas-fir forest near Campbell River on Vancouver Island, Canada. The author, with field help from undergraduate and laboratory associates, conducted an experiment using non-steady-state chamber techniques and gas chromatography to measure fluxes of CO2, CH4 and N2O over 1 year following a surface application of Douglas-fir derived biochar (5 t ha-1) and a surface application of urea fertilizer  v (200 kg N ha-1) in the following year. The author collected and analyzed the data and wrote a paper draft, which was guided and contributed to by Drs. M.S Johnson, T.A. Black, and R. S. Jassal, with additional input at the later editing stages from N.J. Grant. This work was presented at the American Geophysical Union 2013 Annual General Meeting on 16th December 2013. It was also presented at the Western Silvicultural Contractors’ Association on 2nd February 2017, which stimulated discussion with government and private companies on how to increase the scale and scope of biochar research for enhancing carbon sequestration using forest management in BC. The goal is to publish this chapter in a suitable peer-reviewed journal. Of significance were the installation, development and maintenance of the laboratory gas chromatography system. The author acted as supervisor of instrument operations for more than 10 undergraduate and graduate projects collaborating with the Faculty of Forestry, Faculty of Land and Food Systems and the Department of Geography at UBC, and) and with two other university organizations (Quest University, Squamish, BC and Cornell University, Ithaca, New York) to facilitate affordable GHG analysis, helping to promote environmental science across in North America.  Chapter 4 is based on work done using the laboratory incubation system described in Chapter 2 and the field research site described in Chapter 3. The author describes soil pore water extractions from both studies and how the dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) are affected by Douglas-fir derived biochar and urea-N fertilizer application. Measured ultraviolet-visible spectroscopy spectral indices were used to compare the dissolved organic matter molecular signals between the treatments, an area not well researched yet in the current literature available for biochar studies. The author collected and analyzed the data and  vi has worked on a draft of this chapter, which was guided and contributed by Drs. M.S Johnson, T.A. Black, and R. S. Jassal. This work was presented on 2nd February 2017 at the Western Silvicultural Contractors’ Association annual general meeting, further stimulating discussion with government and private companies on how to increase the scale and scope of biochar research for enhancing carbon sequestration using forest management in BC. The aim is to publish this chapter in a suitable peer-reviewed journal.    vii Table of Contents Abstract	...................................................................................................................................................	ii	Preface	....................................................................................................................................................	iv	Table	of	Contents	...............................................................................................................................	vii	List	of	Tables	.........................................................................................................................................	xi	List	of	Figures	......................................................................................................................................	xii	List	of	Abbreviations	........................................................................................................................	xvi	List	of	Symbols	................................................................................................................................	xviii	Acknowledgments	............................................................................................................................	xix	Chapter	1:	Introduction	.....................................................................................................................	1	1.1	Background	and	study	motivation	.....................................................................................................	1	1.2	Research	objectives	.................................................................................................................................	7	1.3	Thesis	overview	........................................................................................................................................	7	Chapter	2:	Douglas-fir	forest	soil	incubation	experiment	to	measure	greenhouse	gas	emissions	after	biochar	application	at	three	rates	with	and	without	urea-N	fertilization	..........................................................................................................................................	10	2.1	Introduction	............................................................................................................................................	10	2.2	Materials	and	methods	........................................................................................................................	12	2.2.1	Soil	...........................................................................................................................................................................	12	2.2.2	Biochar	...................................................................................................................................................................	13	2.2.3	Experimental	incubations	..............................................................................................................................	14	2.2.4	GHG	flux	measurements	.................................................................................................................................	15	 viii 2.2.5	Statistical	analysis	.............................................................................................................................................	17	2.3	Results	and	discussion	........................................................................................................................	18	2.3.1	Temporal	variation	in	GHG	fluxes	..............................................................................................................	18	2.3.2	Effect	of	biochar	application	rates	on	soil	GHG	fluxes	from	unfertilized	soil	..........................	20	2.3.3	Fertilizer	effects	on	soil	GHG	flux	responses	to	biochar	application	rates	...............................	22	2.3.4	Influence	of	soil	moisture	on	GHG	fluxes	................................................................................................	24	2.3.5	Global	warming	potentials	............................................................................................................................	24	2.4	Conclusions	.............................................................................................................................................	26	2.5	Tables	........................................................................................................................................................	27	2.6	Figures	......................................................................................................................................................	28	Chapter	3:	In-situ	measurements	of	greenhouse	gas	emissions	after	biochar	application	with	and	without	urea-N	fertilization	at	a	Coastal	Douglas-fir	forest	......	37	3.1	Introduction	............................................................................................................................................	37	3.2	Methods	....................................................................................................................................................	41	3.2.1	Site	description	..................................................................................................................................................	41	3.2.2	Experimental	design	........................................................................................................................................	42	3.2.3	Biochar	and	fertilizer	application	..............................................................................................................	43	3.2.3	Field	soil	GHG	emission	measurements	..................................................................................................	44	3.2.3.1	Sample	collection	and	ancillary	measurements	...............................................................................	44	3.2.3.2	Greenhouse	gas	analyses	and	flux	calculations	................................................................................	46	3.2.4	Data	analysis	........................................................................................................................................................	46	3.3	Results	.......................................................................................................................................................	48	3.3.1	Soil	climate	...........................................................................................................................................................	48	3.3.2	Soil	greenhouse	gas	fluxes	.............................................................................................................................	49	3.3.2.1	Soil	CO2	emissions	.........................................................................................................................................	49	 ix 3.3.2.2	Soil	CH4	Uptake	...............................................................................................................................................	52	3.3.2.3	Soil	N2O	emission	and	uptake	..................................................................................................................	53	3.3.2.4	Soil	CO2e	and	GWP	........................................................................................................................................	54	3.3.3	Soil	greenhouse	gas	flux	relationships	to	soil	climate	.......................................................................	55	3.4	Discussion	................................................................................................................................................	57	3.4.1	Biochar	treatment	effects	..............................................................................................................................	57	3.4.2	Fertilizer	treatment	effects	...........................................................................................................................	59	3.4.3	Carbon	dioxide	equivalent	fluxes	...............................................................................................................	63	3.5	Conclusions	.............................................................................................................................................	64	3.6	Tables	........................................................................................................................................................	65	3.7	Figures	......................................................................................................................................................	66	Chapter	4:	Laboratory	and	field	measurements	of	dissolved	organic	carbon	and	nitrogen	after	biochar	application	with	and	without	urea-N	fertilization	to	a	West	Coast	Douglas-fir	forest	soil	...........................................................................................................	71	4.1	Introduction	............................................................................................................................................	71	4.2	Materials	and	methods	........................................................................................................................	75	4.2.1	Field	soil	water	sampling	...............................................................................................................................	76	4.2.2	Laboratory	Experimental	Setup	..................................................................................................................	78	4.2.3	Soil	pore	water	analysis	.................................................................................................................................	78	4.2.4	Soil	moisture	characterization	....................................................................................................................	80	4.2.5	Statistical	analysis	.............................................................................................................................................	81	4.2.5.1	Field	experiment	............................................................................................................................................	81	4.2.5.2	Laboratory	experiment	...............................................................................................................................	81	4.3	Results	.......................................................................................................................................................	82	4.3.1	Field	experiment	................................................................................................................................................	82	 x 4.3.1.1	Soil	climate	.......................................................................................................................................................	82	4.3.1.2	Field	ancillary	measurements	of	soil	pore	water	quality	.............................................................	83	4.3.1.3	Field	soil	pore	water	DOC	and	TN	..........................................................................................................	84	4.3.1.4	Field	soil	pore	water	spectral	indices	...................................................................................................	86	4.3.2	Laboratory	experiment	...................................................................................................................................	87	4.3.2.1	Laboratory	ancillary	soil	pore	water	quality	measurements	.....................................................	87	4.3.2.2	Laboratory	soil	pore	water	DOC	and	TN	.............................................................................................	87	4.3.2.3	Laboratory	soil	pore	water	spectral	indices	......................................................................................	89	4.3.2.4	Laboratory	soil	TOC,	NO3-	and	NH4+	post	experiment	...................................................................	89	4.4	Discussion	................................................................................................................................................	91	4.5	Conclusions	.............................................................................................................................................	95	4.6	Tables	........................................................................................................................................................	96	4.7	Figures	......................................................................................................................................................	99	Chapter	5:	Summary	and	conclusions	......................................................................................	109	5.1	Future	work	..........................................................................................................................................	111	5.2	Overall	significance	............................................................................................................................	112	References	.........................................................................................................................................	114	Appendices	........................................................................................................................................	130	Appendix	1	Site	soil	description	............................................................................................................	130	Appendix	2	Biochar	details	.....................................................................................................................	133	Appendix	3	Minimum	detectable	flux	estimation	...........................................................................	134	Appendix	4	Project	instrumentation	...................................................................................................	136	Appendix	5	Site	images	............................................................................................................................	137	   xi List of Tables Table	1.	Summary	of	experimental	treatments	........................................................................................................	27	Table	2.	Greenhouse	gas	annual	(Annl)	and	seasonal	one-way	ANOVA.	Spring-summer	(SS),	Summer-fall	(SF).	HSD	analysis	indicates	which	treatments	were	determined	to	be	significantly	larger	than	others	in	list.	.....................................................................................................................................................	65	Table	3.	Treatment	annual	dependence	of	Rs10	(μmol	CO2	m-2	s-1)	and	Q10Rs	parameters	in	Eq.2.	Also	shown	model	goodness	of	fit	parameters	AIC	and	R2.	Fitted	relationships	for	all	data	can	be	seen	in	Figure	14.	................................................................................................................................................................	65	Table	4.	Field	ECs	and	ECSCL,	pHSCL	and	spectral	indices.	All	values	given	are	mean	(±1	standard	error)	of	all	available	data	in	2013	...................................................................................................................................	96	Table	5.	DOCSCL	vs	TNSCL	linear	regression	analysis	output	...................................................................................	96	Table	6.	Laboratory	EC,	pH	and	spectral	indices.	All	values	given	are	mean	(±1	standard	error)	of	all	data	collected	after	more	than	1	L	of	DDI	had	been	flushed	through	each	incubation.	....................	97	Table	7.	DOCLAB	vs	TNLAB	linear	regression	analysis	output	..................................................................................	97	Table	8.	S275-295	vs	leachate	volume	linear	regression	analysis	output	.............................................................	98	Table	9.	Soil	characteristics	literature	review	.......................................................................................................	130	Table	10.	Soil	characteristics	........................................................................................................................................	131	Table	11	Fine	mineral	fraction	%	...............................................................................................................................	131	Table	12.	Textural	class	soil	organic	matter	content	...........................................................................................	131	Table	13.	Diacarbon	Douglas-fir	derived	biochar	particle	size	distribution	...............................................	133	    xii List of Figures Figure	1.	Incubation	units	attached	to	the	CRDS	greenhouse	gas	analyzer	comprising	a	closed	chamber	soil	flux	measurement	system.	The	four	replicates	of	the	six	treatments	are	shown	in	the	inset	photo,	as	(A)	unamended	soil	(S);	(B)	Soil	+	1%	biochar	(w/w)	(BC1%);	(C)	Soil	+	10%	biochar	(BC10%);	(D)	Soil	+	fertilizer	(200	kg	N	ha-1	equivalent	of	urea	fertilizer)	(S+F);	(E)	Soil	+	1%	biochar	+	fertilizer	(BC1%+F);	(F)	Soil	+	10%	biochar	+fertilizer	(BC10%+F).	..................................	28	Figure	2.	(A)	Cumulative	CO2	effluxes	during	experiment	by	treatment	(means	±	1	standard	error	(SE));	(B)	Daily	mean	CO2	flux	time	series	plot	(means	±	1	standard	error	(SE)).	..............................	29	Figure	3.	(B)	Cumulative	CH4	effluxes	during	experiment	by	treatment	(means	±	1	standard	error	(SE));	(B)	Daily	mean	CH4	flux	time	series	plot	(means	±	1	standard	error	(SE)).	..............................	30	Figure	4.	(A)	Cumulative	N2O	effluxes	during	experiment	by	treatment	(means	±	1	standard	error	(SE));	(B)	Daily	mean	N2O	flux	time	series	plot	(means	±	1	standard	error	(SE)).	..............................	31	Figure	5.	Boxplots	of	soil	CO2	efflux	during	the	post-disturbance	period	by	treatment.	The	thick	horizontal	line	represents	the	means,	the	upper	and	lower	lines	of	the	boxes	indicate	the	means	±	1	SE,	and	the	whiskers	extend	to	the	maximum	and	minimum	fluxes	measured	during	the	post-disturbance	period.	..................................................................................................................................................	32	Figure	6.	Boxplots	of	soil	CH4	efflux	during	the	post-disturbance	period	by	treatment.	The	thick	horizontal	line	represents	the	means,	the	upper	and	lower	lines	of	the	boxes	indicate	the	means	±	1	SE,	and	the	whiskers	extend	to	the	maximum	and	minimum	fluxes	measured	during	the	post-disturbance	period.	..................................................................................................................................................	33	Figure	7.	Boxplots	of	soil	N2O	efflux	during	the	post-disturbance	period	by	treatment.	The	thick	horizontal	line	represents	the	means,	the	upper	and	lower	lines	of	the	boxes	indicate	the	means	±	1	SE,	and	the	whiskers	extend	to	the	maximum	and	minimum	fluxes	measured	during	the	post-disturbance	period.	..................................................................................................................................................	34	Figure	8.	Principal	component	analysis	of	experimental	variables.	..................................................................	35	Figure	9.	Effect	of	treatments	on	net	GHG	emissions	in	terms	of	CO2e	relative	to	unamended	soil.	The	magnitude	of	the	CH4	consumption	is	subtracted	from	the	CO2	plus	N2O	total.	...................................	36	 xiii Figure	10.	Site	soil	climate	variables.	(A)	Daily	cumulative	precipitation	measured	at	a	nearby	climate	station.	Dashed	lines	indicate	biochar	application	(27th	Feb	2012)	and	fertilizer	application	(27th	Mar	2013);	(B)	Daily	mean	Ts15	from	averaging	each	MPS-2	and	GS3	measurements;	(C)	Daily	mean	θ s15	from	averaging	GS3	measurements;	(D)	Logarithm	of	the	daily	mean	Ψ s15	from	MPS-2	measurements.	Dashed	lines	indicate	sample	dates.	....................................................................................	66	Figure	11.	Mean	treatment	CO2	fluxes	for	soil	(S),	biochar	(BC),	fertilizer	(F)	and	Biochar	plus	Fertilizer	(BC+F).	During	2012	when	only	the	biochar	treatment	was	applied	(top	panel);	Measurements	during	2013	when	all	treatment	were	applied	(bottom	panel).	Error	bars	indicate	±	one	standard	error	of	the	mean	treatment	value	and	points	have	been	spread	evenly	for	each	sample	date	to	ease	comparison.	.........................................................................................................................	67	Figure	12.	Methane	2012:2013	seasonal	treatment	effect	(mean	±	1	SE).	Spring-summer	(SS),	Summer-fall	(SF).	Lettering	(a-b)	indicates	pairwise	differences	detected	in	HSD	analysis	when	p	<	0.05.	............................................................................................................................................................................	68	Figure	13.	Nitrous	oxide	2012:2013	seasonal	treatment	fluxes	(Mean	±	1	SE).	Summer-spring	(SS),	Spring-fall	(SF).	Lettering	(a-b)	indicates	pairwise	differences	detected	in	HSD	analysis	when	p	<	0.05.	................................................................................................................................................................................	69	Figure	14.	Relationship	of	soil	CO2	efflux	(Rs)	to	soil	temperature	at	15-cm	depth	for	different	treatments.	..................................................................................................................................................................	70	Figure	15.	Suction	cup	lysimeter	(SCL)	sample	date	(dashed	lines)	daily	(month-day)	soil	climate	at	15-cm	depth.	(A)	Daily	precipitation	with	cumulative	precipitation	prior	to	each	sample	date	is	provided	above	and	below	this	the	cumulative	amount	of	precipitation	since	fertilization;	(B)	Treatment	ECs,	mean	(±1	SE),	1	and	2	asterisks	indicates	where	one	way	ANOVA	detected	statistical	differences	between	treatments	at	p<0.01	and	p	<0.5	respectively;	(C)	Soil	temperature,	site	mean	(±1	standard	deviation);	(D)	Volumetric	soil	water	content	site	mean	(±1	SD);	(E)	Soil	water	matric	potential,	site	mean	(±1	SD).	In	plots	B-E	values	shown	are	the	mean	values	from	soil	sensors	with	one	in	each	of	12	plots,	evenly	distributed	between	treatments.	The	red	dashed	line	indicates	timing	of	fertilization.	...................................................................................	99	 xiv Figure	16.	Field	sample	date	treatment	surface	EC	measurements,	mean	(±1	standard	error).	(i)	2-days.	(ii)	9-days.	(iii)	16-days	and	(iv)	21-days	after	fertilization	on	26	March	2013.	There	is	a	significant	response	to	fertilization	in	F	and	BC+F	treatment	plots	9-days	and	less	so	16-days	after	fertilization.	...................................................................................................................................................	100	Figure	17.	Field	sample	date	treatment	mean	DOCSCL	(±1	standard	error).	(A)	2012;	(B)	2013	...........	100	Figure	18.	Field	sample	date	(month-day)	treatment	mean	TN	(±1	standard	error).	(A)	2012;	(B)	2013	......................................................................................................................................................................................	101	Figure	19.	Suction	cup	lysimeter	mean	(±1	standard	error)	DOC	and	TN	observations	from	data	collected	during	2013	(A)	DOCSCL;	(B)	TNSCL;	(C)	Treatment	DOC	vs	TN	analysis.	Colored	dashed	lines	in	panels	(A)	and	(B)	indicate	the	treatment	to	be	used	as	an	aid	to	identifying	dataset	outliers.	......................................................................................................................................................................	102	Figure	20.	Suction	cup	lysimeter	mean	(±1	standard	error)	(A)	S275-295;	(B)	SUVA254	..............................	103	Figure	21.	Soil	incubation	pore	water	treatment	mean	EC	(A)	and	pH	(B)	measurements	over	the	course	of	the	leaching	experiment.	Error	bars	indicate	one	standard	error.	....................................	104	Figure	22.	Soil	incubation	pore	water	treatment	mean	DOC	(A)	and	TN	(B)	measurements	over	the	course	of	the	leaching	experiment.	Error	bars	indicate	one	standard	error.	....................................	105	Figure	23.	Soil	incubation	pore	water	treatment	boxplots	of	(A)	DOC	and	(B)	TN	after	>1	L	volume	of	leachate	collected.	Error	bars	are	one	standard	error.	(C)	Relationship	between	DOC	and	TN.	The	coloured	dashed	lines	are	the	lines	of	best	fit	for	each	treatment	following	treatment	color	conventions	in	(A)	and	(B).	.................................................................................................................................	106	Figure	24.	Soil	incubation	pore	water	treatment	UV-Vis	spectroscopy-based	indicators	of	DOC	quality	(A)	S275-295	and	(B)	SUVA254	..................................................................................................................................	107	Figure	25.	(A)	Amount	of	nitrogen	species	(NH4+-N	and	NO3--N)	remaining	in	soil	following	the	soil	incubation	experiment	for	each	treatment;	(B)	Mean	treatment	ratio	of	nitrogen	species	(NH4-N/NO3-N).	Error	bars	are	one	standard	error.	.............................................................................................	108	Figure	26.	This	image	shows	a	slash	piles	in	the	foreground,	with	eight	more	visible	in	the	background	at	a	near-by	(within	500	m	of	research	location)	harvested	forest	location	that	could	have	been	feedstock	for	biochar	production.	....................................................................................................................	137	 xv Figure	27.	This	image	shows	the	slash	piles	burned	to	improve	access	for	replanting	and	reduce	forest	fire	hazard.	...............................................................................................................................................................	137	Figure	28.	Research	site	soil	profile.	..........................................................................................................................	138	Figure	29.	This	image	shows	a	static	non-steady	state	chamber	between	two	large	trees	with	the	chamber	lid	on	during	a	site	greenhouse	gas	flux	measurement	campaign.	.....................................	138	Figure	30.	This	image	shows	the	ancillary	GS3	instrument	in	use	for	surface	soil	electrical	conductivity	(ECs),	volumetric	water	content	and	temperature	measurements	in	close	proximity	to	a	static	non-steady	state	chamber	collar.	Also	seen	are	5	vials	used	for	greenhouse	gas	sample	storage	in	preparation	for	chamber	flux	measurements.	.............................................................................................	139	Figure	31.	Biochar	being	hand	applied	on	date	of	application	(27th	Feb	2012).	.........................................	139	Figure	32.	Completed	biochar	application,	showing	contrasting	color	with	untreated	surrounding	area.	............................................................................................................................................................................	140	   xvi List of Abbreviations ANOVA – analysis of variance AIC – Akaike information criterion UBC – the University of British Columbia C - carbon CO2 – carbon dioxide CO2e – carbon dioxide equivalent CH4 - methane CEC – cation exchange capacity DDI – deionized distilled water df – Degrees of freedom EC – electrical conductivity F – F statistic DOC – dissolved organic carbon FID – flame ionization detector GC – gas chromatograph GHG – greenhouse gas GPP – gross primary productivity GWP – global warming potential HCL - hydrochloric acid IBI – international biochar initiative IPCC – Intergovernmental Panel on Climate Change KCL – potassium chloride  xvii µECD – micro-electron capture device MC – molecular weight N – nitrogen NEP – net ecosystem productivity NH4+ - ammonium NO3- – nitrate N2O – nitrous oxide NPOC – non-purgeable organic carbon p - probability value PTFE - polytertraflouroethylene SCL – suction cup lysimeter SE – standard error SNSS – static non-steady state chamber SOC – soil organic carbon SOM – soil organic matter (~58% carbon) SUVA254 – specific UV absorbance at 254 nm TN – total nitrogen TOC – Total organic carbon Tukey HSD – Tukey honest significant difference test UV-vis – Ultraviolet visible     xviii List of Symbols Ts  - soil temperature (0-5 cm)   θs - volumetric soil water content (0-5 cm) σs - bulk electrical conductivity (0-5 cm) θ15 - volumetric soil water content (15-cm depth)  σ15 - bulk electrical conductivity (15-cm depth) T15 - soil temperature (15-cm depth) Ѱ15 - soil water matric potential (15-cm depth) Ra – autotrophic soil respiration Re – ecosystem respiration Rh – heterotrophic soil respiration Rs – total soil respiration Rs10 – reference respiration rate at 10 °C Q10Rs - relative change in Rs for 10 °C change in shallow surface soil temperature     xix Acknowledgments This research in this thesis was a financially supported through a Natural Resources and Applied Sciences Research Team Grant from the BC Innovation Council to MSJ and TAB, with additional support from the Natural Sciences and Engineering Research Council Discovery grants to MSJ and TAB.  The author is thankful to Decagon Devices, Inc. for support through the Grant A. Harris Fellowship.  The work carried out in this thesis could not have been achieved without the network of support that I receive at UBC. Thank you to all of the undergraduate students and graduate students, in particular Cameron Webster and Mathias Meyer, who have helped chip away at the task in hand. The UBC Biometeorology and Soil Physics Group and members, Nicholas Grant, Zoran Nesic, Hughie Jones and past member Rick Ketler have been instrumental in providing essential field, laboratory and moral support. Thanks guys!  I would like to thank Dr. Mark Johnson for much more than being my supervisor, his support and guidance has been bottomless, Dr. Andrew T. Black for guiding me academically to value the attention to detail that he applies to every endeavor and Dr. Ulrich Meyer for responding promptly, effectively and kindly whenever called.  I would like to thank my family for always believing in me. Finally, Christine, THANK YOU, you have kept me in check, making me a stronger and happier person.    I would like to dedicate this thesis to David Gaumont-Guay (1972-2016) and Thomas Hilker (1976-2016). They remain inspirational.   1 Chapter 1: Introduction 1.1 Background and study motivation  Global climate change caused by increasing concentrations of greenhouse gases (GHGs) in the Earth’s atmosphere is creating an uncertain climate future for generations to come (IPCC, 2014). There are no human systems exempt from a direct or indirect dependency on natural resource supply and climate. A broad recognition of this has pushed forward the issue of how to best reduce the anthropogenic contribution to global warming through quantifying sinks and sources of GHGs, finding ways to maximize the efficiency with which we manage natural resources and acknowledging ecosystems for their services (Foley et al., 2005). The most significant long-lived GHGs in terms of atmospheric concentrations are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). It is estimated that CO2 accounts for >65% of the increase in radiative forcing when expressed over a 100 year time period (IPCC, 2014). Referencing the radiative forcing of other non-CO2 gases to the radiative forcing of CO2 provides a metric commonly referred to as the global warming potential (GWP) which can be used to compare emissions from different sources/sectors and landscape treatments on different spatial scales (IPCC, 2014; Lashof and Ahuja, 1990; Montzka et al., 2011). The GWP is calculated on a mass basis with CH4 and N2O each having 28 and 298 times the radiative forcing of CO2 over a 100 year time period, respectively (IPCC, 2014).  One way to reduce the global climatic impact of increasing CO2 and other GHGs is to store carbon (C) in forms other than as CO2 in the atmosphere. Soil C, at 4.5 times the size of the biotic C pool and 3.3 times the size of the atmospheric C pool, is hugely important in the global C cycle (Lal et al., 2004).  Forests absorb CO2 through photosynthesis then release a portion of it back through respiration. On balance, forests in recent decades have acted as a net sink for as  2 much as 30 % of annual anthropogenic CO2 emissions (Houghton, 2000, Pan et al., 2011). Reducing CO2 emissions from deforestation and forest degradation, enhancing C sequestration rates in existing and new forests, substituting wood fuels for fossil fuels, and providing wood products in place of more energy-intensive materials are all recognized options for improving forest management to help mitigate climate change (IPCC, 2014). Increasing soil C stocks through biochar addition provides an additional option that has been receiving increasing attention in recent years (IPCC, 2014).  Biochar is a product of the thermal decomposition of organic matter in a reduced oxygen environment (i.e. pyrolysis). Biochar typically has high C content and has the potential to be used as a climate change mitigation strategy by reducing the amount of CO2 in the atmosphere through increased C sequestration in soils (Lehman et al., 2006). It is resistant to decomposition in soils due to a prevalence of aromatic structures containing few functional groups (Dai et al., 2005, Zimmerman, 2010), which has been shown to be particularly true when the O:C ratio of the biochar is less than 0.2 (Spokas, 2010). Biochar has a lower specific density compared to mineral soils, with larger surface areas (Brewer et al., 2014). When incorporated into soil, biochar alters the physical structure of soil, generally decreasing its bulk density (Major et al., 2010; Lim et al., 2016), increasing the exposure of chemically reactive surfaces, and providing refugia for microbes (Lehmann et al., 2011). Changes to the physical structure of soils impacts soil water-holding characteristics, and leads to alterations in biotic and abiotic processes affecting fluxes of soil CO2, CH4 and N2O (Major et al., 2010; Sohi et al., 2010). Biochar is known to affect soil inorganic nitrogen (N) dynamics differently when applied with or without fertilizers (Nguyen et al., 2017), affecting N contents in plants (Ling et al., 2017). It is advisable therefore that agronomic and silvicultural systems needing to increase productivity where  3 inorganic N is limited must consider effects of biochar application with and without fertilization, as well as the timing of applications (Nguyen et a., 2017). To do this, policy makers need clear information from a broad range of identifiably important forest and soil types. The global forest resource assessment in 2015 determined that deforestation has slowed and reforestation has increased globally since 1990, though not in poorer tropical countries (Sloan and Seyer, 2015). In 2015, Natural Resources Canada reported in their annual State of Canada’s forests report (Natural Resources Canada, 2016), that Canada has 9 % of the world’s forests, exceeded only by Russia and Brazil. This equates to 347 million hectares of forest cover of which 166 million hectares is independently certified as being sustainably managed and 24 million hectares are protected. Notably, the Canadian forest industry generated more than $20 billion CAD in 2015 towards the gross domestic product and provided more than 200,000 jobs. This puts Canada in a unique position to act as a global economic leader in forest C management.  In Canada, some the of the most productive and economically valuable second-growth forests are the stands of Coastal Douglas-fir in British Columbia, where active forestry is an essential part of the local, provincial and national economy. These production forests in the coastal region of British Columbia are typically N-limited systems, and have been found to respond significantly to N-fertilizer additions (Chappell et al., 1991). Fertilizing Coastal Douglas-fir forests remains a common silvicultural practice in British Columbia’s Coastal Forest Action Plan offers fiscal incentives to increase productivity of second-growth coastal forests using fertilization to provide a potential benefit of reducing GHG emissions through increased C sequestration while maintaining employment levels in the forest product sector (BCMFR, 2007). While fertilization can significantly increase gross primary productivity, not all fertilizer applied is taken up by the trees, with estimates of up to 20% of the applied fertilizer lost as N2O gas or  4 through leaching losses of ammonium (NH4- ) and nitrate (NO3-). Strategies to increase soil C sequestration for mitigating climate change that also improve the sustainability of the production of forest products and minimize nutrient losses are urgently needed. Biochar has been suggested as a means for doing so, though studies of biochar use in forested systems are currently limited (Thomas and Gale, 2015).  Soil CO2 emissions, also termed soil respiration (Rs), are controlled by a complicated balance of biotic and abiotic processes that encompass the cumulative release of CO2 from autotrophic (root and rhizosphere, Ra) and heterotrophic (soil organisms, Rh) respiration and some chemical weathering of carbon compounds (Kuzyakov, 2006). Many studies have shown strong and significant positive correlations between Rs and near surface soil temperature (Ts) (Davidson and Janssens, 2006). It is uncertain if surface applied biochar would alter or change this soil Rs - Ts relationship. It is possible that the biochar surface application could reduce the surface albedo leading to changes in thermal soil properties (Usowicz et al., 2016) and potentially increase surface warming and enhance Ts and Rs.  In terms of other primary biogenic GHGs, forest soils can be a source or sink of CH4 and N2O depending on the balance of biotic and abiotic processes, in particular those that regulate microbial activity. For CH4, two pathways exist: methanogenesis that produces methane under anoxic, often water saturated, conditions, and methanotrophy by bacteria that consume CH4 under oxic conditions. Both pathways can exist in the same soil and the resultant CH4 flux is the net balance between them (Hiltbrunner et al., 2012). Studies have found that biochar can increase CH4 oxidation, possibly through improving soil aeration (Van Zwieten et al., 2009, Spokas and Reicosky, 2009), although decreases in CH4 oxidation have also been observed due to reductions  5 in methanotrophic activity (Spokas, 2013), possibly attributable to biochar inhibiting microbial intracellular signaling (Masiello et al., 2013). The soil surface flux of N2O represents the net balance between nitrification, occurring under aerobic conditions, and denitrification, occurring where low O2 conditions exist (Parton et al 1996; Hang Wei et al., 2015). In N deficient soils, emissions of N2O are typically small, with net consumption overall being possible (Chapius-Lardy et al., 2007; Jassal et al., 2010). Studies have shown that biochar additions to soil can reduce N2O emissions, and some of this reduction could be due to N retention directly onto the surfaces of the biochar (Jassal et al., 2015). Biochar has often been reported to reduce N2O emissions from soils (Spokas et al., 2010; Cayuela, 2010; Cayuela, 2014; Harter et al., 2014), although biochar with high N content (e.g., biochar made from animal manure or food waste), has been observed to stimulate N2O fluxes (Spokas and Reicosky, 2009; Singh et al., 2010; Van Zweiten et al., 2010). With uncertainty surrounding how biochar application will influence net GHG fluxes, and with very few field studies in forest soils, there is a need to quantify these fluxes when biochar is applied with and without fertilizer N application.  The impact of biochar application on soil water dynamics depends on the structure and surface properties of the biochar, as controlled by the feedstock type and pyrolysis conditions, as well as by the rate of application and how it is applied, and the characteristics of the soil it is applied to. The porous nature of biochar could improve a soil’s ability to retain moisture by reducing the unsaturated hydraulic conductivity of a soil, though the exact mechanism of such a result remains elusive (Novak et al., 2009). This effect could also help reduce loss of dissolved ions and suspended solids as well as ease crop moisture stress potentially providing higher yields. It may also regulate ammonium-N (NH4+-N) availability through increases in redox  6 potential and cation exchange capacity (CEC). The likely increase in CEC over time resulting from biochar inputs (Lehmann, 2007; Liang et al., 2006) and subsequent absorption of positively charged N-based ions (e.g., NH4+) in biochar amended soils could result in a reduction of dissolved N, causing an initial decrease in plant N availability followed by increasing availability to plants over longer time periods. As the biochar becomes saturated with positive ions, its ability to retain nutrients will decrease. Specifically, NH3 formation after urea-N fertilization and its subsequent adsorption onto the biochar could reduce the inorganic-N pool available for nitrifiers and thus NO3- concentrations in leachate may be reduced.  Biochar application studies in the laboratory have reported differing effects on GHG emissions, soil pore water quality and plant growth over a broad range of mainly agricultural soils. There has been less study of biochar effects on forest soils (which are typically more heterogeneous than agricultural soils), and so there is a need to investigate its potential benefits and pitfalls. Applying biochar to a site could have broad implications for GHG emissions and nutrient leaching, some of which may not be beneficial and may change over time depending on initial biochar physical and chemical properties and resulting interactions with the surrounding soil. Complex interactions between applied biochar and prevailing biogeochemical nutrient cycles and climate restricts our ability to forecast exact effects of biochar application at any site. Thus, field-based studies are needed for establishing feedstock and reaction parameters where organic waste is available. In particular, biochar applications should be investigated as part of a system based approach, where biochar produced from specific site-based biomass waste can be applied on site, which could be the most cost effective for climate change mitigation taking into account additional economic and carbon costs of transportation.   7 The abundant supply of woody materials left on-site after forest harvesting represents a potential feedstock for biochar and energy production via pyrolysis. At present, forest harvest residues are burned in coastal British Columbia to facilitate replanting and reduce fire hazards during warm-dry months, reducing the C sequestration potential of the forest life cycle by releasing significant amounts of GHGs rapidly back to the atmosphere (British Columbia, Ministry of Environment, 2012). However, positive results are being reported from the utilization of biochars derived from woody debris for improving carbon sequestration, and soil water and nutrient dynamics. 1.2 Research objectives This thesis presents a series of studies focused on evaluating the effects of application of Douglas-fir derived biochar in combination with N-fertilizer on the biogeochemistry and hydrology of a forest soil from an intermediate aged Douglas-fir (Pseudotsuga menziesii [Mirbel] Franco) stand on Vancouver Island. The specific research objectives were to: 1. Evaluate impacts of differing biochar application rates on soil fluxes of CH4, N2O and CO2 in combination with fertilizer application. 2. Determine effects of biochar application on dissolved organic concentrations of C and N in leachate in relation to fertilizer application.  1.3 Thesis overview Chapter 2 describes a laboratory based soil incubation experiment utilizing a state of the art measurements system capable of quantifying difficult to detect GHG fluxes of carbon dioxide CO2, CH4 and N2O using laser-based cavity ring-down spectroscopic (CRDS) GHG analyzer (G2508 Greenhouse Gas Analyzer, Picarro Inc., Santa Clara, CA, USA). A comparison of treatment effects on soil GHG effluxes after application of a Douglas-fir forest soil with 0%, 1%  8 and 10% biochar derived from Douglas-fir residual materials was evaluated with and without 200 kg ha-1 urea-N fertilizer. Measurements were performed over a 4-week period, with soil mixtures being kept consistently warm (23 °C) and moist (~40% relative saturation) and open to the atmosphere between measurements. The results of this study demonstrated that CO2 was the dominant gas controlling net GHG fluxes from the soils, with CO2 representing more than 98% contribution to the total calculated CO2e flux for all treatments. Emissions of CO2 and N2O were found to be significantly greater than the control soil for the 10% mixtures without fertilization, while both low (1%) and high biochar application rates (10%) resulted in decreased CH4 consumption. The techniques outlined in this chapter offer a fast and effective way to quantify GHG fluxes in soil incubations, and could be expanded to any mixture of soils and treatments. Chapter 3 presents a field experiment conducted with a biochar surface application rate of 5 t ha-1 biochar (<1% biochar incorporation in top 10 cm of soil with bulk density close to 1.35 g cm-3) with and without fertilization. Using static non-steady state chamber flux measurements techniques analyzed with an Agilent 7890A (G3440A) Gas Chromatograph (GC) system, flux determinations for three GHGs of interest (CO2, CH4 and N2O) was possible. Temperature was found to be the dominant control on CO2 emissions from the field plots. Biochar application was found to have little impact on the GHG fluxes from the soil, although slightly reduced CO2 emissions were observed for the biochar-amended plots during drought conditions in late summer and fall. Fertilizer applications to the forest soil plots following industry-standard practices neither significantly increased nor decreased the treatment CO2e fluxes in relation to biochar additions. Therefore, in agreement with Chapter 2, low rates of biochar addition to this forest soil were found to improve soil C sequestration, both with or without fertilization.   9 In Chapter 4, effect of the biochar and fertilizer applications on laboratory and field soil pore water DOC, TN and spectral indices were evaluated. Methods included utilizing a vacuum manifold in the laboratory, and suction cup lysimeters in the field. Samples were analyzed using a UV-vis spectrophotometer immediately after collection, followed by DOC and TN analysis. Data from the laboratory data showed that the high rate biochar addition significantly increased DOC in free draining water after a period of drying while helping reduce TN leaching, with and without fertilization. Furthermore, the high rate biochar treatments were shown to a have decreasing values S275-295 with successive flushed of deionized water, as well as significantly lower SUVA254 values. This suggests that more labile forms of DOM were being flushed out first, and that biochar additions resulted in a significant contribution of highly aromatic molecules to the DOC. However, the losses of DOC relative to the total amount of C added as biochar were extremely low (<1%). In the laboratory study, there was a significant difference detected in the N species retained, with increased retention of N as both NH4+-N and NO3--N in 10% biochar applications (with and without fertilization). No significant treatments effects on DOC or TN were observed in the field study. Chapter 5 presents a summary of the conclusions drawn from research described in Chapters 2, 3 and 4.    10 Chapter 2: Douglas-fir forest soil incubation experiment to measure greenhouse gas emissions after biochar application at three rates with and without urea-N fertilization  2.1 Introduction The relationship between soil respiration (i.e., CO2 emissions from soils) and biochar additions to soil is central to understanding the sequestration potential of biochar-amended soil in specific systems. Since soil respiration is one of the largest fluxes of the global terrestrial C cycle, representing about 70% of total ecosystem respiration in temperate forests (Ryan and Law, 2005), soil management strategies such as biochar addition should be carefully evaluated with respect to changes in soil CO2 fluxes. (Ryan and Law, 2005) In a meta-analysis of 46 studies, Sagrilo et al. (2015) found when studies were grouped by the ratio of added biochar C to soil organic C (SOC), only those with a ratio > 2 showed a significant increase in CO2 emissions.  Biochar has been shown to have the potential to reduce methane (CH4) emissions in water-logged rice paddies (Liu et al., 2011) and enhance CH4 uptake in aerobic soils (Karhu et al., 2011). However, clear patterns in the impact of biochar applications on CH4 dynamics have been difficult to identify (Gurwick et al., 2013). A recent study evaluating the impacts of biochar addition on greenhouse gas (GHG) fluxes from a temperate forest soil found no differences in CH4 fluxes between biochar-amended soil and control soil (Sackett et al., 2015). Similarly, nitrous oxide (N2O) emissions from soils are also an important consideration for soil management (Zhang et al., 2015). Biochar has shown significant potential for reducing N2O fluxes, with factors such as biochar feedstock, pyrolysis conditions, and soil C/N ratio  11 influencing the efficacy of biochar addition (Cayuela et al., 2014; Case et al., 2015). However, some studies have found that biochar with high nitrogen (N) content, or biochar applied with urea can increase soil N2O emissions (Chen et al., 2015). The likelihood for reduction or enhancement of soil N2O fluxes is strongly linked to which N2O formation pathway is followed, which is soil specific (Sánchez-García et al., 2014). It is important to note that the majority of biochar studies related to GHG fluxes have been performed on agricultural soils (Spokas et al., 2009). However, little is known about the effect of biochar additions on forest soil GHG fluxes despite the potential of biochar to enhance the size of the forest C sink by increasing forest productivity (Thomas and Gale, 2015).  Of particular relevance to this study is that slash material remaining after forest harvest is commonly burnt to facilitate replanting and reduce fire hazard, and that this directly releases large amounts of GHGs (~8 Mt CO2 equivalent annually) into the atmosphere (British Columbia Ministry of Environment, 2012). An alternative would be to adopt a systems-based approach and convert the slash into biochar that could be incorporated back into the forest soil to enhance soil quality for the subsequent rotation and help reduce the large C footprint of forest harvesting on managed land (de Ruiter et al., 2014). Fertilization is a common forest management practice to enhance biomass production rates of N-limited forests in the Pacific North West of North America, with typical application rates of 200 kg N ha-1 (Hanley et al., 1996; Jassal et al., 2010). In these circumstances, excess N can result in large N2O emissions from soils during the first year following fertilizer application (Jassal et al., 2008; Jassal et al., 2010; Shrestha et al., 2014) that significantly reduce the global warming benefit of additional forest C uptake in response to fertilizer addition (Jassal et al., 2011). However, very little biochar research to date has been conducted on forest soils using a systems-based approach, and none to our knowledge has  12 addressed N fertilizer interactions with biochar and the resulting GHG emissions for a forested humo-ferric podzol, the dominant forested podzol great group in Canada.  In order to evaluate the potential for biochar incorporation into forest management practices in British Columbia, Canada, where a forest carbon offset protocol is already in place and under critical review (Peterson St-Laurent et al., 2017), we measured the GHG fluxes from a forest soil amended with different rates of biochar produced from Douglas-fir harvest residuals, in combination with unfertilized soil and soil receiving an N fertilizer application of 200 kg N ha-1. We designed a laboratory incubation study using a state-of-the-art cavity ring-down spectroscopic (CRDS) gas analyzer to simultaneously measure soil CO2, CH4 and N2O fluxes in relation to different rates of biochar and urea-N application under controlled laboratory conditions. We tested the hypotheses that (i) biochar application would increase CO2 emissions from an N-fertilized soil, (ii) biochar application would suppress N2O emissions from an N-fertilized forest soil, and (iii) biochar would enhance CH4 uptake by the forest soil regardless of N fertilization. We also sought to determine the influence of biochar and fertilizer applications on soil CO2 fluxes, and to quantify the relative magnitude of GHG fluxes by accounting for their individual global warming potentials (i.e., CO2 equivalent fluxes), and relative to C added as biochar.  2.2 Materials and methods  2.2.1 Soil Soil was collected from a coastal Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco var. menziesii) forest located near Campbell River on Vancouver Island, British Columbia (BC), Canada (49° 52’N, 125 20’W, 320 m.a.s.l.). Stand density in the area is typically ~1100 trees ha-1 , less than 60 years old and composed primarily of Douglas-fir (80%), western red cedar (Thuja  13 plicata Donn, 17%) and western hemlock (Tsuga heterophylla (Raf.) Sarg., 3%) (Humphreys et al., 2006). The majority of the old-growth forest in this area was harvested in the early 1900s with land being managed for forest products on a 50 to 90 year rotation thereafter (Spittlehouse, 2003).  The soil is classified as a humo-ferric podzol with a gravelly loamy-sand texture and is described in more detail in Appendix 1. The top 20 cm of the mineral soil layer was collected from an area of the forest that was not previously fertilized. During collection, the soil was sieved to 1 cm, removing large stones and roots. Soil was transported to the laboratory and stored in darkness at 5 °C for one week before being sieved to <2 mm for use in the experiment.  2.2.2 Biochar The biochar used was supplied by Diacarbon Inc. (Burnaby, Canada) and was made from Douglas-fir slash feedstock, chipped to 2-cm pieces and pyrolyzed for 30 min at 420 °C. The C content of the biochar was 78% on a dry matter basis, with low volatiles and ash contents (18.8 and 2.4%, respectively). The biochar was sieved to <2 mm for use in the experiment, and comprised a wide range of particle sizes. The largest fraction (32%) was in the range of 425 to 991 µm, and the next largest fraction (<150 µm) was 24%. Biochar pH and electrical conductivity were determined following the International Biochar Initiative (IBI) protocols (International Biochar Initiative, 2012), and were found to be 6.86 (± 0.04) and 86 (± 2) µS/cm, respectively. The skeletal (i.e., particle) density of the sieved biochar was 1.33 (± 0.03) g cm-3 which is within the range of other wood derived biochar investigated in Brewer et al., (2014). The biochar had been stored for more than two years after production in airtight steel drums, which were opened occasionally for use in other experiments.   14 2.2.3 Experimental incubations The incubation study was conducted using 250 cm3 Steri-fil® Asceptic filtration units (EMD Millipore, Darmstadt, Germany) coupled in line with the laser-based CRDS GHG analyzer (G2508 Greenhouse Gas Analyzer, Picarro Inc., Santa Clara, CA, USA). Mixtures of air-dry soil, biochar and fertilizer in different proportions were added to the incubation units in 20-cm3 volumes until reaching a total volume of 200 cm3. Mixtures were compacted and the upper surface loosened to avoid layering between each addition.  Six experimental mixtures, consisting of combinations of soil, biochar (application rates of 0, 1% and 10% biochar on a mass basis) and fertilizer (application rates of 0 and 200 kg N ha-1 equivalent of urea fertilizer) (Table 1), were replicated four times, yielding a total of 24 incubation units. Soil and biochar components for each of the six treatments were mixed independently in batches, with fertilizer applied to the assigned treatments at the initial wetting of the soil-biochar mixture before packing. Using a soil mineral particle density of 2.65 g cm-3, the total porosity of the mixture when packed to 200 cm3 was ~0.61 cm3 cm-3. The initial wetting using deionized water raised the relative saturation to ~40%.  During the experiment, water was added to the incubation units to maintain the relative saturation in the range of 15-40%. The incubation units were weighed daily during the week to estimate soil evaporation, with deionized water added on Mondays, Wednesdays and Fridays to return incubations to soil water contents equivalent to ~40% relative saturation. In this way, the units were rewetted every 48 hours from Monday to Friday, and after 72 hours over weekends. Added water was allowed to spread throughout the incubation units for 24 hours prior to the GHG flux measurements.   15 GHG fluxes and incubation masses were measured daily for four weeks from Monday to Friday. Wetting of the soil in the incubation units occurred immediately following the GHG flux measurements on Tuesdays and Fridays, with subsequent flux measurements made ~24, 48 and 72 hours after wetting. The incubations units were left open to the atmosphere in a dark laboratory at 23 °C between measurements. Relative saturation was determined for each flux measurement based upon incubation masses. At the end of the experiment, the incubation units were destructively sampled and their oven dry (105 °C for 24 hours) mass was used to calculate bulk density. 2.2.4 GHG flux measurements GHG flux measurements were made on each incubation unit daily between 10 AM and 12 PM over a 25-day period during November and December 2014. This length of time was chosen based on biochar incubation studies that have shown relatively constant CO2 fluxes during 100-day incubations (Spokas and Reicosky, 2009) and stabilized N2O fluxes within 30 days of fertilizer application (Cayuela et al., 2013). The non-steady state flow-through incubation system (Figure 1) was constructed to permit in-line measurements of CO2, CH4 and N2O fluxes. Each incubation unit headspace was sealed using Steri-fill caps during flux measurements. The caps were additionally sealed with Parafilm and secured by firmly pressing down on the cap during measurements to ensure ambient air did not enter the air stream circulating between the incubation headspace and the CRDS analyzer. Rubber septa were placed to block two of the four ports in the caps, leaving two ports for air entry and exit. Air was circulated between the incubation unit headspace and the CRDS analyzer at 250 mL min-1 using a low-leak diaphragm pump (A0702, Picarro, Santa Clara, CA, USA). The total volume of the system was 215 mL comprising the 105 mL Picarro G2508 standard system configuration volume, 30 mL comprised  16 of 24 cm of 3.175 mm (1/8”) ID Bev-A-Line® (manufacturer) tubing including a 1.0-µm inlet filter, and the 80 mL incubation unit headspace. The twenty-four incubation units were analyzed in random order each day, with each unit coupled to the analyzer for five minutes.  During the 5-min flux measurement closure time, CO2, CH4 and N2O mixing ratios (mol of GHG per mol of dry air) were measured every 2 s. Default analyzer settings used in others studies (Fleck et al., 2013, Christiansen et al., 2015) were used throughout the experiment. Immediately after lid closure, the headspace was flushed via a three-way valve for 1 minute at 1 L min-1 with laboratory reference air (mixing ratios of 420 µmol mol-1 CO2, 1.96 µmol mol-1 CH4 and 0.72 µmol mol-1 N2O) to provide the same ambient conditions in all treatments. Prior to the experiment, this reference air was obtained as air that was pumped from outside a laboratory window overlooking a lightly treed lawn using an air compressor system to fill an empty gas cylinder. The laboratory reference air was analyzed on a gas chromatograph (Agilent 7890A, Mississauga, CA) that was calibrated using certified standards (Air Liquide America Specialty Gases LLC, Houston, USA). The hourly fluxes (Fx, µmol g-1 h-1) for GHGx, where x represents each GHG, at time t0 after flushing with reference gas were calculated using Eq. (1) which is the same as Eq. (6) in Jassal et al. (2012), except that ground surface area has been replaced by sample mass, the mass of soil (~200 g) inside each incubation unit (ms, kg): Fx = (ρaV/ms) dsx/dt     (1) where ρa is the density of dry air (mol m-3), V is the total system volume (m3), and dsx/dt is the rate of change of the mixing ratio of GHGx (µmol mol-1 s-1). ρa is given by P/[RT(1 + sw)], where sw is the water vapour mixing ratio (µmol mol-1) (average value during the measurement), P is atmospheric pressure (Pa), and T is absolute temperature (K). Using a hand-held infrared  17 thermometer, we found the soil surface temperature was not affected by contact with air warmed by the CRDS during the measurement interval.  To track system drift and leakages during headspace air circulation through the measurement system, a non-soil control incubation unit containing acid-washed glass beads rather than soil was used. GHG fluxes were measured daily using the same method as for the soil incubation units, and fluxes from the glass beads unit, were subtracted from the daily soil incubation measurements. The starting time (t0) for rate of change of mixing ratio determination was 5 s after the completion of air flushing to avoid associated pressure perturbations. This also permitted enough time for air to circulate through the system to remove any lag effect. Generally, the rate of change in mixing ratio (dsx/dt) for CO2 was non-linear, declining with time because the gradient declined as the volume filled, and so the exponential model described by Jassal et al. (2012) was used to determine dsx/dt at t0 for this gas. A linear fit was used for determination of dsx/dt for CH4 and N2O since linear changes were observed for these gases. The manual switching of incubation units, flushing with laboratory reference gas and closures did result in occasional time series for which calculated fluxes were unreliable. After testing the flux-filtering approaches of Jassal et al (2012) and Christiansen et al (2015), only extreme outliers (defined as fluxes with RMSE more than 3 standard deviations from the mean RMSE for all fluxes for each GHGx) were removed, which corresponded to <3% of data. Cumulative fluxes were calculated using the procedure outlined by Yang and Cai (2005). 2.2.5 Statistical analysis Data were analyzed using a one-way analysis of variance (ANOVA) to test for significant differences. Biochar treatments (S, BC1%, and BC10%) were compared independently of  18 fertilization treatments (S+F, BC1%+F and BC10%+F). When F values indicated significant differences, a post-hoc Tukey’s Honest Significant Differences (HSD) test was conducted to determine which pair-wise differences were statistically significant.  Independent samples t-tests were conducted to further compare biochar with and without fertilization (BC1%, BC1%+F, BC10%, BC10%+F). Following this, independent samples t-tests were conducted to compare the responses of unamended soil (S) and soil with a low biochar application rate (BC1%) to fertilizer application (i.e., S + F and BC1% +F). Differences in soil moisture between treatments were investigated using measurements of relative saturation as the main factor in an ANOVA for the three GHGs. Where F values indicated significant difference, a post-hoc Tukey’s Honest Significant Differences (HSD) was conducted. The effect of soil moisture on GHG fluxes was then investigated within each treatment by performing independent linear regressions. The net GHG flux in terms of CO2 equivalent (CO2e) was calculated for each treatment using mean fluxes from the last two weeks of the experiment using global warming potentials (GWP) of 1 for CO2, 34 for CH4, and 298 for N2O (IPCC, 2014). To evaluate net CO2e of soil GHG fluxes relative to C added in the biochar treatments (g biochar (g soil)-1), soil gas fluxes without biochar were subtracted from soil gas fluxes with biochar using the differences determined from the treatment post-hoc Tukey’s HSD tests and converted to CO2e fluxes. Greenhouse gas flux calculations and statistical analyses were performed in R version 3.3.1 (2016-06-21) (RCore Team, 2016) 2.3 Results and discussion 2.3.1 Temporal variation in GHG fluxes For all treatments, the measured CO2 fluxes were high on the first day of incubation  19 (DOI-1), decreasing substantially through DOI-3, after which values remained lower for all treatments (Figure 2). The highest CO2 flux (0.75 µmol CO2 (g soil)-1 h-1) was measured on one of BC10%+F samples on DOI-2, with an average value of approximately 0.55 µmol CO2 (g soil)-1 h-1) obtained for both BC10% and BC10%+F. Although these high CO2 effluxes did not persist past DOI-3, the cumulative efflux over the three days was a major contributor to total CO2 emissions during the experiment, representing 18% - 25% of total CO2 effluxes for the treatments (Figure 2).  In general, the unamended soil (S) had the lowest CO2 effluxes measured during the disturbance period (i.e. over the first 3-days), as well as over the remaining 22-days (Figure 2). The biochar treatments BC10% and BC10%+F had the largest CO2 fluxes of all treatments in the first few days of the experiment, with the rank order treatments for CO2 fluxes persisting throughout the experiment as BC10%+F > BC10% > BC1%+F > S+F > BC1% > S (Figure 2). Overall, the cumulative soil CO2 flux from BC10%+F was 55% larger than S.  Soil CH4 fluxes were mainly negative, indicating net consumption of CH4 by methanotrophic soil microbes in all treatments. Differences in treatment responses were pronounced and consistent throughout the entire experiment (Figure 3). A disturbance effect was also observed during the first 3 days for CH4, which can be observed in the rapid increases in CH4 uptake (e.g. more negative CH4 fluxes) for DOI-3 compared to DOI-1. The BC10% and BC10%+F treatments clearly exhibited the lowest rates of CH4 consumption, and in some cases showed signs of net CH4 production, particularly for BC10%+F (Figure 3). The cumulative values at the end of the experiment in ranked order from highest to lowest CH4 consumption varied as: S > BC1% > S+F >BC1%+F > BC10% > BC10%+F (Figure 3). The S treatment consistently had the strongest CH4 consumption, followed by BC1%, which had 35% less CH4  20 consumption compared to the unamended soil (treatment S). The highest rate of CH4 consumption (-6.41 x 10-5 µmol CH4 (g soil)-1 h-1) was measured for the S treatment on DOI-16. Unlike the CO2 fluxes, there was little initial disturbance effect in N2O flux magnitudes, which were generally highest towards the end of the experiment (Figure 4). The cumulative values at the end of the experiment in ranked order from largest to smallest are BC10% > BC10%+F ~ S+F > BC1%+F > S = BC1% (Figure 4). While emissions were highest for BC10% and BC10%+F throughout the experiment, an increase in emissions can be observed towards the end of the experiment for S+F after DOI-14. Total N2O emissions from the largest emitters, BC10% and BC10%+F were 191% and 169% greater, respectively, than S. However, there were clear reductions in N2O fluxes when biochar was applied to N-fertilized soil at the 1% rate, as cumulative N2O fluxes for BC1%+F were 28% lower than for S+F (Figure 4). 2.3.2 Effect of biochar application rates on soil GHG fluxes from unfertilized soil Comparing individual treatment pairs after the disturbance effect diminished (Figure 5), we found that CO2 fluxes in BC10% were significantly larger than in BC1% (p <0.01) and BC1% was significantly larger than S (p < 0.01, Figure 5).  Studies have reported that biochar addition can at first increase soil respiration rates, but then result in a decline over the following weeks to years (Cross and Sohi, 2011; Major et al., 2010; Steinbeiss et al., 2009). Specifically, some incubation studies have shown that, after subtracting measured raw biochar CO2 emission from those measured from soil biochar mixtures, biochar reduces soil CO2 emissions (Spokas et al., 2009, Zimmerman et al., 2011) or does not effect it, and where increases have been recorded they have been attributed to abiotic CO2 release directly from the biochar (Thomazini et al., 2015). Additionally, Zimmerman et al. (2011) found increased CO2 emission more likely during early stages of incubation (<90 days)  21 for soils treated with biochar produced at lower temperature (250 – 400 °C) and from grasses. In contrast, Spokas (2013) investigated the effect of weathering on soil CO2 emission response after wood-derived biochar addition and concluded that biochar increases soil CO2 emissions in the long-term, suggesting that there was an enhancement in the rate of microbial mineralization of weathered biochar. In a meta-analysis of 106 studies, Wang et al. (2015) concluded that biochar additions stimulated soil organic matter mineralization in nutrient poor sandy soils by as much as 20% and suggested that this would largely be due to biochar stimulating microbial activity. Biochar application significantly decreased CH4 uptake (AOV F2,214 = 470.99, p < 0.01, Figure 6), with the net CH4 consumption decreasing with increasing rates of biochar application (F(1,167), p < 0.01, R2 = 0.87). The post-hoc Tukey HSD test showed that the BC1% and BC10% differed significantly from S and from each other (p < 0.01). This is at first surprising given that biochar is expected to improve conditions for CH4 oxidation and uptake by soil microbes, and by reducing bulk density and increasing porosity (Van Zwieten et al., 2009). However, we ensured that bulk density was not significantly different between control and biochar application treatments, so it is possible that differences observed in this study are due to biochar increasing pH thereby improving conditions for methanogens (Inubushi et al., 2005). This remains to be proven and our results differ from those described in Jeffery et al. (2016) where biochar addition increased CH4 uptake in soils of pH < 5 with no effect in soils with pH > 5. Spokas et al. (2009) also found reductions in CH4 oxidation after wood derived biochar was applied to a silt loam soil, hypothesizing that there could be an inhibitor to methanotrophic activity found on the biochar particle surface area or that the methanotrophs are selectively using another organic compound sorbed to the biochar surface before potentially returning to CH4 over time.  22 There was a significant effect of biochar additions on N2O fluxes (AOV F2,214 = 23, p < 0.01), with the BC10% having significantly higher emissions than BC1% and the unamended soil (S) (Figure 7). The post-hoc Tukey HSD test showed that N2O emissions from BC1% were not significantly different from S (p > 0.97). Other studies have shown that biochar addition reduces N2O emissions (Cayuela et al., 2010) in relation to soil moisture (Yanai et al., 2007; Spokas et al., 2009). Spokas (2013) found that field-aged biochar did not suppress N2O emissions. Nitrification, converting ammonium to nitrate and releasing N2O and nitric oxide (NO), occurs in well-aerated soils. Sanchez-Garcia et al., (2014) suggested that the fact that biochar increased measured N2O emissions in a Haplic Calcisol soil may have resulted from improved conditions for microbial ammonia-oxidizer populations. Similarly, Gundale and Deluca (2006) found that Douglas-fir derived biochar applied to Western Montana forest soil (sandy-skeletal, mixed, frigid Typic Dystrustept) increased ammonification and nitrification rates. In this study, where relative saturation was generally low, it is possible that the increase in N2O emissions was due to an improvement in conditions favouring nitrification, and it is unlikely that this is due to improved oxygen availability (Prommer et al., 2014).  2.3.3 Fertilizer effects on soil GHG flux responses to biochar application rates  T-tests were performed on treatment pairs S vs. S+F, BC1% vs. BC1%+F and BC10% vs. BC10%+F to test the influence of fertilizer on CO2 fluxes. In contrast to the significant increase in CO2 efflux (p < 0.05) for fertilized soil (S+F) compared to soil without fertilizer (S), we found no significant difference between BC1% and BC1%+F, or between BC10% and BC10%+F (Figure 5). In a meta analysis Liu et al. (2016) found that biochar in combination with synthetic-N fertilizers led to the most significant increases in soil CO2 emissions and microbial biomass-C, likely due to a shift in the C/N ratio favoring microbial soil C mineralization.  23 Comparing fertilized and non-fertilized treatments, fertilizer addition appeared to decrease CH4 uptake, with significant differences (p < 0.01) for S vs. S+F and BC1% vs. BC1%+F, but not for BC10% vs. BC10%+F (Figure 6). Adding Fertilizer appears to further reduce CH4 uptake. This is in agreement with Jassal et al (2011) for this forest soil. While biochar is generally expected to improve conditions for CH4 uptake, we did not find this to be the case in this study (i.e., BC1% +F and BC10% +F were virtually the same as S + F). Fertilizer application significantly increased N2O fluxes (S vs. S+F, Figure 7), which is similar to measurements made in the field (Jassal et al., 2010, Jassal et al 2011) close to where the soil used in this study was collected. When fertilized, biochar addition at the 1% application rate (BC1%+F) reduced N2O emissions relative to the fertilized soil with no biochar (S+F) (p < 0.05); however, N2O fluxes for BC10%+F were not significantly different than from S+F. Furthermore, N2O fluxes for BC10%+F were not significantly different from those measured for BC10% (Figure 7). This suggests that for high biochar application rates, large N2O fluxes resulting from biochar additions overwhelmed any reduction in N2O emissions resulting from fertilizer additions observed at the lower biochar application rate. Lan et al. (2017) found that different biochar significantly reduced N2O emissions after N-fertilization in Australian Tenosols and this was not consistent in Ferrosols. They concluded that this was a result of modification in the ratio of dissolved organic-C and nitrate. Our results suggest that there may be a maximum rate at which biochar can be added in combination with urea-N fertilizer for which reductions in N2O emissions can be expected. As with treatments with only biochar, this could be the result of high rates of biochar improving conditions for nitrification, while fertilizer provides more available N enhancing N2O emissions.  24 2.3.4 Influence of soil moisture on GHG fluxes We performed a principal component analysis for the post-disturbance period to identify patterns between dependent and independent variables (Figure 8). The incubation length (e.g. day of experiment) was strongly related to N2O fluxes, but less associated with CO2 and CH4 fluxes. Time since wetting was not a significant factor for either of the principal components (standardized loadings of 0.02 and -0.01 of PC1 and PC2, respectively). Among dynamic parameters, relative saturation was the strongest explanatory variable (whereas biochar application rate and bulk density represent static parameters). Exploring the relationships between soil moisture and soil GHG fluxes, we found a significant, though weak, correlation between soil CO2 fluxes and relative saturation (p < 0.001, R2 = 0.09). The relationship was stronger between CH4 fluxes and relative saturation (p < 0.001, R2 = 0.27). There was not a significant relationship between N2O fluxes and soil moisture (p > 0.05, Figure). Any differences in saturation between treatments during the experiment were limited to the high (10%) biochar application rate and were not significant. Relationships between GHG fluxes and soil moisture were not detected within treatments. 2.3.5 Global warming potentials The total global warming potential (GWP) for each treatment was determined by summing the 100-year radiative forcings associated with each of the three measured GHGs (GWP factors of 1 for CO2, 34 for CH4, and 298 for N2O, respectively (IPCC, 2014)). In this experiment, soil CO2 effluxes represented >95% of the total GWP for all treatments (Figure 9). Cayuela et al., (2010) also showed that CO2 contributed the largest percentage to the GWP, when biochar was added to a sandy soil. The increases in N2O emissions after biochar and fertilizer additions increase total GWPs, as do the reductions in CH4 consumption. However, the effects of  25 these two GHGs had very little impact on the total CO2e of any treatment (Figure 9). The BC10% treatment had the largest total CO2e emissions, followed by BC10%+F > BC1%+F > BC1% > S+F >1% > S.  We also estimated the net climate change impact of biochar additions by considering both the C addition represented by biochar amendments in addition to the GWP resulting from changes in GHG fluxes. This calculation was based on the mean flux measured during the last two weeks of the experiment, which we used to provide a first-order approximation of the length of time during which the enhanced C sequestration due to biochar additions would be “offset” due to the enhanced CO2e emissions of the treatments relative to the unamended soil. We subtracted the CO2e of the unamended soil from the CO2e of treatments receiving biochar additions. This provided a metric for characterizing the impacts of biochar additions on the net GWP of a soil (Wang et al, 2015).  The treatment with the longest time interval before the C sequestration resulting from biochar additions was offset by the total CO2e of GHG emissions from the soil was the BC10%+F at 17.7 years. This was slightly longer than the value calculated for the BC10% treatment (16.3 years). Though these high biochar application rates have the highest CO2e values (Figure 9), they also represent the highest C additions. Repeating the calculation using the radiative forcings of CH4 and N2O for a 20-year period (GWP of 72 and 289, respectively) (IPCC, 2014), the C-neutrality time remained similar: 17.4 and 16.1 years for BC10%+F and BC10%, respectively. This is due to the fact that CO2 fluxes dominate the total GWP. For the lower biochar addition rate (BC1%), CO2e from added biochar would be lost through soil GHG fluxes after 6.7 years. With fertilization (BC1%+F), the climate benefit of biochar to soil is reduced to just 3.9 years of equivalent C sequestration.   26 Steinbeiss et al. (2009) calculated the mean residence time for biochar added to a forest soil to be 6 and 12 years, with results differing based on biochar feedstock material. These values are similar to the offset periods calculated in the present study. In a meta-analysis of 24 studies Wang et al. (2015) concluded that the mean residence time of biochar-C ranged from 108 days and 556 years for labile and recalcitrant biochar C pools, respectively. The high rates of C loss we measured could be the result of stimulated soil organic matter mineralization and the labile component of the biochar being more readily mineralized. Clearly, short-term laboratory incubation studies are not representative of the ecosystem responses to biochar, which would include vegetation dynamics, seasonal climate drivers, and other factors influencing the overall climate impact of biochar amendments with and without fertilizer. Field studies should include these considerations in future measurements in order to provide policy guidance based on the effects of each environmental variable on a soil receiving biochar additions. 2.4 Conclusions 1. Biochar application at high (10%) application rates increased CO2 and N2O emissions when applied without urea-N fertilizer. 2. Biochar application at both low (1%) and high (10%) application rates decreased CH4 consumption when applied without urea-N fertilizer. 3. Biochar application with urea-N fertilization did not increase CO2 emissions compared to biochar amended soil without fertilizer.  4. In terms of CO2e, net change in GHG emissions was mainly controlled by CO2 emissions with CH4 and N2O together accounting for less than 1.5% of the total emissions.  2.5 Tables Table 1. Summary of experimental treatments Treatment code Treatment details S Soil with no amendments BC1% Soil + 1% biochar1 BC10% Soil + 10% biochar S+F Soil + fertilizer2 BC1%+F Soil + 1% biochar + fertilizer BC10%+F Soil + 10% biochar + fertilizer 1 Biochar mixed with soil as a percentage of total mass  2 200 kg N ha-1 equivalent of urea fertilizer    28 2.6 Figures  Figure 1. Incubation units attached to the CRDS greenhouse gas analyzer comprising a closed chamber soil flux measurement system. The four replicates of the six treatments are shown in the inset photo, as (A) unamended soil (S); (B) Soil + 1% biochar (w/w) (BC1%); (C) Soil + 10% biochar (BC10%); (D) Soil + fertilizer (200 kg N ha-1 equivalent of urea fertilizer) (S+F); (E) Soil + 1% biochar + fertilizer (BC1%+F); (F) Soil + 10% biochar +fertilizer (BC10%+F).   29  Figure 2. (A) Cumulative CO2 effluxes during experiment by treatment (means ± 1 standard error (SE)); (B) Daily mean CO2 flux time series plot (means ± 1 standard error (SE)). ●●●●●●●●●●●●●●●●●●A0204060801000 5 10 15 20 25Cumulative CO2flux (µmol CO2 (g soil)−1 )●● ●●●● ● ● ●● ●●● ●● ● ● ●B00.10.20.30.40.50.60 5 10 15 20 25Day of ExperimentCO2 flux (µmol CO2 (g soil)−1  h−1)● S BC1% BC10% S+F BC1%+F BC10%+F 30  Figure 3. (B) Cumulative CH4 effluxes during experiment by treatment (means ± 1 standard error (SE)); (B) Daily mean CH4 flux time series plot (means ± 1 standard error (SE)). ●●●●●●●●●●●●●●●●●A−3.0 ×10−2−2.5 ×10−2−2.0 ×10−2−1.5 ×10−2−1.0 ×10−2−5.0 ×10−3 0.0× 10+00 5 10 15 20 25Cumulative CH4 flux (µmol CH4 (g soil)−1 )●●●●●●●●● ●●●● ●●●●B−6× 10−5−4× 10−5−2× 10−5 0 ×10+0 2 ×10−50 5 10 15 20 25Day of ExperimentCH4 flux (µmol CH4 (g soil)−1  h−1)● S BC1% BC10% S+F BC1%+F BC10%+F 31  Figure 4. (A) Cumulative N2O effluxes during experiment by treatment (means ± 1 standard error (SE)); (B) Daily mean N2O flux time series plot (means ± 1 standard error (SE)). ●● ●●● ● ●● ●● ● ● ●●●●●●A0.0× 10+05.0× 10−41.0× 10−31.5× 10−32.0× 10−32.5× 10−30 5 10 15 20 25Cumulative N 2O flux (µmol N 2O (g soil)−1 )●●●●●●●●●●● ●●●●●●●B−4× 10−6−2× 10−6 0 ×10+0 2 ×10−6 4 ×10−6 6 ×10−6 8 ×10−6 1 ×10−50 5 10 15 20 25Day of ExperimentN 2O flux (µmol N 2O (g soil)−1  h−1)● S BC1% BC10% S+F BC1%+F BC10%+F 32  Figure 5. Boxplots of soil CO2 efflux during the post-disturbance period by treatment. The thick horizontal line represents the means, the upper and lower lines of the boxes indicate the means ± 1 SE, and the whiskers extend to the maximum and minimum fluxes measured during the post-disturbance period. 00.020.050.080.10.120.15SBC1%BC10% S+FBC1%+FBC10%+FTreatmentMean CO2 flux (µmol CO2 (g soil)−1  h−1) 33  Figure 6. Boxplots of soil CH4 efflux during the post-disturbance period by treatment. The thick horizontal line represents the means, the upper and lower lines of the boxes indicate the means ± 1 SE, and the whiskers extend to the maximum and minimum fluxes measured during the post-disturbance period. −6× 10−5−5 ×10−5−4× 10−5−3 ×10−5−2× 10−5−1 ×10−5 0 ×10+0SBC1%BC10% S+FBC1%+FBC10%+FTreatmentMean CH4 flux (µmol CH4 (g soil)−1  h−1) 34  Figure 7. Boxplots of soil N2O efflux during the post-disturbance period by treatment. The thick horizontal line represents the means, the upper and lower lines of the boxes indicate the means ± 1 SE, and the whiskers extend to the maximum and minimum fluxes measured during the post-disturbance period.  −3× 10−6−1 ×10−6 1 ×10−6 3 ×10−6 5 ×10−6 7 ×10−6 9 ×10−6SBC1%BC10% S+FBC1%+FBC10%+FTreatmentMean N 2O flux (µmol N 2O (g soil)−1  h−1) 35  Figure 8. Principal component analysis of experimental variables.   ●−2 −1 0 1 2−1.0−0.50.00.51.0Variables factor map (PCA)Dim 1 (39.11%)Dim 2 (17.04%)Bulk.DensityN2O.fluxCO2.fluxCH4.fluxday.of.experimentdays.since.wetting Biochar.rateFertilizer.raterelative.saturationl . it.fl.fl.fl. f. i t. i . tti i . ttili . tl ti . t tirr rr r rr rl . it.fl.fl.fl. f. i t. i . tti i . ttili . tl ti . t til . it.fl.fl.fl. f. i t. i . tti i . ttili . tl ti . t ti 36  Figure 9. Effect of treatments on net GHG emissions in terms of CO2e relative to unamended soil. The magnitude of the CH4 consumption is subtracted from the CO2 plus N2O total.           −0.010.000.010.020.030.040.050.060.070.080.090.100.110.120.130.140.15S BC1% BC10% S+F BC1%+F BC10%+FTreatmentCO2e (µmol (g soil)−1  hr−1)CH4CO2N2O 37 Chapter 3: In-situ measurements of greenhouse gas emissions after biochar application with and without urea-N fertilization at a Coastal Douglas-fir forest  3.1 Introduction Rising atmospheric concentrations of greenhouse gases (GHGs) - carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) - linked to climate change have stimulated a global effort to mitigate atmospheric concentrations of these gases. There is an urgent need to increase awareness of land management impacts on GHG flux dynamics to facilitate the development of mitigation strategies that minimize GHG emissions, and to reduce concentrations of CO2, CH4, and N2O in the atmosphere, as land management strategies can have significant impacts on soil GHG fluxes. Forest management options for climate adaptation and mitigation include reducing emissions from deforestation and forest degradation, enhancing the carbon (C) sequestration rate in existing and new forests, substituting wood fuels as a substitute for fossil fuels, and providing wood products in place of more energy-intensive materials. Increasing soil C stocks through biochar addition is an additional option (IPCC, 2014) that has yet to be extensively investigated in forest ecosystems. To determine if biochar addition to forest soil is a beneficial climate action requires information on its effect on both C stocks and soil GHG fluxes.  Biochar is produced through the thermal decomposition of organic matter in a reduced oxygen environment (i.e. pyrolysis). Biochar typically has high C content and has the potential to be used as a climate change mitigation strategy by reducing the amount of CO2 in the atmosphere through increased C sequestration in soils (Lehman et al., 2006). It is resistant to decomposition  38 in soils due to a prevalence of aromatic structures containing few functional groups (Dai et al., 2005, Zimmerman, 2010), which has been shown to be particularly true when the O:C ratio of the biochar is less than 0.2 (Spokas, 2010). Biochar has a lower specific density compared to mineral soils, with larger surface areas (Brewer et al., 2014). When incorporated into soil, biochar alters the physical structure of soil, generally decreasing its bulk density (Major et al., 2010; Lim et al., 2016) increasing the exposure of chemically reactive surfaces, and providing refugia for microbes (Lehmann et al., 2011). Changes to the physical structure of soils impacts soil water-holding characteristics, and leads to alterations in biotic and abiotic processes affecting fluxes of soil CO2, CH4 and N2O (Major et al., 2010; Sohi et al., 2010). Biochar has been shown to reduce soil CO2 emissions when applied at a ratio less than twice the level of soil organic carbon stock (SOC), but can increase soil CO2 fluxes at higher application rates (Sagrilo et al., 2015). These reductions are possibly the result of biochar’s ability to sorb soluble organic matter and stabilize SOC, reducing the amount of organic substrates available for respiration (Lehman and Joseph, 2012; Spokas et al., 2009; Chintala et al., 2014; Chang et al., 2016). Other studies have concluded that biochar can stimulate soil CO2 emission potentially due to increased mineralization of the labile fraction of biochar C, changes in the soil C:N ratio and nutrient availability, and enhanced microbial mineralization of weathered biochar (Kolb et al., 2009; Zimmerman et al., 2011, Spokas, 2013).  Soil CO2 emissions, also termed soil respiration (Rs), are controlled by a complicated balance of biotic and abiotic processes that encompass the cumulative release of CO2 from autotrophic (root and rhizosphere, Ra) and heterotrophic (soil organisms, Rh) respiration and some chemical weathering of carbon compounds (Kuzyakov, 2006). Many studies have shown strong and significant positive correlations between Rs and near surface soil temperature (Ts)  39 (Davidson and Janssens, 2006). It is uncertain if surface applied biochar would alter or change this soil Rs - Ts relationship. It is possible that the biochar surface application could reduce the surface albedo (Usowicz et al., 2016) and potentially increasing surface warming and enhancing Rs.  In terms of other primary biogenic GHGs, forest soils can be a source or sink of CH4 and N2O depending on the balance of biotic and abiotic processes, in particular those that regulate microbial activity. For CH4, two pathways exist; methanogenesis that produces methane under anoxic, often water saturated, conditions, and methanotrophy by bacteria that consume CH4 under oxic conditions. Both pathways can exist in the same soil and the resultant CH4 flux is the net balance between them (Hiltbrunner et al., 2012). Studies have found that biochar can increase CH4 oxidation, possibly through improving soil aeration (Van Zwieten et al., 2009, Spokas and Reicosky, 2009), although decreases in CH4 oxidation have also been observed due to reductions in methanotrophic activity (Spokas, 2013), possibly attributable to biochar inhibiting microbial intracellular signaling (Masiello et al., 2013). The soil surface flux of N2O represents the net balance between nitrification, occurring under aerobic conditions, and denitrification, occurring where low O2 conditions exist (Parton et al 1996; Hang Wei et al., 2015). In nitrogen (N) deficient soils, net emissions of N2O are typically small, while net consumption could be possible (Chapius-Lardy et al., 2007; Jassal et al., 2010). Studies have shown that biochar addition to soil can reduce N2O emissions, and some of this reduction could be due to N retention directly onto the surfaces of the biochar (Jassal et al., 2015). Biochar has often been reported to reduce N2O emissions from soils (Spokas et al., 2010; Cayuela, 2010; Cayuela, 2014; Harter et al., 2014), although biochar with high N content (e.g., biochar made from animal manure or food waste), has been observed to stimulate N2O  40 fluxes (Spokas and Reicosky, 2009; Singh et al., 2010; Van Zweiten et al., 2010). With uncertainty surrounding how biochar application will influence net GHG fluxes, and with very few field studies in forest soils, there is a need to quantify these fluxes when biochar is applied with and without fertilizer N application.  Production forests in the coastal region of British Columbia are typically N-limited systems, and have been found to respond significantly to N-fertilizer additions (Chappell et al., 1991). British Columbia’s Coastal Forest Action Plan offers fiscal incentives to increase productivity of second-growth coastal forests using fertilization to provide a potential benefit of reducing GHG emissions through increased C sequestration while maintaining employment levels in the forest product sector (BCMFR, 2007). While fertilization can significantly increase gross primary productivity, it also comes at a cost to the climate system in terms of N2O emissions resulting from fertilizer additions: these N2O emissions have been found to negate much of the climatic benefit of increased C uptake in response to forest fertilization in the first year after application (Jassal et al., 2008). If biochar additions reduced fertilization-related N2O emissions the potential would exist to maximize the benefits of forest management strategies by including changes to soil C stocks and net GHG fluxes on a CO2 equivalent (CO2e) basis (relative to CO2, CH4 and N2O have global warming potentials (GWP) of 34 and 298, respectively, on a 100-yr basis (IPCC, 2013)). The abundant supply of woody materials left on-site after forest harvesting is a potential pyrolysis feedstock for biochar and energy production. At present, harvest residues are burned in coastal British Columbia to facilitate replanting and reduce fire hazards during warm-dry months, reducing the C sequestration potential of the forest life cycle by releasing significant  41 amounts of GHGs rapidly back to the atmosphere (British Columbia, Ministry of Environment, 2012).  In this paper, we report on a controlled field-based experiment that we conducted to investigate the effects of biochar application to a forest soil, with and without N fertilization, on soil fluxes of CO2, CH4 and N2O in a Pacific Northwest Douglas-fir forest. We hypothesized that biochar additions would (i) increase CO2 emissions irrespective of fertilizer additions, (ii) increase CH4 uptake (or decrease CH4 emissions) in fertilized and unfertilized soils, and (iii) reduce N2O emissions in fertilized soils, but increase N2O emissions in un-fertilized soils in this N-limited system. The overall objective was to determine whether biochar additions could reduce net soil GHG fluxes on a CO2e basis in response to fertilizer application. 3.2 Methods 3.2.1 Site description The research site is a Douglas-fir forest on the eastern side of Vancouver Island 10 km SW of Campbell River, British Columbia and close to a long-term eddy-covariance site (Paul-Limoges et al., 2015) where local climate data was measured (49°52’N. 125°20’W, 300 m.a.s.l). Located in the dry maritime Coastal Western Hemlock Biogeoclimatic subzone, the forest has been classified as seasonally dry temperate rain forest--a region that experiences cool summers and relatively warm winters--with a mean annual temperature of 9.1 °C and annual precipitation of 1500 mm yr-1 (Pojar, 1987). The second-growth forest stand was planted in 1949 and comprises 80% Douglas-fir, 17% western red cedar (Thuja plicata Donn), and 3% western hemlock (Tsuga heterophylla (Raf.) Sarg) with a relatively sparse understory (Humphreys et al., 2006, Morgenstern et al., 2004). The stand is tall (30-35 m) and dense (1100 stems ha-1) (Hilker et al., 2010), with a leaf area index of 7.3 m2 m-2 (Chen et al., 2006) keeping the soil surface well  42 shaded. Understory vegetation is sparse (LAI<1), consisting mainly of salal (Gaultheria shallon Pursh.), Oregon grape (Berberis nervosa Pursh.), vanilla-leaf dear foot (Archlys triphylla DC) and a shallow mat (<1 cm) of ferns and mosses. The soil is classified as duric humo-ferric podzol of morainal origin with a Quimper gravelly-loamy-sand texture commencing below a variable, litter-fermented-humified (LFH) organic layer that ranges in depth from 9 to 19 cm. More detailed information, summarized from available publications and sampling is available in Appendix 1.  3.2.2 Experimental design A previously unfertilized area of the forest stand was selected and sixteen 4 m x 4 m plots were assigned in a randomized complete-block design with four blocks each of the following four treatments: i) soil only (S; i.e. control), ii) 5 Mg ha-1 biochar (BC), iii) 200 kg N ha-1 urea fertilizer pellets (F), and iv) 5 Mg ha-1 biochar with 200 kg N ha-1 urea pellets (BC + F). Each plot was positioned to have two large trees with diameter at 1.3 m > 31 cm within its boundary (Jassal et al., 2011).  Each plot contained a PVC collar to be used with a static non-steady-state (SNSS) chamber (for details see below) for the measurement of soil CH4, CO2 and N2O fluxes at the soil surface. Measurements from the SNSS chambers commenced in September 2011, with monthly measurements during September to December 2011 prior to snowfall, and biweekly measurements following snowmelt in April 2012. Measurements continued until September, 2013.  Additional instrumentation was used to monitor soil properties in twelve of the 16 plots (three replicates for each of the four treatments), starting in April 2012. This instrumentation consisted of sensor clusters at the 15-cm depth to measure volumetric water content (θ15), bulk  43 electrical conductivity (σ15), and soil temperature (T15) (model GS3, Decagon Devices Inc. Pullman, WA), and soil matric potential (Ѱ15, model MPS-2 sensors, Decagon Devices Inc.).  Sensors were installed at the 15-cm depth on opposing sides of a trench (50 cm long x 25 cm wide  x 15 cm deep), which was then backfilled. Signals were measured using a CR1000 data logger (Campbell Scientific Inc, Logan, UT) powered by a 12-V-DC 70-amp-hour lead-acid battery. The battery charge was maintained using a 60-W solar panel with a charge controller, supplemented through winter months (October-April) by recharging using a portable generator. Measurements were made at 5-min intervals and average output tabulated every 30 min.  3.2.3 Biochar and fertilizer application The biochar used was supplied by Diacarbon Inc. (Burnaby, Canada) and was made from Douglas-fir slash feedstock, chipped to 2-cm pieces and pyrolyzed for 30 min at 420 °C. The C content of the biochar was 78% on a dry matter basis, with low volatiles and ash contents (18.8 and 2.4%, respectively). Biochar pH (6.86 ± 0.04, mean ±1 SD) and electrical conductivity (σ, 86 ± 2 µS cm-1) were determined following the International Biochar Initiative (IBI) protocols (International Biochar Initiative, 2012). The skeletal (i.e., particle) density of the sieved biochar was 1.33 (± 0.03) g cm-3 which is within the range of other wood derived biochars investigated in Brewer et al., (2014). The biochar had a wide range of particle sizes, with the largest fraction (32%) in the 425-991 µm range. Additional information describing the biochar is available in Appendix 2. Prior to application, the biochar was stored for up to 2 years in 55 gallon steel drums sealed from the atmosphere.  The biochar was applied in late February 2012 following a strategy recommended by J. Lehmann (pers. comm.) as specific methodologies for best practices in applying biochar to forest soils are not yet established. We used a drip-bag surface application approach. Plastic bags  44 containing 3 kg of biochar for application over 4 m2 (5 t ha-1) were prepared with an additional 1500 ml of tap-water (< 3 mg L-1 C and < 0.2 mg L-1 N) to prevent loss of airborne particles during application. The biochar was spread over a plot by cutting a hole in the corner of the sealed bag and slowly walking along a 4-m line distributing the biochar over a 1-m wide swath. In this way a homogeneous application equivalent to 5 t ha-1 of biochar (3.95 t C ha-1) was consistently applied to plots. Images taken at the site immediately after biochar application can be found in Appendix 5. Urea-based fertilizer (N:P2O5:K2O, 46:0:0) (Agrium Inc.) was carefully applied by hand in late March 2013 at a rate equivalent to 200 kg N ha-1 (Hanley et al., 1996). The delay in the fertilizer application was intended to allow the biochar to become activated with local microbial communities, and to ensure N application during typically cool moist conditions, which is recommended for forest fertilization in BC (Lousier et al., 1991). This approach facilitates the rapid transfer of urea-N into the deeper soil profile near the roots and avoids N loss through NH3 volatilization. Hence, the timing of N application provides 1-year window to investigate the short-term biochar application effects on GHG fluxes prior to fertilization. 3.2.3 Field soil GHG emission measurements 3.2.3.1 Sample collection and ancillary measurements The SNSS chambers consisted of a permanent chamber collar (21-cm inner diameter x 10-cm long PVC pipe) installed to 5-cm depth or until contact with the mineral soil (Schiller and Hastie, 1996, Jassal et al., 2008). The chambers cover a total area (A) of 350 cm2 with a headspace volume (V) of ~1.72 L, depending on depth of insertion limited by coarse soil fraction and tree roots. Headspace volumes were determined for each measurement date by measuring collar height. Soil cover within the collars included small ground herbs or grasses. Measurements  45 were made by placing an acrylic lid (0.4-cm thickness) with high-density foam attached to seal the outside edge of the collar. A small fan powered by a 9-V battery and a 10-cm-long venting tube (6.35 mm OD) attached to the lid were used to keep headspace air well mixed and equilibrate the internal volume of the chamber with atmospheric pressure (Hutchinson & Livingston, 2001), respectively.  Headspace gas samples (20-ml) were drawn from the chamber using a medical grade plastic syringe with needle (23-gauge) and piercing a 2-cm diameter butyl-rubber septum in the lid. Samples were injected into 12-cm3 pre-evacuated exetainers (Labco Limited, Buckinghamshire, UK). Samples were collected from the chambers at discrete time intervals to enable flux calculations. Initially, samples were collected at 0, 3, 10, 20, and 30-minutes after placing the lid on the collar (i.e. the total gas removal was 100 ml). Based on initial data analysis and logistical considerations, samples for 2012 onwards were collected at 0, 3, 6, 9 and 12 minutes following chamber closure. This approach allowed greater precision in GHG flux determination by focusing sampling closer to when the lid was placed on the collar during the time of the most rapid change in chamber headspace GHG concentrations. .  In combination with the sampling for GHG flux calculations, measurements of surface (0-5 cm) soil temperature (Ts), volumetric water content (θs), and bulk electrical conductivity (σs) were also made. This was done using a mobile hand-held unit equipped with a GS3 sensor (with factory calibration to convert the dielectric permittivity measurement to θs) at three locations triangulated within a radius of 20 cm centered on each chamber immediately after chamber measurements were made. Half-hourly measurements of precipitation and air temperature were obtained from the nearby climate station (Paul-Limoges et al., 2015).  46 3.2.3.2 Greenhouse gas analyses and flux calculations GHG analyses were performed using a 100-vial autosampler connected to an Agilent 7890A (G3440A) Gas Chromatograph (GC) system fitted with a flame ionisation detector (FID) for detecting CH4 and CO2 following conversion to CH4 with an inline methanizer. A micro-electron capture detector (µECD) was used to measure N2O. The GC system thus evaluated the three GHGs (CO2, CH4, and N2O) from each sample vial using a single injection split between parallel flowpaths to the FID and the µECD. Mixing ratio values were calculated from calibration curves determined from autosampler injections sampled from vials filled with certified standards (Air Liquide Inc, Burnaby, BC and Praxair, Aldergrove, WA), as were methodological standards for QA/QC, which to simulate actual field sampling, were sampled in the field (Air Liquide Inc, Burnaby, BC GC Calibration Standard mix of 1 ppm N2O, 5 ppm CH4, 600 ppm CO2) and analyzed in parallel with the collected samples. All calibration standards were prepared as mixtures blended with pure N2.  GHG fluxes for the SNSS chambers were calculated for each plot from GHG mixing ratios determined by the GC using linear and non-linear techniques with the HMR package for Flux Estimation with Static Chamber Data (Pedersen et al., 2010). The linear and non-linear fits for each chamber closure were compared independently for each of the three GHGs, with flux calculations discarded for the few closures for which fits were not obtained.  3.2.4 Data analysis Data were analyzed using a one-way analysis of variance (ANOVA) to test for significant differences between treatments and corresponding GHG fluxes, the total treatment GWP (the sum of CO2, CH4-CO2e and N2O-CO2e) and soil climate variables (Ts, T15, θs, θ15, σs, σ15 and Ψ15). This was followed by Tukey’s Honest Significant Differences (HSD) test when Treatment  47 was found to be a significant source of variance. It was possible to investigate the effects of BC treatments relative to S (i.e., control) plots using data from 2012 as well as 2012 and 2013 data combined. From data collected in 2013 after spring fertilization it was possible to investigate the newly fertilized treatments including F and BC+F. Date was included as an interacting factor with treatment in a two-way ANOVA. The peak in annual Ta occurred on 25 July for both years. This date was used to segregate sampling periods for both years into the spring through early summer time-period (pre 25 July; hereafter referred to as spring-summer, SS) and post 25 July time periods (referred to hereafter as summer-fall, SF).  Annual and seasonal relationships between GHG fluxes with Ts, T15, θs, θ15, σs, σ15 and Ψ15 were explored using linear regression. Additionally, for CO2 we calculated the reference respiration rates (Rs at 10 °C, 𝑅!!") and the temperature sensitivity parameters Q10 (the relative change in Rs for a 10 °C change in shallow surface soil temperature) after Davidson et al. (1998):  𝑅! = 𝑅!!"𝑄!"!!(!!"!!")/!" (2) Equation 2 was fit to treatment CO2 flux data and T15 using nonlinear least-squares estimates. While this approach does not ensure unbiased estimates of respiration rates (Lloyd and Taylor, 1994; Howard and Howard, 1979), it can be useful for comparisons between and within study sites. In this study we used it to compare differences between fitted values for the different treatments.  The net GHG flux in CO2 equivalent terms (CO2e as g CO2e m-2 s-1) was calculated for each treatment using mean SNSS measured fluxes from all sampling dates and the 100-yr GWP of 1 for CO2, 34 for CH4, and 298 for N2O (IPPC, 2013) using equation 3: 𝐹!"!! = (𝐹!"!  × 𝑀!"!)+  (𝐹!"!× 𝑀!"!  × 34)+  (𝐹!!!× 𝑀!!! × 298) (3)  48 where the 𝐹!"!, 𝐹!"! and 𝐹!!! are molar fluxes (mol m-2 s-1 and, 𝑀!"!, 𝑀!"! and 𝑀!!! are molar masses (g mol-1) and GWPs are global warming potentials. All calculations and statistical analyses were performed in R version 3.3.1 (2016-06-21) (RCore Team, 2016). .  3.3 Results 3.3.1 Soil climate  The study site on eastern Vancouver Island typically experiences cool and wet winters with warm and dry summers. The mean annual Ta (°C) was 8.1 ± 6.9 (mean ±1 SD) in 2012 and 8.7 ± 7.0 in 2013. Both years were within one SD (standard deviation) of the 30-year mean recorded at the near-by Campbell River airport. The spring-summer mean Ta calculated from measurement days was similar for 2012 and 2013 (12.8 ± 2.6) and 11.6 ± 3.0), respectively. However, summer-fall Ta calculated from measurement days was higher in 2013 (19.6 ± 2.1) than in 2012 (15.4 ± 5.8). Precipitation in 2012 (1434 mm) was 40% more than in 2013 (840 mm), which was characterized by fewer intense (>15 mm/day) rainfall events. However, in 2013 precipitation was more evenly distributed with several small rainfall events (< 10 mm) throughout the growing season, and >50 mm in August, which is typically a very dry month.  Daily mean values of T15, θ15, and Ψ15 in the experimental area, and daily P measurements at a nearby (0.5 km) climate station, are presented during the two years when flux data were collected (Figure 10). Soil volumetric water content (θ15) in early spring in both years shows drying trends frequently replenished by > 5 mm rainfall events. Both years experienced their driest periods from July to October. However, the more even distribution of rainfall during 2013, though lower in total compared to 2012, served to maintain θ15 at marginally higher values and Ψ15 at much higher values during summer-fall sample dates in 2013 (mean ± 1 standard deviation, θ15 = 0.22 ± 0.04 cm3 cm-3; Ψ15 = -406 ± 550 kPa) than in 2012 (θ15 = 0.20 ± 0.06 cm3  49 cm-3; Ψ15 = -1944 ± 2117 kPa). The volumetric water content values measured at the surface (θs) were also marginally larger in summer-fall sample dates of 2013 (θs = 0.14 ± 0.03 cm3 cm-3) compared to 2012 (0.12 ± 0.04 cm3 cm-3).  ANOVA analyses revealed that θs during spring-summer in 2013 was significantly different between treatments (F(3,136) = 8.05, p << 0.01 ), and HSD analysis showed that it was lower in BC+F compared to BC and F (p < 0.05), while θs in S it was significantly less than in BC (p < 0.05). The effect of biochar application on measured θ15 was undetected. Using the ANOVA to assess surface σ during summer-fall in 2012 and 2013 was found to be 37% greater in the biochar plots than the control plots (F(1,28) = 3.843, p = 0.054 in 2012 and F(1,30) = 3.794, p = 0.061 in 2013). This could be explained by increased concentrations of exchangeable anions or cations compared to the control soil (Liang et al., 2006). After fertilization in 2013, σs was significantly greater (>35 %) in both the F and BC+F compared to the BC and S treatments (F(3, 204), p << 0.001), which was possibly due to increased NH4+ following hydrolysis of applied urea; σs subsequently decreased due to NH4+ uptake by plant roots, volatilization of NH4+ as NH3 gas or immobilization as a result of binding to soil organic matter or, (in the case of the BC+F), the biochar surface. Depending on the soil conditions influencing the rate of urea oxidation (temperature, moisture and pH) this increase could also be indicative of both increasing NO2- and NO3- after nitrification of NH4+.  3.3.2 Soil greenhouse gas fluxes 3.3.2.1 Soil CO2 emissions Mean CO2 emissions from control (S) and biochar treated (BC) plots increased from 1.35 (±0.86, SE) and 1.1 (±0.78) µmol CO2 m-2 s-1, respectively, on 11 May 2012 to 4.25 (±1.70) and 3.2 (±2.17) µmol CO2 m-2 s-1, respectively on 25 July 2012 (Figure 11). From all sample dates  50 prior to 25 July 2012, CO2 emissions from S were on average 11 % higher than BC, peaking at nearly 33 % higher on 25 July 2012. After the 2012 spring-summer period, CO2 emissions from S and BC decreased to mean values of 1.537 (±0.471) and 0.897 (±0.868) µmol CO2 m-2 s-1, respectively on 9 October 2012, coinciding the lowest Ψs15 values and decreasing temperatures (Figure 10). Fluxes were, on average, 19 % greater from S than BC during the summer-fall. The mean flux from all measurements was 2.55 (±1.53) for S and 2.17 (±1.644) µmol CO2 m-2 s-1 for BC. These results, especially for S, are consistent with those obtained in other studies investigating soil CO2 emission at the site (Drewitt et al., 2002; Jassal et al., 2005, 2008, 2010) from measurements at research plots within 500-m of the study location.  Table 2 summarizes the one-way ANOVA for annual and seasonal CO2 fluxes. In 2012, there was a statistically significant (p < 0.1) effect of treatments on CO2 emissions during summer-fall months. The HSD analysis determined that fluxes from S were 0.57 µmol CO2 m-2 s-1 higher (p = 0.082) than from BC, suggesting that biochar decreased net CO2 emissions towards the end of summer into fall. Including date as the second factor in the two-way ANOVA with treatment as the main factor, a significant effect for sampling date (F(9,134) = 4.23, p < 0.001) was identified, indicating pronounced seasonal dynamics. Results showed the CO2 emissions from S during 2013 had similar seasonal trends as in 2012, with maximum CO2 fluxes occurring for July through August (Figure 11). Both the fertilizer treated plots (F) and biochar plus fertilizer treated plots (BC + F) had a noticeably early season peak on 28 March 2013 shortly after fertilization (26 March 2013), with these treatment fluxes being about 75 and 60 % higher than from S, respectively. This increase could be the result of a CO2 flux from the hydrolysis of the applied urea ((NH2)2CO + 3H2O → 2NH4+ + CO2  51 + 2OH−) as well as increased Ra and Rh from fertilization. The F treatment had the largest CO2 emissions throughout 2013, in particular for July to September with a summer peak on 25 July 2013 of 10.60 (±2.35) µmol CO2 m-2 s-1. The CO2 flux from S reached a maximum of 4.60 (±1.59) µmol CO2 m-2 s-1 on the same date. The emissions of CO2 measured from S were generally smaller than all other treatments throughout the year. The CO2 emissions from BC peaked somewhat later than S at 5.91 (±4.03) µmol CO2 m-2 s-1 on 2013-09-05 and showed similar flux magnitudes as the BC+F, with the latter also reaching a maximum on that date of 5.21 (±2.83) µmol CO2 m-2 s-1.  During 2013, there was a highly significant (p < 0.001) treatment effect on annual and seasonal CO2 emissions (Table 2). The HSD analysis determined CO2 fluxes from F were 3.31, 2.30 and 2.24 µmol m-2 s-1 higher than from S, BC and BC+F, respectively (p < 0.01). There was a significant effect for sampling date (F(13,162) = 10.31, p < 0.001) when included as the second factor in ANOVA, supporting the earlier observation of temporal variability. During spring-summer 2013, the HSD analysis determined that CO2 emissions from F were 2.94, 2.18 and 1.8 µmol m-2 s-1 greater than from S (p < 0.001), BC (p < 0.001) and BC+F (p = 0.001) treatments, respectively. Furthermore, the analysis detected significant differences between S and BC+F, with BC+F being 1.14 µmol m-2 s-1 greater than S. In summer-fall, the HSD analysis determined that CO2 emissions from the F treatment were 5.69, 5.14 and 4.11 µmol m-2 s-1 greater than from S (p < 0.001), BC (p < 0.001) and BC+F (p < 0.001) treatments, respectively.  Taken together, these results strongly suggest that biochar alone did not increase CO2 emissions compared to the control plots. They also suggest that fertilization significantly increased CO2 emissions in early spring and summer, and that biochar applied prior to fertilization helped reduce these emissions. Furthermore, these results suggest that fertilization  52 significantly increased CO2 emissions more during late summer/fall than during spring/early summer in 2013 with biochar mitigating these higher fluxes (Figure 11). 3.3.2.2 Soil CH4 Uptake Uptake of CH4 in this forest soil was observed throughout 2012 for both S and BC (Figure 12). The net consumption of CH4 increased during warmer and drier soil conditions starting in July with a maximum uptake measured in summer-fall, at 4.12 (±0.0.30) and 4.49 (±0.62) µmol CH4 m-2 h-1 for S and BC, respectively. Mean fluxes for just S in 2012 averaged 3.58 µmol CH4 m-2 h-1, which was close to the annual mean uptake of 4 µmol m-2 h-1 in a nearby control (without any treatment) soil surface measured by Jassal et al. (2011). On average CH4 uptake was 15 % greater for BC when compared with S, although differences were not statistically significant (Table 2). The CH4 uptake for S and BC treatments in 2013 was greater and shared similar seasonal trends as found in 2012, with the maximums again being again occurring in summer-fall (Figure 12). Throughout 2013, CH4 uptake in S was approximately 60% of that measured in F, which had a maximum uptake of 11.86 (±5.1) µmol CH4 m-2 h-1 on 14 August 2013, closely matching that in BC with maximum uptake of 10.37 (±6.74) µmol CH4 m-2 h-1 on 5 September 2013 (not shown).  During 2013 there was a significant effect of treatments on CH4 uptake (p < .05) annually as well as seasonally. Including date as the second factor in the two-way ANOVA with treatment as the main factor, a significant effect for sampling date during 2012 and 2013 (F(9,107) = 2.16, p < 0.03, F(13,124) = 2.68, p < 0.002, respectively) was identified, supporting the observations of seasonal dynamics (Figure 12). The HSD comparisons for the one-way ANOVA determined that uptake in the F treatment was significantly larger than in S (p = 0.012) with an annual average of  53 2.21 µmol m-2 h-1 more CH4 uptake in F. Similar results were observed for spring-summer (p = 0.012) and summer-fall (p = 0.039) with CH4 uptake higher in F than in S by 1.47 and 4.89 µmol m-2 hr-1, respectively. The lack of significant difference between other treatments suggests that fertilization-induced increases in CH4 uptake were lower if fertilizer was applied to soils pre-treated with biochar.  3.3.2.3 Soil N2O emission and uptake During 2012, N2O emission and uptake were very small and well below the minimum detectable flux (Appendix 3) in most plots. Higher fluxes were more commonly found from BC during warmer days in the spring when the soil was wetter than in summer and fall (Figure 13). The N2O emission peaked for BC on 20 June 2013 at 0.085 (±0.053) µmol m-2 hr-1 (not shown), while, at the same time, flux magnitudes were lower for S. There was a significant (p < 0.1) effect of treatments on N2O emissions annually and during the spring-summer period in 2012 (Table 2). The HSD comparisons determined that for 2012, BC had, on average, emissions 0.027 µmol m-2 hr-1 greater than S (p = 0.068).  During 2013, S displayed similar trends as in 2012, generally emitting N2O in early spring when conditions were warm and moist, followed by a transition to N2O uptake in summer and fall months (Figure 13). BC and F were consistently very small sources of N2O to the atmosphere, and on average, F, BC and BC+F were 9, 11 and 19 times larger N2O sources than S, respectively. In general, fertilization increased N2O emissions throughout 2013, and there was a difference in peak timing with F peaking first on 26 June 2013 at 0.323 (±0.477) µmol N2O m-2 h-1, followed by BC+F on 10 July 2013 at 0.266 (±0.312) µmol N2O m-2 h-1 and BC on 14 August 2013 at 0.300 (±0.560) µmol N2O m-2 h-1 (not shown).   54 There was as modest effect of treatments determined from annual and spring-summer one-way ANOVA during 2013 (p < 0.1). The HSD comparisons determined that the 2013 annual emissions from S were on average lower by 0.103 µmol m-2 hr-1 compared to the BC+F (p = 0.006) and not significantly different from F. The HSD comparisons also determined that the spring-summer emissions from S were on average lower by 0.109 µmol m-2 hr-1 compared to the BC+F (p = 0.003) and that the emissions from BC were on average lower by 0.074 µmol m-2 hr-1 compared to the BC+F (p = 0.003). The results suggest that biochar application stimulated N2O emissions, both with and without fertilizer application.  3.3.2.4 Soil CO2e and GWP The seasonal pattern of the net CO2e emissions was strongly correlated with actual CO2 emissions. For all treatments, CO2 emissions were almost equal to the calculated CO2e, indicating an almost undetectable contribution from CH4 uptake and N2O emissions to the total treatment GWP during this study with and without biochar application. On average, the CO2 emissions accounted for ~100 % CO2e emissions from S and BC and 99 % from F and BC+F.  There was a significant treatment effect on CO2e emissions only in 2013 and this mimics differences already identified in the CO2 analysis. The annual HSD comparisons showed that F was significantly greater than all other treatments (p < 0.001) exceeded S by 2.93 µmol m-2 s1, BC by 2.47 2.36 µmol m-2 s-1and BC+F by 2.36 µmol m-2 s-1, respectively. In summary, in the first year after winter application of biochar it likely decreased CO2 emissions in summer-fall, while fertilization (F) in the following year increased CO2 emissions immediately after application, especially in summer, with BC and BC+F showing no significant difference when compared with S. From these results we would reject the hypothesis that biochar additions would increase CO2 emissions irrespective of fertilization. Biochar application had no  55 effect on CH4 uptake while fertilization increased CH4 uptake and biochar likely reduced this enhanced uptake. From these results we would reject the hypothesis that biochar additions before fertilization would increase CH4 uptake (or decrease CH4 emissions) in fertilized and unfertilized soils. In the first year (2012) after winter application of biochar, N2O emissions increased during spring-summer, while in 2013 annual N2O emissions were larger for all treatments and during spring summer for BC+F. From these results we would accept the hypothesis that biochar additions would increase N2O emissions in un-fertilized soils in this N-limited system and reject the hypothesis that prior biochar additions would reduce N2O emissions in fertilized soils. The net effect on GWP was that biochar did not significantly increase emissions (with no fertilizer addition) and helped to reduce CO2e emissions when applied before fertilization.   3.3.3 Soil greenhouse gas flux relationships to soil climate Overall, CO2 emissions during 2013 were positively correlated with T15 for all treatments (r = 0.71, 0.49 and 0.74, n = 42, 42 and 34, for S, BC and F, respectively at p < 0.01), somewhat weakly for BC+F (r = 0.23, n = 34, p < 0.01). During 2013, CO2 emissions in BC and F treatments were negatively correlated with θ15, particularly when θ15 > 0.25, (r = -0.42 and -0.28, n = 37 and 35, respectively at p < 0.01), less so in S (r = -0.08, n = 42, p = 0.07) and not at all in BC+F treatments. Both correlations of CO2 emissions with T15 and θ15 were heavily chamber dependent:  chambers that indicated stronger correlations with T15 also indicated stronger correlations with θ15, suggesting variability in other controlling variables was having more impact at other chamber locations. This could be due to a large number of measurements in 2013 from early spring (Figure 11) made when water availability was not limiting to stand photosynthesis and associated Ra, and may also have resulted from the wetter conditions expressed through consistently higher Ψ15 through summer-fall in that year.  56 For CH4 uptake, there were weak but significant soil climate correlations determined for the BC and F treatments. For BC, CH4 uptake during 2012 and 2013 was found to have a positive correlation with Ts (r = 0.06, n = 92, p = 0.02) and T15 (r  = 0.09, n = 78, p = 0.01), and negatively correlated to θs (r = 0.09, n = 87, p < 0.01), σs (r = 0.09, n = 96, p < 0.01) and σ15 (r = 0.12, n = 78, p  < 0.01). For F, CH4 uptake during 2013 was found to have a positive correlation with Ts (r = 0.19, n = 29, p = 0.01) and T15 (r = 0.29, n = 29, p < 0.01), and a negative correlation with θ15 (r = 0.32, n = 29, p < 0.01) and Ψ15 (r = 0.42, n = 6, p = 0.08) and σ15 (r = 0.13, n = 29, p = 0.04). The only significant although weak correlation found for the BC+F treatment was a positive response for CH4 uptake with σs (r = 0.18, n = 38, p = 0.01).  For N2O emissions, very weak but significant soil climate correlations were determined for only the BC+F in 2013, which was also the only treatment during this time period where non-zero N2O fluxes from the soil to the atmosphere were detected. Very weak positive correlations were determined between N2O fluxes and θ15 (r = 18, n = 34, p = 0.01) and between N2O fluxes and σ15 (r = 0.16, n = 34, p = 0.02).    In general, the strongest response to climate variables across treatments and individual chambers was that of CO2 fluxes to changes in soil temperature. Figure 14 shows the fitted lines for equation 2 with the estimated F10, Q10 values and statistics for each treatment given in Table 3. All parameter fits were significant at p << 0.001. The highest R2 were observed during 2013, which, similar to the results obtained for the CO2 response to T15 using linear regression, we attribute to the greater number of measurements during early spring (Figure 11) when water availability was not limiting and the wetter conditions that occurred during the summer-fall 2013. Of particular interest is the high R2 value (0.75) for the F treatment compared to others (0.68 and 0.47 for S and BC, respectively) and the contrastingly low R2 value for the BC+F  57 treatment (0.21). The F treatment showed the highest Rs10, followed by BC+F, BC and S with the latter almost half of the F Rs10. The BC treatment had the greatest sensitivity to temperature increase (Q10 = 3.15) followed by F, S, then BC+F. 3.4 Discussion 3.4.1 Biochar treatment effects Biochar decreased net CO2 emissions compared to control plots towards the end of summer into fall in the first year after application. Taking care to subtract the control CO2 from the biochar emissions, Spokas et al., (2009) showed that the addition of biochar reduced Rs in an incubation experiment, while Lui et al., (2016) showed no net effect on CO2 emissions when biochar was added to Chinese agricultural soils.  The soil C resident in the surface organic horizon is estimated to be between 244-257 mg g-1 dry soil (Humphreys et al., 2006), making it more than 50% of the rate of biochar C addition in this study. The resulting decrease in CO2 emissions is therefore in line with findings from a meta-analysis by Sagrilo et al. (2015), who concluded that biochar generally reduces CO2 emissions when applied at rates less than twice the amount of the soil organic matter C. Studies reporting increased CO2 emissions following biochar application to soils have attributed the rise to mineralization of the available labile C pool associated with the biochar and enhanced mineralization of weathered biochar over time by microbes. It is possible that in this study the lack of any detected increase in CO2 emissions associated with biochar effects on soil mineralization is the result of variably charged minerals like iron (Fe) and aluminum (Al), associated with humo-ferric podzol soils (Evans and Wilson, 1985), sorbing onto typically electronegative biochar surfaces (Mukherjee et al., 2011) and serving to stabilize the biochar  58 (Fang et al., 2014), although this more commonly occurs in soils with high clay contents (Brodowski et al 2006).  This study found no significant effect on net CH4 uptake after biochar addition, while there was a trend toward increased consumption following fertilizer application. The magnitude of CH4 uptake measured during this study was comparable to measurements made at this forest site by Jassal et al., (2011), and by Crill (1991) in a temperate forest near New York, as well as at sites further afield and from other ecosystem types during summer months as summarized by Sabrekov et al. (2016). The increased CH4 uptake in fall suggests that low soil water content did not induce a biological limitation (Del Grosso et al., 2000), but rather remained within the optimal range (70-20 % ) for methane consumption in the surface soil layer (Mosier et al., 1996). The soil at this site is highly acidic and experiences short term flooding from high rainfall events (see Figure 10, year 2013), while the soil texture (gravelly-sandy-loam) also helps it to drain rapidly. In a meta-analysis by Jeffery et al (2016), biochar was found to mitigate CH4 emission in acidic soils that are periodically flooded. Masiello et al (2013) found that biochar can stimulate a range of effects on microbial gene expression that are dependent on intercellular communication and hence it could regulate microbial-dependent soil processes like net CH4 uptake through the balance between methanotrophic (uptake) and methanogenic activity (production). This study showed that biochar modestly increased N2O emissions during the first year after application. The N2O emissions measured in this study were notably smaller than those measured at nearby (200 m away) research plots by Jassal et al., (2008, 2010 and 2011) following spring and winter fertilizations at 200 kg N ha-1. From a meta-analysis, Cayuela et al (2014) concluded that biochar reduced N2O emissions from agricultural soils in both field and laboratory studies by up to 54%. This was directly correlated to the amount of biochar applied.  59 Harter et al. (2014) specifically related these decreases in water-saturated soils to the structure and function of the N-cycling microbial community. Whether similar behavior holds for the soil microbial community in rapidly drained, N-deficient forest soils is unknown at present. In contrast, by utilizing microcosm experiments with the addition of different types of fertilizer-N and using stable isotope techniques with and without the use of a nitrification inhibitor, Sanchez-Garcia et al., (2014) found that biochar can increase soil N2O emissions produced by nitrification-mediated pathways. 3.4.2 Fertilizer treatment effects Fertilized plots were consistently large sources of CO2 compared to all other treatments, while fertilized plots with biochar were significantly larger sources than control plots only during spring and early summer periods of relatively low fluxes (Figure 11). The significantly larger CO2 emissions observed immediately after the fertilization of the control plots in this study are consistent with the increases in Rs observed in the field measurements of Jassal et al. (2010) and Shrethsa et al. (2014), each of whom attributed the short-term increase in Rs to increases in Ra more than from Rh, with possible contributions from CO2 released from urea hydrolysis (Jassal et al., 2010). Raich et al. (1994) found increased root growth after fertilization of N-limited forest soils, which would be reflected in increased contributions of Ra to Rs. In this study, after an initial increase in CO2 emissions following fertilization, differences between treatments diminished through spring into early summer, possibly due to decreased Rh as labile substrates were used up and the remaining litter quality was low (Knorr et al., 2005). Another explanation could be changes to the microbial structure after fertilization (Cleveland et al., 2007; Levy-Booth, 2016). Fray et al. (2004) detected shifts in fungal diversity; reductions in active fungal  60 biomass and the activity of ligninolytic enzymes (e.g., phenol oxidase), which all influence rates of degradation of recalcitrant organic material after N-fertilization of pine and hardwood forests. Significant differences between the fertilized plots and all others were found throughout the summer period when N2O fluxes were largest.  The increase in measured CO2 emissions from fertilized plots after June 2013 suggests that there could have been increased plant productivity and associated Ra as a result of fertilization. In this study fertilizer was applied more than 15 days earlier than other in studies that measured CO2 emissions from Douglas fir forest soils after spring fertilization (Jassal et al., 2010; Shrestha et al., 2014). Seasonal changes in microbial biomass and nutrient flush have been recorded in forest soils and complex relationships between fertilizer timing, microbial biomass, nutrient mineralization and climate could have contributed to the different CO2 fluxes measured in this study (Diaz-Ravina et al., 1995; Levy-Booth, 2016). Unlike the fertilized plots without biochar, those with biochar did not have elevated CO2 emissions compared to the control plots throughout the summer (Figure 11). It is possible that the biochar retained some released NH4+-N after urea hydrolysis, through physical entrapment in its pores (Jassal et al., 2015) rendering it unavailable for microbial or plant uptake or subsequent nitrification to plant available NO3--N. Zheng et al. (2012), found that biochar decreased extractable NO3- in N-fertilized treatments by 8% with mixed effects on NH4+ in two temperate soils. Biochar absorption of NH4+ released via hydrolysis of urea would likely reduce microbial activity, decrease Rh, and slow the conversion of NH4+ through nitrification to NO3-, which in combination could effectively reduce Ra. Soil fluxes of GHGs share soil temperature and soil moisture as common controlling variables (Smith et al., 2003). The effect of biochar application on these, with and without fertilization in a forest soil is unknown. Other important variables, known to affect gas diffusion  61 gradients and microbial activity include θ, Ψ, C/N, organic matter content, redox potentials and pH (Mosier et al., 1996; Del Grosso et al., 2000; Jassal et al., 2005; Jassal et al., 2010; Jassal et al., 2011; Hu et al., 2015). Analysis and comparison between treatments and the soil climate variables for all gases was made difficult by the large spatial and temporal variability in this heterogeneous forest soil, that is not ideally captured by SNSS chamber techniques and where these biogenic GHG fluxes are the expression of complex abiotic and biotic relationships. Analysis of the relationship between the CO2 emissions and temperature for the different treatments indicated that this relationship was strongest in fertilized plots suggesting that the partitioning between Rh and Ra could be different between treatments. The reduced temperature sensitivity of Rs observed in the biochar plots indicates that the effects of biochar application on Ra and Rh differ from those of fertilizer application. Eberwein et al. (2015) found that the response of Rh to N-enrichment and changes in temperature was dependent on the C availability of soil substrates, and when C was abundant, N enrichment increased Rh. Furthermore, they found that while the complexity of the C source was important, abundance of C was more so. In this study we found that the addition of biochar, in the absence of fertilizer, resulted in slightly higher Q10 estimates during 2013 when surface conditions were generally wetter. After fertilization of biochar treated plots, there was no noticeable differences in CH4 uptake in BC + F compared to biochar without fertilization (BC); however, there was a noticeable increase in CH4 uptake in fertilized plots without biochar application (F). The fertilization results differ from those observed by Jassal et al. (2011) who found that N addition reduced CH4 uptake from an average of 4 µmol m-2 h-1 in the control plots to 2 µmol m-2 h-1 in the fertilized plots. Jassal et al.’s (2011) results are consistent with the likely inhibition of methanotrophic activity caused by competitive inhibition of the enzyme methane monooxygenase (MMO) by NH4+. The work of  62 Krus and Everson (1995) may help explain the differences in the observed CH4 uptake response to N addition between Jassal et al. (2011) and the present study. They found that fertilization of nutrient poor ecosystems may have a stimulating effect on CH4 oxidation and associated this with likely increases in ammonium concentrations resulting from more rapid nutrient cycling (Goldman et al., 1995). Furthermore, Bodelier at el. (1999) found that urea-N fertilization stimulated methane oxidation around the roots of rice plants with an increase in Type-I methanotrophs being measured, with N known to be selective for those methane oxidizers. It is possible we detected increased CH4 uptake and Jassal et al. (2011) detected decreased CH4 uptake after fertilization because less mineral-N was available in the soil prior to fertilization in our study (not measured), as is indicated by the difficulties in determining significant N2O fluxes before and after fertilization in out study as well as the recorded significant increase in CO2 emissions, thought to be related to increase Ra and Rh after fertilization. Using the SNSS technique, Jassal et al. (2007) measured peak emissions of 26 µmol N2O m-2 h-1 on 24 July 2007, 3-months after spring fertilizer application, at a nearby (200 m away) location in this stand. Other studies have shown lesser N2O emissions but similar timing after urea-N fertilization (Shrestha, et al., 2014). The small increase in N2O emissions that was observed after fertilization, is unusual as it is more usual to detect increases from 20-500% after fertilization (Magill et al., 2000; Papen et al., 2001; Jassal et al., 2008; Koehler et al., 2009; Shrestha et al., 2014). It is likely that the very small amount of rain that fell immediately following fertilization (< 15 mm over 12 days, with only 1-day receiving > 5 mm), could have resulted in poor incorporation of the urea-N into the deeper soil profile and increased loss of N as NH3 (Fox and Hoffman, 1981). Another factor contributing to the low N2O emissions observed could be that the applied N was quickly bound to organic matter surfaces with high CEC in the  63 upper LFH layer, rendering it unavailable for nitrifying bacteria (Chapell et al., 1999; Prescott et al., 1993). The transient, sporadic nature of elevated N2O emissions often associated with rainfall events and nitrifier denitrification would have been missed by the biweekly sampling (Barton et al., 2015). Furthermore, the delayed peak in N2O emissions experienced after fertilization (Jassal et al., 2008; Shrestha et al., 204) is often short-lived (Peng et al., 2001; 2-3 weeks) and so can be easily missed with low temporal sampling frequency.  Jassal et al. (2010) reported short-term significant increases in N2O emissions following fertilization and hypothesized that this was due to a lack of bioavailable C in acidic forest soils which would facilitate immobilization of NH4+-N in microbial assimilates (Aber et al., 1998). The small increase in N2O emissions after biochar addition and fertilization could be the result of improved bioavailability of C. In a meta analysis, Cayuela et al (2014) found that emissions of N2O after biochar application were influenced by the biochar feedstock, pyrolysis conditions and the C/N ratio; interactions between soil texture and the biochar, and, if applied, the chemical form of N-fertilizer, were also found to be important. 3.4.3 Carbon dioxide equivalent fluxes In this experiment, soil CO2 effluxes represented > 99% of the CO2e for all treatments. The unexpected increase in net-CH4 uptake after fertilizer additions was not sufficient to significantly decrease CO2e, and the N2O emissions were also not sufficiently large to have an effect. When we take into account the added biochar-C (3.9 t ha-1) and consider that there was no detected increase in the CO2 emissions after application to unfertilized plots, we can conclude that the biochar application to soil successfully sequestered more C in the short-term in the soil. Without a more thorough knowledge of the effects of fertilization on stand productivity, it is impossible to ascertain if there was an increase in C sequestration as a result of biochar  64 applications to a fertilized forest. Jassal et al. (2008) determined a net increase of 64% in net ecosystem productivity (NEP), from 3.3-5.3 Mg-C ha-1 yr-1 following fertilization. Based on this, and because we found that biochar reduced CO2 emission after fertilization, we estimate that biochar would further improve C-sequestration by an additional 15% to 5.9 Mg-C ha-1 during the first year after fertilization compared to fertilization alone. 3.5 Conclusions The results presented show the effects on soil GHG emissions after application of 5 t ha-1 of Douglas-fir derived biochar to a Douglas-fir forest soil in the first year followed by application of fertilizer N as urea in the second year. The results support that soil CO2 emissions are strongly controlled by soil temperature, furthermore that the relationship is stronger after fertilization suggesting that the partitioning between Ra and Rh can vary with different treatments. Overall the results suggest that the effect of adding low amounts of Douglas-fir derived biochar to the surface of the dominant soil type supporting Douglas-fir stand growth in Western Canada, would have little effect on GHG emissions and their total CO2e fluxes. Additionally, applying biochar prior to fertilizer applied following industry-standard practices neither significantly increased nor decreased the treatment CO2e fluxes. Fertilization did have a significant effect, increasing CO2 emissions. In conclusion, low rates of biochar addition to this forest soil would further improve soil C sequestration with or without fertilization.      65 3.6 Tables Table 2. Greenhouse gas annual (Annl) and seasonal one-way ANOVA. Spring-summer (SS), Summer-fall (SF). HSD analysis indicates which treatments were determined to be significantly larger than others in list. Gas Year Season df F p HSD CO2 2012  Annl 1,152 2.155 0.144   SS 1,76 0.099 0.750   SF 1,74 3.106 0.082 S > BC 2013 Annl 3,212 22.610 << 0.001 F > S, BC, BC+F SS 3,164 16.560 << 0.001 F > S, BC, BC+F & BC+F > S   SF 3,44 14.650 << 0.001 F > S, BC, BC+F CH4 2012  Annl 1,125 1.656 0.2   SS 1,61 1.001 0.32   SF 1,62 0.804 0.37   2013 Annl 3,173 4.447 0.005 S > F SS 3,133 3.159 0.03 S > F SF 3,36 2.703 0.056 S > F N2O 2012 Annl 1,130 2.241 0.012   SS 1,71 3.428 0.068 BC > S SF 1,57 0.231 0.632   2013 Annl 3,190 4.227 0.006 BC+F > S  SS 3,149 4.460 0.005 BC+F > S &  BC+F > BC  @ p < .1 SF 3,37 1.479 0.235    Table 3. Treatment annual dependence of Rs10 (µmol CO2 m-2 s-1) and Q10Rs parameters in Eq.2. Also shown model goodness of fit parameters AIC and R2. Fitted relationships for all data can be seen in Figure 14. Years Treatment Rs10 Q10Rs AIC R2 All S 2.55 2.94 317.23 0.31 All BC 3.36 3.15 420.89 0.21 2012 S 2.61 2.67 199.28 0.09 2012 BC 2.76 1.37 205.67 -0.01 2013 S 2.48 3.06 100.67 0.68 2013 BC 4.38 3.13 193.84 0.47 2013 BC+F 3.87 1.97 159.73 0.21 2013 F 6.02 3.08 137.24 0.75   66 3.7 Figures  Figure 10. Site soil climate variables. (A) Daily cumulative precipitation measured at a nearby climate station. Dashed lines indicate biochar application (27th Feb 2012) and fertilizer application (27th Mar 2013); (B) Daily mean Ts15 from averaging each MPS-2 and GS3 measurements; (C) Daily mean θs15 from averaging GS3 measurements; (D) Logarithm of the daily mean Ψs15 from MPS-2 measurements. Dashed lines indicate sample dates. A B C D  67  Figure 11. Mean treatment CO2 fluxes for soil (S), biochar (BC), fertilizer (F) and Biochar plus Fertilizer (BC+F). During 2012 when only the biochar treatment was applied (top panel); Measurements during 2013 when all treatment were applied (bottom panel). Error bars indicate ± one standard error of the mean treatment value and points have been spread evenly for each sample date to ease comparison. Year: 2012Year: 2013051015051015MarAprMayJun JulAugSepOctNovDecDateCO2 flux (µmol m−2 s−1)CO2 flux (µmol m−2 s−1)SBCSBCFBC+F 68  Figure 12. Methane 2012:2013 seasonal treatment effect (mean ± 1 SE). Spring-summer (SS), Summer-fall (SF). Lettering (a-b) indicates pairwise differences detected in HSD analysis when p < 0.05.  a a a ab b ab a ab b ab a a  69  Figure 13. Nitrous oxide 2012:2013 seasonal treatment fluxes (Mean ± 1 SE). Summer-spring (SS), Spring-fall (SF). Lettering (a-b) indicates pairwise differences detected in HSD analysis when p < 0.05.  b a  70  Figure 14. Relationship of soil CO2 efflux (Rs) to soil temperature at 15-cm depth for different treatments.        71 Chapter 4: Laboratory and field measurements of dissolved organic carbon and nitrogen after biochar application with and without urea-N fertilization to a West Coast Douglas-fir forest soil  4.1 Introduction  The growth of a tree is limited ecologically by the niche it occupies. Temperate and boreal forest ecosystems are generally nitrogen (N) limited (Vitousek and Howarth, 1991). Plant available forms of N include ammonium (NH4+), naturally provided through ammonification of organic N, and, nitrate (NO3-) from nitrification. Fertilization with N can result in increasing carbon (C) sequestration (Aber et al., 1995; Aber et al., 1998). Studies have shown that the effects of N addition are positive on tree growth and also mostly positive on soil C sequestration in the Northern Hemisphere (Aber et al., 1995; Jassal et al., 2008; Liu and Greaver, 2009; Vitousek and Howarth, 1991).  In the N-limited Douglas-fir stands of the Pacific Northwest, N is mostly stored in soil organic matter (Chapell et al., 1991, Humphreys et al., 2006) and its availability to plants is largely dependent on the ammonification rate through to NH4+ as the typically high C/N ratios and low nitrification rates limit the availability of NO3-. Chapelle et al. (1991) note that fertilization programs have focussed predominantly on increasing stand growth, as nutrient deficiencies are not enough to prevent the establishment Douglas-fir stands. Because of the low rates of natural addition of N to the soils through symbiotic fixation and N deposition, it is of utmost importance to ensure that leaching losses enhanced by nitrification are kept to a minimum. In work done by Heilman and Gessel (1971), close to 30% of fertilizer N applied as  72 urea was found to be taken up by Douglas-fir trees. Jassal et al. (2008) concluded that fertilization of a 54-year-old Douglas-fir forest increased net ecosystem productivity (NEP) by 60%, from 326 to 535 g C m-2 yr-1 following fertilization. When accounting for changes to CH4 and N2O fluxes expressed in global warming potential (GWP) terms as CO2 equivalents, fertilizer additions resulted in an additional uptake of 2.28 t CO2 ha-1 one year after fertilization.  Chen et al. (2011) concluded that urea-N fertilization increased NEP in a Douglas-fir forest by 83% after fertilization through increases in gross primary productivity (GPP) and reduced ecosystem respiration (Re), largely through a reduction in soil respiration (Rs), where NEP = GPP - Re. Clearly any opportunity to increase the low total uptake of applied urea-N (~30%) by the Douglas-fir trees could have financial benefits realized through further increased stand growth. Additionally this would increase C sequestration, reducing CO2 concentrations in the atmosphere, helping to mitigate its contribution to climate change. Realizing this, British Columbia’s Coastal Forest Action Plan offers fiscal incentives to increase productivity of second-growth coastal forests using fertilization to provide a potential benefit of reducing greenhouse gas (GHG) emissions through increased C sequestration while maintaining employment levels in the forest products sector (BCMFR, 2007).  Biochar, a C rich product resulting from pyrolysis of organic matter, has shown significant potential for use as a soil amendment that may increase tree growth, enhance the C sequestration potential of a fully managed forest cycle, and reduce nutrient loss after fertilization and harvesting while diversifying the forest economy (Clough and Condron, 2010; Jeffery et al., 2010; Lehman et al., 2006; Lehmann and Joseph, 2009; Thomas and Gale, 2015; IPCC, 2014).  The properties of a particular biochar are largely determined by the feedstock and pyrolysis conditions (Sohi et al., 2010). The prevalence of aromatic structures containing few functional  73 groups enhances the resistance of biochar to decomposition in soils (Dai et al., 2005, Zimmerman, 2010). This is particularly true when the O:C ratio of the biochar is less than 0.2 (Spokas, 2010).  Compared to mineral soils, biochar has a lower particle density and larger surface area (Brewer et al., 2014). When added to soil, biochar alters the soil’s physical structure, generally decreasing its bulk density (Major et al., 2010; Lim et al., 2016). This can serve to increase the exposure of chemically reactive surfaces to aqueous and gaseous exchange, and provide refugia for microbes (Lehmann et al., 2011).   The effect of biochar on dissolved organic carbon (DOC) is not well known, and could represent a significant loss of C when soil hydraulic conductivity is high. Major et al. (2009) used stable isotopes to demonstrate that biochar applied to a savanna Oxisol in Columbia resulted in increased biomass production and DOC leaching, but attributed enhanced DOC leaching to biochar-stimulated increases in below-ground net primary productivity rather than to leaching of biochar-derived DOC. In a soil column study without plants, Eykelbosh et al. (2015) found that biochar attenuated total DOC leached over the course of an experiment using filtercake-derived biochar on DOC and NO3- leaching from a vinasse-treated soil (5 % w/w). Hartley et al. (2016) determined that wood waste-derived biochar increased pore water DOC relative to controls in a sandy soil. Tang et al. (2016) showed that biochar increased dissolved organic matter and that DOC content and estimated aromaticity (SUVA254) was dependent on the biochar feedstock. In terms of N cycling, biochar could regulate ammonium-N (NH4+-N) availability through increases in redox potential and CEC. The likely increase in CEC capacity over time resulting from biochar additions (Lehmann, 2007; Liang et al., 2006) and its subsequent  74 absorption of positively charged N based ions onto or into biochar amended soils could result in a reduction in dissolved N, leading to an initial decrease in plant N availability. However, as the biochar becomes saturated with positive ions, its ability to retain nutrients will decrease and an increased availability of N to plants over longer time periods could result. Specifically, NH4+ formation after urea-N fertilization and its subsequent adsorption onto the biochar could reduce the inorganic-N pool available for nitrifiers, and thus NO3- concentrations in leachate may be reduced.  It is important to note that results from biochar studies on N cycling have been contradicting. In a meta-analysis comparing 56 studies performed between 2010 and 2015, Nguyen et al. (2017) determined that biochar generally reduced soil inorganic nitrogen (SIN). They found that woody biochars reduce SIN less than other biochar types and that interaction between biochar and environmental factors, pyrolysis temperature and surface properties of biochar were the main factors affecting SIN. Some studies have reported increases in soil N mineralization and nitrification by as much as 269 % and 34 %, respectively in biochar mixtures with soil (Case et al., 2015). Singh et al. (2010) reported an increase in NO3- leaching from poultry manure derived biochar, as well as a decrease in NH4+ leaching from Eucalyptus and poultry-manure-derived biochar mixed with soil. Others have not detected any effect on NO3- leaching (Eykelbosh et al., 2015). Atanu and Zimmerman (2013) showed that for a range of biochar studied, NH4-N was generally the more abundant N form in leachates, with NO3-N also abundant in the biochar made from grass.  It is clear that applying biochar could have broad implications for DOC and total N (TN) in soil pore water and leachate. Complex interactions between applied biochar and biogeochemical cycles make it difficult to forecast the exact effects of biochar application at any  75 site, and so field-based studies are essential. Most work in this area has focused on either agricultural systems or on charcoal resulting from natural forest fires (Jeffreys et al., 2014; Thomas and Gale, 2015), as studies on the use of biochar as a silvicultural treatment are uncommon. Since field conditions in forest soils are typically highly spatially heterogeneous, controlled laboratory studies that investigate the dominant bulk soil contributing to components of the biogeochemical cycles can be highly informative.  Research Objectives  The objectives of this study were to investigate the effects of biochar application to a forest soil with and without N fertilization on soil pore water dissolved organic carbon (DOC) and total nitrogen (TN) in a Pacific Northwest Douglas-fir forest. Specifically, the research sought to determine the effects of differing biochar application rates on soil water DOC and TN concentrations, and to investigate differences in soil pore water quality using UV-Vis absorbance spectra. We hypothesized that biochar additions would (i) increase DOC in drainage water, (ii) decrease N in drainage water, with concentrations hypothesized to increase for larger biochar application rates and in response to fertilization additions. The goal for the study was to determine whether biochar additions could reduce TN leaching losses without significantly contributing to DOC leaching. This information could assist in future efforts to maximize forest fertilization efficiency and improve carbon sequestration potential throughout the production cycle of these important forests. 4.2 Materials and methods This chapter describes a study on water quality that is comprised of field and laboratory components that utilized the experimental plots described in Chapter 3 and the soil columns described in Chapter 2. Briefly, the field-based experiment was set up in previously unfertilized  76 area of the forest stand and consisted of sixteen 4 m x 4 m plots assigned in a randomized complete-block design with four blocks of the following four treatments: i) soil only (S; e.g. control), ii) 5 Mg ha-1 biochar (BC), iii) 200 kg N ha-1 urea fertilizer pellets (F), and iv) 5 Mg ha-1 biochar with 200 kg N ha-1 urea pellets (BC+F). Biochar was applied to plots assigned to BC and BC+F treatment in late February 2012. Urea-based fertilizer (N:P2O5:K2O, 46:0:0) (Agrium Inc.) was applied in late March 2013 to plots assigned to F and BC+F treatments. These plots were utilized for water sampling in the present study, focusing on wet season conditions in the months following fertilizer application.  The laboratory component of this study (described fully in Chapter 2) consisted of mixtures of soil, biochar and fertilizer incubations in volumes of 200 cm3. Six experimental mixtures, consisting of combinations of soil, biochar (application rates of 0, 1% and 10% biochar on a mass basis) and fertilizer (application rates of 0 and 200 kg N ha-1 equivalent of urea fertilizer, were replicated four times, yielding a total of 24 incubation units. The laboratory component of the present study utilized the incubations several months after the experiment described in Chapter 2. The present study thus followed several months of drying, with the wetting cycles described below simulating the rapid wet-up in late fall that follows the dry summer that is typical of the summer-fall weather pattern in the study region. 4.2.1 Field soil water sampling Soil suction cup lysimeters (SCL) (model 1900, SoilMoisture Equipment Corp, Santa Barbara, CA) were installed at the 25-cm depth in each of the 16 field plots which were evenly allocated among the following treatments: biochar (BC, where soils were amended with 5 t ha-1 Douglas-fir derived biochar), fertilized (F, where soils were amended with 200 kg ha-1 urea-N fertilizer), biochar plus fertilizer (BC+F, where soils were amended with 5 t ha-1 and 200 kg ha-1  77 urea-N), and control plots (unamended soils, S). Full details on the experimental design and set-up of the plots is given in Chapter 3.  Each SCL consisted of a 25-cm long PVC tube with 4.8-cm outside diameter (OD) and a porous ceramic cup with a 2 bar (200 kPa) air-entry value. The PVC tube was capped with a Santoprene stopper assembly with compressible Neoprene access tubing (1/4” ID) and clamping ring (Z1900-200, SME, Santa Barbara). To install the SCL, a 7-cm diameter auger was used to bore the hole into which the SCL was placed. The space around the auger was then backfilled with a soil slurry (2-mm sieved) to well above the suction cup, capped with Bentonite, then backfilled with soil extracted during the excavation (SME, 2007).  The decreasing tension method (Titus, 1996) was used to collect mobile phase soil pore water using the SCL between site visits. Briefly, the SCL internal volume was evacuated using a Model 2005G2 Vacuum Hand Pump (1900, Soil Moisture Equipment Corp, Santa Barbara, CA) in the field. On the subsequent return visit, water samples were collected from the SCLs using the vacuum hand pump to create a negative pressure in the tubing thus transferring the sample from the SCL to a collection vessel. The SCL were evacuated monthly from November 2011 to March 2012 to their maximum vacuum (approximately -65 to -80 kPa) to allow the SCL body materials to reach chemical equilibrium with the surrounding soils. During this equilibration period, water samples were not analyzed.  Sampling was conducted on two dates in spring 2012 following biochar application. Seven sampling campaigns were conducted in 2013, primarily in the spring following fertilizer application, but continuing as soil water contents and field logistical arrangements permitted.    78 For sampling, a negative pressure (-65 kPa) was applied to the internal volume and water accumulating in the volume between visits was collected for analysis. Samples were placed in brown PTFE containers (250 ml) and transported to the UBC Soil Water Atmosphere Laboratory (SWAL) for analysis. Before analysis, samples were refrigerated at 4 °C. Limited sample volumes were obtained over July through September due to dry soil conditions (< - 60 kPa) and a loss of hydraulic connectivity with the surrounding soil.  4.2.2 Laboratory Experimental Setup Following the greenhouse gas experiment described in detail in Chapter 3, the incubations were left to air dry in the laboratory for three months, simulating the dry season. After this period, incubations were wet up with DDI water to saturation (visually confirmed by the appearance of surface pooling). This water was then allowed to freely drain until field capacity was reached. All incubations were then leached with 0.1 L de-ionized water (DDI) on Tuesdays and Wednesdays for two months, for a total 1.7 L of DDI water additions. After each 0.1 L wetting, the entire suite of 24 incubations was attached to a vacuum manifold and soil pore water was extracted at 30 kPa for 2 hrs. Between wettings, incubations were left uncovered at room temperature (23 °C) in the dark. Water samples were immediately analyzed with surplus water labeled and refrigerated in brown glass bottles at 4 °C in case repeated analysis was required.  4.2.3 Soil pore water analysis  All soil pore water samples from the field and laboratory were analyzed using a ultraviolet- visible (UV-VIS) spectrophotometer (Spectro::lyzer, s::can, Vienna, Austria).  Laboratory samples were analyzed directly following collection, while field samples were analyzed on the spectrophotometer within 24 hours of collection. The instrument produces an  79 absorption spectrum from 200 to 750 nm that was used to calculate a range of spectral indices described below. The instrument applies a global calibration to the absorbance spectra to estimate DOC and NO3-, which were both useful for monitoring experimental progress. Since the calibration equation was developed for surface water rather than soil water these numbers were not used in analysis. The spectral slope for the 275-295 nm wavelength band (S275-295) was calculated after Helms et al., (2008). S275-295 has been shown to increase with decreasing molecular weight of DOM. Specific UV absorbance at 254 nm (SUVA254) has been used as an indicator of aromaticity. SUVA254 was calculated by dividing sample absorbance at 254 nm (m-1) by the DOC concentration (mg L-1) (Weishaar et al., 2003). Increasing SUVA254 indicates increasing aromaticity of DOM.  Following this analysis, samples were filtered through 0.7 µm glass fiber filters (Whatman Ltd. Kent, UK) to remove suspended solids and refrigerated at 4 oC in brown glass bottles until further analysis was performed.  The soil pore water dissolved organic carbon (DOC) and total nitrogen (TN) concentrations were analyzed on a TOC-Vcsh Total Carbon Analyzer (Shimadzu Corp, Japan) with an inline TNM-1 (Shimadzu Corp, Japan) system connected to a ASI-V autosampler (Shimadzu Corp, Japan).  Prior to sample injection, the autosampler performed sample acidification (7.5 µl 2N HCL) and sparging (ultra-zero compressed air) to remove any inorganic carbon (IC), allowing quantification of DOC as Non-Purgable Organic Carbon (NPOC), hereafter referred to as DOCSCL when referring to SCL field samples and DOClab for laboratory incubation soil pore water samples. After sample injection (50µl) into the system column (platinum beads), and  80 catalytic oxidation via dry combustion at 720 oC, the resulting CO2 gas was picked up in the carrier gas stream (ultra-zero compressed air carrier gas) and passed through a highly sensitive non-dispersive infrared (NDIR) detector for measurement of CO2 which was converted to DOC via a standards-based calibration curve.  Following the NDIR, the gas stream was carried to the TNM-1 system where a chemi-luminescence detector determines TN in the sample, hereafter referred to as TNSCL when referring to SCL field samples and TNlab for laboratory incubation soil pore water samples. This combined system (TOC-vcsh + TNM-1) was configured to carry out 5 injections to achieve a within-sample coefficient of variation of 2%. Calibrations for all methods were performed after periodic maintenance and distilled DDI and 5 mg L-1 standards were loaded with each sample run to determine the system detection threshold and to monitor calibration changes.  A Mettler Toledo benchtop meter fitted with pH and electrical conductivity (EC) probes was used to measure soil pore water pH and EC after transferring samples into 40 mL vials for analysis of DOC and TN. Hereafter pH and EC measurements for SCL field samples and laboratory incubation soil pore water samples are referred to as pHSCL and ECSCL, and pHlab and EClab, respectively. Following the laboratory flushing experiment soil incubations were destructively sampled and analysed for total organic carbon (TOC) using the loss on combustion technique and extractable NH4+ and NO3- after KCL extraction using method version EE-SOP# A.04.01. 4.2.4 Soil moisture characterization Field sensors  At the field site, additional instrumentation installed at 15-cm depth was used to monitor soil water conditions in twelve of the 16 plots (three replicates for each of the four treatments).  81 This instrumentation consisted of sensor clusters to measure volumetric water content (θ15), bulk electrical conductivity (EC15), and soil temperature (T15) (model GS3, Decagon Devices Inc. Pullman, WA), and soil water matric potential (Ѱ15, model MPS-2 sensors, Decagon Devices Inc.). Signals were measured using a CR1000 data logger (Campbell Scientific Inc, Logan, UT) powered by a 12-V-DC 70-amp-hour lead-acid battery. Measurements were made at 5-min intervals and average output tabulated every 30 min. Furthermore, on each sample date a handheld GS3 sensor was used to measure surface volumetric water content (θS), bulk electrical conductivity (ECS), and soil temperature (TS). 4.2.5 Statistical analysis 4.2.5.1 Field experiment  To determine if there was any effect of biochar application on the ECSCL, pHSCL, DOCSCL and TNSCL in relation to fertilizer application, a two way ANOVA was first used to test the effect of location and time, followed by a one-way ANOVA to test treatment differences during specific times periods. Data were log-normalized prior to analysis and Tukey-adjusted differences of least square means (HSD) were used to compare treatments when significant differences were detected in the one-way ANOVA. Linear regression was used to investigate the relationship between DOCSCL and TNSCL for all data and between treatments, with the slope of significant relationships being used as in indicator of soil pore water C/N.  4.2.5.2 Laboratory experiment To determine if there was any effect of biochar application on the EClab, pHlab, DOClab, and TNlab and in relation to fertilizer application, a one-way ANOVA was first used to test the effect of treatment on the first flushing date available, followed by a one-way ANOVA to test treatment differences during the period after signals had stabilized. When there was indication of non- 82 normal distributions of residuals during ANOVA, data were log-normalized prior to further analysis. Tukey-adjusted differences of least square means (HSD) were used to compare treatments when significant differences were detected in the one-way ANOVA. Linear regression was used to investigate relationships between DOCLAB and TNLAB for all data and between treatments, with the slope of significant relationships being used as in indicator of soil pore water C/N. Spectral indices (S275-295 and SUVA254) measured in the laboratory experiment were tested for treatment effects using a one-way analysis of variance. 4.3 Results 4.3.1 Field experiment 4.3.1.1 Soil climate Cumulative precipitation for 2012 from January 1 up to the first day of sampling (date here) was 593 mm, equivalent to >40 % of the 1434 mm total for 2012. Total precipitation for 2013, however was only 840 mm, which was well distributed into the summer, keeping soil conditions moist through to the end of June (Figure 15). Following fertilization, there was an unexpectedly low amount of precipitation. During 2013, the maximum θ15 mean (±SD) measured was 36 (± 0.7)% and θ15 was above field capacity (21%, Appendix 1) 90% of the time (not shown) during 2013. Sampling of SCL always occurred when measured Ψ15 exceeded -15 kPa, which occurred 65% of the time in 2013.  The soil required more than 10 mm of precipitation in a daily rainfall event or cumulative over a period of days to replenish soil water conditions close the maximum measured at the site (θ15 ~36 %). In figure 15 it can be seen that over the 16 days after fertilization (28 March 2013 - 11 April 2013), only 25 mm was recorded. Evidence of the fertilizer application was not detected in surface EC measurements until 9-days after fertilization, as indicated by significant increase in  83 measured surface EC for F and BC (Figure 15 and 16). Data shows that between 03 May 2013 and 31 May 2013 there was a brief period of decreased θ15 and Ψ15, likely resulting from increasing evapotranspiration due to increasing temperatures (Figure 15), and clear sky conditions with increasing photosynthetic photon flux leading into the summer (not shown). Soil temperatures (T15) followed an increasing trend throughout the sample period from a mean (±SD) value of 4.1 (0.2) °C on 26 March 2013 to a maximum of mean (±SD) of 13.5 (0.3) °C on 11 July 2013.  4.3.1.2 Field ancillary measurements of soil pore water quality The mean (± 1 SE) soil pore water (ECs) was highest for BC+F, 22.20 (±1.80) µS cm-1 (Table 4). There was a significant effect from SCL location (F(15,344) = 9.61 , p < 0.001 ) and date (F(23,344) = 15.55 , p < 0.001), as well as treatment (F(3,222) = 17.20 , p < 0.001 ). The order of increasing ECs by treatment was S < BC < BC+F < F, with Tukey HSD analysis confirming the significant effect of the fertilization on both fertilized treatments (F & BC+F) compared to unfertilized treatments (S & BC).  The mean (± 1 SE) soil pore water (ECSCL) was highest for BC+F, 91.44 (± 15.31) µS cm-1 (Table 4). The soil extract EC was independently measured to be higher than the soil + biochar (1% w/w) soil extract EC at 46 (± 20) and 31 (± 15) µS cm-1, respectively. There was a highly significant effect from SCL location (F(15, 123) = 101.91 , p < 0.001), though data was inconsistent with assumptions needed for ANOVA and was log-normalized for further analysis.  A closer look at the individual ECSCL measurements for each SCL revealed that one BC+F plot had consistently higher values (ECSCL >100 µS cm-1) than all others with all measured (ECSCL < 75 µS cm-1). The ECSCL responded significantly to treatment (F(3,135) = 13.81, p < 0.01). The order of increasing ECSCL by treatment was BC < F < S < BC+F with HSD analysis detecting a  84 significant difference between BC and S, and, BC+F and BC suggesting that factors other than fertilization were determining the difference in contrast to results from ECs.  The mean (± 1 SE) SCL soil pore water pH (pHSCL) was lowest for BC, 6.2 (± 0.10) (Table 4). There was a highly significant effect from SCL location (F(15,123) = 28.23 , p < 0.001 ) though data was inconsistent with assumptions needed for ANOVA and log normalized for analysis hereafter. A closer look at the individual pHSCL measurements for each SCL revealed that one BC plot (SCL 10) had consistently lower pHSCL values (pH < 5.5) than all others (pH > 6). The order of increasing pHSCL by treatment was S < BC+F < F < BC, with no significant difference detected between treatments or sample date and no interactions between the two. The pH of soil at the 25-cm depth was 5.6 (± 0.05), which was lower than the pH of the biochar itself (6.86 ± 0.01). While it might have been expected that biochar would increase the bulk soil pH, the biochar was surface applied, and any impact on soil pH was difficult to discern in this study. 4.3.1.3 Field soil pore water DOC and TN Data available for 2012 shows that BC generally had higher DOCSCL concentrations in soil pore water (Figure 17), although these differences were not significant and the concentrations were similar to those measured in a near-by (500 m away) headwater catchment stream during peak flows (Jollymore et al., 2012) when soil pore water could be contributing significantly to surface water DOC fluxes. For 2013, the highest treatment mean (± 1 SE) soil pore water DOCSCL was detected for BC on 20 June 2013, 8.5 (±3.4) mg L-1. There was a highly significant effect from SCL location (F(15,188) = 51.06, p < 0.01) though data was inconsistent with assumptions needed for ANOVA and log normalized for analysis hereafter. A closer look at the individual DOCSCL measurements for each SCL revealed that four of the SCLs (1,10,11 and 14), one in each of BC and F, and two S, had consistently higher DOCSCL values (DOCSCL > 10  85 mg L-1) than all others (DOCSCL < 5 mg L-1) (Figure 17). The order of increasing DOCSCL by treatment in 2013 was BC+F < S < F < BC, with no significant difference detected between treatments and significant differences detected over time (F(3,178) = 2.19, p < 0.04). There was no noticeable difference in treatment soil pore water TNSCL, and values were low in this nitrogen-limited system in 2012 (Figure 18). Following fertilizer application in 2013, there was a highly significant effect from SCL location (F(15,191) = 24.83, p < 0.01) on TNSCL response, though data was inconsistent with assumptions needed for ANOVA and log normalized for statistical analysis hereafter. The highest mean (± 1 SE) soil pore water TN measured 0.24 (± 0.11) mg L-1 for F on the 11 April 2013, 16 days after fertilization. A closer look at the individual TNSCL measurements for each SCL revealed that three of the same SCL (1,10 and 11) identified to have above average for DOCSCL, also had consistently higher TNSCL values (TNSCL > 0.2 mg L-1) than all others (TNSCL <0.2 mg L-1). On four of the seven sample days following fertilization, F plots recorded the highest TNSCL values, only exceeded by S on two occasions, and exceeded by BC+F on one of the remaining sample dates (Figure 18).  The overall order of increasing TNSCL by treatment was BC < BC+F < F < S, with no significant difference detected between treatments or sample date. Unlike measurements at the surface (ECS), there was surprisingly no significant response from fertilization detected at 25cm via the TNSCL signal.  Linear regression analysis revealed a weak but significant correlation between DOCSCL and TNSCL data for the entire dataset (R2 = 0.54; Table 5). Analyzing the data by individual treatment revealed much stronger and significant correlations for S (R2 = 0.86), BC (R2 =0.79) and F (R2 =0.98) than for BC+F. By comparing the TN values to DOC in this way the slope of the linear regression can be used as a proxy for the C/N of the soil pore water, and it can be seen  86 that BC had a much higher C/N ratio than all other treatments, with BC+F having the lowest (Figure 19). 4.3.1.4 Field soil pore water spectral indices The spectral indices S275-295, SR, and SUVA 254 were significantly different between SCL and over time (p < 0.01). A lower value for S275-295 generally indicates higher molecular weights (MW) in DOM in freshwater samples (Helms et al. 2008). The S275-295 measured for individual SCL show that SCL identified earlier to have high DOCSCL and TNSCL also tend to have low S275-295 indicating that those samples had higher MW contributions to their DOM. After log normalization of the SUVA254 data, a one-way ANOVA determined significant differences between treatments (F(3,126) = 3.01, p = 0.03). The proceeding HSD analysis determined that S > BC+F (p = 0.07). As SUVA254 increases, the percentage of aromaticity increases (Chin et al. 1994; Fuentes et al. 2006) and this would suggest that there was a greater contribution of humic substances in S. In summary there was no significant effect of biochar application on bulk DOC and TN in soil pore water measured during the first or second year after biochar application, when soil moisture conditions were favorable for downward percolation at the field site, although there were indications of differences in the composition of DOC as evidenced by the spectral indices. No effect was detected from fertilization in the second year on either DOC or TN at the 25-cm depth. The influence of fertilization was detected in electrical conductivity measurements at the surface close to the time of fertilization in plots with and without biochar application, suggesting that any fertilization responses diminished with increasing soil depth. Highly variable DOC/TN slope estimates between the treatments (13.02 to 114.86) were heavily influenced by outlying data identified to have increasing molecular weights using spectral indices.   87 4.3.2 Laboratory experiment 4.3.2.1 Laboratory ancillary soil pore water quality measurements The EClab measurements remained above 25 µS cm-1 for all treatments throughout the flushing experiment, and differences can be noticed more clearly at the earlier stages of the experiment prior to the cumulative addition of 1 L of leachate volume (LV) (Figure 21). The mean (± 1 SE) EC values throughout the experiment were largest for BC1%+F and S+F starting at 489 (± 58.92) µS cm-1 and 481.5 (± 36.81) µS cm-1 respectively, with S and BC1% being approximately 35% smaller and BC10%+F and BC10%, being less than 25% of the magnitude of S and BC1%. During the period of flushing from 0 to1 L, there were significant differences between treatments (F(5,30) = 30.69, p < 0.01) and over time (F(6,30) = 25.68, p < 0.01). The significant differences as determined by HSD analysis using the first available sampling were S+F > BC10%, S+F > BC10%+F, BC1%+F > BC10%. After 1.2 L of LV, all treatments were in the range of 50-75 µS cm-1 and were not statistically different from each other. The pH measurements were relatively stable throughout the experiment (Figure 21), and maintained significant differences between treatments throughout (F(5,36) = 3.843, p < 0.01). HSD analysis detected significant differences between BC10% and all other treatments and BC10%+F and all other treatments (p < 0.01). The pH of the leachate was significantly higher (~10%) with high rates of biochar applications (Table 6). 4.3.2.2 Laboratory soil pore water DOC and TN  From the first flush, DOClab measurements exhibited significant differences between treatments (F(5,17) = 68.19, p <0.01), with all treatments exceeding 10 mg L-1 (Figure 22). The HSD analysis confirmed that BC10% and BC10+F were statistically similar and both were statistically greater (p < 0.01) than all other treatments (BC1%, BC1%+F, S, S+F). As was the  88 case for EClab, these differences diminished as more volume of water was flushed through, eventually reaching a rough equilibrium after 1 L. Regardless of this diminishing difference, treatments BC10% and BC10%+F remained statistically different from all other treatments (F(5,34) = 21.73, p <0.01) after more then 1L had been flushed through the incubations (Figure 23, Table 6). The cumulative loss of DOC from was largest for 10% mixtures. To investigate if the increase in DOC leaching detected at the higher rate biochar application was significant relative to the amount of biochar added (20 g x 78% C = 15 g C) we subtracted the mean cumulative loss of DOC of the control treatments (106 mg L-1) from the 10% mixtures (180 mg L-1) and multiplied this by the total amount of water leached (1.7 L). In this way we calculated that the total loss of DOC was <1% of the total amount of biochar-C added. From the first flush, TNlab measurements exhibited clear and significant differences between treatments (F(5,16) = 28.39, p <0.01), with all treatments, excluding BC10% and BC10%+F, exceeding 10 mg L-1 (Figure 22). The HSD analysis confirmed that BC10% and BC10+F were statistically similar and, in contrast to DOClab, both were statistically smaller (p < 0.01) than all other treatments (BC1%, BC1%+F, S, S+F). Similar to EClab, these differences diminished as more water was flushed through, eventually reaching a rough equilibrium after 1 L. There was strong positive correlation between TNlab and EClab (R2 = 0.96, F(1,225)=5781, p < 0.01). Regardless of the diminishing difference between treatments, BC10% and BC10%+F remained statistically different from all other treatments (F(5,36) = 30.4, p < 0.01) after more than 1L had been flushed through the incubations (Figure 23, Table 5).  Linear regression revealed a very weak but significant correlation between DOCSCL and TNSCL data (R2 = 0.04, Table 7). Analyzing the data by treatment revealed strong and significant correlations with the addition of biochar, and more so for biochar in combination with fertilizer  89 (Table 7). It can be seen that BC10% with and without fertilizer had a much higher DOC/TN ratio than all other treatments, with the treatments listed in decreasing order as follows: BC10% > BC10%+F > BC1% > BC1%+F > S > S+F (Figure 23, Table 7). The application of fertilizer decreased the DOC/TNN slopes by as much as 20% for biochar treatments and less than 5% for S+F. 4.3.2.3 Laboratory soil pore water spectral indices   Values for S275-295 ranged between 0.11 and 0.14 and were all below values measured from a biochar leaching experiment described by Jamieson et al., (2014) and below surface water measurements made by Helms et al., (2008). The HSD analysis confirmed that BC10% and BC10+F were statistically similar and both were statistically smaller (p < 0 .01) than all other treatments (BC1%, BC1%+F, S, S+F). Over the course of the experiment, the S275-295 values decreased in all treatments (Figure 24). Linear regression indicated that the slopes were significant and the correlation coefficients higher for increasing rates of biochar (Table 8). The SUVA254 values ranged between mean (± 1 SE) of 0.54 (± 0.2) for BC10% and 1.72 (± 0.08) for BC1%, and were well below the range of values measured by Helms et al. (2008) and similar in range to those measured from the biochar leaching experiment described in Jamieson et al., (2014). There were significant differences detected between treatments (F(5,30) = 17.67 , p <0.01), and the HSD analysis confirmed that BC10% and BC10%+F were statistically similar, and like S275-295, both were statistically smaller (p < 0.01) than all other treatments (BC1%, BC1%+F, S, S+F).  4.3.2.4 Laboratory soil TOC, NO3- and NH4+ post experiment   At the end of the experiment, at which point the incubations had been subjected to several wetting and drying cycles and 1.7 L of water had been flushed through them, destructive  90 sampling and analysis indicated some interesting differences between treatments for TOC, extractable NH4+, extractable NO3- and the ratio of NH4+/ NO3- (Figure 25). Significant differences were detected between treatments for TOC (F(5,12) = 33.83, p < 0.01), NH4+ (F(5,12) = 10.10, p < 0.01), NO3- (F(5,12) = 96.88, p < 0.01) and NH4+/ NO3- (F(5,12) = 11.94, p < 0.01) measurements. For TOC, Tukey HSD analysis confirmed that BC10% and BC10%+F were significantly greater than all other treatments, equivalent to about 46% more TOC (Table 6). For extractable NH4+ the HSD analysis confirmed that S+F had statistically higher amounts remaining than S and BC1% (p<0.02). The same analysis also concluded that BC1%+F had statistically higher amounts of NH4+ remaining than S and BC1% (p < 0.02). Furthermore this analysis concluded that BC10%+F had statistically higher amounts remaining than S, BC1% (p <0.03). For extractable NO3- HSD analysis concluded that BC10% had statistically (p<0.01) higher amounts remaining than BC1% and S, and that BC1% also had statistically (p<0.01) higher amounts remaining than S. Furthermore, this analysis concluded that BC10%+F had statistically (p<0.01) higher amounts remaining than all other treatments. These results suggest that with fertilization biochar retained more extractable NH4+. Even without fertilization, BC can help retain extractable NO3- during high flushing events and increasing rates of biochar increases retention of extractable NO3-, which could be directly related to surface charge density on the biochar and an affinity for negatively charged ions.   In summary, DOC leaching was greatly increased at the high rates of biochar application (10%), though not the low application rate of (1%), both with and without fertilization.  Both EC and TN leaching decreased when biochar application rates were increased to 10% for both fertilized and unfertilized treatments. Also important for plant productivity is the increase in pH that was only found at higher rates of biochar application independent of fertilization status.  91 Using DOC/TN to infer differences in C/N ratios showed that the high biochar application rate increased the C/N ratio significantly. Fertilization was found to reduce this by up to 20% when applied to biochar treatments. The S275-295 spectral index decreased as more water was flushed through the incubations, suggesting contributions of DOM with increasing molecular weight as the experiment continued.  4.4 Discussion The results outlined in this chapter indicate that low rates of surface applied biochar did not impact soil pore water DOC at the field site during freely draining soil moisture conditions. Other field studies have detected low rates of biochar leaching as DOC. Major et al., (2010) found that in total, less than 1% of applied biochar was detected in percolating water over two growing seasons in a highly weathered tropical soil. Other studies have found that the magnitude of reported early flushes of bioavailable DOC leached from biochar soil mixtures is dependent on feedstock, production technique and soil temperature as well as soil type (Lin et al., 2012). In our study considering field and laboratory biochar application to the same soil, we found that the homogenized soil with no root activity showed increased DOC leaching at only the 10% application rate. Our field study was unable to detect differences related to a biochar application rate similar to that of the 1% application rate in the laboratory study. It is likely that a contribution to DOC from field-applied biochar was undetected; the rapidly flushed DOC in the laboratory experiment indicates that it could have moved quickly through the sample. It has been shown that biochar can promote soil organic carbon and that this was stored in the soil and not mineralized to CO2 (Jaing et al., 2016), which could make it available for rapid leaching. The difficulty of obtaining field samples of mobile phase soil water from a remote site using suction cup lysimeters resulted in a lower sampling frequency than that of the laboratory study. This  92 difference in sampling frequency, plus the active root processes in the mature forest plots compared to root-free laboratory soil, makes the comparison between field and laboratory challenging. The cumulative amount of DOC leached at the high rate application (10% w/w) in the laboratory study was much larger than for both the low (1% w/w) biochar application and the control treatments, and was similar for both fertilized and unfertilized applications. This suggests that the biochar application rate is the primary control on early flush DOC leaching here. Hartley et al. (2016) showed that DOC in soil pore water samples in a sandy agricultural soil was increased after application of biochar made from forest clearance operations. Liu et al. (2016) determined that increased DOC leaching measured after biochar applications to sand was affected by both the rate of application and particle size. Jaing et al. (2016) showed that biochar additions were less of an influence on SOC mineralization than N additions. In a batch experiment mixing two different biochars into an acrisol at 10% w/w increased the soil pH from 4.9 to 8.7 and caused a 15-fold increase in DOC loss (Smebye et al., 2016). Liu et al. (2016) estimated an equivalent loss of 0.06 – 0.18 % by weight of biochar added C as DOC and as with this study (<1%) concluded that biochar-derived C would contribute little to surface and ground water C.  In the present laboratory study using the ratio of NH4+/ NO3-, the HSD analysis concluded that S+F had significantly higher proportion of the N species left as NH4+ than NO3-, which suggests that biochar application increased nitrification rates in this study, which is in agreement with other findings (Case et al., 2015). Overall, however, we found that the higher rate of biochar application increased total N retention as NH4+ and NO3- in this soil, which could be due to increased physical entrapment in biochar pores (Jassal et al., 2015), and contradicts the  93 general finding in a meta-analysis by Nguyen et a. (2017), who found that biochar generally decreased SIN. Mukherjee and Zimmerman (2013) found that NH4-N was usually the more abundant inorganic N in leachate from most biochars they studied, which is in agreement with the increased retention of NO3-N relative to NH4-N retained in this study. It is likely that organic N contributed significantly to the total N lost in this study (Zimmerman, 2013) and that availability of any retained NO3-N would not be high (Lan et al., 2017).   We also found very strong correlations between DOC and TN in both the laboratory and field components, which suggests that to reduce the magnitude of N leaching relative to DOC leaching, a higher rate biochar application could be favorable. In our laboratory study the DOC and TN in leachate decreased quickly with successive DDI flushes. Lan et al. (2017) found DOC derived from biochar in a mixture with a ferrosol soil  increased the DOC/NO3- ratio early in their incubation experiment and this decreased quickly. In our field study, there would have been enough precipitation after  ~11 days following fertilization (after considering interception from the canopy) to move the surface-applied fertilizer in solution past the depth of the SCL during sampling. However the effect of fertilization was only detected in surface electrical conductivity measurements, supporting findings that very little amounts of nitrogen species are lost through leaching in these podzol forest soils (Chapelle et al., 1991). In the field study, the S275-295 values were lower than those of water samples collected directly from biochar leachates by Jamieson et al. (2014), suggesting that the addition of biochar would tend to decrease the DOM MW (Eykelbosh et al. 2015). This effect was not clear in this study with BC having the lowest S275-295 and BC+F the highest values (Table 4), which might suggest that the effect of the biochar application was masked by other soil processes at this depth in the field. The range of values for S275-295 (Table 4) were comparable to “dark” freshwater  94 samples described in Helms et al. (2008) and to 23 freshwater samples collected from sites influenced by agriculture and vegetation as described in Chen et al. (2011). Furthermore, Chen et al., (2011) measured S275-295 values in the range of 0.009-0.0130 from three humic acids over a pH range of 2-11, suggesting that the humic acid content in the forest soils could be masking the influence of biochar on the S275-295 signal.  The SUVA254 values measured from SCL were similar to all Helms et al. (2008) tidally influenced samples in Chesapeake Bay water. Contrastingly, the SUVA254 from our field samples were higher than those from free-draining leachate collected from root-free column study using a mineral soil collected from a Brazilian sugar cane field and mixed with filtercake biochar (Eykelbosh et a., 2015). In our laboratory study, SUVA254 values were well below the range of values measured by Helms et al. (2008) and similar in range to those measured from the biochar leaching experiment described in Jamieson et al., (2014).  Here, we compared differences between treatments for both field and laboratory findings since all treatments used the same soil. In the field, highly variable DOC/TN slope estimates between the treatments (13.02 to 114.86) were heavily influenced by apparent outliers, which were identified to have optical indices values indicative of increasing molecular weights. This supports the idea that treatment effects were not observed due to other stronger influences in soil humification processes. The SUVA254 values in the laboratory component were lower for higher biochar application rates, without any influence from fertilization, in line with results from Eykelbosch et al. (2015), which showed that biochar preferentially retained high-molecular weight, more humified DOC species. Our results suggest that labile components contributed significantly to leachate DOC. This can be inferred by observing that there was no slope change  95 in the SUVA254 over time in the laboratory, while S275-294 decreased with increasing flushing volume.  4.5 Conclusions These results suggest that the high and low rate of Douglas-fir derived biochar application to this forest soil could be beneficial for both increasing C-sequestration and N-retention in this context, despite higher rates of DOC leaching at the high rate of biochar application in the laboratory. The increased retention of TN as both NH4+-N and NO3--N in 10% biochar applications with and without fertilization suggests there could be an added benefit to stand productivity through high rates of biochar application.  Stronger correlations between DOC and TN were observed in the laboratory leaching experiment for the higher rate of biochar addition compared to lower rate of biochar addition. Based on optical characteristics of leachate in the laboratory experiment, it appears that labile components contributed significantly to leachate DOC. Furthermore, that there was no significant effect of biochar application on DOC and TN leaching measured in the field after biochar application provides further evidence that there is a net carbon sequestration and N retention benefit at the 5 t ha-1 field application rate.    96 4.6 Tables Table 4. Field ECs and ECSCL, pHSCL and spectral indices. All values given are mean (±1 standard error) of all available data in 2013 Variable Treatment  S  BC  F  BC+F  ECS (µS cm-1) 11.65 (0.60)a 16.08 (0.98)a 22.66 (2.23)b 22.20 (1.80)b ECSCL (µS cm-1) 38 (2.5)a 27.42 (1.65)b 33.45 (1.98)a 91.44 (15.31)a pHSCL 6.5 (0.04)a 6.2 (0.10)a 6.6 (0.03)a 6.5 (0.03)a DOCSCL (mg L-1) 4.47 (0.57)a 6.62  (1.19)a 5.29 (0.86)a 2.99 (0.30)a TNSCL (mg L-1) 0.18 (0.01)a 0.15 (0.01)a 0.17 (0.02)a 0.16 (0.01)a S275-295 0.015 (0.000)a 0.012 (0.001)a 0.014 (0.000)a 0.016 (0.000)a SUVA254 (L m-1 mg-1) 5.73 (0.55)a 5.53 (0.58)ab 4.43 (0.35)ab 3.86 (0.38)b Data points with differing italic lowercase letters are significantly different from each other at p < 0.05 Table 5. DOCSCL vs TNSCL linear regression analysis output Treatment df F p R2 Equation All (1,130) 145.70 <0.01 0.53 y = 47.64x-2.94 S (1,28) 180.89 <0.01 0.86 y = 36.69x-2.21 BC (1,34) 131.31 <0.01 0.31 y = 114.86x-10.43 F (1,34) 2296.37 <0.01 0.79 y = 55.89x-4.43 BC+F (1,28) 12.34 <0.01 0.99 y = 13.02x+1.09    97 Table 6. Laboratory EC, pH and spectral indices. All values given are mean (±1 standard error) of all data collected after more than 1 L of DDI had been flushed through each incubation. Variable   Treatment     S  BC1%  BC10%  S+F  BC1%+F  BC10%+F  EClab (µS cm-1) 64.17 (8.33)abc 69.33 (8.22)abc 53.5 (2.57)a 56.5 (4.82)b 59.67 (2.32)bc 51.33 (3.4)ac pHlab 6.35(0.06)a 6.15 (0.12)a 6.93 (0.05)b 6.33 (0.09)a 6.2 (0.1)a 6.87 (0.06)b DOClab (mg L-1) 22.1 (1.08)a 19.47 (1.40)a 67.31 (10.35)b 18.30 (3.29)a 24.14 (2.57)a 75.32 (4.22)b TNlab  (mg L-1) 2.76 (0.45)a 3.64 (0.36)a 0.82 (0.07)b 3.11 (0.51)a 3.79 (0.33)a 0.99 (0.07)b S275-295 0.0133 (0.002)a 0.0134 (0.002)a 0.0116 (0.001)b 0.0138 (0.001)a 0.0136 (0.001)a 0.0117 (0.001)b SUVA254 (L m-1 mg-1) 1.7 (0.09)a 1.72 (0.08)a 0.54 (0.02)b 1.62 (0.08)a 1.54 (0.08)a 0.55 (0.02)b TOC (mg g-1) 0.09 (0.00)a 0.11 (0.00)a 0.16 (0.00)b 0.10 (0.01)a 0.11 (0.00)a 0.16 (0.01)b NH4+ (mg g-1) 0 (0.00)a 0 (0.00)a 0.02 (0.01)a 0.05 (0.01)b 0.07 (0.02)b 0.05 (0.01)b NO3-  (mg g-1) 0.13 (0.01)a 0.39 (0.05)b 0.82 (0.04)c 0.23 (0.08)abc 0.44 (0.05)abc 1.39 (0.01)d Data points with differing italic lowercase letters are significantly different from each other at p < 0.05= Table 7. DOCLAB vs TNLAB linear regression analysis output Treatment df F p R2 Equation All 1,273 13.67 < 0.01 0.04 y = -0.194x + 27.67 S 1,44 12.94 < 0.01 0.23 y = 0.156x + 14.171 BC1% 1,45 31.49 < 0.01 0.41 y = 0.260x + 12.173 BC10% 1,42 786.47 < 0.01 0.95 y = 20.52x + 8.285 S+F 1,45 39.43 < 0.01 0.47 y = 0.162x + 11.82 BC1%+F 1,44 66.13 < 0.01 0.60 y = 0.213x + 13.71 BC10%+F 1,43 193.54 < 0.01 0.82 y = 16.38x + 3.061     98 Table 8. S275-295 vs leachate volume linear regression analysis output Treatment df F p R2 Equation All 1,64 5.34 0.02 0.06 y = -0.00073x + 0.01389 S 1,9 13.62 <0.01 0.60 y = -0.00086x + 0.01448 BC1% 1,9 12.03 <0.01 0.57 y = -0.00091x + 0.01459 BC10% 1,9 90.28 <0.01 0.91 y = -0.00144x + 0.01359 S+F 1,9 3.19 0.11 0.26 y = 0.00045x + 0.01320 BC1%+F 1,9 1.79 0.21 0.17 y = -0.00035x + 0.01410 BC10%+F 1,9 92.82 <0.01 0.91 y = -0.00127x + 0.01345    99 4.7 Figures   Figure 15. Suction cup lysimeter (SCL) sample date (dashed lines) daily (month-day) soil climate at 15-cm depth. (A) Daily precipitation with cumulative precipitation prior to each sample date is provided above and below this the cumulative amount of precipitation since fertilization; (B) Treatment ECs, mean (±1 SE), 1 and 2 asterisks indicates where one way ANOVA detected statistical differences between treatments at p<0.01 and p <0.5 respectively; (C) Soil temperature, site mean (±1 standard deviation); (D) Volumetric soil water content site mean (±1 SD); (E) Soil water matric potential, site mean (±1 SD). In plots B-E values shown are the mean values from soil sensors with one in each of 12 plots, evenly distributed between treatments. The red dashed line indicates timing of fertilization.  236 244 268 269 294 390 4376 25 26 46 120 1600102030Precipitation (mm)● ● ● ●●● ●● ●●●020406080DateECs(µS m−1)● S BC F BC+F051015T 15  (o C)010203040Θ15 (%)−25−20−15−10−5018−0325−0301−0408−0415−0422−0429−0406−0513−0520−0527−0503−0610−0617−0624−06Date 2013Ψ15 (kPa)A B C * E **  100  Figure 16. Field sample date treatment surface EC measurements, mean (±1 standard error). (i) 2-days. (ii) 9-days. (iii) 16-days and (iv) 21-days after fertilization on 26 March 2013. There is a significant response to fertilization in F and BC+F treatment plots 9-days and less so 16-days after fertilization.     Figure 17. Field sample date treatment mean DOCSCL (±1 standard error). (A) 2012; (B) 2013 ● ● ●●●●●●2013−03−28 2013−04−042013−04−11 2013−04−16020406080020406080020406080020406080S BC F BC+F S BC F BC+FS BC F BC+F S BC F BC+FTreatmentEC (µS m−1)●●024681012141612−0319−0326−0302−0409−0416−0423−0430−0407−0514−0521−0528−0504−0611−0618−0625−0602−07Date 2012DOC (mg L−1 )● ●● ●●●●●●●●● ●024681012141611−0318−0325−0301−0408−0415−0422−0429−0406−0513−0520−0527−0503−0610−0617−0624−0601−07Date 2013DOC (mg L−1 )● ●S BC F BC+FA B  101  Figure 18. Field sample date (month-day) treatment mean TN (±1 standard error). (A) 2012; (B) 2013  ● ●0.00.10.20.30.40.512−0319−0326−0302−0409−0416−0423−0430−0407−0514−0521−0528−0504−0611−0618−0625−0602−07Date 2012TN (mg L−1 )●●● ● ●●●●● ●●●0.00.10.20.30.40.511−0318−0325−0301−0408−0415−0422−0429−0406−0513−0520−0527−0503−0610−0617−0624−0601−07Date 2013TN (mg L−1 )● ●S BC F BC+FA B  102  Figure 19. Suction cup lysimeter mean (±1 standard error) DOC and TN observations from data collected during 2013 (A) DOCSCL; (B) TNSCL; (C) Treatment DOC vs TN analysis. Colored dashed lines in panels (A) and (B) indicate the treatment to be used as an aid to identifying dataset outliers. ●●●●● ●●●051015202530DOC SCL (mg L−1 ) ● ●S BC F BC+F●●●● ●●●●0.00.10.20.30.41 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16LysimeterTNSCL (mg L−1 )●●●●●● ●●●●●●● ●●●●●●●0510152025300.000.050.100.150.200.250.300.350.400.450.50TNSCL (mg L−1)DOC SCL (mg L−1 )A B C  103  Figure 20. Suction cup lysimeter mean (±1 standard error) (A) S275-295; (B) SUVA254 ●●●●●●●●0.0000.0050.0100.0150.020S 275−295● ●S BC F BC+F●●●●●●●●02468101 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16LysimeterSUVA 254A B  104  Figure 21. Soil incubation pore water treatment mean EC (A) and pH (B) measurements over the course of the leaching experiment. Error bars indicate one standard error. ●● ●●●● ● ●●● ●● ●●●●●●●● ●●●● ● ● ●0100200300400500600EC (µS m−1)●BC1%BC10%S + FBC1% + FBC10% + F●● ● ●● ● ●● ● ● ● ● ●● ●● ●●● ● ● ● ●● ● ● ● ●●3456780.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7Leachate Volume (L)pHA B  105  Figure 22. Soil incubation pore water treatment mean DOC (A) and TN (B) measurements over the course of the leaching experiment. Error bars indicate one standard error.  ●●●●●●●● ●●● ● ●●● ● ●●●●●●●●●● ●●● ● ● ●0204060800.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7Leachate Volume (L)DOC (mg L−1 )●● ●● ●●●● ●● ● ● ● ● ● ● ●●● ●●●●●●●●●0204060800.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7Leachate Volume (L)TN (mg L−1 )●●SBC1%BC10%S + FBC1% + FBC10% + FA B  106  Figure 23. Soil incubation pore water treatment boxplots of (A) DOC and (B) TN after >1 L volume of leachate collected. Error bars are one standard error. (C) Relationship between DOC and TN. The coloured dashed lines are the lines of best fit for each treatment following treatment color conventions in (A) and (B). ●●051015202530SBC1%BC10% S + FBC1% + FBC10% + FTreatmentDOC (mg L−1 )●●012345SBC1%BC10% S + FBC1% + FBC10% + FTreatmentTN (mg L−1 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●01020304050607080901000 10 20 30 40 50 60 70 80 90 100TN (mg L−1)DOC (mg L−1 )A B C  107  Figure 24. Soil incubation pore water treatment UV-Vis spectroscopy-based indicators of DOC quality (A) S275-295 and (B) SUVA254 ● ● ● ●●● ●● ●●●● ●●● ● ● ●●● ●0.0000.0040.0080.0120.0160.020S 275−295●●SBC1%BC10%S + FBC1% + FBC10% + F● ●●●●●●●●●●●●●●●●●●●●●0.00.51.01.52.02.50.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7Leachate Volume (L)SUVA 254B A  108  Figure 25. (A) Amount of nitrogen species (NH4+-N and NO3--N) remaining in soil following the soil incubation experiment for each treatment; (B) Mean treatment ratio of nitrogen species (NH4-N/NO3-N). Error bars are one standard error.   0.00.10.20.3N (mg g−1 soil)NH4−NNO3−N●●0.000.250.500.751.001.25S BC1% BC10% S+F BC1%+F BC10%+FTreatmentNH4+1−N/NO 3−1 −NA B  109 Chapter 5: Summary and conclusions This thesis describes the effects of biochar application on CH4, N2O and CO2 fluxes for a forest soil on the west coast of Canada in relation to fertilizer use. In the laboratory soil incubation study described in Chapter 2, biochar application at high (10%) application rates increased CO2 and N2O emissions when applied without urea-N fertilizer, and decreased CH4 uptake with and without fertilization. Furthermore, biochar application with urea-N fertilization did not increase CO2 emissions compared to biochar-amended soil without fertilization. This is important given that CO2 contributed > 97% of the CO2e measured in the laboratory experiment. Regardless of the increased CO2 emissions detected after biochar application, the treatment with the longest time interval for which C sequestration resulting from biochar additions would be negated by increased total CO2e of GHG emissions from the soil was the BC10%+F at 17.7 years. This was slightly longer than the value calculated for the BC10% treatment (16.3 years), suggesting that biochar helps reduce soil CO2 emission when fertilizer is applied.  As a result of the low impact on CO2e from N2O and CH4 in this system, these results did not significantly change when using the stronger global warming potentials (GWPs) for the 20-year residency time period compared to the GWP over a 100-year period. It should be noted that the short-term laboratory incubation study did not account for important effects from changing climatic conditions, changes in biochar and soil interactions over time, or plant-root interactions. The field study (Chapter 3) was conducted to better understand these influences. As a result of CO2 fluxes remaining high, the low rates of biochar application in the field study did not appear to significantly improve the climate benefit of C sequestration in this soil (6.7 years), and with fertilization (BC1%+F), the climate benefit of biochar to soil was reduced to just 3.9 years of equivalent C sequestration, when using mean CO2 flux values from all of the data.  110 In the field study, soil CO2 emissions were strongly controlled by soil temperature. This relationship was found to be stronger after fertilization, suggesting that the partitioning between autotrophic and heterotrophic respiration can vary with different treatments. It was determined that the effect of adding low amounts of Douglas-fir derived biochar to the surface of the dominant soil type (humoferric podzol) supporting Douglas-fir stand growth on the west coast of Canada would have little effect on GHG emissions and their total CO2e fluxes. It was concluded over all that low rates of biochar addition to this forest soil would improve soil C sequestration in the soil, with or without fertilization.  The carbon sequestration potentials discussed in this work do not take into account the amount of direct CO2 loss to the atmosphere that were avoided through biochar conversion rather than on site burning or any improvements to stand productivity that could be realized from liming of the acidic soils and N-species (NH4+ and NO3-) retention favourable for Douglas-fir growth possibly realized when higher rates of biochar are applied. Combining studies in the field and in the laboratory (Chapter 4) was a productive way to determine the effects of biochar application on dissolved organic concentrations of C and total N in leachate in relation to fertilizer application. Regardless of increased CO2 and DOC losses in the laboratory incubation experiment with higher biochar additions, there was still 50% more TOC remaining in the soil/biochar mixture after the incubation experiment, with or without fertilization, when compared to the lower biochar treatment. From the laboratory study it was concluded that carbon sequestration potential is increased by adding biochar to a disturbed forest soil of this kind. The results for TN presented were interesting as they showed that higher rates of biochar application likely suppress TN leaching and over time, although it may also increase  111 nitrification rates leading to more loss of applied N during periods of heavy rain and rapid percolation.  From the laboratory soil greenhouse gas emission and flushing experiments, it was concluded that gains could be small with regards to carbon sequestration potential by adding biochar in a single large application to a disturbed forest soil of this kind, as the net CO2 efflux rates were increased along with DOC losses, although the latter was very small, and was less than 1% loss over the course of the experiment.  Recommendations for field application resulting from this study are to apply multiple small amounts, perhaps 10 x 5 t ha-1, of biochar spaced out over time, while monitoring DOC leaching to minimize early flush losses and maximize carbon retention resulting from biochar applications. Given that the fertilization in the field was only detected as an increase in soil surface electrical conductivity and not in leachate C and N measurements suggests that managing biochar applications more specifically for carbon sequestration through direct increases in soil carbon and not leachate reduction may be a relevant focus when working in N-deficient undisturbed soils. 5.1 Future work Voltaire is reported to have said, “it is not the answer you give, but the questions you ask”, which the author takes as the premise that we are never finished scientific research. In Chapter 3 a disparity between N2O and CH4 fluxes measured at the field site in this work and those measured by Jassal et al. (2008, 2010, 2011) at another research site close-by (200 m away) after fertilization were identified. The N2O fluxes measured by the author were considerably smaller and did not indicate any increase following fertilization, regardless of a clear spike in ECs after fertilization documented in Chapter 4. Also, no fertilization effect was  112 observed when analyzing TN data from field leachate, which suggests that it did not percolate down to this depth or was lost to deeper percolation or as gaseous N.  To properly address this, the author believes that another independent study directly investigating N2O and CH4 fluxes after fertilization utilizing an improved automated chamber system capable of high frequency measurements, supplemented with careful soil microbial population profiling could help to discern treatment effects. Additionally, there is a need to better understand the effect of increasing the rate of application of biochar on the measured greenhouse gas fluxes in this study. It was stated in Chapter 3 that without a more thorough knowledge of the impact on stand productivity, it is difficult to ascertain through investigation of soils alone whether the effects of biochar application in combination with fertilization on stand productivity resulted in an increase in C sequestration. Based on the results of this investigation, it is possible that biochar would increase C-sequestration by 15% during the first year after fertilization compared to fertilization in the absence of biochar application. That is, the increase in stand C sequestration of a Douglas-fir forest resulting from fertilization estimated by Jassal et al. (2008) of 64% (3.3 to 5.3 Mg-C ha-1 yr-1) following fertilization could be increased by applying biochar to result in a total annual C sequestration of 5.9 Mg-C ha-1. Ideally, this would be confirmed through the adoption of suitable methodologies such as eddy covariance and a larger scale application of biochar.  5.2 Overall significance In this thesis, a fast effective laboratory method for quantifying fluxes of CO2, CH4 and N2O resulting from different biochar application rates with and without N fertilization was developed. Furthermore, this technique allowed quantification of differences in soil pore water DOC and TN. The method was described at the Western Silvicultural Contractors Association  113 Annual General Meeting (Feb 2017) where private companies and government agencies expressed interest in using it.  The results presented herein show promise for incorporating biochar production and application into the current forest economy of BC, and of Canada as a whole. First Nations, private companies and government land managers have expressed interest in using this information to guide large-scale pilot studies in British Columbia. The author has been invited to participate in a working group to assemble this information. This thesis provides the first study of a potential holistic biochar system, evaluating the potential to make environmentally beneficial uses of residual materials remaining following forest harvest. At present, forest harvest residues are typically burned in British Columbia, reducing air quality, diminishing soil carbon stocks, and increasing the greenhouse gas loading to the atmosphere. As an alternative, these forest harvest residual materials can be converted via pyrolysis to biochar, and returned to forest soils when incorporated into existing silvicultural techniques along with ongoing forest fertilization practices.     114 References  Aber, J. D., Magill, A., Mcnulty, S. G., Boone, R. D., Nadelhoffer, K. J., Downs, M., & Hallett, R. (1995). Forest biogeochemistry and primary production altered by nitrogen saturation. 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Ahn (2011), Positive and negative carbon mineralization priming effects among a variety of biochar-amended soils, Soil Biology and Biochemistry, 43(6), 1169-1179, doi:10.1016/j.soilbio.2011.02.005.    130 Appendices Appendix 1 Site soil description The site soil is classified as duric humo-ferric podzol from morainal origin with a Quimper gravelly-sandy-loam texture commencing below a variable litter-fermenting- humified (LFH) organic layer (0.09-0.193 cm). Table 9. Soil characteristics literature review Humphreys (1999)    Bulk density  (± SE) (upper 10 cm, kg m-3) 971 (± 221)     Drewitt (2002)   Bulk density  (± SE) (10-80 cm) (kg m-3) 1353 (± 51) Soil porosity (10-80 cm, %) 49.0  Coarse fraction (> 2mm) 31     Jassal et al., (2005)   Bulk density (0-10 cm)  (kg m-3) 1050 Bulk density (50 cm)  (kg m-3) 1500     Humphreys et al.,  (2006)    Soil mineral fraction (m) 0-1 Texture Gravelly loamy sand Wilting point (m3 m-3) 0.06 Field capacity (m3 m-3) 0.21 Available water content (mm) 150 Soil mineral fraction (m) 0 - 0.15 C (mg g-1 dry soil) 18-74 N (mg g-1 dry soil) 0.4-4.4 ratio (L,M,H) 45,19,17 Surface organic horizons   C (mg g-1 dry soil) 244-527 N (mg g-1 dry soil) 6.8-16.5 % (L,M,H) 36,33,32 Average thickness (m) 0.09-0.193   131 A total of 12 soil core were taken from 3 locations spread around the perimeter of the biochar plots. At each location a trench approximately 30 cm x 30 cm was dug to 15 cm deep. From each of the four walls of the pit one bulk density core (7.3 cm diam. x 3.8 cm depth) was inserted horizontally 2-3 cm below the organic-mineral transition. Results of the measured soil characteristics are shown in Table 10, 11 and 12. Table 10. Soil characteristics Bulk Density (kg m-3) Fine Fraction (<2mm) Percent Coarse Fraction (>2mm) Organic Matter Content (%) Carbon Content (%) Volume-weighted particle density  (kg m-3) Porosity  (m-3 m-3) 1133.1 (133.6) 46.1 (9.2) 53.9 (9.2) 6.8 (1.8) 3.1 (0.8) 2570.7 (17.4) 0.559 (0.053)  Table 11 Fine mineral fraction % Sand Silt Clay 74.5( 3.7) 18.5 (3.5) 7.0 (1.5)  Table 12. Textural class soil organic matter content  Textural Class Soil Organic Matter  (%) Sand: 5.1 Silt: 6.1 Clay: 12.5  Two additional soil pits (50 cm depth) were excavated at the site and two differing soil types thought to be associated with topography were identified. On flat areas the soil (S1) was found to be higher in organic matter and have a more gravelly-loamy-sand texture (27% coarse fraction (> 2 mm diameter)), less easily drained than that found on more sloping locations (S2) (53% coarse fraction > 2mm).  132 Additional soil was also collected from the same depth described above and brought back to the lab for use in an incubation experiment. This soil was analyzed for total organic carbon, cation exchange capacity, nitrate and ammonia concentrations and microbial abundance.    133 Appendix 2 Biochar details The biochar particle size distribution described in Table 1 was produced using 1 cm chipped Douglas-fir woody materials from British Columbia heated in a pyrolysis oven to 420 oC for 32 min (Diacarbon Energy Inc. Canada). The resulting biochar has high carbon content (78.8%), low volatiles and ash content (18.8 & 2.4%, respectively). After production the char was transported to UBC in 55 gallon steel drums sealed from the atmosphere. Table 13. Diacarbon Douglas-fir derived biochar particle size distribution Size Fraction (µm) Average % of Sample (± 1Standard Deviation) >2000 5 (2) 991-2000 17 (1) 425-991 32 (4) 250-425 13 (1) 105-250 19 (2) 53-105 7 (1) <53 7 (4)  Values in table 13 were determined after sieving four 100 g samples taken from three incremental depths in the biochar supply containers.    134 Appendix 3 Minimum detectable flux estimation Using methodology outlined in Christiansen et al., (2015), the minimum detectable flux (MDF) was estimated for each gas. Briefly, the sensitivity of the sample collection and measurement procedure as characterized by the MDF is quantified by defining the minimum quantification limit (MQL), following Corley et al., (2003), where analytical accuracy is determined from multiple standard assessment as 3 x standard deviation x t99% (t99% taken from t-table at respective degrees of freedom) and the MDF was defined as the flux equivalent to the analytical precision of raw output divided by closure time:   𝑀𝐷𝐹 = 𝐴!𝑡!  ×  𝑉 × 𝑃𝑆 × 𝑅 × 𝑇  (4)  where Aa is the analytical accuracy of the instrument (ppm), tc is the closure time of the chamber in hours, V is the chamber volume (m3) P is the atmospheric pressure (Pa), S is the chamber surface area (m2), R is the ideal gas constant (m3 Pa K-1 mol-1) and T is the ambient air temperature (K).  The largest contribution to error in this study was likely due to sampling, storage and vial sampling and injection into the GC. Currently, following Christiansen et al., 2015 we were able to achieve an Aa of 13 ppm CO2, 0.0998 ppm for CH4 and 0.013 ppm for N2O from GC autosampler vial analysis. The Aa for our system from direct GC injections was 6 ppm CO2, 0.096 ppm CH4 and 0.036 ppm N2O. Notice the increase in Aa for direct injections compared to the autosampler for N2O. This can be attributed to human error, due to unavoidable differences between manual injections (e.g., pace and force).   135 None of the CH4 and N2O emissions measured in the field were calculated to be above the MDF. Consequences in regards to the conclusions drawn from this thesis are inconsequential given their small contribution to the total CO2e estimations. Methods for improving future studies would need to involve in-situ estimation using sensitive flow through detectors as mentioned in Chapter 5.     136 Appendix 4 Project instrumentation Table 14. Field and laboratory equipment  Manufacturer Name Detector/Hardware Measurement Symbol/acronym Range Units Decagon Devices Inc. GS3 Electrode resistance Matric Potential θ  % .  Electrode resistance Bulk Electrical Conductivity EC  dS/m .  Thermistor Temperature T  °C Decagon Devices Inc. MPS-2 Electrode resistance Soil water potential Ѱ -5 to -500 kPa -.  Thermistor Temperature T -40 to +50 °C SoilMoisture Equipment Corp L1900 Suction cup lysimeter  Soil water sampler SCL 0 to 250 ml ml Campbell Scientific CR1000 Datalogger Controls measurements and stores data    University of British Columbia Static Non-steady state chamber PVC collar + Lid Greenhouse gas in headspace (CH4, CO2, N2O)  SNSS    EMD Millipore, Darmstadt, Germany  Soil incubation Steri-fil® Asceptic filtration units     Agilent 7890A GC Flame ionisation detector + methanizer [CH4], [CO2]   ppm Agilent 7890A GC Electron Capture N2O   ppm Shimadzu TOC-V CSH Nondispersive Infrared TOC, NPOC                    IC = TC-NPOC   mg/l Shimadzu TNM-1 Chemiluminescence TN   mg/l                 137 Appendix 5 Site images    Figure 26. This image shows a slash piles in the foreground, with eight more visible in the background at a near-by (within 500 m of research location) harvested forest location that could have been feedstock for biochar production.  Figure 27. This image shows the slash piles burned to improve access for replanting and reduce forest fire hazard.  138  Figure 28. Research site soil profile.  Figure 29. This image shows a static non-steady state chamber between two large trees with the chamber lid on during a site greenhouse gas flux measurement campaign.  139  Figure 30. This image shows the ancillary GS3 instrument in use for surface soil electrical conductivity (ECs), volumetric water content and temperature measurements in close proximity to a static non-steady state chamber collar. Also seen are 5 vials used for greenhouse gas sample storage in preparation for chamber flux measurements.  Figure 31. Biochar being hand applied on date of application (27th Feb 2012).  140  Figure 32. Completed biochar application, showing contrasting color with untreated surrounding area.     

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