A quantitative review of the effects of biochar application to soils on crop productivity using meta
Agriculture,Ecosystems and Environment 144(2011)175–187
Contents lists available at SciVerse ScienceDirect
Agriculture,Ecosystems and
Environment
j o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /a g e
e
Review
A quantitative review of the effects of biochar application to soils on crop productivity using meta-analysis
S.Jeffery a ,?,F.G.A.Verheijen a ,d ,M.van der Velde a ,b ,A.C.Bastos c
a
European Commission,Joint Research https://www.360docs.net/doc/8d3983980.html,nd Management &Natural Hazards Unit.Institute for Environment &Sustainability (IES),Ispra (VA),Italy b
International Institute for Applied Systems Analysis (IIASA),Schlossplatz 1,A-2361Laxenburg,Austria c
Department of Biology &CESAM (Centre for Environmental and Marine Studies),University of Aveiro,3810-193Aveiro,Portugal d
Department of Environment and Planning &CESAM (Centre for Environmental and Marine Studies),University of Aveiro,3810-193Aveiro,Portugal
a r t i c l e i n f o Article history:
Received 20December 2010
Received in revised form 24August 2011Accepted 24August 2011
Available online 23September 2011Keywords:Biochar Soil
Crop productivity Meta-analysis Effect size Crop yield
a b s t r a c t
Increased crop yield is a commonly reported bene?t of adding biochar to soils.However,experimental results are variable and dependent on the experimental set-up,soil properties and conditions,while causative mechanisms are yet to be fully elucidated.A statistical meta-analysis was undertaken with the aim of evaluating the relationship between biochar and crop productivity (either yield or above-ground biomass).Results showed an overall small,but statistically signi?cant,bene?t of biochar application to soils on crop productivity,with a grand mean increase of 10%.However,the mean results for each analysis performed within the meta-analysis covered a wide range (from ?28%to 39%).The greatest (positive)effects with regard to soil analyses were seen in acidic (14%)and neutral pH soils (13%),and in soils with a coarse (10%)or medium texture (13%).This suggests that two of the main mechanisms for yield increase may be a liming effect and an improved water holding capacity of the soil,along with improved crop nutrient availability.The greatest positive result was seen in biochar applications at a rate of 100t ha ?1(39%).Of the biochar feedstocks considered and in relation to crop productivity,poultry litter showed the strongest (signi?cant)positive effect (28%),in contrast to biosolids,which were the only feedstock showing a statistically signi?cant negative effect (?28%).However,many auxiliary data sets (https://www.360docs.net/doc/8d3983980.html,rmation concerning co-variables)are incomplete and the full range of relevant soil types,as well as environmental and management conditions are yet to be investigated.Furthermore,only short-term studies limited to periods of 1to 2years are currently available.This paper highlights the need for a strategic research effort,to allow elucidation of mechanisms,differentiated by environmental and management factors and to include studies over longer time frames.
?2011Elsevier B.V.All rights reserved.
Contents 1.Introduction (176)
2.
Methods https://www.360docs.net/doc/8d3983980.html,parisons using meta-analysis...........................................................................................................1772.2.Data sources and treatment.................................................................................................................1772.3.Data groupings...............................................................................................................................1772.4.Meta-analysis................................................................................................................................1772.5.Presentation of graphs.......................................................................................................................1793.
Results...............................................................................................................................................1793.1.Application rate..............................................................................................................................1793.2.pH............................................................................................................................................1793.3.Soil texture...................................................................................................................................1803.4.Biochar and fertilizer interaction............................................................................................................1803.5.Crop..........................................................................................................................................
181
?Corresponding author.Tel.:+390332783682;fax:+390332786394.E-mail address:simon.jeffery@jrc.ec.europa.eu (S.Jeffery).0167-8809/$–see front matter ?2011Elsevier B.V.All rights reserved.doi:10.1016/j.agee.2011.08.015
176S.Jeffery et al./Agriculture,Ecosystems and Environment144(2011)175–187
3.6.Biochar feedstock (181)
3.7.Plant biomass,crop yield and trial type (181)
4.Discussion (182)
4.1.Data and analysis (182)
4.2.Environmental and management representativity (184)
4.3.Auxiliary variables (185)
4.4.Reporting guidelines for‘biochar-crop production’experiments (185)
4.5.Maintenance of this MA (185)
5.Conclusions (185)
Acknowledgements (186)
References (186)
1.Introduction
Biochar is a predominantly stable,recalcitrant organic carbon (C)compound,created when biomass(feedstock)is heated to temperatures usually between300and1000?C,under low(prefer-ably zero)oxygen concentrations(Verheijen et al.,2010).Biochar application to soils is currently being considered as a means of mitigating climate change by sequestering C,while concurrently improving soil properties and https://www.360docs.net/doc/8d3983980.html,paring the incorpo-ration of biochar versus that of‘fresh’crop residues into soils may provide an insight into the mechanism of C sequestration through application of biochar to soils.Carbon dioxide from the atmosphere is?xed in vegetation through photosynthesis.Biochar is subse-quently created through pyrolysis of the plant material,thereby increasing its inherent recalcitrance with respect to the original biomass.The estimated C-residence time of biochar in soils is in the range of hundreds to thousands of years,while that of crop residue is in the range of decades(Lehmann et al.,2006).Conse-quently,incorporating biochar from such a feedstock into soils has the potential to reduce the CO2release back to the atmosphere.It is posited that,if other greenhouse gas emissions from soils are not elevated as a consequence of biochar application,and if those emis-sions associated with production and transport of biochar and/or its feedstocks do not off-set the sequestered C,then the overall greenhouse effect will be abated(Roberts et al.,2010).The amount of feedstock required for conversion to biochar in order to achieve such a result,is critically dependent on the C retention(i.e.the ratio of the C in the biochar over the C in the initial dry biomass feed-stock).Carbon retention of49%has been reported for slow pyrolysis at atmospheric pressure,while higher C retention(100%)resulted in less stable biochar,with residence times of4–29years(Woolf et al.,2010).
Concomitant with carbon sequestration,biochar is intended to improve soil properties and functions relevant to agronomic and environmental performance(Lehmann and Joseph,2009; Woolf et al.,2010).Hypothesised mechanisms for such a potential improvement are mainly enhanced water and nutrient retention (as well as improved soil structure and drainage).Furthermore, there is experimental evidence that soil microbial communities and their activity,which hold key roles in sustaining soil health and functioning,are directly affected by the addition of biochar to soils(Ogawa,1994;Rondon et al.,2007;Warnock et al.,2007; Steiner et al.,2008).The full range of mechanisms and conse-quences behind these effects remain poorly elucidated.However, it is likely that changes in soil microbial activity,community struc-ture and functional diversity could impact on crop productivity. For example,it has been previously shown that biochar addition to soil increases N2?xation by both free-living and symbiotic dia-zotrophs(Ogawa,1994;Rondon et al.,2007).Whether this can be explained by an increase in diazotrophic biomass or enhanced metabolic activity is yet to be investigated.However,as N is often the limiting factor for crop productivity,particularly in agricultural scenarios,improved N?xation by soil microorganisms seems likely to explain to some extent the increase in overall crop productivity.
There is a poor understanding of the general relationship between soil organic matter(SOM)and crop yield(e.g.Loveland and Webb,2003),the same is currently true regarding the interac-tion between biochar and crop productivity.In experimental?eld trials,it is often dif?cult or impossible to fully account for or con-trol all environmental variables in an experimental design,such as meteorological factors and their annual and inter-annual variabil-ity.This can lead to weaknesses in the data obtained from these experiments,and reduce con?dence when extrapolating results and formulating predictions for other probable effects under other environmental conditions.
In recent years,various factors have highlighted biochar as a rel-evant topic for research,as well as for policy and the wider society. In addition to mitigating climate change and offering the poten-tial for organic waste disposal,helping to achieve food security is an important driver.The global human population is expected to increase to9.2billion by2050(U.N.Population Division,2008). Currently,more than99%of food supplies(calories)for human con-sumption comes from the land(FAO,2003)and this seems unlikely to decrease.Veri?cation of the effects of biochar on crop yields would,therefore,demonstrate the potential for biochar to help or hinder global food security.
Before the large scale implementation of biochar can be con-templated seriously and developed into policy,a robust body of scienti?c evidence regarding its effects on soil properties,processes and functions is paramount.Some progress has been made in this direction(e.g.Lehmann and Joseph,2009;Shackley and Sohi,2010; Verheijen et al.,2010;Woolf et al.,2010),but substantially more work remains to be done.Experimental results,when available, are often inconsistent and largely dependent on experimental con-ditions and design,while causative mechanisms remain unclear (Atkinson et al.,2010).Increased crop production is the most com-monly reported effect of biochar application to soils.Considering the strong and manifold drivers for biochar implementation,and the many sources of heterogeneity between experiments(differ-ent crops,feedstocks,soil types and climatic conditions,etc.),a sound quantitative meta-analysis(MA)of current results in the literature is pertinent,if not vital,to allow a clear picture to be drawn,as well as to highlight areas where further research must be targeted.Representativity and potential causative mecha-nisms are discussed,while recommendations for future research and reporting guidelines are put forward,including those with respect to the maintenance of this MA(as further reports become available).
The use of MA allows for increased objectivity of systematic reviews based on studies involving a range of soil properties,as well as environmental and management conditions.However,as stated by Bobko and Stone-Romero(1998),MA is an“imperfect procedure”owing to the necessary decisions regarding the man-ner in which data are handled.For a MA to be undertaken,it
S.Jeffery et al./Agriculture,Ecosystems and Environment144(2011)175–187177
is necessary that each study must allow the comparison of an experimental treatment with a control,with the control being con-sistently de?ned across all studies used.These criteria were ful?lled using the methodology described below.
2.Methods
https://www.360docs.net/doc/8d3983980.html,parisons using meta-analysis
For this MA,the control was de?ned as being identical to the experimental treatment with regard to all variables apart from the addition of biochar.Therefore,data were extracted from treatments in each study,where a control with zero biochar input could be compared to an equivalent treatment with biochar,at either a sin-gle or multiple application rates,with all other factors unchanged. The“effect size”was then calculated for a wide range of inde-pendent variables,such as biochar feedstock,the type of fertilizer, soil and crop used,as well as overall plant biomass versus crop yield.
2.2.Data sources and treatment
For the MA,an extensive literature search was performed using Scopus with the search terms“biochar AND crop productivity OR crop production OR crop yield”shows23studies published before the cut off date of1st March2010.However,many of such studies were not found to include suf?cient information regarding envi-ronmental parameters or variance in results,or were not relevant in the context of this paper.Searches were also performed using Science Direct and Google Scholar but no additional studies were found suitable for inclusion into the MA.From those23studies, 14(+2from grey literature)were considered relevant for inclu-sion in the MA.To maximise the number of studies,both pot and?eld experiments were recorded,providing the results were quantitative.Studies that did not report quantitative results where excluded from the MA.When no measures of variance were given, efforts were made to obtain these from the corresponding authors, which in most cases were successful.If not,those studies were also excluded from the analysis.Similarly,efforts were made to contact lead researchers on the topic of biochar for the inclusion of unpublished data into the MA,in an attempt to overcome the problems of publication bias.Such efforts resulted in the inclusion of data from one unpublished study(Wisnubroto et al.,2010),as well as from one Master’s thesis(Nehls,2002),meaning a total of 16studies and that of177“treatments”were used:Chidumayo (1994);Ishii and Kadoya(1994);Nehls(2002);Lehmann et al. (2003);Yamato et al.(2006);Blackwell et al.(2007);Chan et al. (2007);Steiner et al.(2007);Chan et al.(2008);Kimetu et al. (2008);van Zwieten et al.(2009);Asai et al.(2009);Gaskin et al. (2010);Hossain et al.(2010);Major et al.(2010);Wisnubroto et al. (2010).
Relevant data were extracted from each study regarding soil type(texture,pH,CEC),biochar feedstock and application rate, fertilizer type and application rate,crop type and the growing season(which in all cases was>2years highlighting the cur-rent paucity of long time scale studies),and on whether above ground biomass was quanti?ed,in addition to crop yield.In instances where such relevant information was omitted,the MA was undertaken using solely those categories for which data was available in all selected studies.As a result,it was not possible to include categories,such as that of the CEC,or to per-form a regression MA.When data was only provided in graphic format,DataThief III(Tummers,2006),was used to extract rel-evant data points.The selected data were inserted into Excel, with each row representing a‘treatment’and then exported to MetaWin Version2.The cut-off date for publications to be consid-ered for inclusion in this MA was1st March2010.The database containing extracted data which was used for the MA will be publically available on the website of the European Soil Data Cen-tre.(http://eusoils.jrc.ec.europa.eu/library/esdac/index.html).This database will be updated periodically as new data becomes avail-able and included in up-coming MA.A concise summary of the studies that contributed to can be found in Table1.
As with all MA,there is a possibility for publication bias to affect the results.This effect was initially reported by Rosenthal and Rosnow(1991)who suggested that published articles are likely to be drawn from the pool of statistically signi?cant results,while studies showing no signi?cant effects are often not considered for publication.Therefore,it is likely that the primary literature may provide a biased sample of all of the studies undertaken in a given ?eld,reporting a higher proportion of statistically signi?cant?nd-ings than actually exist.The inclusion in the MA of data from the grey literature is topic of scienti?c debate.For example,Cook et al. (1993)found that30%of editors surveyed would not publish a MA that included unpublished material.However,McAuley et al.(2000) reported that the use of grey literature is generally regarded as reducing bias and,therefore,preferable.For this MA,two studies from the grey literature were included,where suf?cient informa-tion was available for con?dent assessment of the soundness of their experimental design.
2.3.Data groupings
In some instances,data required pre-grouping before the MA could be conducted,aiming for maximal in-group homogenisation. For example,with regard to soil pH,the use of ungrouped data would have lead to many exclusions due to insuf?cient treatments (i.e.<2)within each group.This is due to the continuous nature of the variable and the relatively high precision of its reporting(1or2 decimal places).Therefore,these data were formed into three cat-egories‘Very acidic’pH<5,‘Acidic’5
Soil texture was grouped into three basic classes(?ne,medium, coarse),because of the inconsistent reporting of soil texture in the literature(e.g.particle size distribution,soil taxonomical unit, qualitative description)using expert judgement,where required. For example,heavily oxidised tropical soils often have a?ne pri-mary texture,while the secondary texture is coarse on account of sesquioxides,causing water-stable micro-aggregation(see e.g. Pochet et al.,2007and Barthès et al.,2008).In such instances,sec-ondary texture,i.e.‘effective texture’,was used and these soils were classi?ed as‘coarse’.
2.4.Meta-analysis
An MA(Rosenberg et al.,2000)was conducted to quantify the effects of biochar addition to soil on crop productivity.For each study,the control mean and experimental means were recorded,or calculated where necessary.Standard deviation was used as a mea-sure of variance,and included,when present,or calculated from the published measure of variance in each study.
Standardisation of the literature results was undertaken through calculation of the effect size.This allows quantitative statistical information to be pooled from,and robust statistical comparisons to be made between effects from a range of studies that reported results based on different experimental variables. Firstly,the data was normalised using a square-root transforma-tion.The effect size was then calculated in MetaWin Version2 statistical software(Rosenberg et al.,2000)using the transformed
178S.Jeffery et al./Agriculture,Ecosystems and Environment144(2011)175–187
Table1
Matrix showing which studies provided data for each categorical grouping on which the meta-analyses were run.
Grouping Sub-heading Number of
studies
Authors
Soil texture Coarse7Gaskin et al.(2010),Ishii and Kadoya(1994),Lehmann et al.(2003),Major et al.(2010),Nehls
(2002),van Zwieten et al.(2009),Wisnubroto et al.(2010)
Medium8Asai et al.(2009),Blackwell et al.(2007),Chan et al.(2008),Hossain et al.(2010),Kimetu et al.
(2008),van Zwieten et al.(2009),Yamato et al.(2006)
Fine1Asai et al.(2009)
pH class(<5,5–6,>6)<57Blackwell et al.(2007),Chan et al.(2007),Chan et al.(2008),Hossain et al.(2010),Major et al.
(2010),van Zwieten et al.(2009),Yamato et al.(2006)
5–68Asai et al.(2009),Blackwell et al.(2007),Chan et al.(2008),Chidumayo(1994),Gaskin et al.
(2010),Kimetu et al.(2008),Lehmann et al.(2003),Wisnubroto et al.(2010)
>65Asai et al.(2009),Gaskin et al.(2010),Ishii and Kadoya(1994),Kimetu et al.(2008),van
Zwieten et al.(2009)
Latitude Tropical7Asai et al.(2009),Chidumayo(1994),Kimetu et al.(2008),Lehmann et al.(2003),Major et al.
(2010),Steiner et al.(2007),Yamato et al.(2006)
Subtropical7Blackwell et al.(2007),Chan et al.(2007),Gaskin et al.(2010),Hossain et al.(2010),Ishii and
Kadoya(1994),Nehls(2002),van Zwieten et al.(2009)
Feedstock Poultry litter1Chan et al.(2008)
Acacia bark1Yamato et al.(2006)
Paper pulp and wood chips1van Zwieten et al.(2009)
Wastewater sludge1Hossain et al.(2010)
Green waste2Chan et al.(2007),Wisnubroto et al.(2010),Lehmann et al.(2003),Steiner et al.(2007),
Chidumayo(1994),Nehls(2002),Kimetu et al.(2008),Blackwell et al.(2007)
Wood8Asai et al.(2009),Major et al.(2010)
Peanut hull1Gaskin et al.(2010)
Pine chip1Gaskin et al.(2010)
Biosolids1Wisnubroto et al.(2010)
Biochar application rate1–39t ha?112Asai et al.(2009),Blackwell et al.(2007),Chan et al.(2007),Chan et al.(2008),Gaskin et al.
(2010),Hossain et al.(2010),Kimetu et al.(2008),Major et al.(2010),van Zwieten et al.
(2009),Wisnubroto et al.(2010),Yamato et al.(2006)
40–79t ha?15Chan et al.(2007),Chan et al.(2008),Ishii and Kadoya(1994),Lehmann et al.(2003),Yamato
et al.(2006)
>80t ha?12Chan et al.(2007),Lehmann et al.(2003)
Fertilizer coaddition None10Asai et al.(2009),Chan et al.(2007),Chan et al.(2008),Chidumayo(1994),Gaskin et al.(2010),
Ishii and Kadoya(1994),Nehls(2002),van Zwieten et al.(2009),Wisnubroto et al.(2010) Inorganic10Asai et al.(2009),Blackwell et al.(2007),Chan et al.(2007),Chan et al.(2008),Gaskin et al.
(2010),Kimetu et al.(2008),Steiner et al.(2007),van Zwieten et al.(2009),Wisnubroto et al.
(2010)
Organic4Asai et al.(2009),Hossain et al.(2010),Lehmann et al.(2003),Nehls(2002)
Both2Asai et al.(2009),Major et al.(2010)
Crop type Rice2Asai et al.(2009),Nehls(2002)
Wheat2Blackwell et al.(2007),van Zwieten et al.(2009)
Radish3Chan et al.(2007),Chan et al.(2008),van Zwieten et al.(2009)
Bauhinia trees1Chidumayo(1994)
Maize4Gaskin et al.(2010),Kimetu et al.(2008),Major et al.(2010),Yamato et al.(2006)
Tomato1Hossain et al.(2010)
Satsuma mandarin trees1Ishii and Kadoya(1994)
Cowpea2Lehmann et al.(2003),Yamato et al.(2006)
Ryegrass1Wisnubroto et al.(2010)
Soybean1van Zwieten et al.(2009)
Sorgum1Steiner et al.(2007)
Experiment type Pot7Chan et al.(2007),Chan et al.(2008),Hossain et al.(2010),Ishii and Kadoya(1994),Lehmann
et al.(2003),van Zwieten et al.(2009),Wisnubroto et al.(2010)
Field7Asai et al.(2009),Blackwell et al.(2007),Chidumayo(1994),Gaskin et al.(2010),Kimetu et al.
(2008),Major et al.(2010),Yamato et al.(2006),Steiner et al.(2007),Nehls(2002)
data taken as the natural logarithm of the response ratio by using
the following equation(Rosenberg et al.,2000):
ln R=ln
ˉx E
ˉx C
whereˉx E:mean of experimental group;andˉx C:mean of control group.
For calculation of grouped effect sizes,a categorical random effects model was used.Groups with fewer than two treatments were excluded from each analysis.Resampling tests were gen-erated from999iterations.For each of the analyses,grouped by different categorical predictors,data were analysed using a random effects model,except in instances where the estimated pooled variance was≤0,in which case a?xed effects model was used.
To test the effects of publication bias(Rothstein et al., 2005)and the robustness of the MA,the Fail-safe N technique (Orwin,1983;Rosenthal and Rosnow,1991)was used.This involved computing the combined P value for all of the stud-ies included,and calculating the number of additional studies showing no effect(i.e.average Z value of0)that would be needed in order to change the P value from signi?cant to non-signi?cant at P=0.05.Therefore,the robustness of the?ndings of the MA is directly correlated with the size of the Fail-safe N number.
S.Jeffery et al./Agriculture,Ecosystems and Environment144(2011)175–187
179
Fig.1.A forest plot showing the mean change in crop productivity as a percentage of the control,for a range of different biochar application rates.Points show means of treatments,bars show95%con?dence intervals.Numbers to the right of bars show biochar application rates(t ha?1),while numbers in the two columns on the right show the total number of‘replicates’(n)from the combined studies upon which the statistical analysis is based(bold),and the number of‘experimental treatments’that have been grouped for each analysis(italics).
All results are stated as being statistically signi?cant if P≤0.05.
2.5.Presentation of graphs
All graphs show forest plots showing the effect size calculated from each group.Each point represents the mean effect size for each grouping,with the lines representing95%con?dence intervals (CIs).All graphs are formatted with the greatest mean effect shown at the top and the smallest or most negative mean effect size at the bottom.On the x-axis,the‘effect size’was exponentially trans-formed and multiplied by100to obtain the percentage change in crop productivity.Numbers to the right of each bar show the group-ing upon which the mean and95%CIs are based.The numbers in the two columns on the right show the total number of‘replicates’(n) from the combined studies(bold)and the number of‘experimental treatments’(italics)respectively,included in each grouping.
3.Results
Rosenthal’s Fail Safe N for the following analyses fell between 1483and1612,depending on the number of studies that needed to be excluded from each analysis(due to a lack of data,or fewer than two treatments being available for a given category).This means that a minimum of1483studies with an average Z value of0would need to be included in the MA,for the P value indicating statistically signi?cant effects of biochar application to soil(as shown by the “Grand Mean”),to be reduced to being not statistically signi?cant at P=0.05.It suggests that it is unlikely that publication bias exists in the literature to such an extent as to affect the overall statistical signi?cance of these results.
3.1.Application rate
Fig.1shows the effect of biochar addition to soil on crop productivity,grouped by application rate and reported as t ha?1.Unfortunately,biochar incorporation depth in the soil was insuf-?ciently reported.The sample means indicate a small but positive effect on crop productivity,with a grand mean(being the mean of all effect sizes combined)of approximately10%.However,there was no statistically signi?cant difference(P>0.05)between any of the application rates.Application rates of10,25,50and100t ha?1 were all found to signi?cantly increase crop productivity when compared to controls,which received no biochar addition.How-ever,other application rates within the range investigated,such as40and65t ha?1,showed no statistically signi?cant effect of biochar addition to soil on crop yield.Results demonstrated that while biochar addition to soil may increase crop productivity,there was no correlation between application rate and the effects on crop productivity(r2=0.1).
Data from within individual application rate treatments were highly variable.No single biochar application rate was found to have a statistically signi?cant negative effect on the crops from the range of soils,feedstocks and application rates studied.
3.2.pH
Fig.2a shows the effects of biochar addition to soil on crop productivity,grouped by pH.A statistically signi?cant(P<0.05) increase in crop productivity occurred upon biochar addition to soil in both‘Acidic’and‘Neutral’soils.There was no statistically signi?cant(P>0.05)change in crop productivity upon biochar addi-tion to soil in the‘Very Acidic’grouping,nor between the three pH groupings.
Fig.2b shows the effects of biochar addition to soil on crop pro-ductivity,categorised by associated changes in soil pH.Studies that found biochar addition to soil to reduce(?0.5to0.0grouping)and to increase(0.6–1.0pH units)the pH,showed no statistically signif-icant(P>0.05)effect on crop productivity.However,the remaining groupings,which showed an increase in soil pH upon addition of biochar,showed a positive effect on crop productivity(P<0.05),
180S.Jeffery et al./Agriculture,Ecosystems and Environment 144(2011)
175–187
Fig.2.(a)A forest plot showing the mean change in crop productivity as a percentage of the control for different pH ranges of soils.Points show means of treatments,bars show 95%con?dence intervals.‘Very acidic’=pH <5,‘Acidic’5≥pH ≤6and ‘Neutral’=pH >6.Numbers in the two columns on the right show the total number of ‘replicates’(n )from the combined studies upon which the statistical analysis is based (bold)and the number of ‘experimental treatments’that have been grouped for each analysis (italics).(b)A forest plot showing the mean changes in crop productivity as a percentage of the control,as in?uenced by changes in soil pH,upon addition of biochar.Points show means of treatments,bars show 95%con?dence intervals.Numbers beside bars show pH groupings in 0.5unit intervals.Numbers in the two columns on the right show the total number of ‘replicates’(n )from the combined studies upon which the statistical analysis is based (bold)and the number of ‘experimental treatments’that have been grouped for each analysis (italics).
with a general positive trend for crop productivity to increase with increasing pH.3.3.Soil texture
Fig.3shows the effects of biochar addition to soil on crop productivity,grouped by ‘effective’soil textural class (see Section 2.3).Signi?cant (P <0.05)increases in crop productivity occurred in soils of both medium and coarse textures.In contrast,no signi?-cant (P >0.05)effects of biochar application on crop productivity were found in ?ne-textured soils.The variance in results from the ?ne textural classes was more than double that of either the medium or coarse textural classes.Grouping by ‘effective’textural classes,showed no statistically signi?cant (P >0.05)negative effects of biochar application to soil on crop productivity.
3.4.Biochar and fertilizer interaction
There was no statistically signi?cant effect of biochar applica-tion to soil between groups as categorised by fertilizer addition (P >0.05).The control,against which each group was measured to calculate the effect size,was the same as the experimental treatment,but without biochar addition.For example,the group ‘inorganic fertilizer’shows the effect size between crop productiv-ity for use of inorganic fertilizer alone (control),compared to the experimental treatment of inorganic fertilizer (at various applica-tion rates)applied with biochar.No signi?cant difference was found in the effects of biochar on crop productivity whether inorganic,organic,both,or no fertilizer was used (P >0.05;Fig.4).However,statistically signi?cant increases in crop productivity were seen when biochar was applied concurrently with inorganic fertilizer,
S.Jeffery et al./Agriculture,Ecosystems and Environment144(2011)175–187
181
Fig.3.A forest plot showing the mean change in crop productivity as a percentage of the control in response to different rates of biochar application grouped by‘effective’soil textural class,as indicated beside bars.Points show means of treatments,bars show95%con?dence intervals.Numbers in the two columns on the right show the total number of‘replicates’(n)from the combined studies upon which the statistical analysis is based(bold)and the number of‘experimental treatments’that have been grouped for each analysis(italics).
compared to applying inorganic fertilizer alone,as well as when biochar was applied to soil without any fertilizer addition(P<0.05). There was no statistically signi?cant effect of concurrent applica-tion of biochar with organic fertilizer,compared to that of organic fertilizer alone or when biochar was applied concurrently with both inorganic and organic fertilizer(P>0.05).
3.5.Crop
Statistically signi?cant increases in crop productivity were found to occur in both radishes and soybean upon addition of biochar to soil(P<0.05),while the opposite was observed in rye-grass(P<0.05).For the remaining crop types investigated,no statistically signi?cant effects were seen on application of biochar to soil(P>0.05).Levels of variance within treatments ranged con-siderably with crop type,with peanuts and satsumas showing particularly variable effects regarding yield.3.6.Biochar feedstock
Fig.6shows the results of crop productivity experiments using biochar from a range of feedstocks.Signi?cant positive effects (P<0.05)were found for the following feedstocks:wood,paper pulp,wood chips and poultry litter(both when pyrolysed at450?C, and when pyrolysed at550?C and activated).A signi?cant nega-tive effect on crop productivity(P<0.05)was found for biosolids. All other feedstocks investigated showed no statistically signi?cant effects on crop productivity(P>0.05).
3.7.Plant biomass,crop yield and trial type
Fig.7a and b show two forest plots with similar means and con?-dence intervals.Biochar application lead to a statistically signi?cant positive effect on both biomass and yield(fruit or grain;P<0.05). The effect on biomass productivity showed a signi?cant
increase
Fig.4.A forest plot showing the mean change in crop productivity as a percentage of the control resulting from the interaction of biochar and type of fertilizer.Note that while three of the groupings show statistically signi?cant increases in the mean between experimental treatment(with biochar)versus control treatment(without biochar), there is no statistically signi?cant difference between the groups.Points show means of treatments,bars show95%con?dence intervals.Numbers in the two columns on the right show the total number of‘replicates’(n)from the combined studies upon which the statistical analysis is based(bold)and the number of‘experimental treatments’that have been grouped for each analysis(italics).
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Fig.5.A forest plot showing the mean change in crop productivity as a percentage of the control for different crop types.Points show means of treatments,bars show 95%con?dence intervals.Numbers in the two columns on the right show the total number of ‘replicates’(n )from the combined studies upon which the statistical analysis is based (bold)and the number of ‘experimental treatments’that have been grouped for each analysis (italics).
(mean being approximately three times higher)when compared to crop yield (fruit or grain;Fig.7a).
Biochar application in both pot and ?eld trials also showed a statistically signi?cant positive effect (P <0.05).Furthermore,the mean increase in crop productivity for pot trials was approximately three times greater than that for ?eld trials (Fig.7b).4.Discussion 4.1.Data and analysis
A limited number of studies were available for inclusion in this MA,partly due to the relatively recent focus of research on the rela-tionship between biochar and crop productivity.Some categories within the analysis contained as few as two experimental treat-ments (as de?ned above),which were combined for calculation of the effect size (and in some instances only one experimental treat-ment was available leading to their exclusion).Both the number of replicates (n )and the number of experimental treatments are reported for each effect size,to show the con?dence associated with a result,as well as to identify where paucity of data exists;this is useful for guiding future research.No studies were found in the literature that had run for more than 2years.More than 90%of the studies included in this analysis showed results over 1growing season.Owing to possible changes upon ageing of biochar within the soil,this highlights the urgent need for long term studies on the impacts of biochar applications to soil on crop productivity.
The grand mean in all ?gures shows a statistically signi?cant positive effect on crop production of approximately 10%,as a response to biochar application to soil.Variability in the grand mean can be seen between ?gures due to the exclusion of individ-ual studies in some instances where insuf?cient data were available for a particular analysis.
Variance was heterogeneous between categories in most ?g-ures.This is to be expected as the analyses include data from a range of soil types,climatic conditions,experimental designs,biochar application rates and crop types.However,in some instances,the high levels of variance (as shown by the 95%con?dence inter-val bars),may be accounted for by the low number of studies included in some categories.Further work is necessary to inves-tigate whether large variability is an inherent trait of the category reported,or an artefact of the paucity of data currently available in that given category.
Two ?gures (Fig.2a and b)were included to provide information regarding the interaction of biochar and soil pH on crop productiv-ity.This is because one of the hypothesised mechanisms by which biochar addition to soil affects crop productivity is through a liming effect,resulting in the increase of soil pH (van Zwieten et al.,2009).This is possibly due to biochar raising the soil pH past the thresh-old of Al 3+toxicity (i.e.pH 4.8–5.0).However,analysis of the pH change around this threshold was inconclusive (data not shown).Biochar addition to soil in the ‘Very Acidic’category did not show a signi?cant effect on crop productivity (Fig.2a),which might be due to the liming effect of biochar addition to soil not being suf?cient to raise the pH of the soil past any metal ion toxicity thresholds.
Varying levels of liming effect were seen from biochar appli-cation experiments,for which pre and post amendment soil pH was reported.However,a large amount of variability in the magni-tude of any liming effect existed,independently of the feedstock used.For example,wood was used as a feedstock by Blackwell et al.(2007)who reported no change in pH in virtually all instances (starting soil pH of 5.53and 4.8)and also by Chidumayo (1994)who reported an increase in soil pH of 5.5to 7upon application of biochar made from a wood feedstock.This highlights the need for accurate reporting of feedstock as different species of tree wood may lead to different levels of liming effect,a hypothesis that could not be tested using the data available in the literature,as many investigators have reported the umbrella term “wood”.
There was a general trend of biochar addition to soil leading to enhanced soil pH and a concurrent increase in crop productivity (Fig.2b).This effect was not strictly linear,e.g.the effect size for the pH category 1.1–1.5units,was lower than that of 0.6–1.0units.
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Fig.6.A forest plot showing the mean change in crop productivity as a percentage of the control upon application of biochar from a range of feedstocks to the soil.Points show means of treatments,bars show95%con?dence intervals.Numbers in the two columns on the right show the total number of‘replicates’(n)from the combined studies upon which the statistical analysis is based(bold)and the number of‘experimental treatments’that have been grouped for each analysis(italics).
This may be due to the initial soil pH for different studies varying relative to the Al3+toxicity threshold,although further research is needed to test this hypothesis.There was no correlation between biochar application rate and change in soil pH post amendment (data not shown).Further to Al3+toxicity effects,increasing soil pH may also have an effect on nutrient availability by raising the CEC(particularly in the organic matter fraction)of soils.However, pre and post amendment CEC data was not reported consistently or different methodologies were used for its determination.As a result,it was not possible to further investigate through this MA the interactions with CEC.
Regarding the concurrent application of fertilizer and biochar (Fig.4),there was no statistically signi?cant effect of biochar application to soil between groups(as grouped by fertilizer addi-tion),regardless of whether fertiliser was applied concurrently,or whether organic or inorganic fertiliser was used.This is contrary to speci?c recommendations in the literature,advocating fertilizer addition in order to maximise the positive impacts of biochar appli-cation to soil(Yamato et al.,2006;Steiner et al.,2007;Asai et al., 2009).Care must be taken when interpreting Fig.4.The effect sizes were based on the difference between‘controls without biochar’vs.‘treatments with biochar’and thus,the“None”treatment(i.e.no fertilizer application)represents the relative effect of biochar addi-tion to soil alone.In the remaining groupings,the controls include the addition of fertiliser in the absence of biochar.In both instances where a signi?cant effect was observed(‘Inorganic’and‘None’), biochar addition to soil enhanced crop productivity by approxi-mately10%.Whereas in the‘Inorganic’treatment,this was a10% increase in addition to the fertilizer effects,in the‘None’treat-ment,it represented a10%increase in response to the addition of biochar alone,compared to that in the absence of biochar.Chan et al.(2007)reported a lack of response upon addition of biochar alone,i.e.without concurrent N addition.Therefore,it seems likely that available N in soil was not the limiting factor to crop productiv-ity,explained either by the quantity and quality of native SOM,or by previous applications of fertilizer and/or cropping with legumes.On the other hand,the combined addition of biochar with organic fertiliser was found to have no statistically signi?cant effect when compared to the application of organic fertiliser alone.This may be explained by the high levels of variance associated to the latter treatments.
The only statistically signi?cant negative effects were found when ryegrass(Fig.5)was grown in the presence of biochar derived from biosolids(Fig.6).However,there seem to be no practical rea-sons why rye grass may perform differently to other grasses.It is important to note that because the only studies that used ryegrass also used biosolids as a feedstock(Wisnubroto et al.,2010),it is not currently possible to elucidate mechanisms or distinguish whether the negative effect occurred due to the crop type,feedstock,or a combination of the two.Further research is needed,therefore,to investigate whether ryegrass often,or always,responds negatively to interactions with biochar in the soil.Alternatively,unreported associated factors may also explain the negative effect,either in combination with the previous or on their own(e.g.by intro-ducing heavy metals and/or other contaminants in the biochar; Bridle and Pritchard,2004;Hospido et al.,2005;Chan and Xu, 2009).
Fig.7a and b demonstrate,in two graphs,that a strong confound-ing effect exists between?eld/pot experiments(upper graph)and biomass/yield parameters(lower graph).In the literature included in this MA,98%of?eld trials focused on crop yield(i.e.grain or fruit production),while70%of pot trials investigated crop biomass. This indicates that it is not possible,on the basis of this analysis, to determine whether the increased effect on crop productivity is differentiated between crop biomass and grain or fruit yield,or whether biochar causes an increased effect in pot trials compared to?eld trials,or a combination of the two.This highlights the need for further long-term experiments that quantify both total crop biomass and yield(grain or fruit)following biochar application in ?eld trials.Only once this information is available in the literature will elucidation of the effects of pot vs.?eld or biomass vs.yield be possible through meta-analysis.
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Fig.7.(a).A forest plot showing the mean change in crop productivity as a percentage of the control upon addition of biochar to soil.Points show means of treatments,bars show 95%con?dence intervals.Biomass refers to the total above ground plant biomass,whereas yield refers to the production of fruit or seeds (depending on crop).Numbers in the two columns on the right show the total number of ‘replicates’(n )from the combined studies upon which the statistical analysis is based (bold)and the number of ‘experimental treatments’that have been grouped for each analysis (italics).(b).A forest plot showing the mean change in crop productivity as a percentage of the control upon addition of biochar to soil on plants taken from either pot or ?eld trials.Points show means of treatments,bars show 95%con?dence intervals.Numbers in the two columns on the right show the total number of ‘replicates’(n )from the combined studies upon which the statistical analysis is based (bold)and the number of ‘experimental treatments’that have been grouped for each analysis (italics).
4.2.Environmental and management representativity
In order to reduce bias,care has been taken to include all available data,both in the primary,as well as in the grey liter-ature.While the possible in?uence of publication bias has been accounted for by the calculation of the Fail Safe N Number,the majority of studies were based on trials undertaken in tropical (38%)and subtropical (55%)latitudes,except for one study (report-ing 7%of all treatments used)conducted at a temperate latitude >40?(Wisnubroto et al.,2010).Whether it is possible to extrapo-late these results to temperate regions remains unclear (Atkinson et al.,2010).The inclusion of data from non-tropical environments has helped to reduce bias,by gathering results as representative as possible of biochar effects from a range of biochar feedstocks,under a range of environmental conditions and crop types.Meta-analyses allow for results to be extrapolated to different soil conditions with increased con?dence than is possible when looking at each study individually.
Of the total 177observations,112(63%)were ?eld studies and 65(37%)were mesocosm experiments conducted in pots.The mean reported time period between biochar application and data mea-surement for the ?eld studies was 1.3year (range 0.29–4year),and for the pot experiments <1year (often only 1to 2months).The majority of the ?eld trial data included in this MA were obtained in short-term experiments of 1–2years.Long-term studies are therefore,urgently required,particularly concerning effects of soil tillage/cultivation on behaviour,mobility and fate of biochar in soil,but also in relation to the in?uence of biochar application strate-gies on its functioning.For example,little is still known on the way CEC of biochar changes over time in arable soils,as it weathers and as in?uenced by soil management,with potentially relevant implications for soil nutrient retention and crop productivity.
A cautionary note should also be made regarding the classic difference between carefully managed scienti?c ?eld trials and ‘real-life’farming.In this context,long-term effects of agricultural management practices on biochar properties that are bene?cial to crop production may not be well represented by scienti?c trials.Accelerated ageing of biochar to study long-term processes cur-rently lacks standardised methods (Sohi et al.,2009;Cross et al.,2010)and are unlikely to incorporate the complex interactions that can develop over time with soil organic and inorganic mat-ter (Brodowskia et al.,2005),as well as with soil organisms (Saito,1990;Pietik?inen et al.,2000),which may lead to changes in crop productivity.This demonstrates that the experiments are not repre-sentative of what would occur in soil upon biochar addition,where any interactions between the biochar,soil and biota can develop over a period of years or decades.
While only limited statistically signi?cant negative effects (that of the in?uence of biochar application to soil on ryegrass produc-tivity or that of the in?uence of use of biosolids as a feedstock),was identi?ed by this MA,it must be stressed that the range of studies included here does not cover a wide range of latitudes,with data being heavily skewed towards (sub)tropical conditions.While this analysis provides evidence of the generally positive or not statistically signi?cant effects of biochar addition to soil on crop productivity,care needs to be taken when extrapolating these results to higher latitudes,crops,soil types and agricultural sys-tems that are not covered in the current analysis.Importantly,agricultural management practices may not always be bene?cial to both components of the dual objective of biochar (i.e.increasing
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crop production and carbon sequestration).Certain agricultural practices such as tillage,ploughing,etc.may increase crop produc-tion when performed in combination with biochar addition,but may actually be disadvantageous to carbon sequestration through biochar.
In most of the studies in the literature,soil texture was described,although particle size distributions were mostly not reported.Soil taxonomical classi?cation was sometimes provided, in different taxonomical systems and to different degrees of detail. Many combinations of soil-climate-hydrology-topography factors have not been included in reported experiments so far,including those combinations that are largely found in agricultural areas,e.g. chernozems,vertisols,histosols.A lack of data also exists for studies in semi-arid and arid regions.
All studies in this analysis were conducted under arable or horti-cultural land use.Additional land uses where biochar application is considered possible,such as pasture and forest,need to be included. Data on soil management(e.g.tillage and cultivation type and intensity)are also lacking,while a variety of mainly arable(com-binable)and some horticultural crops has been reported.However, arable root and permanent crops,pasture,forest and shrubland were not represented in the data set analysed due to a lack of studies in the literature involving these crops.
4.3.Auxiliary variables
Only62%of studies reported the value of soil pH both before and after biochar application,reducing the number of studies that could be included in this analysis.Similarly,only38%of studies reported soil CEC before and after biochar addition to soil,while methodologies for CEC determination varied between studies.Fur-thermore,data on soil texture was reported inconsistently,while that on soil structure was mostly lacking.These data are vital for clear elucidation of possible mechanisms and,as such,it is strongly recommended that such data are reported in upcoming studies to aid future meta-analysis and reviews of mechanisms.
It is also worth emphasising that the dependent variable of this study was crop productivity and that any results described here do not hold any relevance to other soil(sub)functions,like for example a possible priming effect(soil organic carbon regulation function), or potential toxicity to parts of the soil biota(habitat function). 4.4.Reporting guidelines for‘biochar-crop production’
experiments
Soils are highly heterogeneous systems at a multitude of spatiotemporal scales,while biochar itself can also be very hetero-geneous when physical–chemical characteristics are concerned.As new studies emerge,this MA on the effect of biochar application to soil on crop production can be updated(and re?ned)periodically, as necessary.In addition,the effects of biochar at other levels of soil processes and functions can be analysed by MA once a large enough body of research has been established.
It is paramount that upcoming studies on the effects of biochar provide as much information as possible in regard to the study conditions,as well as a consistent and complete description of the data.Such a description should also include the Z or F statistic and clear measures of variance for comparative data analysis,ideally as standard deviations or standard errors for each treatment and the control,rather than an LSD(least signi?cant difference),which has been pooled for several treatments.In all cases,it should be absolutely clear what the sample number is for every treatment (including the control).To enable MA on effects of a factor that is not the dependent variable of a study,it is also recommended to include all sample numbers,standard deviations or standard errors of other parameters measured as auxiliary variables(e.g.CEC,pH,bulk density,microbial activity,etc.).Finally,it is recommended that all data be reported in tabular format,possibly as an annex. This will help to ensure that data can be effectively included in future MAs and those results are obtained with the highest possible con?dence.
4.5.Maintenance of this MA
The Cochrane Collaboration handbook(Higgins and Green, 2009)stated that systematic reviews should be updated every two years.This time period was selected for studies in medical inter-vention,while those in environmental or agricultural sciences may generally be expected to be less‘time pressing’.Nevertheless,in the case of biochar,and considering strong drivers from climate change,waste management,food security,commercial pyrolysis facility producers,and soil quality requirements,it seems reason-able that a regular and frequent update of this MA is warranted. Substantial amounts of data derived from biochar?eld and pot (greenhouse)trials(including crop production data)are expected to become available during the next few years,as many projects have started during the preparation of this manuscript.Moreover, a proportion of the?eld experiments used in this MA(mostly of1–2year duration)will be maintained during the next years, providing more and longer-term data,assuming the results are published in the primary literature or made available via other means.
All existing data should be made available as much as possible in a transparent way,with full disclosure of data,statistics and fund-ing.This can imply translation of research papers into English or the posting of experimental results in an online public database. In all cases,it is advised that studies are reported according to the guidelines described above.The database used for this MA is pub-licly available to download at ESDAC,the European Soil Data Centre (ESDAC,2010).
5.Conclusions
Evaluating the evidence regarding the relationship between biochar and crop productivity,this MA shows an overall relatively small(approximately10%)but statistically signi?cant,positive effect of biochar application to soils on crop production.Such a result is robust and useful,as it provides a sound basis for the potential bene?ts of biochar use on crop productivity.However, it must be stressed that this should not be taken to imply that the random addition of biochar to a?eld anywhere in the world, will always lead to a small yield increase,nor does it provide any information about additional potential effects and consequences of such a practice(e.g.regarding the environmental regulation function of soils).It is worth stressing that this MA is not capa-ble of predicting the longevity of effects of biochar addition to soil.In fact,this MA highlights the need for long-term experi-ments in order to include and quantify the in?uence of ageing of biochar in soil on the expected effects of its application on productivity.
The greatest positive effects were seen in biochar application rates of100t ha?1(39%).Other positive effects were seen in acidic (14%)and neutral pH soils(13%),and in soils with a coarse(10%)or medium texture(13%).This suggests that two of the main mech-anisms for yield improvement may be a liming effect and the in?uence on the water holding capacity of the soil.
The variation in effect sizes ranged considerably,both within and between treatments.To understand the variation in effect sizes,the MA data were partitioned,thereby providing useful pre-liminary insights into the relationship between biochar and crop production,as well as experimental methodologies However,what
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Table2
Main weaknesses in the current scienti?c evidence on the relationship between biochar addition to soils and crop production(see Sections4.1to4.5for more detailed discussion).
Categories Short description
Biochar properties Some properties are known to vary widely,while other properties are unknown and/or not measured/reported.Feedstocks are
not always representative of likely feedstocks under consideration.
Environmental Reported studies are limited to(sub)tropical regions mostly.Other regions across a range of soil types and environmental
conditions,with large global agricultural areas have been scarcely studied.
Land use All reported studies involving effects of biochar on crop productivity are currently under arable and horticultural land use.Other land uses where biochar implementation is considered possible,such as pasture and forest,need to be included.
Land/soil management Data lacking on soil management(e.g.tillage and cultivation type and intensity).A variety of mainly arable(combinable)and
some horticultural crops has been reported.However,arable root crops,permanent crops,pasture,forest and shrubland are not
represented.
Pot Pot experiments often only measure plant biomass,and commonly maintain the soil at?eld capacity.Most experiments have a
duration of<6months.Methodologies that integrate?eld with pot experiments in long-term trials could prove useful to evaluate
mechanisms.
Field Most?eld data are available for only1–2years.Much longer term data is required,particularly concerning effects of soil
tillage/cultivation on biochar.Studies using methodologies for accelerated biochar ageing by tillage/cultivation,freezing/thawing
and wetting/drying simulations have not been reported.
Study design Even basic auxiliary data,e.g.pH and CEC of soil,are often not measured both before and after biochar application,thereby
disabling statistical analysis of potential causal mechanisms.Other,potentially important auxiliary variables,e.g.soil texture,
structure,changes in plant-available soil water retention,are measured sporadically.Much wider and consistent measurement of
auxiliary variables needs to be considered in study design.
Study reporting Future publication of longer term?eld data should be accompanied by site-speci?c meteorological data.Data on all variables
needs to be reported with a full and coherent statistical description.
Numbers The number of observations(experimental treatments)is low considering the variation in dependent variables(environmental
and management),as well as in different parameters used as the independent variable.
Crop production parameters Studies measured/reported different crop production parameters,e.g.plant biomass;grain yield,plant height,etc.Although this
may be useful for mechanistic understanding,if measured consistently,the necessary inclusion of different parameters may have
‘masked’effects in this MA.
is evident from Figs.1–7,are the very large95%con?dence inter-vals around the mean effect size for each partitioning.This is due to the amount of variation within the data concerning both independent and dependent variables,relative to the number of observations.
For a responsible implementation of biochar policy at any scale (in any area),there is an urgent need for both qualitative and quan-titative understanding of the causative mechanisms behind the range of effects of biochar on soil functions,as well as of the envi-ronmental and management factors relevant to that scale/area and of the half-life of biochar in soil,as in?uenced by the site-speci?c characteristics.Currently,mechanistic understanding remains very limited,particularly for longer time series(i.e.more than a few years).Besides time series,three additional main weaknesses in the current scienti?c evidence need to be addressed:representa-tivity,auxiliary data,and observations,as discussed in detail above and summarized in Table2.It is strongly recommended that these issues are taken into account in future studies. Acknowledgements
We would like to acknowledge the work carried out by the researchers whose published data was used for this meta-analysis. Particular thanks go to Wisnubroto et al.,for giving us permission to use their pre-published data.Thanks also to the European Soil Data Centre for hosting the MA raw data on their servers to allow for the updating of this MA as new results become available.Finally,this work was partly funded by iSOIL-Interactions between soil related sciences–Linking geophysics,soil science and digital soil mapping, a Collaborative Project(Grant Agreement number211386)co-funded by the Research DG of the European Commission within the RTD activities of the FP7Thematic Priority Environment;iSOIL is one member of the SOIL TECHNOLOGY CLUSTER of Research Projects funded by the EC.This publication re?ects the authors’views.The European Commission is not liable for any use that may be made of the information contained therein.References
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