biochar effects on rice paddy: meta-analysis · 2018. 10. 26. · table 1 statistical analysis for...

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Biochar Effects on Rice Paddy: Meta-analysis

Awad, Yasser M.; Wang, Jinyang; Igalavithana, Avanthi D.; Tsang, Daniel C. W.;Kim, Ki-Hyun; Lee, Sang S.; Ok, Yong Sik

Advances in Agronomy

DOI:10.1016/bs.agron.2017.11.005

Published: 01/01/2018

Peer reviewed version

Cyswllt i'r cyhoeddiad / Link to publication

Dyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA):Awad, Y. M., Wang, J., Igalavithana, A. D., Tsang, D. C. W., Kim, K-H., Lee, S. S., & Ok, Y. S.(2018). Biochar Effects on Rice Paddy: Meta-analysis. Advances in Agronomy, 148, 1-32.https://doi.org/10.1016/bs.agron.2017.11.005

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08. Jan. 2021

https://doi.org/10.1016/bs.agron.2017.11.005

https://research.bangor.ac.uk/portal/en/researchoutputs/biochar-effects-on-rice-paddy-metaanalysis(c7dc52f4-62f1-454f-8895-aa67515a640a).html

https://research.bangor.ac.uk/portal/en/researchoutputs/biochar-effects-on-rice-paddy-metaanalysis(c7dc52f4-62f1-454f-8895-aa67515a640a).html


Biochar Effects on Rice Paddy:Meta-analysisYasser M. Awad*,†, Jinyang Wang‡, Avanthi D. Igalavithana*,Daniel C.W. Tsang§, Ki-Hyun Kim¶, Sang S. Lee||,1, Yong Sik Ok*,1*Korea Biochar Research Center, O-Jeong Eco-Resilience Institute (OJERI), Korea University, Seoul, SouthKorea†Faculty of Agriculture, Suez Canal University, Ismailia, Egypt‡State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy ofSciences, Nanjing, China§The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China¶Hanyang University, Seoul, South KoreajjYonsei University, Wonju, South Korea1Corresponding authors: e-mail address: [email protected]; [email protected]

Contents

1. Introduction 22. Materials and Methods 3

2.1 Data Compilation 32.2 Data Processing and Meta-analysis 4

3. Results 73.1 BC and Soil pH 73.2 Rice Grain Yield 73.3 Greenhouse Gas Emissions 83.4 Heavy Metal Bioavailability and Translocation in Rice 19

4. Discussion 204.1 Rice Paddy Soil pH and Productivity 204.2 Greenhouse Gas Emissions 214.3 Heavy Metal Immobilization 234.4 Meta-analysis 24

5. Conclusions and Research Need 24References 25Further Reading 32

Abstract

Rice is staple for nearly half of the world population. Biochar (BC) improves crop yields,reduces greenhouse gas (GHG) emissions, and immobilizes heavy metals in the soil. Thisstudy was aimed to meta-analyze the data from the published articles focused on thevarious BCs’ effects on rice yield, soil acidity, GHG emissions, and bioavailability of Cdand Pb. The data of pyrolysis temperature, application rate, and feedstock of BCs werecategorized by using the MetaWin software for calculating the mean effect sizes (E) with

Advances in Agronomy # 2017 Elsevier Inc.ISSN 0065-2113 All rights reserved.https://doi.org/10.1016/bs.agron.2017.11.005

1

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95% confidence intervals (CI). Compared to the control, the BCs increased soil pH and riceyield by 11.8% (medium E+: 0.436 to 0.439) and 16% (large E+: 0.790 to 0.883), respectively.Applying BCs derived from different feedstocks and pyrolysis temperatures reduced N2Oemissions from rice paddies (large E�:�0.692 to�0.863). The BCs produced at 550–600°Creduced the GHG emission with medium to large negative effects (E�: �1.571 to�0.413). Applications of BCs at a range of 41–50 t ha�1 were the best for rice produc-tivity. Applications of all types and rates of BCs showed the significant decrease of avail-able Cd by 35.4%–38.0% in a soil and led to the Cd reduction by an average of 43.6% inrice grains compared to the untreated soils. Applying BC is a promising approach tomeet the challenges of sustainable global rice production, and the properties of BCsshould be fully characterized and designed depending on its needs prior to itsapplication.

1. INTRODUCTION

The Rice Market Monitor Report 2016 by the Food and Agriculture

Organization (FAO) announced that annual yields of rice reached 209.5 and

5.6 million metric tons in China and Korea, respectively (FAOSTAT, 2016).

High-yielding rice cultivars have been used in 74% of the rice cultivated areas

over the world because of high demand of food needs (Cao and Yin, 2015;

Dowling et al., 1998). However, the intensive rice cultivation is readily

degrading the soil quality and brings to a shortage of soil organic matter

(Huang et al., 2013; Lee et al., 2009). It also leads to a threat of optimal levels

of essential nutrients, such as N and P, in the soils via leaching and emission as

N gasses (Cao et al., 2014; Chen et al., 2013a). On the contrary, the overuse of

N and P fertilizers on agricultural areas increases possibilities to threaten sur-

rounding ecosystems through runoff and soil loss (Kim et al., 2006).

Greenhouse gas (GHG) emission from rice paddies is of great environ-

mental concern for global warming (Ali et al., 2013; Liu et al., 2011). Rice

productivity relies on fieldmanagement practices such as tillage, fertilizer, and

irrigation, which also influence the GHG emission (Balesdent et al., 2000;

Doran and Smith, 1987). For instance, the N loss to the atmosphere as

N2O emission accounts for up to 48% of N fertilizer applied to the irrigated

rice paddies (Reddy et al., 1980) and the emission of CH4 is concerned in the

agricultural fields under the waterlogged or anaerobic condition (Dong et al.,

2013; Sch€utz et al., 1989). Intergovernmental Panel on Climate Change

(IPCC) reported that the global warming potentials of CH4 and N2O are

28 and 265 times greater than that of CO2, respectively (IPCC, 2015).

The occurrence of heavy metal contamination leads adverse effects on

the soil, plant, and human health (Bian et al., 2014; Kim et al., 2015;

2 Yasser M. Awad et al.

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Usman et al., 2012). The release of toxic heavy metals severely degrades the

agricultural areas by accumulating them into grains and surrounding envi-

ronmental components (Bian et al., 2013a, 2014). In case of Cd contamina-

tion, the human health can be threatened via a food chain, causing itai-itai

disease as the mass Cd poisoning (Bian et al., 2013a; IPCS, 1992; Nakadaira

and Nishi, 2003).

Biochar (BC) is a carbon-rich product from pyrolysis of biomass such as

wood, crop residues, and manure (Lehmann et al., 2015). The use of BC is

shapely increasing as a soil amendment for sustaining soil fertility, remediating

organic/inorganic contaminants, mitigating GHG emission, and facilitating

environmental management (Bian et al., 2013a; Fang et al., 2016; Jeffery

et al., 2011; Ok et al., 2015a). Pyrolysis conditions and feedstock type of

BC predominantly determine its physicochemical properties, and both neg-

ative and positive impacts on plant growth and productivity (Enders et al.,

2012; Liu et al., 2013). However, its mechanistic evidence is still ambiguous

(Clough et al., 2013; Jeffery et al., 2015; Lehmann et al., 2015).

Therefore, the meta-analysis of BCs’ impacts using the published empir-

ical data was done to elucidate mechanism and effectiveness of BC on soil

environments and related outcomes. This study used the published data of

soil quality, rice grain yield, net GHG emission, and bioavailability of Cd

and Pb in paddy soils amended with BCs which had various feedstock type,

pyrolysis condition, application rate, etc.

2. MATERIALS AND METHODS

2.1 Data CompilationData were compiled from the published articles related to the effects of BC

application on soil acidity, grain yield, GHG emissions, and bioavailability of

Cd and Pb in rice paddy soils. The articles were collected and sorted by

experiment types using the electronic databases of ISI Web of Science

and Google Scholar (Ali et al., 2013, Asai et al., 2009; Barbosa de Sousa

et al., 2014; Bian et al., 2013a,b, 2014; Cao and Yin, 2015; Chen et al.,

2013a; Cui et al., 2011, 2013; Dong et al., 2013, 2015; Feng et al., 2012;

Haefele et al., 2011; Huang et al., 2013, 2014; Jones et al., 2012; Khan

et al., 2013, 2014; Knoblauch et al., 2011; Lai et al., 2013; Li and Xu,

2015; Liang et al., 2014; Liu et al., 2011, 2012, 2013, 2014, 2015, 2016;

Lugato et al., 2013; Ly et al., 2015; Ma et al., 2014; Nehls, 2002; Qian

et al., 2014; Shen et al., 2014; Singla and Inubushi, 2014; Singla et al.,

2014; Van der Gon, 1995; Wang et al., 2011; Wu et al., 2015; Xie et al.,

3Biochar Effects on Rice Paddy: Meta-analysis

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2013; Xu et al., 2012; Yang et al., 2016; Yoo and Kang, 2012; Zhang et al.,

2010, 2012, 2013a,b, 2015; Zhao et al., 2014; Zheng et al., 2015). The data

were selected with a limitation of experiment duration for a column exper-

iment having longer than 45 days, a pot experiment having longer than

3 months, and a field experiment having longer than a year.

The published data of soil pH and texture, N fertilizer, feedstock type,

pyrolysis temperature and application rate of BC, concentrations of available

Cd and Pb, and rice grains were used as shown in Table 1. The data of CO2,

N2O, and CH4 emissions were also derived and compiled from the publi-

shed articles conducted as the field, pot, and incubation experiments, and the

compiled data were grouped by BC factors (i.e., pyrolysis temperature,

application rate, and feedstock type) and soil factors (i.e., texture and pH)

for data homogenization and equilibrium.

2.2 Data Processing and Meta-analysisData were extracted from tables and figures in the published articles. The

Web Plot Digitizer Software (http://arohatgi.info/WebPlotDigitizer/) was

used to achieve the data values from figures. The pooled variance was

also estimated if necessary (Curtis andWang, 1998). The variance was calcu-

lated as:

V ¼ Nc +Ntð Þ= Nc�Ntð Þ (1)

where N is the number of replicates for control (Nc) and treatment (Nt)

(Wang et al., 2016).

The repeated measurements in the data set were meta-analyzed using the

MetaWin software version 2.1 based on a mixed model effect (Adams et al.,

1997) to calculate the effect size (d stands for Cohen’s measure to standardize

the quantity for difference between the means). The chi-square test was per-

formed to investigate the effects of BC on rice grain yield, available Pb and

Cd in soil, uptake of Pb and Cd by rice plant, and GHG emissions in each

group (Table 1).

MetaWin software was also used to calculate the mean effect sizes and

95% CI based on sample size, mean, and standard deviation. The computing

natural log-transformed response ratio (RR) and Hedges’d were estimated

from studies as a measure of effect size (Hedges et al., 1999) as follows:

lnRR¼ ln Xt=Xcð Þ (2)

E¼ Xt�Xcð Þ=Swithin (3)


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http://arohatgi.info/WebPlotDigitizer/

http://arohatgi.info/WebPlotDigitizer/

Table 1 Statistical Analysis for the Biochar Effects on Rice Grain Yield, Greenhouse Gas Emission, and Bioavailability of Cd and Pb to Rice Plants

CategoricalVariables Group

Rice Grain Yield Soil pHCumulative GHG Emission:CO2–CH4–N2O Cd–Pb in Soil Cd–Pb in Rice Grains

No.Observations

Chi-SquareTest

No.Observations

Chi-SquareTest

No.Observations

Chi-SquareTest

No.Observations Chi-Square Test

No.Observations

Chi-SquareTest

N fertilizer

(kgN ha�1)

0 17 84.22 17 36.29 — — — — — —

15–60kg 11 11 —

120–220 30 30 —

225–350 20 20 —

360–750 — — —

Feedstock

type

Maize straw — 151.53*** — 29.98 NO–16–10 1487.67*** — 22.45– — 9.96–

Manure and

sludge

— — 7–11–NO —

385.01***2–2 49.11*** — 0.31

Rice husk 18 18 55–17–32 — — —

Rice straw 14 14 35–38–NO 101.96*** — —

Wheat straw 28 28 NO–8–20 39–18 33–18

Wood 9 9 127–36–8 — —

Pyrolysis

temperature

(°C)

400–450°C 8 74.12 8 35.68 — — — 22.45– 9.96–

500–550°C 55 55 39–24 80.72*** 33–18 0.31

600–800°C 9 9 —

Pyrolysis

temperature

(°C)

290–400°C — — — — 51–18–15 933.62*** — — — —

450–500°C 34–6–33 —

550–600°C 88–92–19 335.46**–

550–600°C — 116.95***

Continued

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Table 1 Statistical Analysis for the Biochar Effects on Rice Grain Yield, Greenhouse Gas Emission, and Bioavailability of Cd and Pb to Rice Plants—cont’d


Rice Grain Yield Soil pHCumulative GHG Emission:CO2–CH4–N2O Cd–Pb in Soil Cd–Pb in Rice Grains

No.Observations

Chi-SquareTest

No.Observations

Chi-SquareTest

No.Observations

Chi-SquareTest

No.Observations Chi-Square Test

No.Observations

Chi-SquareTest

Application

rate (tha�1)

<1 — 122.26*** — 42.91 — 1041.79*** — 22.46–73.17*** — 9.96–

1–10 17 17 33–37–13 — 10–8 8–6 0.31

11–20 11 11 NO–5–9 466.20*** 16–8 13–6

21–30 12 12 103–37–20 — 14–8 12–6

31–40 12 12 60–60–29 261.60*** — —

41–50 18 18 32–6–32 — —

70–160 — — NO–6—11 — —

Texturea Lighta 11 69.34 11 39.59 — — — — — —

Medium 32 32

Heavy 27 27

Soil pH

classesbNeutralb 26 80.37 26 70.63 — — — — — —

Acidic 25 25

Very acidic 27 27

aLight: sandy and sandy loam soils; medium: loamy sand and loam soils; heavy: clay loam, silty clay, and clay soils.bNeutral: pH >6–7, acidic: pH 5–6, very acidic: pH <5, slightly alkaline: >7–8.All units were unified as tha�1 for the grain yields and BC application rate, mgm�2 h�1 for GHG emission, and kgha�1 for cumulative GHG efflux and N fertilizer. Significant levels at *P�0.05, **P�0.01, and***P�0.001. NO: no observations.

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where Xt is the means of BC-treated soils, Xc is the control without BC,

and Swithin is the pooled across groups (standard deviation within groups)

(Borenstein et al., 2009).

The RR and CI of treatments were back-transformed from lnRR and

value/variance of d. Confidence limits for the means were also calculated.

The positive and negative lnRR indicate an increase or decrease of the

variables after BC addition, respectively. The data categorized in various

groups were meta-analyzed to calculate the mean effect sizes (E) and

95% CI (Table 1). The meta-analysis software was run at 4999 times of

iterations. Agreement rate percentage (AR) was calculated as (Borenstein

et al., 2009):

AR¼ number of observations agreed upon=total number of observations

(4)

3. RESULTS

3.1 BC and Soil pHBased on the calculated mean effect sizes and 95% CI, the type of different

feedstock, application rate, and pyrolysis temperature of BC changed soil pH

significantly with medium positive effect size (0.436–0.439 E+) and agree-

ment rates (ARs) of about 60%–75%. The overall increases of 11.5%–11.9%in soil pH were estimated with the BC compared to the untreated soil

(0.244–0.635 CI95%). The largest increase in soil pH was observed with

the wood BC (0.518 E+ and �0.028 to 1.065 CI95%), application rate at

41–50 t ha�1 (0.427 E+ and 0.014 to 0.841 CI95%), and pyrolysis temper-

ature of 500–550°C (0.453 E+ and 0.229 to 0.677 CI95%). The addition

of BC increased the soil pH in medium-textured soils such as loamy sand

and loam soils (0.584 E+ and 0.288 to 0.880 CI95%, 46% AR), compared

to the light-textured (sandy and sandy loam) and heavy-textured (clay loam,

silty clay, and clay) soils. The addition of BC also greatly changed the initial

pH values of very acidic soil compared to the neutral soil (Fig. 1).

3.2 Rice Grain YieldApplication rates of BC at 21–40t ha�1 and feedstock types of rice straw and

wood increased the rice grain yield with the large positive effect size

(0.790–0.883 E+) and 33%–60% AR (Fig. 2 and Tables 2 and 3). The pyrol-

ysis temperature showed amedium effect size (0.309–0.776E+) for rice grain


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yield. With the BC addition, an increase of grain yield with medium effect

size values was estimated in the different ranges of soil pH (neutral, acidic, and

very acidic; 0.414–0.553 E+) and N fertilizer. The increase of grain yield

with the BCs produced from manure and sludge at temperatures of

500–550°C or rates of 41–50t ha�1 was the largest. This increase occurred

in the heavy-textured soil rather than the light- and medium-textured soils.

3.3 Greenhouse Gas EmissionsChange of CO2 emission was not significant with an application rate and a

pyrolysis temperature of BC, based on the overall mean effect size near to

zero (�85% AR). Feedstock type showed negative or positive medium

effect size (�0.467 E� to 0.776 E+) on cumulative CO2 emission from rice

paddies (Fig. 3 and Table 2).

Fig. 1 Mean effect size on soil pH in response to biochar addition among various groupsof application rate, feedstock, pyrolysis temperature, N fertilizer application, soil pH, andtexture classes. Symbols represent mean effect sizes with 95% confidence intervals(horizontal bars). The numbers shown in parentheses correspond to observations infeedstock class upon which the statistical analysis is based.


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Rice husk BC increased the cumulative CO2 emission by 12.8% with a

large E+ value of 3.2. In contrast, the BCs derived from wood and manure/

sludge feedstocks reduced cumulative CO2 emission from rice paddy soils

with significant effects (�2.57 to �6.74 E�). The largest decrease

(23.6%) in cumulative CO2 emission was measured following the applica-

tion of wood BC, compared to the control. Meta-analysis revealed the

largest number of observations on wood feedstock relevant to CO2 (127

observations, a 57% AR). The BCs derived from rice husk and maize straw

significantly reduced cumulative CH4 emission (�0.331 to �8.017 CI95%).

Other than maize straw and rice husk BCs, a feedstock type of BC led to

insignificant changes in cumulative CH4 emission with a negative medium

effect size<0.39 E� (Fig. 4). A feedstock type of BC decreased cumulative

N2O emission by an average of 26.1% with E� values of�0.692 to�0.863

(Fig. 5 and Table 2).

Fig. 2 Mean effect size on rice grain yield in response to biochar addition among var-ious groups of application rate, feedstock, pyrolysis temperature, N fertilizer application,soil pH, and texture classes. Symbols represent mean effect sizes with 95% confidenceintervals (horizontal bars). The numbers shown in parentheses correspond to observa-tions in feedstock class upon which the statistical analysis is based.


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Table 2 Changes of Rice Grain Yield, Greenhouse Gas Emission, and Bioavailability of Cd and Pb to Rice Plants Among the Group of SelectedCategorical Variables


Percentage of Change (% of Control)

Soil pHRiceYield

CumulativeCO2

CumulativeCH4

CumulativeN2O

Cd Contentin Soil

Cd Contentin Grains

Pb Contentin Soil

Pb Contentin Grains

Application

rate (tha�1)

<1 — — Xa X X

1–10 20.90 11.86 �3.10 3.63 �23.99 �23.26 �31.72 �25.39 �10.00

11–20 4.36 56.59 — 33.62 �2.55 �39.47 �44.23 �25.82 �1.13

21–30 6.01 12.39 �4.94 �12.44 �71.20 �46.91 �50.77 �37.31 �13.29

31–40 10.36 12.15 �4.43 �13.34 9.36

41–50 11.46 6.09 42.14 29.29 �55.59

70–160 — — — �19.71 71.95

Grand mean 11.52 17.54 2.07 �5.90 �21.71 �38.02 �43.57 �29.51 �8.14

Feedstock

type

Maize straw — — X �28.64 �0.84

Manure and

sludge

— — �13.17 5.52 X �12.04 �27.55

Rice husk 11.46 6.09 28.20 �8.72 �55.59

Rice straw 20.03 8.72 �12.86 �14.02 X

Wheat straw 5.08 7.74 X 9.53 �24.47 �36.56 �43.57 �29.51 �8.14

Wood 21.14 16.26 �23.64 �4.86 �22.06

Grand mean 11.87 8.62 �8.90 �8.76 �35.04 �35.37 �29.36

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Table 2 Changes of Rice Grain Yield, Greenhouse Gas Emission, and Bioavailability of Cd and Pb to Rice Plants Among the Group of SelectedCategorical Variables—cont’d



Soil pHRiceYield

CumulativeCO2

CumulativeCH4

CumulativeN2O

Cd Contentin Soil

Cd Contentin Grains

Pb Contentin Soil

Pb Contentin Grains

Pyrolysis

temperature

(°C)

400–450°C 25.45 74.12

500–550°C 10.21 14.97 �36.56 �43.57 �29.51 �8.14

600–800°C 9.86 16.70

Grand mean 11.86 21.76

Pyrolysis

temperature

(°C)

290–400°C �15.54 �6.79 �25.32

450–500°C 39.68 31.55 �51.70

550–600°C �4.83 �10.94 �31.17

550–600°C — — —

Grand mean 0.76 �8.10 �39.97

N fertilizer

(kgN ha�1)

0 12.16 10.44

15–60kg 14.72 59.71

120–220 7.14 20.55

225–350 14.29 8.25

360–750 — —


Continued

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Table 2 Changes of Rice Grain Yield, Greenhouse Gas Emission, and Bioavailability of Cd and Pb to Rice Plants Among the Group of SelectedCategorical Variables—cont’d



Soil pHRiceYield

CumulativeCO2

CumulativeCH4

CumulativeN2O

Cd Contentin Soil

Cd Contentin Grains

Pb Contentin Soil

Pb Contentin Grains

Textureb Light 22.34 1.96

Medium 10.51 19.19

Heavy 9.41 31.09


Soil pH

classescNeutral 2.01 7.27

Acidic 12.01 12.16

Very acidic 19.11 41.58


aX: excluded studies due to a limited number of observations.bLight: sandy and sandy loam soils; medium: loamy sand and loam soils; heavy: clay loam, silty clay, and clay soils.cNeutral: pH >6–7, acidic: pH 5–6, very acidic: pH <5, slightly alkaline: >7–8.

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Table 3 Characteristics of the Selected Studies Included in Meta-analysis of Rice Production

Biochar FeedStock

ApplicationRate (tha21)

PyrolysisTemperature(°C) Experiment Type

SoilTexture

N Fertilizer(kgN ha21)

Change inYield (% ofControl) Reference

Wood residuesa 8 100–500 Field Clay loam 0 +27.1 Asai et al. (2009)

50 +48.2

Babassu palm nut 15 Unknown Field Clay 350 +9.5 Barbosa de Sousa

et al. (2014)

Wheat straw 10 350–550 Field Loam 120 No effect Cui et al. (2011)

20

40

Bamboo wood 22.5 600 Field Clay loam 0 No effect Dong et al. (2015)

200 +17.1b

Rice Straw 22.5 600 0 +20.7b

200 +27.8b

Rice huska 41.3 100–500 Field Clay 0 No effect Haefele et al. (2011)

60 No effect

Silty clay 0 +9.2c

60 +6.7c

Loamy

sand

0 No effect

60 +26.1c

Cassava stemsa 17.5 100–500 Pot Clay loam 234 +10 Huang et al. (2014)

35.0 234 +8.1

Continued

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Table 3 Characteristics of the Selected Studies Included in Meta-analysis of Rice Production—cont’d

Biochar FeedStock

ApplicationRate (tha21)

PyrolysisTemperature(°C) Experiment Type

SoilTexture

N Fertilizer(kgN ha21)

Change inYield (% ofControl) Reference

Sewage sludge 80 550 Pot Loamy

sand

120 +151.5 Khan et al. (2013)

160 +178.8

Wheat straw 20 350–550 Field Loam and

sandy

loamy

240 +5.6 Liu et al. (2014)d

40

Maize straw 2.4 400 Field Sandy

loamy

200 +6.0 Liu et al. (2015a)

Orchard pruning 40 550e Field Sandy

loamy

200 +34.4 Lugato et al. (2013)

Tree conifer 40 1000 200 +10.5

Rice straw 5 450 Column with

continuously flooded

Sandy 284 �27.4 Ly et al. (2015)

Column with

alternative wetting

and drying

284 �35.8

Terra preta 16 Unknown Field Clay 30 +>500 Nehls (2002)

Maize straw 0.45 350–450 Field Unknown +10.5 Qian et al. (2014)

Manure compost +13.5

Municipal waste +31.4

Peanut husk +28.1

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Rice straw 30 Unknown Field Loam 150 No effect Shen et al. (2014)

70.5 150

30 Clay loam 120

70.5 120

Wheat straw 10 350–550 Field Loam 0 +12 Zhang et al. (2010)

300 +8.8

40 0 +14

300 +12.1

Wheat straw 10 350–550 Field Loam 300 +9.2 Zhang et al. (2012)

20 300 +10.5

40 300 +11.8

Wheat straw 10 350–550 Field Loam 300 +28.6 Zhang et al. (2013a)f

20 300 +16.7

40 300 +21.4

4.5 350–550 Pot Unknown 250 +6.1 Zhang et al. (2013a)

9.0 250 +22.3

aBiochar was produced from wood residues (Tectona grandis L. and Pterocarpus macrocarpus Kurz) by earth mound method (Booth, 1983).bAverage of grain yields in 2009 and 2010 seasons.cAverage of grain yields from 2005 to 2008 wet seasons.dSurvey study from six locations in China.eOrchard biochar produced from slow pyrolysis at 550°C, while tree conifer biochar produced from gasification at 1000°C.fAverage of grain yields from 2010 to 2012 seasons.

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Fig. 3 Mean effect size on cumulative CO2 from paddy soils treated with biochar amonggroups of application rate (A), feedstock (B), and pyrolysis temperature (C). Symbols rep-resent mean effect sizes with 95% confidence intervals (horizontal bars). The numbersshown in parentheses correspond to observations in feedstock class upon which thestatistical analysis is based.


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Fig. 4 Mean effect size on cumulative CH4 from paddy soils treated with biochar amonggroups of application rate (A), feedstock (B), and pyrolysis temperature (C). Symbols rep-resent mean effect sizes with 95% confidence intervals (horizontal bars). The numbersshown in parentheses correspond to observations in feedstock class upon which thestatistical analysis is based.


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Fig. 5 Mean effect size on cumulative N2O from paddy soils treated with biochar amonggroups of application rate (A), feedstock (B), and pyrolysis temperature (C). Symbols rep-resent mean effect sizes with 95% confidence intervals (horizontal bars). The numbersshown in parentheses correspond to observations in feedstock class upon which thestatistical analysis is based.


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Meta-analysis showed that BC produced at a pyrolysis temperature of

550–600°C decreased cumulative CO2, CH4, and N2O emissions from rice

paddies with �1.838 to �0.098, �0.833 to 0.008, and �2.313 to �0.830

CI95%, respectively, compared to the control (Figs. 3–5). The BCs producedat high pyrolysis temperatures (>500°C) generally showed a relatively high

efficacy in reducing GHG emissions.

3.4 Heavy Metal Bioavailability and Translocation in RiceApplication of wheat straw BC at relatively higher application rates led to the

reduction of available Cd and Pb forms in rice paddy soils (Fig. 6 and

Table 2). According to the published literature, the wheat straw was the

most widely used feedstock for BC (39 observations) for reducing the avail-

able Cd by 36.6% and Pb by 29.5% with medium to very large effects of

�0.511 and �3.2 E�, respectively (�0.786 to �0.235 and �4.97 to

�1.687 CI95%, respectively). The wheat straw BC significantly decreased

Cd in rice grains by 43.6% with a medium effect size of �0.686. At the

BC application rates of 20–30 t ha�1, the largest decreases of available Cd

Fig. 6 Mean effect size on soil available Cd (A and B) and Pb (C and D) contents and ricegrains in paddy soils treated with biochar among groups of application rate. Symbolsrepresent mean effect sizes with 95% confidence intervals (horizontal bars). The num-bers shown in parentheses correspond to observations in feedstock class upon whichthe statistical analysis is based.


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and Pb in soil were observed with a large negative effect size of �0.790 and

�5.490, respectively.

4. DISCUSSION

The pyrolysis temperature is a main factor for determining the physical

properties of BC which affect crop growth and soil quality (Lehmann et al.,

2009, 2015; Ok et al., 2015b). In general, the increasing pyrolytic temper-

ature in BC production enhances the aromatic C structures and increases a

pH value of BC; however, available fractions of C and other nutrients can be

decreased (Ahmad et al., 2014; Chen et al., 2014; Clough et al., 2013;

Oleszczuk et al., 2016). Pyrolysis condition is also determining the surface

area and pore volume size of BC, which control the contained nutrients,

and/or its adsorption capacity of heavy metals in soils (Chen et al., 2014;

Rajapaksha et al., 2016).

4.1 Rice Paddy Soil pH and ProductivityRice paddy is conventionally subjected to flooding after the transplanting of

nursery seedling (Farooq et al., 2009; Liu et al., 2012). Different chemical

and biological processes are observed in the paddy soils due to the redox

condition, compared to the cropland soils (Rinklebe et al., 2016). The addi-

tion of BC assists in controlling fermentation and hydrolysis of organic mat-

ter (OM), dissolved organic carbon (DOC), and dissolved inorganic carbon

(DIC) in the soil (Frohne et al., 2014; Rinklebe et al., 2016). The BC may

also decrease DOC of floodplain soils resulting from carbon consumption

under oxic conditions (Frohne et al., 2014; Rinklebe et al., 2016). In addi-

tion, the BC improves soil fertility by increasing soil water-holding capacity,

enhancing crop available nutrients, and reducing bulk density, and immo-

bilizes inorganic contaminants in the soil (DeLuca et al., 2015; Lee et al.,

2015; Lehmann et al., 2015; Ok et al., 2015a,b), thereby potentially increas-

ing a grain yield (Jeffery et al., 2011). Themajor interaction between BC and

soil particles is illustrated in Fig. 7.

A significant increase in rice yield was reported following the addition of

BC, which may lead to the enhancement of soil environment such as earth-

worm population, microbial biomass, and enzyme activities (Cayuela et al.,

2014). An increase of microorganisms can help increase crop available nutri-

ent and root proliferation through the acceleration of soil OM decomposi-

tion (DeLuca et al., 2015; Lehmann et al., 2011; Noguera et al., 2010). In

this study, the increasing rice grain yield by BC was observed, mainly

resulted from soil acidity correction. This result agreed with a study of


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Crane-Droesch et al. (2013) that soil acidity, soil C, and pyrolysis temper-

ature of BC had significant impacts on grain yield, whereas the effect of clay

content was relatively smaller. The BC increased soil wettability, equilib-

riummoisture content, and release of nutrients from soil particles as reported

previously (Kang et al., 2016; Li et al., 2016; Ojeda et al., 2015). These

results might be additional reasons for the increased rice yield along with

the BC addition (Table 2). There was a positive linear correlation between

characterization of BC and moisture sorption, owing to the improved soil

physicochemical properties (Lin et al., 2016).

4.2 Greenhouse Gas EmissionsApplication of BC is one of the emerging C-negative technologies; how-

ever, both positive and negative impacts of BC on GHGs have been

Fig. 7 Schematic diagram of biochar and soil interaction. Biochar (BC) adsorbsmicrobes, inorganic nutrients, and soil organic matter (A): (1) BC increases water reten-tion because of porous structure of BC particles; (2) BC sorbs inorganic nutrients, owingto increase in plant available nutrients; and (3) BC increases soil exchangeable cationsand neutralizes the acidity in acidic and very acidic soils. A basic model of BC and soilorganic matter (SOM) interaction: BC and labile SOM bind soil particles and form stablemacroaggregates, contributing to soil structure improvement (B).


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reported in many studies (Cayuela et al., 2014; Liu et al., 2011; Ly et al.,

2015; Sun et al., 2016). Meta-analysis revealed a negligible effect of BC

on CO2 emission in some cases because of the slow decomposition rate

of BC as reported by previous studies (Awad et al., 2016; Kuzyakov

et al., 2009, 2014). In contrast, the rice husk BC increased CO2 emission

from soil (Table 2 and Fig. 3). The provision of favorable conditions by

adding BCs such as optimum soil pH and soil–water–air supplies, and high

nutrient supplies may increase the inherent OM decomposition and CO2

emission in rice paddies (Haefele et al., 2011; Koyama et al., 2016; Zech

et al., 1997). The decomposition of labile C in BC enhanced the CO2 emis-

sion (Haefele et al., 2011; Lehmann et al., 2009). Results revealed that wood

BCs had negative impacts on CO2 emission. Awad et al. (2016) reported that

wood BC suppressed the decomposition of 14C-labeled plant residues based

on CO2 emission due to adsorption of OC on BC surfaces or occlusion of

OM in soil aggregates.

It is cleared from the results that the negative mean effect sizes of CH4

emission from rice paddies were attributed to the addition of rice husk and

maize straw BCs (Table 2 and Fig. 4). Furthermore, it has been reported that

the application of rice husk BC at 4% (�80 t ha�1) improved paddy soil

physical properties, owed to the reduction of methane emission by 55%

because of the CH4 oxidation by methanogenic bacteria (Pratiwi and

Shinogi, 2016). Similar reason for CH4 emission reduction was also reported

in studies by Liu et al. (2011) and Feng et al. (2012). However, the positive

mean effect sizes of CH4 emission from rice paddies might be attributed to

the introduction of soluble organic carbon by BC. This may because of the

methanogen activity and CH4 production primarily depend on readily avail-

able carbon substrates in BCs produced at pyrolysis temperatures less than

600°C (Bian et al., 2013b; Van der Gon, 1995; Wang et al., 2017). An

increase of the water-filled soil porosity besides also contributed to increase

CH4 by enhanced methanogenesis in anoxic conditions (Wang et al., 2017).

The negative mean effect size of BC on N2O emission results from var-

ious factors (Table 2 and Fig. 5). For example, Rogovska et al. (2011) and

Oomori et al. (2016) reported that BC application led to a substantial

decrease in N2O emission due to the reduction in soil compaction and bulk

density. Moreover, the addition of BC may inhibit the denitrification of

N2O by interacting with reactive NO2� or N2O in a soil (Cayuela et al.,

2014, 2015; Rubasinghege et al., 2011), adsorbing NO3� (Baggs et al.,

2000; Cayuela et al., 2013; Novak et al., 2010), and reducing the soil C:

N ratio (Huang et al., 2004). While, the positive mean effect size of BC


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onN2O emissionmay result from the increase in soil porosity and aeration as

a critical factor to regulate N2O production and diffusion to the atmosphere

from a waterlogged soil because the water-filled porosity governs the aera-

tion and availability of oxygen to soil nitrifiers and denitrifiers (Heincke and

Kaupenjohann, 1999; Oomori et al., 2016). Additionally, the process of

wetting/drying can enhance the N2O emission due to the changes in oxy-

gen availability and predominant process via nitrification and denitrification

(Cayuela et al., 2013; Zhou et al., 2016). Hence, the BC addition may help

reduce N2O emission by increasing soil porosity (Case et al., 2012; Novak

et al., 2016).

The increase of BC pyrolysis temperature establishes a more systematic

skeleton of BC by enhancing the aromatic carbon structures (Zimmerman,

2010), whereas available carbon fractions and other nutrients decrease with

increasing temperature (Ahmad et al., 2014; Atkinson et al., 2010; Clough

et al., 2013). Hence, microbial activity leading to GHG emissions may have

a negative correlation with the pyrolysis temperature (Lehmann et al., 2011).

As a result, BCs produced at high temperatures have a greater ability to alle-

viate/adsorb GHG emissions in rice paddies because of high surface area and

large pore size of BCs (Wang et al., 2017). For instance, BCs produced at

500°C and 700°C mitigate CH4 and N2O emission by increasing soil pH

and thus decreasing soil microorganisms underpinning GHG-related trans-

formation processes (Wang et al., 2017). Because of increased soil pH, the

maximal impact of BCs on GHG emission was found in slightly acidic and

acidic rice paddy soils as explained by Cayuela et al. (2014, 2015). Nonethe-

less, the majority of the published studies used in this meta-analysis did not

provide the details of BC pyrolysis conditions and process, making it difficult

to draw up a clear conclusion. Further research is required to select the

proper pyrolysis temperature and production conditions of BC, together

with mechanistic evidence, to mitigate the GHG emissions from rice

paddies.

4.3 Heavy Metal ImmobilizationBC protects the plant root system in contaminated soils by sorption of the

toxic compounds onto its surface as reported by Chen et al. (2016) and

Lehmann et al. (2011). The BC treatment precipitates toxic metals through

an increase in the soil pH and net negative charge of soil constituents as

reported previously (Ahmad et al., 2014; Karami et al., 2011; Rinklebe

et al., 2016). The changes of availability of heavy metals are dependent


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on the feedstock type, pyrolysis temperature, and application rate of BC as

well as metal characteristics (Ahmad et al., 2014; Tsang et al., 2016). For

instance, wood-derived BC increased the availability of Cu and As, while

immobilized Cd and Zn as compared to the control (Ahmad et al., 2017;

Beesley et al., 2010). Available P also is a critical parameter controlling

the solubilization or immobilization of heavy metals in soils (Zhang et al.,

2013b). High pyrolysis temperature (550°C) decreased the release of P, K,

and Ca from BC as compared to the low pyrolysis temperature (Uchimiya

et al., 2012). Electrostatic attraction, ion exchange, surface complexation,

and precipitation of cationic metals are the possible mechanisms of BC inter-

actions with heavy metals (Ahmad et al., 2014; Rajapaksha et al., 2016;

Rizwan et al., 2016). Meta-analysis of this study reinforced that BC can effi-

ciently remediate heavy metals from contaminated soils. Specifically, porous

structure and high surface area of BC could mainly immobilize Cd and Pb

through adsorption and complexation as reported by (Hu et al., 2016).

4.4 Meta-analysisThere were very few studies of meta-analysis about the effect of BC on rice

yield and GHG emission from rice paddies (Biederman and Harpole, 2013;

Wang et al., 2011), while no attempt has been made to meta-analyze the

effect of BC on immobilization of heavy metals in paddy soils and their

translocation in rice grains. This study provides the current information

about the impact of BC on rice grain yield, net GHG emissions, and

bioavailability of Cd and Pb in BC-amended paddy soils compared to con-

trol data from the published articles. The selection of management for

reducing the emission of CH4/N2O from rice fields is very important

(Sanchis et al., 2012). Meta-analysis has been recently conducted using a

smaller database to evaluate BC effects on plant productivity and nutrient

cycling (Biederman and Harpole, 2013; Cayuela et al., 2014, 2015;

Crane-Droesch et al., 2013; Harpole and Biederman, 2014). For example,

the several studies of meta-analysis revealed that the addition of BC

decreased N2O emission by�54% (Cayuela et al., 2014, 2015) and the trend

of N2O reduction was similar to the outcome from field studies showing

�28% reduction of N2O.

5. CONCLUSIONS AND RESEARCH NEED

Application of BCs derived from all feedstocks at pyrolysis tempera-

tures of 450–500°C showed significant positive changes in pH, rice grain


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yield, and CH4 emission. Applying BCs to rice paddies substantially reduced

N2O emissions, while the CO2 emission was marginally affected. All BCs

from any feedstock or application rate significantly decreased the accumu-

lation of Cd and Pb in rice grains. Thus, the BC application can be a

pragmatic option to meet the challenge of sustainable global rice production.

However, the understanding of interactions between BC and its dependent

variables such as rice seedlings, iron reduction, soil quality and productivity,

microbial communities and activities, and toxicity mitigation under varying

redox conditions due to flooding and drainage in rice paddies should com-

prehensively be reinforced in the future.

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FURTHER READINGChen, W., Wang, Y., Zhao, Z., Cui, F., Gu, J., Zheng, X., 2013b. The effect of planting

density on carbon dioxide, methane and nitrous oxide emissions from a cold paddy fieldin the Sanjiang Plain, northeast China. Agric. Ecosyst. Environ. 178, 64–70.


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