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Article

Effects of Biochar on Methane Emissions and Crop Yields in East Asian Paddy Fields: A Regional Scale Meta-Analysis

by
Jong-Mun Lee
,
Hyun-Cheol Jeong
,
Hyo-Suk Gwon
,
Hyoung-Seok Lee
,
Hye-Ran Park
,
Guen-Sik Kim
,
Do-Gyun Park
and
Sun-Il Lee
*
National Institute of Agricultural Sciences, Wanju 55365, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(12), 9200; https://doi.org/10.3390/su15129200
Submission received: 28 April 2023 / Revised: 26 May 2023 / Accepted: 30 May 2023 / Published: 7 June 2023
(This article belongs to the Special Issue Green Technologies for Environment, Health and Sustainable Development)
Browse Figures
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Abstract

:
Biochar emerged as a potential solution to mitigating greenhouse gas emissions, though previous studies obtained variable results regarding its effects on methane (CH4) emissions and crop yields. Global meta-analyses were conducted regarding the effectiveness of biochar, though regional meta-analyses are still needed. We performed a meta-analysis of 43 published papers to obtain the central tendency of the response to biochar application in East Asian rice paddies. Biochar application significantly reduced methane emissions while increasing the soil organic carbon (SOC) content and crop yield. We identified the most significant influencing factors on the CH4 emissions, SOC content, and crop yield. Our findings provide a scientific basis for the application of biochar to East Asian rice paddies, as well as to study the effects of biochar application in East Asian rice paddies. The numbers in parentheses represent the sample sizes.

1. Introduction

Climate change is having a serious impact on global food production and greenhouse gas (GHG) emissions. In particular, it disrupts the soil, which is the largest carbon reservoir in terrestrial ecosystems [1,2]. Mitigating climate change and maintaining food security are considered major challenges for agriculture worldwide [3]. Agriculture accounts for 10–12% of total GHG emissions, which is equivalent to 5.1–6.1 Pg per year [4,5], as well as 50% of global methane (CH4) emissions [6]. CH4 is a significant GHG with a global warming potential that is 28 times higher than that of carbon dioxide (CO2) [7].
Rice paddies are considered a major artificial source of CH4, and they may also affect nitrous oxide (N2O) emissions through intermediate water management practices [7,8]. Therefore, the mitigation of GHG emissions from rice paddies is of great importance. Rice (Oryza sativa L.) is an important staple crop that >50% of the world’s population depends on, and its production is predicted to increase to >700 million tons by 2025 [9,10]. Asia produces 90% of the world’s rice, and East Asian countries alone account for 33.6% of total production [10]. During the rice-growing season, the soil in paddy fields is submerged in freshwater, and CH4 is generated via the anaerobic decomposition of organic matter [11]. Fertilizers increase soil fertility and crop yields to meet the demand for greater crop production, though they also inevitably increase CH4 emissions [12,13,14]. An effective solution is needed to maintain high crop productivity while mitigating GHG emissions in East Asia. Recently, the application of biochar was considered as a potential means of increasing crop yields while suppressing or offsetting CH4 emissions [15,16]. However, some conflicting results were reported regarding its effectiveness.
A meta-analysis is a statistical approach to synthesizing data from individual studies conducted under various conditions [17] that is used to systematically quantify results obtained at various spatial and temporal scales [18]. Meta-analyses on the effectiveness of biochar at GHG mitigation were previously conducted on a global scale [17,19,20,21]; however, they also obtained conflicting results. For example, Jeffery et al. [22] reported a 12% decrease in CH4 emissions from biochar application in paddy fields. However, He et al. [23] found no significant difference in CH4 emissions through their meta-analysis, while Zhang et al. [24] reported a 15% increase in CH4 emissions from biochar application. For a given farmland, the trends and amounts of GHG emissions depend on the management practices, climate, and soil conditions [25]. Thus, comprehensive analyses at the regional level are necessary to compare studies under similar climatic conditions and using similar farming methods. Although some studies reviewed the overall impact of biochar on the global soil carbon cycle and CH4 emissions [22,24,25,26], knowledge of the effect of biochar on a regional scale is insufficient.
In this study, our objective was to comprehensively and quantitatively understand the effects of biochar on rice paddies in East Asia. We performed a meta-analysis on related studies in the literature to determine the effects of biochar on CH4 emissions, soil organic carbon (SOC) content and crop yield, as well s to evaluate the effects of different experimental conditions, biochar characteristics, and soil properties so that we could identify key influencing factors.

2. Methodology

2.1. Data Collection

We collected studies published from 2010 to March 2022 using the following search engines: Google Scholar, ScienceDirect, SCOPUS, PubMed, and Korea Citizenship Index. We used the following search terms: “biochar”, “methane”, “CH4”, “greenhouse gas”, “soil organic carbon”, “crop yield”, “agricultural land”, “cropland”, “rice field”, and “paddy”. To standardize the dataset, we established selection criteria:
  • Experimental studies comparing at least one control group with a treatment group;
  • Studies presenting the physicochemical characteristics of biochar;
  • Studies presenting country and climate zones;
  • Studies presenting standard deviation or standard errors;
  • Studies presenting the physicochemical characteristics of soil.
Most of the studies presented emission data in tables. In cases where the emission and standard deviation data were presented in figures, the final data were extracted by running the software Plot Digitizer (Ver. 2.6.9). The cumulative CH4 emission values presented in each study were extracted, and different units were converted to kilograms per hectare (kg ha−1). Table 1 presents the 40 published studies that satisfied all of the above selection criteria.
The data categories in Table 2 were used to evaluate the effects of biochar application and identify the following potential influencing factors [17,25]: experimental conditions, biochar characteristics, and soil properties. The following experimental conditions were considered: type (field, incubation, and pot), cropping system (rice, rotation, and none), duration (≤0.5 years, ≤1 year, ≤2 years, and >2 years), and fertilizer application rate (≤150, ≤300, ≤500, and >500 kg ha−1) [24]. The following biochar characteristics were considered: feedstock [herbaceous (straw, bamboo and green waste), biosolids (sewage sludge from treatment plants), wood (willow, pine, oak, sycamore, and wood mixture), manure (pig, poultry, and cattle), and lignocellulosic waste (rice husk and nuts shells)], pyrolysis temperature (≤400, ≤500, ≤600, and >600 °C), pH [≤6.5 (acidic), 6.6–7.3 (neutral), and >7.3 (alkaline)], C/N ratio (≤50, ≤150, ≤300, and >300), and application rate (≤10, ≤20, ≤30, ≤40, and >4 ton ha−1) [19]. The following soil properties were considered: pH [≤6.5 (acidic), 6.6–7.3 (neutral), and 7.3 (alkaline)], C/N ratio (≤10 and >10), and soil types [fine (silt clay, clay, sandy clay), medium (loam, clay loam, silt, silty clay loam, and silt loam), and coarse (sandy clay loam, sandy loam, and loamy sand) soil] [66].

2.2. Meta-Analysis

We used the response ratio (RR) to standardize the results obtained from individual studies [67]:
ln (RR) = ln (Xt/Xc) = ln (Xt) − ln (Xc)
where Xt represents the treatment result (i.e., application of biochar), and Xc represents the control result. If a selected study only presented the standard error (SE), it was converted into the standard deviation (SD) as follows:
SD = SE   ×   n
where n represents the number of repetitions. The variance (v) was estimated as follows:
v = SDt2/NtXt2 + SDC2/NCXC2
where SDt and SDC are the standard deviations of the treatment and control groups, respectively, and Nt and NC are the sample numbers of these groups, respectively.
The mean effect size of the dataset was analyzed using R software, and the 95% confidence interval (CI) was calculated to determine the statistical significance of any differences in the CH4 emissions and crop yield between the treatment and control groups. For ease of understanding, the effect size, which was derived as a response ratio, was converted into a percentage [21,26]:
RR = (eLn(RR) − 1) × 100%
where RR is the weighted response ratio, which is the percentage change (%) of the treatment compared with the control.

2.3. Statistical Analysis

We performed the paired t-test in SPSS (Version 25, IBM, Armonk, NY, USA) to examine the statistical significance of differences in CH4 emissions and crop yield with and without biochar application. We performed Pearson’s correlation analysis using the corrplot packages in R (version 4.2.2) to calculate the effect sizes of the experimental conditions, biochar characteristics, and soil properties on the CH4 emissions, SOC content, and crop yield.

3. Results

3.1. Effects of Biochar Application on CH4 Emissions, SOC Content, and Crop Yield

Table 3 presents the results of applying biochar in East Asian paddy fields. On average, CH4 emissions decreased by 22.9% from 154.4 ± 14.7 kg ha−1 to 118.6 ± 10.7 kg ha−1 (95% CI = −24.9%, 20.9%; p < 0.000), the SOC content increased by 40.5% from 17.7 ± 1.0 Mg ha−1 to 24.3 ± 1.2 Mg ha−1 (95% CI = 38.5%; 42.5%, p < 0.000), and the crop yield increased by 16.2% from 9.9 ± 0.9 Mg ha−1 to 11.4 ± 1.0 Mg ha−1 (95% CI = 15.2%, 17.2%; p < 0.000).

3.2. Effects of Experimental Conditions

Figure 1 shows the effects of the experimental conditions. Across experimental conditions, the CH4 emissions decreased by 24.4%. The CH4 emissions decreased by 29.5% to 20.5%, depending on the experiment type. The greatest reduction in CH4 emissions was observed with incubation experiments (29.5%; 95% CI = 32.5%, 26.5%), followed by pot and field experiments. With regard to the cropping system, the greatest reduction in CH4 emissions was observed without crops (41.1%; 95% CI = 53.8%, 28.4%), and the lowest reduction was observed with crop rotation (14.8%; 95% CI = 17.8%, 11.8%). With regard to the duration of the studies, the effect of biochar application increased significantly when the research period exceeded 2 years (40.5%; 95% CI = 42.5%, 38.5%). With regard to the fertilizer application rate, the greatest reduction in CH4 emissions with biochar application was observed at a rate of 150–300 kg ha−1 (27.4%; 95% CI = 29.4%, 25.4%). Across experimental conditions, the SOC content increased by 36.3% (95% CI = 31.2%, 41.4%). Among experiment types, the increase in SOC content was highest in field experiments (47.7%; 95% CI = 45.7%, 49.7%). Among cropping systems, the increase in SOC content was higher with a single crop (41.9%; 95% CI = 40.9%, 42.9%) than with crop rotation. In regard to the study duration, the increase in SOC content was highest when biochar was applied for more than 2 years (60.0%; 95% CI = 54.9%, 65.1%). Across experimental conditions, the crop yield increased by 12.7% (95% CI = 14.4%, 21.0%). Among experiment types, the increase in crop yield was significant in pot experiments (44.8%; 95% CI = 41.8%, 47.8%), but was not significant in field experiments (−1%). Among cropping systems, the increase in crop yield was greater with crop rotation (40.5%; 95% CI = 36.4%, 44.6%) than with a single crop. The crop yield increased by 5.1–27.1% (95% CI = 2.1%, 8.1% to 24.1%, 30.1%) for study periods lasting for 0.5 years or more. The crop yield also increased with the fertilizer application rate.

3.3. Effects of Biochar Characteristics

Figure 2 shows the effects of the biochar characteristics. Across biochar characteristics, CH4 emissions decreased by 12.2% (95% CI = 24.3%, 0.01%). Among feedstocks, the herbaceous feedstock resulted in the greatest reduction in CH4 emissions (25.9%; 95% CI = 28.9%, 22.9%). Among pyrolysis temperatures, the greatest reduction in CH4 emissions was observed at 400–500 °C (30.9%; 95% CI = 33.9%, 27.9%). Among C/N ratios, the greatest reduction in CH4 emissions was observed at C/N ≤ 50 (32.3%; 95% CI = 35.3%, 29.3%), and the amount of CH4 emissions increased with the C/N ratio. CH4 emissions decreased proportionally to the biochar application rate, with the greatest reduction observed at 20–30 ton ha−1 (42.9%; 95% CI = 49.1%, 36.7%). Across biochar characteristics, the SOC content increased by 47.7% (95% CI = 35.0%, 60.4%). Among feedstocks, the greatest increase in SOC content was observed with woody biomass (89.6%; 95% CI = 46.3%, 135.9%). Among pyrolysis temperatures, the greatest increase in SOC content was observed at <400 °C (232.0%; 95% CI = 197.0%, 267.0%). The SOC content increased proportionally with the biochar application rate. Across biochar characteristics, the crop yield increased by 6.2% (95% CI = 2.1%, 10.3%). Among feedstocks, the greatest increase in the crop yield was observed with woody biomass (19.7%; 95% CI = 11.4%, 28.0%). Among pyrolysis temperatures, the greatest increase in the crop yield was observed at 400–500 °C (16.2%; 95% CI = 15.2%, 17.2%). Among C/N ratios, the greatest increase in the crop yield was observed at C/N < 50 (19.7%; 95% CI = 17.7%, 21.7%). Among biochar application rates, the greatest increase in the crop yield was observed at 20–30 ton ha−1 (11.6%; 95% CI = 8.6%, 14.6%).

3.4. Effects of Soil Properties

Figure 3 shows the effects of the soil properties. Across the soil properties, the CH4 emissions decreased by 11.3% (95% CI = 41.0%, 14.8%), though the decrease was not significant. Among soil pH values, the greatest reduction in CH4 emissions was observed with alkaline soils (25.9%; 95% CI = 21.8%, 30.0%). Among C/N ratios, the greatest reduction in CH4 emissions was observed at C/N < 10 (28.8%; 95% CI = 22.6%, 25.0%). Among soil types, CH4 emissions decreased in loamy soils (16.5%; 95% CI = 12.4%, 20.6%) but increased in sandy soils (105.4%; 95% CI = 84.5%, 126.3%). Across soil properties, the SOC content increased by 46.2%. The greatest increases in the SOC content were observed in neutral soils (78.6%; 95% CI = 65.9%, 91.3%) and with C/N < 10 (60%). Across soil properties, the crop yield increased by 8.3% (95% CI = −2.2%, 18.8%), though the increase was not significant. The greatest increase in crop yield was observed in alkaline soils (36.3%; 95% CI = 33.3%, 39.3%). The crop yield increased at C/N < 10 (7.3%; 95% CI = 5.3%, 9.3%), but did not differ significantly at C/N ≥ 10 or higher.

3.5. Correlation Analysis

Figure 4 shows the results of the correlation analysis. The CH4 emissions showed significant positive correlations with biochar total carbon (BTC, r = 0.72, p < 0.001), biochar pH (BPH, r = 0.77, p < 0.001), soil total carbon (STC, r = 0.73, p < 0.001), soil total nitrogen (STN, r = 0.64, p < 0.01), and N-fertilizer (NF, r = 0.77, p < 0.001), as well as significant negative correlations with pyrolysis temperature (PT, r = −0.60, p < 0.01), soil pH (SPH, r = −0.51, p < 0.05), and Soil C/N (SCN, r = −0.53, p < 0.05). The SOC content showed significant positive correlations with the biochar application rate (BAR, r = 0.92, p < 0.001), BTC (r = 0.44, p < 0.05), and NF (r = 0.41, p < 0.05). The crop yield had significant positive correlations with SPH (r = 0.61, p < 0.001) and SCN (r = 0.63, p < 0.01), as well as significant negative correlations with BTC (r = −0.51, p < 0.01), BTN (r = −0.53, p < 0.01), STC (r = −0.53, p < 0.05), STN (r = −0.59, p < 0.01), and NF (r = −0.63, p < 0.01).

4. Discussion

4.1. Effect of Biochar on CH4 Emissions

Our results showed that biochar application significantly decreased CH4 emissions by 22.9% in East Asian rice paddies. This result is similar to the trend observed by previous global scale meta-analyses on biochar [22,60,68], although they observed smaller reductions of 11.2–17.5% [60] and 12% [22]. Biochar may reduce CH4 emissions by improving the aeration of the rice paddies, which would significantly increase the abundance of methanotrophic proteobacteria and decrease the proportion of methanogenic archaea [29,69,70].
The greater reduction in CH4 emissions observed at the regional scale can most likely be attributed to the biochar feedstock specific to East Asia [23,26,71]. Other countries often produce biochar from wood and manure [19,72,73], while East Asian countries often use herbaceous feedstock (Figure 2). Manure-based biochar was shown to increase microbial growth, while plant-based biochar tends to inhibit microbial growth [74]. Manure-based biochar contains protein-derived compounds and has a relatively high nitrogen ratio, which can stimulate microbial activity, depending on the soil characteristics, and increase GHG emissions via respiration [23,26,71,74]. Shakoor et al. [26] found that manure-based biochar actually increased CH4 emissions, while herbaceous biochar reduced CH4 emissions. However, they used a relatively small sample size of biochar produced from wood and manure and compared to herbaceous biochar; thus, there may be problems with their quantitative analysis, and further research may be necessary.
Many studies considered the short-term effects of applying biochar on CH4 emissions [68,71,75]. However, uncertainty over the long-term effects led recent studies to evaluate the application of aged biochar [76]. Nan et al. [39] found that biochar reduced CH4 emissions significantly in long-term experiments and that applying a small amount of biochar annually was more effective than applying a large amount at once. These results are consistent with our results, showing that the continuous application of biochar for more than 2 years obtained the greatest reduction in CH4 emissions. However, some studies showed that the effect of biochar is short-term and decreases over time, as the C substrate is gradually consumed by micro-organisms [29,77,78]. This issue highlights the need for further research on the effect of the duration of biochar application on CH4 mitigation.
Biochar affects the CH4 flux of soil through the adsorption of CH4-related substances on its porous surface and its improvement of soil aeration, which promotes CH4 oxidation [29,74]. Biochar is more effective at reducing GHG emissions with a high C/N ratio than with a low C/N ratio [79]. However, in anaerobic environments, the unstable carbon in biochar can be used to generate CH4 [58]. Similarly, Feng et al. [29] reported greater CH4 mitigation when biochar was applied to cultivated soils with a low C/N ratio. These reports are consistent with our results, showing significant positive correlations between CH4 emissions and both BTC (r = 0.72, p < 0.001) and STC (r = 0.73, p < 0.05).
Biochar was also found to have a greater effect on CH4 emissions at soil C/N ratios of ≤10 rather than >10. Under aerobic conditions, a low C/N ratio can increase N2O emissions [80]. Under anaerobic conditions, however, a significant negative correlation was observed between CH4 emissions and the soil C/N ratio (r = −0.53, p < 0.05) (Figure 4). These results indicate the need for further investigation of the effects of the soil C/N ratio on GHG emissions under both anaerobic and aerobic conditions.

4.2. Effect of Biochar on SOC Content

We found that applying biochar in East Asian rice paddies increased the SOC content by 40.5%, which agrees with previous studies [81,82]. Our results are similar to the 40% increase reported by Liu et al. [82], and are greater than the results obtained via global scale meta-analyses, which reported increases of 9.3–34.8% [20,21,60]. The effect of biochar on SOC content depends on the amount of biochar applied and the soil properties [19,72,83]. We found that increasing the biochar application rate increased the SOC content, and the highest increase of 78.6% was observed in neutral soil (Figure 3 and Figure 4). In addition, a significant positive correlation was observed between BAR (r = 0.92, p < 0.001) and the SOC content (Figure 4). Biochar had limited effects on the SOC content at low application rates of less than 10 ton ha−1, though its effects increased significantly at application rates of 10 ton ha−1 or more. Lee et al. [80] reported that short-term experiments showed that applying biochar to carbon-poor soils increased the SOC content by more than 10% compared with fertile soils. However, Sheng and Zhu [84] reported that the long-term carbon sequestration effect of biochar can be affected by other factors, such as the total carbon and the clay content. Further research is needed on the long-term effects of the biochar application rate and soil characteristics on SOC content.

4.3. Effect of Biochar on Crop Yield

Our results showed that applying biochar in East Asian paddy fields increased the crop yield by 16.2%. This result is greater than the increase of 15.1% for paddy fields treated only with biochar, but less than the increase of 48.4% for paddy fields treated with both biochar and fertilizer [20]. Global meta-analyses that did not consider fertilizer application obtained lower increases in the crop yield of 9.0–11.3% [60,85]. On the other hand, [86] suggested that applying only compost under low-moisture conditions could result in a 7–38% reduction in crop yields. These results indicate that biochar and fertilizer should be applied together to increase crop yields [13]. Biochar has an excellent capacity to hold nutrients and moisture because of its high surface area, though its performance varies depending on the feedstock [15,86,87]. However, excessive fertilizer use does not guarantee a sustained increase in yield and may result in low nutrient utilization efficiency [88]. The correlation analysis showed a significant negative correlation between the fertilizer application rate and the crop yield (Figure 4). This result may occur because the nutrient-holding capacity of biochar can result in unwanted side effects if the fertilizer application rate is excessive.
Studies using pot experiments found that applying biochar had a greater effect on the crop yield than those using field experiments. This result may be because pot experiments allow for easier control of variables, have shorter experimental periods, and facilitate large biochar application rates [89]. Biochar can improve crop yields by improving soil fertility [15]. Biochar has a porous structure that increases the moisture and nutrient retention capacities of soil [90]. This fact not only reduces nutrient leaching from the soil, but also increases nutrient availability for plants [91,92]. Thus, biochar can improve soil structure, reduce soil compaction, and enhance root growth and nutrient uptake in plants. Biochar was shown to alleviate soil acidity, which may help plants that prefer a more neutral soil pH [20]. More research is needed to fully understand the relationship between biochar and crop yields.

5. Conclusions

In this study, we conducted a meta-analysis to quantify the effect of biochar on the CH4 emissions, SOC content, and crop yields of paddy fields in East Asia. As a result, we found that the biochar application in the paddy field can reduce methane emissions by 22.9%. Furthermore, biochar increases SOC by 36.3% and increases crop yield by 16.2%. Our results suggest that biochar can be an effective solution for mitigating the GHG emissions and increasing the SOC content and crop yield in this region. This study can be used as a foundation for predicting the effects and managing the application of biochar on East Asian paddy fields in the future.

Author Contributions

Conceptualization, S.-I.L.; Data curation, J.-M.L.; Formal analysis, J.-M.L.; Investigation, J.-M.L. and G.-S.K.; Methodology, J.-M.L. and S.-I.L.; Project administration, J.-M.L.; Supervision, S.-I.L.; Validation, S.-I.L.; Visualization, J.-M.L.; Writing—original draft, J.-M.L.; Writing—review & editing, J.-M.L., H.-C.J., H.-S.G., H.-S.L., H.-R.P., D.-G.P. and S.-I.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was carried out with the support of the Cooperative Research Program for Agriculture Science and Technology Development (PJ01559203), Rural Development Administration, Republic of Korea.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Response of (a) methane (CH4), (b) soil organic carbon (SOC), and (c) crop yield to application of biochar under experimental type, cropping system, duration, and fertilizer application rate. Numbers in parentheses represent sample size. The squares in color in the figure caption are an overall average value on properties.
Figure 1. Response of (a) methane (CH4), (b) soil organic carbon (SOC), and (c) crop yield to application of biochar under experimental type, cropping system, duration, and fertilizer application rate. Numbers in parentheses represent sample size. The squares in color in the figure caption are an overall average value on properties.
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Figure 2. Response of (a) methane (CH4), (b) soil organic carbon (SOC), and (c) crop yield to application of biochar under feedstock, pyrolysis temperature, soil pH, soil C/N, and biochar application rate. Numbers in parentheses represent sample size. The squares in color in the figure caption are an overall average value on properties.
Figure 2. Response of (a) methane (CH4), (b) soil organic carbon (SOC), and (c) crop yield to application of biochar under feedstock, pyrolysis temperature, soil pH, soil C/N, and biochar application rate. Numbers in parentheses represent sample size. The squares in color in the figure caption are an overall average value on properties.
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Figure 3. Response of (a) methane (CH4), (b) soil organic carbon (SOC), and (c) crop yield to application of biochar under feedstock, pyrolysis temperature, soil pH, soil C/N, and biochar application rate. Numbers in parentheses represent sample size. The squares in color in the figure caption are an overall average value on properties.
Figure 3. Response of (a) methane (CH4), (b) soil organic carbon (SOC), and (c) crop yield to application of biochar under feedstock, pyrolysis temperature, soil pH, soil C/N, and biochar application rate. Numbers in parentheses represent sample size. The squares in color in the figure caption are an overall average value on properties.
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Figure 4. Correlation between RR and influence indicators responding to CH4 emissions, SOC contents, and crop yield. SD, study duration; PT, pyrolysis temperature; BCN, biochar C/N; BTC, biochar total carbon; BTN, biochar total nitrogen; BPH, biochar pH; BAR, biochar application rate; SPH, soil pH; SCN, soil C/N; STC, soil total carbon; STN, soil total nitrogen; NF, nitrogen fertilizer; CH4, ln(RR) of methane emission; SOC, ln(RR) of soil organic carbon contents; yield, ln(RR) of crop production. A blue circle means a positive correlation and a red circle means a negative correlation.
Figure 4. Correlation between RR and influence indicators responding to CH4 emissions, SOC contents, and crop yield. SD, study duration; PT, pyrolysis temperature; BCN, biochar C/N; BTC, biochar total carbon; BTN, biochar total nitrogen; BPH, biochar pH; BAR, biochar application rate; SPH, soil pH; SCN, soil C/N; STC, soil total carbon; STN, soil total nitrogen; NF, nitrogen fertilizer; CH4, ln(RR) of methane emission; SOC, ln(RR) of soil organic carbon contents; yield, ln(RR) of crop production. A blue circle means a positive correlation and a red circle means a negative correlation.
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Table
Table 1. Description of experimental methods and physiochemical properties of soil and biochar included in this meta-analysis.
Table 1. Description of experimental methods and physiochemical properties of soil and biochar included in this meta-analysis.
AuthorYearCountryExperimental MethodCropStudy Duration (Days)FeedstockPyrolysis Temperature (°C)Biochar C/NBiochar PHBiochar Application Rate (t ha−1)Soil
pH		Soil
C/N
[27]2018ChinaFieldRice>2Herbaceous≤500≤150Alkaline≤30Acidic≤10
[28]2017ChinaFieldRice>2Herbaceous≤500≤50Alkaline≤10Acidic≤10
[29]2012ChinaPotNone≤0.5Herbaceous≤400≤50Alkaline≤30Alkaline≤10
[30]2022ChinaFieldRice≤0.5Herbaceous≤500≤150Alkaline≤10Alkaline≤10
[31]2022ChinaFieldRice≤2Herbaceous≤500≤50Alkaline≤10Acidic≤10
[32].2019ChinaFieldRice≤0.5Herbaceous≤500≤50Alkaline>40Acidic>10
[33]2020JapanPotRice≤0.5Manure≤500≤50Alkaline≤20Acidic>10
[34]2015JapanFieldRice≤0.5Herbaceous≤400≤150Alkaline≤10Neutral>10
[35]2022ChinaFieldRice≤0.5Herbaceous≤600---Acidic>10
[36]2021ChinaIncubationRice≤0.5Herbaceous≤500≤150Alkaline≤10Acidic≤10
[37]2019ChinaFieldRotation≤2Herbaceous≤500≤50Alkaline≤20Alkaline≤10
[38]2020ChinaFieldRice≤0.5Herbaceous-≤50Alkaline≤10Acidic>10
[39]2020ChinaFieldRice≤1Herbaceous≤500≤50-≤10Acidic>10
[40]2016JapanFieldRice≤1Herbaceous>600≤150Alkaline≤10Acidic≤10
[41]2017ChinaFieldRice≤0.5Herbaceous≤500≤150Alkaline≤40Alkaline≤10
[42]2020ChinaFieldRice≤0.5Herbaceous≤500≤150Alkaline≤10Acidic>10
[43]2020ChinaPotRice≤1Herbaceous≤500≤150Alkaline≤10Alkaline>10
[44]2021ChinaPotRice≤0.5Herbaceous≤500≤50Alkaline≤10Alkaline>10
[45]2014ChinaFieldRice≤0.5Herbaceous≤500≤150Alkaline≤10Acidic≤10
[46]2014JapanPotRice≤0.5Herbaceous≤400≤50Alkaline≤10Acidic>10
[47]2016ChinaFieldRice>2Herbaceous≤500≤150Alkaline≤10Neutral≤10
[48]2019ChinaFieldRice≤0.5Herbaceous≤500≤50Alkaline≤20Acidic-
[49]2020ChinaFieldRice≤0.5Herbaceous≤500≤150Alkaline≤20Alkaline>10
[50]2016ChinaFieldRice>2Wood≤500≤50-≤10Acidic≤10
[51]2020ChinaFieldRice≤1Herbaceous≤600≤50Alkaline≤10Acidic≤10
[52]2019ChinaFieldRice≤1Lignocellulosic waste≤500≤300Alkaline≤30Neutral≤10
[53]2018ChinaFieldRice>2Herbaceous≤500≤150Alkaline≤30Acidic>10
[54]2017ChinaFieldRice≤0.5Herbaceous-≤50-≤10Acidic>10
[55]2019ChinaFieldRice≤1Herbaceous≤600≤50-≤10Acidic>10
[56]2018ChinaFieldRice≤0.5Herbaceous≤500≤150Alkaline>40Acidic>10
[57]2020ChinaFieldRice≤0.5Herbaceous≤600≤50-≤10Acidic>10
[58]2012ChinaFieldRice≤0.5Lignocellulosic waste≤500≤150Alkaline≤30Neutral>10
[59]2017ChinaFieldRice≤1Herbaceous≤500≤150Alkaline≤10Alkaline>10
[60]2019ChinaFieldRotation>2Herbaceous≤500≤150Alkaline≤20Acidic>10
[61]2019ChinaFieldRotation≤1Herbaceous≤400≤150Alkaline≤10Acidic≤10
[16]2021ChinaFieldRice≤0.5Wood≤600≤50-≤10Acidic≤10
[62]2015ChinaFieldRice≤0.5Herbaceous≤600≤150Alkaline≤10Acidic>10
[63]2010ChinaFieldRice≤0.5Herbaceous≤600≤150Alkaline≤10Acidic>10
[64]2013ChinaFieldRice≤0.5Herbaceous≤600≤150Alkaline≤10Acidic>10
[65]2012ChinaFieldRice≤0.5Herbaceous≤600≤150Alkaline≤10Acidic>10
Table
Table 2. Factors categorized as predictive variables in this meta-analysis.
Table 2. Factors categorized as predictive variables in this meta-analysis.
FactorSpecific ConditionsLevels
Experimental conditionsType of experimentField; pot; incubation
Cropping systemRice; rotation; none
Duration (year)≤0.5; ≤1; ≤2; >2
Fertilizer (kg N ha−1)≤150; ≤300; ≤500; >500
Biochar
properties		FeedstockHerbaceous; lignocellulosic waste; wood; manure
Pyrolysis temperature (°C)≤400; ≤500; ≤600; >600
Biochar pH≤6.5 (acidic); 6.6–7.3 (neutral); >7.3 (alkaline)
The application rate of biochar (ton ha−1)≤10; ≤20; ≤30; ≤40; >40
Soil
properties		Soil pH≤6.5 (acidic); 6.6–7.3 (neutral); >7.3 (alkaline)
Soil C/N≤10; >10
Soil textureFine; medium; coarse
Table
Table 3. Data distribution of CH4 emission, SOC, and crop yield from our datasets.
Table 3. Data distribution of CH4 emission, SOC, and crop yield from our datasets.
DivisionMSEtp
CH4 (kg ha−1)C154.414.74.445 ***0.000
T118.610.7
SOC (Mg ha−1)C17.71.3−7.873 ***0.000
T24.21.7
Crop yield (Mg ha−1)C9.90.9−6.077 ***0.000
T11.41.0
Note: T, application of biochar; C, no application of biochar; M, mean; SE: standard error, ***, p < 0.001.
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MDPI and ACS Style

Lee, J.-M.; Jeong, H.-C.; Gwon, H.-S.; Lee, H.-S.; Park, H.-R.; Kim, G.-S.; Park, D.-G.; Lee, S.-I. Effects of Biochar on Methane Emissions and Crop Yields in East Asian Paddy Fields: A Regional Scale Meta-Analysis. Sustainability 2023, 15, 9200. https://doi.org/10.3390/su15129200

AMA Style

Lee J-M, Jeong H-C, Gwon H-S, Lee H-S, Park H-R, Kim G-S, Park D-G, Lee S-I. Effects of Biochar on Methane Emissions and Crop Yields in East Asian Paddy Fields: A Regional Scale Meta-Analysis. Sustainability. 2023; 15(12):9200. https://doi.org/10.3390/su15129200

Chicago/Turabian Style

Lee, Jong-Mun, Hyun-Cheol Jeong, Hyo-Suk Gwon, Hyoung-Seok Lee, Hye-Ran Park, Guen-Sik Kim, Do-Gyun Park, and Sun-Il Lee. 2023. "Effects of Biochar on Methane Emissions and Crop Yields in East Asian Paddy Fields: A Regional Scale Meta-Analysis" Sustainability 15, no. 12: 9200. https://doi.org/10.3390/su15129200

APA Style

Lee, J.-M., Jeong, H.-C., Gwon, H.-S., Lee, H.-S., Park, H.-R., Kim, G.-S., Park, D.-G., & Lee, S.-I. (2023). Effects of Biochar on Methane Emissions and Crop Yields in East Asian Paddy Fields: A Regional Scale Meta-Analysis. Sustainability, 15(12), 9200. https://doi.org/10.3390/su15129200

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