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Article

Biochar: An Option to Maintain Rice Yield and Mitigate Greenhouse Gas Emissions from Rice Fields in Northeast China

by
Wenjun Dong
1,*,†,
Frederick Danso
2,3,†,
Ao Tang
1,
Jun Zhang
2,
Youhong Liu
1,
Ying Meng
1,
Xijuan Zhang
1,
Lizhi Wang
1 and
Zhongliang Yang
4
1
Institute of Crop Cultivation and Tillage, Heilongjiang Academy of Agricultural Sciences/Heilongjiang Provincial Key Laboratory of Crop Physiology and Ecology in Cold Region, Harbin 150023, China
2
Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
CSIR-Oil Palm Research Institute, Kade P.O. Box 74, Ghana
4
Institute of Biotechnology, Heilongjiang Academy of Agricultural Sciences, Harbin 150023, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(12), 3050; https://doi.org/10.3390/agronomy14123050
Submission received: 3 December 2024 / Revised: 17 December 2024 / Accepted: 19 December 2024 / Published: 20 December 2024
(This article belongs to the Special Issue Net-Zero Emissions for Sustainable Food Production and Land Management)
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Versions Notes

Abstract

:
Crop production is heavily dependent on fertilizers that negatively impact the environment; therefore, research on biochar to improve the soil’s properties and reduce greenhouse gas emissions has intensified over the years. To elucidate rice yield and greenhouse gas emission (GHG) arising from the application of biochar and N fertilizer on paddy soil in Northeast China, a 3-year (2015–2017) field experiment was established. Adopting a split-plot design with three replicates, two nitrogen (N) fertilizer levels in the main plots were designated as follows: 120 kg N ha−1 (N1, 2/3 of N application rate for optimal local rice yield); 180 kg N ha−1 (N2, full N application rate for optimal local rice yield); and four biochar application rates of no biochar (C0, control); 1.0 t ha−1 biochar (C1); 1.5 t ha−1 biochar (C2); and 2.0 t ha−1 biochar (C3) were designated as sub-treatments. The results showed that in 2015, biochar amendment increased GHG emissions while between 2016 and 2017, biochar amendment of 1.5 t ha−1 decreased CH4 emissions, global warming potential (GWP), and greenhouse gasses intensity (GHGI) by 11.3%, 10.9%, and 17.0%, respectively. On average, for the years 2016 and 2017, the N2O fluxes were 17.0% lower in the N2 plots compared to the N1 plots. Biochar amendment of 1.5 t ha−1 recorded an 8.6% increase in rice yield compared to the control. The soil properties of the study site showed that biochar amendment of 1, 1.5, and 2 t ha−1 augmented soil organic matter by 3.3%, 5.3%, and 5.2%, respectively, and soil phosphorus availability by 6.4%, 11.2%, and 22.6%, respectively. The co-application of biochar at 1.5 t ha−1 and 180 kg N ha−1 effectively regulated GHG emissions while maintaining crop yield. Appropriate co-application of biochar with N fertilizer can be adopted for emission reduction and rice yield maintenance while maintaining soil fertility in Northeast China.

1. Introduction

Rice production over the years has contributed to global greenhouse gasses (GHGs) estimates. Estimates of CH4 and N2O emissions from paddy soils account for 19% and 11%, respectively, of total global agricultural emissions [1,2]. The use of fertilizers in rice production contributes considerably to emissions leading to environmental consequences [3,4]; hence, the urgency to reduce emissions arising from rice production. Therefore, emphasizing sustainable and environmentally friendly agricultural management practices, that boost rice productivity and mitigate GHG emissions is of importance.
Incorporating crop residues and animal waste into agricultural soils plays a pivotal role in improving SOC stocks and soil fertility [5,6]. Undoubtedly, this practice has transitioned into a common practice though it shows varying levels of soil functions due to the slow release of nutrients from organic inputs. Thus, a combined application of organic and inorganic fertilizer is a better alternative for the maintenance of soil fertility and crop production than the use of either amendment [7]. However, evidence points to the superior performance of biochar in increasing SOC storage and mitigating GHG emissions from the soil [8,9].
It is worth noting the importance of biochar in agricultural production. Biochar, an adaptable organic amendment, exhibits the potential to increase SOC content, accelerate microaggregate formation improve soil fertility, increase crop yield, and mitigate global warming effects [10,11,12]. However, previous studies on GHG emissions response to biochar application showed contradicting results, such as a decrease in emissions [13], an increase in emissions [14], and neutral effects [15] on GHG emissions. To fully harness the potential of biochar while minimizing N losses in the paddy field, a combinatorial application of biochar and N fertilizer, which could simultaneously achieve low emissions and increase rice yield, is proposed. Huang et al. [16] demonstrated that biochar application increased the uptake of N fertilizer by rice by 23–27%, thus increasing grain yield by 8–10%. Studies demonstrated that biochar interacted with the N fertilizer by retaining mineral N through surface adsorption [17], and by absorbing and holding it in its internal pores [18]. The high stability, oxygen-rich carboxyl and hydroxyl groups of biochar serve as a carbon carrier which allows for the adsorption of nutrients [19]. However, biochar of different stocks produces variable mechanisms of influence on the N in the paddy soil environment owing to their specific physical and chemical properties and biochar dosage [20].
Northeast China, which is the main production area for japonica rice in China [21], is characterized by a temperate climate. Though in temperate agriculture, biochar addition has been reported, fewer studies exist on the use of the combined application of biochar and N fertilizer for rice production in the Northeast region of China. We hypothesized that biochar and nitrogen fertilizer optimization will mitigate GHG emissions while maintaining yields in rice paddies of Northeast China. An in-depth knowledge of the combinatory application of biochar and N fertilizer as an agronomically sustainable option in GHG emissions mitigation is imperative for the recommendation of low-emission and high-yielding practices. Accordingly, the objective of this study is to evaluate the effects of varying levels of biochar and its co-application with two different nitrogen levels, on greenhouse gas emissions and rice yield in the Northeast of China.

2. Materials and Methods

2.1. Site Description

At the experimental station of the National Modern Agricultural Demonstration Park in Minzhu town, Harbin City, Heilongjiang Province, China (45°49′ N, 126°48′ E, 117 m a.s.l.) the three-year field experiment (2015–2017) was established. The site is characterized by a typical single rice cropping system on a Chernozem soil (a Mollisols in USA-ST) with basic soil properties of total N 1.2 g kg−1, total P 0.5 g kg−1, total K 18.6 g kg−1, available N 82.4 mg kg−1, available P 19.8 mg kg−1, available K 147.8 mg kg−1, pH 8.6, and soil organic matter 23.2 g kg−1. Dominated by a northern temperate climatic condition, the experimental location has a mean annual precipitation of 508–583 mm, effective accumulated temperature (≥10 °C) 2600 °C·d–2700 °C·d, annual sunshine time of 2668 h and 131–146 days frostless period. The meteorological parameters during the rice growing season at the experimental site are presented in Figure 1, Figure 2 and Figure 3.

2.2. Experimental Design

The experiment was set up by adopting a split-plot design with three replicates on plot sizes of 15 m × 10 m each. Two nitrogen (N) fertilizer levels in the main plots were designated as 120 kg N ha−1 (N1, 2/3 of N application rate for optimal local rice yield) and 180 kg N ha−1 (N2, full N application rate for optimal local rice yield) while four biochar application rates of no biochar (C0, control), 1.0 t ha−1 biochar (C1), 1.5 t ha−1 biochar (C2), and 2.0 t ha−1 biochar (C3) were designated as sub-treatments. The treatment details are shown in Table 1.

2.3. Crop Management

Longdao 21 rice seeds a local japonica rice variety were nursed on 16 April, 18 April, and 14 April in 2015, 2016, and 2017, respectively. At planting density of three to four seedlings hill−1 and 25 hills m−2 the rice seedlings were manually transplanted into each plot on 16 May, 18 May, and 17 May in 2015, 2016, and 2017, respectively. At the appropriate rate per plot rice straw biochar was evenly spread in each treatment plot prior to rice transplanting. The N fertilizer in the form of urea and diammonium phosphate was applied as 50% N basal dressing two days prior to transplanting, 30% as side dressing in the form of urea at the tillering stage, and the other 20% in the form of urea at the panicle stage. The phosphatic fertilizer of P2O5 at 70 kg ha−1 in the form of diammonium phosphate and the potash fertilizer of K2O at 50 kg ha−1 in the form of potassium sulfate, respectively, were applied as basal fertilizers. Each plot was serviced with an independent irrigation system and rice crops were maintained under continuous flooding irrigation water management. Mature rice crops were harvested on 23 September in 2015, 18 September in 2016, and 21 September in 2017.

2.4. Gas Sampling and Measurement

The CH4 and N2O gasses were sampled and the fluxes were measured during the growing season of 2015–2017 using the static closed chamber and gas chromatography methods, respectively [16,17]. In each plot, a PVC frame (53 cm × 47 cm × 28 cm, length × width × height) with a groove to accommodate the chamber was permanently installed to enclose six hills of rice throughout the cropping season. Polyvinyl chloride (PVC) swathed aluminum foil chamber of dimensions (length × width × height) 50 cm × 50 cm × 50 cm and 50 cm × 50 cm × 100 cm in accordance with plant height were equipped with a battery-operated internal fan to ensure complete gas mixture in the head space. Preceding the gas sampling the PVC chamber was placed in the groove and filled with water to form an airtight seal in the upper chamber during the period of sampling. Subsequently, after chamber closure internal chamber temperature was recorded using an attached digital thermometer and gas samples were collected from the chamber headspace through a 50 mL airtight syringe with a three-way stop-cork into pre-evacuated vacuum tubes at fixed intervals of 0, 5, 10, and 15 min. At weekly intervals during the rice growing season of 2015 to 2017, the gas samples were collected between the hours of 9:00 to 11:00 am until rice maturity. A gas chromatograph (Agilent 7890A, USA Agilent 7890A, Agilent Technologies, Wilmington, DE, USA) equipped with a flame ionization detector (FID) to detect CH4 and an electron capture detector (ECD) to detect N2O were used to determine CH4 and N2O concentrations in the gas samples. Hourly emissions of CH4 and N2O were determined from the slope of the mixing ratio change in four sequential samples. CH4 and N2O flux measurements were selected if the r2 of the linear correlation coefficient of the fluxes were >0.90. Cumulative CH4 and N2O emissions for the entire cropping period were sequentially accumulated from the emissions averaged on every two adjacent intervals of the measurements [22,23].

2.5. Estimation of Greenhouse Gas Emissions

Integrated evaluation of GHG emissions was expressed as GWP and greenhouse gas intensity (GHGI). The GWP was calculated as (GWP = 25 × CH4 + 298 × N2O kg CO2 equivalent ha−1). The GHGI was calculated by dividing GWP by rice yield to evaluate biochar and N fertilizer impact on GHG emissions and rice yields.

2.6. Estimation of Biomass and Yield

Five hills of plant samples were randomly taken from each plot at maturity, oven-dried at 80 °C to a constant weight, and weighed to determine the biomass. Three replicates of one m2 rice plant at maturity of each treatment were harvested using one m2 quadrant. Rice plants within the quadrant were harvested for yield determination. The grain yield was adjusted to 14.5% moisture content.

2.7. Determination of SOC and Soil Available Nutrients

At harvest, three replicates of soil samples collected at a depth of 0–20 cm per plot, were composited, air-dried, and ground to pass through a 2 mm sieve. The SOC was determined by the potassium dichromate volumetric method, the alkaline nitrogen content was determined by the alkali-hydrolyzed diffusion method, the available phosphorus content was determined by 0.5 mol L−1 NaHCO3 extraction spectrophotometry while the available potassium content was determined by flame photometry.

2.8. Statistical Analysis

Data analyses were computed on SPSS version 23.0, IBM, USA. A two-way analysis of variance (ANOVA) was used for the double factors experiment to test the effects of the different treatments. Significant differences in treatments were determined at p < 0.05 while plotted graphs were performed using Microsoft Office Excel 2019.

3. Results

3.1. CH4 Emissions

The impact of biochar and N fertilizer on the pattern of CH4 fluxes was similar under all the treatments for 2015, 2016, and 2017 with peaks of CH4 emission at the active tillering and jointing-booting stages (Figure 4). In 2015, the CH4 flux was the highest in the N1C1 plots which were 87.5% more compared to the control (p < 0.05). The highest CH4 flux in 2016 was noted in N1C2 while in 2017 it was observed in N2C3. In 2015, the CH4 fluxes in the C2 plots were 56.4% higher than those in the C0 plots; however, in both 2016 and 2017, the CH4 fluxes in the C2 plots were 13.7% lower than those in the C0 plots (p < 0.05) (Figure 4). The average CH4 flux correlated significantly with biochar amendment. Similarly, the interaction between the year and biochar application correlated significantly with the average CH4 flux (p < 0.01) level (Table 8).

3.2. Cumulative CH4 Emission

Generally, the 2016 production year produced the lowest cumulative CH4 emissions in comparison with both 2015 and 2017 rice production years (Table 2). In 2015, the cumulative CH4 emissions in the N2 plot were 25.3% lower than those in the N1 plot (p < 0.05). However, in 2016 and 2017, the cumulative CH4 emissions in the N2 plot were 8.6% higher than those in the N1 plot (Table 2). The cumulative CH4 emissions were 32.7%, 19.0%, and 26.6% were higher in the C1, C2, and C3 plots, respectively, than in the C0 plot in 2015 (p < 0.05). However, the cumulative CH4 emissions on average were reduced by 11.3% in the C2 plots compared with the C0 plots in 2016 and 2017 (p < 0.05) (Table 2). In 2015 and 2016, the cumulative CH4 emissions in the N2C2 plots were 16.1% lower than those in the N1C0 plots (Table 2). However, in 2017, the cumulative CH4 emissions in the N2C2 plots were 17.3% higher than those in the N1C0 plots (p < 0.05) (Table 2). The cumulative CH4 emissions were significant at p < 0.05 and p < 0.01 level under biochar amendment and the interaction of year and N application, respectively (Table 8).

3.3. N2O Emission

The seasonal dynamics of N2O fluxes recorded a high temporal variation, exhibiting a sporadic and pulse-like pattern with two high peaks at the heading and grain filling stage (Figure 5). Averagely for the years 2016 and 2017, the N2O fluxes were 17.0% lower in the N2 plots compared to the N1 plots (p < 0.05) (Figure 5). However, there was no difference between N1 and N2 treatments in 2015. The differences in N2O fluxes were significant under biochar amendment and year of application (p < 0.05) (Table 8).

3.4. Cumulative N2O

On average, across the years 2016 and 2017, there was a 19.3% reduction in the cumulative N2O emissions in the N2 plots compared to the N1 plots; however, this increased by 11.5% in 2015 (p < 0.05) (Table 3). There was a significant correlation (p < 0.01) between year and cumulative N2O emissions during the rice cropping system (Table 8).

3.5. Rice Biomass and Yield

In comparison with N1 treatment, N2 treatment increased the aboveground biomass by 14.0%, on average across the three years (Figure 6). The aboveground biomass production significantly increased by 8.4%, 11.5%, and 11.9% on average in the C1, C2, and C3 plots, respectively, compared with the control during the three years. Larger aboveground biomass was noted in the N2C2 and N2C3 plots. Significant differences were noted in the aboveground biomass between N fertilizer treatments and among biochar-amended treatments (Table 8).
The grain yields were significant and averaged 7.3% higher in the N2 plot than in the N1 plot over the three years (Figure 7). Additionally, the grain yields in the C1, C2, and C3 plots averaged 6.3%, 8.6%, and 6.9%, respectively, higher and significant than in the C0 plot across the three years. The highest grain yield was recorded in the N2C2 plot. Significant differences in the grain yield were observed between N fertilizers treatments and biochar-amended treatments (Table 8).

3.6. Impact of Biochar and N Fertilizer on GWP and GHGI

In 2015, the GWPs in the N2 plot were lower than those in the N1 plot. However, in both 2016 and 2017, the GWPs in the N2 plot were higher than those in the N1 plot (Table 4). The GWPs were higher in the C1, C2, and C3 plots than in the C0 plot in 2015. On average, GWPs were reduced by 10.9% in C2 plots compared with the C0 plots for 2016 and 2017. In 2015 and 2016, the GWPs in the N2C2 plots were lower than those in the N1C0 plots (Table 4). However, in 2017, the GWPs in the N2C2 plots were higher than those in N1C0 plots. The GWPs significantly correlated with both biochar and the interactive effects of year and N application on rice production (Table 8). On average, the N2 plot recorded lower GHGI than those in the N1 plot for 2015 and 2016 and remained almost unchanged between the N1 and the N2 plots in 2017 (Table 5). The GHGIs were higher in the C1, C2, and C3 plots than in the C0 plot in 2015 (Table 5). The GHGIs across the years of 2016 and 2017 were on average reduced by 17.0% in the C2 plots compared with the C0 plots (Table 5). Across the three years, the GHGIs recorded a reduction of 19.8% on average in the N2C2 plots compared with the N1C0 plots (Table 5). The GHGIs were significant under biochar and N fertilizer treatments (Table 8).

3.7. Soil Organic Carbon

The average changes in the SOC of the N2 plots compared to the N1 plots were not obvious across the three years (Table 6). Compared with the control, SOC on average in the C2, C3 and C1 plots were significantly increased by 5.3%, 5.2% and 3.3%, respectively, during the three years period (Table 6). The biochar increased the SOC content in 2016 and 2017. Higher SOC content ranging from 5.5% to 15.6% were observed in N1C1, N1C2, N1C3, N2C1, N2C2, and N2C3 plot than in N1C0 plot in 2017. There were significant differences in the SOC content among the biochar treatments (Table 8).

3.8. Soil Available Nutrients

In comparison with the N1 plots, the alkaline nitrogen content on average did not significantly change in the N2 plots, across three years (Table 7). The alkaline nitrogen content was significantly decreased by 5.1% and 5.2% in the C2 and C3 plots, respectively, in 2015 and 2016 while it was significantly increased by 8.4% and 22.4% in the C1 and C2 plots, respectively, for 2017 compared with the control (Table 7). Averagely across the two years of 2015 and 2016, the alkaline nitrogen was reduced 6.9%, 6.1%, 5.6% and 5.3% in the N1N2, N1C3, N2C2 and N2C3 plots, respectively, compared with the N1C0 plots. However, it was recorded a significant increase of 30.0% and 22.8% in alkaline nitrogen for the N1C2 and N2C2 plots, respectively, in 2017 (Table 7). There were significant differences in the alkaline nitrogen among biochar amendments treatments (Table 8). The available phosphorus on average did not significantly change in the N2 plots compared to N1 plots across the three years (Table 7). Compared with the control, available phosphorus was averagely increased by 6.4%, 11.2% and 22.6% in the C1, C2 and C3 plots, respectively, across the study period with significant differences been observed in C3 plot (Table 7). Compared with N1C0 plot, the available phosphorus was significantly increased by 25.3% in N2C2 plot in 2015, and significantly increased by 24.0%, 20.4% and 23.3% in the N1C1, N1C3 and N2C3 plots, respectively, in 2016 (Table 7). However, in 2017 there were no significant differences among the different treatments in 2017 (Table 7). Significant differences in available phosphorus were found among biochar amendments (Table 8). Additionally, the available phosphorus was significantly different under biochar-amended treatments and N fertilizer treatments (Table 7). Compared with the N1 plots, the available potassium content did not significantly change on average in the N2 plots for 2015 and 2017; however, it significantly decreased by 11.4% in the N2 plots in 2016 (Table 7). On average the available potassium content was significantly increased by 7.0% and 11.8% in the C2 and C3 plots, respectively, compared with the control, across the three years (Table 7). Compared with the N1C0 plot, the available potassium was significantly increased by 19.6% and 22.8% in the N1C3 and N2C3 plots in 2017, respectively. However, there were no significant differences under the different treatments for the year 2015 and 2016 (Table 7). Significant differences in the available potassium content were noted among the biochar treatments (Table 8).
Table 7. Interactive effects of biochar and N fertilizer on alkaline nitrogen, available phosphorus and available potassium from 2015–2017.
Table 7. Interactive effects of biochar and N fertilizer on alkaline nitrogen, available phosphorus and available potassium from 2015–2017.
YearTreatmentAlkaline Nitrogen
(mg kg−1)		Available Phosphorus
(mg kg−1)		Available Potassium
(mg kg−1)
2015N1C086.03 ± 2.80 a16.83 ± 0.02 b146.17 ± 0.75 a
N1C184.22 ± 1.06 a19.20 ± 0.31 ab142.03 ± 4.39 a
N1C276.77 ± 0.77b20.08 ± 0.45 ab151.80 ± 6.89 a
N1C379.84 ± 1.58 ab20.53 ± 0.64 ab154.50 ± 6.86 a
N2C082.36 ± 1.10 ab19.80 ± 1.28 ab147.77 ± 3.64 a
N2C181.70 ± 2.37 ab19.39 ± 0.62 ab154.85 ± 0.45 a
N2C279.51 ± 1.16 ab21.09 ± 0.96 a147.33 ± 8.04 a
N2C383.57 ± 3.66 ab20.51 ± 1.41 ab155.53 ± 3.54 a
2016N1C0121.65 ± 1.21 a27.29 ± 1.51cd177.50 ± 4.15 ab
N1C1121.42 ± 1.37 a33.83 ± 1.07 a172.38 ± 3.69 ab
N1C2117.99 ± 4.98 a31.21 ± 0.81 abc181.37 ± 2.70 a
N1C3115.48 ± 3.18 a32.85 ± 2.35 ab185.05 ± 9.44 a
N2C0120.74 ± 2.86 a26.56 ± 1.20 cd144.93 ± 6.97 c
N2C1122.11 ± 2.40 a25.79 ± 1.75 d153.05 ± 4.82 bc
N2C2117.31 ± 3.30 a28.70 ± 1.85 bcd171.38 ± 4.08 ab
N2C3117.42 ± 0.75 a33.64 ± 2.04 a165.02 ± 8.85 abc
2017N1C0101.14 ± 9.06 cd25.84 ± 2.81 a151.83 ± 2.02 bc
N1C1114.07 ± 3.56 bc28.72 ± 4.05 a146.28 ± 4.53 bc
N1C2131.49 ± 8.65 a30.13 ± 2.51 a164.27 ± 5.24 abc
N1C399.79 ± 4.70 cd36.23 ± 2.71 a181.65 ± 0.70 a
N2C0107.63 ± 3.49 cd25.85 ± 1.92 a151.40 ± 1.90 bc
N2C1112.22 ± 4.37 bc24.61 ± 3.15 a142.18 ± 5.93 c
N2C2124.15 ± 1.18 ab26.75 ± 1.74 a168.80 ± 10.23 ab
N2C395.46 ± 1.36 d32.14 ± 1.92 a186.48 ± 7.09 a
Different superscript letters in a single column indicate difference (p < 0.05) among the treatments in 2015, 2016, and 2017, respectively. Data are means ± SE (n = 3).
Table 8. Significance values and degree of freedom of ANOVA analysis (three-way).
Table 8. Significance values and degree of freedom of ANOVA analysis (three-way).
SourcedfAboveground
Biomass		Grain YieldAverage CH4 FluxAverage N2O FluxCH4 EmissionN2O EmissionGWPGHGISOCAlkaline NitrogenAvailable Phosphorus Available Potassium
Year2**************ns********
N1****nsnsnsnsns**nsnsnsns
C3******ns*ns*********
Year × N2nsns**ns**ns****nsnsns**
Year × C6nsnsns*nsnsnsnsns*****
N × C3ns*nsnsnsnsnsnsnsns*ns
Year × N × C6nsnsnsnsnsnsnsnsnsnsnsns
Error46
N and C represent the treatments of N fertilizers and biochar amendments, respectively. “ns”, “*” and “**” mean being insignificant and significant at 0.05 level and 0.01 level, respectively. GWP and GHGI represent the global warming potential and GWP in terms of grain yield, respectively. ANOVA: analysis of variance.

4. Discussion

4.1. Effects of Biochar and N Fertilizer on CH4 Emissions

During the rice-growing period of this study, similarities in the CH4 flux pattern as a resultant effect of co-application of biochar and N fertilizer for 3 years might have occurred from the non-suppressive effect of the applied biochar on emission (Figure 4). However, differences in the peak flux among the different years confirms the assertion that biochar application shows a variable response, such as a decrease in emissions [13], increase in emissions [14], and even neutral effects [15] on GHG emissions.
The conditioning effect of biochar might have advanced soil aeration shifts which favored the population of higher methanogens to methanotrophs ratios causing the reduction in CH4 emissions [24]. This might account for the cumulative decline in the emission of CH4 from the first to the second year of biochar application. However, increases in cumulative CH4 emissions were noted in the third year of use of biochar. Mukome et al. [25] explained that factors such as pyrolysis temperature, application rate, experimental method, and the duration of the study could affect the overall response effect of GHG emissions to biochar amendment. Though the inclusion of biochar as a soil amendment is critical in enabling the soil to negate GHG emissions [26] most biochar application rates, except <20 and <40 t ha−1 demonstrated a significant reduction in CH4 emissions [9]. The biochar application averaged across treatments over the three years was significantly correlated with an average CH4 flux and CH4 emission of p < 0.01 and p < 0.05, respectively (Table 8). Additionally, increasing biochar rates did not translate into a significant reduction in cumulative CH4 during the period of study. A plausible reason is the lower rates of biochar application and duration of this study which could not modify or alter soil biochemical functions mediating CH4 emissions rates significantly. According to Mukome et al. [25] these factors could affect the general outcome of GHG emissions to biochar application.

4.2. Effects of Biochar and N Fertilizer on N2O Emissions

There was a 19.3% reduction in the cumulative N2O emissions in the N2 plots compared to the N1 plots for both 2016 and 2017 (Figure 5, Table 2). According to Paustian et al. [27], the exact mechanisms involved are uncertain since many of the controls on nitrification and denitrification processes (by which N2O emissions occur), for example, pH, mineral N concentrations, soil moisture, and O2 concentrations can be impacted by the presence of biochar. The co-application of biochar with N fertilizer produced lower N2O fluxes in the second year compared to the first and third year. The increasing rate of biochar in both the lower and higher rates of the N plots did not minimize N2O fluxes as expected during the period of the study. Cayuela et al. [28] explained that given that biochar improves soil conditioning attributes, such as pH, porosity, and nutrient transformation, these modifications to the soil environment may adjust nitrifier and denitrifier activity with some feedback effect, either reduction and/or increase, on N2O emission flux.

4.3. Effects of Biochar and N Fertilizer on GWP and GHGI

The global warming potential (GWP) is a basic index that foresees the probability of GHG-related impact on the lifetime and radiative power of these species [29] showed that the GWPs of the C2 plots for 2016 and 2017 (Table 2) were lower compared with both the C1 and C3 plots while non-significance in GWP arising from the use of biochar and N was evident in this study. Some findings suggest that GWP response to biochar application showed variable response, such as neutral effects on emissions increase in emissions and decrease in emissions [8,9,10]. Soil biochemical functions mediating emissions rates due to differences in feed-stock materials, experimental conditions, crop types, and intensities could be plausible for such observations of biochar application on the GWP [9]. The interactive effect of the application of biochar and N fertilizer on GHGI reduction occurred in the second year of the study while the interaction of the years and N application showed significant correlation with GHGI (p < 0.01). In a meta-analysis of 81 trials, Liu et al. [30] showed that the application of biochar could significantly reduce GHGI as evident in our study. Indeed, in the current study among the biochar-treated plots, the use of N2C2 largely showed a reduction in GHGI across the years an indication that higher amounts of N application and moderated rate of biochar is important in minimizing the GHGI effect. Therefore, the choice of an appropriate of combination of biochar and N fertilizer may go a long way in promoting sustainable and ecologically safe rice production in the cold regions of China.

4.4. Effects of Biochar and Fertilizer on Yield of Rice and Biomass

The application of biochar to soil evidently tend to increase crop yield [31]. Previous studies showed that biochar reloads soil nutrients due to its unique surface charge density, and the predominant negatively charged surfaces which were important in the promotion of cation adsorption [32]. Both aboveground biomass and grain yield increased under biochar treatment in our study. This supported the assertion of Zhang et al. [33] who found that biochar amendment significantly increases rice yield by 10% in the first production cycle and by 9.5–29.0% in a subsequent production cycle. The aboveground biomass of our study was significantly increased by 8.4%, 11.5%, and 11.9% on average in the C1, C2, and C3 plots, respectively, compared with the control during the three years. Additionally, N2 treatment increased the aboveground biomass by 14.0% on average across the three years (Figure 6, Table 5). Additionally, the grain yields in the C1, C2, and C3 plots were on average 6.3%, 8.6%, and 6.9%, respectively, higher and significant than in the C0 plot across the three years (Figure 7, Table 5). This is lower than the earlier work of Liu et al. [34] who reported increases of 8.5–10.7% in rice grain yield. These increases likely resulted from the relatively high soil available P and K content of biochar soils. Some findings have explained that if biochar is added to the soil with enough chemical fertilizer in the rice season, it would not cause nutrient limitations in rice production [35]. Therefore, Qi et al. [36] suggest that fertilizer rates should not be reduced to account for the biochar’s nutrients in order to achieve rice production similar to that of chemical fertilizer application alone.

4.5. Effects of Biochar and Fertilizer on Soil SOC and Available Nutrients

Biochar has been applied in a variety of terrestrial ecosystems in order to sequester biomass carbon into the ecosystems. The combination of biochar and mineral fertilizers has been shown to synergistically affect crop yield and growth in field studies [37]. There were significant differences in the alkaline nitrogen among biochar amendment treatments (Table 5). Duku et al. [38] reported that biochar application in soils minimizes N loss through leaching and NH3 volatilization. De Gryze et al. [39] also opined that biochar decreases the possibility of nutrient loss in soils and enhances nutrient recycling, resulting in positive impacts on crop yields in the long run through the slow release of nutrients to the soil. Significant differences in available phosphorus were found among biochar amendments with some recording a reduction in available P (Table 5). This supports the assertion that biochar can reduce the available P in soils [40]. Available phosphorus was generally higher under biochar and N fertilizer-amended plots. This was consistent with the results from El-Eyuoon and Amin [41] who reported significant increases in available P content in soil with biochar application. On the contrary, some studies have shown increased P content in biochar soils while some previous works have reported a non-significant effect of biochar on P availability in soils [42].

5. Conclusions

Assessing the combinatory use of biochar and N fertilizer in paddy rice production is imperative in the choice of low-emission and high-yielding practices in Northeast China. This current study elucidates the GHG mitigation effects of biochar as a soil amendment in rice paddies. The application rate of 180 kg N ha−1 and biochar amendment at 1.5 t ha−1 was the most effective rate of mitigating GHG emissions while maintaining good crop yield during the period of study. An appropriate rate of biochar can be applied to attain the goal of emission reduction while maintaining yield and soil fertility in rice paddies in the cold region Northeast of China. However, more data are needed on the type of biochar, chemical properties of feedstock, pyrolysis conditions, bulk density, and physiochemical properties appropriate for paddy rice production in Northeast China. Though soil microbial studies were a limitation for this study, to further appreciate the mitigation effects of biochar on GHG emissions and soil microbial activities in paddy fields, long-term studies are proposed. Future biochar studies could explore and monitor the relationship between biochar and the soil physico-chemical properties to determine treatment effects on deeper SOC-cycling processes over time.

Author Contributions

Conceptualization, W.D.; methodology, W.D.; software, W.D. and F.D.; validation, W.D., F.D. and A.T.; formal analysis, W.D. and F.D.; investigation, W.D., F.D., A.T., J.Z., Y.L., Y.M. and X.Z.; resources, L.W. and Z.Y.; data curation, W.D.; writing—original draft preparation, W.D. and F.D.; writing—review and editing, W.D., F.D., A.T., J.Z., Y.L. and Y.M.; visualization, W.D., F.D. and A.T.; supervision, Y.L., Y.M., X.Z., L.W. and Z.Y.; project administration, L.W.; funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Heilongjiang Province Agricultural Science and Technology Innovation Span Project (CX23GG12) and the Key Program of Research Funds for the Research Institutes of Heilongjiang Province (CZKYF2021-2-B019).

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy limitations.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Win, E.P.; Win, K.K.; Bellingrath-Kimura, S.D.; Oo, A.Z. Greenhouse gas emissions, grain yield and water productivity: A paddy rice field case study based in Myanmar. Greenh. Gas Sci. Technol. 2020, 10, 884–897. [Google Scholar] [CrossRef]
  2. FAO. Food and Agriculture Organization of the United Nations; FAO: Rome, Italy, 2019. [Google Scholar]
  3. Kalu, S.; Kulmala, L.; Zrim, J.; Peltokangas, K.; Tammeorg, P.; Rasa, K.; Kitzler, B.; Pihlatie, M.; Karhu, K. Potential of biochar to reduce greenhouse gas emissions and increase nitrogen use efficiency in boreal arable soils in the long-term. Front. Environ. Sci. 2022, 10, 914766. [Google Scholar] [CrossRef]
  4. Wang, Z.; Zhang, X.; Liu, L.; Wang, S.; Zhao, L.; Wu, X.; Zhang, W.; Huang, X. Inhibition of methane emissions from Chinese rice fields by nitrogen deposition based on the DNDC model. Agric. Syst. 2020, 184, 102919. [Google Scholar] [CrossRef]
  5. Ndzelu, B.S.; Dou, S.; Zhang, X. Corn straw return can increase labile soil organic carbon fractions and improve water-stable aggregates in haplic cambisol. J. Arid. Land 2021, 12, 1018–1030. [Google Scholar] [CrossRef]
  6. Gross, A.; Glaser, B. Meta-analysis on how manure application changes soil organic carbon storage. Sci. Rep. 2021, 11, 5516. [Google Scholar] [CrossRef]
  7. Iqbal, A.; Xie, H.; He, L.; Ahmad, S.; Hussain, I.; Raza, H.; Khan, A.; Wei, S.; Quan, Z.; Wu, K. Partial substitution of organic nitrogen with synthetic nitrogen enhances rice yield, grain starch metabolism and related genes expression under the dual cropping system. Saudi J. Biol. Sci. 2021, 28, 1283–1296. [Google Scholar] [CrossRef]
  8. Yousaf, B.; Liu, G.; Wang, R.; Abbas, Q.; Imtiaz, M.; Liu, R. Investigating the biochar effects on C-mineralization and sequestration of carbon in soil compared with conventional amendments using the stable isotope (δ13C) approach. GCB Bioenergy 2017, 9, 1085–1099. [Google Scholar] [CrossRef]
  9. Shakoor, A.; Arif, M.S.; Shahzad, S.M.; Farooq, T.H.; Ashraf, F.; Altaf, M.M.; Ahmed, W.; Tufail, M.A.; Ashraf, M. Does biochar accelerate the mitigation of greenhouse gaseous emissions from agricultural soil?—A global meta-analysis. Environ. Res. 2021, 202, 111789. [Google Scholar] [CrossRef]
  10. Ashiq, W.; Nadeem, M.; Ali, W.; Zaeem, M.; Wu, J.; Galagedara, L.; Thomas, R.; Kavanagh, V.; Cheema, M. Biochar amendment mitigates greenhouse gases emission and global warming potential in dairy manure-based silage corn in boreal climate. Environ. Pollut. 2020, 265, 114869. [Google Scholar] [CrossRef]
  11. Yin, X.; Chen, N.; Cao, F.; Tui, Z.; Huang, M. Short-term application of biochar improves post-heading crop growth but reduces pre-heading biomass translocation in rice. Plant Prod. Sci. 2020, 23, 522–528. [Google Scholar] [CrossRef]
  12. Ding, X.; Li, G.; Zhao, X. Biochar application significantly increases soil organic carbon under conservation tillage: An 11-year field experiment. Biochar 2023, 5, 28. [Google Scholar] [CrossRef]
  13. Zhang, D.; Pan, G.; Wu, G.; Kibue, G.W.; Li, L.; Zhang, X.; Cheng, K.; Joseph, S.; Liu, X. Biochar helps enhance maize productivity and reduce greenhouse gas emissions under balanced fertilization in a rainfed low fertility inceptisol. Chemosphere 2016, 142, 106–113. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, Y.; Bai, R.; Di, H.J.; Mo, L.Y.; Han, B.; Zhang, L.M.; He, J.Z. Differentiated mechanisms of biochar mitigating straw-induced greenhouse gas emissions in two contrasting paddy soils. Front. Microbiol. 2018, 9, 2566. [Google Scholar] [CrossRef]
  15. Li, J.; Kwak, J.H.; Chang, S.X.; Gong, X.; An, Z.; Chen, J. Greenhouse gas emissions from forest soils reduced by straw biochar and nitrapyrin applications. Land 2021, 10, 189. [Google Scholar] [CrossRef]
  16. Huang, M.; Yang, L.; Qin, H.; Jiang, L.; Zou, Y. Fertilizer nitrogen uptake by rice increased by biochar application. Biol. Fert. Soils 2014, 50, 997–1000. [Google Scholar] [CrossRef]
  17. Yao, Y.; Gao, B.; Zhang, M.; Inyang, M.; Zimmerman, A.R. Effect of biochar amendment on sorption and leaching of nitrate, ammonium, and phosphate in a sandy soil. Chemosphere 2012, 89, 1467–1471. [Google Scholar] [CrossRef]
  18. Turunen, M.; Hyväluoma, J.; Heikkinen, J.; Keskinen, R.; Kaseva, J.; Hannula, M. Quantifying the pore structure of different biochars and their impacts on the water retention properties of sphagnum moss growing media. Biosyst. Eng. 2020, 191, 96–106. [Google Scholar] [CrossRef]
  19. Zhao, Y.; Jiang, H.; Gao, J.; Feng, Y.; Yan, B.; Li, K.; Zhang, W. Effects of nitrogen co-application by different biochar materials on rice production potential and greenhouse gas emissions in paddy fields in northern China. Environ. Technol. Innov. 2023, 32, 103242. [Google Scholar] [CrossRef]
  20. Liao, P.; Sui, F.; Tang, J.; Zeng, Y.J.; Wu, Z.M.; Shi, Q.H.; Huang, S. Effects of biochar amendment on the global warming potential and greenhouse gas intensity in a double rice-cropping system. J. Nucl. Agric. Sci. 2018, 32, 1821–1830. [Google Scholar] [CrossRef]
  21. Xu, C.; Ji, L.; Chen, Z.; Fang, F.P. Historical review and prospect of China’s rice production, market and import and export trade. J. China Rice. 2021, 27, 17–21. [Google Scholar] [CrossRef]
  22. Wang, J.Y.; Jia, J.X.; Xiong, Z.Q.; Khalil, M.A.K.; Xing, G.X. Water regime–nitrogen fertilizer–straw incorporation interaction: Field study on nitrous oxide emissions from a rice agroecosystem in Nanjing, China. Agric. Ecosyst. Environ. 2011, 141, 437–446. [Google Scholar] [CrossRef]
  23. Zou, J.; Huang, Y.; Jiang, J.; Zheng, X.; Sass, R. A 3-year field measurement of methane and nitrous oxide emissions from rice paddies in China: Effects of water regime, crop residue, and fertilizer application. Glob. Biogeochem. Cycles 2005, 19, 153–174. [Google Scholar] [CrossRef]
  24. He, Y.; Zhou, X.; Jiang, L.; Li, M.; Du, Z.; Zhou, G.; Shao, J.; Wang, X.; Xu, Z.; Bai, S.; et al. Effects of biochar application on soil greenhouse gas fluxes: A meta-analysis. GCB Bioenergy 2017, 9, 743–755. [Google Scholar] [CrossRef]
  25. Mukome, F.N.D.; Six, J.; Parikh, S.J. The effects of walnut shell and wood feedstock biochar amendments on greenhouse gas emissions from a fertile soil. Geoderma 2013, 200–201, 90–98. [Google Scholar] [CrossRef]
  26. Montanarella, L.; Lugato, E. The application of biochar in the EU: Challenges and opportunities. Agron 2013, 3, 462–473. [Google Scholar] [CrossRef]
  27. Paustian, K.; Larson, E.; Kent, J.; Marx, E.; Swan, A. Soil C sequestration as a biological negative emission strategy. Front. Clim. 2019, 1, 8. [Google Scholar] [CrossRef]
  28. Cayuela, M.L.; van Zwieten, L.; Singh, B.P.; Jeffery, S.; Roig, A.; Sanchez-Monedero, M.A. Biochar’s role in mitigating soil nitrous oxide emissions: A review and meta-analysis. Agric. Ecosys. Environ. 2014, 191, 5–16. [Google Scholar] [CrossRef]
  29. IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team; Pachauri, R.K., Meyer, L.A., Eds.; IPCC: Geneva, Switzerland, 2013. [Google Scholar]
  30. Liu, X.; Mao, P.N.; Li, L.H.; Ma, J. Impact of biochar application on yield-scaled greenhouse gas intensity: A meta-analysis. Sci. Total Environ. 2019, 656, 969–976. [Google Scholar] [CrossRef]
  31. Schulz, H.; Dunst, G.; Glaser, B. Positive effects of composted biochar on plant growth and soil fertility. Agron. Sustain. Dev. 2013, 33, 817–827. [Google Scholar] [CrossRef]
  32. Kongthod, T.; Thanachit, S.; Anusontpornperm, S.; Wiriyakitnateekul, W. Effects of biochar and other organic soil amendments on plant nutrient availability in an Ustoxic Quartzipsamment. Pedosphere 2015, 25, 790–798. [Google Scholar] [CrossRef]
  33. Zhang, A.; Liu, Y.; Pan, G.; Hussain, Q.; Li, L.; Zheng, J.; Zhang, X. Effect of biochar amendment on maize yield and greenhouse gas emissions from a soil organic carbon poor calcareous loamy soil from Central China Plain. Plant Soil 2012, 351, 263–275. [Google Scholar] [CrossRef]
  34. Liu, Y.; Lu, H.; Yang, S.; Wang, Y. Impacts of biochar addition on rice yield and soil properties in a cold waterlogged paddy for two crop seasons. Field Crops Res. 2016, 191, 161–167. [Google Scholar] [CrossRef]
  35. Wang, C.; Liu, J.; Shen, J.; Chen, D.; Li, Y.; Jiang, B.; Wu, J. Effects of biochar amendment on net greenhouse gas emissions and soil fertility in a double rice cropping system: A 4-year field experiment. Agric. Ecosyst. Environ. 2018, 262, 83–96. [Google Scholar] [CrossRef]
  36. Qi, L.; Pokharel, P.; Chang, S.X.; Zhou, P.; Niu, H.; He, X.; Wang, Z.; Gao, M. Biochar application increased methane emission, soil carbon storage and net ecosystem carbon budget in a 2-year vegetable–rice rotation. Agric. Ecosyst. Environ. 2020, 292, 106831. [Google Scholar] [CrossRef]
  37. Atkinson, C.J.; Fitzgerald, J.D.; Hipps, N.A. Potential mechanisms for achieving agricultural benefits from biochar application to temperate soils: A review. Plant Soil. 2010, 337, 1–18. [Google Scholar] [CrossRef]
  38. Duku, H.M.; Gu, S.; Hagan, E.B. Biochar production potentials in Ghana—A review. Renew. Sustain. Energy Rev. 2011, 15, 3539–3551. [Google Scholar] [CrossRef]
  39. De Gryze, S.; Cullen, M.; Durschinger, L.; Lehmann, J.; Bluhm, D.; Six, J. Evaluation of opportunities for generating carbon offsets from soil sequestration of biochar. In An Issues Paper Commissioned by the Climate Action Reserve, 5th ed.; Terra Global Capital LLC: Oakland, CA, USA, 2010; pp. 1–99. Available online: http://www.terraglobalcapital.com/press/Soil (accessed on 23 May 2019).
  40. de Vasconcelos, A.C.F.; Chaves, L.H.G.; Gheyi, H.R.; Fernandes, J.D.; Tito, G.A. Crambe growth in a soil amended with biochar and under saline irrigation. Commun. Soil Sci. Plant Anal. 2017, 48, 1291–1300. [Google Scholar] [CrossRef]
  41. El-Eyuoon, A.; Amin, A.Z. Impact of corn cob biochar on potassium status and wheat growth in a calcareous sandy soil. Commun. Soil Sci. Plant Anal. 2016, 47, 2026–2033. [Google Scholar] [CrossRef]
  42. Amendola, C.; Montagnoli, A.; Terzaghi, M.; Trupiano, D.; Oliva, F.; Baronti, S.; Miglietta, F.; Chiatante, D.; Scippa, G.S. Short-term effects of biochar on grapevine fine root dynamics and arbuscular mycorrhizae production. Agric. Ecosyst. Environ. 2017, 239, 236–245. [Google Scholar] [CrossRef]
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Figure 1. Daily mean precipitation, air and soil temperature at the experimental site during rice growing season of 2015.
Figure 1. Daily mean precipitation, air and soil temperature at the experimental site during rice growing season of 2015.
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Figure 2. Daily mean precipitation, air and soil temperature at the experimental site during rice growing season of 2016.
Figure 2. Daily mean precipitation, air and soil temperature at the experimental site during rice growing season of 2016.
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Figure 3. Daily mean precipitation, air and soil temperature at the experimental site during rice growing season of 2017.
Figure 3. Daily mean precipitation, air and soil temperature at the experimental site during rice growing season of 2017.
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Figure 4. Interactive effects of biochar amendment and N fertilizer on the CH4 flux during rice growing seasons of (a) 2015 (b) 2016 and (c) 2017. Data are means ± SE (n = 3).
Figure 4. Interactive effects of biochar amendment and N fertilizer on the CH4 flux during rice growing seasons of (a) 2015 (b) 2016 and (c) 2017. Data are means ± SE (n = 3).
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Figure 5. Interactive effects of biochar amendment and N fertilizer on N2O flux during rice growing seasons of (a) 2015, (b) 2016, and (c) 2017. Data are means ± SE (n = 3).
Figure 5. Interactive effects of biochar amendment and N fertilizer on N2O flux during rice growing seasons of (a) 2015, (b) 2016, and (c) 2017. Data are means ± SE (n = 3).
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Figure 6. Interactive effects of biochar and N fertilizer on aboveground biomass at maturity for 3 years. Different letters in a single column indicate difference (p <0.05) among the treatments. Data are means ± SE (n = 3).
Figure 6. Interactive effects of biochar and N fertilizer on aboveground biomass at maturity for 3 years. Different letters in a single column indicate difference (p <0.05) among the treatments. Data are means ± SE (n = 3).
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Figure 7. Interactive effects of biochar and N fertilizer on grain yield for 3 years. Different letters in a single column indicate difference (p<0.05) among the treatments. Data are means ± SE (n = 3).
Figure 7. Interactive effects of biochar and N fertilizer on grain yield for 3 years. Different letters in a single column indicate difference (p<0.05) among the treatments. Data are means ± SE (n = 3).
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Table 1. Treatment details of both N fertilizer and biochar application rates.
Table 1. Treatment details of both N fertilizer and biochar application rates.
TreatmentN Rate (kg N ha−1)Biochar Rate (t ha−1)
N1C01200.0
N1C11201.0
N1C21201.5
N1C31202.0
N2C01800.0
N2C11801.0
N2C21801.5
N2C31802.0
Table 2. Impact of biochar and N fertilizer on cumulative CH4 for three years.
Table 2. Impact of biochar and N fertilizer on cumulative CH4 for three years.
TreatmentCumulative CH4 (kg ha−1)
201520162017
N1C0193.34 ± 29.39 ab188.04 ± 16.44 a132.38 ±2.58 c
N1C1272.68 ± 21.83 a164.16 ± 4.09 a220.41 ± 9.85 ab
N1C2255.91 ± 11.76 ab155.90 ± 2.20 a177.91 ± 25.60 abc
N1C3242.61 ± 30.87 ab158.46 ± 33.49 a233.16 ± 17.90 a
N2C0151.76 ± 21.42 b180.57 ± 25.55 a225.85 ± 20.09 ab
N2C1224.05 ± 62.56 ab167.52 ± 6.09 a239.35 ± 30.25 a
N2C2187.37 ± 5.23 ab153.43 ± 14.95 a156.80 ± 19.46 bc
N2C3206.99 ± 20.61 ab190.47 ± 2.60 a240.02 ± 27.98 a
Different superscript letters in a single column indicate difference (p < 0.05) among the treatments in 2015, 2016, and 2017. The same applies to the following tables. Data are means ± SE (n = 3).
Table 3. Impact of biochar and N fertilizer on cumulative N2O for three years.
Table 3. Impact of biochar and N fertilizer on cumulative N2O for three years.
TreatmentCumulative N2O (kg ha−1)
201520162017
N1C01.15 ± 0.16 a0.78 ± 0.12 ab1.13 ±0.12 a
N1C11.05 ± 0.29 a1.07 ± 0.11 a0.87 ± 0.17 a
N1C21.20 ± 0.11 a0.85 ± 0.10 ab1.10 ± 0.30 a
N1C31.47 ± 0.22 a0.78 ± 0.08 ab0.75 ± 0.06 a
N2C01.43 ± 0.09 a0.54 ± 0.01 b0.91 ± 0.06 a
N2C11.45 ± 0.18 a0.93 ± 0.06 ab0.76 ± 0.04 a
N2C21.45 ± 0.04 a0.56 ± 0.02 b0.77 ± 0.08 a
N2C31.10 ± 0.06 a0.68 ± 0.02 ab0.76 ± 0.13 a
Different superscript letters in a single column indicate difference (p < 0.05) among the treatments in 2015, 2016, and 2017, respectively. The same applies to the following tables. Data are means ± SE (n = 3).
Table 4. Interactive effects of biochar and N fertilizer on GWP for three years.
Table 4. Interactive effects of biochar and N fertilizer on GWP for three years.
TreatmentGWP (kg ha−1)
201520162017
N1C05176.6 ± 597.9 ab4934.0 ± 446.7 a3644.7 ±83.9 c
N1C17130.1 ± 464.0 a4423.6 ± 133.2 a5770.6 ± 267.5 ab
N1C26755.2 ± 263.3 ab4149.3 ± 28.4 a4775.1 ± 723.2 abc
N1C36501.9 ± 780.2 ab4193.0 ± 853.5 a6051.3 ± 438.1 a
N2C04220.6 ± 508.2 b4674.1 ± 639.6 a5917.8 ± 504.1 ab
N2C16033.4 ± 1511.5 ab4466.2 ± 141.2 a6209.2 ± 760.6 a
N2C25114.8 ± 133.1 ab4003.3 ± 397.2 a4149.2 ± 467.1 bc
N2C35502.4 ± 761.5 ab4963.0 ± 98.8 a6225.9 ± 695.9 a
Different superscript letters in a single column indicate difference (p < 0.05) among the treatments in 2015, 2016 and 2017, respectively. The IPCC GWP factors (mass basic, kg CO2 equivalent ha−1) for CH4 and N2O are 25 and 298 in the time horizon of 100 years, respectively. Data are means ± SE (n = 3).
Table 5. Interactive effects of biochar and N fertilizer on GHGI for three years.
Table 5. Interactive effects of biochar and N fertilizer on GHGI for three years.
TreatmentGHGI (kg kg−1)
201520162017
N1C00.63 ± 0.07 abc0.70 ± 0.05 a0.50 ± 0.02 bc
N1C10.82 ± 0.05 a0.52 ± 0.01 bc0.72 ± 0.04 a
N1C20.76 ± 0.05 ab0.57 ± 0.02 abc0.58 ± 0.08 abc
N1C30.76 ± 0.14 ab0.51 ± 0.10 bc0.74 ± 0.04 a
N2C00.55 ± 0.07 bc0.57 ± 0.07 abc0.70 ± 0.06 ab
N2C10.52 ± 0.17 c0.56 ± 0.06 abc0.70 ± 0.09 ab
N2C20.55 ± 0.01 bc0.43 ± 0.02 c0.46 ± 0.06 c
N2C30.61 ± 0.07 abc0.62 ± 0.03 ab0.69 ± 0.07 ab
Different superscript letters in a single column indicate difference (p < 0.05) among the treatments in 2015, 2016 and 2017, respectively. GHGI (kg CO2 equivalent kg−1 grain yield) is calculated by dividing GWPs of CH4 and N2O emissions by rice yield. Data are means ± SE (n = 3).
Table 6. Interactive effects of biochar and N fertilizer on soil organic carbon (SOC) content for three years.
Table 6. Interactive effects of biochar and N fertilizer on soil organic carbon (SOC) content for three years.
TreatmentSOC (g kg−1)
201520162017
N1C013.65 ± 0.30 a13.92 ± 0.73 a13.44 ± 0.92 b
N1C113.84 ± 0.44 a13.75 ± 0.72 a14.94 ± 0.77 ab
N1C213.86 ± 0.22 a14.61 ± 1.10 a14.97 ± 0.41 ab
N1C314.16 ± 0.59 a13.33 ± 0.38 a15.53 ± 0.94 a
N2C013.44 ± 0.09 a13.68 ± 0.48 a13.96 ± 0.67 ab
N2C114.10 ± 0.04 a13.99 ± 1.39 a14.18 ± 0.36 ab
N2C214.12 ± 0.03 a13.99 ± 0.39 a14.88 ± 0.24 ab
N2C314.21 ± 0.15 a13.90 ± 0.94 a15.24 ± 0.27 a
Different superscript letters in a single column indicate difference (p < 0.05) among the treatments in 2015, 2016 and 2017, respectively. Data are means ± SE (n = 3).
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Dong, W.; Danso, F.; Tang, A.; Zhang, J.; Liu, Y.; Meng, Y.; Zhang, X.; Wang, L.; Yang, Z. Biochar: An Option to Maintain Rice Yield and Mitigate Greenhouse Gas Emissions from Rice Fields in Northeast China. Agronomy 2024, 14, 3050. https://doi.org/10.3390/agronomy14123050

AMA Style

Dong W, Danso F, Tang A, Zhang J, Liu Y, Meng Y, Zhang X, Wang L, Yang Z. Biochar: An Option to Maintain Rice Yield and Mitigate Greenhouse Gas Emissions from Rice Fields in Northeast China. Agronomy. 2024; 14(12):3050. https://doi.org/10.3390/agronomy14123050

Chicago/Turabian Style

Dong, Wenjun, Frederick Danso, Ao Tang, Jun Zhang, Youhong Liu, Ying Meng, Xijuan Zhang, Lizhi Wang, and Zhongliang Yang. 2024. "Biochar: An Option to Maintain Rice Yield and Mitigate Greenhouse Gas Emissions from Rice Fields in Northeast China" Agronomy 14, no. 12: 3050. https://doi.org/10.3390/agronomy14123050

APA Style

Dong, W., Danso, F., Tang, A., Zhang, J., Liu, Y., Meng, Y., Zhang, X., Wang, L., & Yang, Z. (2024). Biochar: An Option to Maintain Rice Yield and Mitigate Greenhouse Gas Emissions from Rice Fields in Northeast China. Agronomy, 14(12), 3050. https://doi.org/10.3390/agronomy14123050

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