Three years of biochar and straw application could reduce greenhouse gas and improve rice productivity

ABSTRACT

Applying soil amendments such as biochar has been proposed as a means to improve soil properties and reduce greenhouse gas emission from agricultural soils. The objective of this study was to evaluate and compare the effects of biochar and straw application on rice yield, select soil properties, global warming potential (GWP), and greenhouse gas intensity (GHGI) over the 3-year period. The study treatments included control (CN), barley straw biochar (BC, 2,000 kg ha⁻¹), barley straw (BS, 2,000 kg ha⁻¹), and BC+BS (each 1,000 kg ha⁻¹). During the rice-growing season, significant interactive effects of BC and BS treatment were observed in major soil properties. The average rice yields in BC, BS, and BC+BS treatments increased by 4.6%, 4.7%, and 18.7%, compared with CN treatment, respectively. Also, BC+BS treatment produced relatively stable yield during the 3-year study. Both BC and BS applications, either alone or in combination, lowered soil bulk density values compared to the CN treatment. Further, BC application produced a low total CH₄ flux than other treatments. The total produced GWP over the 3 years occurred in the order: BS ≧ CN > BC+BS > BC treatment. These results clearly demonstrate the advantages of biochar and straw applications in improving rice yield and soil fertility while reducing GWP and GHGI.

KEYWORDS:

• Barley straw
• biochar
• global warming potential
• greenhouse gas intensity
• paddy

1. Introduction

Global atmospheric concentration of greenhouse gases has increased markedly from pre-industrial levels (Qin et al. 2016; Qi et al. 2018). Researches are underway in multifold sectors to reduce greenhouse gas emissions and to adapt to climatic variability (Sun et al. 2018; Yang et al. 2019). Agriculture is a major source of three primary greenhouse gas [carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)] emissions and thus anthropogenic radiative climate forcing (Acharya, Rasmussen, and Eriksen 2012). As such, climate-smart technologies, improved agricultural management, and conservation measures are necessary to lower greenhouse gas emissions.

The warming of climate systems is unequivocal; one principal cause being the anthropogenic emission of CO₂, CH₄, and N₂O into atmosphere (2007; Thangarajan et al. 2013). The greenhouse effect of CH₄ and N₂O is 25 and 298 times, respectively, to that of CO₂, making them important greenhouse gases (Linquist et al. 2012; IPCC 2013; Wang et al. 2017). Paddy fields, which produce the most important food resource in many Asian countries (Ghimire et al. 2017), are particularly recognized as major sites of CH₄ and N₂O emission and must be managed to prevent further climate warming (Ghimire et al. 2017; Jeong et al. 2017).

CH₄ emission from rice fields is affected by methanogenic bacteria, organic matter, and soil redox potential levels; methanogenesis varying with soil, climate, and management factors (Liu and Wu 2004; Liu et al. 2011). Particularly, CH₄ fluxes depend on agricultural water and fertilizer management. In addition, the level of CH₄ generated greatly depends on measurement time and area. N₂O emissions, on the other hand, are primarily affected by N fertilizer – type (inorganic fertilizer, straw, compost, manure, etc.), placement, time, and application methods (single vs. split) and environmental factors such as soil moisture, temperature, and aeration (Bouwman, Boumans, and Batjes 2002; Ruser et al. 2006). In recent years, there has been surge in studies on methods to stabilize or reduce CH₄ and N₂O emissions from rice-based cropping systems but long-term studies are generally few (Zhang et al. 2014; Gutekunst, Vargas, and Seyfferth 2017; Liang et al. 2017; Zhou et al. 2018).

Rice continues to be one of the most important food crops and major staple food for over half of the global population including South Korea (Cheng 2020). It provides up to 50% dietary caloric supply and a source of income for Asia’s poor population (Muthayya et al., 2014). Despite Asia contributing about 90% of global rice production, there are considerable uncertainties on future growth in rice yield and water savings due to climatic change. In South Korea, with the introduction of rice tariffication law and diminishing rice prices, rice acreage is decreasing (Jeong et al. 2021). For example, rice production in South Korea in 2016 was 14.6% lower than that in 2009. Global rice production continues to face challenges from excessive use of inorganic fertilizers, strong summer rainfall, and intensive tillage practices. Such practices on cropland could significantly deteriorate in soil health and physical quality and increase CH₄ and N₂O emissions. Globally, rice fields contribute to 30% and 11% of agricultural CH₄ and N₂O emissions, respectively (Hussain et al. 2015). As such, improved management is necessary to enhance soil properties and crop yield and reduce greenhouse gas emission. While researchers globally have attempted to use amendments like straw, green manures, and compost to overcome environmental challenges and sustain productivity in the face of climate change, little to no research is available on the use of biochar in South Korea.

Biochar is a carbon-rich material obtained after pyrolyzing biomass under anaerobic conditions. Applying biochar to soil systems has been proposed as a ‘carbon negative strategy’ to reduce greenhouse gas emission (Lehmann 2007; Major 2010; Sriphirom et al. 2020). Kang et al. (2021) highlighted biochar potential in reducing CH₄ and N₂O from paddy fields in South Korea. However, in a meta-analysis, He et al. (2017) reported that biochar increased soil CO₂ fluxes by 22.14%, reduced N₂O fluxes by 30.92%, and had no effect on CH₄. Decrease in N₂O fluxes is often associated with increase in aeration, soil pH, and N immobilization. Similarly, decrease in CH₄ may result from decrease in methanogenic archaea to methanotrophic proteobacteria ratio. Alternate wetting and drying water management has shown potential to reduce CH₄ emission by 19% without significantly affecting rice yields (Sriphirom et al. 2020). Although biochar amendments have shown increased rice production and reduced greenhouse gas emissions in East and Southeast Asia, most of these available results are reported from short-term studies (Yagi et al. 2020). Indeed, the effects of biochar on GHG fluxes are highly uncertain and site-specific depending on soil properties, crop management, and biochar rate and properties (He et al., 2017). Over the last few years, BC has, however, gained much attention as a soil conditioner and an amendment (Blanco-canqui et al., 2020; Kul et al. 2021). Liu et al. (2016) reported 4.4–10.6% increases in rice yield with biochar application compared with control. Biochar amendment may improve soil quality, fertilizer use efficiency, nutrient bioavailability, and crop productivity apart from environmental advantages like waste management (Blanco-Canqui 2017; Bohara et al. 2019; Blanco-canqui et al., 2020).

Barley straw could serve as an important biomass to ameliorate soil physical properties, but soils amended with barley straw may show significant increase in organic acids and decrease in nitrogen levels. This phenomenon occurs primarily due to decomposition of straw and partly due to co-occurring of the barley harvesting and rice transplantation period in the South Korean cropping systems (Kang et al. 2016). Hence, the application of biochar derived from barley straw could be among portfolio of options available for recycling of resources, environmental quality, and soil health in agricultural systems. Here, the objective of this study was to compare the effects of biochar and straw application on rice yield, soil properties, and fluxes of greenhouse gas during a 3-year period. The results from this study will be beneficial in developing sustainable strategies for improving soil and environmental quality and crop productivity in rice-based cropping systems.

2. Materials and methods

2.1. Raw materials

Here, soil was classified as sandy loam with a bulk density of 1.31 Mg m⁻³, porosity of 56.3%, pH of 5.87, and cation exchange capacity (CEC) of 6.64 cmol_c kg⁻¹. Barley Straw (BS) feedstock was collected from local farm in Gwangyang City, South Korea, and biochar was produced at the Sunchon National University. The organic matter (OM), TN, P₂O₅, and K₂O contents of the BS feedstock were 93.2%, 0.27%, 0.11%, and 2.34%, respectively. The straw was placed in a covered stainless-steel container and subjected to pyrolysis in a furnace [DK-1015(E), STI tech, Gumi, Republic of Korea] under oxygen-limited conditions (Kang et al. 2016). N₂ gas was used to purge the stainless-steel container to allow limited oxygen, and the temperature in the internal biomass chamber was ramped to 400°C at 3°C min⁻¹. The temperature was maintained at this peak temperature for nearly 1 hour. The barley straw biochar (BC) obtained was ground and filtered through a 2 mm sieve for use in the field experiment. The pH, total nitrogen (TN), P₂O₅, K₂O, and CEC of the BC were 7.72, 0.42%, 0.30%, 1.41%, and 21.7 cmol_c kg⁻¹, respectively. The C content in the BC was >70%.

2.2. Experimental design

The field experiment was carried out to evaluate and compare the effects of BC and BS applications on rice yield, soil properties, and GWP at Sepung-ri (34º 56ʹ 33ʺ N, 127º 33ʹ 56ʺ E), Gwangyang-eup, Gwangyang-si, Jeollanam-do, South Korea. This study is a continuation of study examining the effect of biochar and straw treatments by Kang et al. (2021). The temperatures and precipitation recorded during the experimental period are illustrated in Figure 1. The experimental area had a mild oceanic climate with mean annual temperature of 15.2ºC (range of 14.8 ~ 15.5ºC) and mean annual precipitation of 1212 mm (range of 824 ~ 1607 mm).

Figure 1. Mean daily precipitation and air temperature during the rice cultivation year.

The field experiment was conducted from 2015 to 2017 and consisted of four treatments: BC (barley straw biochar 2,000 kg ha⁻¹), BS (barley straw 2,000 kg ha⁻¹), BC+BS (barley straw biochar 1,000 kg ha⁻¹ + barley straw 1,000 kg ha⁻¹, respectively), and an untreated control (CN) plot to separate each treatment. Each treatment area was 100 m² (10 × 10 m) in size. The experiment was replicated 3 times, thus producing a total of 12 plots. BC, BS, or BC+BS were evenly spread in the respective treatments 14 days prior rice transplanting and incorporated to a depth of 15 cm. BC was applied once in 2015 only. However, BS was applied every year (Table 1). Although BC was applied only once, the effects on yield are expected to remain for several years (Dumortier et al. 2020). No inorganic fertilizers were used during rice cultivation. Rice plots were irrigated after transplantation, and drainage was performed 3 weeks before harvesting.

Table 1. Field management for rice cultivation.

Items	2015	2016	2017
Flooded period (days)	105	112	98
Rice transplanting (month/day)	5/28	5/24	5/27
Rice harvesting (month/day)	9/30	9/26	9/29
Barley straw biochar	○	×	×
Barley straw	○	○	○

○, application; ×, no application.

2.3. Sampling and soil analysis

After crop harvesting in 2017, soil samples were collected from the near-surface layer (15 cm) using a soil auger. Randomized soil samples were obtained from each plot, and a total of 14 soil samples were collected from each treatment. Soils were air-dried and sieved through a 2 mm sieve. Subsequently, all soil analyses were performed using the methods described in NIAST (2000). The soil pH and electrical conductivity (EC) were measured in a soil–water ratio of 1:5 after shaking the mixture for 30 min. Soil organic carbon (SOC) was measured using a total organic carbon analyzer (Shimadzu TOC-V_CPN) (Bhattacharjya et al., 2016), whereas the total nitrogen (TN) and available P₂O₅ (Avail. P₂O₅) analyses were performed using the Kjeldahl and Lancaster methods, respectively. Soil exchangeable cations were extracted by 1N-NH₄OAc. At the end of the experiment, soil bulk density was measured at random locations in each treatment using the core method (Grossman and Reinsch 2002).

2.4. Quantitative real-time PCR analyses

Approximately 1.0 g of soil from different treatment locations was used to extract total DNA using the FastDNA® SPIN Kit (MP-Biomedicals, U.S.A.). The extracted DNA was resuspended in 50 μL DES (DNase/Pyrogen-Free Water), and DNA concentration and quality were measured using an Optizen NanoQ spectrophotometer (Optizen, Korea). The total bacterial content was quantified using SYBR Green-based quantitative real-time PCR (qPCR). The primers used were 1114-F (5ʹ-CGGCAACGAGCGCAACCC-3ʹ) and 1275-R (5ʹ-CCATTGTAGCACGTGTGTAGCC-3ʹ) (Denman and McSweeney 2006). Quantitative PCR was performed in a 20 µL reaction mixture consisting of 10 µL of 2× QuantiSpeed SYBR mix (PhileKorea, Korea), 0.8 µL of each 10 µM primer, and 50 ng of template DNA using an Eco™ Real-Time PCR (Illumina, San Diego, CA, U.S.A.). The qPCR reactions were performed in triplicate under thermal cycler conditions of one cycle at 50°C for 2 min, and 95°C for 2 min, followed by 40 cycles at 95°C for 15 s, 60°C for 1 min and 72°C for 30 s.

2.5. Monitoring of methane (CH₄) and nitrous oxide (N₂O)

CH₄ and N₂O fluxes were measured using a static chamber with 0.02 m² area and 0.01 m³ volume. Gas sampling was performed weekly between 1 and 2 p.m. local time. The chamber was inserted to a soil depth of 20 cm between rice plants. CH₄ and N₂O measurements were made at 0, 10, 20, and 30 min after chamber closure using a 25 mL gas-tight syringe and samples were instantly transferred into gas-evacuated 10 mL soda glass vials. Samples were simultaneously analyzed on a gas chromatograph (GC-2014, Shimadzu) equipped with a flame ionization detector (FID) and an electron capture detector (ECD) for CH₄ and N₂O measurements, respectively. In FID, we maintained temperatures at 55ºC for the column, 100ºC for the injector, and 230ºC for the detector, whereas temperatures were maintained at 50ºC for the column and 310ºC for the detector in the ECD. Carrier gases were used in the experiment, which included a gas mixture of argon and methane for CH₄ and nitrogen gas for N₂O. Both CH₄ and N₂O fluxes were estimated using the Rolston equation (Rolston 1986; Kang et al. 2016; Yun et al. 2022).

F = ρ × (V/A) × (Δc/Δt) × (273/T)

where F is either CH₄ or N₂O flux, ρ is the density of CH₄ or N₂O density, V is the chamber volume (m³), A is the chamber area (m²), Δc/Δt is the increase in the rate of gas concentration per 10 min (Δc is concentration difference before and after sample collection; Δt is sampling time) and T is 273 + mean temperature in the chamber (ºC).

The total CH₄ and N₂O fluxes for the entire rice cultivation period were computed using Kang et al. (2016).

Total CH₄ and N₂O flux = $\sum _i^n\mathit{\hspace{0.17em}}\left(\mathrm{Ri}\times \mathrm{Di}\right)$

where n is the number of sampling intervals, Ri is the rate of CH₄ or N₂O emission from rice plots during the sampling interval, and Di is the number of days in the sampling interval

In addition, the effect of BC on GWP was estimated from the total CH₄ and N₂O fluxes (TF) over the rice cultivation period using the IPCC method (2007) below. This approach has been utilized in many other studies (Kang et al. 2016):

Total GWP (g CO₂ m⁻²) = 25 × TF – CH₄+298 × TF – N₂O

Rice yield (Y) and GWP were used to calculate Greenhouse Gas Intensity (GHGI) as described by Zhang et al. (2012).

GHGI = GWP/Y

2.6. Statistical analysis

Data analyses were conducted using JMP statistical analysis software from SAS 14.2 (SAS Institute Inc.). The mean values were computed as an average of three replicates. Each mean value was subjected to one-way analysis of variance (ANOVA), and a comparison of the treatments was performed using Tukey’s test. Two-way ANOVA was used to determine the differences between years, treatment conditions, and their interaction on CH₄ and N₂O fluxes, GWP, and GHGI.

3. Results

3.1. Effect of biochar and straw application on rice yield

The rice yields during the 3-year study were significantly affected by treatment types (Table 2). Significant yield difference was particularly evident in Year 2017. Rice yields for CN, BC, BS, and BC+BS treatments ranged on average from 473 to 515, 497 to 532, 516 to 528, and 583 to 602 g m⁻², respectively. Among the treatments, the BC+BS treatment produced the highest average rice yield. There was no significant difference in rice yield under BC+BS treatment within three different years. Under the BS treatment condition, rice yield tended to increase year by year, with the final yield (year 3) showing an increase of 2.4% from that in year 1. On the other hand, under the BC treatment condition, the rice yield tended to decrease gradually, and in 2017, the rice yield decreased by about 6.7% compared with Year 2015. The rice yield under BC treatment decreased in the third rice harvest cycle and was lower than that in the BS treatment. However, in the first and second rice harvest cycles, the rice yield under BC treatment was 3.22% and 2.41% higher than that of BS treatment. On an average, rice yield under combined treatment (i.e., BC+BS) was significantly higher than remaining single treatments. Average rice yield under BS, BC, and CN treatments largely remained similar.

Table 2. Rice yield (mean ± SD) under different biochar and straw treatments during the 3 years of study.

Treatment	Rice yield (g m^−2)
	2015	2016	2017	Ave.
CN	515 ± 64.1b	508 ± 26.2b	473 ± 24.1c	499 ± 28.6b
BC	532 ± 17.2ab	536 ± 20.0b	497 ± 29.5bc	522 ± 18.7b
BS	516 ± 23.9b	524 ± 5.8b	528 ± 12.1b	523 ± 8.8b
BC+BS	593 ± 36.4a	583 ± 26.6a	602 ± 34.0a	592 ± 25.4a

Different letters within the same column indicate significant differences, as determined by Tukey’s test with p < 0.05.

3.2. Effect of biochar and straw application on soil properties

Table 3 shows concentration of select soil physicochemical properties under different treatments after the final rice harvesting. Briefly, soil bulk density ranged between 1.25 and 1.33 Mg m⁻³, pH between 5.78 and 5.96, EC between 0.18 and 0.20 dS m⁻¹, SOC content between 9.04 and 10.4 g kg⁻¹, TN content between 1.39 and 1.69 g kg⁻¹, Avail. P₂O₅ content between 55.5 and 60.0 mg kg⁻¹, and CEC between 6.25 and 6.80 cmol_c kg⁻¹. Soil bulk density and pH were improved in all BC, BS, BC +BS treatments compared with the CN treatment. The SOC and TN content after BC application increased by 0.56 and 0.08 g kg⁻¹, respectively, compared to control. Further, SOC and TN content increased by 0.89–1.36 and 0.16–0.3 g kg⁻¹ after BS and BC+BS application, respectively. The soil CEC values after BC, BS, and BC+BS treatment were 0.55, 0.37, and 0.49 cmol_c kg⁻¹ greater than under CN plot, respectively. The total bacteria 16S rRNA gene components under the BC, BS, and BC+BS treatments were similar but significantly higher than under the CN treatment (Figure 2).

Figure 2. Quantification of total bacterial 16S rRNA gene components after final rice harvesting. Different letters indicate significant differences among treatments at a 5% probability level. Error bars represent standard deviations (n = 12).

Table 3. Soil properties (mean ± SD) under different treatment sites after rice harvesting.

Treatment	Bulk density	pH	EC	SOC	TN	Avail. P2O5	CEC	Total bacteria
	(Mg m^−3)	(1:5 H2O)	(dS m^−1)	—– (g kg^−1) —–		(mg kg^−1)	(cmolc kg^−1)	(log10 copies g^−1)
CN	1.33 ± 0.1a^1	5.78 ± 0.0b	0.18 ± 0.2c	9.04 ± 0.7b	1.39 ± 0.1d	56.5 ± 3.1ab	6.25 ± 0.1b	6.90 ± 0.3b
BC	1.26 ± 0.0b	5.94 ± 0.0a	0.19 ± 0.2ab	9.60 ± 1.0ab	1.47 ± 0.0c	55.5 ± 3.3b	6.80 ± 0.3a	8.05 ± 0.3a
BS	1.25 ± 0.0b	5.93 ± 0.0a	0.18 ± 0.2bc	10.4 ± 0.7a	1.69 ± 0.1a	58.6 ± 2.7ab	6.62 ± 0.4ab	7.65 ± 0.1a
BC+BS	1.26 ± 0.0b	5.96 ± 0.1a	0.20 ± 0.2a	9.93 ± 0.6ab	1.55 ± 0.0b	60.0 ± 1.1a	6.74 ± 0.3a	7.68 ± 0.4a

^aDifferent letters within the same column indicate significant differences, as determined by Tukey’s test with p < 0.05.

3.3. Effect of biochar and straw application on CH₄ and N₂O emission

The change in the CH₄ emission rates during rice cultivation under different treatment conditions is shown in Figure 3. Irrespective of the experimental years, the CH₄ emission rate ranged from 3.31–42.10 mg m⁻² hr⁻¹ in CN, 1.32–28.01 mg m⁻² hr⁻¹ in BC, 0.56–44.90 mg m⁻² hr⁻¹ in BS, and 1.45 ~ 35.82 mg m⁻² hr⁻¹ in BC+BS treatment areas during the rice cultivation. CH₄ emission patterns were similar in all treatments and years and peaked at 14–35 days after rice transplantation, gradually decreasing thereafter until rice harvesting. N₂O emissions, on the other hand, varied greatly across treatments depending on the treatment conditions (irrigation and drainage) and sampling date (Figure 4). It increased shortly after irrigation but decreased after drainage. The average rates of N₂O emission were highest in 2016, with 73.3 µg m⁻² hr⁻¹ for CN, 93.5 µg m⁻² hr⁻¹ for BC, 155.8 µg m⁻² hr⁻¹ for BS, and 134.0 µg m⁻² hr⁻¹ for BC+BS. The N₂O emission patterns were significantly different depending on the presence or absence of straw. Straw in general tended to increase both CH₄ and N₂O emission.

Figure 3. Changes in CH₄ emission rates during each of the three rice-growing seasons. Error bars represent standard deviations (n = 3).

Figure 4. Changes in N₂O emission rates during each of the three rice-growing seasons. Error bars represent standard deviations (n = 3).

3.4. Effect of biochar and straw application on total flux

The total CH₄ flux was significantly higher under straw-treated (BS and BC+BS) conditions. However, BC application produced a low total CH₄ flux compared to that of the other treatments. In 2015, the CH₄ flux of BC+BS and BC treatment areas was not significantly different, but in 2016 and 2017, the CH₄ flux of the BC+BS treatment area increased by 24.8% and 16.2% over that of the BC treatment area. Meanwhile, total CH₄ flux in the BC treatment area decreased by 2.9–40% compared to the CN area during the 3 year’s period (Table 4).

Table 4. Total CH₄ and N₂O fluxes, GWP, and GHGI (mean ± SD) during three rice-growing seasons.

Year	Treatment	Total fluxes (g m^−2)		GWP	GHGI
		CH4	N2O	(Mg CO2 ha^−1)
2015	CN	42.0 ± 1.7a^1	0.16 ± 0.0c	10.98 ± 0.1a	2.18 ± 0.4a
	BC	25.2 ± 0.7b	0.25 ± 0.0bc	7.03 ± 0.0b	1.32 ± 0.1b
	BS	38.8 ± 1.5a	0.37 ± 0.0a	10.81 ± 0.0a	2.11 ± 0.1a
	BC+BS	25.7 ± 1.4b	0.32 ± 0.0ab	7.38 ± 0.0b	1.25 ± 0.0b
2016	CN	38.0 ± 2.2b	0.22 ± 0.0b	10.15 ± 0.1b	2.00 ± 0.1b
	BC	26.9 ± 3.3c	0.28 ± 0.0b	7.56 ± 0.1c	1.41 ± 0.1d
	BS	45.3 ± 0.3a	0.47 ± 0.0a	12.72 ± 0.0a	2.43 ± 0.0a
	BC+BS	33.5 ± 2.7b	0.41 ± 0.0a	9.59 ± 0.1b	1.65 ± 0.1c
2017	CN	40.9 ± 4.0ab	0.22 ± 0.1b	10.88 ± 0.1ab	2.34 ± 0.3a
	BC	29.7 ± 3.1c	0.27 ± 0.0ab	8.25 ± 0.1c	1.68 ± 0.2b
	BS	43.9 ± 2.4a	0.37 ± 0.0a	12.09 ± 0.1a	2.30 ± 0.1a
	BC+BS	34.6 ± 3.5bc	0.33 ± 0.1ab	9.62 ± 0.1bc	1.59 ± 0.1b
Total	CN	120.8a	0.60c	32.01a
	BC	81.8b	0.80b	22.84c
	BS	128.0a	1.22a	35.63a
	BC+BS	93.8b	1.06a	26.60b
Statistical analysis^2
Year (A)		ns	ns	ns	ns
Treatment (B)		***	***	***	***
A × B		ns	***	ns	ns

^aDifferent letters within the same column indicate significant differences between treatments, as determined by Tukey’s test with p < 0.05.

^bns, *, **, and *** denote not significant (ns) and significant different differences at the 5, 1, and 0.1% levels, respectively.

The total N₂O flux during the rice-growing period occurred in the order: BS ≥ BC+BS > BC > CN (Table 4). Similarly, the GWP over the 3 years occurred in the order: BS ≥ CN > BC+BS > BC treatment.

4. Discussion

Biochar and straw application to agricultural fields could potentially improve crop yield by improving soil properties; however, the efficacy may vary with extent, rate and type of biochar, pyrolysis conditions, soil, climate, and management practices (Burrell et al. 2016; Bonin et al. 2018). Yield effects under biochar are mostly apparent in nutrient-poor and marginal soils (Laird et al. 2017). In this study, we observed positive effects of the BC and BS application in select soil chemical properties such as pH, SOC, TN, and CEC after final rice harvesting in 2017. Also, a decrease in the soil bulk density was observed. Microbial activity appeared to be maintained at a higher level following biochar and straw application compared with the control treatment. Indeed, biochar could lower soil bulk density through either dilution effects or improved soil porosity. Biochar and straw application could increase exchangeable cations in soils by improving soil organic matter content and therefore surface area for cation adsorption (Blanco-Canqui 2017). BC treatment increased rice yield by 3.30–5.51% compared to control in each of the 3 years. The BS treatment, encompassing straw application every year, showed a steady increase in rice yield and produced a slightly different trend from that of the BC treatments. These observations agree with the report by Zhang et al. (2012), Liu et al. (2016) and Hu et al. (2016) that showed significant positive effect of biochar and straw amendment on rice yield. Indeed, rice yield may differ with soil type, nutrient status, physical properties, climate, and management, with the straw application showing a significant positive effect.

Yield gains with BC and BS applications are governed by a number of mechanisms. Jeffery et al. (2011) highlighted the benefits of improved water-holding capacity and liming effects in crop yields. Others have correlated yield with increase in water holding and plant available water (Zhou et al. 2019). Indeed, biochar application to soil improves soil bulk density, aeration, water retention, and SOC content and, therefore, soil health and productivity (Purakayastha, Kumari, and Pathak 2015; Fahad et al. 2016; Liu et al. 2016; Mohammadi et al. 2016). Straw, on the other hand, may have been mineralized during the 3-year study, resulting in an increase in nutrient use efficiency, and influencing rice yields (Thammasom et al. 2016; Zhang et al. 2017).

The application of BC+BS mixture appears to maintain stable rice production and soil quality. While BS can compensate for insufficient nutrients in BC, BC will retain nutrient elution by BS. Many previous studies reported higher nutrient availability to crops when BC was combined with additional nutrients in agricultural soils (Chan et al. 2007; Agegnehu, Nelson, and Bird 2016). Thus, the combination of BC and BS is likely to improve yield by potentially improving nutrient availability compared to sole application. We observed a lower GWP with BC application compared with other treatments, which is in par with several previous studies (Liu et al. 2014; Shen et al. 2014). Interestingly, BC was applied only once at the beginning of the study, but the GWP was significantly lower in all biochar-amended soils both alone or combined in all 3 years. On an average, BC had the lowest GWP compared with other treatments – In a global meta-analysis, Xu et al. (2021) reported 27% reduction in GWP with biochar application. The effect of biochar on GWP is largely dependent on biochar’s CN ratio. An increase in biochar CN ratio is likely to decrease soil N mineralization intensity. Further, biochar increases cation exchange capacity through increased surface area for cation adsorption, thus adsorbs NH₄+ (adsorbed on negatively charged COOH and OH groups) and NO₃- (retained in intrapores of biochar or adsorbed by positively charged base functional groups) to reduce N₂O emission (Xu et al. 2021)

GWP is highly dependent on CH₄ emission in the paddy environment. The anaerobic decomposition of organic materials and the processes of nitrification and denitrification can affect CH₄ and N₂O emissions. Biochar may, however, increase N₂O due to improved soil water content and thus denitrification. CH₄ is likely to increase due to inhibition of soil methanotrophs (He et al., 2016) significant tradeoff between CH₄ and N₂O may exist with shifts from continuous flooding to midseason drainage management in rice paddies (Zou et al. 2005). Indeed, CH₄ and N₂O fluxes depend on soil texture, soil temperature, organic matter, organic amendments, soil microorganisms, and water management, among other factors (Minami 1994; Ruser et al. 2006; Sander, Samson, and Buresh 2014; Hu et al. 2016). In this study, the BS application acted as a source of decomposable carbon and nitrogen, increasing CH₄ and N₂O emissions and producing a relatively high GWP. Similar increase in GWP following straw application has been reported by other researchers. Thammasom et al. (2016) reported higher CH₄ emissions and GWP from straw application than those from biochar treatments. Zhang et al. (2012) reported that straw application generates more CH₄ emissions because it promotes methanogenesis with abundant methane-producing substrates. Also, compared to N₂O emissions, CH₄ emissions from rice fields have been reported to contribute significantly more to GWP increases (Cai, Shan, and Xu 2007; Bridgham et al. 2013; Pandey et al. 2014; Wang et al. 2018, 2019). We also observed consistently lower GHGI under BC+ BS treatment due largely to consistent rice yield and lower GWP. Because straw application (BS) produced a higher GWP than the other treatments, our results suggest on combining BS with biochar (BC+ BS) for reducing CH₄ and N₂O fluxes and GWP. However, long-term biochar studies under different soil amendments, nutrients, and water management are necessary to better understand GWP and minimize CH₄ and N₂O emissions in paddy fields.

5. Conclusion

This study comparing biochar and straw application effects on rice yield, select soil properties, and GWP indicates that BC and BS have potential to maintain or improve soil quality and rice yields, and lower greenhouse gas emission. However, BC alone resulted in inconsistent rice production, and BS-alone increased greenhouse gas emissions. Thus, our results suggest for a combination of a single BC treatment and annual BS supplementation to increase rice yield and lower GWP. Our results also suggest the benefits of using BC and BS as soil amendment to reduce dependency on inorganic fertilization and to improve agricultural sustainability.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2022R1F1A1064576), and this research was supported by “Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021RIS-002). The authors also acknowledge the support of “Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ015568)” Rural Development Administration, Republic of Korea.