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Article

Biochar Co-Compost: A Promising Soil Amendment to Restrain Greenhouse Gases and Improve Rice Productivity and Soil Fertility

by
Muhammad Umair Hassan
1,
Guoqin Huang
1,*,
Rizwan Munir
2,
Tahir Abbas Khan
1 and
Mehmood Ali Noor
1
1
Research Center on Ecological Sciences, Jiangxi Agricultural University, Nanchang 330045, China
2
School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang 330013, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(7), 1583; https://doi.org/10.3390/agronomy14071583
Submission received: 12 June 2024 / Revised: 4 July 2024 / Accepted: 13 July 2024 / Published: 20 July 2024
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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Abstract

:
Agriculture is a major source of greenhouse gas (GHG) emissions. Biochar has been recommended as a potential strategy to mitigate GHG emissions and improve soil fertility and crop productivity. However, few studies have investigated the potential of biochar co-compost (BCC) in relation to soil properties, rice productivity, and GHG emissions. Therefore, we examined the potential of BC, compost (CP), and BCC in terms of environmental and agronomic benefits. The study comprised four different treatments: control, biochar, compost, and biochar co-compost. The application of all of the treatments increased the soil pH; however, BC and BCC remained the top performers. The addition of BC and BBC also limited the ammonium nitrogen (NH4+-N) availability and increased soil organic carbon (SOC), which limited the GHG emissions. Biochar co-compost resulted in fewer carbon dioxide (CO2) emissions, while BC resulted in fewer methane (CH4) emissions, which was comparable with BCC. Moreover, BC caused a marked reduction in nitrous oxide (N2O) emissions that was comparable to BCC. This reduction was attributed to increased soil pH, nosZ, and nirK abundance and a reduction in ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) abundance. The application of different amendments, particularly BCC, favored rice growth and productivity by increasing nutrient availability, soil carbon, and enzymatic activities. Lastly, BCC and BC also increased the abundance and diversity of soil bacteria, which favored plant growth and caused a reduction in GHG emissions. Our results suggest that BCC could be an important practice to recycle organic sources while optimizing climate change and crop productivity.

1. Introduction

Food production nourishes the world; however, it warms the planet by generating approximately one-third of the total greenhouse gas emissions (GHGs [1]). The global average temperature is continuously soaring, and it is projected to increase by 2 °C by the end of the 21st century [2] due to a substantial increase in global warming and GHG emissions [3]. Agriculture is one of the main sources of GHG emissions, and it accounts for 58%, 51%, and 20% of anthropogenic nitrous oxide (N2O), methane (CH4) emissions, and carbon dioxide (CO2) emissions, respectively [4,5,6,7]. Agriculture is the single largest source of N2O emission, and more than 50% of N2O emission is contributed by fertilized soils [8]. Thus, reducing the GHG emissions would reduce global warming and subsequently improve the stratospheric ozone layer’s stability [8].
Rice is a leading cereal crop and a staple food for more than 50% of the world’s population [9]; however, a significant amount of GHGs is emitted from rice fields. For instance, N2O and CH4 emissions from rice fields account for 10% and 20% of N2O and CH4 emissions from the agriculture sector [6,9]. Soils are an important source of carbon storage, and they store three times more carbon compared to the atmosphere; however, industrialized agriculture is leading to a rapid loss of carbon from soils [10]. This loss of carbon can compromise the capacity of the agro-ecosystem to produce food and transfer it from GHG sinks into a net source [11]. Therefore, efforts must be put forth to increase soil carbon to promote soil health and combat climate change [12]. The application of organic amendments, like biochar (BC) and compost, is considered an important strategy to improve soil organic carbon (SOC) and mitigate climate change [13,14]. The ability of BC to reduce GHGs has further reinforced their importance in mitigating climate change. Different recent studies have recorded that BC application reduces soil CO2, N2O, and CH4 emissions [4,15,16]. However, the extent and magnitude of the effect of BC on GHG depends on the feedstock type, pyrolysis conditions, the rate of BC application, soil properties, and climatic conditions [17].
Biochar application reduces the soil bulk density; improves soil texture, soil pH, and porosity [18,19,20]; and modifies soil microbial communities linked to GHG emissions [20,21,22]. Biochar also reduces soil ammonification and nitrification, which in turn affects GHG emissions from agricultural soils [23,24]. Besides this, BC also directly absorbs N2O and causes a reduction in production and emission rates of N2O [25], while another group of authors reported that BC can increase N2O emissions [26]. This difference could be due to the feedstock type, biochar properties, and soil and climatic conditions [27]. Furthermore, BC also reduces CH4 emissions by increasing the abundance of methanotrophic proteobacteria and decreasing the proportion of methanogenic archaea [28]. Additionally, BC has a large surface area and oxygenated function groups, which adsorb the CH4 and promote its oxidation and, therefore, reduces CH4 emission [29].
Due to different opinions on the impacts of BC in mitigating GHG emissions, the modification of BC has been employed to reduce GHG emissions. Globally, BC is used in combination with different fertilizers and organic amendments (manures and compost) to improve its efficiency in reducing GHG emissions. For instance, BC, in combination with nitrogen, reduced the N2O emissions by 7.57–12.93% [30], while Harrison et al. [31] reported that biochar composting appreciably decreases CH4 emissions. Recently, biochar co-compost (BCC) has emerged as an effective strategy to improve crop productivity and soil fertility and reduce GHG emissions. The application of BCC improves crop yield and soil health [32,33], and the effect of BCC may lead to agronomic advantages compared to other amendments [33]. These advantages may be due to the high retention of feedstock N [33,34] and the adsorption of ammonium (NH4+) and nitrate (NO3−) on the BC surface due to the formation of a mixed-charged organo-mineral layer because of the composting process [35,36]. Furthermore, much remains unknown about how agro-ecosystems would respond to BCC application when considering the agronomic, soil, and environmental (GHG) emission impacts. However, there could be some limitations to using BC and BCC; for instance, BC and BCC may carry toxic metals, which can affect plant growth and development [37]. Their benefits can have different effects under different soil and climate conditions, and they do not have positive effects on all soil types [38].
The effect of BC on soil fertility, rice productivity, and GHG emissions is well-documented in the literature. However, in the literature, no study is available regarding the impact of BCC on rice productivity, soil fertility, gene abundance, microbial activities, and GHG emissions. We hypothesized that the impact of BC and BCC on GHG emissions will depend on soil properties, gene abundance, and soil microbial activities. Thus, this study was performed with the following objectives: (i) to compare the impacts of BC, compost, and BCC on GHG emissions; (ii) to test the impact of BC, compost, and BCC on soil properties and rice productivity; and (iii) to assess the effects of BC, compost, and BCC on gene abundance and microbial diversity and abundance.

2. Materials and Methods

2.1. Experimental Details

The present incubation and pot studies were conducted to determine the effect of BC, compost, and BCC on rice crop performance soil properties, and fluxes of GHG emissions. The studies were performed at Jiangxi Agricultural University (28°46′ N, 115°55′ E) in Nanchang, China. Soil samples for the incubation and pot studies were collected from the experimental field (0–20 cm). The study site has a humid subtropical climate with a rainy monsoon climate. The collected soil was dried and sieved to remove all the debris and then used to determine different properties. The soil had a silt loam texture (sand: 25.1%, silt: 57.2%, and clay: 17.3%), with a pH of 5.39, 11.62 g kg−1 of organic carbon, 26.33 and 108.13 of mg kg−1 available phosphorus (P) and potassium (K), total nitrogen (N) of 1.56 g kg−1, and a cation-exchange capacity of 7.38 cmol kg−1. The study comprised different treatments: control, biochar (BC: 2%), compost (2%), and biochar co-compost (BCC: 2%).

2.2. Preparation of Biochar and Biochar Co-Compost

Biochar produced at temperatures of 500–700 °C has shown promising results in improving soil fertility and reducing GHG emissions [39,40,41]. Therefore, to prepare biochar for this study, maize straws were collected, dried, and pyrolyzed at 600 °C for 8 h to prepare BC. The prepared BC was sieved (2 mm) and tested for different properties following standard procedures. The resulting BC had an alkaline pH (9.90), total carbon content of 640 g kg−1, and nitrogen (N) content of 4.52 g kg−1. The compost was prepared using dairy manure and crop residues. To prepare BCC, dairy manure and crop residues were mixed. The mixture was turned weekly until the final preparation of BCC. Biochar was added at the rate of 6% to prepare BCC, as application rates of 3–9% have shown beneficial results such as increased nutrient retention and reduced GHG emission during composting [42,43]. The compost used in the study had a pH of 7.32, total carbon content of 354 g kg−1, cation-exchange capacity (CEC) of 8.2 cmol kg−1, and N content of 5.33 g kg−1, while BCC had pH of 8.98, total carbon content of 539 g kg−1, CEC of 12.2 cmol kg−1, and N content of 6.98 g kg−1. The pH of all amendments was measured using pH meter (10 (water): 1 (BC, CP, BCC) [44], while N and C concentrations were determined using an elemental analyzer [45]. The biochar, compost, and BCC had a C:N ratio of 114.59, 66.41, and 77.22, respectively.

2.3. Soil Incubation Experiment, Gases’ Sampling, and Analysis

The incubation experiment was conducted in a completely randomized design (CRD) with three replicates to determine the impact of different treatments on soil properties, GHG emissions, gene abundance, and microbial activities. The experiment was conducted in 500 mL glass jars, each filled with 100 g of soil. Before starting the incubation experiments, the soil was incubated at 40% water filler pore spaces (WFPS) at 25 ± 1 °C for 7 days to stimulate the microbial activities [46]. Thereafter, BC, compost, and BCC were added to the jars and mixed thoroughly, and WFPS was increased to 60% (33). The experiment was conducted for 90 days, and gas samples were collected from the head spaces of glass jars after adding the treatments at 0, 1, 2, 3, 7, 10, 13, 17, 20, 25, 30, 35, 40, 45, 51, 58, 65, 72, 79, 86, and 90 days of incubation. We placed an airtight lid with a rubber septum to close the jars for gas sampling. Gas samples were collected twice: immediately after closing the jars (T0) and then after 60 min (T60). The air-tight lid contained a three-way stop-cock syringe for gas sampling. The gas samples were collected in air bags and analyzed for gas concentrations using a gas chromatograph (Agilent 7890B Santa Clara, CA, USA). The gas fluxes were calculated by using the following equation: F = p × V/W × ΔC/Δt × 273/(273 + T). Here, F is the indicating rates of gases in μg kg−1 h−1, p is the density of gases at standard conditions, V is jar volume (500 mL), W is the soil weight, Δc is the gas concentration change over 1 h, Δt is the sealing time, and T is the incubation temperature (25 °C).

2.4. Determination of Soil Properties, Genes, Abundance, and Microbial Activities

To determine soil pH and N dynamics, 1000 mL glass beakers were filled with 600 g of soil, and the same treatments were set up. The glass jars were incubated under the same conditions, and soil samples were collected at different intervals (0, 1, 10, 20, 30, 45, 60, 75, and 90 days) to determine soil pH and N dynamics. Soil pH was measured with a pH meter using a soil-to-water ratio of 1:5. Nitrate (NO3−) and ammonium (NH4+-N) concentrations were determined using the potassium chloride (KCl) extraction method.
Soil organic carbon (SOC) was determined by concentrated sulfuric acid–potassium dichromate external heating method. Available soil phosphorus was determined with sodium bicarbonate extraction (NaHCO3-extractable P) as suggested by the Olsen method [47]. Available potassium contents were determined using ammonium acetate extraction method [48]. For total nitrogen determination, 2 g of soil was digested with 10 mL of concentrated H2SO4 for 2 h at 370 °C. The concentration of total N was determined using Kjeldahl method, as detailed by Bao [49]. Soil urease activity was assessed with sodium hypochlorite-sodium phenate colorimetry assay with urea as the substrate [50]. On the other hand, soil catalase was assessed with a permanganimetric assay with hydrogen peroxide as the substrate [51,52]. The activity of urea and catalase was expressed as mg of NH4+-N g−1 24 h−1 and 1 µmol of H2O2 g−1 day−1, respectively. To determine soil microbial biomass carbon (MBC), fresh soil (20 g) was fumigated with chloroform for 24 h. Then, both fumigated and non-fumigated soils were taken and extracted with 0.5 K2SO4, filtered, and extract was obtained. Thereafter, the concentration of carbon in both fumigated and non-fumigated soil samples was determined by using a carbon analyzer.
Soil samples were collected and immediately brought to the laboratory and stored at −80 °C to determine soil microbial activities. The soil samples were analyzed following standard procedures of Meiji Biomedical Technology Co., Ltd., Shanghai, China, for high-throughput sequencing. A deoxyribonucleic acid (DNA) kit named DNeasy Power Soil Pro Kit (QIAGEN, Germantown, MD, USA) was used to extract the DNA from soil samples, and then both the concentration and purity of DNA in samples were determined with an ultra-micro spectrophotometer. Later, DNA integrity was assessed using 1% agarose gel electrophoresis, and primer-338F (ACTCCTACGGGGAGGCAGCAG) and primer-806R (GGACTACHVGGGTWTCTAAT) were used to amplify 16S rRNA of soil bacteria. Functional genes in soil samples were quantified using DNA extraction and quantitative PCR methods [35]. DNA was extracted with the special kit (Nohe Zhiyuan Science and Technology Co., Ltd., Beijing, China), and genes were quantified to measure the copy number of genes per g of dry soil by normalizing the extraction yield.

2.5. Pot Experiment

A pot experiment was also conducted in a CRD with three replications to determine the impact of the same treatments (BC: 2%, compost: 2%, and BCC: 2%) on rice productivity. Pots with a diameter of 28 cm and length of 35 cm were filled with 8 kg of soil (dry weight), and the same treatments were set up. The experiment was conducted in an open greenhouse with a rain shed to avoid washing with rainwater. Soil from each pot was placed on a plastic sheet, mixed homogeneously with biochar, compost, and BCC, and water was added to attain 100% field capacity. The pots were left for one week to allow the treatments to stabilize; thereafter, five seedlings (30 days old) of rice (variety: Zhongjiazao 17) were transplanted into each pot. The pots were visited daily, and a water depth of 2–3 cm above the soil was maintained throughout the growth period. Weeds growing in the pots were manually uprooted, and no insect/pest attacks or diseases were observed during the study. At harvesting, root length, plant height, tillers per plant, panicle length, and kernel per panicle were taken from all plants, which were measured and averaged. Entire pots were harvested to determine kernel and biomass yield, as well as the harvest index.

2.6. Statistical Analysis

The data were tested for normal and homogeneity of variances (Bartlett’s test) before analysis. Two-way analysis of variance (ANOVA) was conducted to study the effect of different treatments on soil pH, GHG fluxes over time, and nitrogen dynamics, while one-way ANOVA was applied to analyze cumulative GHGs, soil properties, gene abundance, yield traits, and microbial data. Tukey’s honestly significant difference (HSD) test (p ≤ 0.05) was used to separate the significant ANOVA sources by using Statistics 8.1. Further, permutational multivariate analysis of variance (PERMANOVA) was used to assess the impact of different treatments on soil microbial communities. Differences in diversity and composition were determined using the Maaslin2 R package.

3. Results

3.1. Effect of BC, Compost, and BCC on Soil pH and Nitrogen Dynamics

The application of BC, compost, and BCC showed a contrasting impact on soil pH during the study period (Table 1). Initially, there was a non-significant impact (p ≤ 0.05) of different treatments on soil pH. However, over time, the differences among treatments became more significant (p ≤ 0.05), and a maximum increase in soil pH was observed with BC application (Table 1). The soil pH value in all treatments reached a plateau after 30 days of incubation, followed by a continuous decrease in soil pH throughout the study period (Table 1). The application of different treatments showed a significant (p ≤ 0.05) impact on soil NH4+-N and NO3−-N contents during the study period. At the start of the experiment, there was a minor difference in NH4+-N and NO3−-N among treatments. However, this difference increased over time (Table 1). The maximum concentration of NH4+-N (31.20 mg kg−1) was observed after 30 days of incubation in control, while the lowest NH4+-N concentration was observed in the BC treatment. The concentration of NO3−-N showed an opposite trend as compared to NH4+-N (Table 1). The maximum concentration of NO3−-N throughout the study period was observed in the BCC treatment, followed by CP and BC, with the lowest NO3−-N throughout the study observed in the control (Table 1).

3.2. Effect of BC, Compost, and BCC on Fluxes of GHG Emissions

The application of different amendments showed a contrasting impact on the fluxes of GHG emissions (Figure 1). The results indicate that CO2 flux was higher at the initial phase of the study, then showed a downward trend until the 17th day. Afterward, it exhibited an inconsistent trend, increasing again until the 65th day, followed by a continuous decrease until the end of the study. The maximum CO2 emissions were observed in the control, and the lowest CO2 emissions were observed in BCC, which remained consistent with BC application (Figure 1A).
CH4 emissions also showed an inconsistent trend throughout the study. Initially, CH4 emissions were higher at the start of the study and showed a continuous decline until the 17th day. There was an increase on the 20th and 25th days, followed by a continuous decline until the end of the study (Figure 1B). Overall, BC remained the top performer and resulted in the lowest CH4 emissions, comparable to BCC, while the maximum CH4 emissions were observed in control (Figure 1B). The significant differences in CH4 among treatments could be explained by the impact of amendments on soil pH. The increased soil pH from different amendments enhanced methanotrophic activities, which increased CH4 uptake and decreased CH4 emissions.
N2O emissions were significantly high at the start of the study and thereafter showed a decline until the 10th day, then increasing on the 13th and 17th days (Figure 1C). Afterward, N2O emissions showed a decline until the 35th day then an increasing trend until the 45th day, and later continuously decreased until the end of the study. Overall, the lowest N2O emissions were observed with BC application, comparable to BCC, while the maximum N2O emissions were noted in the control (Figure 1C). The application of different treatments also showed a significant impact on cumulative GHG emissions. The lowest cumulative CO2 emissions were recorded with BCC, while the lowest CH4 and N2O emissions were observed with BC, comparable to BCC. The highest fluxes of GHGs were observed in the control (Figure 2).

3.3. Effect of BC, Compost, and BCC on Soil Nutrients, Gene Abundance, and Microbial Activities

Biochar and BCC showed a remarkable impact on soil properties (Table 2). The maximum pH value was observed with BC (5.82), followed by BCC (5.70) and CP (5.56), with the lowest pH value (5.39) observed in the control (Table 2). The application of BC and BCC also significantly increased the concentration of soil NPK compared to the control. Biochar co-compost increased the soil N, P, and K concentration by 80.59%, 44.27%, and 57.58% compared to the control, while BC increased soil N, P, and K concentration by 59.54%, 31.90%, and 19.69%, respectively (Table 2). The application of different treatments also showed a noteworthy impact on soil organic carbon (SOC) and MBC content. The maximum SOC (19.23 mg kg−1) was recorded with BC application comparable to BCC (17.31 mg kg−1), with the lowest SOC (12.49 mg kg−1) recorded in the control (Table 2).
Opposite to this, the maximum soil MBC (412.67 mg kg−1) was noted in BCC treatment followed by BC, and the lowest soil MBC (297.32 mg kg−1) was observed in the control (Table 2). The application of diverse treatments also induced a significant impact on soil enzymatic activities as compared to control. The maximum urea and catalase activities were observed with BCC that remained comparable with BC and the lowest urease and catalase activities were recorded from control pots (Table 2).
The application of different treatments showed a significant impact on ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) gene abundance (Figure 3A,B). The maximum AOA (6.38 × 106 g−1 dry soil) and AOB (4.37 × 106 g−1 dry soil) abundance was observed in the control, followed by CP and BCC, and the lowest AOA (5.15 × 106 g−1 dry soil) and AOB (3.60 × 106 g−1 dry soil) were obtained with application of BC (Figure 3B). Biochar and BCC also showed a significant impact on nitrous oxide reductase (nosZ) and nitrite reductase (nirK) gene abundance (Figure 3C,D). The maximum nosZ (5.44 × 107 g−1 dry soil) and nirK genes (3.73 × 106 g−1 dry soil) were noted with BC application, followed by BCC and CP, and the lowest AOA and AOB abundance was observed in the control (Figure 3C,D).
Biochar, CP, and BCC applications showed significantly different impacts on bacterial abundance and diversity. The results indicate a significant impact of different treatments on OTUs, with BC application resulting in the maximum OUTs, followed by BCC, and the lowest OUTs were observed with CP application (Figure 4). The application of diverse treatments also showed a remarkable impact on bacterial abundance at the phylum level (Figure 5). Gemmatimonadota, Myxococcota, Acidobacteriota, Actinobacteria, Actinobacteria, Planctomycetota, Bacteroidota, Firmicutes, and Proteobacteria were recognized as the top 10 phyla after the application of different treatments (Figure 5). The application of BCC, CP, and BC significantly increased the richness of Proteobacteria, followed by Firmicutes, Bacteroidota, and Acidobacteriota (Figure 5).
The results also indicate that different treatments showed a significant impact on soil bacterial communities at the family level (Figure 6). The application of BC and BCC increased the richness of Spirochaetota, Bdellovibrionote of Bacteroidota, Parcubacteria, Proteobacteria, Actinobacteriota, and Actinobacteria (Figure 6).

3.4. Rice Growth and Yield Characteristics

Different treatments showed a variable impact on the root characteristics of rice crops (Table 3). The longer roots (58.27 cm) with the highest fresh (12.23 g) and dry weight (5.44 g) were observed with the application of BCC, followed by BC application. Shorter roots (44.37 cm) with a minimum fresh weight (8.03 g) and dry weight (4.03 g) were noted in the control treatment (Table 3). The maximum plant height (PH: 75 cm) with maximum kernels/panicle (KPP: 75.76) and 1000-grain weight (TGW: 3.14 g) was obtained with BCC, comparable to BC application, and the lowest PH, KPP, and TGW were obtained in the control (Table 3). We also found a significant impact of different treatments on kernel yield (KY), biological yield (BY), and harvest index (HI: Table 3). The application of BCC resulted in the highest KY (29.97 g/pot), BY (62.24 g/pot), and HI (48.16%); however, it remained the same for the BC application. Furthermore, the lowest KY (20.30 g/pot), BY (47.30 g/pot), and HI (42.91%) were recorded in the control treatment (Table 3). Different treatments showed a marked impact on abortive kernel (AK) and sterile kernels (SK), and the maximum percentage of both AK and SK was observed in the control, followed by CP, and the lowest percentage of AK (7.67%) and SK (10.67%) was observed with the BCC, which were comparable to BC application (Table 3).

4. Discussion

The present study aimed to determine the potential of BC and BCC on GHG emissions, soil fertility, and rice productivity. Biochar and BCC significantly increased the soil pH owing to the basic charged groups and alkaline nature of BC, which increases the soil pH [53]. Biochar also decreases the soil aluminum (Al) concentration through its binding on functional groups, increasing base cations, and leading to an increase in the soil pH [54]. Biochar application increased the soil pH initially, which then decreased over time, possibly due to the aging of applied amendments. The results also indicate that NO3−-N increases initially and then decreases, suggesting that nitrification occurred. The nitrification decreased over time, likely resulting in a significant decrease in the soil pH [55].
The results indicated that NH4+-N and NO3−-N were high at the initial period of study, which could be attributed to NH4+ desorption and the mineralization of organic nitrogen from applied amendments and native soil nitrogen [56]. Biochar and BCC decreased the soil NH4+-N and increased NO3−-N availability (Table 1). Biochar has excellent adsorption properties and absorbs a significant amount of NH4+-N on its surface, thereby leading to decreased NH4+-N availability. Furthermore, compost and BCC increased NO3−-N, which could be ascribed to the higher amount of N, increasing soil nitrification [57]. Both BC and BCC increased SOC and MBC owing to effective carbon sequestration [58,59]. The presence of BC modulates the soil microbial composition involved in mineral nutrients, resulting in a substantial increase in soil nutrient availability (Table 2, [60]).
At the start of incubation, CO2 emissions were significantly high and inconsistent, and a decreasing trend was observed over time. The high rate of nitrification provides more N substrates to microbes, thereby increasing CO2 emissions [56]. Therefore, the higher availability of mineral N at the start of the study was a reason behind the increase in CO2 emission. Thereafter, mineral N availability decreased over time, resulting in a significant decrease in CO2 emissions.
BCC significantly decreased CO2 emissions, comparable to BC, while the highest CO2 emissions were observed with compost application. This indicates that compost might contain more degradable carbon compared to BC, which therefore resulted in maximum CO2 emission [61,62]. Biochar is an excellent amendment to improve soil fertility [63,64] and reduce GHG emissions [65]. The significant increase in MBC and lowest CO2 emissions in BCC implies a higher microbial carbon use efficiency. This indicates its excellent potential to increase soil carbon storage through the contribution of microbial necromass [66,67].
Both BC and BCC significantly reduced CH4 emissions (Figure 1), which could be ascribed to BC’s ability to decrease the activity of methanogenic archaea and increase the activity of methanotrophic and proteobacteria [28]. The soil pH is an important factor that significantly affects the activity of methanogens and methanotrophs that control both CH4 uptake and emissions [68]. In the present study, both BC and BCC increased the soil pH, which indicates that the activity of methanotrophs was increased with an increasing soil pH. Furthermore, CH4-monooxygenase is an important enzyme that causes the oxidation of CH4 and is active at a higher pH [69]. This indicates that an increase in the soil pH after BC and BCC application increased the activity of both methanotrophs and the CH4-monooxygenase enzyme, leading to increased CH4 oxidation and uptake, resulting in lower CH4 emissions.
Apart from this, BC also contains oxygenated functional groups and has a higher surface area, which allows CH4 adoption on the BC surface and leads to a reduction in CH4 emissions [29]. The BC and BCC used in the current study had a higher C:N ratio, which also contributed to a reduction in methane emissions. Moreover, BC also improves soil aeration and bulk density, enhancing the soil’s sink capacity and reducing CH4 emissions [70,71]. The application of BC also promotes the methanotrophic CH4 intake and subsequent CH4 oxidation by methanotrophic organisms at the root surface, therefore, leading to a reduction in CH4 emissions [72,73].
The soil pH is an important factor that fundamentally affects N2O emissions from soils [53]. The application of BC and BCC significantly increased the soil pH and reduced the N2O emissions, consistent with earlier studies indicating that BC application reduces N2O emissions by increasing the soil pH [74,75,76]. Biochar application significantly decreases N2O emissions due to its liming effect, which reduces the N2O/N2 ratio and results in lower N2O emissions [29,77]. Thus, the reduction in N2O emissions following BC and BCC application could be attributed to the liming effect of BC, which might favor N2 formation [78]. Nitrous-oxide reductase (N2OR) is an important enzyme involved in the conversion of N2O to N2 during nitrification. It is documented that a lower soil pH can decrease the assembly and function of the N2O reductase (N2OR) enzyme, which reduces N2O to N2 in denitrification, leading to lower N2O emissions [79]. The addition of BC and BCC enhanced the soil pH, which might increase the functioning of N2OR, leading to a marked reduction in N2O emission (Figure 1). The positive impact of BC and BCC on reducing N2O production can also be ascribed to their capacity to inhibit nitrification (Table 1) and decrease N2O losses from de-nitrification.
The bacterial-encoded nosZ gene is involved in the synthesis of N2OR enzymes. In the present study, BC and BCC increased nosZ abundance and subsequent N2OR activity, resulting in a marked N2O production [80]. The increase in nosZ activity was linked with an increase in the soil pH following different amendments. The increase in the soil pH increases the abundance of nosZ encoding the N2O reductase, which increases the ratio of denitrification product (N2/N2O), thereby reducing the N2O emission [81,82].
The maximum N2O emission was observed at the start of the study, followed by a continuously decreasing trend. This could be attributed to the transformation of mineral nitrogen and an increase in N availability. A significantly higher AOA and AOB abundance was seen under the control. The addition of BC and BCC decreased the abundance of both AOA and AOB, with AOA showing more abundance than AOB. This indicates that the nitrification effect in this study was dominated by AOA rather than AOB. These results coincide with the findings of Ye and Zhang [83], who found that AOA can outnumber AOB. Likewise, Shi et al. [84] and Wu et al. [85] also reported that an increase in the soil pH after BC application decreased AOB abundance. The application of fertilizers can also decrease AOA and AOB abundance by changing the concentration of soil available phosphorus [86]. The increase in nutrient availability after BC and BCC might have allowed the plants and microbes to increase access to limited nutrients, thus maintaining a dynamic balance of stoichiometry. Biochar and BCC mediated increases in P availability increased N absorption by plants, reducing the availability of N to AOA and AOB microbes, thereby inhibiting their growth and abundance. Therefore, a reduction in AOA and AOB abundance could also be another important reason for BC and BCC-mediated decrease in N2O emissions.
Biochar and BCC showed a marked improvement in yield and biomass production (Table 3). The higher availability of N and P explained the difference in biomass and yield after the application of different treatments. These findings are in line with earlier studies indicating that BC and BCC can increase crop growth and yield by improving soil nutrient availability [87,88,89]. Our findings also indicate that nutrients present in compost were retained during composting, and the addition of BC further improved nutrient retention, leading to improved plant performance [37]. Furthermore, BCC also increased the P and K availability, water retention, and soil carbon contents [90,91], which could also reason behind improvd rice growth and productivity in the current study. Biochar co-compost reduced sterile and abortive kernels, which could be attributed to improved nutrient availability, SOC, and microbial activities.
The relative abundance and diversity of soil bacteria are important indicators of soil functioning [92]. The abundance of Proteobacteria, Firmicutes, Bacteroidota, and Acidobacteriota was significantly increased following BC and BCC application. This is consistent with the findings of Xia et al. [93], who reported that BC application can increase bacterial abundance and diversity. Further, Lei et al. [94] also found a significant relationship between Firmicutes and Proteobacteria and variations in N2O and NH3 emissions. The results indicate that all the treatments increased the abundance of Actinobacteria, which inhibit the proliferation of methanogens, thereby reducing the CH4 emissions [95]. The results also indicated that biochar treatments decreased NH4+-N over time, which reduced the availability of N substrates for nitrification and denitrification processes, thus resulting in reduced N2O emissions. All the treatments increased the abundance of Proteobacteria, which are involved in carbon decomposition, and led to an increase in soil organic carbon and a decrease in CO2 and N2O emissions [96]. Biochar and BCC facilitate changes in the soil bacterial community, therefore leading to abated GHG fluxes and improved rice yield. Moreover, the BC and BCC might also create new ecological niches and provide more nutrients (Table 1 and Table 2) for microbial growth, thereby resulting in improved bacterial abundance and diversity, which in turn affects GHG emissions [97,98].

5. Conclusions

The application of all the organic amendments significantly increased rice growth and productivity. However, biochar co-compost and biochar provided better benefits compared to compost alone. BC and BCC increased rice growth and productivity by increasing the nutrient (NPK) availability, soil carbon, soil enzyme activity, genes abundance, and diversity of soil bacteria. Furthermore, BCC significantly reduced CO2 emissions, while biochar significantly reduced CH4 and N2O emissions, with similar results observed for BCC. The reduction in GHGs following BC and BCC was linked with an improved soil pH, modulated gene abundance, nitrogen dynamics, and bacterial abundance. These findings suggest that BCC offers an excellent opportunity to reduce GHG emissions while improving rice productivity. The present short-term experiment was conducted under controlled conditions, which is a major limitation of this study. Therefore, field studies must be conducted under a wide range of soil and climate conditions to determine the impact of BCC on soil fertility, rice productivity, and microbial activities. Besides this, the present study was conducted in acidic soil, highlighting the need for more studies in neutral and alkaline soils before fully assessing the impact of BC and BCC on soil fertility, rice productivity, and microbial activities.

Author Contributions

Conceptualization, M.U.H. and G.H., Investigation, M.U.H. and G.H., writing—original draft preparation, M.U.H. and G.H., writing—review and editing, R.M., T.A.K. and M.A.N. formal analysis, R.M., supervision, G.H., funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Project, “Optimal allocation mechanism and efficient Planting mode of double cropping rice in the middle Reaches of Yangtze River”, No. 2016YFD0300208: the National Natural Science Foundation of China, “Effects of nitrogen application on soil organic carbon and greenhouse gas emission under straw Returning condition” (41661070), as well as a Study on the Key Pattern and Technology of Paddy Field Cyclic Agriculture in Winter in Jiangxi Province (20161BBF60058).

Data Availability Statement

Data are contained within the article, and further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The effects of biochar, compost, and biochar co-compost on CO2 (A), CH4 (B), and N2O (C) emissions. Data are mean values (n = 3) with ±SE.
Figure 1. The effects of biochar, compost, and biochar co-compost on CO2 (A), CH4 (B), and N2O (C) emissions. Data are mean values (n = 3) with ±SE.
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Figure 2. The effects of biochar, compost, and biochar co-compost on cumulative CO2 (A), CH4 (B), and N2O (C) emissions. Data are mean values (n = 3) with ±SE. Different letters with mean values (n = 3) indicate significant differences (p ≤ 0.05) with Tukey’s test.
Figure 2. The effects of biochar, compost, and biochar co-compost on cumulative CO2 (A), CH4 (B), and N2O (C) emissions. Data are mean values (n = 3) with ±SE. Different letters with mean values (n = 3) indicate significant differences (p ≤ 0.05) with Tukey’s test.
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Figure 3. The effects of biochar, compost, and biochar co-compost on AOA (A), AOB (B), nosZ (C), and nirK (D) genes’ abundance. Different letters on bars indicate significant differences (p ≤ 0.05) with Tukey’s test.
Figure 3. The effects of biochar, compost, and biochar co-compost on AOA (A), AOB (B), nosZ (C), and nirK (D) genes’ abundance. Different letters on bars indicate significant differences (p ≤ 0.05) with Tukey’s test.
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Figure 4. Venn diagram about the effects of biochar, compost, and biochar co-compost on OTUs distribution of bacteria. T1: control, T2: biochar, T3: compost, and T4: biochar co-compost.
Figure 4. Venn diagram about the effects of biochar, compost, and biochar co-compost on OTUs distribution of bacteria. T1: control, T2: biochar, T3: compost, and T4: biochar co-compost.
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Figure 5. The effect of different treatments on bacterial composition at phylum level. T1: control, T2: biochar, T3: compost, and T4: biochar co-compost.
Figure 5. The effect of different treatments on bacterial composition at phylum level. T1: control, T2: biochar, T3: compost, and T4: biochar co-compost.
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Figure 6. The effect of different treatments on bacterial composition at family levels. T1: control, T2: biochar, T3: compost, and T4: biochar co-compost.
Figure 6. The effect of different treatments on bacterial composition at family levels. T1: control, T2: biochar, T3: compost, and T4: biochar co-compost.
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Table 1. The effects of biochar, compost, and biochar co-compost on soil pH and nitrogen dynamics.
Table 1. The effects of biochar, compost, and biochar co-compost on soil pH and nitrogen dynamics.
TreatmentsTime
Day 0Day 1Day 10Day 20Day 30Day 45Day 60Day 75Day 90
Soil pH
Control5.381b5.403c5.443d5.433d5.387c5.328d5.373c5.318d5.303d
BC5.393ab5.497ab6.012a6.240a6.503a6.219a6.120a5.993a5.860a
CP5.393ab5.443bc5.762c5.880c6.132b5.920c5.880b5.650c5.400c
BCC5.400a5.480a5.924b6.00b6.410a6.060b5.902b5.737b5.618b
NH4+-N
Control23.283a25.330a28.667a27.260a31.200a35.700a33.567a32.967a29.434a
BC23.233a24.200a25.742c24.767b25.860c29.033c27.833c26.870c25.000b
CP23.267a24.713a27.700ab26.707a28.267b34.270a32.168a30.700b28.600a
BCC23.387a24.337a26.200bc25.693ab27.328bc31.300b30.200b28.169c25.467b
NO3−-N (mg kg−1)
Control32.230a33.067c35.037bc35.233c40.167c42.730c38.667b34.833c35.167a
BC32.230a34.633b37.033b37.680ab43.200b46.067ab40.667ab35.700b35.700a
CP32.430a33.400bc35.200c35.967bc41.034bc44.833bc38.932b35.367b35.367a
BCC33.230a35.967a39.333a38.977a46.367a47.967a42.067a36.500a36.500a
BC: biochar, CP: compost, BCC: biochar co-compost. Different letters with mean values (n = 3) indicate significant differences (p ≤ 0.05) with Tukey’s test.
Table 2. The effects of biochar, compost, and biochar co-compost on soil characteristics.
Table 2. The effects of biochar, compost, and biochar co-compost on soil characteristics.
TreatmentsSoil pHAvailable Phosphorus (mg kg−1)Available Potassium (mg kg−1)Total Nitrogen (g kg−1)Soil Organic Carbon
(mg kg−1)		Soil Microbial Biomass Carbon (mg kg−1)Urease Activity
(mg NH4+-N g−1 day−1)		Catalase
(1 µmol H2O2 g−1 day−1)
Control5.390d12.830d59.607d0.660c12.493c297.322c0.343c9.900c
BC5.820a20.473b78.632b0.790b19.230a385.000b0.474b15.530ab
CP5.557c17.177c70.600c0.827b14.892b312.394c0.437b13.568b
BCC5.702b23.267a86.000a1.040a17.307a412.667a0.550a17.730a
BC: biochar, CP: compost, BCC: biochar co-compost. Different letters with mean values (n = 3) indicate significant differences (p ≤ 0.05) with Tukey’s test.
Table 3. The effects of biochar, compost, and biochar co-compost on the growth and yield characteristics of rice.
Table 3. The effects of biochar, compost, and biochar co-compost on the growth and yield characteristics of rice.
TreatmentsRL (cm)RFW (g)RDW (g)PH (cm)TPPPL (cm)KPPTKW (g)KY/Pot (g)BY/Pot (g)HI (%)AK (%)SK (%)
Control44.367c ± 0.508.033c ± 0.074.030c ± 0.04563b ± 0.717.00a ± 0.4710.640d ± 0.06756.667c ± 0.982.130d ± 0.16320.300b ± 0.4747.300b ± 0.9042.907c ± 0.269.000a ± 0.4712.666a ± 0.27
BC52.400b ± 0.4310.400a ± 0.334.970b ± 0.06374a ± 2.067.33a ± 0.2712.540b ± 0.16770.000ab ± 1.242.783b ± 0.22227.410a ± 0.3058.733a ± 0.3946.669ab ± 0.805.669b ± 0.2810.000bc ± 0.47
CP47.433bc ± 1.239.053bc ± 0.254.590b ± 0.07565b ± 0.727.33a ± 0.2711.367c ± 0.09863.000bc ± 1.252.456c ± 0.04222.233b ± 0.1650.900b ± 0.2943.691bc ± 0.697.668a ± 0.2710.670ab ± 0.55
BCC58.267a ± 1.2312.230a ± 0.475.440a ± 0.12375a ± 1.688.67a ± 0.5414.447a ± 0.16275.668a ± 1.653.138a ± 0.01729.967a ± 0.6462.240a ± 1.4348.158a ± 0.184.667b ± 0.278.333c ± 0.27
RL: root length, RFW: root fresh weight, RDW: root dry weight, PH: plant height, TPP: tillers/plant, PL: panicle length, KPP: kernels/panicle, TKW: thousand-kernel weight, KY: kernel yield, BY: biological yield, HI: harvest index, AK: abortive kernel, SK: sterile kernel. BC: biochar, CP: compost, BCC: biochar co-compost. Different letters with mean values (n = 3) indicate significant differences (p ≤ 0.05) with Tukey’s test.
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Umair Hassan, M.; Huang, G.; Munir, R.; Khan, T.A.; Noor, M.A. Biochar Co-Compost: A Promising Soil Amendment to Restrain Greenhouse Gases and Improve Rice Productivity and Soil Fertility. Agronomy 2024, 14, 1583. https://doi.org/10.3390/agronomy14071583

AMA Style

Umair Hassan M, Huang G, Munir R, Khan TA, Noor MA. Biochar Co-Compost: A Promising Soil Amendment to Restrain Greenhouse Gases and Improve Rice Productivity and Soil Fertility. Agronomy. 2024; 14(7):1583. https://doi.org/10.3390/agronomy14071583

Chicago/Turabian Style

Umair Hassan, Muhammad, Guoqin Huang, Rizwan Munir, Tahir Abbas Khan, and Mehmood Ali Noor. 2024. "Biochar Co-Compost: A Promising Soil Amendment to Restrain Greenhouse Gases and Improve Rice Productivity and Soil Fertility" Agronomy 14, no. 7: 1583. https://doi.org/10.3390/agronomy14071583

APA Style

Umair Hassan, M., Huang, G., Munir, R., Khan, T. A., & Noor, M. A. (2024). Biochar Co-Compost: A Promising Soil Amendment to Restrain Greenhouse Gases and Improve Rice Productivity and Soil Fertility. Agronomy, 14(7), 1583. https://doi.org/10.3390/agronomy14071583

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