Biochar application increased methane emission, soil carbon storage and net ecosystem carbon budget in a 2-year vegetable–rice rotation

Agriculture, Ecosystems and Environment 292 (2020) 106831

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Biochar application increased methane emission, soil carbon storage and net ecosystem carbon budget in a 2-year vegetable–rice rotation

T

Le Qia,b, Prem Pokharelb, Scott X. Changb,c,**, Peng Zhoua, Haidong Niua, Xinhua Hea,d, Zifang Wanga, Ming Gaoa,* a

College of Resources and Environment, Southwest University, Chongqing, 400716, China Department of Renewable Resources, University of Alberta, Edmonton, T6G 2E3, Canada c State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300, China d School of Biological Sciences, University of Western Australia, Perth, 6009, Australia b

ARTICLE INFO

ABSTRACT

Keywords: Carbon dioxide Greenhouse gas Methanogen Methanotroph Nitrous oxide Soil organic carbon

The effect of biochar application on the net ecosystem carbon budget (NECB) and the mechanism controlling methane (CH4) emission in paddy soils under vegetable–rice rotations are poorly understood. A 2-year field experiment was conducted with three treatments: control (no fertilizer or biochar application), chemical fertilizer (BC0) and biochar plus chemical fertilizer application (BC1) to analyze greenhouse gas (GHG) fluxes, soil organic carbon (SOC) content, as well as the abundance and community structure of methanogens and methanotrophs in a vegetable–rice rotation. Biochar addition (BC1) did not affect the yield, or the emission of total CH4 or nitrous oxide (N2O) but significantly increased carbon dioxide (CO2) emission as compared to BC0 in the vegetable season. Rice yield in BC1 was 14.1 % higher than in the control but was lower than in BC0 because of lower available nutrients in BC1 than in BC0. During the rice season, cumulative CH4 emission under BC1 was increased by 2.65 times as compared with BC0 (P < 0.01), in association with an increase in methanogenic and decrease in methanotrophic gene abundances. The cumulative CO2 emission was not different between BC0 and BC1 while cumulative and yield-scaled N2O emissions were significantly higher in BC1 than in BC0 in the rice season. However, BC1 increased SOC and NECB, but decreased the ratio of carbon (C) emission to C sequestration, net global warming potential (NGWP) and greenhouse gas emission intensity (NGHGI) as compared to the BC0 and control treatments (P < 0.01). The increase in GHG emissions in the biochar-amended soil was compensated by the increase in soil C storage and C uptake by rice, based on NGWP and NGHGI. The increase in NECB and SOC in the BC1 treatment indicates the benefit of biochar in restoring SOC during the rice season. This study provides insights into the effects of biochar addition on changes in bacterial abundance and community structure which increased CH4 emission in the rice season of a vegetable–paddy rotation.

1. Introduction Soil organic carbon (SOC) in agricultural soils is an active and vital carbon (C) pool in terrestrial ecosystems (Schmidt et al., 2011), especially in paddy soils (Xu et al., 2011). To increase crop production for sustaining a fast-growing population, farmers in China are inclined to increase chemical fertilizer input to their cropland (Cao et al., 2013), resulting in serious environmental problems such as increased decomposition of native SOC and soil greenhouse gas (GHG) emissions (Ju et al., 2009; Lu et al., 2014). In addition, it is a common practice to convert arable soil used for vegetable cultivation to rice cultivation, and vice-versa, in order to increase crop production in China (Wu et al., ⁎

2018). Land use change can have substantial effects on SOC loss and GHG emission from the soil (Nishimura et al., 2008). Therefore, it is important to explore measures for increasing SOC storage in paddy fields that were previously used for vegetable cultivation that can support climate change mitigation by reducing GHG emission as well as increasing crop production. The application of biomass wastes (such as crop residues) to croplands can help increase SOC but its direct application may increase GHG emissions and the risk of crop disease, because abundant nutrients present in biomass wastes favor the growth of pests (Procházková et al., 2003). Pyrolysis of those biomass wastes into biochar reduces the risk of crop disease, and biochar application to croplands can increase

Corresponding author. Corresponding author at: Department of Renewable Resources, University of Alberta, Edmonton, T6G 2E3, Canada. E-mail addresses: [email protected] (S.X. Chang), [email protected] (M. Gao).

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https://doi.org/10.1016/j.agee.2020.106831 Received 16 July 2019; Received in revised form 9 January 2020; Accepted 13 January 2020 0167-8809/ © 2020 Published by Elsevier B.V.

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nutrient conservation, improve soil quality and reduce GHG emission (Steiner et al., 2007; Zhang et al., 2012; Lehmann and Joseph, 2015). Previous studies have shown positive (Biederman and Harpole, 2013; Pokharel and Chang, 2019), negative or no effects (Martinsen et al., 2014; Bamminger et al., 2017; Jeffery et al., 2017) of biochar application on crop yield. Improvements in crop production due to biochar application are often reported in highly degraded and nutrient-poor soils, and only a few studies have shown significant improvement in crop yield in fertile soils (Hussain et al., 2017). The effects of biochar application on emissions of the three major GHGs, methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O), have been widely studied and found to be highly variable, with increases (Knoblauch et al., 2011; Sagrilo et al., 2015; Singla et al., 2014), decreases (Feng et al., 2012; Han et al., 2016; Cayuela et al., 2013) or no effects (Liu et al., 2016; Xie et al., 2013) reported for different agricultural systems, due to differences in soil properties, pyrolysis conditions and the feedstock type used for biochar production (Wang et al., 2019). The increase in N2O emission from the cropland caused by excessive use of chemical fertilizers has been one of the serious environmental issues for climate change mitigation (Battye et al., 2017). In a meta-analysis, Cayuela et al. (2014) has shown significant positive effects of biochar application on reducing N2O emission from different croplands. Although biochar’s effect may not be substantial in reducing CH4 emission from upland soils, Jeffery et al. (2016) observed significant effects on reducing CH4 emission from paddy soils. In China, rice is a major staple crop, mainly cultivated on paddy soils that are important sources of CH4 emission (Jeong et al., 2017). Biochar application changes microbial community composition in paddy soils that can subsequently change in CH4 emission (Wang et al., 2019). Thus it is very important to understand how biochar alters the microbial community composition to achieve anticipated reduction in CH4 emission from paddy soils. Analysis of the abundance of genes such as the methyl coenzyme M reductase (mcrA) and particulate methane monooxygenase (pmoA) of methanogens and methanotrophs, respectively, can help us explore the net effect on CH4 emission by changes in microbial CH4 production or CH4 consumption in the soil (FernándezBaca et al., 2018). Biochar additions can decrease CH4 emission from rice paddy soils due to increased methanotrophic proteobacterial abundances and a decreased ratio of methanogenic to methanotrophic (mcrA/pmoA) abundance (Feng et al., 2012); or they can increase total CH4 emission by an inhibitory effect of chemicals contained in biochar on the activity of the methanotrophs (Zhang et al., 2010). There are only a few studies that have directly compared CH4 emission with changes in the abundance and community composition of methanogens and methanotrophs after biochar application in paddy fields (Singla et al., 2014; Wang et al., 2019), and they show contrasting results. For instance, Wang et al. (2019) and Feng et al. (2012) found biochar with high available nutrients increased methanotrophic and methanogenic gene abundances, but Han et al. (2016) found a decrease in the activity of methanogens, while Singla et al. (2014) did not observe any effects on methanogenic diversity in a biochar-amended paddy soil. In particular, the relative abundances of the dominant genera that can substantially alter methanogenic and methanotrophic processes in biocharamended paddy soils have not been well studied (Feng et al., 2012; Singla et al., 2014; Han et al., 2016; Wang et al., 2019). One of the most important aspects of biochar application is to enhance soil C sequestration (Lehmann, 2007; Stewart et al., 2013; Lorenz and Lal, 2014). Contrasting results have been found in the effect of biochar application on C cycling in paddy soils. For example, Wang et al. (2018) reported that biochar applied at 24 Mg ha−1 had no effect on net primary productivity (NPP, C production in grain, straw, root, litter and rhizodeposits of crop plants) in the first three years of biochar application. However, Zhang et al. (2013) observed an increase in NPP in the first year of biochar application in paddy fields. Soil C balance in an agro-ecosystem can be determined by the net ecosystem C budget (NECB), which is the difference between C input and output in a system

over a given period of time (Smith et al., 2010b). Wang et al. (2018) found that biochar had a positive effect on NECB in paddy fields, but they did not identify the reason for the increase in NECB. Therefore, the main objectives of this study were: i) to assess the effects of biochar application on emission of the three major trace GHGs (CH4, CO2 and N2O) and C sequestration, ii) to examine biochar’s effects on crop yield, emission factor of N2O and yield-based N2O emission in a vegetable-rice rotation, and iii) to explore the changes in the abundance of microbial genes related to CH4 emission and their microbial community composition during the rice cultivation after biochar application. In addition, C emission ratio (C emission per unit of soil C sequestered), NECB, net global warming potential (NGWP), and net greenhouse gas emission intensity (NGHGI) were also determined in this study, as they are the important parameters for understanding the effect of biochar addition on net C balance in an agroecosystem. The mechanism underlying the observed effects of biochar on CH4 emission in terms of changes in the abundance and community composition of methanogens and methanotrophs were also investigated in the biocharamended soil in the rice season of a vegetable-paddy rotation. 2. Materials and methods 2.1. Experimental site A field experiment was conducted at the research station established in the National Monitoring Base for Purple Soil Fertility and Fertilizer Efficiency (29°48′N, 106°24′E, 266.3 m above sea level) located in Beibei District, Chongqing, China (Fig. S1). The study site has a subtropical monsoon humid climate with an annual average temperature and precipitation of 18.4 °C and 1105.5 mm, respectively, based on data collected between 1998 and 2017. The soil was developed from a gray brown purple sand shale parent material, and is classified as an Orthic Entisol in the Chinese soil classification system (Huang et al., 2018) or as a Regosol under the FAO system (FAO, 2006). The soil had a pH of 6.3 (soil to water ratio of 1: 2.5 w: v), soil organic C of 11.1 g kg–1, available nitrogen (N) of 96.9 mg kg–1, available phosphorus (P) of 51.3 mg kg–1 and available potassium (K) of 208.8 mg kg–1 before vegetable cultivation (see below for the methods of analyses). 2.2. Experimental design A total of 9 plots (1 × 2 m each) were established in a completely randomized factorial design in a vegetable–rice rotation at the research station (Fig. S1). The vegetables were chili (Capsicum annuum L.), lettuce (Lactuca sativa L. var. angustana L.) and cabbage (Brassica rapa subsp. Chinensis L.) grown in a sequence in the first year followed by rice (Oryza sativa L. var. Longliangyou 1206) grown in the second year of the rotation. Three treatments were established: (1) control (without any fertilizers or biochar addition), (2) chemical fertilizer application alone (BC0), and (3) biochar applied with chemical fertilizers (BC1). The treatments were replicated three times. Biochar was applied with chemical fertilizers as it is not a significant source of nutrients on its own for crop production (Asai et al., 2009; Wang et al., 2018). The biochar used in this study was purchased from Sichuan Jiusheng Agricultural Technology Development Corporation, China. The biochar was made from rape straw after heating at 500 °C for 2 h without oxygen. The biochar had a pH of 8.9, organic C of 625.8 g kg–1 and dissolved organic C (DOC) of 1.06 g kg−1 (see below for the methods of analyses). In BC0 and BC1 treatments, biochar was applied at 0 and 10 Mg ha−1, respectively, and mixed manually into the top 10 cm of soil using a shovel. Biochar was applied in both the first year before chili cultivation and the second year before rice cultivation. The chemical fertilizers were applied to the BC0 plots in the chili, lettuce and rice cultivations (no fertilizer was applied in cabbage cultivation) at locally recommended rates: (1) chili: urea (300 kg N ha−1), calcium superphosphate (80 kg P ha−1) and potassium chloride (150 kg K 2

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ha−1); (2) lettuce: urea (300 kg N ha−1), calcium superphosphate (90 kg P ha−1) and potassium chloride (150 kg K ha−1); (3) rice: urea (150 kg N ha−1), calcium superphosphate (75 kg P ha−1) and potassium chloride (75 kg K ha−1) (Table S1). The rate of chemical fertilizer application in the BC1 plots was the same as that applied to the BC0 plots after accounting for the 0.44 % N, 0.0.7 % P, and 1.05 % K contained in the biochar. During the chili cultivation, 50 % of the N fertilizer, 50 % of the K fertilizer and all of the P fertilizer were applied as base fertilizers on May 10, 2017, and the remaining 50 % of the N and K fertilizers were top-dressed on June 19, 2017 (Huang et al., 2019). During lettuce and rice cultivations, 60 % of the N fertilizer and all of the P and K fertilizers were applied as base fertilizers on September 21, 2017 and April 28, 2018, respectively, and the remaining 40 % of the N fertilizer was top-dressed on November 1, 2017 and May 23, 2018, respectively. Chili, lettuce and cabbage were grown in a sequence in the research plots in the arable soil before the soil was flooded for the preparation of rice cultivation. Chili and lettuce seedlings, and cabbage seeds used in this experiment were bought from a local nursery. Twenty-one seedlings of chili were transplanted into each plot on May 11, 2017 and harvested on September 14, 2017. After three days of chili harvest, 21 seedlings of lettuce seedlings were transplanted in the same plots (at the rate of 21 seedlings per plot) and grown until they were harvested on November 27, 2017. After three days of harvesting the lettuce, 50 seeds of cabbage were sown in each plot and the cabbage was harvested in three months of the seed sowing. The chili seedlings were watered twice a week to keep the soil moist at 30–50 % water holding capacity (WHC), while the lettuce and cabbage were not irrigated, as there was enough precipitation to keep the soil moisture content at 30–50 % WHC during lettuce and cabbage cultivations. After 42 days of cabbage harvest, biochar was applied to the soil and the plots were irrigated until there was standing water on the soil surface in preparation for rice cultivation. Rice seeds were sown on March 12, 2018 on a bed prepared separately from the experimental plots. Twenty-one rice seedlings were transplanted into each experimental plot on May 2, 2018 and were grown until harvest on August 12, 2018 (102 days after transplanting rice seedlings). The paddy field was drained on August 5, 2018 for one week until the rice was harvested.

diffusion method (Yang et al., 2008). For available P determination, soil samples were extracted with a 0.5 M NaHCO3 solution (pH 8.5), then used molybdenum blue colorimetric method. Available K was determined by a flame photometer (FP6400A, Shanghai, China) after extraction with 1 M NH4Ac (pH 7.0). Soil MBC was determined by chloroform fumigation-extraction (Pokharel and Chang, 2019) for which soil samples were extracted with 0.5 M K2SO4 at a soil: K2SO4 solution ratio of 1:4 (w: v). The soil extracts were then analyzed for extractable C by K2Cr2O7 oxidation and FeSO4 titration method. The DOC of soil and biochar was measured according to Qi et al. (2019) with an automated TOC analyzer (Multi N/C 2100, Analytik Jena AG Co. Ltd., Germany). The bulk density (ρ) of the 0–10 cm soil was determined using 100 cm3 cylinders (diameter 50.5 mm and height 50.0 mm) before rice planting and after rice harvesting. The total N, P and K of the biochar were determined by the Kjeldahl method, vanadium molybdate yellow colorimetric method, and flame photometry method, respectively (Yang et al., 2008), after the biochar was digested with a 95 % H2SO4 solution (1: 10, w:v) and 30 % H2O2 solution. During vegetable sampling, all the plants in each plot were collected, roots were separated from the aboveground parts and washed with deionized water to remove soil from the roots. Aboveground parts were also washed with deionized water. For the purpose of yield measurement, we used fresh weight of fruits for chili and all aboveground parts for lettuce and cabbage. For rice plant sampling, five out of twenty-one rice plants grown in each plot were randomly selected and dug up at harvesting time. The plants were cut at the base of the stem, the above- and belowground parts were washed several times with deionized water, and then oven-dried at 60 °C for 72 h. These samples were used for measuring agronomy indicators such as plant height at tiller, number of kernels per spike, 1000-grain weight and belowground NPP while the remaining plants in each plot were used to measure rice grain yield, aboveground biomass and aboveground NPP from each plot after collecting them by cutting at ground level, then washing with tap water and drying in an oven at 60 °C for 72 h. Subsamples of aboveground biomasses (including grain and straw) and roots were ground to pass through a 0.15-mm sieve separately for total C analysis. 2.4. Measurement of greenhouse gas fluxes

2.3. Soil and plant sampling and analysis

A static-chamber method (Pramanik et al., 2014) was used to collect greenhouse gas (CH4, CO2 and N2O) samples during the vegetable and rice seasons. In each plot, a base frame made of stainless steel (0.5 × 0.5 m) was inserted 25 cm into the soil. Gas chambers (0.5 m long × 0.5 m wide × either 0.5 m or 1.0 m high; chambers of different heights were used depending on the height of the crop plants) made of stainless steel were wrapped with styrofoam and aluminum foil to prevent the chamber from heating up during gas sampling. At each sampling, the chamber was placed onto the frame, which had a 3 cm deep ×3 cm wide groove. Water was added to the groove to create a gas tight seal between the chamber and the base frame during gas sampling. A fan (12 V, diameter 8 mm) was installed inside each chamber to mix the air. Air temperature inside the chamber was measured using a portable digital thermocouple (JM624, Jinming Co. Ltd., China). Gas sampling was done between 8:30 and 10:30 am once a week (except that the gas sampling was done three times in the first week after each time of chemical fertilizer application). At each gas sampling, four gas samples were collected at 0, 10, 20, and 30 min after closing the chamber using a 60 mL plastic syringe equipped with a 3way stopcock. After collecting gas samples, the syringes were closed and brought to the laboratory for immediate sample analysis. Concentrations of CH4, CO2 and N2O in the gas samples were determined using a gas chromatograph (Agilent GC-7890A, Agilent Technologies, USA) equipped with a flame ionization detector for determining CH4 concentration, a thermal conductivity detector for determining CO2 concentration, and an electron capture detector for determining N2O

A composite soil sample was collected from 0 to 10 cm depth in each plot from five random points in an “S” shape using a 0.5 m long stainless-steel auger with a boring crown that had a 38 mm inner diameter. Since the focus of this research was on the rice season, we did not collect soil samples during the vegetable cultivation as frequently as during the rice cultivation. However, to determine SOC, microbial biomass C (MBC) and DOC during the vegetable season, we collected soil samples before chili cultivation and after cabbage cultivation. During the rice cultivation, soil samples were collected twice every month from the seedling stage (April 26, 2018) to the ripening stage of rice growth (August 12, 2018). Visible plant residues and coarse fragments in the soil samples were removed manually, then the samples were divided into two parts. One part of the soil was stored at 4 °C for the measurement of DOC and MBC, and the other part was air-dried at room temperature (25 °C) for analyzing soil pH, SOC, and available N, P and K. Soil samples for microbial analysis of mcrA and pmoA genes were collected on July 20, 2018 (80 days after transplanting rice seedlings). A composite sample per plot was collected following the method described above. The samples were kept in a cooler for transportation to the laboratory and stored at −20 °C until the samples were used for DNA extraction. Soil pH was determined using a 1:2.5 (w:v) soil to water ratio with a pH meter (PB-10, Sartorius, Göttingen, Germany). Soil organic C was determined by K2Cr2O7 oxidation and FeSO4 titration method (Huang et al., 2018). Available N was determined by an alkaline hydrolysis 3

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concentration. The GHG fluxes were calculated based on each gas concentration using Eq. (1) (Haque et al., 2015):

F=

×(

V c 273 )×( )×( ) A t 273 + t −2

paddy soils (Haque et al., 2016). The NGWP coefficients for CH4 and N2O in a 100-year time frame were considered as 28 and 265, respectively (Intergovernmental Panel on Climate Change (IPCC, 2014). The NGHGI (net greenhouse gas intensity, expressed as Mg CO2-eq Mg–1 grain yield) of rice production for the entire season was calculated as (Ma et al., 2013; Zhang et al., 2017):

(1) −1

−2

−1

where F is the CH4 (mg m h ), CO2 (mg m h ), or N2O (μg m−2 h−1) flux; ρ is the gas density of CH4, CO2 or N2O (mg cm−3); V represents the volume of the chamber (m3); A represents the surface area of chamber (m2); Δc/Δt is the rate of change in gas concentration in the chamber (mg m−3 h−1 or μg m−3 h−1), and t is the mean temperature (°C) in the chamber. The cumulative emissions of CH4, CO2 or N2O for the vegetable and rice seasons were calculated by linear extrapolation between consecutive measurements of GHG fluxes.

NGHGI = NGWP / Y

where NGWP is the net global warming potential (Mg CO2 ha ), Y is the rice grain yield (Mg yield ha–1) in each treatment. The N2O emission factor (EFN2O) (g N2O-N kg–1 N) due to the application of N fertilizer or by N contained in the biochar applied with the chemical fertilizer was determined as follows (Neto et al., 2016):

2.5. Soil organic C sequestration, C emission ratio and NPP

EFN2O =

Soil C sequestration (kg C ha–1) for the rice growing season was calculated as follows (Koyama et al., 2015): Soil C sequestration (kg ha–1) = 100 × h × ρ × (Ca – Cb)

aboveground

+ NPP

root

+ NPP

litter

+ NPP

Total DNA was extracted from 0.5 g homogenized soil using a FastDNA SPIN Kit for soil (MP Biomedicals, USA). The DNA was then stored at −20 °C for polymerase chain reaction (PCR) analysis. Quantitative real-time PCR assays were used to determine the copy numbers of mcrA and pmoA genes using a TIB 8600 Cycle Real-time PCR System (Tep Bioscience Co. Ltd., China) with three replicates. The primer pairs of mcrAF/ mcrAR (Luton et al., 2002) and A189/ A650 (Tuomivirta et al., 2009) were used to quantify abundances of methanogens and methanotrophs, respectively (Table S2). Standard curves were generated using 10 × stepwise dilution series of plasmid DNA with the target genes. The reaction efficiencies were 85–111 % and R2 was 0.995-0.999. The extracted DNA was amplified using a set of primers listed in Table S3. All PCR reactions were performed in triplicate with 1 μL of DNA, 1 μL of each primer (forward primer labeled with the fluorescent dye carboxyfluorescein), 12.5 μL 10 mM dNTP mix, and 9.5 μL PCRgrade water to make a final volume of 25 μL (Barbier et al., 2012). The archaeal mcrA genes were amplified using the primer pair M13 F/M13R (Luton et al., 2002). For the methanotroph pmoA gene amplification, the primer pairs A189f/A682 r and A189f/mb661 r were applied in a semi-nested PCR approach (Costello and Lidstrom, 1999; Holmes et al., 1999). The PCR reactions were visualized on a 2 % agarose gel and the PCR products were then purified using a DNA Purification Kit (Tiangen, China). The purified PCR products were quantified by spectrophotometry using a microplate reader (BioTek FLX800, USA) and sent out for sequencing (Personalbio Co. Ltd., China) by Illumina MiSeq. All the sequences determined in this study were deposited in the GenBank database under accession numbers SRR 8269038-SRR 8269046 for mcrA genes and SRR 8269089-SRR 8269097 for pmoA genes.

(3)

2.6. Calculation of NECB, NGWP, NGHGI and N2O emission factor The NECB (Mg C ha–1) in the rice growing season was calculated using the following equation (Wang et al., 2018; Wu et al., 2018):

+ TRh ×

Coutput = Cbiochar + NPP– Charvest + T (CH 4) ×

12 16

12 44

(4)

T (CO2) = TRa + TRh

(5)

TRa = GPP – NPP

(6)

where Cbiochar is the organic C input from biochar application, Charvest is the organic C output of harvested straw and grain, T (CH4) is the soil CH4 emission, and TRh is the soil heterotrophic respiration. As TRh was not meastured in this study but T (CO2) which is the total respiration in the ecosystem that is equal to the sum of plant respiration (TRa) and TRh. TRa was calculated as the difference between GPP and NPP (Wu et al., 2018). GPP is the gross primary production, which was calculated from NPP by the ratio of NPP/0.52. The NPP/GPP ratio of 0.52 was deduced from the result of Moderate Resolution Imaging Spectroradiometer products and has been used in previous papers such as Wu et al. (2018) and Zhang et al. (2009). The NGWP (kg CO2-eq ha–1) was calculated as follows (IPCC et al., 2014; Haque et al., 2016; Zhang et al., 2017): NGWP = 28 × T (CH4) + 265 × T (N2O) – 44/12 × SOC change

(9)

2.7. Real-time PCR and illumina MiSeq sequencing

The NPP of litter was estimated as 5 % of the aboveground biomass (Kimura et al., 2004) and of rhizodeposits as 15 % of the total rice biomass (Mandal et al., 2008).

NECB = Cinput

Tco

where, T (N2O) (g N2O-N ha ) is the total emission of N2O-N in BC1 or BC0 treatment, Tco (g N2O-N ha–1) is the total emission of N2O-N in the control treatment, Nap (kg N ha–1) is the amount of N applied as fertilizer or biochar plus fertilizer.

(2)

rhizodeposit

T (N2 O) Nap

–1

where h is soil depth (cm) which was set to 10 cm, ρ is soil bulk density (g cm–3); Ca is SOC (g C kg–1) after rice harvested, and Cb is SOC (g C kg–1) before rice planting. The C emission ratio for the rice season was calculated as the cumulative C emission (CH4-C and CO2-C kg ha–1) divided by the total C sequestered in the soil (kg C ha–1). The NPP (Mg C ha–1) of rice was calculated as follows (Wu et al., 2018): NPP = NPP

(8) –1

2.8. Statistical analysis Statistical analyses were conducted using SPSS 22.0 software (IBM, USA). One-way analysis of variance (ANOVA) was used to test the significance of the treatment effects on rice growth parameters, yield, cumulative CH4, CO2 and N2O emissions, average SOC, MBC, DOC and pH in the vegetable and rice seasons, diversity of genes of soil methanogens and methanotrophs, C emission ratio, NECB, NGWP, and NGHGI in the rice season. The effects of treatments on SOC, MBC, DOC and pH were also tested separately for soil samples collected at different stages of rice growth using a one-way ANOVA. The data were checked for normality (Shapiro-Wilk test) and homogeneity of variance (Levene's

(7)

where T (CH4) (kg CH4 ha–1) and T (N2O) (kg N2O ha–1) are the cumulative CH4 and N2O emissions for the entire rice cultivation. The SOC change was calculated from the NECB using a coefficient of 0.213 for 4

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Table 1 Effects of biochar and chemical fertilizer treatments on yield of vegetables (chili, lettuce and cabbage), and height at tiller, number of kernels per spike, weight of 1000 grains, plant biomass and yield of rice. Parameter　

Vegetable Rice

Treatment

Chili yield (Mg fresh weight ha−1) Lettuce yield (Mg fresh weight ha−1) Cabbage yield (Mg fresh weight ha−1) Height at tiller (cm) Number of kernels per spike 1000-seed weight (g) Plant biomass (Mg dry weight ha−1) Rice yield (Mg dry weight ha−1)

Control

BC0

BC1

0.58 ± 0.04b 28.31 ± 3.11b 15.28 ± 1.78b 110.85 ± 1.29b 237 ± 3a 23.85 ± 0.65b 10.5 ± 0.4b 7.68 ± 0.39c

1.25 ± 0.27a 35.32 ± 0.21a 24.40 ± 1.51a 115.18 ± 0.12a 216 ± 8b 24.38 ± 1.08ab 13.1 ± 1.2a 10.57 ± 0.013a

1.07 ± 0.12a 37.99 ± 2.58a 22.28 ± 1.38a 115.77 ± 2.32a 211 ± 17b 25.55 ± 1.12a 11.6 ± 0.7ab 8.76 ± 0.56b

Notes: Control = no biochar and chemical fertilizer application; BC0 = chemical fertilizer application; BC1 = biochar plus chemical fertilizer application. Data (means ± SD, n = 3) followed by different letters indicate significant differences between treatments at P < 0.05.

test) before ANOVA and all data were found to be normally distributed with homogenous variances. Significant differences between treatments were determined by the least significant difference (LSD) at P < 5 %. Differences of EFN2O between treatments in the vegetable–rice rotation were measured using a T-test. Pearson correlation analysis (P < 0.05 or 0.01) and multiple linear regression analysis (P < 0.05) were used to determine the relationships among soil pH, SOC, DOC, MBC, methanogenic and methanotrophic gene abundances at the 80th day of transplanting rice seedlings, cumulative CH4 and CO2 emissions, and yield in the entire rice season.

that in the control (P < 0.05; Table 2). In both vegetable and rice seasons, the rate of N2O fluxes was affected by chemical fertilizer application events, with the fluxes in the BC0 and BC1 treatments peaking immediately after chemical fertilizer applications (Fig. 3). In the vegetable cultivations, the cumulative N2O emissions were greater in BC0 and BC1 than in control but were not different between BC1 and BC0. In the rice season, the cumulative N2O emissions in the BC0 and BC1 treatments were 2.6 and 6.1 times, respectively, higher than that in the control (P < 0.05). Biochar did not change EFN2O in the vegetable season but increased in the rice season. The EFN2O was more than doubled in BC1 (7.89 g N2O-N kg–1 N) as compared with BC0 (3.35 g N2O-N kg–1 N) (Table 2). The yield scaled N2O emission was also not different between BC1 and BC0 in the vegetable season but higher in BC1 than in BC0 in the rice season (P < 0.05; Table S5).

3. Results 3.1. Effects of biochar and chemical fertilizer applications on the production of vegetables and rice

3.3. Effects of biochar and chemical fertilizer applications on soil C sequestration, NPP, NECB, NGWP and NGHGI

The yields of chili, lettuce and cabbage were significantly greater in BC1 and BC0 than in the control with no significant difference between BC1 and BC0 (Table 1). The rice yield was 14.1 % higher in BC1 than in the control (P < 0.05) but was 20.6 % lower in BC1 than in BC0 (P < 0.05). The height at tiller of rice was greater in BC0 and BC1 than in the control. Although the 1000-grain weight of rice was 7.1 % higher in BC1 than in the control (P = 0.009), the number of kernels per spike was 10.9 % lower in the former. The rice plant biomass was not different between BC1 and BC0 but was 25.1 % greater in BC0 than in the control treatment.

Soil organic C was higher in BC1 than in the other two treatments in both the vegetable and rice seasons (P < 0.01; Tables 2 and S4). The MBC and DOC were not affected by the treatments in the vegetable and rice seasons (Table 2) but both varied significantly with rice growth stage (Table S4). The mean soil pH during the vegetable season was not significantly different between the treatments but was higher in BC1 than in BC0 in the rice season (P < 0.05; Table 2). The C emission ratio was 94.5 and 79.8 % lower (P < 0.01) in the BC1 than in the BC0 and control treatments, respectively, in the rice season (Table 3). The NPP of rice was 33.9 and 40.1 % higher in BC1 and BC0, respectively, than in the control (P < 0.05; Table 3). The total amount of C sequestered in root and rhizodeposits of rice was higher in BC1 than in BC0 (P < 0.05). The total C sequestered in seed, straw and litter of rice was 21.3, 23.5 and 25.3 %, respectively, lower in BC1 than in BC0 (P < 0.05). The NECB in the BC1 treatment was 5.42 and 7.99 Mg C ha–1 higher (P < 0.01) than that in the control and BC0 treatments, respectively, in the rice season (Table 3). The NGWP in the BC1 treatment was 91.2 and 94.1 % lower in the rice season (P < 0.01), than that in the BC0 and control treatments, respectively. The NGHGI was also 89.4 % lower in BC1 than in BC0 in the rice season (P < 0.01).

3.2. Effects of biochar and chemical fertilizer applications on greenhouse gas emissions The CH4 emission did not show large variations in any of the vegetable cultivation treatments (Fig. 1). The treatments did not have any effects on total CH4 emission during the vegetable season (P > 0.05; Table 2). In the rice season, the rate of CH4 flux in all treatments remained stable from day 1 to day 45 after the transplanting of the seedlings, then the flux in BC1 and the control treatments started to increase to a peak at the heading stage (88 days after rice transplanting) before decreasing sharply 98 days after rice transplanting (Fig. 1). The sharp decrease in CH4 emissions occurred immediately after drainage in all the treatments. The cumulative CH4 emissions were 2.65 times higher in BC1 than in BC0 but were similar in the BC1 and control treatments of the rice season. During the vegetable season, the total CO2 emissions were 30.0 and 40.1 % higher in BC1 than in control and BC0, respectively (P < 0.05; Table 2). During the rice season, the CO2 flux increased continuously along with the growth of rice seedlings and peaked at 98th day, immediately after the drainage, then decreased sharply at 105th day, after rice harvest, in all treatments (Fig. 2). The cumulative CO2 emissions for the entire rice growing season in the BC0 and BC1 treatments were 81.5 and 57.2 %, respectively, greater than

3.4. Soil methanogenic and methanotrophic gene abundances and community structures in the rice season The copy number of mcrA genes in BC1 was 170.7 % higher than that in BC0 (P < 0.01), but 53.3 % lower than in the control (Fig. 4a). The copy number of pmoA genes was 77.6 % lower in BC1 than in BC0 but was not different from the control treatment (Fig. 4b). The abundance ratio of mcrA/pmoA in BC0 was 93.4 % lower than that in BC1 with no significant difference between BC1 and control treatments (Fig. 4c). Total CH4 emissions were positively related to the gene 5

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Fig. 1. Methane (CH4) fluxes in biochar and chemical fertilizer treatments in the vegetable (chili, lettuce and cabbage) and rice growing seasons. Arrows in the graph represent fertilizer addition and drainage events, BF represents base fertilizer, TD represents top dressing and D represents drainage. The treatments are control = no biochar and chemical fertilizer application, BC0 = chemical fertilizer application, and BC1 = biochar plus chemical fertilizer application.

Table 2 Effects of biochar and chemical fertilizer treatments on cumulative methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O) emissions, the N2O emission factor (EFN2O), average soil organic carbon (SOC), microbial biomass carbon (MBC), dissolved organic carbon (DOC) and pH in the vegetable and rice seasons. Crop

Treatment

Vegetable

Control BC0 BC1 Control BC0 BC1

Rice

CH4 (kg ha–1)

CO2 (Mg ha–1)

N2O (kg ha–1)

EFN2O (g kg–1)

SOC (g kg–1)

MBC (mg kg–1)

DOC (mg kg–1)

1.44 ± 1.57a 0.40 ± 0.69a –0.58 ± 0.77a 177.97 ± 36.05a 35.96 ± 21.99b 131.18 ± 53.48a

46.18 ± 4.72b 42.88 ± 5.17b 60.06 ± 8.90a 26.17 ± 2.94b 47.52 ± 10.32a 41.14 ± 4.38a

3.53 9.84 7.70 0.30 1.09 2.16

– 6.69 ± 3.79a 4.42 ± 1.31a – 3.35 ± 1.23b 7.89 ± 1.44a

11.80 11.99 19.16 10.56 11.14 19.92

259.02 262.27 222.82 214.85 217.01 211.96

165.68 ± 120.35a 118.57 ± 102.86a 114.69 ± 93.92a 329.63 ± 96.25a 288.05 ± 65.76a 315.38 ± 55.66a

± ± ± ± ± ±

1.52b 2.65a 1.83a 0.20c 0.49b 0.54a

± ± ± ± ± ±

0.79b 0.84b 3.14a 0.75b 0.42b 1.12a

± ± ± ± ± ±

37.82a 39.51a 22.75a 27.05a 49.57a 39.17a

pH 6.34 6.01 6.28 6.51 6.27 6.70

± ± ± ± ± ±

0.25a 0.43a 0.34a 0.09a 0.10b 0.11a

Notes: Control = no biochar and chemical fertilizer application; BC0 = chemical fertilizer application; BC1 = biochar plus chemical fertilizer application. Data (means ± SD, n = 3) followed by different letters indicate significant differences between treatments at P < 0.05. Fig. 2. Carbon dioxide (CO2) fluxes in biochar and chemical fertilizer treatments in the vegetable (chili, lettuce and cabbage) and rice growing seasons. Arrows in the graph represent fertilizer addition and drainage events, BF represents base fertilizer, TD represents top dressing and D represents drainage. The treatments are control = no biochar and chemical fertilizer application, BC0 = chemical fertilizer application, and BC1 = biochar plus chemical fertilizer application.

abundance of mcrA and mcrA/pmoA and negatively related to the gene abundance of pmoA (P < 0.05) (Tables S6 and S7). The BC1 and control treatments had a greater relative abundance of Methanobacterium than the BC0 treatment but the BC0 treatment had a higher relative abundance of Methanocella than the BC1 and control treatments (P < 0.05; Fig. 5a). The relative abundance of Methanoregula was 10.8 % higher in BC1 than in BC0 but was not different from the control. For the community structure of methanotrophs, the relative

abundance of Methylococcus was higher in BC1 than in BC0 but was not different from the control (Fig. 5b). 4. Discussion 4.1. Effects of biochar and chemical fertilizers on crop production As compared to the chemical fertilizer treatment, biochar addition 6

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Fig. 3. Nitrous oxide (N2O) fluxes in biochar and chemical fertilizer treatments in the vegetable (chili, lettuce and cabbage) and rice growing seasons. Arrows in the graph represent fertilizer addition and drainage events, BF represents base fertilizer and TD represents top dressing and D represents drainage. The treatments are control = no biochar and chemical fertilizer application, BC0 = chemical fertilizer application, and BC1 = biochar plus chemical fertilizer application.

Table 3 Soil carbon (C) sequestration, C emission (CH4-C and CO2-C), C emission ratio, net primary productivity (NPP), net ecosystem carbon budget (NECB), net global warming potential (NGWP) and net greenhouse gas intensity (NGHGI) in biochar and chemical fertilizer treatments in the rice season. Parameter 　 Soil C sequestration (Mg C ha–1) C emission (Mg C ha–1) CH4-C/Total C sequestered CO2-C /Total C sequestered C emission ratio Plant C (Mg C ha–1)

NPP (Mg C ha–1) C input (Mg C ha–1) Charvest (Mg C ha–1) Coutput (Mg C ha–1) NECB (Mg C ha–1) NGWP (Mg CO2 ha–1) NGHGI (Mg CO2 Mg–1)

Treatment

CH4-C CO2-C

Seed Straw Root Litter Rhizodeposit

Control

BC0

BC1

0.99 ± 0.08b 0.133 ± 0.014a 7.14 ± 0.44b 0.14 ± 0.03a 7.21 ± 0.81b 7.36 ± 0.84b 1.89 ± 0.20b 1.67 ± 0.15b 0.192 ± 0.001c 1.15 ± 0.01c 3.46 ± 0.03c 8.36 ± 0.31b 8.36 ± 0.31c 3.39 ± 0.27b 10.66 ± 0.61c –0.39 ± 0.07b 5.37 ± 1.06a 0.70 ± 0.14a

0.48 ± 0.42b 0.026 ± 0.008b 12.96 ± 1.54a 0.06 ± 0.03b 27.00 ± 5.86a 27.06 ± 5.89a 2.49 ± 0.34a 2.26 ± 0.28a 0.204 ± 0.013b 1.86 ± 0.21a 5.07 ± 0.53b 11.88 ± 1.05a 11.88 ± 1.05b 4.36 ± 0.34a 17.35 ± 1.88a –2.96 ± 0.08c 3.61 ± 0.75a 0.34 ± 0.08b

7.56 ± 1.97a 0.098 ± 0.020a 11.22 ± 0.74a 0.01 ± 0.01c 1.48 ± 0.16c 1.49 ± 0.18c 1.96 ± 0.16b 1.73 ± 0.17b 0.248 ± 0.037a 1.39 ± 0.12b 5.99 ± 0.37a 11.32 ± 0.85a 17.28 ± 0.91a 3.65 ± 0.33b 14.97 ± 1.08b 5.03 ± 2.06a 0.32 ± 1.62b 0.036 ± 0.187c

Notes: Control = no biochar and chemical fertilizer application; BC0 = chemical fertilizer application; BC1 = biochar plus chemical fertilizer application. C emission ratio represents the ratio of cumulative emission (CH4-C and CO2-C) (kg C ha−1) to total soil carbon sequestration. Data (means ± SD, n = 3) followed by different letters indicate significant differences between treatments at P < 0.05.

did not affect vegetable production most likely due to higher N application rates in the vegetable fields in China (Jia et al., 2012a) and the background mineral soil N concentrations during the vegetable season were found much greater than the concentrations reported in other upland agriculture ecosystems (Li et al., 2015). The lower yield of rice in BC1 than in BC0 was associated with the lower amounts of the easily available nutrients (Kavitha and Subramanian, 2007) that are easily available for rice to uptake in the BC1 treatment because the amount of applied chemical fertilizers in BC1 was reduced by accounting for N, P and K contents to make total nutrient contents equal to that of BC0. Although there are results in previous studies showing that biochar can provide some nutrients (such as inorganic N) for the crop (Agegnehu et al., 2017), most of the nutrients present in biochar are in organic form that can’t be released to the soil for a long time because of recalcitrant nature of biochar (Kuzyakov et al., 2014; Table S5). Another possible reason may be the physical adsorption of nutrients on biochar surfaces (Clough and Condron, 2010) that could reduce availability of

nutrients for rice growth. But if biochar is added to the soil with enough chemical fertilizer (without accounting for the N, P and K contained in biochar) in the rice season, it would not cause nutrient limitations in the rice production as shown in some other studies (Jones et al., 2012; Shen et al., 2014; Wang et al., 2018). Therefore, our results 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. 4.2. Effects of biochar and chemical fertilizers on GHG emissions, emission factor, NGWP, NGHGI, and the mechanism controlling CH4 emissions In this study, the CH4 emission was not affected by biochar addition in the vegetable season (Table 2), which is a result similar to Jia et al. (2012a). Biochar addition probably did not make substantial changes to soil porosity of the well aerated soil (Angst et al., 2014) and thus no significant change in the methanogenic gene abundance in the soil 7

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plant and microbial respiration) in the rice season in BC0 and BC1 treatments as compared with the control, might be a result of chemical fertilizer application (alone and in combination with biochar) increasing nutrient availability for plants and soil microbes, so leading to increased ecosystem respiration (Baumann et al., 2009). The immediate increase of CO2 in all treatments after drainage was due to the removal of the diffusion barrier caused by floodwater (Miyata et al., 2000). The sharp decrease in CO2 emissions in all treatments occurred on the 105th day largely due to the rice harvest on the 102th day) (as the CO2 emissions measured in this experiment included plant and soil respiration) and partly due to the decrease in soil heterotrophic respiration caused by lowered soil moisture content following field drainage (Moyano et al., 2013). In addition, the higher DOC in BC1 as compared with the control in the initial months of rice seedling growth (Table S4) could enhance soil microbial respiration (Wang et al., 2018). The highest N2O emissions observed immediately after fertilization in both vegetable and rice seasons corresponded to the increase in available N in the BC1 and BC0 treatments (Hoben et al., 2011; Bayer et al., 2015). The difference in biochar’s effects on N2O emission between vegetable and rice seasons (neutral in the vegetable and positive in the rice season) is probably linked with biochar’s effects on nitrifier and denitrifier activities (Cayuela et al., 2014). Nitrification is the main pathway of N2O emission in arable soil (in the vegetable season) where biochar addition did not change soil aeration (Bateman and Baggs, 2005). But in anaerobic soils, biochar’s role is often described to decrease denitrification by increasing oxygen availability for the reduction of N2O emission, suggesting that other factors of biochar such as liming and increasing N availability for enhanced denitrification caused the increase in N2O emission during the rice season (Clough et al., 2013). The result of higher N2O emission in BC1 compared to that in BC0 in the rice season was similar to the result of Lin et al. (2017) where the increase in N2O emission in biochar-amended soil was attributed to the increase in bacterial amoA gene abundance caused by the increase in soil pH. In addition, reductions in NO3− leaching by biochar addition can increase NO3− availability for denitrifiers and increase N2O emission (Cayuela et al., 2014). Since the total amount of N applied was equal in BC1 and BC0 treatments, greater cumulative emission of N2O in BC1 made EFN2O greater in BC1 than in BC0 in the rice season. Biochar reduced rice yield but increased cumulative N2O emission resulting in greater yield-scaled N2O emission in BC1 than in BC0 (Tables 1 and 2). With the neutral effects in the vegetable season and positive and negative effects on yield-scaled N2O emission and crop yield, respectively, in the rice season suggests that biochar application is not recommended in vegetable-rice rotation for mitigating N2O emission. The lower NGWP and NGHGI in BC1 than in BC0 in the rice season was associated with the increase in SOC (Zhang et al., 2017). Shang et al. (2011) found that CH4 and SOC were the main two factors controlling NGWP as they were the major source and sink of NGWP in paddy fields. Although there was an increase in GHG emissions from biochar application during the rice season (Figs. 1 and 3), the NGWP was lower in BC1 than in the other two treatments. This is because biochar has a greater impact on increasing SOC than on increasing C emission, leading to a greater reduction in the C emission ratio (Table 3; Paustian et al., 2016). The lower NGHGI in BC1 than in BC0 suggested that, with the same rice yield, biochar addition reduced environmental impacts caused by GHG emission (Wang et al., 2018). The greater methanogenic gene abundances in BC1 than in BC0 were attributed to the increase in soil NH4+–N, SOC and pH as a result of biochar addition to the soil (Wang et al., 2019). Increased inorganic N and SOC contents in the soil can increase substrate availability for the growth of methanogens (Banger et al., 2012; Zhang et al., 2018) resulting into greater CH4 production. Increased pH can be another factor to stimulate the growth of methanogens in biochar-amended paddy soils (Yu et al., 2013). The net emission of CH4 from the soil depends on the production (facilitated by methanogenic bacteria) and consumption

Fig. 4. Effects of biochar and chemical fertilizer application on copy numbers of methyl coenzyme M reductase (mcrA) (a) and particulate methane monooxygenase (pmoA) (b), and the ratios of copy numbers mcrA to pmoA (c) in different treatments in the rice growing season. The treatments are control = no biochar and chemical fertilizer application, BC0 = chemical fertilizer application, and BC1 = biochar plus chemical fertilizer application. Data (means ± SD, n = 3) followed by different letters indicate significant differences between treatments at P < 0.05.

during the vegetable season. The higher CH4 emission in the rice season in BC1 than in BC0 may be attributed to the increased SOC in the biochar-amended soils (Singla et al., 2014), as SOC strongly affects CH4 emission in paddy soils (Watanabe et al., 1998; Yan et al., 2005). The increased CO2 emission in BC1 relative to BC0 during the vegetable season was probably due to the increased availability of labile organic substrates for microbial decomposition added with the biochar (Wang et al., 2015). The drainage at the end of the rice season significantly reduced CH4 emission in the paddy soil because of the increase of soil redox potential (Eh) (Minamikawa and Sakai, 2006), soil porosity and aeration (Wang et al., 2019). The higher CO2 emission (which includes 8

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Fig. 5. Effects of biochar and chemical fertilizer application on the relative abundance [%] of operational taxonomic units (OTUs) classified at genus level of soil methanogens (a) and OTUs classified at genus level of soil methanotrophs (b) in the rice growing season. The OTUs with < 0.10 % abundance were neglected. The relative abundance of methyl coenzyme M reductase (mcrA) and relative abundance of particulate methane monooxygenase (pmoA) OTUs of all three replicates in each treatment is shown in the figures. The treatments are control = no biochar and chemical fertilizer application, BC0 = chemical fertilizer application, and BC1 = biochar plus chemical fertilizer application. Vertical bars represent standard errors (n = 3).

(facilitated by methanotrophic bacteria) of CH4 (Borrel et al., 2011; Feng et al., 2012; Han et al., 2016). These processes are controlled by the abundances and community structure of methanogens and methanotrophs (Feng et al., 2012; Liu et al., 2019; Wang et al., 2019). In this study, the abundance of methanogens and the relative abundance of Methanoregula, an important genus of methanogenic bacteria (Tong et al., 2017), were increased (Figs. 4 and 5) and the abundance of methanotrophs was decreased (Fig. 4), which were associated with the increase in CH4 emission in the BC1 as compared to the BC0 treatment (Tables S6 and S7). The relative abundance of Methanoregula was found to be positively correlated with CH4 emission in an earlier study (Cai et al., 2018). The significant decrease in methanotrophic bacterial diversity (as shown by the values of Chao1, Shannon and operational taxonomic units) did not result in the reduction of CH4 emission from biochar-amended soils (Table S8), indicating that the change in methanotrophic bacterial diversity may not be the main factor controlling CH4 emission from the biochar-amended paddy soil. Perhaps the mcrA/ pmoA transcript and abundance ratios are key factors controlling the

CH4 emission from the biochar-amended paddy soil (Lee et al., 2014; Fernández-Baca et al., 2018). 4.3. Carbon sequestration and NECB as affected by biochar and chemical fertilizer applications in the rice season The greater soil C sequestration in BC1 than in the BC0 and control treatments was due to the increase in SOC by biochar addition (Table 2). Increasing the SOC pool is a good strategy to mitigate climate change in biochar-amended soils (Lehmann, 2007). Although C (CH4 and CO2) emissions from biochar-amended soils were increased in this study, the decrease in the C emission ratio caused by biochar addition has an important implication for decreasing net C loss from the soil (Luo et al., 2011). Our results showed that the greater soil DOC and MBC in BC1 than in the control was found only in the initial months after biochar application (Table S4). This short-lived biochar effect might be caused by the release of bio-oil condensates (a form of labile C) formed during pyrolysis (Smith et al., 2010a). Although the organic 9

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matter present in rhizodeposits is an easily available source of C and energy for microbial decomposition, increased C sequestration in rhizodeposition could be stabilized by biochar through the formation of organo-mineral complexes and the protection of C from microbial decomposition (Keith et al., 2015). An increase in NPP by chemical fertilizer application is common in croplands (Yang et al., 2015), but addition of biochar together with chemical fertilizers did not cause significant change in NPP in paddy fields compared with BC0, perhaps the negative effect of the lower amount of easily available nutrient input on the NPP was offset by the increased photosynthetic C fixation by biochar application (Lorenz and Lal, 2014). The negative NECB observed in BC0 and control illustrates the net C loss from the ecosystem in those two treatments (Jia et al., 2012b) during the rice cultivation. However, the NECB was positive in BC1 (5.03 Mg C ha–1), which was significantly greater than that of BC0 and control, illustrating high potential of biochar application to sequester C in the rice season (Wang et al., 2018; Wu et al., 2018). Although C output was increased by 40.4 % in BC1 as compared to the control, the increase of C input to the soil overweighed the increase of C output in BC1 (Table 3). Therefore, biochar applied to the soil can be effective in offsetting the C emitted from the soil with chemical fertilizers and in maintaining ecosystem C balance in the rice season because the C added to the soil with the biochar (Paustian et al., 2016; Smith, 2016).

anonymous reviewers for their constructive comments that substantially improve the quality of an earlier version of this paper. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agee.2020.106831. References Agegnehu, G., Srivastava, A., Bird, M.I., 2017. The role of biochar and biochar-compost in improving soil quality and crop performance: a review. Appl. Soil Ecol. 119, 156–170. Angst, T.E., Six, J., Reay, D.S., Sohi, S.P., 2014. Impact of pine chip biochar on trace greenhouse gas emissions and soil nutrient dynamics in an annual ryegrass system in California. Agric. Ecosyst. Environ. 191, 17–26. Asai, H., Samson, B.K., Stephan, H.M., Songyikhangsuthor, K., Homma, K., Kiyono, Y., Inoue, Y., Shiraiwa, T., Horie, T., 2009. Biochar amendment techniques for upland rice production in Northern Laos: 1. Soil physical properties, leaf SPAD and grain yield. Field Crops Res. 111, 81–84. Bamminger, C., Poll, C., Marhan, S., 2017. Offsetting global warming-induced elevated greenhouse gas emissions from an arable soil by biochar application. Glob. Change Biol. 24, 318–334. Banger, K., Tian, H., Lu, C., 2012. Do nitrogen fertilizers stimulate or inhibit methane emissions from rice fields? Glob. Change Biol. 18, 3259–3267. Barbier, B.A., Dziduch, I., Liebner, S., Ganzert, L., Lantuit, H., Pollard, W., Wagner, D., 2012. Methane-cycling communities in a permafrost-affected soil on Herschel Island, Western Canadian Arctic: active layer profiling of mcrA and pmoA genes. FEMS Microbiol. Ecol. 82, 287–302. Bateman, E., Baggs, E., 2005. Contributions of nitrification and denitrification to N2O emissions from soils at different water-filled pore space. Biol. Fertil. Soils 41, 379–388. Battye, W., Aneja, V.P., Schlesinger, W.H., 2017. Is nitrogen the next carbon? Earths Future 5. Baumann, K., Marschner, P., Smernik, R.J., Baldock, J.A., 2009. 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5. Conclusions Reducing of chemical fertilizer by the amounts of nutrients (N, P and K) added through biochar had negative effects on rice yield but not on vegetable yield implying that biochar can reduce the requirement of chemical fertilizer in vegetable production. Yield-based N2O emission was increased because of increase in N2O emission and decrease in rice yield in the biochar-amended paddy soil. But the increase in NECB by biochar application shows that itis effective for offsetting C emission and for increasing C storage in the soil during the rice season. Although biochar increased CH4 and N2O emissions during the rice season, the significant reduction in C emission ratio, NGWP and NGHGI demonstrates the potential of biochar to reduce net C loss from the soil and help in mitigating climate change. The increase in GHG emissions due to biochar addition is more than compensated for by large increases in soil C uptake and storage, enough to overcome the GWP of increased CH4 and N2O emissions in biochar-amended soils. This study concludes that in order to ensure no nutrient limitation occurs, the chemical fertilizer should not be reduced by the amount of nutrients present in the biochar to maintain crop yields and increase SOC storage during rice cultivation. This study provides an insight on the biological control of CH4 emission from biochar-amended paddy soil that was previously arable and was used for vegetable production. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We gratefully acknowledge funding from the Chongqing Graduate Student Research Innovation Project (CYB18091), the China Scholarship Council, National “Five-Year" Key Research and Development Program (2017YFD0800101), State Cultivation Base of Eco-agriculture for Southwest Mountainous Land and the National College Students Innovation and Entrepreneurship Training Program (201910635077). The authors would also like to acknowledge Rong Huang, Xiaomin Guo, Yingyan Wang, Yingxiao Hu, Ruijie Jia, Ting Li, Jinlin Deng, Fuhua Wang, Jiacheng Li, Jiao Li, Guoxin Xu, Jinzhu Wang and Meng Rao for their help in data collection. We also thank three 10

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