1. Introduction
The huge demand for food along with the rapid growth of world’s population puts a severe pressure on food security. Rice (Oryza. sativa L.) is one of the most important staple foods for human consumption, attributing rice cultivation as the most important activity for the global population. As the second most important greenhouse gas after carbon dioxide (CO2), CH4 is produced naturally where organic matter decomposes under anaerobic circumstances [1]. In past decades, rice cultivation has been regarded as one of the most important anthropogenic sources of CH4 emissions, and it contributes about 25–37 Tg CH4 yr−1 [1].
Previous studies have demonstrated that CH4 emissions are regulated by several processes, including CH4 production, consumption, and migration [2]. In a typical flooded rice paddy system, the determining factor of CH4 emissions is the production process [3], which can be regulated by many farming practices such as irrigation regimes [4] and straw retention [5,6]. During rice cultivation, fore-rotating crop straw can be incorporated into rice paddies as a kind of organic amendment to maintain soil fertility by increasing the soil’s organic carbon stock. However, CH4 emissions from rice paddies could be affected by straw incorporation, and in most cases, it will stimulate the CH4 emissions from rice paddies [7,8,9]. Thus, it is hard to strike a balance between soil carbon sequestration and CH4 mitigation in rice paddies when it comes to the usage of raw crop straw as the soil organic amendment.
Previous studies suggested that the advantages of soil carbon sequestration that resulted from crop straw retention could be offset by the crop straw-induced excessive CH4 emissions [10]. Furthermore, besides the massive CH4 emissions, the inefficient soil carbon sequestration ability of straw incorporation is another adverse factor [11]. Han et al. [12] indicated that after a two-year straw incorporation treatment, the amount of the incremental portion of soil organic carbon (SOC) only accounted for 24.88% of the total straw carbon inputs. From the perspective of low-carbon agriculture, the incorporation of crop straw as a soil organic amendment in rice paddies is an unwise investment indeed. Therefore, it is urgent to optimize straw retention technology to further sequester soil carbon and reduce CH4 emissions to achieve sustainable and low-carbon agricultural production.
Biochar is a carbon-based solid material resulted from the thermal conversion of biomass in an anaerobic environment, and it has been reported for various uses in agricultural practices [13,14]. Field and laboratory experiments have demonstrated that biochar has a lower carbon mineralization rate in comparison with raw biomass wastes, such as crop straw and stubble [15,16]. The carbon turnover time of straw-derived biochar is much longer than raw crop straw in farmland soils. In some specific cases, nine years after the incorporation of biochar, the carbon loss of the applied biochar was only 10.3–11.8% [17]. Moreover, previous research suggests that, in the long term, biochar–soil interaction can promote soil carbon storage via the processes of soil organic matter sorption to biochar and physical protection [15].
The aim of the present study was to comparatively evaluate the effects of different fore-rotating crop straw retention strategies on the rice paddy carbon budget. Therefore, a field experiment with a four-year continuous in situ CH4 emissions observation and five-year soil physicochemical property monitoring was conducted to quantitatively evaluate the effects of different crop straw retention strategies on CH4 emissions and carbon sequestration rates in rice paddy fields.
2. Materials and Methods
2.1. Study Site Description
The in situ field experiment was carried out at the Zhuanghang Comprehensive Experiment Station (121°23′53′ E, 30°53′37′ N), Shanghai City, China. The study site is located in the Yangtze River Delta with a typical subtropical monsoon climate. The annual average temperature is 16.8 °C, with an annual average rainfall precipitation mount of 1332 mm (2012–2016). The mean temperature of rice growing seasons from 2012 to 2016 was 22.9 °C. Soil is classified as Typical Haplaquept (USDA taxonomy) with an average pH value of 7.52 (S:W = 1:5).
2.2. Field Experiment Design
Five treatments of non-fertilization and non-straw (Control), conventional fertilization and non-straw (CF), conventional fertilization and raw wheat straw (FS), slow-release fertilizer and raw wheat straw (SFS), and conventional fertilization and wheat-straw-derived biochar (FB) were employed in the field experiment during rice growing seasons from 2012 to 2016. Parametric details of each treatment during rice growing seasons are summarized in Table 1. During the wheat growing seasons, 3 t·hm−2 of raw rice straw were incorporated in FS and SFS treatment plots, and 1 t·hm−2 of rice-straw-derived biochar were applied in FB treatment plots. The field experimental plots were arranged as a completely randomized block design. Each treatment had three replications, giving a total of 15 filed experimental plots. The size of each field experimental plot was 60 m2 (6 m × 10 m). The edges of the field experimental plots were molded using concrete to unify the configuration of each plot. Special rubberized membranes (high density polyethylene) were vertically mounted in the paddy soil around each experimental plot to alleviate lateral seepage. The soil physicochemical properties were analyzed from 2012 to 2016, and the in situ observation of CH4 emissions from rice paddies was carried out from 2013 to 2016 in the rice growing seasons.
2.3. In Situ Gas Sampling
Gas samples for determining the CH4 exchange rates between the atmosphere and the rice paddy systems were collected using the in situ static chamber method. The gas sampling device was composed of several parts, including a transparent open bottom acrylic gas sampling chamber (L × W × H = 50 cm × 40 cm × 50 cm), an acrylic chamber base (L × W × H = 50 cm × 40 cm × 5 cm), and two alternative acrylic frames (L × W × H = 50 cm × 40 cm × 40 cm or 50 cm × 40 cm × 20 cm) for sampling chamber height extension. In the representative in situ sampling processes, the sampling chamber was placed into the chamber base. The chamber base was pre-installed simultaneously with rice transplanting. The top of the chamber base was manufactured as a liquid seal groove to prevent the seams of the chamber and the chamber base from leaking gas. Five minutes after the sampling chamber was water sealed, four gas samples (500 mL gas each) were sequentially collected at time intervals of 6 min by using an automatic gas sampler. The collected gas samples were encapsulated in the gas sampling bags (LB-101, Delin Dalian, China) prior to analysis.
2.4. Soil Physicochemical Properties Determination
Soil samples were collected from the rice paddy plough layer at a depth of 0 to 10 cm with a manual soil sampler from five places in each treatment plot. Samples were dried naturally, ground, and sieved to 2 mm for the following physicochemical analysis. The concentrations of carbon (C) and nitrogen (N) in the soil samples were determined by using a CN analyzer Vario EL Cube (Elementar Analysensysteme GmbH, Hanau, Germany).
2.5. Methane Emission and Greenhouse Gas Intensity Determination
The methods of calculating CH4 fluxes and seasonal cumulative emissions are described in detail elsewhere in Sun et al. (2016) [18] and Zou et al. (2005) [19], respectively. The CH4-induced greenhouse gas intensity (GHGI) of rice production was calculated by using the following equations [20]
CH4-induced GWP = CH4 (kgCH4-C ha−1)/12 × 16 × 27, (1)
GHGI = CH4-induced GWP/rice grain yield (2)
where the constant 12 is the molecular weight of C in CH4 and 16 is the molecular weight of CH4. The global warming potential of 1 kg CH4 from non-fossil sources is equivalent to 27 kg CO2 based on 100 years [1]. The unit of CH4-induced GWP and GHGI are kg CO2-eq and kg CO2-eq kg−1 rice grain, respectively.2.6. Statistical Analyses
Two-factor repeated measures analysis of variance (ANOVA) and ANOVA along with the Duncan test to assess pairwise differences were carried out using SPSS 22.0.0.0 (IBM Co., New York City, NY, USA). Path analysis was conducted using AMOS 21.0.0 software (IBM Amos Development Co., Meadville, PA, USA). Multivariate Box–Cox transformation was carried out to solve the latent multi-collinearity issues among variables in the path analysis model by using Minitab Statistical Software 17.1.0.0 (Minitab Inc., Pennsylvania, PA, USA). Each datum point in Figures represents an average value; vertical bars represent the standard error (SE) of the means. All significant levels were set at p < 0.05 unless otherwise stated.
3. Results
3.1. Methane Emissions
The seasonal dynamics of CH4 fluxes from rice paddies showed an identical pattern among treatments but differed in yearly variations (Figure 1). The occurrence of CH4 fluxes peaks can be observed within 40 days after rice plant transplanting. During the initial flooding phase, CH4 fluxes increased steadily to the maximum value and then drop dramatically with the following midseason drainage. Thereafter, with the termination of the midseason drainage, the CH4 fluxes increased gradually and then maintained at the relatively lower CH4 fluxes rates until the end of the rice growing season. The CH4 fluxes peaks under FS and SFS treatments were consistently higher in comparison with the other treatments (Figure 1). Straw incorporation markedly increased the amplitudes of CH4 fluxes from rice paddies. The yearly maximum CH4 fluxes under FS and SFS treatments were 22.7–50.6 mg CH4 m−2 h−1 and 22.0–44.9 mgCH4 m−2 h−1, respectively.
The seasonal cumulative CH4 emissions varied greatly among years, but the rand–size relationships of seasonal cumulative CH4 emissions among the applied treatments did not change much. As shown in Figure 2, the seasonal cumulative CH4 emissions of the FS treatment were roughly equivalent to the SFS treatment, and about 2 to 3 times higher than those of the CF and FB treatments. In contrast, there was no significant difference among Control, CF, and FB treatments in seasonal cumulative CH4 emissions in the 2013–2016 rice growing seasons.
3.2. Soil Carbon Properties
The results of rice paddy soil TC contents under different treatments are summarized in Table 2. The dynamics of soil TC contents varied slightly with year and showed an increasing tendency except for the Control treatment.
As shown in Table 3, a two-way repeated measures ANOVA was conducted to evaluate the accumulative rates of soil TC contents among treatments; the results indicated that the rice paddy soil TC contents were significantly affected by the duration of continuous rice–wheat rotation (refer to the Time factor in Table 3) (p < 0.01). The cumulative rates of rice paddy soil TC contents varied greatly among treatments (p < 0.01), while there was no interaction between Treatment and Time factors (refer to the Time × Treatment factor in Table 3). Compared with the treatments without any exogenous organic carbon inputs, FS, SFS, and FB treatments that incorporated with straw or straw-derived biochar showed the higher soil carbon sequestration rates. Soil TC contents were significantly positively correlated with the duration of rice–wheat rotation under CF, FS, SFS, and FB treatments (Figure 3a–e). The annual average increments of soil TC contents under CF, FS, SFS, and FB treatments were 0.32, 0.47, 0.66, and 0.89 g C kg−1 soil, respectively.
3.3. Rice Grain Yields and Greenhouse Gas Intensities
Rice grain yields and the corresponding seasonal CH4 emissions under different treatments were statistically evaluated in this study. As shown in Figure 4, the rice grain yields under different treatments varied over years. The average rice grain yields of CF, FS, and FB treatments were approximately equal, and were significantly lower than that of the SFS treatment (p < 0.05), but higher than that of the Control treatment (p < 0.01).
The results of CH4-induced GHGI indicated that, in comparison with the other three treatments, the FS and SFS treatments significantly increased CH4-induced GHGI in rice paddy systems (p < 0.05). In contrast, the Control, CF, and FB treatments showed no significant differences over the four rice growing seasons (Table 4).
The results of the two-way repeated measures ANOVA showed that the CH4-induced GHGI could markedly be affected by both the durations of continuous rice–wheat rotation and the applied treatments, but there was no interaction between these two factors (Table 5).
3.4. Environmental Factors
A path analysis was conducted to simultaneously estimate the effect sizes of the direct and indirect factors on CH4 emissions. As shown in Figure 4, the results showed that rice biomass significantly increased CH4 emissions from rice paddies (standardized path coefficient = 0.25, p < 0.01). In contrast, temperature, as an environmental factor, showed a negative effect on CH4 emissions from rice paddy systems (standardized path coefficient = −0.18, p < 0.05). The seasonal cumulative SOC was significantly positively correlated with rice biomass (standardized path coefficient = 0.28, p < 0.05), but CH4 emissions from rice paddies were not affected by rice biomass via SOC promotion. There was no significant association between rice biomass and climatic factors; the rice biomasses were mainly influenced by applied treatments. Thus, the interannual variances of CH4 emissions of different treatments were not resulted from the indirect effects of temperature or precipitation factors via regulating rice biomasses. The soil C properties, including soil C content, labile soil C content, seasonal cumulative SOC, and soil C/N ratio showed no direct effects on CH4 emissions.
4. Discussion
4.1. CH4 Emissions
The present study demonstrated that the applied treatments mainly affected the amplitudes of CH4 emissions, but the seasonal dynamic patterns of CH4 fluxes did not vary with different treatments. It is suggested that the dynamic patterns of seasonal CH4 fluxes mostly depend on irrigation regimes and water conditions in rice paddies [19,21]. In agreement with previous studies, the application of fore-rotating crop straw can significantly increase CH4 fluxes and seasonal emissions from rice paddies [8,22,23]. The application of raw crop straw not only provides a massive organic carbon source for methanogenesis, but it also provides anaerobic soil conditions, which may consequently facilitate CH4 production processes in the rice paddy soil systems [24,25].
The continuous inputs of biochar can mitigate CH4 emissions from rice paddies, which may be attributed to the improvement of soil aeration and methanotrophs abundance [26,27]. However, in present study, compared with the CF treatment, the application of wheat-straw-derived biochar showed no reduction effect on CH4 emissions from rice paddies over the four rice growing seasons. One possible explanation for this is the application amount differences. Previous studies indicated that CH4 emissions from rice paddies could be reduced when the yearly application amount of biochar was at a range of 2.8–22.5 t ha−1 [27,28,29]. In the present study, in order to make it comparable with the application rate of raw wheat straw in the FS and SFS treatments, the application rate of biochar in FB treatment was only 1 t ha−1. Nan et al. (2020a) [27] suggested that there could be an optimal application amount of biochar for CH4 mitigation. Thus, the application rate of biochar in the present study might be lower than the effective threshold, which may consequently weaken the effect of biochar on CH4 mitigation.
The pyrolysis temperature difference may have been another influencing factor on the CH4 mitigation effect discrepancy. Crop straw biochar production processes can be separated into three main pyrolysis stages: pre-pyrolysis, main-pyrolysis, and carbonaceous formation [30] in which the physiochemical characteristics of biochar can be generated depending on different pyrolysis temperatures [31]. Higher pyrolysis temperatures (usually above 500 °C) may lead to an increase in carbonized matter content and pH value, and these changes may consequently influence the CH4 production processes in the rice paddy system [27]. In this study, the pyrolysis temperatures of wheat straw biochar production were only 300–400 °C. Thus, the wheat-straw-derived biochar in the present study might have a higher labile carbon content and lower pH value than that of other reported study cases. The biochar-induced labile carbon inputs may provide an exogenous carbon substrate for the methanogens and weaken the CH4 mitigation effects of biochar application.
In comparison with the CF treatment, the FB treatment significantly reduced 66.73% of the total CH4 emissions from rice paddies (p < 0.01). As mentioned above, many studies suggested that a vital influencing factor of rice paddy CH4 emissions is the availability of organic carbon sources [32,33] and the activities of methanogens and methanotrophs in rice paddy soil systems [27,34]. The methanogenesis proceeds via three biochemical pathways depending on the carbon sources, including hydrogenotrophic, aceticlastic, and methylotrophic methanogenesis [35,36]. In a typical rice paddy system, the key regulating factor to these methanogenesis processes is the concentration of organic carbon substrates. Raw wheat straw incorporation provided substantial organic carbon sources, which may consequently lead to the massive CH4 emissions from rice paddies [37]. In contrast, the application of wheat-straw-derived biochar did not increase CH4 emissions from rice paddies through four successive rice growing seasons. Wang et al. (2015) [38] indicated that the mean residence time of labile and recalcitrant biochar carbon pools were estimated to be about 108 days and 556 years with pool sizes of 3% and 97%, respectively. Thus, the low bioavailability could slow down the biodegradation of the biochar carbon pool, and as a result, restrict the carbon availability in the biochar carbon pool as the CH4 synthetic substrate.
The application of raw wheat straw biochar not only affected carbon bioavailability to methanogens, but it also impacted soil microbial processes. In the flooded rice paddy system, the incorporation of raw crop straw develops strictly anaerobic soil conditions [25], which may consequently stimulate CH4 production and weaken the CH4 oxidation processes [39,40]. On the contrary, Nan et al. (2020a) [27] indicated that the application of biochar could reduce CH4 emission from rice paddies by promoting the abundance of methanotroph.
The application of slow-release fertilizer is an effective way to mitigate CH4 emissions from rice paddies [41,42]. It can slow down the dissolution rates of nitrogen fertilizer contents and reduce the maximum concentration of NH4+-N. Previous studies suggested that NH4+-N has an inhibitory effect on CH4 oxidation processes [43,44]. Dong et al. (2021) [45] suggested that the cumulative CH4 emissions from rice paddies were significantly positively correlated to NH4+-N. In comparison with the straw amended rice paddies with conventional fertilizer, the combination of slow-release fertilizer and wheat straw tended to reduce CH4 emission, which may be owing to the delayed peak of NH4+-N concentration in soil pore water or the intensified oxygen secretion via the improved rice aerenchyma and root morphology in the field applied with slow-release fertilizer [46]. However, in this study, the combination of slow-release fertilizer with wheat straw did not change the characteristics of CH4 emissions from rice paddies in comparison with the FS treatment. Compared with the CF treatment, the SFS treatment tripled seasonal CH4 emissions.
4.2. Rice Paddy Soil Carbon Sequestration
Soil carbon sequestration rates are determined by the dynamic equilibrium of exogenous carbon inputs and carbon losses via carbon mineralization processes. In the agroecosystem, the exogenous carbon sources mainly derive from crop photosynthesis carbon and crop straw retention. In the present study, the exogenous carbon inputs were wheat straw and wheat-straw-derived biochar. In the present study, the yearly average carbon sequestration rates under FS, SFS, and FB treatments were 0.47‰, 0.66‰, and 0.89‰, respectively. We noticed, however, the net annual carbon inputs of the FS and SFS treatments were much higher than that of the FB treatment. These carbon input–output discrepancies were mainly attributed to the proportional differences of labile and recalcitrant carbon contents. As discussed above, the longer turnover time of recalcitrant carbon contents in biochar, including amorphous and aromatic carbon, can reduced carbon losses from the rice paddy soil systems [38,47,48] and, consequently, promoted the soil carbon accumulative rate of the FB treatment.
Apart from the direct wheat straw and biochar inputs, the rice plant photosynthesis was another exogenous carbon source for rice paddy soil carbon sequestration. In this study, despite having the same wheat straw application level, the accumulative rate of soil carbon under SFS treatment was significantly higher than that of FS treatment. One possible explanation for this is the promotion effect of slow-release fertilizer on rice biomass [46,49]. The application of slow-release fertilizer increased the rice plant biomass and resulted in the increment of organic carbon input via rice root exudation [50] and root biomass [51].
Soil carbon loss is another considerable factor in soil carbon sequestration. Previous studies indicated that the priming effect of exogenous carbon inputs on soil organic carbon decomposition could affect soil carbon sequestration processes [52,53]. The effect of straw-incorporation-induced priming is superior to that of biochar [53]. Thus, in the present study, the application of biochar not only added exogenous carbon to the rice paddy soil carbon pool, but it also alleviated the decomposition processes of the intrinsic soil organic carbon pool in comparison to the raw wheat straw incorporation. In conclusion, due to the long turnover time and a minor priming effect, the application of biochar could significantly increase rice paddy soil carbon sequestration rates in comparison with straw incorporation.
4.3. Greenhouse Gas Intensity and Influencing Factors of CH4 Emission
To quantitatively assess the rice-production-induced greenhouse effect, the concept of greenhouse gas intensity (GHGI) was proposed. The CH4 emissions from rice paddy systems were accounted for using the Intergovernmental Panel on Climate Change (IPCC) literature and methodology [1]. Compare with other treatments, the FS and SFS treatments significantly increased CH4-induced GHGI during rice production. This was due mainly to the massive CH4 emissions during rice cultivation. The increment of rice grain yield under SFS treatment cannot equalize the influence of raw wheat straw incorporation induced CH4 emissions. It is generally believed that rice production with lower GHGI could be an effective way to alleviate the impact of rice cultivation on climate change [20]. However, it is unwise to pursue CH4 mitigation at the sacrifice of rice production. For instance, in this study, the Control treatment achieved the lowest CH4 emission and a relatively lower GHGI at the expense of a reduction of 52.9% grain yield in comparison with the CF treatment.
Many environmental factors, such as soil water conditions [54], soil nutrient contents [55], pH [56], and temperature [57] have been shown to affect CH4 emissions from rice paddies. A path analysis was carried out to comprehensively evaluate the effect of different factors on rice paddy CH4 emissions. Previous studies suggested that the current season photosynthate from rice plants was an important carbon source to CH4 production, which may contribute up to 60–85% of CH4 emissions depending on the rice growing stage [58,59,60]. In the present study, the rice biomasses were significantly positively correlated with CH4 emissions. This can be attributed to the increments of photosynthetic carbon input that resulted from the increased rice plant biomasses [59]. Soil carbon content is an important factor in regulating CH4 emissions [61]. In the present study, however, the soil carbon factors, including soil TC, labile C content, seasonal cumulative SOC, and soil C/N ratio, showed no clear correlation with CH4 emissions. This may partially be due to the component differences of the soil carbon contents among different treatments. The lower labile carbon contents in biochar limited the carbon source availabilities for methanogens under FB treatment, resulting in high soil carbon contents with low CH4 emissions.
5. Conclusions
Rice paddies play an amplifying role in carbon biogeochemical cycle processes by converting soil organic carbon into CH4 instead of emitting CO2 via soil respiration. On the other hand, in comparison with the upland agroecosystems, the anaerobic soil conditions of rice paddies can slow down the mineralization rate of the soil organic carbon pool, thereby promoting the soil carbon sequestration of rice paddy systems. In the present study, the incorporation of raw wheat straw and straw-derived biochar significantly increased the soil carbon sequestration of rice paddies. However, the application of raw wheat straw not only promoted soil carbon accumulation, but also stimulated CH4 emissions from rice paddies, which may consequently offset the carbon sequestration effect of straw retention. In contrast, the application of wheat-straw-derived biochar was superior to raw wheat straw retention in soil carbon sequestration, and it showed no positive effect on CH4 emissions. In conclusion, instead of raw wheat straw retention, the application of wheat-straw-derived biochar is an effective way to promote soil carbon sequestration and mitigate CH4 emissions from rice paddies. These findings provide a comprehensive insight into low-carbon rice production, and also contribute to the implementation of the utilization of agricultural waste.
C.W.: Investigation, methodology, data curation, writing—review and editing. H.S.: Investigation, data curation. X.Z.: Data curation. J.Z.: Investigation, data curation. S.Z.: Conceptualization, methodology, supervision. All authors have read and agreed to the published version of the manuscript.
Data is unavailable due to privacy and ethical restrictions.
This research was supported by the funding from the National Key Research and Development Program of China (grant number 2022YFD2300304), the National Natural Science Foundation of China (grant number 41907065), and Science and Technology Commission of Shanghai Municipality (grant number 22dz1208300). We are grateful to the editor and the anonymous referees for their insightful comments which helped us to improve our paper substantially.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 1. The seasonal dynamics of CH4 fluxes from rice paddies under different treatments. (a) Rice growing season in 2013. (b) Rice growing season in 2014. (c) Rice growing season in 2015. (d) Rice growing season in 2016.
Enlarge this image.Figure 2. Seasonal cumulative emissions of CH4 under different treatments. Error bars represent standard errors. Means labelled with different letters indicate significant differences among treatments at the p < 0.05 level.
Enlarge this image.Figure 3. Correlations between soil TC contents and the continuous tillage duration of the rice–wheat rotation system. (a) Control treatment. (b) CF treatment. (c) FS treatment. (d) SFS treatment. (e) FB treatment. Double-asterisk represents a significant difference level at p < 0.01.
Enlarge this image.Figure 3. Correlations between soil TC contents and the continuous tillage duration of the rice–wheat rotation system. (a) Control treatment. (b) CF treatment. (c) FS treatment. (d) SFS treatment. (e) FB treatment. Double-asterisk represents a significant difference level at p < 0.01.
Enlarge this image.Figure 4. Path analysis of different latent factors that affect CH4 emissions from rice paddies. Numbers adjacent to the arrows are standardized path coefficients, indicating the direct effect size of the relationship. Blue and red arrows represent positive and negative relationships, respectively. Double-headed arrows indicate correlations between factors, single-headed arrows indicate a one-way directed relationship between factors. Standardized path coefficients labelled with an asterisk and double asterisks represent statistical significance at p < 0.05 and p < 0.01, respectively. The grey circles labelled with e1–e4 represent the unexplained variance (residuals) from variables.
Description of the field experimental design in this study.
| Treatments | Applied Synthetic Fertilizer Properties | Incorporated Straw Properties | |||
|---|---|---|---|---|---|
| Fertilizer Types | Application Rates | Straw Forms | Application Rates | Chemical |
|
| Fertilizer blank + non-straw |
-- | 0 | -- | 0 | -- |
| Conventional fertilization + non-straw |
bulk blended fertilizer | 225 kgN·hm−2 |
-- | 0 | -- |
| Conventional fertilization + straw |
bulk blended fertilizer | 215 kgN·hm−2 |
raw wheat straw | 3 t·hm−2 | C = 44% |
| Slow-release fertilizer + straw |
slow-release fertilizer | 215 kgN·hm−2 |
raw wheat straw | 3 t·hm−2 | C = 44% |
| Conventional fertilization + straw-derived biochar |
bulk blended fertilizer | 215 kgN·hm−2 |
wheat-straw-derived biochar |
1 t·hm−2 | C = 53% |
Soil TC contents under different treatments (mean ± SE).
| Treatments | Year (%) | ||||
|---|---|---|---|---|---|
| 2012 | 2013 | 2014 | 2015 | 2016 | |
| Control | 0.88 ± 0.01 a * | 1.01 ± 0.01 b | 0.96 ± 0.03 b | 0.99 ± 0.01 c | 0.96 ± 0.05 b |
| CF | 0.90 ± 0.01 a | 1.07 ± 0.03 b | 1.01 ± 0.04 ab | 1.08 ± 0.01 b | 1.07 ± 0.05 b |
| FS | 0.92 ± 0.01 a | 1.11 ± 0.03 ab | 1.06 ± 0.01 ab | 1.13 ± 0.03 b | 1.15 ± 0.08 ab |
| SFS | 0.91 ± 0.01 a | 1.16 ± 0.01 a | 1.06 ± 0.03 ab | 1.21 ± 0.03 a | 1.17 ± 0.03 ab |
| FB | 0.90 ± 0.01 a | 1.18 ± 0.06 a | 1.12 ± 0.04 a | 1.24 ± 0.03 a | 1.33 ± 0.10 a |
* Different lower case letters represent a significant difference at p < 0.05.
Results of the two-way repeated measures analysis of variance (ANOVA) on soil TC contents.
| Factors | df | F | Sig. | ||
|---|---|---|---|---|---|
| Time | 3 | 29.753 | <0.01 ** | ||
| Time × Treatment | 12 | 1.577 | 0.15 | ||
| Treatment | 4 | 8.396 | <0.01 ** | ||
| Post hoc comparison | Control | CF | FS | SFS | FB |
| Control | -- | ||||
| CF | ns | -- | |||
| FS | 0.01 * | ns | -- | ||
| SFS | <0.01 ** | ns | ns | -- | |
| FB | <0.01 ** | <0.01 ** | 0.04 * | ns | -- |
* Significant level at p < 0.05. ** Significant level at p < 0.01.
CH4-induced greenhouse gas intensities (GHGI) under different treatments (mean ± SE).
| Treatments | Year (kg CO2-eq kg−1 Rice Grain Yield) | |||
|---|---|---|---|---|
| 2013 | 2014 | 2015 | 2016 | |
| Control | 0.33 ± 0.04 b * | 0.15 ± 0.02 b | 0.12 ± 0.01 c | 0.24 ± 0.10 abc |
| CF | 0.36 ± 0.09 b | 0.09 ± 0.02 b | 0.19 ± 0.10 a | 0.08 ± 0.01 c |
| FS | 0.98 ± 0.19 a | 0.40 ± 0.06 a | 0.57 ± 0.21 a | 0.34 ± 0.02 a |
| SFS | 0.83 ± 0.17 a | 0.37 ± 0.10 a | 0.52 ± 0.16 ab | 0.27 ± 0.01 ab |
| FB | 0.36 ± 0.04 b | 0.12 ± 0.01 b | 0.14 ± 0.02 bc | 0.15 ± 0.06 bc |
* Different lower case letters represent a significant difference at p < 0.05.
Results of the two-way repeated measures ANOVA on GHGI.
| Factors | df | F | Sig. | ||
|---|---|---|---|---|---|
| Time | 3 | 17.631 | <0.01 ** | ||
| Time × Treatment | 12 | 1.383 | 0.23 | ||
| Treatment | 4 | 11.23 | <0.01 ** | ||
| Post hoc comparison | Control | CF | FS | SFS | FB |
| Control | -- | ||||
| CF | ns | -- | |||
| FS | <0.01 ** | <0.01 ** | -- | ||
| SFS | <0.01 ** | <0.01 ** | ns | -- | |
| FB | ns | ns | <0.01 ** | <0.01 ** | -- |
** Significant level at p < 0.01; “ns” mark represents a non-significant correlation between treatments.
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; Sun, Huifeng 1 ; Zhang, Xianxian 1 ; Zhang, Jining 1 ; Zhou, Sheng 1
1 Shanghai Academy of Agricultural Sciences, No. 1000 Jinqi Road, Fengxian District, Shanghai 201403, China;