Next Article in Journal
Experimental Study on Water Distribution and Droplet Kinetic Energy Intensity from Non-Circular Nozzles with Different Aspect Ratios
Next Article in Special Issue
The Response of Grain Yield and Quality of Water-Saving and Drought-Resistant Rice to Irrigation Regimes
Previous Article in Journal
Parameter Optimization of Reciprocating Cutter for Chinese Little Greens Based on Finite Element Simulation and Experiment
Previous Article in Special Issue
Response of Grain Yield and Water Use Efficiency to Irrigation Regimes during Mid-Season indica Rice Genotype Improvement
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 12
Issue 12
10.3390/agriculture12122134
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Rice Cultivar Renewal Reduces Methane Emissions by Improving Root Traits and Optimizing Photosynthetic Carbon Allocation

by
Siyu Li
1,†,
Lu Chen
1,2,†,
Xian Han
1,
Kai Yang
1,
Kun Liu
1,
Jun Wang
1,
Yun Chen
1,3 and
Lijun Liu
1,*
1
Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation Centre for Modern Production Technology of Grain Crops/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Yangzhou University, Yangzhou 225009, China
2
Taicang Agricultural Technology Extension Center, Suzhou 215400, China
3
College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2022, 12(12), 2134; https://doi.org/10.3390/agriculture12122134
Submission received: 9 November 2022 / Revised: 7 December 2022 / Accepted: 9 December 2022 / Published: 12 December 2022
(This article belongs to the Special Issue Crop Cultivation Physiology and Farmland Greenhouse Gas Emission Reduction)
Browse Figures
Review Reports Versions Notes

Abstract

:
Cultivar renewal (CR) contributes greatly to rice yield increase in China and even in the world. However, few studies were focused on the impact and mechanism of CR on field methane (CH4) emissions. A 2-year field experiment was conducted using 14 typical japonica rice cultivars released in the Yangtze River Basin of China during the last 70 years. The grain yield, root morphophysiological traits and their relationships with CH4 emissions were examined. The results showed that the grain yields of cultivars in the 1960–2010s increased by 18.8–93.9% while the CH4 emissions decreased by 9.5–41.2% compared with the 1950’s cultivars. The daily and cumulative CH4 emissions during the panicle differentiation stage (PD) were reduced significantly, which contributed greatly to the CH4 mitigation of the whole growing season. The CR notably increased root biomass, root/shoot ratio, root oxidation activity, and the total organic carbon in root exudates (ETOC), and decreased the ratios of ETOC/yield, ETOC/root biomass and ETOC/shoot biomass. Nitrogen fertilizer applied during panicle differentiation could improve the root physiology and decrease the ETOC/yield and ETOC/root, therefore reducing CH4 emissions. Our findings illustrated that CR reduced CH4 emissions by improving root traits and by optimizing the photosynthate allocation to biomass and grain yields. Applying nitrogen fertilizer during panicle differentiation could further mitigate the CH4 emissions in paddies.

1. Introduction

Rice yields have to increase by 28% to meet the growing global demand by 2050 [1]. China’s rice yield increased from 2 t ha−1 in 1950 to 7.1 t ha−1 in 2019, contributing greatly to global food security. Since the Green Revolution, rice varieties have improved dramatically, accounting for 50% of the yield growth in decades [2]. From the 1950s to the 1980s, the rice yield increase was mainly due to the promotion of dwarfing genes, which increased the harvest index and reduced lodging. The yield increase after the 1980s was mainly due to the promotion and application of high-yield varieties (including hybrid and ‘Super’ rice varieties) [3,4]. The Yangtze River basin is the main rice-producing area in China, accounting for 51.3% of the total planting area and 51.2% of the total production in China [5,6]. Japonica rice varieties are widely cultivated in this region, but few studies were conducted on the effect of cultivar renewal (CR) on the yield of japonica rice in this region.
Rice fields are an important source of atmospheric methane (CH4) emissions. Although the atmospheric CH4 concentration (1.8 ppmV) is much lower than that of CO2 (357 ppmV), the global warming potential of CH4 (in 100-year terms) is 15–34 times greater than that of CO2. Moreover, the contribution of CH4, second only to CO2, contributed 15–20% to the global greenhouse effect [7]. CH4 emissions from paddy fields accounted for about 48% of global CH4 emissions from agricultural sources and 16% of global anthropogenic CH4 emissions [8,9,10]. Therefore, it is of great significance to study CH4 mitigation in paddy fields to slow global warming. CH4 emissions from the paddy field are a net effect of CH4 production and oxidation [11]. CH4 is produced by the anaerobic methanogens in paddy soil and is diffused to topsoil due to concentration gradients or is absorbed by rice roots. About 10–90% of CH4 is oxidized by methanotrophs at the aerobic–aerobic interfaces, such as the soil–water interface and the rice root rhizosphere soil [12,13].
Measures to reduce methane emissions from paddy fields can be divided into short-term measures, such as water-saving irrigation and optimized fertilization methods, and long-term measures, such as improved tillage practices and CR [10,14,15,16,17]. Rice plants play an essential role in CH4 production, oxidation and transmission in paddy fields. Rice root exudates and residues provide a rich carbon (C) source for methanogens [18]. About 40–60% of in-season CH4 emissions are caused by rice root exudates and root turnover, including dead roots and sloughed-off cells [19]. Photosynthetic C is transported to the rice root system within a few hours after being synthesized in leaves and is involved in root growth, metabolism, and secretion. Approximately 20–30% of photosynthetic C is secreted into the rhizosphere soil by root exudation and turnover [20,21]. Jiang et al. [22] found that the increase in the harvest index during CR had no obvious effect on CH4 emissions in paddy fields through meta-analysis, indicating that the photosynthetic C allocation among plant aboveground parts may not be the dominant factor of CR affecting CH4 emissions in paddy fields. Chen et al. [18] concluded that rice varieties with a high proportion of photosynthetic products allocated to root growth have a higher potential for CH4 mitigation. It is unclear whether CR will affect CH4 emissions in rice fields by changing the allocation of photosynthetic C to the shoot growth, root growth and secretion. Rice roots could provide a suitable habitat for methanotrophs by forming a relatively aerobic rhizosphere through oxygen loss. Therefore, rice varieties with strong root morphophysiology and well-developed aerenchyma may promote CH4 oxidation in the rhizosphere and reduce CH4 emissions in rice fields [8,13,23]. The research on the variation of rice root morphophysiological traits during the japonica rice CR and its effect on CH4 emissions in rice fields is still inefficient. Here, we hypothesized that (1) the allocation ratio of rice photosynthate in yield formation, shoot and root growth, and root exudation will influence CH4 emissions in paddy fields, and (2) the japonica rice CR could affect CH4 emissions by influencing root morphophysiology and the content or allocation ratio of root-secreted C.

2. Materials and Methods

2.1. Experimental Site and Designs

The experiment location was a research farm in Yangzhou University, Jiangsu Province, China (32°30′ N, 119°25′ E). Two field experiments were carried out during the rice-growing seasons (May to October) in 2019 and 2020. The rice was grown in a sandy loam (Typic fluvaquents, Etisols (US taxonomy)) soil. The contents of organic matter, alkali hydrolyzable N, Olsen-P, and exchangeable K in soil was 24.2 g kg−1, 95.2 mg kg−1, 34.75 mg kg−1, and 67.2 mg kg−1, respectively. Meteorological data were measured at a weather station close to the experimental location (Figure S1).

2.1.1. Experiment 1: Effects of Rice CR on Grain Yield and CH4 Emissions in Paddy Field

Fourteen typical japonica rice cultivars grown in the Yangtze River basin (with annual planting areas over 6.67 × 104 ha) since the 1950s were selected and classified into seven types: 1950’s, 1960’s, 1970’s, 1980’s, and 1990’s in the 20th century and 2000’s and 2010’s in the 21st century according to the decade of release. Each cultivar could head normally. The growth duration of all 14 cultivars is listed in Table 1. Total N fertilizer of 300 kg N ha−1 was applied at 1 d before transplanting, 7 DAT, and 35 DAT (the initiation of panicle differentiation stage, panicle fertilizer,) at the proportion of 50%, 10%, and 40%, respectively.

2.1.2. Experiment 2: Effects of Panicle Fertilizer on Grain Yield and CH4 Emissions in Paddy Fields

Two rice cultivars, i.e., Nanjing9108 and Wuyunjing27 in experiment 1 were used in experiment 2. Furthermore, 150 kg N ha−1 and 30 kg N ha−1 were applied at 1 d before transplanting and 7 DAT, respectively. Two nitrogen application rates at the initiation of the panicle differentiation stage, including zero N (0N) and 120 kg N ha−1 (NN), were set in experiment 2.
Urea (N 46.4%) was applied as nitrogen fertilizer in this study. P2O5 (40 kg ha−1) and K2O (100 kg ha−1) were applied as basal fertilizer 1 day before transplanting. Rice seedlings were raised in the seedbed with sowing date on 11 May and transplant date on 10 June. The seedlings were manually transplanted with a hill spacing of 13.5 × 25 cm and two seedlings per hill. Both experiments were arranged in random block design with three replicates. The plot size for each treatment was 5 × 4 m. Bunds covered with plastic sheeting were built between plots to minimize seepage. A water layer of 2 to 3 cm above the ground was kept from transplanting to a week before harvest, except for the mid-season drainage (30–37 days after transplanting, DAT). The preceding crop was wheat (Triticum aestivum L.) in both experiments.

2.2. CH4 Sampling and Measurement Methods

CH4 flux was measured between 0800 and 1000 h every 5–7 days during the rice growing season, using the static chamber method described previously with a gas chromatograph (7890A, Agilent Technologies Inc. (Santa Clara, CA, USA) [8]. Cumulative CH4 emissions were calculated as the sum of daily CH4 fluxes using piecewise linear interpolation [8,24]. The greenhouse gas intensity (GHGI) of CH4 was calculated to assess the climate impacts of producing per kilogram of grain yield as follows [18]:
GHGI of CH4 (kg CO2−eq kg−1 season−1) = seasonal CH4 emissions × 25/grain yield.

2.3. Rice Root Traits

At the initiation of the panicle differentiation stage (PD), booting stage (usually recognized as the end of panicle differentiation), and heading stage of each cultivar, rice roots were sampled for the determination of root oxidation activity, root oxygen loss, root physiological traits (root length, root number, and specific root length) and root biomass. A block of soil (20 × 20 × 20 cm) around the rice roots was dug up for each root sampling. The roots were rinsed with a hydro-pneumatic device and detached from the nodal bases. The root oxidation activity was determined as described by Liu et al. [25] and expressed as the oxidation rate of alpha naphthylamine (α-NA) per gram of root dry weight per hour (μg α-NA g−1 DW h−1). Root radial oxygen loss was determined using the method described by Chen et al. [23]. Three hills of rice plants in each treatment were cut off at 12 cm above the ground at 1800 h of the sampling date to determine the root bleeding rate. The pre-weighed absorbent defatted cotton covered with a polyethylene sheet was wrapped around the cut of the stem. At 6:00 am the following morning, the cotton was retrieved to weigh the root bleeding sap and to calculate the root bleeding rate [26].

2.4. Root Exudated Total Organic Carbon (ETOC)

The root exudates were collected using the filter paper method [14]. Samples of each rice variety were taken every seven days from the initiation of the PD to the day before heading. The organic carbon content in root exudates was analyzed using a TOC analyzer (Analytik Jena, Jena, Germany) as described by Chen et al. [23]. The ETOC in PD was calculated from accumulating ETOC fluxes between two consecutive sampling dates throughout PD.

2.5. CH4 Production Potential and CH4 Oxidation Potential

The CH4 production potential and CH4 oxidation potential in paddy soil were determined as described by Krüger et al. [27]. For the CH4 production potential measurement, 10 g of fresh soil and 10 mL sterile water (1:1 soil water ratio) were placed into a 150 mL Erlenmeyer flask with N2 flushed for 15 min. The flask was then sealed and incubated for 48 h under dark conditions on a horizontal shaker (25 °C, 150 rpm). Five-milliliter headspace gas was collected to determine the CH4 concentration. The CH4 production potential was calculated on the difference in CH4 concentration between the beginning and the end. To determine the CH4 oxidation potential, 5 g of fresh soil and 5 mL sterile water (1:1 soil water ratio) were placed into a 150 mL Erlenmeyer flask with rubber stoppers. Then, 1 mL of air was pumped out, and 1 mL of pure CH4 was injected into the flask. The CH4 concentration in the reaction system was approximately 7500 ppm. The flask was kept at 25 °C in an incubator under dark conditions for 24 h with shaking at 150 rpm. We took the first sample at 0.5 h after incubation and collected samples at 2 h intervals until the end. The CH4 oxidation potential was calculated from the linear regression of CH4 concentrations over time.

2.6. Grain Yield

Grain yield was measured from a harvest area of 5 m2 in each plot and adjusted to a moisture content of 14%, according to Liu et al. [26]. The plants in the three rows on each side of the plot were discarded to avert border effects.

2.7. Statistical Analysis

An analysis of variance (ANOVA) was performed using the SAS/STAT statistical analysis package (version 6.12, SAS Institute, Cary, NC, USA). Means were tested by least significant difference (LSD) test at p = 0.05 (LSD 0.05). Data were visualized using SigmaPlot 11.0 (SPSS Inc., Point Richmond, CA, USA).

3. Results

3.1. Differences in Experimental Factors

For the rice growth duration, grain yield, total CH4 emissions, cumulative CH4 emissions of PD, ETOC, root biomass, root oxidation activity, and root oxygen loss, the ANOVA showed significant variation (p < 0.05) among cultivars, indicating that CR had a significant effect on CH4 emissions in paddy fields and root traits (Table S1). No significant variations were detected among years for grain yield and rice root traits except for the root oxygen loss and CH4 emissions. Furthermore, no significant interactions between rice cultivars and experimental years were observed in CH4 emissions, grain yields, and all the root traits. Therefore, all data in experiment 1 were presented as the means of two experimental years.

3.2. Grain Yield

Compared to the 1950’s, the grain yield of 1960’s, 1970’s, 1980’s, 1990’s, 2000’s and 2010’s increased by 18.8%, 43.5%, 60.9%, 81.2%, 95.8%, and 93.9%, respectively, suggesting that CR significantly increased the rice yield from the 1950’s to the 2000’s (Table 2). However, the yield increase has decreased since the 21st century, showing a limited potential for the yield increase. The yield increase was mainly due to the significant increase in the total spikelets caused by the increase in spikelets per panicle, especially for ‘Super’ rice cultivars after the 21st century. Compared with 1950’s cultivars, the spikelets per panicle, total spikelets, and filled grains of 2000’s and 2010’s cultivars were increased by 37.4–42.6%, 39.9–47.5%, and 33.9–38.6%, respectively.

3.3. Methane Emissions

Significant decreasing trends over the years of cultivar release were observed in the total CH4 emissions, cumulative CH4 emissions and daily CH4 flux during PD and GHGI. The total CH4 emissions of rice cultivars released from the 1960s to 2010s decreased by −0.6%, 3.3%, 7.4%, 16.3%, 27.6%, and 26.1% compared to that of 1950’s rice cultivars, with a relative decrease of 5.8% per decade. The total CH4 emissions of the 2000’s and 2010’s cultivars decreased by 21.9% and 20.2%, respectively, relative to that of 1980’s cultivars, indicating a great potential of ‘Super’ rice cultivars for CH4 mitigation. Similar to total CH4 emissions, CH4 emissions at PD decreased gradually due to CR (p < 0.001), with a relative decrease per decade of 11.4% (Figure 1B). The CH4 emissions during PD of rice cultivars released after the 1990s decreased significantly. For example, the CH4 emissions during PD of 2010’s cultivars decreased by 40.4% compared with that of 1980’s cultivars. Cultivar renewal resulted in a notable decrease in the proportion of CH4 emissions during PD in the total CH4 emissions, which decreased from 54% of the 1950’s cultivars to 39% of the 2010’s cultivars, suggesting that CR could mitigate total CH4 emissions by decreasing the CH4 emissions during PD. In addition, since the duration of PD varied little as CR progressed, the daily CH4 flux during PD decreased significantly, with a relative decrease per decade of 12.3% (Figure 1D). The daily CH4 flux during PD of the 2000’s and 2010’s cultivars were 54.8% and 54.0% lower than that of 1950’s cultivars, respectively. Cultivar renewal linearly reduced the GHGI (p < 0.001), with a relative decrease per decade of 15.8% (Figure 1E).

3.4. Shoot Biomass and Root Traits

Cultivar renewal notably increased rice shoot and root biomass production (Table 3). From the 1960s to 2010s, the relative increase per decade of shoot biomass, root biomass and root/shoot ratio at the heading stage were 11.6%, 20.7%, and 9.4%, respectively. The above traits of rice cultivars released after the 1980s were significantly higher than those of previous cultivars, e.g., the shoot biomass and root biomass at booting stage of 1980’s cultivars increased by 53.7% and 76.9%, respectively, compared with those of 1970’s cultivars. No noteworthy differences occurred in the shoot biomass, root biomass, and root/shoot ratio between 2000’s and 2010’s cultivars, which might be one of the reasons for the limited potential of yield increase in the last decade. Cultivar renewal from the 1950s to 1970s and from the 1990s to the 2010s had no notable improvement on the harvest index. However, the harvest index of 1980’s cultivars was significantly higher than those of cultivars released before the 1980s.
The root length and root number at the booting and heading stages significantly increased as the CR progressed, while the specific root length decreased gradually. The relative increase per decade of root length and root number at the heading stage was 3.0% and 4.1%, respectively, and the relative decrease per decade of specific root length was 15.8% (Table S2). Rice root oxidation activity increased progressively over the past decades, showing a significant improvement in the ‘Super’ rice cultivar released after the 21st century. For example, the root oxidation activity of the 2010’s cultivars increased by 44.5% and 68.4% at booting and the heading stages, respectively, compared with the 1950’s variety, and by 12.6% and 13.3%, compared with the 1990’s cultivars (Figure 2A). The root bleeding rate increased significantly from booting stage to the heading stage (Figure 2B). During the same growing period, the root bleeding rate increased gradually as the CR progressed, i.e., the root bleeding rate at booting stage of rice cultivars released from the 1960s to 2010s increased by 9.2%, 14.1%, 18.4%, 37.4%, 44.8%, and 48.5%, respectively, compared with the 1950’s cultivars. The relative increase per decade of root oxygen loss at the heading stage was 6.2% (Figure 2C). Root oxygen loss at the booting and heading stages of 2010’s cultivars were 46.8% and 45.5% higher than those of 1950’s cultivars, respectively.

3.5. Root Exudate Organic Carbon

Cultivar renewal gradually increased the ETOC in PD, with a relative increase per decade of 3.0% (Figure 3A). However, the ratio of ETOC to grain yield (ETOC/yield) decreased as the CR progressed, with a relative decrease per decade of 7.7% (Figure 3B). The decrease in the ETOC/yield has been non-significant since the 1990s, which might be caused by the invisible yield increase. Similar to the ETOC/yield, the CR notably decreased the ETOC/shoot biomass and ETOC/root biomass, i.e., the ETOC/shoot biomass and ETOC/root biomass of the 2010’s cultivar decreased by 32.6% and 61.2% relative to the 1950’s cultivars, respectively (Figure 3C,D). However, no significant difference in the ETOC/yield, ETOC/root biomass, and ETOC/shoot biomass was observed between 1990’s, 2000’s, and 2010’s cultivars.

3.6. Methane Production and Oxidation in Rhizosphere Soil

The CR had no remarkable effect on the CH4 production potential (p > 0.05) but significantly promoted the CH4 oxidation potential in rhizosphere soil (p = 0.007), with a relative increase per decade of 5.1% (Figure 4). For example, the rice cultivar Wuyunjing27 released in the 2010s had the highest CH4 oxidation potential, which was increased by 60.7% relative to the Xudao2 released in the 1970s.

3.7. Relationships between CH4 Emissions and Root Traits and ETOC

The cumulative CH4 emissions in PD were significantly and negatively correlated with grain yields, indicating that the yield increase might have a positive effect on CH4 mitigation (Figure 5A). The cumulative CH4 emissions during PD were positively correlated with the ETOC/yield (r = 0.745, p < 0.01), ETOC/shoot biomass (r = 0.680, p < 0.01), and ETOC/root biomass (r = 0.815, p < 0.01) (Figure 5B–D), while negatively correlated with root length, root number, root oxidation activity, and root oxygen loss (Figure 5E–G and Figure S2A,B). The above results indicated that reducing the proportion of ETOC in photosynthetic C and enhancing the root morphology and physiology could be the reason for CH4 mitigation in the japonica rice by CR. The cumulative CH4 emissions during PD had no significant correlations with the CH4 production potential but showed a negative correlation with the CH4 oxidation potential in rhizosphere soil, suggesting that the promotion of CR on CH4 oxidation in rhizosphere soil rather than CH4 production could contribute to reducing CH4 emissions in paddy fields.

3.8. The Regulation of Panicle Fertilizer on Grain Yield, CH4 Emissions, Root Growth, and Root Exudation

Compared with no panicle fertilizer treatment (0N), the daily CH4 flux, cumulative CH4 emissions in PD and GHGI in NN treatment decreased by 20.7–26.6%, 10.7–12.3%, and 21.9–27.0%, respectively, indicating that the panicle fertilizer application could mitigate the CH4 emissions during PD and GHGI (Figure 6). Panicle fertilizer significantly increased root biomass and reduced the root/shoot ratio and promoted root oxidation activity and root oxygen loss (Table S3). The root biomass, root oxidation activity, and root oxygen loss in NN treatment were increased by 14.3–18.8%, 18.9–24.6%, and 13.7–13.8%, respectively, relative to 0N treatment. Similar to the promotion of root traits, the grain yields in NN treatment were increased by 25.4–29.0% due to the panicle fertilizer (Figure 7A). Compared with 0N treatment, panicle fertilizer application increased the ETOC by 13.7–14.0% and decreased the ETOC/yield and ETOC/root biomass by 9.1–11.9% and 4.4–6.3%, respectively (Figure 7B–D). The above results suggested that panicle fertilizer application could promote the accumulation of photosynthate and root secretion but significantly decreased the allocation of photosynthetic C to root exudation, promoting the rice grain yield increase and root biomass production.

4. Discussion

4.1. The Effect of CR on Rice Yields

In the past 60 years, the grain yield of rice in China has increased threefold, which could be attributed to the CR, improved crop management practices such as N fertilizer application, and plant protection practices [4]. Some studies showed that the contribution of CR and crop management to yield increase reached 38.9–61.7% and 9.3–16%, respectively [28,29]. Since the 1990s, the N fertilizer input has reduced, but the grain yield of rice is still increasing, suggesting that the CR rather than N fertilizer is the main factor influencing the yield increase in rice in recent 20 years [30,31]. The increasing atmospheric CO2 concentration also showed a fertilizer effect on rice growth since it acted as the substrate of plant photosynthesis. A meta-analysis showed that the increase in atmospheric CO2 concentration could increase the grain yield of rice by 12.7–24.7%, showing the highest enhancement on hybrid rice but the lowest effect on japonica rice [32]. Therefore, the increase in grain yield in the region where japonica rice was widely cultivated could be mainly attributed to CR. In this study, grain yield was greatly increased as the japonica rice CR progressed, mainly due to the significant increase in the total number of spikelets and filled grains (Table 2). Compared with the 1950’s cultivars, the grain yield, total number of spikelets, and root biomass of ‘Super’ rice cultivars released after the 21st century increased by 92.3–94.2%, 39.9–47.5%, and 188.2–201.2%, respectively, which was consistent with the results of Yang et al. [33]. The breeding goal of ‘Super’ rice cultivars mainly focused on the large panicles, improved biomass productivity, leaf area, and root system [34,35]. Our results indicated that the CR after the 1980s significantly increased the harvest index of japonica rice, but the increase has not been significant since the 1990s (Table 3). The harvest index of japonica rice in this study reached 0.52, approaching the theoretical upper limit of 0.65. However, the increase in the harvest index has been small since 1990, indicating a limited potential for yield increase by further increasing the harvest index [6,22]. We also suggested that the finite increase in shoot biomass and root/shoot ratio of modern varieties may also be partly responsible for the limited potential for yield increase in the past decade. Therefore, further increases in grain yield require the improvement of rice biomass productivity based on the current or higher harvest index.

4.2. The Effect of CR on CH4 Emissions

Some studies showed that the increase in CH4 emissions in Asian rice fields from 1960 to 1990 was mainly due to the increase in rice planting area from 25 to 132 million ha. However, the CR has shortened the rice growth duration to a certain extent and reduced CH4 emissions significantly [36]. Zhang et al. [10] reported that the CR has decreased the global warming potential (GWP) per area by 2.0–6.4% per decade and decreased the GWP of 2000’s cultivars by 31% relative to the 1960’s cultivars, which was mainly caused by the remarkable reduction in CH4 emissions. In our study, the CR slightly prolonged the rice growth duration but had no significant effect on the duration of PD and its proportion in the whole growth period (Table S1).
Although the CR had a slight influence on the duration of PD, the daily CH4 flux during PD, cumulative CH4 emissions in PD, and its proportion in total CH4 emissions decreased by 12.3%, 11.4%, and 13.5%, respectively, indicating that the CH4 mitigation caused by CR was mainly due to the CH4 reduction during PD (Figure 1). The total CH4 emission of ‘Super’ rice cultivars was 26.1–27.6% lower than that of 1950’s cultivars. The ‘Super’ rice cultivars are widely considered the measure to reduce CH4 emissions in paddy fields while increasing yields [37,38,39]. The physiological characteristics of ‘Super’ rice have remarkable impacts on CH4 production, oxidation, and transport in paddy fields. Rice varieties with higher biomass production activity and grain yields could produce more photosynthates and secrete higher carbon content through roots, which favor the CH4 production process [18,40,41]. Some studies showed that ‘Super’ rice had a higher harvest index than regular varieties, indicating that more photosynthate could be transported to grains after flowering, reducing the C content in root exudations after the heading stage [6,22,33]. In our study, rice yield, shoot biomass, root biomass at the heading stage and harvest index were gradually increased by CR (Table 3). Moreover, grain yield was positively correlated with CH4 emissions (p < 0.001), indicating that the increase in rice yield and biomass production might be beneficial to CH4 mitigation in paddy fields. However, no significant decrease in CH4 emissions occurred after the heading stage as the CR progressed, and the proportion of CH4 emissions after the heading stage in total CH4 emissions was low (18–26%, data not listed). Therefore, the main factor affecting CH4 emissions in paddy fields might not be the increase in harvest index, that is, the photosynthetic C allocation between shoot (including grain) and root biomass but the C allocation between root exudation and biomass production.

4.3. Relationships between Rice Root Traits, ETOC, and CH4 Emissions

The panicle differentiation stage is a crucial growth period during which rice root growth and exudation are vigorous [8,23,42]. We hypothesized that the C allocation between root exudation as well as shoot and root biomass production during PD was crucial for CH4 production. Although the C content in rice root exudation increased gradually during the CR progressed, the ratio of ETOC to biomass production (including shoot and root biomass) at PD decreased significantly (Figure 3), suggesting that the proportion of photosynthetic C allocation to root and shoot growth during PD was gradually increased during the CR progressed, which might also cause the increase in the total number of spikelets of ‘Super’ rice varieties [26]. Correlation analysis showed that the ETOC/grain yield, ETOC/shoot biomass, and ETOC/root biomass were positively correlated with CH4 emissions during PD (p < 0.01, Figure 5B–D). The above results verified that CR could mitigate CH4 emissions by reducing photosynthetic C allocation to root exudation.
These results are consistent with our observation that root biomass and root/shoot ratio significantly increased and root morphology (root length, root number, and specific root length) notably enhanced in rice cultivars released after the 21st century (Table 3 and Table S2). Whether enhanced root morphology and root biomass could promote CH4 mitigation in paddy fields has not been determined. Some studies showed that the increase in rice root biomass would promote root secretion and deposition, providing more substrates for methanogens [19,43]. On the other hand, stronger root morphology could enhance the ability of rice roots to transport oxygen into rhizosphere soil, which could expand the aerobic environment and promote CH4 oxidation in rice rhizosphere [8,23,39,44]. CH4 emissions are determined by the net effect of CH4 production and oxidation in paddy soil [7,45]. Cultivar renewal showed a slight impact on the potential of methanogenesis in rhizosphere soil (p > 0.05), but significantly increased the CH4 oxidation potential, particularly in ‘Super’ rice cultivars (Figure 4). In this study, CH4 emissions during PD showed no significant correlations with CH4 production potential, but were negatively correlated with CH4 oxidation potential (Figure 5H and Figure S2C), indicating that the enhancement of CH4 oxidation by CR was responsible for the CH4 mitigation during PD. Root oxidation activity and root bleeding rate represent the rice root activity, and root radial oxygen loss represents the ability of rice to transport oxygen into the rhizosphere [34,46,47]. The above root physiological traits and root biomass, root length, and root number were negatively correlated with CH4 emissions during PD, suggesting that the CR of japonica rice mitigated CH4 emissions by promoting CH4 oxidation, which could be caused by the enhanced rice root morphology and physiology.
To verify the relationship between rice photosynthate allocation, root morphophysiology and CH4 emissions in paddy fields, a field experiment with two N application rates was set to regulate the photosynthetic C allocation and rice root growth during PD. Nitrogen application at the initiation of PD can effectively promote rice root growth and root secretion [26,42,48,49]. The application of panicle fertilizer significantly reduced the daily CH4 flux and cumulative CH4 emissions during PD and GHGI and increased grain yield (Figure 6). The root oxidation activity, root oxygen loss, and root dry weight at the heading stage were significantly enhanced by panicle fertilizer (Table S3). The C content in root exudation significantly increased, but the ratio of secreted C to yield and root biomass significantly decreased (Figure 7). It was verified that promoting the photosynthetic C to root biomass and yield formation and enhancing root morphology and physiology could contribute to CH4 mitigation in paddy fields.

5. Conclusions

Grain yield gradually increased with the japonica rice CR, which was mainly attributed to the increase in spikelet number per panicle. The CR significantly decreased CH4 emissions during the panicle differentiation stage, contributing greatly to reducing the CH4 emissions of the whole rice growing season. The CR and panicle fertilizer could enhance the rice root traits and optimize the photosynthate allocation to biomass production and grain yields, resulting in the enhanced CH4 oxidation process and CH4 mitigation in paddy fields.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture12122134/s1, Figure S1: Air temperature (A), precipitation (B), and sunlight hours (C) during the growing seasons of rice at the experiment site of Yangzhou, Southeast China in 2019 and 2020; Figure S2: Relationships between cumulative CH4 emissions in panicle differentiation and root length (A), root number (B), and methane production activity in rhizosphere soil (C); Table S1: Analysis-of-variance of F-values of rice growth duration, grain yield, total CH4 emissions during the whole growth duration (Total CH4), cumulative CH4 emissions in panicle differentiation stage (CH4 in PD), rice root biomass (RB), root oxidation activity (ROA), root radial oxygen loss (ROL) and total organic carbon content in root exudate (ETOC) between/among years and cultivars in experiment 1; Table S2: Changes in root morphological traits during japonica rice cultivar renewal; Table S3: Effect of nitrogen application at panicle differentiation stage on root biomass, root oxidation activity, and root oxygen loss.

Author Contributions

S.L.: Conceptualization, Data Curation, Investigation, Writing—original draft, Writing—review and editing. L.C.: Writing—original draft, Data Curation, Investigation. X.H., K.Y., K.L. and J.W.: Investigation. Y.C.: Supervision, Conceptualization, Writing—review and editing. L.L.: Supervision, Resources, Project administration, Conceptualization, Methodology, Funding acquisition, Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China [31871557, 32071947] and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

References

  1. Alexandratos, N.; Bruinsma, J. World Agriculture towards 2030/2050: The 2012 Revision; FAO: Rome, Italy, 2012. [Google Scholar]
  2. Evenson, R.E.; Gollin, D. Assessing the Impact of the Green Revolution, 1960 to 2000. Science 2003, 300, 758–762. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Peng, S.; Khush, G.S.; Virk, P.; Tang, Q.; Zou, Y. Progress in Ideotype Breeding to Increase Rice Yield Potential. Field Crops Res. 2008, 108, 32–38. [Google Scholar] [CrossRef]
  4. Peng, S.; Tang, Q.; Zou, Y. Current Status and Challenges of Rice Production in China. Plant Prod. Sci. 2009, 12, 3–8. [Google Scholar] [CrossRef] [Green Version]
  5. Gu, J.; Chen, J.; Chen, L.; Wang, Z.; Zhang, H.; Yang, J. Grain Quality Changes and Responses to Nitrogen Fertilizer of Japonica Rice Cultivars Released in the Yangtze River Basin from the 1950s to 2000s. Crop J. 2015, 3, 285–297. [Google Scholar] [CrossRef] [Green Version]
  6. Zhang, H.; Chen, T.; Liu, L.; Wang, Z.; Yang, J.; Zhang, J. Performance in Grain Yield and Physiological Traits of Rice in the Yangtze River Basin of China During the Last 60 Yr. J. Integr. Agric. 2013, 12, 57–66. [Google Scholar] [CrossRef]
  7. Malyan, S.K.; Bhatia, A.; Kumar, A.; Gupta, D.K.; Singh, R.; Kumar, S.S.; Tomer, R.; Kumar, O.; Jain, N. Methane Production, Oxidation and Mitigation: A Mechanistic Understanding and Comprehensive Evaluation of Influencing Factors. Sci. Total Environ. 2016, 572, 874–896. [Google Scholar] [CrossRef]
  8. Li, S.; Chen, Y.; Yu, F.; Zhang, Y.; Liu, K.; Zhuo, X.; Qiu, Y.; Zhang, H.; Gu, J.; Wang, W.; et al. Reducing Methane Emission by Promoting Its Oxidation in Rhizosphere through Nitrogen-Induced Root Growth in Paddy Fields. Plant Soil 2022, 474, 541–560. [Google Scholar] [CrossRef]
  9. Carlson, K.M.; Gerber, J.S.; Mueller, N.D.; Herrero, M.; MacDonald, G.K.; Brauman, K.A.; Havlik, P.; O’Connell, C.S.; Johnson, J.A.; Saatchi, S.; et al. Greenhouse Gas Emissions Intensity of Global Croplands. Nat. Clim. Chang. 2017, 7, 63–68. [Google Scholar] [CrossRef]
  10. Zhang, Y.; Jiang, Y.; Tai, A.P.K.; Feng, J.; Li, Z.; Zhu, X.; Chen, J.; Zhang, J.; Song, Z.; Deng, A.; et al. Contribution of Rice Variety Renewal and Agronomic Innovations to Yield Improvement and Greenhouse Gas Mitigation in China. Environ. Res. Lett. 2019, 14. [Google Scholar] [CrossRef]
  11. Cai, Z.; Mosier, A.R. Effect of NH4Cl Addition on Methane Oxidation by Paddy Soils. Soil Biol. Biochem. 2000, 32, 1537–1545. [Google Scholar] [CrossRef]
  12. Serrano-Silva, N.; Sarria-Guzmán, Y.; Dendooven, L.; Luna-Guido, M. Methanogenesis and Methanotrophy in Soil: A Review. Pedosphere 2014, 24, 291–307. [Google Scholar] [CrossRef]
  13. Bodegom, P.; Stams, F.; Mollema, L.; Boeke, S.; Leffelaar, P. Methane Oxidation and the Competition for Oxygen in the Rice Rhizosphere. Appl. Environ. Microbiol. 2001, 67, 3586–3597. [Google Scholar] [CrossRef] [Green Version]
  14. Theint, E.E.; Suzuki, S.; Ono, E.; Bellingrath-Kimura, S.D. Influence of Different Rates of Gypsum Application on Methane Emission from Saline Soil Related with Rice Growth and Rhizosphere Exudation. Catena 2015, 133, 467–473. [Google Scholar] [CrossRef]
  15. Wang, Z.; Gu, D.; Beebout, S.S.; Zhang, H.; Liu, L.; Yang, J.; Zhang, J. Effect of Irrigation Regime on Grain Yield, Water Productivity, and Methane Emissions in Dry Direct-Seeded Rice Grown in Raised Beds with Wheat Straw Incorporation. Crop J. 2018, 6, 495–508. [Google Scholar] [CrossRef]
  16. Fan, D.; Liu, T.; Sheng, F.; Li, S.; Cao, C.; Li, C. Nitrogen Deep Placement Mitigates Methane Emissions by Regulating Methanogens and Methanotrophs in No-Tillage Paddy Fields. Biol. Fertil. Soils 2020, 56, 711–727. [Google Scholar] [CrossRef]
  17. Jiang, Y.; Carrijo, D.; Huang, S.; Chen, J.; Balaine, N.; Zhang, W.; van Groenigen, K.J.; Linquist, B. Water Management to Mitigate the Global Warming Potential of Rice Systems: A Global Meta-Analysis. Field Crops Res. 2019, 234, 47–54. [Google Scholar] [CrossRef]
  18. Chen, Y.; Zhang, Y.; Li, S.; Liu, K.; Li, G.; Zhang, D.; Lv, B.; Gu, J.; Zhang, H.; Yang, J.; et al. OsRGA1 Optimizes Photosynthate Allocation for Roots to Reduce Methane Emissions and Improve Yield in Paddy Ecosystems. Soil Biol. Biochem. 2021, 160, 108344. [Google Scholar] [CrossRef]
  19. Tokida, T.; Adachi, M.; Cheng, W.; Nakajima, Y.; Fumoto, T.; Matsushima, M.; Nakamura, H.; Okada, M.; Sameshima, R.; Hasegawa, T. Methane and Soil CO2 Production from Current-Season Photosynthates in a Rice Paddy Exposed to Elevated CO2 Concentration and Soil Temperature. Glob. Chang. Biol. 2011, 17, 3327–3337. [Google Scholar] [CrossRef]
  20. Kuzyakov, Y.; Gavrichkova, O. Time Lag between Photosynthesis and Carbon Dioxide Efflux from Soil: A Review of Mechanisms and Controls. Glob. Chang. Biol. 2010, 16, 3386–3406. [Google Scholar] [CrossRef]
  21. Kaštovská, E.; Šantrůčková, H. Fate and Dynamics of Recently Fixed C in Pasture Plant-Soil System under Field Conditions. Plant Soil 2007, 300, 61–69. [Google Scholar] [CrossRef]
  22. Jiang, Y.; Qian, H.; Wang, L.; Feng, J.; Huang, S.; Hungate, B.A.; van Kessel, C.; Horwath, W.R.; Zhang, X.; Qin, X.; et al. Limited Potential of Harvest Index Improvement to Reduce Methane Emissions from Rice Paddies. Glob. Chang. Biol. 2019, 25, 686–698. [Google Scholar] [CrossRef] [PubMed]
  23. Chen, Y.; Li, S.; Zhang, Y.; Li, T.; Ge, H.; Xia, S.; Gu, J.; Zhang, H.; Lü, B.; Wu, X.; et al. Rice Root Morphological and Physiological Traits Interaction with Rhizosphere Soil and Its Effect on Methane Emissions in Paddy Fields. Soil Biol. Biochem. 2019, 129, 191–200. [Google Scholar] [CrossRef]
  24. Aulakh, M.S.; Wassmann, R.; Bueno, C.; Rennenberg, H. Impact of Root Exudates of Different Cultivars and Plant Development Stages of Rice (Oryza sativa L.) on Methane Production in a Paddy Soil. Plant Soil 2001, 230, 77–86. [Google Scholar] [CrossRef]
  25. Liu, L.; Xiong, Y.; Bian, J.; Zhang, H.; Gu, J.; Wang, Z.; Yang, J. Effect of Genetic Improvement of Grain Yield and Nitrogen Efficiency of Mid-Season Indica Rice Cultivars. J. Plant Nutr. Soil Sci. 2015, 178, 297–305. [Google Scholar] [CrossRef]
  26. Liu, K.; Li, T.; Chen, Y.; Huang, J.; Qiu, Y.; Li, S.; Wang, H.; Zhu, A.; Zhuo, X.; Yu, F.; et al. Effects of Root Morphology and Physiology on the Formation and Regulation of Large Panicles in Rice. Field Crops Res. 2020, 258, 107946. [Google Scholar] [CrossRef]
  27. Krüger, M.; Potthast, K.; Michalzik, B.; Tischer, A.; Küsel, K.; Deckner, F.F.K.; Herrmann, M. Drought and Rewetting Events Enhance Nitrate Leaching and Seepage-Mediated Translocation of Microbes from Beech Forest Soils. Soil Biol. Biochem. 2021, 154, 108153. [Google Scholar] [CrossRef]
  28. Yu, Y.; Huang, Y.; Zhang, W. Changes in Rice Yields in China since 1980 Associated with Cultivar Improvement, Climate and Crop Management. Field Crops Res. 2012, 136, 65–75. [Google Scholar] [CrossRef]
  29. Song, Y.; Wang, C.; Ren, G.; Zhao, Y.; Linderholm, H.W. The Relative Contribution of Climate and Cultivar Renewal to Shaping Rice Yields in China since 1981. Theor. Appl. Climatol. 2015, 120, 1–9. [Google Scholar] [CrossRef]
  30. Xin, L.; Li, X.; Tan, M. Temporal and Regional Variations of China’s Fertilizer Consumption by Crops during 1998-2008. J. Geogr. Sci. 2012, 22, 643–652. [Google Scholar] [CrossRef]
  31. Sun, W.; Huang, Y. Synthetic Fertilizer Management for China’s Cereal Crops Has Reduced N2O Emissions since the Early 2000s. Environ. Pollut. 2012, 160, 24–27. [Google Scholar] [CrossRef]
  32. Hu, S.; Wang, Y.; Yang, L. Response of Rice Yield Traits to Elevated Atmospheric CO2 Concentration and Its Interaction with Cultivar, Nitrogen Application Rate and Temperature: A Meta-Analysis of 20 Years FACE Studies. Sci. Total Environ. 2021, 764, 142797. [Google Scholar] [CrossRef] [PubMed]
  33. Yang, J.; Du, Y.; Wu, C.; Liu, L.; Wang, Z.; Zhu, Q. Growth and Development Characteristics of Super-High-Yielding Mid-Season Japonica Rice. Front. Agric. China. 2007, 1, 166–174. [Google Scholar] [CrossRef]
  34. Zhang, H.; Xue, Y.; Wang, Z.; Yang, J.; Zhang, J. Morphological and Physiological Traits of Roots and Their Relationships with Shoot Growth in ‘Super’ Rice. Field Crops Res. 2009, 113, 31–40. [Google Scholar] [CrossRef]
  35. Li, G.; Hu, Q.; Shi, Y.; Cui, K.; Nie, L.; Huang, J.; Peng, S. Low Nitrogen Application Enhances Starch-Metabolizing Enzyme Activity and Improves Accumulation and Translocation of Non-Structural Carbohydrates in Rice Stems. Front. Plant Sci. 2018, 9, 1–15. [Google Scholar] [CrossRef]
  36. Van De Gon, H.D. Changes in CH4 Emission from Rice Fields from 1960 to 1990s 1. Impacts of Modern Rice Technology. Glob. Biogeochem. Cycles 2000, 14, 61–72. [Google Scholar] [CrossRef]
  37. Jiang, Y.; Wang, L.; Yan, X.; Tian, Y.; Deng, A.; Zhang, W. Super Rice Cropping Will Enhance Rice Yield and Reduce CH4 Emission: A Case Study in Nanjing, China. Rice Sci. 2013, 20, 427–433. [Google Scholar] [CrossRef]
  38. Liao, P.; Sun, Y.; Jiang, Y.; Zeng, Y.; Wu, Z.; Huang, S. Hybrid Rice Produces a Higher Yield and Emits Less Methane. Plant Soil Environ. 2019, 65, 549–555. [Google Scholar] [CrossRef] [Green Version]
  39. Ma, K.; Qiu, Q.; Lu, Y. Microbial Mechanism for Rice Variety Control on Methane Emission from Rice Field Soil. Glob. Chang. Biol. 2010, 16, 3085–3095. [Google Scholar] [CrossRef]
  40. Pump, J.; Pratscher, J.; Conrad, R. Colonization of Rice Roots with Methanogenic Archaea Controls Photosynthesis-Derived Methane Emission. Environ. Microbiol. 2015, 17, 2254–2260. [Google Scholar] [CrossRef]
  41. Maurer, D.; Kiese, R.; Kreuzwieser, J.; Rennenberg, H. Processes That Determine the Interplay of Root Exudation, Methane Emission and Yield in Rice Agriculture. Plant Biol. 2018, 20, 951–955. [Google Scholar] [CrossRef]
  42. Xiao, M.; Zang, H.; Ge, T.; Chen, A.; Zhu, Z.; Zhou, P.; Atere, C.T.; Wu, J.; Su, Y.; Kuzyakov, Y. Effect of Nitrogen Fertilizer on Rice Photosynthate Allocation and Carbon Input in Paddy Soil. Eur. J. Soil Sci. 2019, 70, 786–795. [Google Scholar] [CrossRef]
  43. Wang, B.; Neue, H.; Samonte, H. Effect of Cultivar Difference (‘IR72’, ‘IR65598’ and ‘Dular’) on Methane Emission. Agric. Ecosyst. Environ. 1997, 62, 31–40. [Google Scholar] [CrossRef]
  44. Jiang, Y.; van Groenigen, K.J.; Huang, S.; Hungate, B.A.; van Kessel, C.; Hu, S.; Zhang, J.; Wu, L.; Yan, X.; Wang, L.; et al. Higher Yields and Lower Methane Emissions with New Rice Cultivars. Glob. Chang. Biol. 2017, 23, 4728–4738. [Google Scholar] [CrossRef] [PubMed]
  45. Conrad, R. Microbial Ecology of Methanogens and Methanotrophs. Adv. Agron. 2007, 96, 1–63. [Google Scholar]
  46. Zhang, H.; Liu, H.; Hou, D.; Zhou, Y.; Liu, M.; Wang, Z.; Liu, L.; Gu, J.; Yang, J. The Effect of Integrative Crop Management on Root Growth and Methane Emission of Paddy Rice. Crop J. 2019, 7, 444–457. [Google Scholar] [CrossRef]
  47. Li, Y.; Wang, X. Root-Induced Changes in Radial Oxygen Loss, Rhizosphere Oxygen Profile, and Nitrification of Two Rice Cultivars in Chinese Red Soil Regions. Plant Soil 2013, 365, 115–126. [Google Scholar] [CrossRef]
  48. Liu, K.; Chen, Y.; Huang, J.; Qiu, Y.; Li, S.; Zhuo, X.; Yu, F.; Gao, J.; Li, G.; Zhang, W.; et al. Spikelet Differentiation and Degeneration in Rice Varieties with Different Panicle Sizes. Food Energy Secur. 2021, 1–18. [Google Scholar] [CrossRef]
  49. Qi, D.; Hu, T.; Song, X.; Zhang, M. Effect of Nitrogen Supply Method on Root Growth and Grain Yield of Maize under Alternate Partial Root-Zone Irrigation. Sci. Rep. 2019, 9, 1–10. [Google Scholar] [CrossRef]
Agriculture 12 02134 g001 550
Figure 1. Changes in total CH4 emissions (A), cumulative CH4 emissions during panicle differentiation stage (B), the proportion of CH4 emissions during panicle differentiation in total CH4 emissions (C), daily CH4 flux during panicle differentiation stage (D), and greenhouse gas intensity (E) during the japonica rice cultivar renewal. PD: panicle differentiation stage. Solid squares indicate the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Figure 1. Changes in total CH4 emissions (A), cumulative CH4 emissions during panicle differentiation stage (B), the proportion of CH4 emissions during panicle differentiation in total CH4 emissions (C), daily CH4 flux during panicle differentiation stage (D), and greenhouse gas intensity (E) during the japonica rice cultivar renewal. PD: panicle differentiation stage. Solid squares indicate the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Agriculture 12 02134 g001
Agriculture 12 02134 g002 550
Figure 2. Changes in root oxidation activity (A), root bleeding rate (B), and root oxygen loss (C) during japonica rice cultivar renewal. BT: booting stage; HD: heading stage. Data are the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Figure 2. Changes in root oxidation activity (A), root bleeding rate (B), and root oxygen loss (C) during japonica rice cultivar renewal. BT: booting stage; HD: heading stage. Data are the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Agriculture 12 02134 g002
Agriculture 12 02134 g003 550
Figure 3. Changes in total organic carbon in root exudation (A) and its ratio to yield (B), root biomass (C), and shoot biomass (D) during japonica rice cultivar renewal. Solid squares indicate the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Figure 3. Changes in total organic carbon in root exudation (A) and its ratio to yield (B), root biomass (C), and shoot biomass (D) during japonica rice cultivar renewal. Solid squares indicate the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Agriculture 12 02134 g003
Agriculture 12 02134 g004 550
Figure 4. Changes in methane production potential (A) and methane oxidation potential (B) in rhizosphere soil during japonica rice cultivar renewal. Solid squares indicate the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Figure 4. Changes in methane production potential (A) and methane oxidation potential (B) in rhizosphere soil during japonica rice cultivar renewal. Solid squares indicate the mean value of two experimental years for each cultivar, and error bars indicate the standard errors of the mean. Solid lines indicate linear regression lines.
Agriculture 12 02134 g004
Agriculture 12 02134 g005 550
Figure 5. Relationships between cumulative CH4 emissions in panicle differentiation and grain yield (A), root exudation (B–D), root physiological traits (E–G), and methane oxidation potential (H) in rhizosphere soil.
Figure 5. Relationships between cumulative CH4 emissions in panicle differentiation and grain yield (A), root exudation (B–D), root physiological traits (E–G), and methane oxidation potential (H) in rhizosphere soil.
Agriculture 12 02134 g005
Agriculture 12 02134 g006 550
Figure 6. Effect of nitrogen application at panicle differentiation stage on daily CH4 flux (A) and cumulative CH4 emissions (B) in panicle differentiation stage and GHGI (C). 0N: no nitrogen application; NN: nitrogen application rate at 120 kg ha−1. * indicates statistical significance within the same rice cultivar at p < 0.05 level.
Figure 6. Effect of nitrogen application at panicle differentiation stage on daily CH4 flux (A) and cumulative CH4 emissions (B) in panicle differentiation stage and GHGI (C). 0N: no nitrogen application; NN: nitrogen application rate at 120 kg ha−1. * indicates statistical significance within the same rice cultivar at p < 0.05 level.
Agriculture 12 02134 g006
Agriculture 12 02134 g007 550
Figure 7. Effect of nitrogen application at panicle differentiation stage on grain yield (A), total organic carbon in root exudation (B), as well as the ratio of total organic carbon in root exudation to yield (C) and root biomass (D). 0N: no nitrogen application; NN: nitrogen application rate at 120 kg ha−1. * indicates statistical significance within the same rice cultivar at p < 0.05 level.
Figure 7. Effect of nitrogen application at panicle differentiation stage on grain yield (A), total organic carbon in root exudation (B), as well as the ratio of total organic carbon in root exudation to yield (C) and root biomass (D). 0N: no nitrogen application; NN: nitrogen application rate at 120 kg ha−1. * indicates statistical significance within the same rice cultivar at p < 0.05 level.
Agriculture 12 02134 g007
Table
Table 1. Release year and growth duration of the selected Japonica rice cultivars.
Table 1. Release year and growth duration of the selected Japonica rice cultivars.
Cultivar TypeCultivarRelease YearPanicle Differentiation Stage (Days)Growth Duration (Days)Proportion of PD in GD (%)
1950’sHuangkezao19523311329.2
Guihuaqiu19582912024.2
Average 31.0 b116.5 bc26.7 b
1960’sJinnanfeng19603511829.7
Guihuahuang19633411828.8
Average 34.5 a118 bc29.2 a
1970’sLiming19702911525.2
Xudao219793211428.1
Average 30.5 b114.5 c26.6 b
1980’sYanjing219832911625.0
Sidao819863211527.8
Average 30.5 b115.5 c26.4 b
1990’sZhendao8819973311828.0
Huaidao519993012524.0
Average 31.5 b121.5 ab26.0 bc
2000’sHuaidao920063012224.6
Lianjing720103112524.8
Average 30.5 b123.5 a24.7 c
2010’sWuyunjing2720123312626.2
Nanjing910820133112325.2
Average 32.0 ab124.5 a25.7 bc
PD: panicle differentiation stage; GD: growth duration. Different letters indicate statistical significance at the p < 0.05 probability level.
Table
Table 2. Changes in grain yield and its components during japonica rice cultivar renewal.
Table 2. Changes in grain yield and its components during japonica rice cultivar renewal.
Cultivar TypeCultivarPanicle Number
(×104 ha−1)		Spikelets per PanicleTotal Numbers of Spikelets (×106 ha−1)1000-Grain Weight
(g)		Filled Grains
(%)		Yield
(t ha−1)
1950’sHuangkezao288.3116.5335.925.654.04.6
Guihuaqiu254.3118.5301.326.072.85.7
Average271.3 b117.5 de318.6 f25.8 a63.4 f5.2 f
1960’sJinnanfeng286.9117.8338.024.665.85.5
Guihuahuang273.6122.6335.425.081.36.8
Average280.3 ab120.2 d336.7 d24.8 bc73.6 e6.1 e
1970’sLiming304.3122.5372.825.580.97.7
Xudao2268.7121.2325.725.586.27.2
Average286.5 ab121.9 cd349.2 e25.5 ab83.5 cd7.4 d
1980’sYanjing2302.4123.4373.225.684.38.1
Sidao8315.8128.8406.824.486.68.6
Average309.1 a126.1 c389.9 c25.0 bc85.5 b8.3 c
1990’sZhendao88287.9141.8408.125.791.29.6
Huaidao5274.5148.3407.125.289.59.2
Average281.2 ab145.0 b407.6 b25.5 ab90.4 a9.4 ab
2000’sHuaidao9276.4169.6468.825.285.310.1
Lianjing7284.6165.6471.325.684.410.2
Average280.5 ab167.6 a470.0 a25.4 ab84.9 bc10.1 a
2010’sWuyunjing27290.3160.7466.625.187.310.2
Nanjing9108262.1162.0425.026.288.49.8
Average276.2 ab161.4 a445.6 a25.7 a87.9 ab10.0 a
Different letters indicate statistical significance at the p < 0.05 probability level.
Table
Table 3. Changes in shoot biomass, root biomass, root/shoot ratio and harvest index during japonica rice cultivar renewal.
Table 3. Changes in shoot biomass, root biomass, root/shoot ratio and harvest index during japonica rice cultivar renewal.
Cultivar TypeCultivarShoot Biomass (g m−2)Root Biomass (g m−2)Root/Shoot RatioHarvest Index
BootingHeadingBootingHeadingBootingHeading
1950’sHuangkezao303.5545.545.549.60.150.090.45
Guihuaqiu353.7575.851.657.00.150.100.47
Average328.2 e561.3 d48.6 d53.3 d0.15 c0.10 d0.46 c
1960’sJinnanfeng337.5613.852.457.70.160.090.47
Guihuahuang406.7684.962.969.90.150.100.45
Average372.0 d650.8 c57.7 c63.8 c0.16 c0.10 d0.46 c
1970’sLiming406.5684.066.572.50.160.110.46
Xudao2374.8596.560.668.00.160.110.46
Average390.7 d638.6 c63.6 c70.3 c0.16 c0.11 c0.46 c
1980’sYanjing2593.2859.8110.5119.50.190.140.51
Sidao8607.4925.6114.4121.20.190.130.48
Average600.4 bc891.7 b112.5 b120.4 b0.19 b0.14 b0.49 b
1990’sZhendao88596.1923.9118.4127.50.200.140.51
Huaidao5569.3849.7114.2124.10.200.150.49
Average582.6 c885.8 b116.3 b125.8 b0.20 b0.14 ab0.50 ab
2000’sHuaidao9604.91005.4136.9148.80.230.150.52
Lianjing7635.11029.4147.6158.50.230.150.51
Average620.2 ab1017.6 a142.2 a153.7 a0.23 a0.15 a0.51 ab
2010’sWuyunjing27657.11100.6149.8162.10.230.150.52
Nanjing9108638.11002.7147.6159.10.230.160.52
Average647.5 a1049.7 a148.7 a160.6 a0.23 a0.15 a0.52 a
Different letters indicate statistical significance at the p < 0.05 probability level.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Li, S.; Chen, L.; Han, X.; Yang, K.; Liu, K.; Wang, J.; Chen, Y.; Liu, L. Rice Cultivar Renewal Reduces Methane Emissions by Improving Root Traits and Optimizing Photosynthetic Carbon Allocation. Agriculture 2022, 12, 2134. https://doi.org/10.3390/agriculture12122134

AMA Style

Li S, Chen L, Han X, Yang K, Liu K, Wang J, Chen Y, Liu L. Rice Cultivar Renewal Reduces Methane Emissions by Improving Root Traits and Optimizing Photosynthetic Carbon Allocation. Agriculture. 2022; 12(12):2134. https://doi.org/10.3390/agriculture12122134

Chicago/Turabian Style

Li, Siyu, Lu Chen, Xian Han, Kai Yang, Kun Liu, Jun Wang, Yun Chen, and Lijun Liu. 2022. "Rice Cultivar Renewal Reduces Methane Emissions by Improving Root Traits and Optimizing Photosynthetic Carbon Allocation" Agriculture 12, no. 12: 2134. https://doi.org/10.3390/agriculture12122134

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop