Maize and rice double cropping benefits carbon footprint and soil carbon budget in paddy field

Field Crops Research 243 (2019) 107620

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Maize and rice double cropping benefits carbon footprint and soil carbon budget in paddy field

T

M. Suna, M. Zhana, , M. Zhaob, L.L. Tanga, M.G. Qina, C.G. Caoa, M.L Caia, Y. Jianga, Z.H. Liua ⁎

a

MOA Key Laboratory of Crop Physiology, Ecology and Cultivation (The Middle Reaches of Yangtze River), College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China b Institute of Crop Science, Chinese Academy of Agricultural Sciences, Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China

ARTICLE INFO

ABSTRACT

Keywords: Double rice cropping Maize-rice rotation Greenhouse gases Carbon footprint Net soil carbon budget

Cropping systems in double rice paddies in subtropical region of China are under conversion to a rice-upland rotation or upland crops production driven by economic benefits and social conditions. However, limited studies have been completed to evaluate the productivity and environmental consequences following the introduction of maize to paddies in this area. Four cropping systems were practiced in our experimental field plots: traditional double rice (R-R), maize-rice rotation (M–R), rice-maize rotation (R-M) and double maize cropping (M-M). The two-year study showed that conversion to maize-related cropping patterns led to a substantial change in GHG emissions, carbon footprint and net soil carbon budget (NSCB) on a short-term timescale. Although the M-M treatment resulted in a 13.5% higher annual grain yield than the R-R treatment, it showed a tremendous increase in total CO2 eq emissions of 47.7% and higher soil carbon loss, thus, it had a higher carbon footprint than the R-R treatment by 30.7% (p < 0.05). The annual sequence of rice and maize rotation showed different features in system functions as well. The R-M rotation could not be an option to replace R-R cropping because of its higher carbon footprint and soil carbon loss. However, compared to the R-R treatment, the M–R rotation increased the annual grain yield by 18.3% (p < 0.05) and, showed comparable total CO2 eq emissions and a lower carbon footprint (1.67 kg CO2 eq. kg−1). Nevertheless, the M–R treatment still displayed a high soil carbon loss (-2127 kg C ha−1 by NSCB analysis) after two crop seasons of cultivation, while the R-R cropping maintained the soil carbon gain. Overall, the M–R rotation could be the better alternative cropping system in paddies in subtropical regions in China. Further studies regarding proper management practices to reduce soil carbon loss under M–R rotation are needed to realize its dual benefits of higher grain yield and lower impact on the soil carbon pool.

1. Introduction Greenhouse gases (GHG), such as CH4, N2O and CO2, are considered important drivers of rapid global climate change, and have had an impact on crop production (Stocker et al., 2013). Agricultural sectors contribute an arresting effect on anthropogenic emissions of up to 30% (Tubiello et al., 2013). Soil tillage, crop cultivation and livestock account for 50%–70% of agricultural greenhouse gas emissions (Gerber et al., 2013). Therefore, assessing the impact of agricultural activities on the environment is increasingly important (Gerber et al., 2013; Smith et al., 2013), and developing alternative agro-ecosystems of low GHG emission is urgently required (Lal, 2007; Valin et al., 2013). However, soil carbon sequestration, GHG emissions and agricultural productivity are closely connected to one another, and, their unitive studies are in need and increasingly in focus (Linquist et al., 2012; Lal, ⁎

2015). Greenhouse gas emissions caused by agricultural production can be quantitatively assessed using the carbon footprint method (Hertwich and Peters, 2009; Pandey et al., 2011). As an equivalent of carbon dioxide, carbon footprint (ISO 14067, 2013) has been increasingly used as an internationally recognized indicator used to quantify the sum of greenhouse gas emissions and removals related to agricultural products during recent years (Cheng et al., 2011; Gan et al., 2012; Wang et al., 2016). It is even considered to be used as a mandatory “carbon-labeling” on agricultural products in near future, to ensure affordable and sufficient food with a low carbon footprint (Gan et al., 2012; Wang et al., 2016). It is believed that the carbon footprint of agricultural products is significantly influenced by agricultural management practices such as farming practices, farming systems and nitrogen fertilizer application (Gan et al., 2012; Pandey and Agrawal, 2014). Assessment

Corresponding author. E-mail address: [email protected] (M. Zhan).

https://doi.org/10.1016/j.fcr.2019.107620 Received 14 January 2019; Received in revised form 11 August 2019; Accepted 3 September 2019 Available online 20 September 2019 0378-4290/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Average daily air temperature (a) and accumulated precipitation (b) in each half month during experimental crop seasons in 2013 and 2014.

of agricultural products’ carbon footprint will help determine potential solutions to adjust to farming practices with lower greenhouse gas emissions. However, agriculture in China is diversified and complex because of its vast territory and diverse climatic zones. More extensive studies of the carbon footprint of crops production in China are urgently needed. As an effective alternative for climate change in the context of mitigation, carbon fixation in soil has received considerable attention (Lal, 2004; McCarl et al., 2007; Gattinger et al., 2012). Soil carbon sequestration is considered a very effective measure to reduce CO2 emissions and contribute 89% of the reduction potential (IPCC, 2007). The agricultural soil carbon pool is among the most active carbon pools in the world, and it is thought to be important in synchronously coordinating crop productivity and the soil carbon cycle (Lal, 2004). Direct measurement of SOC stock is the most convincing evidence to evaluate soil carbon sequestration and the exchange of CO2 between the soil and atmosphere at a long-term timescale (Pan et al., 2004; Shang et al., 2011); however the method is insensitive to seasonal or annual changes (Zheng et al., 2008a). Another indirect estimation approach was proposed using the net ecosystem carbon budget (NECB) analysis (Smith et al., 2010; Jia et al., 2012; Zhang et al., 2014). The NECB analysis is an indirect tool for determining short-term carbon gains/losses relative to changes in SOC reserves (Smith et al., 2010). NECB is a balance between carbon input (Net primary productivity, NPP or Gross primary productivity, GPP) and output (Soil heterotrophic respiration, Rh or Ecosystem respiration, Re) (Zhang et al., 2014). By measurements of carbon exchange, calculation of the NECB can determine that the ecosystem is a carbon source or sink at annual to decadal timescales (Smith et al., 2010). Furthermore, one can assess the potential for soil carbon sequestration under current conditions using the NECB approach and propose new strategies for mitigating greenhouse gas emissions. Paddy fields typically remain flooded during most of the rice growing period, and are widely considered to have a high potential for C fixation (Pan et al., 2004; Yan et al., 2013); therefore, highlights are imposed in the study of the accumulation and stabilization of SOC in paddy soils (Shang et al., 2011; Weller et al., 2016). China accounted for approximately 28% of global rice production in 2013, with approximately 160 million hectares of rice paddies (FAO, 2014). At present, the cropping systems in paddies have changed rapidly with the variation in rural economic and social conditions, as well as, climate change, in China (Ye et al., 2015); with the introduction of more crops to rotate with rice (Li et al., 2015). Previous studies have found that changing from a double-season rice cultivation to paddy-upland crop

rotation changed the soil environmental conditions, decreased methane emissions and increased nitrous oxide emissions; but reduced net CO2 equivalent emissions (Cha-un et al., 2017). Under comparable soil conditions, conversion of a dual-season rice planting pattern to a ricemaize rotation will result in a 15% decrease in SOC, which is equivalent to a loss rate of 1.5 Mg CO2 eq. ha−1y-1 (Pampolino et al., 2006). The Yangtze River Plain is in the subtropical region, and is the major rice production area in China, including different rice-based cropping systems (Ye et al., 2015). In the past, double rice cropping, rice-rapeseed rotation and rice-wheat rotation were the major cropping patterns in this region. However, maize was recently introduced into the paddies, and quickly expanded because of a higher benefit from maize production (Li et al., 2015). A rice–maize rotation has also notably expanded in tropical and subtropical Asia because of increasing demands for maize for fodder and biofuel production (Timsina et al., 2010). Thus, it is necessary to evaluate the influence of a maize-induced cropping shift on the carbon footprint and soil carbon pools compared to that of traditional double rice cropping, and furthermore to help develop sustainable cropping systems to mitigate CO2 emissions and increase soil carbon under climate change pressure. 2. Materials and methods 2.1. Experimental site The field experimental site was in the town of Huaqiao (30°01′N, 115°74′E), Hubei Province, China. The study area has a humid midsubtropical monsoonal climate; and is a typical double cropping area with an early rice-late rice, or rice-upland crop rotation. Meteorological data during the experimental period were collected from a nearby weather station. As shown in Fig. 1, the mean daily temperature and total rainfall from March 16 to October 31 were 23.3 ℃ and 835.5 mm during 2013 and 22.8 ℃ and 1208.5 mm during 2014, respectively. The previous cropping pattern was double rice cropping before the start of this experiment. The basic soil properties at 0–30 cm depth were measured including pH (6.3), total N (1.95 g kg−1), organic matter (27.76 g kg−1), available P (10.72 mg kg−1), available K (108.5 mg kg−1) and the soil bulk density (1.17 g cm-3). 2.2. Experimental design and agronomic managements Four double cropping systems were selected in the experiment, representing the different annual planting patterns of upland crop – 2

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Table 1 Dates of sowing and harvest, inputs applied to crop production under different cropping systems. Treatments

2013 M-M R-M M-R R-R 2014 M-M R-M M-R R-R

Crop season

Dates of Sowing – transplanting (rice) - harvest (month/day)

Inputs (per ha) N (kg)

P2O5 (kg)

K2 O (kg)

Seeds (kg)

Herbicides (kg)

Pesticides (kg)

Electricity (MJ)

Diesel (kg)

First crop Second crop First crop Second crop First crop Second crop First crop Second crop

3/15 7/20 3/29 7/20 3/15 6/24 3/29 6/24

240 240 180 240 240 180 180 180

120 120 90 120 120 90 90 90

180 180 105 180 180 105 105 105

39.6 39.6 40.6 39.6 39.6 30.0 40.6 30.0

0.9 0.9 0 0.9 0.9 0 0 0

2.45 2.20 1.95 2.20 2.45 1.65 1.65 1.95

0 0 389 0 0 691 389 691

22 0 11 0 22 11 11 11

First crop Second crop First crop Second crop First crop Second crop First crop Second crop

3/16 - /- 7/15 7/20 - /- 11/2 3/27 -4/28- 7/19 7/20 - /- 11/3 3/16 - / - 7/15 6/25- 7/26-10/26 3/27- 4/28- 7/19 6/25 -7/26- 10/26

240 240 180 240 240 180 180 180

120 120 90 120 120 90 90 90

180 180 105 180 180 105 105 105

39.6 39.6 40.6 39.6 39.6 30.0 40.6 30.0

0.9 0.9 0 0.9 0.9 0 0 0

2.75 2.36 1.80 2.36 2.75 2.15 1.80 2.15

0 0 346 0 0 518 346 518

22 0 11 0 22 11 11 11

- / - 7/17 - /- 11/6 – 5/1 -7/17 - /- 11/2 -/- 7/17 -7/27-10/25 -5/1- 7/17 -7/26-10/25

upland crop farming, upland crop – rice rotation and early rice – late rice cropping. The four studied cropping systems and their agronomic management practices were as follows:

and all agronomic management practices were the same as the above description for autumn maize in the maize-maize plots. (3) Maize-Rice cropping (M–R): The preceding crop was spring maize, and the subsequent crop was late rice each year. All agronomic management practices were the same as the above description for the spring maize in the maize-maize plots. The late rice was sown during late June for rice seedlings in the nursery, and they were manually transplanted after the spring maize harvest. Once the spring maize was harvested, the plots were immediately soaked and plowed with the rotary tiller. Then the late rice seedlings were transplanted at the same planting density and pattern as the description for early rice in the rice-maize treatment plots. Other agronomic management practices were identical to those of the early rice as well. (4) Rice-Rice cropping (R-R): The early rice was followed by the late rice planting during the year. All agronomic management practices were the same as the a fore mentioned description for early rice and late rice.

(1) Maize-Maize cropping (M-M): Two seasons of maize were annually planted, including spring maize and autumn maize. All agronomic management practices in the plots were identical to those of local farmer’s practice. The dates of sowing and harvesting, and fertilizer application rates are shown in Table 1. Before seeding of spring maize during the middle of March, the experimental land was plowed using moldboard plow and prepared using a rotary tiller. Then, bed-furrows were built with beds 1.0 m in width and furrows 0.2 m in width to alleviate the impacts of waterlogging. Maize seeds were manually sown in two rows on the bed with two seeds per hole and 27.5 cm between holes. The wide and narrow row spacings were 80 and 40 cm, respectively. Plants were thinned at the threeleaf stage to a stand density of 6.0 plants / m2 each year. After spring maize harvest, autumn maize seeds were sown adjacent to the spring maize stubbles on the bed without soil tillage. The planting density and pattern were the same as those of the spring maize. All of the phosphorus pentoxide (12.0% calcium super phosphate), half of the nitrogen (46.0% urea) and half of the potassium oxide (60.0% potassium chloride) were applied as basal fertilizer before the sowing of maize during both seasons. The remainder of the nitrogen and potassium fertilizer were applied at the 12-leaf stage of the maize. (2) Rice-Maize cropping (R-M): The early rice was sown during late March for rice seedlings in the nursey and transplanted during late April. Before early rice transplanting, the plot land was soaked for 3 days and was subsequently plowed and puddled using the rotary tiller. The transplanting density for early rice was approximately 309,000 hills ha−1 with 27 cm in row spacing and 12 cm in hill spacing each year. All of the phosphorus pentoxide (12.0% calcium super phosphate), 40% of the nitrogen (46.0% urea) and half of the potassium oxide (60.0% potassium chloride) were applied as basal fertilizer before early rice transplanting. A total of 20% of the nitrogen fertilizer was topdressed during the tillering stage. Totals of 40% of the nitrogen fertilizer and half of the potassium oxide were topdressed at the booting stage. An alternating wetting and drying irrigation regime was practiced during rice growth stage. The field was drained one week before harvest. After the harvest of early rice during July, autumn maize seeds were sown without soil tillage,

Local extended crop varieties were chosen for this study. The maize hybrid was Zhengdan958, the early rice hybrid was Zhongjiazao17, and the late rice hybrid was Yueyou9113. The four cropping systems were organized in a randomized complete block design with three replications. Each treatment plot area was 54 m2 (6.0 by 9.0 m). The plots were surrounded by ridges 25 cm in height. A strong black plastic film was driven to a depth of 30 cm along the inner edge of the field ridge and covered the ridge to the other side (30 cm at the base and 30 cm in height) to prevent lateral water movement resulting from leakage or permeable lateral flow. All grains and straws of maize and rice in each cropping system were removed from the field at harvest time. The in situ investigation included herbicide, pesticide, electricity for irrigation, seeds, fertilizer, mechanical operations and fuel consumption inputs under different treatments. The information on planting and farm inputs for each cropping system treatment is shown in Table 1. 2.3. Measurements of direct CH4 and, N2O emissions and soil respiration CH4 and N2O emissions were measured in situ using the static chamber-gas chromatograph method according to Xu et al. (2015). Briefly, gas samplings in each plot were collected during the morning (8:30-11:00 am) because the soil temperature during this period was near that of the mean daily soil temperature (Zou et al., 2005). On the 3

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sampling day, steel cylinders 38 cm in diameter and 50 cm or 110 cm in height were placed in a groove (8 cm in depth) of the steel ring, which was inserted into the soil in the previous day. Fans were installed on the inside top of the cylinder to mix the air within the chamber. At each sampling event, the groove of the ring was filled with water to seal the rim of the chamber. Gas inside the chamber was drawn 4 times and injected into the 25-ml plastic syringes at 8-min intervals following the chamber closure. Meanwhile, the temperature inside the chamber was recorded. Four hills of rice plants were covered in the steel cylinders 50 cm or 110 cm in height according to the plant height. However, during the maize growth period, only 50 height cylinders were used between the two rows of maize plants. The gas samples were collected at 10-day intervals during the crops growing period. Sometimes, sampling intervals were adjusted because of heavy rainfall etc. CH4 and N2O concentrations in the sampled gas were simultaneously determined with a modified gas chromatograph (Shimadzu GC-14B) equipped with a flame ionization detector (FID) for CH4 analysis and an electron capture detector (ECD) for N2O analysis. N2 (flow rate: 330 ml min−1), H2 (flow rate: 30 ml min−1), and zero air (flow rate: 400 ml min−1) were used as the carrier, fuel, and supporting gases, respectively. The temperatures of the column, injector, FID and ECD were set to 55 °C, 100 °C, 200 °C and 330 °C, respectively. The CH4 and N2O fluxes were calculated based on changes in their concentrations throughout the sampling period and estimated as the slope of the linear regression between the concentration and time (Zheng et al., 1998). Then, the fluxes of CH4 or N2O fluxes were calculated according to the following equation:

consumption for farm operations and the manufacture, storage, transportation and application of agrochemicals to the farm. Then, the global warming potential (GWP) of the system was summarized in a CO2 eq amount by converting all GHG emissions according to the following equation (Cheng et al., 2011; Wang et al., 2016):

F = ρ × h × dC/dt × 273/(273 + T)

The system boundary and the items of C exchange to soil are shown in Fig. 2 (a). The difference between the C flow into and out through the boundary was termed the net soil carbon budget (NSCB), which can relatively reflect the rate of soil organic C gain or loss in a cropping system (Nishimura et al., 2008; Smith et al., 2010). In the case of crop residue removal from the field, NSCB was calculated using Eq. (2) modified according to the similar ecosystem C balance analysis by Ma et al. (2013) as follows:

−2

GWP = ∑(AIi×EFi) + E(N2O) × fN2O + E(CH4) × fCH4

(1)

where the functional unit for GWP in this study was kg CO2 eq ha−1. AIi is the amount of each item of agricultural input to the cropping system as shown in (Fig. 2b). EFi is the “emission factor”, which refers to the GHG emission rate caused by an individual agricultural input per unit. According to the Chinese Life Cycle Database (CLCD v0.7, IKE Environmental Technology CO., Ltd, China), the EFi values for urea, calcium superphosphate, potassium chloride, diesel oil for machinery and electricity for irrigation are 2.39, 0.30, 0.15, 0.89 kg CO2 eq. kg−1 and 1.23 kg CO2 eq. MJ−1, respectively. The EFi values of seeds, herbicides, and pesticides are 0.58, 10.15 and 16.61 kg CO2 eq. kg−1, respectively, as obtained from Ecoinvent v2.2 (Swiss Centre for Life Cycle Inventories, Switzerland). E(N2O) and E(CH4) are the direct amount of nitrous oxide and methane emissions from the field, respectively. fN2O and fCH4 are the GWP coefficient at a 100-year time horizon and, are 298 and 34 kg CO2 eq. kg−1 (IPCC, 2013), respectively. Thereafter, the carbon footprint (CF) of the system was calculated by dividing the total GWP by the grain yield, and expressed in kg CO2 eq. kg−1. 2.6. Calculation of net soil carbon budget and its system boundary

-1

where F is the CH4 or N2O flux (mg m h ), ρ is the CH4 density at the standard state, h is the height of the chamber above the soil (m), C is the gas mixing ratio concentration (mg m-3), and T is the mean air temperature inside the chamber during sampling. The soil respiration was measured at the same time on the same date of CH4 and N2O gas sampling using an LI-8100A soil CO2 flux system (Li-Cor Inc., Lincoln, NE, USA). The respiratory chamber of the instrument was inserted between the rows of crop plants to determine soil CO2 flux every 20 s for 180 s at three sites in each plot (Li et al., 2013). Then, the gas emission of two consecutive sampling events was calculated by multiplying the averaged gas fluxes with the number of days between two adjacent sampling days. The emissions of each time buckets were summed as the cumulative direct seasonal CH4, N2O and CO2 emissions (Li et al., 2013).

NSCB = (NPPlitter-C + GPPunderground-C + Seed-c + Ifertiliser-C) - (Re(2)

C+CH4-C+D-C)

where Ifertiliser-C is the C input from the organic fertilizer application, which was negligible because no organic fertilizer was used in our study. Re-C and CH4-C is the C loss from soil respiration and CH4 emissions, respectively. D-C is the soil C losses through runoff and leaching, which were typically excluded in the calculation (Smith et al., 2010; Hounkpatin et al., 2018). Therefore, Eq. (2) was simplified to Eq. (3) in our study.

2.4. Plant sampling and measurements

NSCB = (NPPlitter-C + GPPunderground-C + Seed-c) - (Re-C+CH4-C) (3)

Plant samplings were conducted at crop maturity with six representative hills of rice plants or maize plants at the respective plot. The samples were oven-dried to a constant weight at 85 °C. C concentrations in the root, stalk, leaf and grain were determined using an elemental analyzer (VarioMax CNS, Elementar, Germany). Rice plants of three 3-m2 replicates in each plot were harvested to calculate the grain yield. Maize grain yield was determined by the adjacent 50 maize plants in the middle rows of each plot. The final grain yield for rice and maize was adjusted to the standard moisture content (14%).

where NPPlitter-C refers to the C input from dead leaves shedding during the crop growth period. On estimation, 5% of the total biomass of the rice and maize was partitioned to NPPlitter (Kimura et al., 2004; Yang et al., 2015), and the carbon content of leaves was used to calculate NPPlitter-C. GPPunderground-C is the C input from the partition of the gross primary production (GPP) to the root and rhizodeposits. GPP was estimated from net primary production (NPP) via the NPP/GPP ratio of 0.52 (Zhang et al., 2009). NPProot was estimated by the weighting of aboveground dry biomass and the ratio of root to shoot (R/S). The R/S was fixed at 0.10 and 0.09 for rice and maize (Huang et al., 2007a,b), respectively. Then, NPProot-C was determined from NPProot and the corresponding C content of the root. Estimation of NPPrhizodeposit-C by exudates, root hairs and fine roots sloughed off was recommended to be 11% of the total biomass carbon for rice and 8.5% for maize (Jones et al., 2009). Shifts in crop plant photosynthetic C partition to aboveground and belowground in response to management were not considered in the present study. The calculation of NSCB indicates net C loss from soil with negative value, or net C gain with the positive value.

2.5. System boundary and carbon footprint calculations A system boundary is required for the carbon footprint estimation according to the LCA method (Wang et al., 2016). The schematic system boundary in this study is shown in Fig. 2b. The CO2 emissions resulting from agricultural infrastructure were not considered in the CF analysis. The GHG emissions during the two crop growing seasons in the present study were compiled from all indirect and direct emission sources (Fig. 2b). The indirect emission sources were attributed to energy 4

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Fig. 2. System boundary (——) for calculating net soil carbon budget (NSCB) (a) and GHG emissions (b) over the crops growth season under different cropping systems.

2.7. Statistical analysis

seasons (P < 0.05) (Fig. 3 a). Furthermore, higher CH4 fluxes during the earlier stages of late rice under R-R system were detected compared to those under M–R system (P < 0.05) (Fig. S1c, d). In comparison to R-R system, the sum of CH4 cumulative emission during the double crop seasons were decreased by 95.2% under M-M system, 89.1% under R-M system and 82.1% under M–R system (P < 0.05) during 2013(Fig. 3 a). Similar results were obtained for 2014 (Fig. 3 a), indicating that introducing maize into a paddy can significantly reduce CH4 emissions. Cropping systems, crop seasons and years and their interactions had significant effect on N2O emissions as well (Table 2). Inversely, N2O fluxes and seasonal cumulative emissions were higher during the maize growing seasons and, lower during the rice growing seasons (Fig.S2; Fig.3 b). The averaged N2O cumulative emission during the maize growing period across two crop seasons and two years was obviously 14.9 times higher than that during rice growing seasons. Two-year experiments showed that no significant difference in N2O cumulative emissions were detected among the maize plots (or rice plots) during the same season under different cropping systems (Fig. 3 b). However, the autumn maize season exhibited greater N2O fluxes (Fig. S2 a,b,c) and higher cumulative emissions(Fig. S2; Fig. 3 b) than the spring maize season. In total, in comparison to the N2O cumulative emissions of the M-M treatment, those of R-M, M–R and R-R treatments decreased by 42.7%, 57.4% and 92.3% during the two adjacent crop seasons (P < 0.05), respectively.

Analyses of variance were performed using the Statistix 8.0 statistical package. The results are given as the means of three replicats, and the differences between treatments were determined by comparing their means using the least significant difference (LSD) at p < 0.05. 3. Results 3.1. Direct CH4 and N2O emissions from the field Similar seasonal changes in CH4 fluxes during crop growing periods in 2013 and 2014 are presented in Fig. S1. Accordingly, seasonal CH4 cumulative emission under different cropping systems was calculated as shown in Fig. 3 a. Cropping systems, crop seasons and years and their interactions had significant effect on CH4 emissions (Table 2). The highest CH4 flux was clearly observed during rice cultivation, while distinctively lower fluxes were found during the maize season. Thus, distinctly higher CH4 cumulative emissions were obtained during the rice growing seasons than during the maize growing seasons (Fig.3 a). Averaged across two crop seasons in both years, the cumulative emission of CH4 was 292.6 kg ha−1 during rice growing period, while 18.5 kg ha-1 during maize growing period. However, the averaged CH4 cumulative emission during late rice growth period (M–R and R-R system) was obviously 6.6 times higher than that during the first rice

Fig. 3. Seasonal cumulative CH4 (a) and N2O (b) emissions under different cropping systems in 2013 and 2014. Different small letters beside the columns indicate statistically significant differences in seasonal accumulative CH4 and N2O emissions among cropping systems across two crop seasons and years at p < 0.05. Different capital letters over error bars denote statistically significant differences in annual cumulative CH4 and N2O emissions cropping systems at p < 0.05. 5

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Table 2 Analysis of Variance in cumulative CH4 and N2O emissions, grain yield, carbon footprint within each season and sum of double season. sources 　

Within season　　 CH4

N2O

grain yield

carbon footprint

CH4

N2O

grain yield

carbon footprint

Year (Y) Cropping systems (C) Season (S) Y × C　 C × S　 Y × C × S　

** ** ** ** ** **

* ** ** ** ** **

ns ** ** ** ** **

* ** ** * ** ns

** **

* **

ns **

* **

**

**

**

**

* P < 0.05, ** P < 0.01 and

ns

Total of double season

P > 0.05. The same below.

Table 3 Equivalent GHG emissions (kg CO2 eq ha−1) from direct sources (CH4 and N2O) and indirect sources relating to agricultural managements (AI: agricultural inputs; FO: farm operations) during crop growing seasons under different cropping systems in 2013 and 2014. Means followed by different letters are significantly different at p < 0.05. Treatments

Direct emissions CH4

First crop season 　 2013 　 　 　 2014 　 　 Second crop season 　 2013 　 　 　 2014 　 　 year　(Y) cropping systems　(C) season　(S) Y × C　 C × S　 Y × C × S　 　 2013 　 　 　 2014 　 　 Year　(Y) Cropping systems　(C) Y × C　

M-M R-M M-R R-R M-M R-M M-R R-R

570 g 2843 f 649 g 2703 f 459 g 7790 d 526 g 7651 de

M-M R-M M-R R-R M-M R-M M-R R-R

406 g 415 g 6780 e 15039 b 669 g 1327 g 13708 c 23088 a ** ** ** ** ** ** 976 f 3258 e 7429 d 17742 b 1128 f 9116 d 14234 c 30739 a ** ** **

M-M R-M M-R R-R M-M R-M M-R R-R

Indirect emissions

Total emissions

N2O

Total

AI

FO

Total

　 15,244 de 720 f 13423 e 660 f 18016 cd 1789 f 17463 cd 1752 f 　 19226 bc 21414 b 1219 f 1404 f 29055 a 29350 a 1933 f 1484 f * ** ** ** ** ** 34470 b 22134 c 14642 d 2064 e 47072 a 31139 b 19396 cd 3236 e * ** **

　 15814 ef 3563 h 14072 f 3363 h 18476 de 9579 g 17989 de 9402 g 　 19632 cd 21829 c 8000 g 16443 ef 29724 a 30676 a 15641 f 24572 b ** ** ** ns ** ns 35446bc 25392 d 22071 de 19806 e 48199 a 40255 b 33630 c 33975 c ** ** ns

　 1665 1243 1665 1238 1670 1240 1670 1240 　 1661 1661 1231 1236 1663 1663 1239 1239

　 20 488 20 488 20 435 20 435 　 0 0 860 860 0 0 647 647

　 1684 1730 1684 1725 1689 1675 1689 1675 　 1661 1661 2091 2096 1663 1663 1887 1887

3325 2903 2896 2474 3333 2904 2909 2480

20 488 880 1348 20 435 667 1082

3345 3391 3776 3822 3353 3338 3576 3562

3.2. Equivalent CO2 emissions and composition

　 17498 ef 5293 h 15756 f 5088 h 20165 de 11254 g 19678 de 11077 g 　 21293 cd 23490 c 10091 g 18540 ef 31387 a 32340 a 17528 f 26459 b ** ** ** ns ** ns 38791bc 28783 d 25847 de 23628 e 51552 a 43593 b 37206 c 37537 c ** ** ns

much lower than that during the rice season, which was mainly caused by electricity consumption for rice irrigation. Thus, these four cropping patterns had nearly an equal amount of total indirect CO2 emissions (Table 3). Among the direct equivalent CO2 emissions, CH4 and N2O emissions accounted for an overwhelming percentage in the R-R and MM systems, respectively. However, in the maize and rice rotation systems, contribution of N2O emission decreased, but still accounted for a larger proportion of 62% in M–R system and 82% in R-M system. In sum, of all the CO2 emissions during the two successive crop seasons, M-M system ranked first, with an increase by 27%, 44% and 51% averaged across two years, compared with R-M, M–R and R-R system, respectively (Table 3). However, there was no significant difference in total of all the sourced CO2 emissions between M–R and R-R system.

The total equivalent CO2 emission in each cropping system includes direct equivalent CO2 emissions converted from direct CH4 and N2O emission per unit of area and indirect CO2 emission calculated from field management measures, agricultural inputs and seeds. As shown in Table 3, most of the equivalent CO2 emissions originated from direct CH4 and N2O emissions, which contributed 92.4% in the M-M, 90.3% in R-M, 87.9% in M–R and 87.2% in R-R cropping systems of the total averaged across two years. Among the indirect CO2 emissions, CO2 emissions from agricultural inputs were obviously greater than those from farm operations in each cropping system (Table 3). Although CO2 emission from agricultural inputs during maize season was greater than that during the rice season, CO2 emission from the farm operation was 6

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Fig. 4. Crop grain yields (a,b) and carbon footprint (c,d) under different cropping systems in 2013 and 2014. Different small letters over error bars indicate significant differences in seasonal yield and carbon footprint among treatments across two crop seasons at p < 0.05. Different capital letters over error bars denote significant differences in annual yield and carbon footprint among treatments at p < 0.05.

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3.3. Crop yield and carbon footprint

(i.e., CO2-C and CH4-C emission from the soil) under each treatment. By the harvest of first crop in both experimental years, spring maize season showed a significant 1.26 times greater C input into the soil than that of the early rice (p < 0.05). However, C output from the soil during the spring maize season showed an eminent amount as well (Table 4), which was 4.54 times higher than that during the early rice season on average (p < 0.05). As a result, a positive NSCB was found in the early rice plot, while a negative NSCB came out in spring maize plots (Table 4). During the second crop season, an equivalent C input was found between that of the later rice season and autumn maize season, with the exception of an apparent lower C input during autumn maize season under R-M treatment (p < 0.05). The C output during autumn maize season still showed a tremendously higher value than that of the rice plots. Therefore, the late rice plots had a positive NSCB, while the autumn maize plots had a negative NSCB (Table 4). Noticeably, the autumn maize plots under R-M treatment had a significantly lower NSCB than that under M-M treatment, because of their higher C output resulting from CO2 emission during the growing season (p < 0.05). Considering the NSCB in these annual cropping systems, the ultimate NSCB was influenced mainly by the C inputs and outputs of the two crop seasons. In the experimental cropping patterns, only R-R treatment exhibited a higher potential in the soil carbon sink during the season with evidence of a positive NSCB (Table 4). The M-M, M–R and R-M treatments showed a carbon loss, because of their annual C output surpassing the C input (Table 4). The soil carbon loss under M-M treatment was greatest, and it was significantly different from that of the other treatments (p < 0.05), indicating that double-season corn planting is not conducive to low-carbon agriculture. Although the M–R treatment had a higher annual C input and lower C output during the later rice season, its higher C output via CO2 emission during the spring maize season prevailed and resulted in a negative NSCB. However, M–R treatment had the higher potential for balancing soil carbon over that of

Both annual crop yield and carbon footprint were significantly affected by the cropping systems, and nearly similar trends were shown during both experimental years (Table 2; Fig. 4 a–d). The same crop in a same growing season under different cropping patterns had a similar yield, with the exception that the yield of late rice under M–R treatment was higher than that under R-R treatment (p < 0.05) (Fig.4 a, b). In terms of overall crop yield, M–R treatment obtained a yield of 18.87 t ha−1 averaged across both years and ranked the highest during the trial (Fig. 4 a, b). Compared with M–R treatment, R-M treatment and R-R treatment showed an inferior annual yield, with a significant reduction by 17.00% and 15.48% (P < 0.05), respectively, averaged across both years. The maize-rice rotation showed a higher annual yield potential than the other cropping patterns in the experimental area. Furthermore, M–R treatment had the lowest carbon footprint (1.67 kg CO2 eq kg−1) of all the treatments over the two years (Fig. 4 c, d). For the two crop seasons, the crop carbon footprint of the maize season was significantly higher than that of the rice season. Consequently, M-M treatment with two maize seasons had the highest carbon footprint of 2.49 kg CO2 eq kg−1 averaged across two years (Fig. 4 c, d). In conclusion, cropping patterns or even the sequence of crops in the annual rotation should be considered to achieve low-carbon agriculture. 3.4. Net soil carbon budget Estimates of the seasonal net soil carbon budgets (NSCBs) at the time of crop harvest in each cropping system were calculated using Eq. (5); the carbon inputs and outputs are listed in Table 4. Different discrepancies in NSCB can be found under the different treatments and during the different cropping seasons, with a variation from -6428 to 1635 kg C ha−1. This variation depended on the amounts of different carbon inputs (i.e., seed, biomass litter and root residue) and outputs

Table 4 C exchanges and net soil carbon balance (NSCB) during different crop growing seasons under different cropping systems in 2013 and 2014. Means in the columns of the same indicator followed by different letters are significantly different at p < 0.05. Treatments

C input into soil (kg C ha−1)

C output from soil (kg C ha−1)

NSCB (kg C ha−1)

CO2-C

First crop season M-M R-M M-R R-R Second crop season M-M R-M M-R R-R Analyses of variance Year　(Y) Cropping systems(C) Season　(S) Y × C　 C × S　 Y × C × S　 Total of double seasons M-M R-M M-R R-R Analyses of variance Year　(Y) Cropping systems(C) Y × C　

2013

2014

2013

3298 ab 2669 f 3261bc 2612 fg

3165bcd 2434 g 3141bcde 2484 fg

7196 1431 6753 1725

2974 e 3108cde 3481 a 3203bcd

3054 de 2639 f 3211bcd 3180bcd

5483 7475 2723 2065

**

6273 5776 6742 5816 * *

b c a c

6219 5074 6353 5665 **

2014

2013

2014

2013

2014

bc h d gh

5284 e 903 i 5604 e 1018 i

13 63 14 60

10 g 172 d 12 g 169 de

−3910 gh 1175abc −3506 g 828 bcd

−2129 f 1359 a −2475 f 1297 ab

e b f g

6797 8685 1791 2576

9g 9g 150 e 332 b

15 g 29 g 302 c 509 a

−2517 f −4377 h 609 d 806 cd

−3758 g −6075 i 1118abc 95 e

cd a gh f

** ** ** ** ** **

**

** * ** ns

CH4-C

b d b c

g f g f

** ** ** ** ** **

12679 a 8906 b 9476 b 3790 d

12081a 9588 b 7395 c 3594 d

ns ** **

22 f 72 e 164 d 391 b ** ** **

8

ns ** ** ** ** ** 25 f 201d 314 c 678 a

−6428 e −3202 c −2898 c 1635 a ns ** **

−5887 e −4715 d −1356 b 1392 a

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the M-M and R-M treatments (Table 4).

spring maize season, which resulted in the inhibition of the activity of CH4 bacteria or a large number of deaths and could not recover over a short time after the late rice was transplanted (Itoh et al., 2013; Breidenbach et al., 2016; Liu et al., 2015b). Additionally, soil aeration during the spring maize season generated oxidants, particularly Fe (II) re-oxidation, which could extend to the period of the successive crop and maintain a higher availability (Van Bodegom et al., 2000; Conrad, 2002), thus delaying the onset of methanogenesis (Conrad, 2002). Thereby, a reduction in CH4 emission was observed at the beginning of the late rice season under M–R system in contrast to that under R-R treatment (Fig. S1 c, d). This observation was consistent with the report by Weller et al. (2016). Although maize introduction to paddies decreased CH4 emissions, it tremendously increased N2O emissions (Fig. 3 b). Because the soil moisture content during the maize crop season is low and the aeration condition is better, it is beneficial to the nitrification process (Butterbach-Bahl et al., 2013). Another nonnegligible reason was that more nitrogen fertilizer was used for maize production than for rice production (Table 1), and more available nitrogen in the soil enhanced the denitrification processes, which could further explain the increasing N2O emissions during maize season (Fig. 3 b). This explanation also hints at the possibility of reduction in N2O emissions during maize growing season by adopting new low-input nitrogen fertilizer management. In addition, using N2 as the carrier gas in measurement of N2O with a gas chromatograph might overestimate the N2O emission, especially greater during the rice growing season, in which the N2O fluxes were less than 200 μg N m−2 h−1 on most of sampling date (Zheng et al., 2008b). This might generate biases in the GWP estimation of different cropping systems. However, because of the offset in contributions to GWP between CH4 and N2O emissions in our study, the M–R and R-R cropping patterns had comparative direct equivalent CO2 emissions (Table 2), which was inconsistent with the results of previous reports (Weller et al., 2016; Cha-un et al., 2017). In our study, different maize and rice rotation sequences (M–R and R-M treatments) were found to have distinctly different effects on overall annual GHG emissions and GWP (Fig. 3, Table 3). To our knowledge, there are few reports regarding this topic. The annual GWPs in M–R and R-M rotations were 31,527 and 36,188 kgCO2 eq. ha −1, respectively, which were both higher than those in the wheat-rice rotation system of 10,871–22,711 kgCO2 eq. ha −1 reported by Zhang et al. (2016). Although the late rice season emitted more CH4 than early rice season (p < 0.05), the autumn maize season had tremendous N2O emission compared to the spring maize season (Fig. 3 b). Such results likely are related to the higher air temperature in autumn maize season than that in spring maize season (Fig. 1). We deduced that another nonnegligible reason was in the different application practices of nitrogen fertilization during our experiment. Basal nitrogen fertilizer was plowed into the soil before spring maize sowing, while it was broadcasted without plowing before the autumn maize sowing due to the short fallow period between early rice and autumn maize. Further studies are needed to reveal more detail the reasons for such different effect on CH4 and N2O emissions during successive crop seasons by the previous crops. In most cases, increased grain production leads to an increase in carbon footprint, but cropping systems that achieve high yields and low greenhouse gas emissions are not conflicting targets (Grassini and Cassman, 2012). The carbon footprint is a comprehensive indicator that addresses the dual goals of environmental protection and food security (Linquist, et al., 2012). Differences in CF were found in different rice and maize cropping systems (Fig. 4 c, d), with the highest value of 2.49 kg CO2 eq.kg −1 grain in M-M cropping system, and the lowest value of 1.67 kg CO2 eq.kg −1 grain in M–R cropping system. Our estimates on CF under different maize-rice cropping systems are different from those reported on other cropping systems in China, which were higher than those in wheat-rice rotation system from 0.71 to 1.25 kg CO2 eq. kg−1 grain (Zhang et al., 2016), while, close to those of intensive vegetable systems from 1.15 to 2.29 kg CO2 eq kg−1 vegetable

4. Discussion Different cropping systems have different management practices, which combine the biological traits of crops to exert different effects on the soil process and thus on the outputs attributes of the systems (Nishimura et al., 2008; Datta et al., 2011; Cha-un et al., 2017). It is essential and a possibly effective means to use suitable crop rotation systems to mitigate GHG emissions and improve soil fertility (Ali et al., 2012; Cha-un et al., 2017). In our experiment, different cropping patterns showed an apparent discrepancy in total CO2 eq emissions during a single crop season, even for the annual cropping systems with double successive crop seasons (Table 3). In recent evaluations of GWPs and carbon footprints, direct emissions from soil and indirect emissions relating to management are simultaneously involved, and the contributions of direct and indirect emissions to total GWPs have varied considerably in previous studies (Hillier et al., 2009; Wang et al., 2016) depending on the traits of the studied agricultural systems. Although indirect emissions relating to crop management varied in our study, direct CO2 eq emissions contributed the most to the total (Table 3). This may indicate that reducing direct GHG emissions will be a research priority and an effective approach for low carbon agriculture development in the study area. Among the studied cropping systems, the MM treatment ranked the first in total emissions. Our study showed that the transition of one season of rice to maize did not reduce the net CO2 equivalent emissions in comparison to those of the traditional R-R system (Table 3). This result differed from a previous study by Cha-un et al. (2017), who found that upland crop rotations with rice could reduce GHG emissions. Such a discrepancy may be related to the local climate, soil texture and management practices, which resulted in variation in seasonal accumulative CH4 and N2O emissions (Fig. 3). Direct emissions of CH4 and N2O showed significant variation between the rice and maize plots during a same season, and between the early and late seasons of the same crop during the two examined years (Fig. S1, S2, Fig. 3). Trade-offs between CH4 and N2O emissions were also observed in our study (Fig. S1, Fig. 3), as reported in previous studies (Zou et al., 2005; Berger et al., 2013; Xu et al., 2015). Soil water content is a major driver affecting CH4 and N2O emission (Huang et al., 2007a,b; Xiong et al., 2007; Wang et al., 2011, 2012). Thus, distinctly higher CH4 cumulative emissions were obtained during rice growing seasons than during maize growing seasons (p < 0.05). Furthermore, an opposite trend in N2O emission was observed during the two examined years (Fig. 3). Noticeably, during the same crop growth period under the studied annual cropping systems, the CH4 and N2O emissions during the second seasons were intensified in comparison to those during the first crop season (Fig. 3). Such results are likely related to the higher air temperature in the period of gas emission peak during late season than that in early season (Fig.1). Higher temperature can enhance the activity of CH4 and N2O producing bacteria, resulting in higher emission during the late season (Butterbach-Bahl et al., 2013; Liu et al., 2015a). This result indicates that proper crop sequence in a cropping system and related management practices should be considered in compromising CH4 and N2O emissions. Maize is introduced to traditional double rice field and promotes rice–maize rotation expansion in tropical and subtropical Asia to meet demands for fodder and biofuel production (Timsina et al., 2010). Studies on GHGs emissions under maize and rice rotation have recently been conducted by some researchers (Weller et al., 2016; Cha-un et al., 2017). In our study, the M–R pattern significantly reduced CH4 emission by contrast to R-R pattern, which was in line with previous reports (Weller et al., 2016; Cha-un et al., 2017). In addition, lower CH4 emissions during the first season and the late rice season under M–R treatment were observed in comparison with those under R-R system (Fig. 3, Table 3). These results might partially be because the soil water content was lower and the soil aeration was better during the first 9

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(Zhou et al., 2019). It indicates that CF under paddy production system could be lowered by adjusting the cropping systems or improving the farming management. Increases in cereal grain yield could lead to a reduced the carbon footprint at production scales (Gan, et al., 2012). As C4 plant, maize generally has a higher yield potential than other cereal crops. However, only spring maize showed superiority in yield (Fig. 4 a, b), and the yield potential of autumn maize was suppressed due to environmental conditions (Ge et al., 2016). In contrast, late rice showed better yield potential than early rice (Fig. 4 a, b). Therefore, lower grain yield and higher CO2 eq emissions led to a higher carbon footprint under autumn maize than under spring maize production (Fig. 4). This finding further shows the importance in design of cropping sequence to arrange different crops growing in their adaptive seasons to achieve a low carbon footprint scaled by product. In this study, M–R treatment had similar total annual CO2 eq emissions to the traditional R-R cropping during both experiment years (Table 2), but its annual grain yield significantly surpassed that of the R-R treatment (Fig. 4 a, b), and consequently, it had the lowest carbon footprint of all the treatments (p < 0.05) (Fig. 4 c, d). Thus, M–R cropping pattern can be considered as a viable rotation system for practicing low-carbon agriculture in a subtropical region in China. It was also recommended as an alternative cropping system to double rice cropping in previous reports (Weller et al., 2016; Cha-un et al., 2017). In this study, NSCB, which could provide a scientific basis for shortterm soil carbon gains and losses (Smith et al., 2010), was analyzed to understand the potential effect of different cropping systems on soil carbon stocks in the paddy region. During both experimental years, NSCB by single season or double cropping seasons under R-R treatment was obviously positive due to the low soil C output through CO2 emission (Fig. S3), indicating that the RR treated soil showed carbon sinks (Table 4). The positive NSCB value of R-R treatment hinted that the average of 1457 kg C ha−1 was possibly being sequestrated in the paddy soil after R-R seasons, which was higher than the reported value of 549 kg C ha−1 by Cha-un et al. (2017). Previous findings (Pan et al., 2004; Ma et al., 2013; Kim et al., 2017; Wu et al., 2018) also support the C sequestration potential under rice production found in this study. Paddy soil is generally considered a net C sink (Bhattacharyya et al., 2014); arguments have arisen on whether conversion of double rice cropping to a rice-upland rotation would result in paddy soil carbon loss (Kraus et al., 2016; Wu et al., 2018). In our study, the negative value of NSCB indicated soil carbon loss under M-M, M–R and R-M treatments, mainly due to the greater carbon loss during maize growing season by soil respiration (Table 4, Fig. S3). The M–R treatment showed the lowest soil carbon loss in the maize-related cropping pattern in this experiment, with the average of 2127 kg C ha-1 (Table 3), which is higher than the reported carbon loss of 1430 kg C ha−1 under ricemaize rotation by Weller et al. (2016). Pampolino et al. (2006) reported that conversion of the dual-season rice planting pattern to a rice-maize rotation would result in a 15% decrease in SOC. However, other previous studies found a neutral NSCB or weak soil carbon sink under ricemaize rotation (Saree et al., 2012; Cha-un et al., 2017). These differences in net carbon balance may be mainly due to differences in crop varieties, past land use, management practices, postharvest activities, and environmental and climatic conditions. In our experiment, the M–R treated plots had a significantly higher C input into the soil than R-R treatment (p < 0.05), therefore, tremendous C output from soil under M–R treatment is the key reason for the negative NSCB under M–R treatment (Table 4). Previous reports (Nishimura et al., 2008; Kurganova et al., 2014; Hounkpatin et al., 2018) show that the conversion of a rice paddy to upland cultivation is apt to increase SOM mineralization, particularly and most pronouncedly during the initial years, thus causing SOC loss that stabilizes after decades. Consequently, introduction of maize to paddies stimulated apparent C losses particularly during the initial years (Table 4). Nevertheless, it is possible to prevent more soil C loss triggered by rice-upland rotations during the initial years through some management practices such as crop residue

return (Qiu et al., 2009). Further studies are necessary to evaluate the long-term impact of maize-rice rotation on SOC loss related to the total GWP. In fact, detectable changes in SOC pool usually need long-term observations over years to decades (Smith, 2004). However, it is a prerequisite to detect the SOC loss or gain during a short-term period to determine timely countermeasures; NSCB analysis is considered a proper tool for this purpose (Smith et al., 2010). However, some indices relating to crop C partition used in NSCB analysis, which have substantial influence on the calculation, are somewhat cursory and uncertain, and thereby need improvement according to specific crops and sites. Notably, the use of a process-based model may be an effective means of predicting the carbon input from crops in NSCB analysis (Chaun et al., 2015). In addition, in our study, a slightly longer interval of gas sampling (an approximately 10-day interval) was adopted on account of the heavy labor workload and experimental costs. Such infrequent sampling could generate a slight deviation in seasonal GHG fluxes and exaggerate the weight of some individual observations, perhaps yielding biased estimates in total GHG emission and NSCB calculations. 5. Conclusions Introduction of maize to a double rice paddy and conversion to a rice-upland rotation or double maize cultivation has resulted in a substantial change in GHG emissions, carbon footprint and potential soil carbon balance at a short-term timescale. The sequence of rice and maize rotation showed different features in these system functions as well. Compared with double maize cropping and rice-maize rotation, maize-rice rotation had significantly lower annual CO2 equivalent emissions and carbon footprints scaled by grain yield and a lower soil carbon loss estimated by NSCB analysis. However, M–R rotation still displayed a strong soil carbon source (-2127 kg C ha−1 by NSCB analysis) after two crop seasons of cultivation, while R-R cropping maintained a soil carbon gain. Nevertheless, the M–R rotation had somewhat higher annual yield and lower carbon footprint than R-R cropping. In the context of diversified needs for rice and maize production and a decrease in carbon footprint in crop yield, the maize-rice rotation could be a better alternative cropping system than rice-maize rotation in the subtropical region of China. Further studies regarding proper management practices to reduce soil carbon loss under the M–R rotation are needed to realize its dual benefits of a higher grain yield and lower impact on the environment. Acknowledgments This study was financially supported by the National Natural Science Foundation of China (No. 31571622 and 31871579) and the Special Fund for Agro-scientific Research in the Public Interest of China (201503122). We are grateful to Mr. Zhongbin Zhai for his help in managing the experiment fields. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.fcr.2019.107620. References Ali, R.I., Awan, T.H., Ahmad, M., Saleem, M.U., Akhtar, M., 2012. Diversification of ricebased cropping systems to improve soil fertility, sustainable productivity and economics. J. Anim. Plant Sci. 22, 108–112. Berger, S., Jang, I., Seo, J., Kang, H., Gebauer, G., 2013. A record of N 2 O and CH 4 emissions and underlying soil processes of Korean rice paddies as affected by different water management practices. Biogeochemistry 115 (1–3), 317–332. Bhattacharyya, P., Neogi, S., Roy, K.S., Dash, P.K., Nayak, A.K., Mohapatra, T., 2014. Tropical low land rice ecosystem is a net carbon sink. Agr. Ecosystem Environ. 189,

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