Double paddy rice conversion to maize–paddy rice reduces carbon footprint and enhances net carbon sink

Journal of Cleaner Production 258 (2020) 120643

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Double paddy rice conversion to maizeepaddy rice reduces carbon footprint and enhances net carbon sink Zhenhui Jiang, Jingdong Lin, Yizhen Liu, Chaoyang Mo, Jingping Yang* Institute of Environment Pollution Control and Treatment, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 July 2019 Received in revised form 10 November 2019 Accepted 17 February 2020 Available online 19 February 2020

Increasing water scarcity and demand for biofuels and fodder has resulted in a change to traditional farming cropping system from double paddy rice (early riceelate rice, sign as ERLR) to maizeepaddy rice (spring maizeelate rice, sign as SMLR) in Asia. Whether the introduction of SMLR would induce lower greenhouse gas (GHG) emissions and higher net carbon (C) sink remains unclear. The objective of this study is to conduct a comprehensive assessment of the C footprint and net ecosystem carbon budget (NECB) for ERLR and SMLR systems, based on a twoeyear (2017e2018) paddy field experiment in southeastern China. Results showed that introducing SMLR resulted in a slight decrease of grain yield by 3.78% in 2017 and a pronounced increase by 17.6% in 2018. The introduction of SMLR into ERLR significantly reduced the C footprint by 35.1e41.7%. This was attributed to spring maize having a 60.1e64.5% lower C footprint relative to early rice and the C footprint of later rice in SMLR being 17.7e19.0% lower than in ERLR. Methane emissions were the largest contributor to the C footprint in both cropping systems, accounting for 52.7e54.4% and 37.2e39.6% in ERLR and SMLR, respectively. This composition of C footprint was similar to that of early rice in ERLR and late rice in ERLR and SMLR. However, GHG emissions from the manufacture of nitrogen fertilizers were the largest fraction of the C footprint in spring maize production, accounting for 40.0e49.3%. Although SMLR resulted in a greater C output due to increased direct carbon dioxide emissions from soils (by 60.1e142%), the introduction of SMLR produced a higher C input. Furthermore, the increase of C input outweighed the increase of C output, leading to a significant increase of NECB by 80.1e147%. Results from this study demonstrate that the introduction of SMLR into ERLR is highly effective strategy for reducing C footprint and enhancing the net C sink as well as maintaining high grain yield. © 2020 Elsevier Ltd. All rights reserved.

Handling editor. Zhifu Mi Keywords: Uplandepaddy rotation GHG emissions CH4 C sink

1. Introduction Rice (Oryza sativa L.) is a main staple crop in China (FAO, 2016a) which is generally grown under flooded conditions, accounting for 33% of fresh water resources globally (FAO, 2017). Recently, due to a shortage of water resources, the high cost of irrigation energy and an increase in demand for livestock feed, such as maize (Zea mays L.), farmers have switched cropping systems from double paddy rice (early riceelate rice (ERLR)) to maizeepaddy rice (spring maizeelate rice (SMLR)) rotation (Timsina et al., 2010). The Food and Agriculture Organization (FAO) has adopted SMLR as a sustainable and strategic model for agricultural intensification (FAO, 2016b). Compared with ERLR, SMLR has the advantages of a

* Corresponding author. E-mail address: [email protected] (J. Yang). https://doi.org/10.1016/j.jclepro.2020.120643 0959-6526/© 2020 Elsevier Ltd. All rights reserved.

larger productivity, lower energy consumption and nutrient leaching, higher economic benefits, and lower demand on water resources and labor (He et al., 2017; Jat et al., 2019; Weller et al., 2016). Consequently, the use of SMLR rotation has substantially increased in the ERLR region (Agus et al., 2019). In Asia, the planting area of SMLR has increased to more than 3 million ha (Timsina et al., 2010). With the large-scale promotion of spring maize in the ERLR region in southern China, the SMLR system has become the focus of strategic adjustments in paddy rice. It is fully accepted that increasing greenhouse gas (GHG) emissions drive climate change and warm our planet (Montgomery, 2017). Climate change has been shown to seriously endanger food security and public health, and these effects will become more comprehensive in the coming decades (FAO et al., 2017; Watts et al., 2018a, 2018b). Agricultural land-use change (e.g., cropping-system conversion), as a main driver of increased GHG emissions, contributes to climate change greatly (IPCC et al., 2013). Considering the rapid increase in the

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Z. Jiang et al. / Journal of Cleaner Production 258 (2020) 120643

cultivation area of SMLR and much concern on climate change, a better understanding of the effect of introduction of SMLR into ERLR on GHG emissions is crucial for policy initiatives to adjust planting structure and develop strategies for GHG mitigation. Introduction of SMLR rotation into ERLR may change the carbon (C) and nitrogen (N) cycle, and cause ‘pollution-swapping’ of GHG emissions, i.e., from dominant methane (CH4) emissions towards nitrous oxide (N2O) and carbon dioxide (CO2) (Wassmann et al., 2000). He et al. (2015) proposed that introducing SMLR could result in more CO2 emissions from field soils relative to ERLR. Janz et al. (2019) and Weller et al. (2015, 2016) reported that introduction of SMLR could decrease global warming potentials by resulting in an increase in N2O emissions and a decrease in CH4 emissions from paddy soils due to upland maize cultivation. SMLR introduction also leads to changes in indirect GHG emissions (from the application of fertilizer, pesticides, fuel, electricity, etc.) due to the difference of agricultural inputs with rice cultivation. Furthermore, the greater C sequestration potential of maize biomass by absorbing CO2 from atmosphere than rice biomass is observed (Janz et al., 2019). Overall, the ERLR conversion to SMLR will inevitably induce changes of GHG emissions (including direct and indirect GHG emissions) and net C sink. The reduction in GHG emissions do not necessarily lead to increases in net C sink, since net C sink is dependent on C input and C output. To clarify whether the SMLR is a sustainable cropping system with low GHG emissions and high C sink, it is necessary to conduct an analysis to reveal whether there are tradeoffs or synergies between these two indicators. A comprehensive assessment is preferred when seeking to distinguish the tradeoffs or synergies. The co-evaluation of the C footprint and net ecosystem carbon budget (NECB) in crop production can provide a comprehensive approach to identify the structure of GHG emissions and net C sink. The C footprint is calculated as the total GHG emissions of an activity or a product based on the Life Cycle Assessment (LCA) principle. Direct and indirect emissions are included in the C footprint calculation, and it is expressed as the carbon dioxide equivalent (CO2-eq) (Wiedmann and Minx, 2007). The idea of C footprint has been widely accepted as an indicator to address the impact of GHG emissions from a product or an activity on climate change (Wright et al., 2011). The C footprint assessment in agriculture provides a powerful tool for exploring the mitigation options of GHG emissions relative to crop production (Lal, 2004). Due to the large spatial variability of soil organic carbon (SOC) and its relatively small change over a short time period, SOC changes are difficult to detect and reasonably evaluate (Post et al., 2001). The NECB has been widely used to assess short-term C gains/losses (Smith et al., 2010) and identify whether an agroecosystem is a net C sink or a source (Heimann and Reichstein, 2008). The NECB is calculated using the difference between C input and output. The C fixed in biomass is the main pathway for C inputs, contributing to the primary composition of the C budget (Nishimura et al., 2015). However, it has been argued that C inputs via the C uptake of biomass should not be considered (Huang et al., 2013). According to the reports by West and Marland (2002), “C fixation of crops does not accumulate seasonally or annually, as biomass rapidly degrades.” In other words, C fixation by crops is offset by the loss of C from harvested crops outside the system. However, the larger potential of C storage in biomass will be identified when this biomass is converted into bioenergy and biochar (Paustian et al., 2016), therefore, C fixed in biomass should not be neglected. For a comprehensive and reasonable evaluation of NECB, direct and indirect GHG emissions should be included to reduce any trade-off effects (Schlesinger, 2010). Integrated assessment of C footprint and NECB has been increasingly used to evaluate the sustainability of cropping system

in recent years. For example, Zhang et al. (2016) reported that notill could be a “climate-resilient” for reducing C footprint and enhancing NECB in the winter wheatesummer maize system. Jiang et al. (2019) identified that sustainable N fertilizer application rate can obtain low C footprint and benefit NECB in rice production. However, little is known on comprehensive effects of the introduction of SMLR into ERLR on C footprint and NECB because SMLR as a new cropping system receives little attention. The objectives of this work, therefore, are: (1) to assess the comprehensive changes of C footprint and NECB after the introduction of SMLR into ERLR on a field scale; and (2) to identify the main contributor of GHG emissions and NECB. Based on the existing studies that upland maize production had lower C footprint and higher CO2 uptake from atmosphere than paddy rice (Schmitt and Edwards, 1981; Zhang et al., 2018), we hypothesize that the introduction of SMLR into ERLR leads to a decrease in the C footprint and an increase in the NECB. 2. Materials and methods 2.1. Site characteristics The field experiment was undertaken between April 2017 and November 2018 in the experimental station of Hangzhou Academy of Agricultural Sciences (120.16
E, 30.13
N), Hangzhou, China. This region experiences a subtropical monsoon climate with an average annual precipitation and mean annual temperature of 1454 mm and 17.8
C, respectively. Soil in this area, classified as Inceptisols (USDA classification) with a loam-dominated texture, has a pH of 5.52, an N content of 2.32 g kg1 and a SOC of 17.7 g kg1 in the topsoil (0e20 cm) . The abbreviation is shown in Table 1. 2.2. Experimental design and management Two cropping systems, ERLR and SMLR, were arranged in a randomized design with each having three replicates. The size of each replicate plot was 8 m  8 m. The cultivar of spring maize was Meiyu No. 7, early rice and late rice were Yongxian 115 and Xiushui 134, respectively. The growth periods of ERLR and SMLR are shown in Fig. 1 and the time of cultivation and harvesting for each season crop was adjusted according to local climatic conditions. The application rates from fertilizers, electricity consumption for irrigation, diesel, plastic film and pesticide were recorded during the experimental periods. Agricultural inputs are shown in Table 2. In the ERLR cropping plots, two paddy crops, early rice and late rice, were included. Based on local farmland management, early rice and late rice were planted by direct seeding and transplanting, respectively. Early rice was planted using a direct seeding method (405 kg seeds ha1) on April 10th, 2017 and April 11th, 2018, and late rice seedlings (19 days old) were transplanted with a plant spacing of 23 cm and a row spacing of 13 cm on July 22nd, 2017 and July 25th, 2018 (Fig. 1). Both early rice and late rice received the same amount of fertilizer, with 225 kg N ha1 (46.0% urea), 47 kg P2O5 ha1 (18.0% superphosphate) and 41 kg K2O ha1 (62.0% potassium chloride) in 2017 and 2018. Nitrogen fertilizer during each season was applied at a ratio of 3:4:3 (15th, 35th and 60th day after seeding of early rice, and 7th, 30th and 60th day after transplanting of late rice). Before rice sowing/transplanting, all rice plots were plowed (15 cm). During the growth stage of early rice and late rice, paddy fields were remained flooded (3e5 cm) until final drainage two weeks before the rice harvest. Early rice (July 20th, 2017; July 23rd, 2018) and late rice (November 19th, 2017; November 22nd, 2018) were harvested from three 1 m  1 m areas in each plot and grain and straw were oven dried at 75
C until constant weight. Finally, residues were removed from the plots after harvest.

Z. Jiang et al. / Journal of Cleaner Production 258 (2020) 120643

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Fig. 1. Growth periods of early rice, later rice and spring maize in 2017 and 2018.

Table 1 Abbreviations and their full name. Abbreviation

Full name

Abbreviation

Full name

ERLR SMLR C NECB GHG N

Early riceelate rice Spring maizeelate rice Carbon Net ecosystem carbon budget Greenhouse gas Nitrogen

CH4 N2O CO2 LCA SOC SOM

Methane Nitrous oxide Carbon dioxide Life cycle assessment Soil organic carbon Soil organic matter

Table 2 Agricultural inputs (Ai), and related coefficient factors (di) and application rate. Ai

di

Unit

Literature

Agricultural inputs Unit

N fertilizer P fertilizer K fertilizer Plastic film a Diesel for machinery Pesticide Electricity for irrigation a

kg kg kg kg kg kg kg

CO2-eq CO2-eq CO2-eq CO2-eq CO2-eq CO2-eq CO2-eq

1

kg N kg1 P2O5 kg1 K2O kg1 L1 kg1 kwh1

6.38 0.72 0.62 2.50 3.21 14.0 1.12

Yan et al. (2015) Chen et al. (2015) Chen et al. (2015) Energy Source, China (2009) Zhang et al. (2013) Yang et al. (2014) Zhang et al. (2013)

Application rate

1

kg ha kg ha1 kg ha1 kg ha1 L ha1 kg ha1 kwh ha1

Early rice

Late rice

Spring maize

225 47 41 e 178 13 1198

225 47 41 e 178 15 1346

225 75 47 150 106 7 0

Plastic film was only used for spring maize production in 2018.

For the SMLR cropping plots, two crops, upland spring maize and paddy late rice, were included. Spring maize was transplanted with 37,800 plants ha1 in the three-leaf period on April 13th, 2017, and directly seeded with a plastic film cover on April 1st, 2018 (Fig. 1). Ridge culture with a ridge width of 55 cm and a furrow width of 30 cm was used to cultivate spring maize. Each ridge included two rows of maize with an inter-row spacing of 45 cm and intra-row spacing of 35 cm. Maize received 75 kg P2O5 ha1 (18.0% superphosphate) and 47 kg K2O ha1 (62.0% potassium chloride) as a basal fertilizer. Nitrogen fertilizer (225 kg N ha1, 46.0% urea) was applied twice using a spot application at the rate of 40% as basal fertilizer and 60% at the trumpet period. Electricity was not used for irrigation during maize cultivation due to adequate precipitation in 2017 and 2018. Plastic film and direct seeding methods were only used for maize production in 2018 due to low productivity induced by low temperatures during the early stages of maize production in 2017. Other agricultural inputs of spring maize production were kept consistent in both 2017 and 2018. In the whole growth period of spring maize, all the ridges were kept free of flooding by artificial drainage when encountering heavy rainfall. Spring maize was harvested from three ridges in each plot in the form of a fresh crop

on July 2nd, 2017 and July 5th, 2018 (Fig. 1). Grain and straw were oven dried at 75
C until constant weight. The management practices of late rice in the rotation system remained consistent with late rice in the ERLR cropping system. 2.3. Data collection and analysis According to the definition of the PAS 2050 protocol (BSI, 2008), the C footprint refers to GHG emissions from agricultural inputs assessed as the sum of all direct and indirect GHG emissions, expressed as CO2-eq, in the entire production chain. In the current study, the C footprint of crops was assessed based on the entire life cycle with a system boundary from sowing to harvesting (Fig. 2). It is assumed that the system boundary of cropping system is set from sowing of first season crop to harvesting of second season crop with excluding the free period between the harvesting of first season crop and sowing or transplanting of second season crop. In this study, GHG emissions included: (1) production, storage, transportation and application of fertilizers (N, P and K), plastic film and pesticides; (2) diesel fuel consumption for tillage and harvest; (3) electricity consumption for irrigation; and (4) direct CH4 and N2O

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Fig. 2. Life cycle inventory and system boundary for carbon footprint and net ecosystem carbon budget in early rice, later rice and spring maize production. The plastic film was used only for spring maize production in 2018.

emissions from soils during crop growth. Currently, GHG emissions from seeds were not considered due to their relatively small contribution to total GHG emissions.

2.4. C footprint calculation The C footprints of each crop and a certain cropping system were used to assess the intensity of GHG emissions in terms of grain yield using the following equations:

X ðAi  di Þ

(1)

Etotal ¼ Einput þ ECH4 þ EN2 O

(2)

Einput ¼

Etotal Y

(3)

P E CFb ¼ P i Yi

(4)

CFa ¼

where, Einput is the GHG emissions of the agricultural input during one crop production in a certain cropping system (kg CO2-eq ha1); Ai is the quantity of the ith individual agricultural input during one crop production in a certain cropping system; and di is the coefficient factors of an ith individual agricultural input. Data for Ai and di are shown in Table 2. Etotal is the sum of direct and indirect GHG emissions during one crop production in a certain cropping system (kg CO2-eq ha1); ECH4 and EN2 O are the amount of cumulative CH4 and N2O emissions converted to CO2-eq from soils during one crop production in a certain cropping system (kg CO2-eq ha1), respectively; CFa is the C footprint of one crop in a certain cropping system (kg CO2-eq kg1 grain); Y is the grain yield of one crop in a certain cropping system (kg ha1); CFb is the C footprint of certain cropping P system (kg CO2-eq kg1 grain); Ei is the total GHG emissions of a certain cropping system (kg CO2-eq ha1); Yi is the sum of the grain yield in a certain cropping system (kg ha1).

2.5. NECB The system boundary of NECB was remained the same with that for the C footprint. The NECBs of one crop and a certain cropping system were estimated using the following equations:

NECB ¼ Einput  Eoutput

(5)

Eoutput ¼ Etotal þ ECO2

(6)

where, Einput is the total C input calculated by the following Equation (7) (kg CO2-eq ha1); Eoutput is the total C emissions (kg CO2-eq ha1); ECO2 is the direct CO2 emissions from plant respiration and soil microbial respiration (kg CO2-eq ha1). The NECB of cropping system is the sum of NECB from two season crops in a certain cropping system (kg CO2-eq ha1).

2.6. Total C input Total C input based on C fixed in biomass was estimated using the following equation:

Einput ¼ Btotal  f c 

44 12

Btotal ¼ Bgrain þ Bstraw þ Broot þ Blitter þ Brhizodeposites

(7) (8)

where, Btotal is the sum C input, including grain (Bgrain, kg ha1), straw (Bstraw, kg ha1), root biomass (Broot, kg ha1), litter (Blitter, kg ha1), and rhizodepoist (Brhizodeposites, kg ha1). (Bgrain þ Bstraw)/ Broot ¼ 5.66 (rice) or 6.25 (maize) (Salam et al., 1997); Blitter/ (Bstraw þ Broot) ¼ 0.05 (rice and maize) (Huang et al., 2013; Kimura et al., 2004); Brhizodeposites/(Bstraw þ Broot) ¼ 0.15 (rice) or 0.11 (maize) (Gregory, 2006; Mandal et al., 2008). fc is the C percentage in grain (40% for rice; 44% for maize) (Dubery and Lal, 2009).

Z. Jiang et al. / Journal of Cleaner Production 258 (2020) 120643

2.7. Direct GHG emissions from soils Direct GHG emissions (CO2, CH4 and N2O) from soils were measured in each plot using a static chamber (50 cm  50 cm  90 cm) method during the entire crop growth period. In summary, CO2, CH4 and N2O fluxes were collected at 09:00 once per week. The grooved foundation of a static chamber enclosing nine rice seedlings or four maize seedlings was inserted into the soil (15 cm). The chambers were placed into the grooved foundation and filled with water. Gas samples were collected into 100 mL gas-sampling bags using a 60 mL syringe at 0, 10, 20 and 30 min after the chamber was closed. Samples were analyzed using a gas chromatograph (Agilent 7890A, Shanghai, China). GHG fluxes were estimated using the following equations (Rolston, 1986):

dc HMP F¼  dt R  ðT þ 273:2Þ EC ¼ a 

Xn F  1440  D i Þ ð i i 100

5

oxidation caused by aerobic conditions during spring maize season (Liu et al., 2015). The lower CH4 emissions in late rice of SMLR could be explained by the following evidences. Higher plant biomass promotes CH4 production due to higher rhizosphere C inputs enhancing methanotrophs growth (Liang et al., 2013). In addition, increased availability of soil organic matter (SOM) can trigger the growth and activity of methanotrophs, and then increase CH4 oxidation (Cai et al., 1997). ERLR conversion to SMLR accelerates SOM turnover in late rice, and then increased SOM availability (He et al., 2015). Therefore, the lower CH4 emissions in the late rice of SMLR were probably attributed to the increased CH4 production caused by higher rice biomass being counteracted by the increase of CH4 oxidation resulted from higher SOM availability. 3.2. Grain yield and C footprint

(9)

(10)

where, F is GHG emission rates (CO2, CH4 and N2O) (mg m2 min1); dc/dt is the rate of change of GHG concentration within 30 min; M, P, H, T, and R are the molecular weight of the gas (g mol1), atmospheric pressure (Pa), height of the chamber (m), temperature inside the static chamber (
C), and gas constant (8.134 g1 mol1 k1), respectively; 273.2 is the absolute temperature (K); EC is the CO2 emission equivalent of cumulative CO2, CH4 and N2O (kg CO2-eq ha1); a represents the coefficient of global warming potential in a 100-year horizon: CO2 (1), CH4 (34) and N2O (298) (IPCC, 2013); Fi is the emission rate of the ith sampling period of GHG (mg m2 min1); 1440 is the coefficient for converting minutes to days; Di is the number of days between the ith and i-1st sample; 1/100 is the coefficient for converting mg m2 into kg ha1; and n is the number of sampling points. 2.8. Data analysis Statistical analyses were performed using SPSS 22 (SPSS Inc., IL, Chicago, USA) and Origin 2018 (OriginLab Corporation, Northampton, MA, USA). A two-way ANOVA was conducted to assess the main effects of cropping systems and years, and their interaction on GHG emissions, grain yield, C footprint, direct CO2 emissions from soils, C input, C output and NECB using Duncan’s test at P ¼ 0.05. 3. Results and discussion 3.1. GHG emissions The GHG emissions for ERLR and SMLR ranged from 17.4 to 18.0 and 11.2e11.9 Mg CO2-eq ha1 over the two-year period (Table 3), respectively. Introduction of SMLR significantly decreased GHG emissions by 31.5e37.5% compared with ERLR (Table 3, Table 5). This result could mainly be attributed to the following two reasons: 1) the GHG emissions from spring maize production was 60.6e68.2% lower than in early rice due to lower agricultural input (e.g., zero electricity consumption for irrigation) and less CH4 emissions in spring maize production; and 2) introduction of SMLR reduced GHG emissions of late rice by 6.11e14.4% relative to that in ERLR mainly due to a reduction of CH4 emissions of late rice production in SMLR (Table 3). The CH4 emissions from field soils are mainly determined by CH4 production and oxidation (Linquist et al., 2015). Compared with early rice soils, the CH4 emissions from spring maize season were negligible (Table 3 and Fig. 3), as reported in previous studies (Lal et al., 2019), as a result of the increase in CH4

Grain yield varied from 13.4 to 13.5 Mg ha1 in ERLR and from 13.0 to 15.8 Mg ha1 in SMLR (Table 4). On average, the grain yield of SMLR exhibited an increase by 6.90% than ERLR (Table 4 and 5). However, grain yield of SMLR was slightly lower than that ERLR in 2017 (3.78% lower), the possible explanation was that the low temperature during initial stage of spring maize production resulted in a lower grain yield in spring maize than in early rice. In 2018, grain yield for SMLR recorded a significant increase by 17.6% than ERLR (Table 4). This was mainly attributed to the plastic film application for spring maize in 2018 which facilitated initial temperature maintaining for seedling growth and extended the period of the photosynthetic rate of maize, which increased maize growth and yield grain (Zhang et al., 2019). Moreover, grain yield of late rice in SMLR enhanced by 12.4e14.8% compared with that in ERLR over the two-year period (Table 3). Aerobic conditions during the production of spring maize accelerated SOM mineralization which improved the nutrient utilization rate of late rice, resulting in an increase in grain yield of the late rice (He et al., 2017; Weller et al., 2016). Taken together, these results suggest that higher crop productivity could be achieved under ERLR conversion to SMLR (see Table 5). The C footprints of rice (including early rice and late rice) and spring maize were 1.07e1.40 kg CO2-eq kg1 grain and 0.45e0.51 kg CO2-eq kg1 grain, respectively (Table 4). These results were in accordance with previous studies, Xue et al. (2016) reported that the C footprint of rice production ranged from 0.71 to 1.22 kg CO2-eq kg1 grain in China. Zhang et al. (2016) estimated the C footprint of spring maize production to be 0.48e1.12 kg CO2eq kg1 grain in south China. However, results from our study were higher than the 0.80 kg CO2-eq kg1 grain of rice production and 0.28e0.39 kg CO2-eq kg1 grain of maize recorded by Yan et al. (2015). These differences in C footprint may be mainly due to the difference from data sources, grain yield, the definition of system boundaries and agricultural inputs. The introduction of SMLR significantly reduced C footprint by 35.0e41.7% compared with ERLR ( Table 4 and 5). Higher GHG emissions do not necessarily result in larger C footprint, since the C footprint is dependent on grain yield. In SMLR, the lower GHG emissions outweighed the relatively lower grain yield in 2017 and the decrease of GHG emissions combined with the increase of grain yield in 2018, leading to a lower C footprint compared with that in ERLR (Tables 3 and 4). The results indicate that introduction of SMLR into ERLR system provides a feasible system to reduce the C footprint and increase crop productivity. Compared with early rice in ERLR, the C footprint of spring maize in SMLR was 60.1e64.5% lower, which was due to the lower agricultural input and direct CH4 emissions in spring maize production (Table 3). This result was supported by results from Yan et al. (2015), which reported that rice produced higher C footprint

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Z. Jiang et al. / Journal of Cleaner Production 258 (2020) 120643

Table 3 GHG emissions in the early riceelate rice (ERLR) and spring maizeelate rice (SMLR) system (kg CO2-eq ha1), 2017e2018. ER is the early rice; LR is the late rice; SM is the spring maize. Years

2017

Treatments

ERLR

SMLR

2018

ERLR

SMLR

Indirect input

ER LR ERLR SM LR SMLR ER LR ERLR SM LR SMLR

Direct input

Total GHG emissions

N

P2O5

K2O

Plastic film

Diesel

Electricity

Pesticides

CH4

N2O

1436 1436 2871 1436 1436 2871 1436 1436 2871 1436 1436 2871

33.8 33.8 67.7 54.0 33.8 87.8 33.8 33.8 67.7 54.0 33.8 87.8

25.4 25.4 50.8 29.1 25.4 54.6 25.4 25.4 50.8 29.1 25.4 54.6

0 0 0 0 0 0 0 0 0 375 0 375

571 571 1143 340 571 912 571 571 1143 340 571 912

1342 1508 2849 0 1508 1508 1342 1508 2849 0 1508 1508

182 210 392 98 210 308 182 210 392 98 210 308

5186 ± 512 4590 ± 334 9776 ± 849 378 ± 29.4 4064 ± 313 4442 ± 342 4260 ± 210 4890 ± 106 9150 ± 316 400 ± 34.5 4031 ± 200 4432 ± 220

379 ± 42.4 438 ± 36.7 818 ± 79.1 579 ± 49.4 467 ± 38.7 1047 ± 86 388 ± 34.7 447 ± 16.4 835 ± 35.5 859 ± 55.0 493 ± 12.8 1351 ± 41.3

9155 ± 554 8812 ± 374 17967 ± 923 2914 ± 78 8315 ± 352 11229 ± 425 8238 ± 235 9120 ± 116 17358 ± 350 3591 ± 53 8308 ± 192 11899 ± 243

Table 4 Carbon footprint of the early riceelate rice (ERLR) and spring maizeelate rice (SMLR) rotation system. Values are means ± standard error. ER is the early rice; LR is the late rice; SM is the spring maize. Grain yield (Mg ha1)

Years

Treatments Cropping systems

Crop types

2017

ERLR

ER LR ERLR SM LR SMLR ER LR ERLR SM LR SMLR

SMLR

2018

ERLR

SMLR

7.14 6.33 13.5 5.69 7.27 13.0 6.55 6.90 13.4 8.06 7.76 15.8

± ± ± ± ± ± ± ± ± ± ± ±

Carbon footprint (kg CO2-eq kg1 grain)

0.22 0.19 0.08 0.18 0.24 0.07 0.04 0.17 0.18 0.19 0.18 0.18

1.28 1.40 1.33 0.51 1.15 0.87 1.26 1.32 1.29 0.45 1.07 0.75

± ± ± ± ± ± ± ± ± ± ± ±

0.08 0.09 0.07 0.01 0.08 0.04 0.03 0.03 0.02 0.01 0.02 0.01

Table 5 Statistical analysis for the effects of cropping system (CS), year (Y) and their interactions (CSY) on GHG emissions, grain yield, C footprint, direct CO2 emissions from plant respiration and soil microbial respiration (ECO2), C output, C input, and net ecosystem carbon budget (NECB).

CS Y CSY a

GHG emissions

Grain yield

C footprint

ECO2

C output

C input

NECB

<0.001 ns a ns

<0.001 <0.001 <0.001

<0.001 ns ns

<0.001 ns ns

<0.001 ns <0.05

<0.001 <0.001 <0.001

<0.001 <0.01 <0.05

ns denotes not significant.

than maize based on farm survey data from eastern China. Notably, the grain yield of spring maize was reduced by 20.3% relative to early rice in 2017, however, its GHG emissions was 68.2% lower than early rice, inducing a 60.1% decrease of C footprint in spring maize production. Introduction of SMLR decreased the C footprint of late rice by 17.7e19.0% compared with that in ERLR (Table 4), which could be explained by the increasing grain yield and the decreasing GHG emissions in the late rice of SMLR (Tables 3 and 4). 3.3. Composition of C footprint In both ERLR and SMLR, CH4 emissions from field soil were the main component of the C footprint with accounting for 37.2e54.4% (Fig. 3). Early rice in ERLR and late rice in both cropping systems exhibited a similar trend of CH4 emissions. This result was consistent with the findings by Jiang et al. (2019) and Yan et al. (2015), which reported that CH4 emissions were responsible for most of the C footprint in rice production. However, the manufacture of N fertilizer (40.0e49.3%) accounted for the greatest contribution to the C footprint in spring maize production (Fig. 3). Cheng et al.

(2015) identified that GHG emissions related to the manufacture of N fertilizer contributed to more than 50% of the C footprint of upland crop production in China, this being mainly attributed to energy required in the manufacturing process due to burning coal (Zhang et al., 2013b). The related GHG emissions for this fertilizer (including transport emissions) are therefore nearly twice as high as those in developed countries (Zeng et al., 2009). Our results suggest that reducing GHG emissions from CH4 and N fertilizer production are critical to reducing the C footprint in both cropping systems. 3.4. NECB The NECB can provide a valuable tool to assess the short-term net C budget balance via C input and output in an agroecosystem (Smith et al., 2010). In both cropping systems, C input of three crops varied from 20.9 Mg CO2-eq ha1 to 41.0 Mg CO2-eq ha1. This result was sufficient to compensate for C outputs (17.1e26.7 Mg CO2-eq ha1), resulting in each crop production being a net C sink with a positive NECB (3.27e14.2 Mg CO2-eq ha1) (Fig. 4). This result was partially

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Fig. 3. Carbon footprint sources for early riceelate rice (ERLR) and spring maizeelate rice (ERLR). A and B is the composition of C footprint for each crop in 2017 and 2018, respectively. C is the composition of C footprint for a certain cropping system during 2017 and 2018. ER is the early rice; LR is the late rice; SM is the spring maize.

Fig. 4. The C input, C output (including GHG emissions as shown in Table 3 and direct CO2 emissions from plant respiration and soil microbial respiration) in each crop and a certain cropping system in 2017 and 2018. ER is the early rice; LR is the late rice; SM is the spring maize; ERLR is early riceelate rice; ERLR is spring maizeelate rice. Error bars in columns and white boxes represent standard error (n ¼ 3).

supported by results from our previous work which recorded a net C sink in rice production (Jiang et al., 2019), as well as by results which showed a net sink in maize production from Wang et al. (2015). However, a net C source in rice production was observed by Kim et al. (2017) and Jeong et al. (2019). Possible reasons could be the difference in system boundaries, local climate and management practices, which led to variation in C input and output. In our study, cropping system and year had profound impacts on NECB (Table 5). Introducing SMLR resulted in an increase of NECB by 80.1e147% (Fig. 4). Although introduction of SMLR increased C output by 9.61% and 26.8% (2017 and 2018, respectively), the increase of C input (by 15.3% in 2017 and 42.5% in 2018) outweighed the increase of C output in SMLR (Fig. 4). The higher NECB in spring maize than in early rice was mainly attributed to much greater C input counteracting C output in spring maize production. In the late rice of SMLR, the higher increase of C input than C output induced a greater NECB than in ERLR. In addition, the NECB of SMLR showed greater interannual variation (122% vs. 61.6%, as calculated by the value in 2018 relative to in 2017) than ERLR (Fig. 4), indicating SMLR cropping system having large potential of net C sequestration.

Results for SMLR recorded a 15.3e42.6% greater C input compared with ERLR (Fig. 4), this was result of higher C input in spring maize production than in early rice production and the increasing C input of late rice in SMLR. The higher C input in spring maize than early rice was mainly due to higher dry biomass and stronger photosynthesis of spring maize (Schmitt and Edwards, 1981) in spite of a decrease of grain biomass from spring maize in 2017. The increasing C input of late rice was observed in SMLR as explained above in grain yield of late rice. Jat et al. (2019) also recorded higher C input of late rice in SMLR relative to that in ERLR. The SMLR produced higher C output than ERLR. Although lower GHG emissions were observed in SMLR than in ERLR, the increase of direct CO2 emissions from the soils overweighed the decrease of GHG emissions. Compared with early rice, the increased C output in spring maize mainly resulted from the promotion of direct CO2 emissions from the soils. Meanwhile, the higher C output in late rice of SMLR mainly stemmed from the increasing SOM decomposition. The possible explain was that enhanced biomass of late rice in SMLR stimulated SOM decomposition by increasing root exudate (Zhu et al., 2018).

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For both cropping systems, direct CO2 emissions from the soils were the main pathway of C output (51.1e73.5%) (Fig. 4). This finding supported evidence from previous observations (e.g., Nishimura et al., 2015; Wu et al., 2018), suggesting that CO2 emissions from soils was a major contribution of terrestrial C budget. The contribution of direct CO2 emissions from soils to total C output increased from 51.1e52.2% in ERLR to 72.6e73.5% in SMRLR (Fig. 4), which indicated that soil C in SMLR was more vulnerable to decomposition losses. This result could be mainly explained by the fact that the use of spring maize as an upland crop increased direct CO2 emissions by 60.1e142% compared with early rice due to an improvement in aerobic conditions enhancing SOM decomposition (Nishimura et al., 2005; Weller et al., 2016), which was in line with previous report that rice paddy conversion to upland crop enhanced soil CO2 emissions (Breidenbach et al., 2016). However, no significant difference on SOC between ERLR and SMLR in the 2-year study (P > 0.05, Table S1). Weller et al. (2016) observed the opposite results that the introduction of upland crop rotations in the cropping system of ERLR induced a SOC loss. The SOC balance is depend on SOC input and output. This difference may be attributed to the greater SOC input (including root-C input and litter-C) offsetting SOC decomposition loss in spring maize production (He et al., 2015). Overall, these findings reveal that SMLR has the advantage of maintaining soil fertility from the perspective of SOC. In addition, a considerable interannual variation with increasing direct CO2 emissions after the introduction of SMLR was observed in 2017 (60.1%) and 2018 (142%) (Fig. 4), primarily due to the increasing spring maize biomass induced by the plastic film application used in 2018 (Zhang et al., 2019). The increase in maize biomass could stimulate SOM mineralization via an increase in the input of root exudates (Kumar et al., 2016). In addition, the application of the plastic film could improve microbial growth by enhancing ground temperature, thereby accelerating SOM mineralization (An et al., 2015). Although spring maize production caused more C outputs due to the increase of direct CO2 emissions and additional GHG emissions from plastic film input in 2018 relative to that in 2017, the greater C input of spring maize production sufficiently counteracted the increasing C output in 2018 (Tables 2 and 3 and Fig. 4). This effect resulted in a higher net C sink for spring maize production compared with that in 2017 (Fig. 4). 3.5. Limitations and implications In our study, the C footprint was assessed in accordance with the PAS 2050 standard (BSI and Carbon Trust, 2011). The PAS 2050 protocol, which has been extensively used, suggests that the C footprint calculation must include GHG emissions deriving from direct land-use changes, and it should not include SOC changes in agroecosystems (BSI, 2008). However, whether C emissions due to changes in SOC are included in the C footprint calculation is still unclear (Gan et al., 2012; Qi et al., 2018). As management practices that change soil C can also play an essential role in offsetting C emissions and impacts on environmental quality, Ostle et al. (2009) recorded that C gains or losses from soils resulting from farmland management practices should be included in the C footprint calculation. In some agroecosystems, the inclusion of SOC change in the C footprint assessment could result in the C footprint changing from positive to negative values (Gan et al., 2014). Thus, it appears that changes in soil SOC should be considered when calculating the C footprint of crop production. However, SOC change was not included in the C footprint calculation in this analysis due to no significant change of SOC being recorded in both cropping systems (Table S1). In this case,

exclusion of SOC change in the calculation of C footprint would not change the main conclusion of our study. Further investigations are needed to address whether changes in soil C should be included in future C footprint calculations. In the present work, our results indicate that introduction of SMLR in ERLR not only produced higher productivity and lower energy consumption, it also mitigated GHG emissions and increased the net C sink. Considering the future conditions being increasingly challenged by global warming and food demand (Reichstein et al., 2013; Tilman et al., 2011), our results highlight that the SMLR could be an alternative cropping system in the planting area of ERLR. Although the site experiment was located in the typical region of paddy rice cultivation in China, there are still some uncertainties in the assessment of C footprint and NECB for ERLR and SMLR at a regional scale in China due to soil and spatial heterogeneity. Therefore, regional scale and long-term experiments are needed to gain comprehensive scientific information on C footprint and NECB. Our results also highlight that GHG emissions derived from CH4 emissions from paddy fields and the production of N fertilizers contributed to the largest proportion of the C footprint in rice and maize production, respectively. This finding implies that reducing GHG emissions from CH4 emissions from paddy fields and the manufacture of N fertilizers provides strategies for obtaining a synergies between mitigating GHG emissions and increasing net C sink during crop production. Previous studies have shown that practices using no-till methods can not only significantly decrease CH4 emissions in paddy fields (Zhang et al., 2013a), but also reduced the energy consumption of mechanical diesel. Improving the production processes of N fertilizers (Zhang et al., 2013b) and changing the type of N fertilizers used with slow-release biochar-based fertilizers (Qian et al., 2014) offer further mitigation options to reduce GHG emissions.

4. Conclusions The GHG emissions were decreased by 31.5e37.5% under ERLR conversion to SMLR, resulting from reduced GHG emissions of spring maize (e.g., lower CH4 emissions in field soils and zero electricity consumption for irrigation) and late rice in SMLR (mainly by lower CH4 emissions from field soils). Introducing SMLR caused an increase by 6.90% in average annual grain yield. A 35.1e41.7% lower of C footprint was observed in SMLR due to lower C footprint of spring maize than early rice and the decreased C footprint of late rice in SMLR. Compared with ERLR, introduction of SMLR significantly increased the positive NECB by 80.1e147% because of increased C input outweighing the increased C output via direct CO2 emissions from soils in SMLR. These results suggest that introduction of SMLR could be an alternative cropping system with reducing C footprint and increasing net C sink whilst ensuring high crop yield. Considering greater interannual variation of NECB in the SMLR cropping system, further research is required to assess the long-term effects of NECB in SMLR.

CRediT author statement Zhenhui Jiang and Jingping Yang: Conceptualization, Methodology. Zhenhui Jiang, Jingdong Lin, Yizhen Liu, and Chaoyang Mo: Data collection and analysis. Zhenhui Jiang: Writing- Original draft preparation. Jingping Yang: Supervision. Zhenhui Jiang, Jingdong Lin, Yizhen Liu, Chaoyang Mo, and Jingping Yang: Writing- Reviewing and Editing.

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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research was financially supported by the National Key Research and Development Program of China (No. 2016YFD0300203-4). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2020.120643. References Agus, F., Andrade, J.F., Rattalino Edreira, J.I., Deng, N., Purwantomo, D.K.G., Agustiani, N., Aristya, V.E., Batubara, S.F., Herniwati, Hosang, E.Y., Krisnadi, L.Y., Makka, A., Samijan, Cenacchi, N., Wiebe, K., Grassini, P., 2019. Yield gaps in intensive rice-maize cropping sequences in the humid tropics of Indonesia. Field Crop. Res. 237, 12e22. An, T., Schaeffer, S., Li, S., Fu, S., Pei, J., Li, H., Zhuang, J., Radosevich, M., Wang, J., 2015. Carbon fluxes from plants to soil and dynamics of microbial immobilization under plastic film mulching and fertilizer application using 13C pulselabeling. Soil Biol. Biochem. 80, 53e61. Breidenbach, B., Blaser, M.B., Klose, M., Conrad, R., 2016. Crop rotation of flooded rice with upland maize impacts the resident and active methanogenic microbial community: crop rotation impacts microbial community. Environ. Microbiol. 18, 2868e2885. BSI and Carbon Trust, 2011. Specification for the Assessment of the Life Cycle Greenhouse Gas Emissions of Goods and Services, p. 36. Publicly Available Specification-PAS 2050:2011. London, UK. BSI, 2008. PAS 2050: 2008. Specification for the Assessment of Life Cycle GreenHouse Gas Emissions of Goods and Services. British Standards Institute, London, UK. Cai, Z., Xing, G., Yan, X., Xu, H., Tsuruta, H., Yagi, K., Minami, K., 1997. Methane and nitrous oxide emissions from rice paddy fields as affected by nitrogen fertilisers and water management. Plant Soil 196, 7e14. Chen, S., Lu, F., Wang, X., 2015. Estimation of greenhouse gases emission factors of China’s nitrogen, phosphate and potash fertilizers. Acta Ecol. Sin. 35, 6371e6383 (In Chinese with English abstract). Cheng, K., Yan, M., Nayak, D., Pan, G.X., Smith, P., Zheng, J.F., Zheng, J.W., 2015. Carbon footprint of crop production in China: an analysis of National Statistics data. J. Agric. Sci. 153, 422e431. Dubey, A., Lal, R., 2009. Carbon footprint and sustainability of agricultural production systems in Punjab, India, and Ohio, USA. J. Crop Improv. 23, 332e350. Energy Source, China, 2009. 7e16 Energy Consumption Per Unit of Product in Main Enterprises that Consume Much Energy, p. 224 (In Chinese). FAO, 2016a. FAOSTAT. Online available: http://www.fao.org/faostat/en/#compare. (Accessed 18 August 2017). FAO, 2016b. Save and Grow in Practice: Maize, Rice, Wheat-A Guide to Sustainable Cereal Production. FAO, Rome IT, p. 124. FAO, 2017. Rice market monitor. Online available: http://www.fao.org/economic/ RMM. (Accessed 20 December 2017). FAO, IFAD, UNICEF, WFP, WHO, 2017. The State of Food Security and Nutrition in the World 2017. Building Resilience for Peace and Food Security. Food and Agriculture Organization of the United Nations, Rome, p. 117. Gan, Y., Liang, C., Campbell, C.A., Zentner, R.P., Lemke, R.L., Wang, H., Yang, C., 2012. Carbon footprint of spring wheat in response to fallow frequency and soil carbon changes over 25 years on the semiarid Canadian prairie. Eur. J. Agron. 43, 175e184. Gan, Y., Liang, C., Chai, Q., Lemke, R.L., Campbell, C.A., Zentner, R.P., 2014. Improving farming practices reduces the carbon footprint of spring wheat production. Nat. Commun. 5, 5012. Gregory, P., 2006. Roots, rhizosphere and soil: the route to a better understanding of soil science? Eur. J. Soil Sci. 57, 2e12. He, Y., Lehndorff, E., Amelung, W., Wassmann, R., Alberto, Ma C., von Unold, G., Siemens, J., 2017. Drainage and leaching losses of nitrogen and dissolved organic carbon after introducing maize into a continuous paddyerice crop rotation. Agric. Ecosyst. Environ. 249, 91e100. He, Y., Siemens, J., Amelung, W., Goldbach, H., Wassmann, R., Alberto, MaC.R., Lücke, A., Lehndorff, E., 2015. Carbon release from rice roots under paddy rice and maizeepaddy rice cropping. Agric. Ecosyst. Environ. 210, 15e24. Heimann, M., Reichstein, M., 2008. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289e292. Huang, J., Chen, Y., Sui, P., Gao, W., 2013. Estimation of net greenhouse gas balance

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