Carbon and nitrogen footprint of double rice production in Southern China

Ecological Indicators 64 (2016) 249–257

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Carbon and nitrogen footprint of double rice production in Southern China Jian-Fu Xue a , Chao Pu a , Sheng-Li Liu a , Xin Zhao a , Ran Zhang a , Fu Chen a , Xiao-Ping Xiao b , Hai-Lin Zhang a,∗ a b

College of Agronomy and Biotechnology, China Agricultural University, Key Laboratory of Farming System, Ministry of Agriculture, Beijing 100193, China Hunan Soil and Fertilizer Institute, Changsha 410125, China

a r t i c l e

i n f o

Article history: Received 23 January 2015 Received in revised form 22 November 2015 Accepted 4 January 2016 Keywords: Double rice Carbon footprint Nitrogen footprint Life cycle assessment

a b s t r a c t Agriculture plays an important role in greenhouse gases (GHGs) emissions and reactive nitrogen (Nr) loss. Therefore, carbon (C) and nitrogen (N) footprint reductions in agro-ecosystem have become an increasingly hot topic in global climate change and agricultural adaptation. The objective of this study was to assess the C footprint (CF) and N footprint (NF) of double rice (Oryza sativa L.) production using life cycle assessment method in Southern China. The results showed that fertilizer application and farm machinery operation contributed the most to both GHGs and Nr emissions from agricultural inputs in the double rice production process. The CF for the early, late, and double rice was 0.86, 0.83, and 0.85 kg CO2 -eq kg−1 year−1 at yield-scale, respectively. In addition, the NF was 10.47, 10.89, and 10.68 g Neq kg−1 year−1 at yield-scale for the early, late and double rice, respectively. The largest fraction of CF and NF of double rice was the share of CH4 emission and NH3 volatilization from the paddy field, respectively. Higher CF and NF at yield-scale for Guangdong, Guangxi, and Hainan provinces were presented, compared to the average level in double rice cropping for the region, while smaller than those of Jiangxi, Hubei, and Hunan provinces. Some effective solutions would be favorable toward mitigating climate change and eutrophication of the double rice cropping region in Southern China, including reduction of fertilizer application rates, improvements in farm machinery operation efficiencies, and changes in regional allocation of double rice cropping areas. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Anthropogenic environmental degradation (e.g., climate change, water eutrophication, acid rain) seriously threaten the well-being of humankind and other organisms on our planet. In recent decades, climate change has created numerous risks for natural and human systems, due to anthropogenic greenhouse gases (GHGs) emissions such as carbon dioxide (CO2 ), methane (CH4 ) and nitrous oxide (N2 O) (Stocker et al., 2013). Agriculture is one of the principal contributors to anthropogenic GHGs emissions, especially non-CO2 emissions (i.e., CH4 and N2 O emission) (Stocker et al., 2013). Meanwhile, synthetic nitrogen (N) fertilizers are widely applied in agricultural ecosystems to meet the food consumption demands associated with the rapid global population increase. However, excess N fertilizer’s reactive nitrogen (Nr, and all N forms except N2 ) is lost to air, water, and land, and can cause a cascade of

∗ Corresponding author. Tel.: +86 1062733376; fax: +86 1062733316. E-mail address: [email protected] (H.-L. Zhang). http://dx.doi.org/10.1016/j.ecolind.2016.01.001 1470-160X/© 2016 Elsevier Ltd. All rights reserved.

environmental changes (e.g., water eutrophication, smog, acid rain, stratospheric ozone depletion, biodiversity loss) (Galloway et al., 2008). In addition, the indirect effects of secondary air pollutants (e.g., secondary particulate matter) from Nr deposition are more of a concern to human health and surrounding ecosystems (Moldanová et al., 2011). Quantifying and assessing the magnitude of the impacts of carbon (C) and Nr emissions on agro-ecosystems could facilitate a potential solution to mitigate climate change and further environmental issues, and be helpful in raising awareness and decision-making concerning environment-friendly technological development for the general public and policy makers. In recent decades, various footprint-style indicators, such as C footprint (CF), water footprint (WF), N footprint (NF), have been adopted to further understand how humanity exerts pressures on the environment (Fang et al., 2014). The CF concept was introduced to quantify the sum of GHGs emissions and removals as CO2 equivalent (CO2 -eq) in a product system, in order to assess and mitigate climate change (ISO, 2013). The NF is an indicator expressing the total amount of Nr lost to the environment due to human activities (Leach et al., 2012). Further studies have recently assessed the CF of

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field crops under different agricultural practices (Gan et al., 2011). Gan et al. (2014) quantified the CF of alternative wheat production systems suited to semiarid environments. Xue et al. (2014) assessed the CF of double rice (Oryza sativa L.) production under different tillage practices in Southern China. Xu et al. (2013) estimated the CF of rice production through the life cycle assessment (LCA) method in five typical rice cultivation regions in China. In addition, previous studies have aimed to find ways to assess the NF relative to the average per capita per country (Gu et al., 2013; Stevens et al., 2014) and food product types (Pierer et al., 2014; Xue and Landis, 2010; Leip et al., 2014). However, the NF in those reports using input–output methods are based on “virtual N factor” and only estimated N nutrient fluxes, rather than assessing the specific environmental impact of diverse Nr forms. Recently, the LCA method has been widely applied to quantitatively evaluate environmental impacts, such as the CF of a product. However, scarce information regarding NF calculated through LCA has been presented. In order to evaluate the environmental impact associated with Nr emissions in the entire stages of double rice production, LCA method was adopted in the study. Thus, the assessment of both CF and NF of agricultural products (e.g., food staples) is necessary to quantify the impacts of human activities on the environment. In China, rice is a chief food staple. Double cropped rice, consisting of the early and late rice, is one of the most principal cropping systems in Southern China. Generally, the early and late rice are manually transplanted in standing water by manually throwing of rice seedlings in April and July, and combine harvested in July and October, respectively. A large number of agricultural inputs are applied in the process of double rice cultivation, e.g., various fertilizers, pesticides, diesel, and film. Double rice, accounting for ∼45% (∼13.5 million hectare, Mha) of the total rice planting area and ∼40% (∼80 million tons, Mt) of the total rice yield, contributes significantly to national food security in China (Bai, 2013). Paddy fields are one of the most predominant CH4 emission sources globally, emitting approximately 493–723 Mt CO2 -eq year−1 in 2010 (FAO, 2013). In addition, China is the greatest consumer of N fertilizer alt ∼45 Mt, accounting for ∼37.6% of world consumption in 2012 (FAO, 2013). As well, about 37% of world N fertilizer applied for rice production was consumed in China (Peng et al., 2002). Rice produced in China exhibits a low N use efficiency (NUE) at only 35% (MAPRC, 2013), with remaining Nr lost to the environment and contributing to soil, air and water pollution. There have been investigations concerning the CF of Chinese agriculture, however, little information exists concerning the CF assessment of staple grain productions (e.g., rice), and no information has been presented about the NF of food staples using the LCA approach. Quantitative assessments of CF and NF for double rice production are essential in mitigating climate change and reducing environmental pollution. The objectives of this study were to (1) evaluate CF and NF of double rice production, (2) assess the composition of CF and NF for double rice production, and (3) understand the regional distribution of CF and NF for double rice produced in Southern China. Here, the potential contribution of double rice production to global warming was expressed as CO2 -eq by quantifying all GHGs emissions and removals over the whole life cycle and growing stages of double rice production. In addition, the eutrophication potential caused by Nr losses was computed by N-eq in double rice production systems. Moreover, the distribution of CF and NF was analyzed from various provinces in Southern China. 2. Materials and methods 2.1. Study region Generally, double rice, including the early and late rice, is cultivated in Southern China. The annual average yields of double rice

Fig. 1. The distribution of study regions. Table 1 The annual mean precipitation, temperature and sunshine duration for study region during 1980–2012. Provinces

Prep (mm)a

Tavg (◦ C)

Tmin (◦ C)

Tmax (◦ C)

SD (h)

Anhui Hubei Zhejiang Jiangxi Hunan Fujian Guangdong Guangxi Hainan

1205.0 1161.1 1465.7 1694.1 1431.7 1607.8 1752.5 1617.4 1684.9

15.4 16.1 16.9 17.7 17.0 18.8 22.0 21.2 25.1

11.6 12.5 13.8 14.3 13.8 15.6 18.9 18.2 22.5

20.2 21.0 21.0 22.5 21.4 23.6 26.3 25.7 29.0

1881.2 1645.5 1788.3 1641.0 1445.6 1700.9 1781.7 1552.4 2242.0

a Prep was the annual mean precipitation for each province during 1980–2012. Tavg , Tmin and Tmax were the annual mean value of average, minimum and maximum temperature for each province during 1980–2012, respectively. SD was the annual sunshine duration for each province during 1980–2012. The above data sourced from the China Meteorological Data Sharing Service System (CMDSSS, 2014).

were 51–67% higher than those of single rice from 1964 to 2007 in China (Zhu, 2010). The primary double rice production regions, including Zhejiang, Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, and Hainan provinces, were analyzed due to a lack of data from others provinces in this study (Fig. 1). The agro-ecological conditions (e.g., precipitation, temperature, sunshine duration) for the above provinces are showed in Table 1. The annual mean precipitation ranged from 1161.1 to 1752.5 mm for those provinces. The annual values among provinces ranged from 15.4 to 25.1 ◦ C for average temperature, 11.6 to 22.5 ◦ C for minimum temperature, and 20.2 to 29.0 ◦ C for maximum temperature. The annual sunshine duration ranged from 1445.6 to 2242.0 h for different regions. 2.2. System boundaries and functional units The GHGs emissions and Nr losses from agricultural inputs and paddy fields were assessed for the entire production chain of double rice (both early and late rice). The system boundaries of this study included the entire stage of double rice production from raw material acquisition of agricultural inputs, field agricultural production processes to farm gate (rice harvest). The GHGs and Nr emissions included the following: (1) production, storage, and transportation of agricultural inputs (e.g., seeds, films, synthetic fertilizers, pesticides) to the farm gate, and application; (2) energy consumption

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from farm machinery operation (e.g., tillage, irrigation, harvest); (3) total CH4 and N2 O seasonal emissions from paddy fields, as well as NH3 volatilization, and NO3 − and NH4 + leaching during double rice growing periods. Generally, most of double rice is sold as a commodity by individual farmers after harvest. Therefore, the equivalent rice grain yields (kg CO2 -eq kg−1 year−1 and g Neq kg−1 year−1 ) are defined as the functional units in this study. 2.3. Inventory data collection The relative inventory database was compiled on an annual double rice production basis from 2004 to 2012, which was collated from national statistical reports and related literature. The agricultural inputs and rice yields were directly or indirectly collected from the National Cost-Benefit Survey for Agricultural Product (NCBSAP, 2005–2013) from 2004 to 2012 in China. Synthetic fertilizers consisted of N, P2 O5 , K2 O, and compound fertilizers. Data for double rice planting areas of different provinces was obtained from the China Rural Statistical Yearbook (CRSY, 2005–2013) series throughout the period of 2004–2012. The growing periods of the early and late rice for different provinces were calculated through the duration between the transplanting and harvest stage, sourced from the China Meteorological Data Sharing Service System (CMDSSS, 2014). The energy consumption consisted primarily of diesel used for farm machinery operation during the double rice growing seasons. The farm machinery operations required for double rice production primarily include tillage, rice-seedling throwing, harvesting, and transportation associated with machinery operations. The amount of diesel required was estimated by dividing the direct diesel cost by the price of diesel per kilogram (kg) each year. According to Cui et al. (2011), it was hypothesized that the direct diesel cost accounted for ∼21% of total farm machinery operational expenses. The total expenses, which can be determined from the current dataset, included additional service costs (e.g., maintenance, repair, labor). Typically, diesel-powered pumping is the predominant irrigation method in these regions. Thus, diesel consumptions for irrigation and drainage were included with that of farm machinery operation. The diesel use was computed using the same method as previously described for machine operation. The applied pesticide amounts were derived from published literature (He, 1999). The emission factors of GHGs and Nr for all agricultural inputs (e.g., synthetic fertilizers, pesticides, diesel) were obtained from the IKE eBalance v3.0 (IKE Environmental Technology CO., Ltd, China), including the Chinese Life Cycle Database, European reference Life Cycle database and Ecoinvent database.

The CF of rice is determined by dividing all GHGs emissions from agricultural inputs and non-CO2 GHGs emissions (i.e., CH4 and N2 O) from paddy fields by grain yield for the early, late, and double rice. Estimation of CF of double rice was calculated using the following Eq. (1) (ISO, 2013; Gan et al., 2011): (1)

where CFy is the total CF for each kg of rice grain produced, including the early, late, and double rice (kg CO2 -eq kg−1 ha−1 year−1 ); Y is the rice yield (kg ha−1 year−1 ); CEtotal is the total GHGs emissions associated with the entire life cycle of rice production (kg CO2 eq ha−1 year−1 ). CEtotal = CEinputs + CECH4 + CEN2 O

(2)

CEinputs =

(3)

 m

(Qusedm × ım )

where CEinputs is the indirect total amount of GHGs emissions associated with agricultural inputs (kg CO2 -eq ha−1 year−1 ); CECH4 and CEN2 O are the cumulative amounts of direct CH4 and N2 O emissions from paddy fields, converted to CO2 equivalent (kg CO2 eq ha−1 year−1 ) during the double rice growing period; Qusedm is the amount of the mth individual agricultural input applied during double rice production (kg ha−1 year−1 ), including synthetic fertilizers, diesel, film, pesticides and seed; ım is the specific emission factor of an individual agricultural input to GHGs emissions, including those manufactured and/or applied (kg CO2 -eq kg−1 ). The CH4 and N2 O emissions from paddy fields were estimated according to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006). The CH4 emissions from rice cultivation were estimated using Eq. (4): ECH4 = EFijk × tijk × 25

(4)

where ECH4 is the annual methane emission per unit of rice cultivation (kg CO2 -eq ha−1 year−1 ); EFijk is a daily emission factor for i, j, and k conditions from Eq. (5) (kg CH4 ha−1 day−1 ); tijk is the rice cultivation period for i, j, and k conditions (day); i, j, and k represent different ecosystems, water regimes, type and amount of organic amendments, and additional conditions under which CH4 emissions from rice production may vary; 25 is the global warming potential (GWP) of CH4 relative to CO2 , over a 100-year horizon. EFijk = EFc × SFw × SFp × SFo

(5)

where EFijk is the adjusted daily emission factor under common cultivation conditions for double rice in China; EFc is the baseline emission factor for continuously flooded fields without organic amendments, 1.30 kg CH4 ha−1 day−1 ; SFw is the scaling factor to account for the differences in water regime during the cultivation period, there was generally a midseason aeration at tillering stage during the rice cropping season in China (except for end-season drainage at harvest) and the scaling factor is 0.60 for the specific case; SFp is the scaling factor to account for the differences in water regime in the pre-season prior to the cultivation period, the scaling factor is 1 for the disaggregated case because the non-flooded pre-season is <180 days under double cropping rice production; SFo is the scaling factor which varies for both type and amount of organic amendment applied from Eq. (6); it was assumed that rice straw was the main organic amendment in this study area, and the amount of straw retention was half of that from the previous year.

 SFo =

1+



0.59 ROAi × CFOAi

(6)

i

2.4. Carbon footprint calculation

CEtotal CFy = Y

251

where ROAi is the application rate of organic amendment from Eq. (7) (t ha−1 ); CFOAi is the conversion factor for organic amendment i; 1 is the conversion factor of different types of organic amendment to CH4 emission, when rice straw is incorporated into the soil at <30 days before cultivation under double rice cropping conditions. ROAi = Y × 0.623 × 0.5 × 0.85 × 0.001

(7)

where Y is the yield of double rice (kg ha−1 year−1 ), 0.623 is the residue/grain ratio of rice (Li et al., 1998), 0.5 is the coefficient of rice straw retention that indicates the percentage of the amount of straw retention to total straw at the present technological level (Liu et al., 2001), 0.85 is the conversion coefficient from fresh weight to dry weight for rice straw (Lu et al., 2010), 0.001 is a unit conversion factor (t kg−1 ). The direct N2 O emissions from paddy fields were estimated using the follow Eq. (8): EN2 O = FN-input × ε ×

44 × 298 28

(8)

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where FN-input is the quantity of N applications sourced from N fertilizers and compound fertilizers in double rice production (kg N year−1 ); ε is the emission factor of N2 O emissions from paddy fields due to N applications, 0.003 kg N2 O-N kg−1 of N; 44/28 is the molecular weight ratio of N2 O to N2 O-N; 298 is the global warming potential (GWP) of N2 O relative to CO2 over a 100-year horizon. Common products (N:P2 O5 :K2 O = 15:15:15) were the main varieties of compound fertilizers generally available in Chinese markets (Zhang et al., 2009), the pure N concentrations of compound fertilizers were calculated according to the above percentages. 2.5. Calculation of nitrogen footprint In this study, eutrophication potential was chosen to characterize eutrophication releases to the air, water and soil, and to assess the impact associated with Nr emissions and losses during double rice production. And, the NF of rice produced was calculated according to ISO 14044 (ISO, 2006). NFy =

NEtotal Y

NEtotal = NEinputs + NVNH3 + NEN2 O + NLNO3 + NLNH+ NEinputs =

(10)

4

Qusedm × m

(11)

m

where NEinputs have equivalent meanings as Eqs. (10) and (11), which is the indirect total amount of Nr emissions associated with agricultural input applications through multiplying the factual use amount of kinds of agricultural inputs (Qusedm ) by those emission factors (m ) from IKE eBalance v3.0 (IKE Environment Technology CO., Ltd, China) (g N-eq ha−1 year−1 ); Qusedm is the amount of an mth individual agricultural input applied in double rice production (kg ha−1 year−1 ), including synthetic fertilizers, diesel, film, pesticides, and seed; m is the specific emission factor of an individual agricultural input to Nr emissions, including manufactured and/or applied (g N-eq kg−1 ). The Nr emission from foreground system included NH3 volatilization, N2 O emission, NO3 − and NH4 + leaching, which was calculated through multiplying pure N use amount by relative loss coefficient, and converted to the value of eutrophication potential through multiplying eutrophication potential factors based on internationally published manuals (Eqs. (12)–(15)). Here, NVNH3 is the volatilization loss of NH3 from paddy fields due to N application, which was roughly estimated by Eq. (12) (g N-eq ha−1 year−1 ); NEN2 O is the cumulative amount of direct N2 O emissions due to fertilizer applications according to Eq. (13) (g Neq ha−1 year−1 ); Terms NLNO− and NLNH+ are the rate of NO3 − and 3

4

NH4 + leaching from paddy fields, respectively, calculated by Eqs. (14) and (15); EVNH3 = FN-input ×  × EN2 O = FN-input × ε ×

17 × 0.833 × 1000 14

44 × 0.476 × 1000 28

(12) (13)

NLNO− = FN-input ×  ×

62 × 0.238 × 1000 14

(14)

NLNH+ = FN-input ×  ×

18 × 0.786 × 1000 14

(15)

3

4

3. Results 3.1. Greenhouse gases and reactive nitrogen emissions from agricultural inputs

(9)

where NFy is the total NF for each kg of rice grain yield (g Neq kg−1 ha−1 year−1 ); NEtotal is the total Nr emission associated with the entire life cycle of double rice production according to Eq. (10) (g N-eq ha−1 year−1 ), which includes the Nr emission during the process of production of kinds of agricultural inputs (background system) and during the process of rice cropping in the field (foreground system); Y has equivalent meanings as in Eq. (1).



where FN-input and ε have equivalent meanings as Eq. (8); ϕ is the NH3 volatilization loss coefficient, 0.338 for the early and 0.362 for late rice (Wang et al., 2012);  and  are the NO3 − and NH4 + leaching coefficients, 0.26 and 0.85 sourced from the handbook for fertilizer leaching coefficients in China, respectively; 17/14, 62/14 and 18/14 are the molecular weight ratios of NH3 to NH3 -N, NO3 − to NO3 -N, and NH4 + to NH4 + -N, respectively; 0.833, 0.476, 0.238 and 0.786 are eutrophication potential factors of NH3 (kg N-eq kg−1 of NH3 ), N2 O (kg N-eq kg−1 of N2 O), NO3 − (kg N-eq kg−1 of NO3 − ) and NH4 + (kg N-eq kg−1 of NH4 + ), respectively, those related applied eutrophication potential factors were sourced from the CML2002 methodology (Guinée et al., 2002); 1000 is a unit conversion factor (g kg−1 ).

Double rice production was primarily distributed throughout nine provinces in Southern China (Table 2). From 2004 to 2012, the total planting area was 578.2 × 104 and 622.7 × 104 ha year−1 for the early and late rice, respectively. The total planting area from Jiangxi, Hunan, Guangxi, and Guangdong provinces accounted for ∼80% of total double rice planting area. The planting areas of the late rice were larger than those of the early rice in all provinces. The average yields from all regions were 5965.2, 6111.8, and 12,077.0 kg ha−1 year−1 for the early, late and double rice during the period, respectively. The average yields ranged from 5603.6 to 6317.2 kg ha−1 year−1 for the early and 4064.9 to 7059.4 kg ha−1 year−1 for late rice and 9668.5 to 13,005.2 kg ha−1 year−1 , respectively. The yields for the early were greater than that for late rice in Fujian, Guangdong, Guangxi, and Hainan provinces, and the opposite for others provinces. The life cycle inventory dataset, consisting of agricultural inputs and paddy fields, was presented, in detail, based on the above defined system boundaries (Table 3). Based on the above dataset, the average GHGs and Nr emissions from agricultural inputs were calculated and analyzed in Table 4. For double rice production, the average GHGs emission from agricultural inputs was slightly larger for the early than for late rice (Table 4). The agricultural inputs contributed 2054.5, 1811.0, and 3865.4 kg CO2 -eq ha−1 year−1 of GHGs emissions for the early, late, and double rice, respectively. The GHGs emissions of synthetic fertilizers (including N fertilizers, P2 O5 fertilizers, K2 O fertilizers, and compound fertilizers) were the most significant fractions of the total agricultural inputs, accounting for 76.51, 80.85, and 78.55% for the early, late, and double rice, respectively. The GHGs emissions amount from diverse forms of synthetic fertilizers followed the order: N fertilizers > compound fertilizers > P2 O5 fertilizers > K2 O fertilizers for both early and late rice. In addition, GHGs emissions from N and P2 O5 fertilizers were higher for the early than late rice, but larger that for the late than early rice for other fertilizers. Following synthetic fertilizers, farm machinery operation was the second largest contributor to GHGs emissions, accounting for 11.10, 13.59, and 12.27% for the early, late, and double rice, respectively. Meanwhile, greater GHGs emissions from farm machinery operation were observed for the late than for early rice. The GHGs emissions from seeds and films were significantly greater for the early than for late rice. The GHGs emissions from pesticides, associated with herbicides, insecticides, and fungicides, were lowest, only accounting for 1.50, 2.22, and 1.84% for the early, late, and double rice, respectively. Differences on Nr emissions from agricultural inputs were observed when comparing with GHGs emissions (Table 4). The Nr emissions related to agricultural inputs for the early rice

J.-F. Xue et al. / Ecological Indicators 64 (2016) 249–257

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Table 2 The average planting area and yield of double rice from nine cropped provinces of Southern China during 2004–2012. Provinces

Early rice

Late rice

Double rice

Planting area (104 ha year−1 )

Yield (kg ha−1 year−1 )

Planting area (104 ha year−1 )

Yield (kg ha−1 year−1 )

Zhejiang Anhui Fujian Jiangxi Hubei Hunan Guangdong Guangxi Hainan

12.3 27.1 23.0 135.7 35.1 134.7 95.8 100.9 13.5

5905.2 5776.2 6317.2 5891.5 6233.2 5892.4 6006.1 6050.4 5603.6

15.7 28.4 25.0 146.9 41.4 142.1 103.1 102.0 17.9

6695.2 7059.4 6189.5 6316.9 6772.0 6239.9 5829.9 5549.5 4064.9

28.1 55.5 48.0 282.7 76.4 276.8 199.0 202.9 31.4

12,600.4 12,835.6 12,506.6 12,208.4 13,005.2 12,132.3 11,836.0 11,599.9 9668.5

Sum Average

578.2 –

– 5965.2

622.7 –

– 6111.8

1200.8 –

– 12,077.0

Planting area (104 ha year−1 )

Yield (kg ha−1 year−1 )

Dataset of planting area for double rice sourced from the China Rural Statistical Yearbook series during 2004–2012. Dataset of yield for all provinces sourced from the National Cost-Benefit Survey for Agricultural Product during 2004–2012.

Table 3 Life cycle inventory for double rice production in Southern China during 2004–2012 (kg ha−1 year−1 ). Early rice

Late rice

Double rice

Agricultural inputsa Seed Film N fertilizerb P2 O5 fertilizerb K2 O fertilizerb Mixed and compound fertilizerb Farm machinery operationc Herbicided Insecticided Fungicided

47.20 6.03 422.37 173.43 57.61 253.04 45.74 0.05 1.60 0.35

23.83 0.73 406.46 120.88 61.58 265.33 49.37 0.06 2.09 0.46

71.03 6.77 828.83 294.31 119.18 518.37 95.07 0.11 3.69 0.81

Paddy fielde CH4 NH3 N2 O NO3 − NH4 +

112.80 71.31 0.82 2.00 1.90

121.47 76.34 0.82 2.00 1.90

234.26 147.65 1.64 4.00 3.80

a The data of agricultural inputs indicated average value of nine provinces for double rice cropped during 2004–2012. Actual application amount of agricultural inputs were presented here, and the relevant data derived from the National CostBenefit Survey for Agricultural Product during 2004–2012. b Application of fertilizers were calculated through dividing the concentration of effective components for all sorts of fertilizers by the corresponding percentage of effective components. Thereinto, all fertilizers consisted of N, P2 O5 , K2 O, and mixed and compound fertilizer. In current dataset, N fertilizers included mainly urea (46% of N) and ammonium bicarbonate (18% of N). P2 O5 fertilizers covered primarily calcium superphosphate (17% of P2 O5 ). Most of K2 O fertilizers was potassium chloride (55% of K2 O). Compound fertilizer (45% of N, P2 O5 , and K2 O) was the most contributor of mixed and compound fertilizer. c Amount of diesel application was estimated due to farm machinery operation consisting of tillage, seedling throwing, irrigation, and harvesting. d Pesticides application rate sourced from investigation in double rice production. e The direct CH4 and N2 O emissions from paddy field were calculated through the method given by 2006 IPCC guidelines. The NH3 volatilization was estimated through multiplying pure N rate by the loss rate. The quantities of NO3 − and NH4 + leaching were estimated multiplying pure N rate by the leaching coefficient

were nearly equivalent to late rice. Similar to GHGs emissions, the Nr emissions of fertilizer applications also shared the largest percentage of agricultural inputs, being 364.1, 323.5, and 687.6 g Neq ha−1 year−1 for the early, late, and double rice, respectively. Meanwhile, the order of Nr emissions from diverse forms of synthetic fertilizers was consistent with those for GHGs emissions. Next to synthetic fertilizers, farm machinery operation emitted 203.9, 323.3, and 423.8 g N-eq ha−1 year−1 for the early, late, and double rice, respectively, accounting for 29.79, 47.23, and 33.48%, respectively. The Nr emissions from seeds and films for the early rice were also greater than those for the late rice. The pesticides

were still the least contributor of Nr emissions in double rice production, accounting for less than 2% of both early and late rice production.

3.2. Carbon footprint and nitrogen footprint of double rice Larger CF was observed for the early than late rice (Fig. 2). For double rice production, the CF for the early, late and double rice were 0.86, 0.83, and 0.85 kg CO2 -eq kg−1 year−1 at yield-scale, respectively. The CH4 emissions from paddy fields made the largest contribution to the CF of double rice production, accounting for 55.09, 59.64, and 57.36% for the early, late, and double rice, respectively. The GHGs emissions associated with agricultural inputs were the second largest contributor to the CF of double rice production, accounting for 40.14, 35.57, and 37.86% for early, late and double rice, respectively. The N2 O emissions from paddy fields had a small impact on the CF of both early and late rice. The NF for early, late and double rice were 10.47, 10.89, and 10.68 g N-eq kg−1 year−1 at yield-scale, respectively. The overwhelming majority of NF was due to NH3 volatilization from paddy fields associated with N fertilizer applications for double rice production, accounting for 95.13, 95.58, and 95.36% for the early, late, and double rice, respectively.

3.3. Distribution of carbon and nitrogen footprints in double rice cropping region The distribution of total GHGs and Nr emissions per unit area for the process of double rice production among provinces were shown in Fig. 3. For the early rice production process, the total GHGs and Nr emissions ranged from 4.6 to 5.7 Mg CO2 -eq ha−1 year−1 and 53.6 to 70.1 kg N-eq ha−1 year−1 , respectively. Compared to the average of all provinces, both higher total GHGs and Nr emissions per unit area were observed in Guangdong, Guangxi, and Fujian, but lower that in Anhui, Hubei, Hunan, and Jiangxi. There were larger Nr with smaller GHGs emission for Zhejiang province compared to the mean value, and greater GHGs with lower Nr emission for Hainan province. For the late rice production process, the total GHGs and Nr emissions ranged from 4.7 to 5.9 Mg CO2 -eq ha−1 year−1 and 53.7 to 78.3 kg N-eq ha−1 year−1 among provinces, respectively. Both higher total GHGs and Nr emissions per unit area were observed in Zhejiang, Guangdong, Guangxi, and Fujian, in comparison to the mean of the study regions, but lower that in Hainan, Hubei, Hunan, and Jiangxi. Larger Nr with smaller GHGs emissions were observed in Anhui province, compared to the mean values. In addition, the distributions of total GHGs and Nr emissions per unit area for the entire double rice production were similar to that for

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Table 4 The average hidden greenhouse gases (GHGs) and reactive nitrogen (Nr) emissions from agricultural inputs of double rice production in Southern China during 2004–2012. GHGs emission (kg CO2 -eq ha−1 year−1 )

Inputs

Early rice Seeds Films Herbicides Insecticides Fungicides N fertilizers P2 O5 fertilizers K2 O fertilizers Mixed and compound fertilizers Farm machinery operation Total

Late rice

Nr emission (g N-eq ha−1 year−1 ) Double rice

a

Early rice

Late rice

Double rice a

86.6 137.1 0.5 26.6 3.7 648.6 434.3 40.7 448.4 228.1

43.7 16.7 0.6 34.8 4.9 643.6 307.0 43.5 470.2 246.2

130.3 153.7 1.1 61.4 8.6 1292.2 741.3 84.2 918.6 474.1

35.7 72.6 0.2 5.6 2.5 208.7 96.6 1.6 57.1 203.9

18.0 8.8 0.3 7.4 3.2 195.1 66.8 1.7 59.9 323.3

53.8 81.4 0.5 13.0 5.7 403.8 163.4 3.4 117.0 423.8

2054.5

1811.0

3865.4

684.6

684.5

1265.7

a

Double rice consisted of the early and late rice. The dataset in Table 4 were the average hidden GHGs and Nr emissions from agricultural inputs in those process of production, storage, and transportation during 2004–2012, which did not include GHGs and Nr emissions during the double rice cropping period in the field. The hidden GHGs and Nr emissions were calculated through multiplying the actual application amount by the specific emission rate (Eq. (11)). The actual application amount of agricultural inputs was presented in Table 2, and dataset of that derived from the National Cost-Benefit Survey for Agricultural Product during 2004–2012. The specific emission factor of individual agricultural input sourced from the IKE eBalance v3.0 (IKE Environmental Technology CO., Ltd, China).

Fig. 2. The average carbon footprint (CF) and nitrogen footprint (NF) of double rice among provinces in Southern China during 2004–2012.

the late rice production for all provinces, except Hainan. Smaller Nr with greater GHGs emission were observed in Hainan. Differences were presented for the CF and NF of rice production among double rice cropping provinces by kg of rice grain yield (Fig. 3). The CF of the early rice among provinces ranged from 0.79 to 0.97 kg CO2 -eq kg−1 year−1 and 9.31 to 11.67 g N-eq kg−1 year−1 , respectively. Taking the CF and NF for each kg of grain yield into account, both the CF and NF for the early rice were higher for Guangdong and Guangxi provinces than other provinces. The CF of the early rice for Fujian and Hainan provinces were also larger than average, but lower NF were observed. Larger NF and lower CF were observed for Zhejiang and Anhui provinces, in comparison with the average of nine provinces. Both a smaller CF and NF were observed for the early rice at Hubei, Hunan, and Jiangxi. The CF of the late rice ranged from 0.71 to 1.22 kg CO2 -eq kg−1 year−1 and 9.49 to 13.49 g N-eq kg−1 year−1 among provinces, respectively. Both a larger CF and NF for the late rice in Guangxi, Guangdong, Hainan, and Fujian provinces were observed, compared to the mean value of all nine provinces. There were both lower CF and NF for late rice at Hubei, Hunan and Jiangxi provinces. For the entire double rice production, there were greater CF and NF for double rice for Guangxi, Guangdong, and Hainan provinces. Smaller CF and NF were observed for Jiangxi, Hubei, and Hunan provinces. Higher NF but lower CF were observed for Anhui and Zhejiang provinces, and higher CF but lower NF were observed for Fujian province.

4. Discussion The data presented herein indicate that average GHGs emissions from agricultural inputs was higher for the early than late rice. Seasonal difference of GHGs emissions may be due to greater applications of P2 O5 fertilizer, seeds and films for the early rice in the spring season, in spite of larger pesticide, mixed and compound fertilizers applications, and farm machinery operations for the late rice (Table 3). There were small differences in N and K2 O fertilizer applications between the early and late rice production. However, no difference in Nr emission for agricultural inputs was observed between the early and late rice in the study. Higher Nr emissions from farm machinery operations in the late rice season may be the reason for this result. In this study, synthetic fertilizers and farm machinery operations were the main determinants for both GHGs and Nr emissions from agricultural inputs for double rice production. Synthetic fertilizers accounted for >75% of GHGs emissions from agricultural inputs in both the early and late rice seasons. Lower fertilizer applications and farm machinery operations are obviously favorable to reduced GHGs and Nr emissions. For example, utilizing the “National Fertilization According to Soil Test Result” program (NFASTR) could reduce the artificial fertilizer application rate by ∼20% per unit area (CPGPRC, 2012), which can decrease related GHGs and Nr emissions in upstream production links and the direct Nr loss from paddy fields. Moreover, adoption

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Fig. 3. The distribution of carbon footprint (CF) and nitrogen footprint (NF) for double rice production among provinces in Southern China.

of conservation tillage (e.g., minimum tillage, no tillage) can reduce diesel consumption resulting in reduction of GHGs and Nr emissions from farm machinery operations (Xue et al., 2014). Due to lower temperatures during the rice seedling stage, larger seeding and film rates are utilized during the early rice production to ensure good seedling emergence, which results in higher GHGs emissions. Thus, selection of frost-resistant cultivars would be an effective measure to decrease the quantity of seed and film applications. The CF for the early and late rice in the study ranged from 0.79 to 0.97 and 0.71 to 1.22 kg CO2 -eq kg−1 year−1 among provinces, respectively. Xu et al. (2013) showed that the CF of rice production was 2.50, 2.33, 1.89, 1.54, and 1.34 kg CO2 -eq kg−1 on yield-scale in Guangdong, Hunan, Heilongjiang, Sichuan and Jiangsu, respectively. The average CF was estimated as ∼1.36 kg CO2 -eq kg−1 for rice production on the basis of national statistical dataset in China (Cheng et al., 2015). Different values of CF of rice could be primarily attributed to differences of the source and quality of data collection, system boundary, the emission factor of agricultural inputs, and the calculation method among studies. In addition, paddy rice cultivation is a primary contributor to global CH4 emissions (Stocker et al., 2013). The CH4 emission from paddy fields is a primary component of CF of double rice in this study, similar with others studies (Cheng et al., 2015; Cao et al., 2014). Reduction of rice paddy field CH4 emissions would be an efficient solution toward lowering the CF of double rice. Adoption of appropriate farming practices could reduce CH4 emissions from paddy rice cultivation, such as optimizing tillage practice and improving water and fertilizer management. Adoption of reasonable water management (e.g., intermittent irrigation, flooding-midseason drainage-frequent water logging with intermittent irrigation, and flooding-midseason drainagereflooding-moist intermittent irrigation without water logging) have been promoted to reduce CH4 emission compared to continuous flooding during rice growing seasons (Hou et al., 2012).

Moreover, adoption of no tillage can significantly reduce CH4 emissions compared with conventional plow tillage and rotary tillage during the double rice growing seasons (Zhang et al., 2013; Li et al., 2012). Reduction of CH4 emissions from paddy fields would be paramount in reducing the CF of rice production. The average NF of the early and late rice among regions was 10.47 and 10.89 g N-eq kg−1 year−1 in the study, respectively. Xue and Landis (2010) estimated that the NF was ∼2.65 g N-eq kg−1 of cereals production by using the LCA method in the Gulf of Mexico. The NF of cereals on yield-scale was estimated as ∼21.9 g N-eq kg−1 based on the input–output analysis in Austria (Pierer et al., 2014). Difference of the calculation method was a main reason among studies. In addition, the difference of N management in the process of cereals production was also a possible cause resulting in the difference of Nr loss. The NH3 volatilization was the principal NF source during double rice production in this study, similar with the result reported by Leip et al. (2014). The NH3 volatilization increased linearly with the N fertilizer application rates in both early and late seasons (Wang et al., 2012). In addition, the contribution of NH3 volatilization to NF was larger for the late than early rice in the study. The trend could be attributed to higher air and soil temperatures during the late growing season, which would facilitate soil urease activity resulting in higher soil NH4 + -N concentration in the paddy field (Wang et al., 2012). Results indicated that reduction of N fertilization rates and improvements in NUE played an essential role in reducing the NF of double rice production. According to the report of fertilizer use efficiencies of the three main staple crops, the NUE of those crops have improved by 5% since the implementation of the NFASTR program (in 2005) in China (MAPRC, 2013). The integrated high-efficiency practice, combining improved fertilization and irrigation management with improved crop cultivation technologies, is effective in reducing NH3 losses and improving the NUE in rice production systems (Cao et al.,

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2013). The Nr pollution poses an even more serious threat than C, due to the complex process involving many chemical forms (Sutton et al., 2011). However, at the present time, a comprehensive policy aiming to reduce Nr emissions is not yet in existence in China. Therefore, new policies are necessary to promote and encourage changes in farm management practices. Differences in total GHGs and Nr emissions per unit area among provinces were observed for the double rice production. These differences may be attributed to the differences in agricultural input rates (e.g., synthetic fertilizers), farming management (e.g., irrigation, tillage), climate conditions (e.g., precipitation, temperature) and soil types among study regions, which could affect the indirect and direct CH4 and N2 O emissions, and other Nr losses for double rice production. In this study, the CF and NF of double rice were evaluated from the perspective of agricultural production and environmental impacts. How to balance food production and environment burden represents a challenge for the scientific community and policy makers. In addition, the NF at yield-scale increases with the CF for double rice production. The NF reduction for double rice production was equally as important as that of the CF toward the mitigation of environmental pressure. Moreover, the balance between the CF and NF among provinces also represents a significant issue to be addressed regarding the sustainable production of double rice. In recent decades, the percentage of cropping area for double rice in China decreased rapidly from 71.3% of total rice area in 1975 to 40.7% in 2007, due to following: an increase in the single rice planting area, labor shortage, and lower economic profit (Zhu, 2010). Results of this study showed that a lower CF and NF at yield-scale were realized for some provinces (i.e., Jiangxi, Hubei, and Hunan), while larger for others (i.e., Guangxi, Guangdong, and Hainan). Appropriate restoration of double rice planting areas for the above former provinces with lower CF and NF, simultaneously, moderate reductions for latter provinces with higher footprints, could also be a potential solution to mitigate climate change and eutrophication of the entire double rice cropping region of Southern China. Relevant analysis would need to be conducted in the future. However, more agricultural inputs (e.g., synthetic fertilizers, pesticides, and diesel) would be applied to increase grain yields when the planting area of double rice decreased in latter provinces. Therefore, the tradeoff between food security and environmental degradation will remain a challenge for China in the future. Recently, the calculation approach for NF has been heavily discussed in the scientific community. Beside the output–input method, LCA is also one of several environmental management techniques to assess the environmental performance of products in their production cycles (ISO, 2006). Therefore, LCA has been introduced to assess the environmental burden due to Nr losses from double rice production in this study. The virtual N factor, which indicates all Nr losses related to an initial investment of N fertilizer into a system, has been used to calculate Nr loss rates for each food category in existing literature (Leach et al., 2012). The NF has been expressed as environmental impacts through the size of N nutrient flows in other studies. However, there are different and complex consequences for various chemical forms and types (e.g., NH3 , N2 O, and NOx ). Adoption of the LCA approach can provide Nr loss inventories for various Nr forms for each production link in detail, which could then be standardized to the specific impact category. However, only eutrophication potential is considered to be an environmental impact category resulting from all Nr species losses in this study. In fact, excessive Nr losses can also cause a diverse array of environmental degradation (e.g., acidification and global warming). Further exploration of synthesizing various environmental impacts would need to be conducted for a comprehensive assessment of NF in future research.

Some limitations are also presented relating to the dataset collection process of this study. A case was hypothesized that the diesel-powered pump was used for irrigation and drainage in rice production; however, there was actually a certain percentage of electric power associated with irrigation and drainage. Thus, higher Nr emissions but lower GHGs emissions can be presented, which may be due to higher Nr but lower GHGs emissions per unit diesel compared with electric power. In addition, pesticide quantities used were collected from a specific location in other studies. However, slight differences in pesticide application amounts are presented among different regions due to different distributions of disease and insect pests during the double rice cropping season. In spite of the limitation, the general trends of GHGs and Nr emissions from pesticide applications did not vary due to being only a small percentage of total emissions. In our study, kinds of fertilizers application amount sourced from the statistical dataset of the National Cost-Benefit Survey for Agricultural Product indicated the average use amount of fertilizer for each province. However, there were large fluctuations in fertilizer use rate among the different provinces and among the different regions in any province. Moreover, the same loss ratios for NH3 volatilization in double rice cropping system were applied in NF calculations for each province, which could cause some discrepancies with actual values due to the effects of soil properties, climate conditions and farm management practices among regions (Sommer et al., 2004). In addition, a possible difference of N fertilizer use amount was observed in different studies (Zhang et al., 2011; Wang et al., 2012), which could lead to the difference of NH3 volatilization. Despite the above limitations, trends in NH3 contributions would likely not change for the NF of double rice. And, exploring a method to access the NF of rice is a major aim for the current manuscript. The CH4 and N2 O emissions from paddy fields were estimated according to the recommended methods of “2006 IPCC Guidelines for National Greenhouse Gas inventories” in the study, resulting in the imprecise value compared to field measurement. However, that is a reliable method for the estimation of CH4 and N2 O emissions at regional scale. 5. Conclusion On the basis of the above analysis, optimization of synthetic fertilizers application is necessary to reduce the NF of double rice, especially the late rice. An adjustment of planting zones for double rice cropping is some possible measures to balance both CF and NF of double rice among regions. There is a strong need to take some potential measures to mitigate climate change and decrease N-management-induced environmental pollution in Southern China, e.g., fertilizer management, reducing soil tillage, and saving irrigation. Acknowledgments This research was funded by the Special Fund for Agro-scientific Research in the Public Interest in China (201103001) and the Program for New Century Excellent Talents in University of Ministry of Education of China (NCET-13-0567). References Bai, R., 2013. Vigorous promotion for the mechanization of rice production in the double paddy cropping region, http://www.camn.agri.gov.cn/Html/2013 08 19/2 1842 2013 08 19 24841.html (in Chinese). Cao, L.M., Li, M.B., Wang, X.Q., Zhao, Z.P., Pan, X.H., 2014. Life cycle assessment of carbon footprint for rice production in Shanghai. Acta Ecol. Sin. 34 (2), 491–499 (in Chinese with English Abstract). Cao, Y., Tian, Y., Yin, B., Zhu, Z., 2013. Assessment of ammonia volatilization from paddy fields under crop management practices aimed to increase grain yield and N efficiency. Field Crops Res. 147, 23–31.

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