A joint use of emergy evaluation, carbon footprint and economic analysis for sustainability assessment of grain system in China during 2000–2015

WANG Xiao-long, WANG Wei, GUAN Yue-shan, XIAN Yuan-ran, HUANG Zhi-xin, FENG Hai-yi, CHEN Yong

College of Agriculture, South China Agricultural University, Guangzhou 510642, P.R.China

Abstract The rapid growth of grain yield in China accelerates a discussion on whether the grain system in China is sustainable. To answer the question, a comprehensive assessment from economic and environmental points is necessary. This study jointly used economic analysis (ECA), emergy evaluation (EME) and carbon footprint (CF) to analyze the environmental and economic sustainability of the grain production system in China based on the national statistical data during 2000–2015.Results showed that the costs of maize, wheat, rice and soybean had increased by 252-346% from 2000 to 2015, causing the lower proflt of grain system in recent years. The situation resulted in a serious problem on economic sustainability of grain system in China. Meanwhile, the emergy sustainability index (ESI) of maize, wheat, rice and soybean systems were increasing during 2000–2015, and the CF on unit yield of the crops had been reduced by 10-30% in the study period. The results reflected the improved environmental sustainability of grain system in China during 2000–2015. Nevertheless, the emergy flow of industrial inputs for the crops were increased by 4-22% in the study period, and the CF from the inputs presented a growth rate of 16-23% as well during the same period. The results implied that the grain system in China was relying more on fossil-based inputs. Finally, according to the key points of cost, emergy and CF, we suggest that improving labor efflciency, advanced agricultural practices and optimizing cropping pattern will be effective ways to further improve the economic and environmental sustainability of grain system in China.

Keywords: grain, sustainability, emergy, proflt, carbon footprint, time-series

1. Introduction

Chinese agriculture has been experiencing a spectacular development since the 1980s. Grain yield in China has increased from 332.1 million tons in 1979 to 621.4 million tons in 2015 (NBSC 2017), meeting the food demands of the 1.4 billion Chinese people. However, high inputs of energy, labor and materials have been the primary methods to achieve high-yield in grain systems. As reported by offlcial statistical data, the use of chemical fertilizers, pesticides and fuel in China increased by 45, 41 and 55%, respectively,from 2000 to 2015 (NBSC 2017). On one hand, the rapid growth of agricultural inputs inevitably increases the cost of the grain system in China. The situation is not favorable for the competition of China’s grain with other countries and possibly affects the economic proflt of grain production for farmers in China. On the other hand, excessive agricultural inputs have made signiflcant contributions to negative environmental consequences in China (Juet al.2009; Liuet al.2013). As the National Development and Reform Commission of China (NDRC) reported, the total annual greenhouse gas (GHG) emissions from agriculture in China amounted to 820 million tons CO2-eq yr–1(NDRC 2012).The GHG emissions from the grain system account for approximately 46% of the total (Liet al.2017). Meanwhile,the grain system maintained by the high industrial inputs continuously aggravates the resource pressure of Chinese agriculture as well. The circumstance accelerates a debate about whether the grain system in China is sustainable for the long term.

Emergy evaluation (EME) is an effective method to analyze the environmental sustainability of systems; it addresses the weaknesses of traditional energy analysis and analyzes resources and services in both ecological and economic systems on a common energy basis, namely, solar emergy (Odum 1996). The method is particularly suitable for agricultural study, because farming is a system in which natural and human contributions interact to generate a flnal product (Pizzigalloet al.2008). Thus, EME has been widely used to analyze agricultural systems worldwide, such as planting systems (Luet al.2009; Bonillaet al.2010;Giannettiet al.2011; Singhet al.2016), animal rearing(Vigneet al.2013; Wanget al.2015), regional agricultural development (Chenet al.2006; Agostinhoet al.2008;Gasparatos 2011) and bioenergy projects (Pereira and Ortega 2010; Zhang and Chen 2017). In recent years, some studies also used the method to analyze the sustainability of the grain system in China (Liu and Chen 2007; Huet al.2010; Luet al.2010; Taoet al.2013; Chenet al.2014).These studies have made a signiflcant step forward to a better understanding of grain systems in China. However,the studies ignored the GHGs emissions of grain systems,which is a serious environmental issue caused by the grain system in China.

Carbon footprint (CF) is an effective method to account for the GHGs emissions of products and systems. CF is deflned as the total amount of greenhouse gas emissions associated with a food product or a service, expressed in carbon dioxide equivalents (CO2-eq) (Ganet al.2011). Presently, CF has been widely used to compare the globe warming potential of different products, including livestock (Perssonet al.2015; Rakotovaoet al.2017; Salvadoret al.2017), food production (Linet al.2015; Xu and Lan 2016), and planting systems (Ganet al.2011; Singhet al.2016). In recent years,the method was also used to assess the GHGs emissions of grain systems in China (Chenget al.2011, 2015; Yanet al.2015; Xueet al.2016; Xu and Lan 2017; Zhenet al.2017).These studies made contributions that were important for exploring measures for mitigating the GHGs emissions of the grain system in China. Nevertheless, the published papers focused on the single viewpoint of the globe warming potential of grain systems. It is not comprehensive enough to assess a system’s sustainability only by standing at a unique point, because an agricultural practice that will improve one aspect of a system may not be favorable for another aspect of the same system. Energy conservation and carbon reduction are both important considerations that promote the transformation of Chinese agriculture to modern agriculture. Therefore, it is necessary to evaluate the grain production system in China by considering the energy consumption and GHGs emissions at the same time.

The EME reflects an “upstream” sustainability determined by energy needed to support the systems, while the CF mainly represents “downstream” GHGs emissions resulting from the systems. Thus, the joint use of the EME and CF will both contribute to a comprehensive assessment of the environmental sustainability of the grain system in China. However, the primary objective for farmers producing grain is to have an economic beneflt. A policy or suggestion should not be proposed after only considering the environmental consequences without evaluating the economic beneflts. Economic analysis (ECA) reveals the economic sustainability of the grain system in China and is more easily understandable by the producer, consumer and policymakers. If farmers cannot make money by producing grain, the only choice for them is to only plant enough grain to meet their personal food demand. If so, there will not be enough grain supplied to the market. In other words,the grain system in China would not be sustainable from an economic standpoint. Thus, an abbreviated ECA based on the input-output model is necessary for assessing comprehensive assessment of the grain system in China.

The multi-dimensional assessment based on various approaches has been a recently sustained trend in evaluating systems, because a policy or measure to improve the systems cannot be scientiflcally and comprehensively set solely based on one single viewpoint. Some authors have recently described research and policy outreach activities in which they have taken a water-energy-food nexus perspective (Wichelns 2017; Xianget al.2017;Zhang and Vesselinov 2017). Some papers also analyzed agricultural systems by the joint use of various methods in recent years. Pizzigalloet al.(2008) analyzed two wine production processes by a joint use of the EME and life cycle assessment (LCA). Luet al.(2010) developed a combined evaluation of emergy, energy and economic methods to assess rice and vegetable production systems in alluvial paddy flelds. Wilfartet al.(2013) used a holistic approach by combining the EME and LCA methods to assess the environmental consequences of three flsh-farming systems.Singhet al.(2016) assessed energy budgeting and CF in transgenic cotton-wheat cropping system in India. Wanget al.(2016) developed a joint use of LCA, EME and ECA for comparison of four typical pig production models in China. The published studies made important contributions for understanding different agricultural systems in a more comprehensive framework. However, the joint use of EME,CF and ECA for a system evaluation has been still lacking,and a study on evaluating China’s grain system from multiple dimensions has not been reported.

Therefore, this study jointly uses EME, CF and ECA to analyze the environmental and economic sustainability of the grain production system in China during 2000–2015.The objective of this study is to analyze the economic and environmental changes of the grain system in China in the past 15 years. The study will make a few suggestions for exploring the balance of economic and environmental performances of the grain system in China.

2. Materials and methods

2.1. System description and data sources

This study considered the primary grain types in China including maize, wheat, rice and soybean. The seeding area of maize, wheat, rice and soybeans accounted for 34, 21, 27 and 8% of total grain planting area in 2015, respectively (NBSC 2017). The inputs driving the grain system are primarily derived from three sources:environmental resources (E), purchased materials (P) and services (S). Meanwhile, the GHGs emissions are diffused into atmosphere causing global warming potential (GWP).Moreover, money flowing into and out of the system drives the economic activity (Appendix A).

This paper includes data from crop farming in China during 2000–2015. All data only refer to mainland China. For the grain system, the raw meteorological data were obtained from the China Meteorological Data Sharing Service System(http://data.cma.cn/); additional raw input/output data for each crop produced during this period mainly came from the National Agricultural Cost-beneflt Data Assembly over the same years (DPNDRC 2000–2016). Since the inputs of pesticides, electricity and organic fertilizer are recorded in money (CNY) per unit area, the price of each input was collected from the National Data (http://data.stats.gov.cn/)and Price Yearbook of China (WCPYC 2011), to convert from money to quantity of the input. It should be noted that the organic fertilizer in this case is considered to be as pig manure based on the current conditions in rural China.

2.2. Economic analysis (ECA)

The prices of all inputs and outputs in the present case were obtained from DPNDRC (2000–2016). The price of crop straw was accounted for in the study. The cost and proflt of different crops on unit area were calculated to evaluate the economic beneflts of the grain system in China. The formulae to calculate the cost and proflt are shown as follows:

Where,irefers to a different grain category such as maize, wheat, rice and soybean;An,irefers to the consumed amount of agricultural inputnfor cropi;Pn,irefers to the price of the agricultural inputn;Yirefers to the yield of cropi;Py,irefers to the yield price of cropi.

2.3. Emergy evaluation (EME)

EME accountingEmergy is the available energy of one kind that is used directly and indirectly to make a product or service (Odum 1996). In this study, the energy content of each input was calculated using the energy coefflcients from Chen (2011) and the formulae from Odum (1996).Different inputs were organized into emergy evaluation tables and converted into emergy by multiplying by relevant unit emergy values (UEVs). The UEVs refer to the emergy required to obtain one joule or gram of a product or service.The inputs into the systems were divided into renewable and nonrenewable fractions by renewability factors (RNFs).The UEVs and RNFs used in the study are derived from the published papers in Table 1. The global emergy baseline used in the paper was 15.83×1024sej yr–1(Odumet al.2000). Rain and wind are considered co-products of sunlight in EME, so only the item with the highest value was considered in the total amount of emergy to avoid double counting (Odum 1996).

EME indexIn the study, the emergy sustainability index(ESI) was introduced to assess sustainability of grain system in China (Brown and Ulgiati 2002). The formula to calculate the ESI is shown as follows:

Where,Tirefers to the total emergy input of crop systemi,Firefers to the economic feedback emergy flow of crop systemi.NiandRirefer to total nonrenewable and renewable emergy flows of crop systemi, respectively.

2.4. Carbon footprint (CF)

The GHGs emissions, including CO2, CH4and N2O during the whole life cycle of grain system, consists of two components: (1) those (GWPinputs) from the upstream production and transport of agricultural inputs and (2) those(GWPon-fleld) during the cropping period on the fleld.

Formulae and factors for GWPinputs accountingThe GWPinputsemissions (CO2-eq kg ha–1) was calculated according to eq. (4) as follows:

Where,InandCnare the amount of each item of input and its coefflcient for GHG cost, respectively. The coefflcients for the GHG cost are shown in Table 2.

Formulae and factors of GWPon-field accountingThe GWPon-fleld(CO2-eq kg ha–1) consists of N2O, CO2and CH4emissions. It should be noted that the CH4emission is normally negligible for upland crops including wheat, maize and soybean. The 100-year global warming potentials of CH4and N2O are 28 and 265 times the intensity of CO2on a mass basis, respectively (Myhreet al. 2013). The GWPon-fleldemissions was calculated according to eq. (5) as follows:

Where, GWPN2O,i, GWPCO2,iand GWPCH4refer to the N2O, CO2and CH4emissions for cropi, CO2-eq kg ha–1,respectively.

According to IPCC (2006), the anthropogenic emissions of N2O occur through both a direct pathway (chemical nitrogen fertilizer and manure applied on-fleld, crop burning on fleld, and crop straw returned directly to the fleld, which retain residual nitrogen in the root stubble) and through two indirect pathways (following volatilization of NH3and NOxand after leaching and runoff of N from managed soils).In this study, the N2O emissions from crop burning and from straw returned directly on fleld were not considered due to the unavailable data. Therefore, the GWPN2O,iwas calculated according to eq. (6) as follows:

Table 1 The unit emergy values (UEVs) and renewability factors (RNFs) used in the study

Table 2 Greenhouse gas (GHG) emission coefflcients of agricultural input used in the case

Where, N2O-NF,irefers to the direct N2O emissions from chemical fertilizer and manure application on fleld; N2ONC,irefers to the direct N2O emissions from crop residual nitrogen in the root stubble; NH3-NF,iand -NF, irefer to the NH3volatilization and N leaching/runoff from chemical fertilizer and manure application on fleld, respectively; EF1and EF2are factors of indirect N2O emissions from NH3volatilization and N leaching/runoff, which are 1 and 0.75%,respectively, according to IPCC (2006);Cis the conversion factor of N2O–N to N2O emissions, which is the ratio of 44 and 28 (IPCC 2006).

In this study, the direct N2O emissions, NH3volatilization and N leaching/runoff from chemical fertilizer and manure application on-fleld for maize, wheat and rice were estimated based on the models in Chenet al.(2014). The models describe the liner and exponential relationship between nitrogen loss, including direct N2O emissions,NH3volatilization and N leaching/runoff, and the nitrogen application/surplus rate in the primary grain-producing areas of China. The nitrogen surplus rate is 45% according to Ju(2014). Moreover, due to the current lack of more exact factors or models, the direct N2O emissions and N leaching/runoff from chemical fertilizer and manure application onfleld for soybean production were respectively estimated as 1 and 30% of total N input based on IPCC (2006), the NH3volatilization was estimated as 10% of nitrogen fertilizer and 20% of manure, respectively, according to IPCC (2006).

The N2O-NC, iemission was calculated according to eq.(7) as follows:

Where,Rirefers to the ratio of below-ground residues to biomass of cropi;Yirefers to the harvested yield of cropi;Hirefers to the harvest index of cropi; NBG(T),irefers to the N content of below-ground residues for cropi,which are 0.007 for maize, 0.009 for wheat and 0.008 for soybean,respectively, based on IPCC (2006), and the NBG(T)for rice is 0.0037 according to Liuet al.(2017).

According to IPCC (2006), the CO2emission on fleld primarily derives from urea fertilization, which can be estimated with eq. (8) according to IPCC (2006):

Where,Mirefers to the input amount of urea fertilization;EFirefers to the emission factor which is 0.20 (IPCC 2006).

The CH4emissions from rice fleld can be estimated with eq. (9) according to IPCC (2006):

Where,trefers to the cultivation period of rice, which is estimated to 180 days in the case;EFCrefers to the baseline emission factor for continuously flooded flelds without organic amendments, which is 1.30 kg CH4ha–1d–1(IPCC 2006); SFWrefers to the scaling factor to account for the differences in water regime during the cultivation period,which is 0.78 (IPCC 2006); SFPrefers to the scaling factor to account for the differences in water regime in the preseason before the cultivation, which is 1.00 (IPCC 2006);ROA refers to the application amount of manure each year;CFOA refers to the conversion factor for manure, which is 0.14 according to Zhanget al.(2017).

CF index The study compared the CF of different grain types based on the yield-based (CFy,i) assessment index.The formulae to calculate the index is shown as follows:

Where,Yirefers to the yield (kg) of cropi.

3. Results

3.1. ECA results

Indices analysisFigs. 1 and 2 show the cost and proflt of grain system in China during 2000–2015. In general, there was a similar growth trend on the cost of wheat, maize, rice and soybean production systems during the study period.The costs of the crop systems were 1 021-2 388 USD ha–1in 2015, which was increased by 252-346% compared to the cost in 2000. Rice and soybean systems continuously had the highest and lowest costs during the period, respectively.The cost of maize had exceeded that of wheat starting in 2011 and had become the second highest costing crop.

From a proflt standpoint, there was also a similar change for wheat, maize, rice and soybean production systems during 2000–2015. Generally, the proflts of the grain system in China kept growing until 2011. Especially, the proflt of wheat increased from a negative value to 538 USD ha–1during 2000–2011. From 2011–2015, the proflts of grain system in China began to decrease with a percentage of 14-77%. Maize and soybean systems had the most obvious reduction trend for proflt. Rice system kept the highest proflt, although the cost of the system was also the highest.

Key points of cost controlFig. 3 shows the cost contribution of grain production systems in China during 2000–2015. The costs of grain production in China primarily derived from labor, fertilizer, machine and fuel. As shown,the contribution of fertilizer to the cost of grain systems was decreasing, while the cost of labor, machines and fuel were increasing in recent years.

Fig. 1 Cost of grain production system in China during 2000–2015.

Fig. 2 Proflt of grain production system in China during 2000–2015.

3.2. EME results

I

ndices analysisAs shown in Fig. 4, total emergy inputs of the cropping systems in China were decreasing continuously during 2000–2015. The result shows that the resource cost of grain system in China was reduced over 15 years. This was due to the reduction of labor services,because the total emergy flows were increasing without services in the same period (Appendix B). Rice production basically showed the highest emergy input in the period,followed by wheat, maize and soybean. Moreover, the ESI values of maize, wheat, rice and soybean systems were increasing during 2000–2015 (Fig. 5), showing the improved sustainability of the grain system in China.Speciflcally, the ESI of the soybean system showed an obvious growth, increasing by 25% from 2000 to 2015,while the index of maize and rice systems were only increased by 11 and 7%, respectively. The ESI of wheat in 2015 was even reduced by 4% compared to that in 2000,although the general trend was increasing.

Fig. 3 Cost contribution of grain production system in China during 2000–2015.

Fig. 4 Total emergy flow of maize, wheat, rice and soybean systems in China during 2000–2015.

Key points of emergy inputsFig. 6 shows the emergy flows of different industrial inputs of maize, wheat, rice and soybean systems during 2000–2015. Except for the services, chemical fertilizer was the largest emergy input item during the study period, followed by electricity,pesticide, fuel, seeds, agricultural fllm and organic fertilizer.Additionally, the emergy inputs of chemical fertilizer,electricity and pesticide showed an obvious growth trend during 2000–2015.

3.3. CF results

Fig. 5 Emergy sustainability index of grain system in China during 2000–2015.

Fig. 6 Emergy flows of agricultural inputs for grain system in China during 2000–2015.

Indices analysisCarbon footprint based on unit yield(CF-y) was calculated to analyze the GHGs emissions of the grain system in China during 2000–2015 (Fig. 7). There were similar historical changes of CF-y for the different crop types during the study period. The CF-y indices of all crops showed a decreasing trend, except for a lasting growth trend for wheat from 2008 to 2013. In 2015, the CF-y of maize, wheat, rice and soybean was reduced by 28, 29, 20 and 9%, respectively, compared to that of the crops in 2000, indicating that an improvement of carbon efflciency of grain systems in China. Rice had the highest CF-y during 2004–2011, but wheat became the highest CF crop after 2012. This was caused by two reasons. First,the GWPinputsfrom the upstream production and transport of agricultural inputs of wheat were 7-20% higher than that of rice from 2009–2015, mainly resulting from the lasting increased inputs of chemical fertilizers and electricity for wheat. Second, the yield of wheat was 12-39% lower than that of rice during 2000–2015. Thus, wheat had the highest GHGs emissions crop based on unit yield starting in 2012.

Fig. 7 Carbon footprint on unit yield of grain production system in China during 2000–2015. CF-y refers to the carbon footprint on unit yield of different grain types.

Key points of GHGs emissionsFig. 8 shows the source of the carbon footprint of grain systems in China during 2000–2015. On-fleld emissions were continuously the largest source of GHGs during the study period, followed by the production and transportation of fertilizer, electricity,pesticide, fuel and agricultural fllm. As shown, the ratio of on-fleld emission and total CF of grain systems decreased from 41% in 2000 to 34% in 2015. The contribution of fertilizer remained approximately 24% during the study period. However, the contribution of pesticides, electricity and fuel for total GHGs emissions of grain systems were continuously increased during 2000–2015. Generally,the GHGs emissions from the upstream production and transport of agricultural inputs and diesel fuel for use in farming operations (GWPinputs) of grain system in the same period presented an increasing trend.

4. Discussion

4.1. Economic sustainability of grain system in China

In this study, the ECA, EME and CF methods provided three different but complementary insights to analyze grain systems in China over a period of time. Economic interest is undoubtedly the primary driving force for grain production activities, because the flrst objective of farmers to plant crops is to make a proflt. In this study, we found that the costs of wheat, maize, rice and soybean increased from 2000 to 2015. The labor, machines and fuel were the main factors causing the increased cost of grain systems.

Although the EME result indicated the reduction of labor input into grain system, the economic cost of labor on farmland still increased in recent years. The result clearly implies rapid growth of the price of labor in China during the study period. As previously reported, the labor price in China has reached a more than 10% growth annually since 2004(Zhanget al.2013; Yanget al.2016). The labor cost on unit area of maize, wheat, rice and soybean increased by 278,280, 248 and 191% during 2000–2015. At the same time,this study showed the continued reduction of proflts of the grain system in China in recent years, except for a temporary rebound in 2013. The continuous drop of grain proflt has heavily affected the motivation of Chinese farmers to plant grain. In this context, many rural people have chosen to work in off-farm sectors for higher incomes. It has been reported that there are approximately 1.2 million rural people migrating into urban areas to work each year (Tanet al.2013). The shortage of rural labor further aggravates the growth of cost for grain production in China. Consequently,the intensity of cropland use is declining, with abandonment of cropland in some areas (Chenet al.2009; Tianet al.2010; Tanet al.2013). Yang and Xu (2015) indicated that the average percentage of abandoned cropland was 11% in the case study area. The results showed that the high labor cost was the primary reason pushing rapid growth of the cost of grain systems in China. The most effective way to change the situation is possibly to improve labor efflciency.

The costs of machines and fuel are also important factors affecting the economic performance of grain systems in China. The increased costs of machines and fuel are valuable for evaluating agricultural transformation, because they illustrate that the grain system in China is changing from labor-based to machine-based farming. This is a good foundation to improve the economic impact of grain systems in China in the future. However, the high inputs of machine and fuel cause the cost of grains systems to increase and have the potential to have more environmental impacts.Thus, it is necessary to improve the efflciency of machines and fuel for grain systems in China.

Fig. 8 Sources of carbon footprint for grain production system in China during 2000–2015.

Moreover, although the costs of other inputs, including fertilizer, pesticides and so on, did not continuously increase in recent years, the inputs continue to affect the proflts of the grain systems. If the agricultural practices would be improved by better seeds or technologies for crop production,the income of grain systems per unit area of farmland will be increased due to the increased yield. However, as stated by Wanget al.(2017), the characteristics of limited cropland make it very difflcult to promote mechanization and improved agricultural technologies. On one hand,some advanced agricultural technologies such as sprinkler and drip irrigation normally require initial investments too expensive for the farmers. Additionally, the farmers do not have enough motivation to change farming habits for their limited fleld, because improved agricultural practices would not bring an increase in proflt for the landholders.

Therefore, although the grain yield in China has increased from 431 million tons in 2003 to 621 million tons in 2015,maintaining continuous growth for 12 years (NBSC 2017),there is a serious problem of sustainability of the grain system in China from an economic beneflt viewpoint. In other words, the issues context of increased cost and decreased proflt accelerates the question about who would be interested in planting grain in China in the future. Indeed,China’s agriculture developed as a series of small-size family-operated farms during the past thousands of years(Chenet al.2006). A large amount of labor on limited areas is an important reason for the lower labor productivity and proflt. Zhang and Zhang (2012) indicated that the fuel consumption of a wheat harvester for a small-scale farm was 20% higher than that for large-scale farm, so it is possible to improve the efflciency of machine and fuel by expanding farm size. Wanget al.(2017) indicated that the crop farm with larger scale earned more net income annually and contributed to the growth of the income of rural labors.Therefore, expanding the scale of crop production could be an effective measure to improve the economic beneflt of grain systems in China. Presently, some farmers have leased their cropland to other farmers, and the concentration of farmland contributes to the expansion of farm size. In recent years, the Chinese government has been exploring suitable polices for guiding agricultural transformation.Recent offlcial documentation also indicated that familyfarms of sufflcient size were the primary direction for agricultural transformation in China. Therefore, promoting the expansion of planting scale is possibly an effective and simple way to increase the economic beneflts of grain systems in China.

4.2. Environmental sustainability of grain system in China

In this study, the EME and CF methods were used to analyze the environmental sustainability of grain system in China. The joint use of the two methods contributes to the multifaceted assessment of the environmental consequences of grain systems in China. The EME is an upstream method, substantially rooted in the direct and indirect consumption of environmental resources(Ulgiatiet al.2006). Chenet al.(2006) analyzed Chinese agricultural systems during 1980–2000 by using the EME and found that the industrial emergy, including fertilizer,pesticide and machines, increased from 7.26E+23 sej in 1980 to 1.92E+24 sej in 2000. Zhanget al.(2016) reported that the industrial emergy flow was increased by 54%, with an annual average value of 1.36E+24 sej from 2000 to 2010. In this study, our flnding also indicated the industrial emergy flows for grain system were increased by 4-22%during 2000–2015. All of these results demonstrate that the grain system in China has been relying more and more on industrial inputs to maintain the high yield of systems since the 1980s.

CF is a downstream method, focusing on GHGs emissions resulting from the whole process of the systems.Chenget al.(2011) reported a basic estimate of CF of crop production using national statistical data available for the period of 1993–2007, and found that the carbon intensity for land under cultivation increased from 94 Mt CO2-eq in 1993 to 140 Mt CO2-eq in 2007. Xu and Lan (2017)reported that the total GHGs emissions from rice, wheat and maize systems were increased by 2% per year from 2004 to 2013. Huanget al.(2017) showed that the area-scaled CF increased by 109% for rice, 218% for wheat and 157%for maize during 1978-2012. In this study, the GWPinputsof grain system also presented a growth rate of 16-23%during 2000–2015. The relative results demonstrate that the high industrial input is causing the more and more GHGs emissions from upstream production and transport of the inputs and diesel fuel use in farming operations. However,the yield-based CFs of all crops show a decreasing trend,showing an improvement of efflciency of GHGs emissions.

Furthermore, the total emergy flow of the grain system in China was reduced when the service is considered. The results reflect that fewer resources are required for grain production per unit area in China in recent years, showing an improvement of efflciency in grain production systems.Meanwhile, the increased ESI values of maize, wheat, rice and soybean systems show the improved sustainability of grain systems in China during 2000–2015. The reduction of emergy input and the improved sustainability are primarily derived from the decreasing emergy flows of services for grain systems. Services in this study refer to the direct labor investment on farmland and the indirect labor related to agricultural process of grain systems. Therefore, the results imply that the emergy input of the whole life cycle of the grain system is decreasing due to the reduction of labor.In other words, improving the labor efflciency on farmland is possibly an effective pathway to lower the nonrenewable resource consumption related to grain systems. Expanding the planting scale of a unit farm is a useful way to improve labor efflciency. Thus, grain systems with a larger scale will possibly contribute to the improvement of environmental sustainability of grain systems to some extent.

In general, the results in the study indicate that the environmental sustainability of grain production in China was improving during 2000–2015 based on the EME and CF results. However, the high industrial inputs and the high GHGs emissions from the inputs are still a concern for developing a sustainable grain system. This is a challenge that Chinese agriculture must resolve. We cannot arbitrarily assume that the higher inputs and emissions are completely negative, because it is part of the process of Chinese agricultural transformation from traditional labor-based production to fossil fuel-based agriculture. Nevertheless,new and effective agricultural practices are required to solve the issue of high consumption and emissions of grain systems in China. As presented in the study, fertilizer,electricity and pesticides were important sources of emergy inputs and GHGs emissions for grain systems in China.Related studies reported similar results in recent years(Chenet al.2006; Juet al.2009; Chenget al.2011, 2015;Linet al.2015; Huanget al.2017). Therefore, advanced agricultural practices for fertilization, irrigation and pesticides are key points to improve the environmental sustainability of grain systems in China in the future. A further objective for grain systems in China is the advanced transformation from fossil fuel-based to modern sustainable agriculture

4.3. Comparison of different grain types

As presented in the study, the proflts of maize and soybean production in China dropped rapidly in recent years. The proflts of these were higher than that of wheat in 2010, but,only flve years later, the proflts of maize and soybean were decreased by 52 and 32%, respectively, compared to that of wheat. The situation resulted from the unreasonable planting pattern of grain in China to some extent. In recent years, soybean farmland in the Northeast China has been replaced by maize, as the latter has a higher yield, and society requires more maize for other sectors. This change of crop patterns results in surplus maize. The maize storage in barns increased from 60 million tons in 2001 to approximately 170 million tons in 2015 (Zhao and Zhong 2016). This impacts the price of maize in markets. At the same time, a large quantity of soybeans must be imported to meet the demand in China. According to offlcial data, the imported quantity of soybean in China was 81 million tons in 2015. In 2013, imported soybean in China accounted for 80% of the domestic supply (Zhao and Zheng 2015)and 62% of the total imported quantity all over the world(FAO 2017). The situation has become a serious problem affecting grain security in China.

For environmental sustainability of different grain types,although the EME and CF results of all crops in this study present almost similar trends during 2000–2015, differences among the crops still exist. During the study period, rice production had the highest total emergy input, followed by maize, wheat and soybean. The ESI of soybean was 42-54% higher than that of wheat, maize and rice. Zhanget al.(2005) reported similar results based on a study in Northeast China. For the CF results, this study showed that the highest CF-y crop was wheat in recent years,followed by maize, rice and soybean. These results imply a possible measure to make an emergy efflcient and GHGs-reducing grain system in China: the emergy inputs and GHGs emissions from grain crops can be reduced by substituting a higher CF crop (i.e., wheat) with lower CF crops (i.e., rice and maize) and by substituting a higher EME crop (i.e., wheat) with lower EME crops (i.e., soybean). The preliminary results in this study are not enough to provide a useful suggestion for replacing crop in different regions, and it is very necessary to analyze the question more carefully.However, this study demonstrates a fact: optimizing the crop pattern would possibly be an effective measure to improve the economic and environmental sustainability of grain systems in China.

5. Conclusion

Since the reform and opening in 1978, the grain systems in China have been improved greatly, exhibiting a trend of rapid yield growth. According to historical data on grain systems in China during 2000–2015, this study indicates that there is currently a serious problem of economic sustainability of grain systems in China, although the grain yield in China has sustained continuous growth for 12 years.The environmental sustainability of grain systems in China has been improved to some extent during the study period.Moreover, this study also indicated that the industrial inputs and GHGs emissions from the inputs increased during 2000–2015. Expanding cropping scale, optimizing the cropping pattern, and advanced agricultural practices are possibly effective measures to further improve the economic and environmental sustainability of grain systems in China.

Acknowledgements

This work was flnancially supported by the Natural Science Foundation of Guangdong Province, China(2017A030310055) and the National Key Research and Development Program of China (2017YFD0201305). We thank anonymous reviewers and editors for their helpful comments and suggestion of the manuscript.

Appendicesassociated with this paper can be available on http://www.ChinaAgriSci.com/V2/En/appendix.htm