Provincial level-based emergy evaluation of crop production system and development modes in China

Ecological Indicators 29 (2013) 325–338

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Case study

Provincial level-based emergy evaluation of crop production system and development modes in China Jin Tao ∗ , Meichen Fu, Xinqi Zheng, Jianjun Zhang, Dingxuan Zhang School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

a r t i c l e

i n f o

Article history: Received 3 November 2012 Received in revised form 7 January 2013 Accepted 18 January 2013 Keywords: Crop production system Emergy indicator Cluster analysis Development mode China

a b s t r a c t This paper presented a multi-objective indicator system for the performance evaluation of crop production system based on emergy method. Eight emergy indicators were selected to analyze the crop production system in the 31 provinces of mainland China to compare intensive use, scale management, investment density, environmental pressure, output benefit, output density, output per capita, and economic benefit. This paper conducted a comparison of each indicator in different provinces, and divided the 31 provinces into 10 groups by cluster analysis. The results show that: the crop production system among provinces has a significant difference and it is summarized into 10 development modes; with the development of modern agriculture, Chinese crop production system still complies with the input–output balance; the majority of provinces cannot achieve win-win between economic investment and environmental health; most of the underdeveloped provinces with high productivity may not be able to obtain good economic benefit; there is a significant positive correlation between the scale management degree and emergy output per capita in the crop production system; 10 development modes and policy suggestions are deduced. It is concluded that emergy-based multi-objective indicators can serve as an effective method in the evaluation of crop production system. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Crop production system provides a material basis for human survival and development, such as rice, wheat, corn, soybean, cotton, and oil-bearing crops. With the accelerating economic development, the growing population and the decreasing arable land, the crop production system is confronted with great challenges from natural environment and human society. These two kinds of external impact forces jointly determine the crop productivity. On one hand, natural environment provides light, temperature, rain, soil, etc. for crop growth; on the other hand, human society provides labor, mechanical force, fertilizers and others. Over the last several decades, in order to increase crop productivity, many industrial inputs were intensely used, such as crop mechanization, chemical fertilizers, and pesticides. Though this pattern leads to high harvests, it also results in a series of environmental and social problems, including soil erosion, biodiversity depletion, and contamination of natural resources and food (Pimentel and Pimentel, 1979). Considering sustainable development being the goal, it is essential to have a comprehensive

∗ Corresponding author. Tel.: +86 1082322205; fax: +86 1082322205. E-mail address: [email protected] (J. Tao). 1470-160X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2013.01.014

understanding about input–output status of crop production system, and obtain optimal evaluation results. To integrate the values of free environment investment, goods, services and information in a common unit, the ecologist H.T. Odum began the research of ecosystem energy in the 1950s, then he proposed the systemic ecology theory in the 1970s, and developed the theory of emergy analysis in the 1980s (Odum and Pinkerton, 1955; Odum and Brown, 1975; Odum, 1988). Emergy is defined as the available energy of one kind previously used up directly and indirectly to make a service or product (Zhang et al., 2012). It is usually quantified in solar energy equivalents and expressed as solar emJoules (sej) (Odum, 1996; Ulgiati and Brown, 2009). The ratio of the emergy required to make a product to the available energy of the product is defined as the solar transformity of the product. Correspondingly, the emergy content of a product or service is the unit of energy multiplying by its appropriate transformity. The unit of the solar transformity is solar emJoules J−1 , abbreviated as sej J−1 . Emergy method sets an effective and comparable criterion to evaluate different types of energy, substances of natural environment, and human economic activities. The goal of emergy analysis for a specific ecosystem is to identify the balance between the socio-economic development and natural environment. Emergy analysis applied to agricultural system can quantify the system’s efficiency by calculating sunlight energy and its derivatives (wind and rain), the same for machinery, fossil fuels, fertilizers, pesticides and other materials (Torbjörn and

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Andrew, 2006). So far, the emergy theory and method have been widely used in various agricultural fields, including food production (Odum and Odum, 1984; Ulgiati et al., 1993; Martin et al., 2006; Cuadra and Rydberg, 2006; de Barros et al., 2009; Cavalett and Ortega, 2009; Gasparatos, 2011; Giannetti et al., 2011), livestock production (Castellini et al., 2006; Rótolo et al., 2007), bioethanol production from crops (Dong et al., 2008; Liu et al., 2012a,b), agriculture and society (Rydberg and Haden, 2006; Cuadra and Björklund, 2007; Lu and Campbell, 2009; Alfaro-Arguello et al., 2011), and soil erosion (Lefroy and Rydberg, 2003; Cohen et al., 2006). In China, researchers in this filed have focused their studies on specific regions, such as the cropping and pasture ecotone in North China (Fu et al., 2005; Li et al., 2006; Dong et al., 2006), the arid region of Northwest China (Min et al., 2004; Liu et al., 2004, 2005), and the Loess Plateau (Dong et al., 2004; Zhang, 2004). Lan presented a preliminary study about three departments-farming, animal husbandry and fishery with the acquired data in 1988 and 1998 (Lan et al., 2002). Chen et al. (2006) revealed the overall panorama of the Chinese agriculture during 1980–2000 considering historical background with drastic political and socio-economic transitions, in which emergy analysis was applied to illustrate China’s agro-ecosystem. Liu and Chen (2007) thought efficiency and sustainability of grain production had been an important issue in China, and had a case study of Jiangsu and Shaanxi Provinces of China. Lu et al. (2010) accomplished integrated emergy, energy and economic evaluation of rice and vegetable production systems in alluvial paddy fields. In the above-mentioned work, emergy indicators were widely employed in different scales and different types of agricultural field. However, emergy analysis has rarely been applied to the evaluation of the overall crop production system. Considering the special characteristics of crop production system and the advantages of emergy, this paper proposes a framework of multi-objective evaluation indicators based on emergy. The indicators system is used to evaluate the crop production systems in the 31 provinces of mainland China. In the last section, policy suggestions are presented for the improvement of crop production system according to evaluation results.

and maintenance of the resource, product or service of interest, and the higher position in the energy hierarchy it resides (Odum, 1996, 1988). Emergy evaluation takes into account the contributions of every resource from nature and human economy (Odum, 1988). Based on the Energy Systems Language introduced by Odum (1996), a typical diagram of the crop production system is presented in Fig. 1. The diagram illustrates the boundary, main components and interactions as well as emergy driving incentive for the system (Jiang et al., 2007; Zhang et al., 2011). In general, inputs of the crop production system can be categorized into four types as shown in that diagram: (1) Local renewable resources (R), such as sunlight, rain, wind and earth circle, (2) local nonrenewable resources (N), such as net loss of topsoil, (3) purchased nonrenewable resources (FN ), such as mechanical equipments, purchased diesel, chemical fertilizers, and other materials, and (4) purchased renewable resources (FR ), such as labor, irrigating water, and seeds. The products of this system mainly include rice, wheat, corn, soybean, tubers, oil-bearing crops, cotton, sugarcane, sugar beet, tobacco, vegetable, fruit, etc. The detailed items associated with five kinds of fluxes (R, N, FN , FR , Y) can be found in Appendix A. Due to the complexity and multi-objective of crop production system, it is difficult to use a single indicator to comprehensively evaluate it. In previous studies, some comprehensive emergy indicators have been widely used, such as Emergy sustainability index (ESI), which is defined as the ratio of Net emergy yield ratio (NEYR) to Environmental loading ratio (ELR) (Brown and Ulgiati, 1997; Chen et al., 2006). Comprehensive emergy indicators usually are calculated by several basic emergy indicators, and the single evaluation value is difficult to describe the multiple characteristics of the system. So this paper selected some basic emergy indicators with different objectives for the evaluation. On the basis of the fluxes as mentioned above, a series of system indicators are introduced and elucidated in detail as follows (Odum and Odum, 1983; Ulgiati et al., 1995; Odum, 1996; Brown and Ulgiati, 1997; Ulgiati and Brown, 1998; Chen et al., 2006).

2. Methodology

This indicator is defined as the ratio of the total emergy input (U) to the arable land area (area). It is deduced as the intensity of the emergy input per unit area, demonstrating the degree of intensive use of the arable land.

Biosphere derives from solar energy directly or indirectly. Correspondingly, solar transformity is used to account for the quality of energy and its position in the universal energy transformation hierarchy with solar emjoules per joule (sej/J) as its unit. The larger the transformity, the more solar energy required for the production

Emergy input density (EID) =

U R + N + FN + FR = area area

Scale management degree (SMD) =

Fig. 1. Typical diagram associated with crop production system.

U R + N + FN + FR = Labor Labor

(1)

(2)

J. Tao et al. / Ecological Indicators 29 (2013) 325–338

It is the ratio of the total emergy input (U) to the emergy input of human labor (Labor). The higher the ratio, the less human labor needed in the crop production process. Emergy investment radio (EIR) =

FN + FR R+N

(3)

EIR is defined as the ratio of the purchased emergy investment to the free environmental emergy inputs. Generally, the higher the ratio, the higher the economic development level. Environmental loading ratio (ELR) =

N + FN R + FR

(4)

It is the ratio of the non-renewable emergy flows to renewable emergy flows, indicating the load of the environment generated by human-dominated non-renewable flows. The lower the ratio, the lower the stress to the environment. Net emergy yield ratio (NEYR) =

Y FN + FR

Y area

4. Results

(6)

It is taken as the intensity of the emergy output (Y) per unit area, denoting the output density of the arable land. Emergy output per capita (EOP) =

Y P

(7)

EOP is the ratio of the total emergy output (Y) to the employed population (P). It can be used as a measure of the potential mean standard of population living, and a large ratio suggests a high level of emergy output intensity per capita. Emergy dollar ratio (EDR) =

U R + N + FN + FR = GDP GDP

cultivated. China’s arable land, which is 10% of the total arable land in the world, supports over 20% of the world’s population. Limited arable land has been a problem throughout China’s history, leading to long-term food shortage. The production efficiency of arable land has improved over time, but efforts in Western and Northern China has seen little improvement of crop production, generally, due to the colder and drier soil comparing to that in eastern China. Since the 1950s, farm space has also been pressured by the increasing needs for industrialization and urbanization. In this paper, the 31 provinces in mainland China are taken as the case study area (Fig. 2). The collected data are mainly from China Statistical Yearbook (2011), and China Rural Statistical Yearbook (2011). Eight emergy indicators are calculated and listed in Table 1 to evaluate crop production systems in different provinces. The detailed items of the original data can be found in Appendix B.

(5)

This indicator is taken as the emergy output (Y) divided by the emergy input as feedback from the outside economy. The higher the value, the greater the return obtained per unit of emergy invested. Emergy output density (EOD) =

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(8)

EDR is the ratio of the total emergy input (U) to GDP within the crop production system. It is defined as the emergy input for generating per unit of money, indicating the economic benefit of the system. The lower the value is, the better the economic benefit is. Among the above 8 indicators, SMD, EOD and EOP are newly added indicators for this study compared to the other five conventional indicators. SMD is a unique emergy indicator for this study, meanwhile, considering the limited cultivated land area, EOD and EOP are important and necessary in the study which can reflect the productivity and the support capability of crop production. Therefore, the three new indicators are meaningful for this study. These indicators consist of the basis of the evaluation for the crop production system’s intensive use, scale management, investment density, environmental pressure, output benefit, output density, output per capita, and economic benefit. 3. Case study China has a long history of crop production which dated back to 7000 years ago (Wang, 1999). Over the history, the development of crops in China has made significant contributions to the development of world agriculture. However, the arable land in China has been undergoing dramatic reduction along with rapidly growing economy and increasing population in recent years. China’s crop production needs to enhance land productivity relying on agricultural chemical products, which inevitably results in land degradation and environmental pollution (Zhou, 1999; Ye et al., 2002). Although the aggregate output of Chinese crop production is the largest in the world, only about 15% of its total land can be

4.1. Emergy analysis We classified the 31 provinces into five classes using natural breaks method provided in ArgGIS software, namely high level, medium-high level, medium level, low-medium level and low level, respectively. The final classification results are presented in Fig. 3.

(1) Emergy input density (EID) EID reflects the degree of intensive cultivation. As showed in Fig. 1(a), the areas with high level of EID are all located in southeastern China, including Fujian, Guangdong and Hunan. In these provinces, the high level is mainly resulted from much more purchased nonrenewable resources input, such as diesel oil and fertilizers. From the southeast to the northwest of China, the level of EID has a decreasing trend. However, as a special case, Xinjiang has much more irrigating water supply than its neighboring provinces. (2) Scale management degree (SMD) Human is the manager of the crop production system, so the higher the ratio, the less human labor needs. The levels of SMD in the 31 provinces also present a significant spatial pattern, as the levels of SMD in north China are higher than those in south China (Fig. 1(b)). This phenomenon is mainly attributed to China’s terrain. In China, the northern provinces have more plain areas and less mountain areas than the southern counterparts. Flat terrain is favorable to the scale management of arable land. (3) Emergy investment radio (EIR) This indicator reflects the intensity of purchased resources investment. As Fig. 1(c) shows, the high EIRs locate in Xinjiang, Tianjin, Ningxia and Hebei. The high levels in Tianjin and Hebei are mainly due to the large purchased nonrenewable emergy input; and in Xinjiang and Ningxia, purchased renewable resources input accounts for a large proportion in the total input. Guizhou’ EIR is the lowest, only 1.28, less than one third of Xinjiang’s. (4) Environmental loading ratio (ELR) The more the consumption of non-renewable resources, the heavier load of the environment. Excessive loading on environment by human might bring severe degradation in ecological function (Ulgiati and Brown, 1997). Generally speaking, the value of ELR below two indicates relatively low environmental impact (Cavalett et al., 2006). However, the ELR values of Tianjing, Shanxi and Hebei are a little higher than this level. The lowest is Jiangxi, only 0.76. (5) Net emergy yield ratio (NEYR)

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Fig. 2. Locations of the 31 provinces in China.

It is a useful indicator to reflect how efficiently the purchased input emergy is used. The higher the ratio, the more the system yields per purchased input emergy. As Fig. 1(e) shows, only three provinces have high-level of NEYR. However, some crop production systems in developed areas

have much lower level of NEYR, including Beijing, Tianjin, Guangdong, which indicates crop production and economic development present a much uncoordinated state in these provinces. The NEYR value of Tibet is the lowest amongst the 31 provinces.

Table 1 Emergy evaluation of crop production systems in 31 provinces (2010). No.

Provinces

EID (×1015 sej/ha)

SMD

EIR

ELR

NEYR

EOD (×1015 sej/ha)

EOP (×1015 sej/per capita)

EDR (×1011 sej/$)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

5.89 5.51 6.30 3.23 3.39 4.15 3.66 3.23 7.05 7.66 6.90 6.64 9.54 7.58 6.80 7.50 7.35 8.31 9.01 6.20 6.68 4.69 5.36 3.53 3.96 3.71 4.22 3.11 4.15 5.03 6.11

15.20 29.67 18.61 21.98 45.45 27.50 46.75 56.00 32.43 26.45 20.44 16.01 15.20 16.79 16.34 11.20 23.55 9.87 13.87 15.45 24.90 12.82 10.10 13.72 15.11 19.60 18.55 21.61 17.87 37.85 45.96

3.23 3.84 3.73 1.92 1.90 1.77 1.69 1.64 2.43 2.30 2.28 1.86 3.05 1.39 2.79 3.30 2.09 1.92 2.08 2.25 1.82 1.57 1.88 1.28 1.75 2.59 2.33 2.16 2.59 3.84 4.41

1.55 2.39 2.04 2.14 1.54 1.58 1.74 1.34 1.34 1.06 1.13 1.04 1.13 0.76 1.53 1.50 1.17 0.78 0.88 0.99 1.10 1.27 0.98 0.93 1.32 1.16 1.84 1.43 0.87 1.07 0.81

2.24 2.20 2.40 2.01 2.25 2.72 3.02 3.28 3.18 3.44 2.65 3.53 1.93 3.98 3.37 3.50 3.05 3.63 2.07 2.30 2.09 4.37 3.74 2.70 2.11 1.28 2.31 2.48 2.15 2.26 3.57

10.10 9.61 11.91 4.26 5.00 7.21 6.96 6.58 15.85 18.39 12.73 15.22 13.86 17.56 16.86 20.13 15.14 19.83 12.61 9.88 9.01 12.52 13.09 5.35 5.31 3.44 6.83 5.27 6.42 9.03 17.79

3.60 5.57 5.12 2.71 6.26 4.21 7.33 10.05 10.74 9.92 3.86 5.67 2.89 5.73 6.32 5.88 7.67 4.01 2.41 2.65 2.95 4.42 3.63 2.01 1.93 1.34 3.23 3.36 2.83 7.81 16.83

5.86 9.56 10.67 12.96 17.82 9.84 15.49 18.47 7.33 10.65 8.43 16.31 8.60 17.72 9.23 11.12 11.81 10.13 9.59 12.93 9.43 11.15 10.20 17.87 17.20 19.26 10.23 12.65 16.18 18.89 12.11

J. Tao et al. / Ecological Indicators 29 (2013) 325–338

Fig. 3. The levels of the 31 provinces for the eight indicators.

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(6) Emergy output density (EOD) This indicator reflects the output benefit of crop production, the higher the EOD, the more the emergy output per unit area. As seen in Fig. 1(f), the high and medium-high level areas are all located in the southeast China except Xinjiang. The Xinjiang case is mainly attributed to the dominant product cotton with high emergy transformity. Low level provinces are mainly located in the northern and southwest China. The EOD of Tibet is the lowest due to the much lower crop yield per unit area. (7) Emergy output per capita (EOP) The provinces with the high level of EOP include Xinjiang, Shanghai, Heilongjiang, and Jiangsu, indicating crop production in these provinces may have a high standard of living. The EOP of Xinjiang is the largest, 11 times more than the lowest value of Tibet. (8) Emergy dollar ratio (EDR) EDR can be used to value the purchasing power. Generally speaking, developing countries have a higher emergy dollar ratio, because their economy involves more direct use of environmental resources without money exchange. Thus, one dollar in these countries can buy more real wealth than that in developed countries (Yang et al., 2010). In Chinese crop production systems, EDR also complies with this rule. As shown in Fig. 1(h), provinces with high level EDR are mainly located in the northern, central and southwest China, where the economy is less developed than that in the other provinces. The highest EDR is in Tibet, two times more than the lowest EDR in Beijing. This result means that Tibet needs more emergy input in the crop production system compared with other provinces for producing the same GDP value.

1.0 EDR

EID

0.8

SMD

0.6 0.4 0.2 0.0

EOP

EIR

EOD

ELR NEYR

Group 1 Group 4

Group 2 Group 5

Group 3

(a) Group 1-5

1.0 EDR

0.8 0.6

4.2. System development mode analysis

0.4

A cluster analysis is conducted in this paper to classify these provinces into groups with minimal differences between the provinces in the cluster and maximum differences between the clusters. In this study, 8 variables for 31 provinces are processed using the SPSS software. Cluster analysis based on K-means can assess group associations in the data. Grouping is thereby performed by minimizing the Euclidean distances among provinces and the corresponding cluster centroid. By examining the Euclidean distance between a province and the center of its cluster, it is possible to identify representative provinces for each cluster (Liu et al., 2009). Based on the clustering analysis, the 31 provinces were divided into 10 clusters. The normalized mean values for the 8 indicators within each cluster were shown in Fig. 4, and the spatial distribution of 10 groups was shown in Fig. 5.

0.2 EOP

EID SMD

0.0

EOD

Group 6 Group 9

EIR

ELR NEYR Group 7 Group 10

Group 8

(b) Group 6-10 Group 1: As seen in Fig. 5, this group includes Heilongjiang, Inner Mongolia and Jilin, has larger SMD than the other groups, due to the more use of mechanical equipments. EID of this group is the lowest, because the multiple cropping index (MCI) values are almost equal to 1, which greatly limit the emergy input. The low MCI values in these provinces are mainly affected by the cold climate, which makes the crop production cycle longer than that in the southern areas of China. Group 2: Gansu, Shaanxi, Shanxi and Liaoning belong to the group 2, with low EID and high ELR. Group 2 is mainly located in northern China, and is characterized by cold weather and poor-quality arable land. The level of total input is considerably low, among which non-renewable resources account for a large proportion, leading to the high environmental pressure.

Fig. 4. Mean values of the eight indicators within each cluster.

Group 3: This group only includes one province, Xinjiang, which has the largest EIR and EOP in the 31 provinces, due to high emergy transformities of cotton and sugar beet as the dominant crops in Xinjiang. The yields of these two kinds of crops in Xinjiang account for nearly half of the total yields in China. Group 4: This group includes Beijing, Tianjin, and Hebei, with larger ELR than all other groups, due to the more use of fertilizers. Although this group has low NEYR, the economic benefit is really better than that of other groups because of the low EDR. Especially Beijing has the lowest EDR in the 31 provinces.

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Fig. 5. The spatial distribution of the groups.

Group 5: This group includes Guizhou, Yunnan, Qinghai and Tibet. EDR of this group is high, and the other indicators are considerably low. Especially for EIR, EOD and EOP, all of them are the lowest among the 10 groups. The dominant terrains of these areas are mountains and plateaus, unsuitable for crop production. So emergy input and output are both severely restricted. Meanwhile, agricultural economies in these areas are relatively underdeveloped and the economic benefits are worse than other areas. Group 6: Provinces in this group include two inland provinces and three coastal provinces (Fig. 5). Except EOD, the other 7 indicators of this group are all in a medium range (Fig. 4). So the development of this group performs well on multi-objective coordination. Group 7: Ningxia is the only province in this group characterized by the largest EDR among the 10 groups. This group has high EIR, but ELR and NEYR are very low, which indicates that non-renewable resources account for a small proportion in the total input, and the output benefit performs not very well. Group 8: This group includes three inland provinces (Hunan, Anhui and Jiangxi), and has high level of EID and NEYR, accompanied with low level of SMD and ELR. The results indicate that input density, environmental health and output benefit perform well in this group, and low SMD denotes the large number of agricultural laborers. Group 9: This group includes two provinces (Sichuan and Chongqing), with the lowest SMD and the largest NEYR among the 10 groups. The NEYR values of Chongqing and Sichuan are respectively the first and the third largest of the 31 provinces, indicating good output benefit in this group. The relatively low EDR indicates the crop economic benefit of this group is also very well.

Group 10: All five provinces of this group are coastal provinces. This group has the largest EID among the 10 groups, which is attributed to the high level of fertilizers and pesticide inputs. However, the NEYR of this group is the lowest, as the dominant crop product rice presents low level of emergy transformity. Fortunately, the EDR of this group is much lower, meaning that the economic benefit is not so bad compared with the low output benefit. 5. Discussions 5.1. Emergy input density and emergy output density There is a proverb in China: “The more you plough the more you gain”, which literally means, in crop production system, the larger the input density, the higher the output density is. We use EID to represent the input level, and EOD to represent the output level. Then the comparison of input level and output level in the 31 provinces is presented in Table 2. As showed in Table 2, 16 provinces have the same level of EID and EOD; 10 provinces’ EID and EOD have a one-level difference; and the input level and output level in Xinjiang and Chongqin have a two-level difference. Thus, with the development of modern agriculture, China’s crop production system still complies with the input–output balance. Five provinces have low level of EID and EOD, and they are Tibet, Shanxi, Inner Mongolia, Guizhou and Gansu. High altitude and poor natural conditions in these provinces limit the emergy input level and result in low-level emergy output. 5.2. Emergy investment radio and environmental health index Modern crop production is characterized by the intensive use of industrial inputs, which would badly impact the environment. So

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J. Tao et al. / Ecological Indicators 29 (2013) 325–338 Table 2 The comparison of EID and EOD in 31 provinces.

Note: Provinces in gray color have the same level of EID and EOD.

Table 3 The comparison of EIR and EHI in 31 provinces. EIR

High Medium-high Medium Low-medium Low

EHI High

Medium-high

Xinjiang

Ningxia

Qinghai, Guangdong Hunan Jiangxi, Guizhou

Jiangsu, Guangxi Sichuan, Anhui

Medium

Low-medium

Low

Fujian Zhejiang, Tibet, Hubei Yunnan, Hainan Chongqing

Shandong, Henan, Beijing Shanghai, Gansu Liaoning, Jilin, Inner Mongolia, Heilongjiang

Tianjin, Hebei

the relationship between economic investment and environmental performance is worthy of investigation. EIR is used to represent economic investment ratio, while environmental health index (EHI) represents environmental performance, which is the normalized reciprocal of ELR. This paper also uses natural break method to classify the 31 provinces into 5 levels based on EHI. The comparison of economic investment and environmental performance in the 31 provinces was shown in Table 3. As Table 3 shows, only Xinjiang’s EIR and EHI are both in the high level. Tianjin and Hebei have high level EIR but low level EHI, which means that the high economic investment results in the strong environmental pressure. There are 23 provinces have medium or less than medium level EIR, and 20 provinces have medium or less than medium level EHI. The results indicate that the economic investment and environment health are both in a relatively low level in China, while most provinces cannot achieve win-win between economic investment and environment health.

Shaanxi Shanxi

5.3. Emergy output benefit and economic benefit Emergy output benefit reflects the productivity of crop production system, and economic benefit reflects the economic vigor of the system in the external environment. The productivity is the basis of the economic vigor, and the economic vigor can actively promote productivity. NEYR is used to represent the productivity of the system, and dollar emergy ratio (DER) denotes the economic vigor, which is the normalized reciprocal of EDR. We also use natural break method to classify the 31 provinces into 5 levels based on DER. Then the comparison of emergy output benefit and economic benefit in the 31 provinces was shown in Table 4. As Table 4 shows, there is no province with high level of both NEYR and DER simultaneously. Beijing has high level DER but its NEYR is just in the low-medium level, which means Beijing’s productivity is relatively low in contrast to high economic vigor. Jiangxi has high level NEYR but low level DER, because the economic vigor is uncoordinated compared with the productivity.

Table 4 The comparison of NEYR and DER in 31 provinces. NEYR

DER High

High Medium-high Medium Low-medium Low

Shanghai Beijing

Medium-high

Medium

Sichuan Shandong, Jiangsu, Hunan Zhejiang, Liaoning Tianjin, Shaanxi, Hainan, Guangdong, Fujian

Chongqing Xinjiang, Hubei, Henan Hebei, Gansu Shanxi, Guangxi

Low-medium Jilin, Anhui Yunnan, Qinghai

Low Jiangxi Heilongjiang Guizhou Ningxia, Inner Mongolia Tibet

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Table 5 The comparison of SMD and EOP in 31 provinces.

Note: Provinces in gray color have the same level of SMD and EOP.

Overall, the productivity and the economic vigor of crop production system do not behave an obvious relationship in China. The economic vigor is mainly decided by local economy, which is the reason for Beijing’s high level of DER in contrast to Jiangxi’s low level. The results indicate that underdeveloped provinces with high productivity may not be able to obtain good economic benefit. 5.4. Scale management degree and emergy output per capita Scale management of crop production system can enhance the emergy output per capita and improve the income level of laborers. The comparison of SMD and EOP in the 31 provinces was shown in Table 5. As Table 5 shows, the two indicators have a significant positive correlation between each other. SMD and EOP of Xinjiang and Heilongjiang are both in the high level. Henan has low level of SMD but high level EOP, because of the poor quality of arable land and the large population. Tibet has medium level SMD and low level EOP, which means Tibet has a certain degree of scale management, while the emergy output per capita is considerably low. 5.5. Policy suggestions for different groups According to the analysis presented above, it is evident that the 8 indicators have significant differences among 10 groups. Each group is named after two indicators that are most typical to represent the group, based on which the paper proposed relevant policy suggestions for potential improvement (Table 6).

5.6. Application prospects of the method In previous studies, emergy analysis on a special system usually used some traditional emergy indicators for the evaluation, and rarely adopted new emergy indicators considering the characteristics of the system (Brown and Ulgiati, 1997; Chen et al., 2006; Liu et al., 2009). So, based on the multiple characteristics of crop production system, this study established a multi-objective emergy indicator system, and carried out a comprehensive evaluation. The evaluation results are more accurate and practical compared with only using traditional emergy indicators. Therefore, this study has two aspects of application prospects: firstly, it provides an appropriate emergy indicator system for crop production system or other agricultural systems; secondly, future researchers can reference the ideas of this study to establish emergy indicator system for evaluating a specific system. 5.7. Limitations and uncertainties This paper established a solar transformity system by reference to a variety of literatures and concluded the unified solar transformity at the national scale, for the purpose of eliminating possible differences when compare. However, the resulting problem is the accuracy of evaluation at a provincial scale. Therefore, the provincial solar transformity needs to be adjusted with the local conditions. The established multi-objective emergy-based evaluation indicators for crop production system have been tested in the 31 provinces of mainland China with promising results. However, the research on reliable and easy-to-use indicators, especially the three

Table 6 The development mode description for each group. Group No.

Development mode

Policy suggestions

Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7 Group 8 Group 9 Group 10

Low EID-high SMD mode Low EOD-high ELR mode Medium EID-high EOP mode Low EDR-high ELR mode Low EIR-high EDR mode Low SMD-high EOD mode Low NEYR-High EIR mode Low SMD-high NEYR mode Low EOP-low EDR mode Low NEYR-high EID mode

Encourage laborers to increase multiple cropping index; improve the intensive use of arable land. Reduce non-renewable resources input; increase renewable resources input; relief environmental pressure. Appropriately increase investment; maintain high output level. Strengthen arable land protection; promote sustainable land. Strengthen crop production input, and gradually increase the prices of crop products. Maintain the balanced development state, appropriately increase mechanization rate. Strengthen land consolidation; improve the quality of arable land Greatly enhance the scale management degree; maintain output benefit. Gradually reduce the number of agricultural laborers; improve the income level per capita. Strengthen land consolidation; reduce nonrenewable resources input, such as chemical fertilizer and pesticides.

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J. Tao et al. / Ecological Indicators 29 (2013) 325–338

newly added indicators – SMD, EOD and EOP, needs further validation and improvement. According to the calculation results, the seven provinces with the highest emergy yields – Henan, Shandong, Jiangsu, Anhui, Heilongjiang, Sichuan and Hebei, contribute to more than half of the total emergy yield in China. However, this paper mainly focused on the comparison among the 31 provinces, not the production characteristics of major crop production provinces in China. Therefore, it hardly takes the further detailed analysis about food security, high yields, agricultural economy, etc. in these provinces, related to food security, high yields, agricultural economy, and others, into consideration. The insufficiencies in these fields need more comprehensive research. 6. Conclusion Emergy method enhances our knowledge in functioning and production of crop production system. Emergy is actually very valuable both for natural and anthropic systems. The wide applicability is probably the principal advantage of this method. This study focused on the crop production system, which is an open, complex and multi-target system. Therefore, it is difficult to seek a comprehensive indicator for evaluation. According to the characteristics of emergy inputs and outputs, this study selected eight appropriate emergy indicators for the evaluation. 31 provinces in China as case study have been verified with multi-objective evaluation results.

Based on the cluster analysis, the study area was divided into 10 groups. Finally, this paper summarized the development mode for each group, and proposed useful suggestions for policy maker in future. The assessment objectively reflects the situation of China’s crop production, as well as its heterogeneity in different provinces. The results provide certain reference value for the national as well as provincial governments in terms of policy-making.

Acknowledgements This article is supported by National Natural Science Foundation of China (Grant No.: 41101531), the Research Fund for the Doctoral Program of Higher Education of China (Grant No.: 20110022120010) and China Scholarship Council (CSC). The paper made references to large amount of documents to define the solar transformities. Here, authors express gratitude to the documents’ authors. We like to thank the reviewers for the detailed and thoughtful comments and their suggestions for improvements.

Appendix A. For the detailed items associated with five kinds of fluxes (R, N, FN , FR , Y), see Table A1.

Table A1 The items associated with the five kinds of fluxes (R, N, FN , FR , Y). No.

Units

Solar transformity (sej/unit)

References

Local renewable resources (R) 1 Sunlight 2 Wind, kinetic energy Rain 3 4 Earth cycle

Item

J J J J

1.00E+00 2.45E+03 3.06E+04 5.80E+04

Odum (1996) Odum (2000) Odum (1996) Odum (2000)

Local nonrenewable resources (N) Net loss of topsoil 5

J

7.40E+04

Brown and Bardi (2001)

Purchased nonrenewable resources (FN ) Mechanical equipments 6 7 Diesel Nitrogen fertilizer 8 Phosphate fertilizer 9 Potash fertilizer 10 Compound fertilizer 11 Pesticides 12 Plastic mulch 13

g J g g g g g g

6.79E+09 6.60E+04 3.80E+09 3.90E+09 1.10E+09 2.80E+09 1.60E+09 3.80E+08

Brown and Arding (1991) Odum (1996) Odum (1996) Odum (1996) Odum (1996) Odum (1996) Odum (1996) Odum (1996)

Purchased renewable resources (FR ) Labor 14 Irrigating water 15 Seeds 16

J J J

3.80E+05 4.10E+04 2.00E+05

Lan et al. (1998) Brown and Arding (1991) Odum (1996)

Products of agro-ecosystem (Y) Rice 17 Wheat 18 Corn 19 Straw 20 Chaff 21 The other cereals 22 Soybean 23 24 Tubers 25 Rape seed Peanut 26 The other oil-bearing Crops 27 Cotton 28 29 Sugarcane 30 Sugar beet Tobacco 31 32 Vegetable 33 Fruit

J J J J J J J J J J J J J J J J J

5.18E+04 9.26E+04 6.03E+04 4.45E+04 2.95E+05 3.59E+04 7.64E+04 2.60E+05 8.88E+04 7.72E+04 6.90E+04 8.60E+05 4.50E+04 6.14E+04 8.30E+04 2.70E+04 5.30E+05

Ulgiati et al. (1993) Ulgiati et al. (1993) Ulgiati et al. (1993) Liu et al. (2012a,b) Liu et al. (2012a,b) Du (2008) Ulgiati et al. (1993) Ulgiati et al. (1993) Ulgiati et al. (1993) Ulgiati et al. (1993) Li (2010) Odum (1996) Ulgiati et al. (1993) Brown and Ulgiati (2001) Li (2010) Liu (2005) Liu (2005)

J. Tao et al. / Ecological Indicators 29 (2013) 325–338

335

Appendix B. For the original data used in this study, see Table B1.

Table B1 The original data used in this study. No.

Item

Units

Beijing

Tianjin

Hebei

Shanxi

Inner Mongolia

Liaoning

Jilin

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Sunlight Wind, kinetic energy Rain Earth cycle Net loss of topsoil Mechanical equipments Diesel Nitrogen fertilizer Phosphate fertilizer Potash fertilizer Compound fertilizer Pesticides Plastic mulch Labor Irrigating water Seeds Rice Wheat Corn Straw Chaff The other cereals Soybean Tubers Rape seed Peanut The other oil-bearing Crops Cotton Sugarcane Sugar beet Tobacco Vegetable Fruit arable land area GDP Population Efficient production perioda

J J J J J g J g g g g g g J J J J J J J J J J J J J J J J J J J J ha $ person Year

7.02E+18 3.90E+15 3.84E+15 3.15E+13 2.53E+15 2.31E+10 3.74E+14 6.87E+10 8.80E+09 7.20E+09 5.20E+10 3.97E+09 1.35E+10 2.36E+14 4.54E+15 6.23E+14 2.76E+13 3.78E+15 1.18E+16 3.88E+15 7.11E+14 6.48E+13 1.75E+14 1.91E+14 5.81E+11 3.55E+14 1.79E+13 0.00E+00 0.00E+00 0.00E+00 0.00E+00 7.57E+15 1.13E+15 2.32E+05 2.33E+09 6.50E+05 0.64

1.04E+19 5.78E+15 3.87E+15 4.67E+13 4.81E+15 4.93E+10 2.68E+15 1.18E+11 3.87E+10 1.64E+10 7.72E+10 3.72E+09 1.20E+10 2.15E+14 7.02E+15 1.01E+15 1.62E+15 7.08E+15 1.30E+16 8.74E+15 1.60E+15 3.48E+13 2.88E+14 6.49E+13 0.00E+00 1.18E+14 5.54E+13 1.18E+15 0.00E+00 0.00E+00 0.00E+00 1.05E+16 9.49E+14 4.41E+05 2.54E+09 7.60E+05 0.50

2.01E+20 1.12E+17 9.12E+16 9.03E+14 6.89E+16 8.51E+11 7.63E+16 1.53E+12 4.73E+11 2.68E+11 9.56E+11 8.46E+10 1.19E+11 5.63E+15 8.49E+16 2.07E+16 7.86E+15 1.64E+17 2.11E+17 1.74E+17 3.20E+16 8.01E+15 5.03E+15 1.38E+16 7.63E+14 3.05E+16 3.15E+15 1.07E+16 0.00E+00 1.37E+15 1.02E+14 1.77E+17 1.65E+16 6.32E+06 3.73E+10 1.47E+07 0.68

8.30E+19 4.60E+16 3.27E+16 3.72E+14 4.42E+16 2.35E+11 6.63E+15 4.00E+11 2.00E+11 8.51E+10 4.18E+11 2.61E+10 3.89E+10 1.57E+15 1.98E+16 7.69E+15 6.67E+13 3.09E+16 1.07E+17 3.16E+16 5.79E+15 5.76E+15 3.61E+15 3.65E+15 1.68E+14 4.98E+14 5.73E+15 1.30E+15 0.00E+00 6.30E+14 1.88E+14 2.27E+16 2.19E+15 4.06E+06 1.01E+10 6.38E+06 0.43

1.46E+20 8.11E+16 7.18E+16 6.56E+14 7.79E+16 2.54E+11 1.93E+16 8.05E+11 3.09E+11 1.46E+11 5.13E+11 2.43E+10 6.06E+10 1.40E+15 1.00E+17 1.16E+16 1.08E+16 2.20E+16 2.05E+17 3.26E+16 5.97E+15 1.82E+16 2.49E+16 2.41E+16 5.91E+15 6.80E+14 3.97E+16 2.11E+13 0.00E+00 4.49E+15 2.37E+14 3.38E+16 7.95E+15 7.15E+06 1.36E+10 5.71E+06 0.43

7.87E+19 4.37E+16 8.54E+16 3.53E+14 4.45E+16 1.88E+11 1.71E+16 6.83E+11 1.14E+11 1.22E+11 4.81E+11 6.94E+10 1.25E+11 1.62E+15 5.26E+16 4.80E+15 6.64E+16 4.92E+14 1.61E+17 6.26E+16 1.15E+16 1.03E+16 5.55E+15 7.23E+15 1.44E+13 2.27E+16 1.31E+15 1.23E+13 0.00E+00 1.36E+14 4.48E+14 6.67E+16 6.98E+15 4.09E+06 1.72E+10 7.00E+06 0.41

1.00E+20 5.56E+16 9.21E+16 4.49E+14 6.03E+16 1.80E+11 2.21E+16 6.69E+11 6.63E+10 1.22E+11 9.71E+11 4.28E+10 5.26E+10 1.14E+15 6.46E+16 6.12E+15 8.24E+16 1.65E+14 2.81E+17 7.73E+16 1.42E+16 1.27E+16 1.69E+16 1.06E+16 0.00E+00 8.75E+15 1.29E+16 9.68E+13 0.00E+00 2.16E+14 1.14E+15 2.70E+16 5.05E+15 5.53E+06 1.31E+10 5.25E+06 0.38

No.

Item

Units

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

Fujian

Jiangxi

Shandong

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Sunlight Wind, kinetic energy Rain Earth cycle Net loss of topsoil Mechanical equipments Diesel Nitrogen fertilizer Phosphate fertilizer Potash fertilizer Compound fertilizer Pesticides Plastic mulch Labor Irrigating water Seeds Rice Wheat Corn Straw Chaff The other cereals Soybean Tubers Rape seed Peanut The other oil-bearing Crops Cotton Sugarcane Sugar beet

J J J J J g J g g g g g g J J J J J J J J J J J J J J J J J

2.28E+20 1.27E+17 1.41E+17 1.02E+15 1.29E+17 3.13E+11 4.91E+16 7.74E+11 4.74E+11 3.08E+11 5.94E+11 7.38E+10 6.94E+10 1.79E+15 1.87E+17 1.55E+16 2.67E+17 1.23E+16 3.25E+17 2.63E+17 4.82E+16 3.78E+15 9.03E+16 1.78E+16 4.87E+13 1.16E+15 8.65E+15 0.00E+00 0.00E+00 4.88E+15

7.87E+18 4.37E+15 9.30E+15 3.53E+13 2.66E+15 8.72E+09 4.78E+15 6.19E+10 1.01E+10 5.90E+09 4.05E+10 7.04E+09 2.11E+10 1.39E+14 5.24E+15 8.03E+14 1.31E+16 2.56E+15 4.13E+14 1.49E+16 2.73E+15 5.88E+14 1.77E+14 1.37E+14 5.38E+14 5.80E+13 3.86E+12 6.61E+13 6.31E+13 0.00E+00

1.63E+20 9.05E+16 2.22E+17 7.31E+14 5.19E+16 3.30E+11 1.44E+16 1.80E+12 4.77E+11 2.08E+11 9.31E+11 9.01E+10 1.00E+11 3.63E+15 1.35E+17 1.81E+16 2.62E+17 1.34E+17 3.06E+16 3.82E+17 7.01E+16 1.20E+16 1.28E+16 5.56E+15 2.97E+16 8.90E+15 7.07E+14 4.90E+15 2.34E+14 2.63E+12

4.31E+19 2.39E+16 7.79E+16 1.93E+14 2.09E+16 2.03E+11 1.29E+16 5.25E+11 1.20E+11 7.17E+10 2.06E+11 6.51E+10 5.54E+10 1.71E+15 5.90E+16 3.35E+15 9.40E+16 3.28E+15 1.70E+15 9.13E+16 1.67E+16 2.21E+15 4.56E+15 5.82E+15 8.78E+15 1.27E+15 3.25E+14 5.52E+14 1.71E+15 0.00E+00

1.94E+20 1.08E+17 2.67E+17 8.70E+14 6.25E+16 4.53E+11 2.30E+16 1.12E+12 3.59E+11 3.18E+11 1.40E+12 1.17E+11 8.07E+10 6.25E+15 8.06E+16 2.15E+16 2.01E+17 1.60E+17 4.38E+16 3.51E+17 6.44E+16 1.32E+15 1.83E+16 6.69E+15 3.53E+16 2.04E+16 2.89E+15 5.94E+15 5.15E+14 0.00E+00

3.82E+19 2.12E+16 6.41E+16 1.71E+14 1.45E+16 1.01E+11 1.85E+16 4.77E+11 1.71E+11 2.47E+11 3.16E+11 5.82E+10 5.71E+10 2.20E+15 6.16E+16 2.68E+15 7.37E+16 1.36E+14 2.13E+15 6.91E+16 1.27E+16 2.64E+14 2.80E+15 1.65E+16 3.83E+14 5.91E+15 6.29E+13 0.00E+00 1.42E+15 0.00E+00

9.12E+19 5.06E+16 2.11E+17 4.09E+14 3.08E+16 3.19E+11 6.73E+15 4.34E+11 2.21E+11 2.12E+11 5.09E+11 1.07E+11 4.55E+10 3.36E+15 7.15E+16 6.43E+15 2.69E+17 2.81E+14 1.18E+15 2.52E+17 4.63E+16 1.39E+14 4.19E+15 8.04E+15 1.69E+16 9.63E+15 1.13E+15 2.46E+15 1.36E+15 0.00E+00

2.57E+20 1.43E+17 2.21E+17 1.15E+15 8.19E+16 9.75E+11 4.82E+16 1.63E+12 4.99E+11 4.64E+11 2.16E+12 1.65E+11 3.23E+11 8.24E+15 9.58E+16 2.85E+16 1.54E+16 2.74E+17 2.70E+17 2.94E+17 5.39E+16 1.31E+15 6.16E+15 2.67E+16 7.02E+14 8.00E+16 1.75E+14 1.36E+16 0.00E+00 0.00E+00

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J. Tao et al. / Ecological Indicators 29 (2013) 325–338

Table B1 (Continued ) No.

Item

Units

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

Fujian

Jiangxi

Shandong

31 32 33 34 35 36 37

Tobacco Vegetable Fruit arable land area GDP Population Efficient production period

J J J ha $ person Year

1.50E+15 1.81E+16 7.69E+15 1.18E+07 2.07E+10 7.75E+06 0.41

0.00E+00 9.95E+15 1.91E+15 2.44E+05 2.34E+09 3.60E+05 0.68

8.13E+12 1.06E+17 1.66E+16 4.76E+06 3.43E+10 8.83E+06 0.73

4.98E+13 4.47E+16 1.05E+16 1.92E+06 1.57E+10 6.34E+06 0.48

4.68E+14 5.34E+16 1.88E+16 5.73E+06 2.33E+10 1.54E+07 0.72

1.97E+15 3.91E+16 2.58E+15 1.33E+06 1.47E+10 6.37E+06 0.61

5.90E+14 2.79E+16 5.65E+15 2.83E+06 1.21E+10 8.67E+06 0.68

1.09E+15 2.26E+17 4.47E+16 7.52E+06 5.54E+10 2.00E+07 0.73

No.

Item

Units

Henan

Hubei

Hunan

Guangdong

Guangxi

Hainan

Chongqing

Sichuan

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Sunlight Wind, kinetic energy Rain Earth cycle Net loss of topsoil Mechanical equipments Diesel Nitrogen fertilizer Phosphate fertilizer Potash fertilizer Compound fertilizer Pesticides Plastic mulch Labor Irrigating water Seeds Rice Wheat Corn Straw Chaff The other cereals Soybean Tubers Rape seed Peanut The other oil-bearing Crops Cotton Sugarcane Sugar beet Tobacco Vegetable Fruit arable land area GDP Population Efficient production period

J J J J J g J g g g g g g J J J J J J J J J J J J J J J J J J J J ha $ person Year

3.40E+20 1.89E+17 2.14E+17 1.52E+15 8.64E+16 8.54E+11 3.36E+16 2.44E+12 1.18E+12 6.16E+11 2.32E+12 1.25E+11 1.47E+11 1.40E+16 7.42E+16 3.99E+16 6.83E+16 4.10E+17 2.29E+17 4.82E+17 8.84E+16 2.99E+15 1.40E+16 1.93E+16 2.35E+16 1.01E+17 9.36E+15 8.41E+15 6.01E+14 0.00E+00 4.51E+15 1.66E+17 5.27E+16 7.93E+06 5.35E+10 2.71E+07 0.91

1.61E+20 8.96E+16 2.26E+17 7.24E+14 5.08E+16 2.83E+11 1.47E+16 1.56E+12 6.58E+11 3.03E+11 9.83E+11 1.40E+11 6.38E+10 3.83E+15 9.49E+16 1.55E+16 2.26E+17 4.56E+16 3.65E+16 2.58E+17 4.73E+16 1.93E+15 6.64E+15 1.37E+16 6.14E+16 1.52E+16 5.71E+15 8.87E+15 7.44E+14 0.00E+00 1.94E+15 7.83E+16 1.13E+16 4.66E+06 2.90E+10 9.21E+06 0.73

1.42E+20 7.86E+16 2.41E+17 6.35E+14 4.13E+16 3.90E+11 8.09E+15 1.10E+12 2.67E+11 4.05E+11 5.90E+11 1.19E+11 7.32E+10 8.40E+15 1.17E+17 9.81E+15 3.63E+17 1.32E+15 2.35E+16 3.41E+17 6.26E+16 8.22E+14 6.04E+15 1.66E+16 4.40E+16 6.44E+15 5.19E+14 4.27E+15 1.76E+15 0.00E+00 3.49E+15 7.81E+16 1.08E+16 3.79E+06 3.11E+10 1.87E+07 0.79

7.68E+19 4.26E+16 1.89E+17 3.45E+14 3.09E+16 1.97E+11 1.54E+16 1.00E+12 2.15E+11 4.70E+11 6.88E+11 1.04E+11 4.21E+10 4.83E+15 1.15E+17 5.30E+15 1.54E+17 3.27E+13 1.01E+16 1.44E+17 2.64E+16 4.61E+14 2.75E+15 2.29E+16 2.11E+14 2.06E+16 9.11E+13 0.00E+00 2.99E+16 0.00E+00 8.65E+14 6.80E+16 3.54E+15 2.83E+06 2.66E+10 1.48E+07 0.58

9.95E+19 5.53E+16 1.43E+17 4.47E+14 4.60E+16 2.32E+11 1.71E+16 6.99E+11 2.88E+11 5.32E+11 8.51E+11 6.45E+10 3.51E+10 4.45E+15 1.33E+17 6.92E+15 1.63E+17 7.58E+13 2.92E+16 1.52E+17 2.79E+16 3.11E+14 3.55E+15 7.92E+15 3.88E+14 1.03E+16 3.21E+14 3.89E+13 1.64E+17 0.00E+00 4.21E+14 5.32E+16 8.34E+15 4.22E+06 2.02E+10 1.57E+07 0.50

1.40E+19 7.78E+15 3.59E+16 6.29E+13 7.93E+15 3.56E+10 4.47E+15 1.38E+11 3.07E+10 7.18E+10 2.24E+11 4.55E+10 1.61E+10 5.14E+14 1.92E+16 9.75E+14 2.01E+16 0.00E+00 1.27E+15 1.88E+16 3.44E+15 7.38E+12 3.23E+14 4.32E+15 0.00E+00 2.18E+15 9.77E+13 0.00E+00 8.86E+15 0.00E+00 0.00E+00 1.11E+16 2.96E+15 7.28E+05 5.16E+09 2.22E+06 0.41

6.33E+19 3.52E+16 6.92E+16 2.84E+14 2.44E+16 8.98E+10 3.81E+15 4.93E+11 1.75E+11 5.24E+10 1.91E+11 2.09E+10 3.66E+10 2.15E+15 1.29E+16 5.00E+15 7.52E+16 6.11E+15 3.52E+16 7.66E+16 1.40E+16 9.50E+14 6.29E+15 4.12E+16 9.03E+15 2.14E+15 4.55E+14 0.00E+00 2.69E+14 0.00E+00 1.27E+15 3.27E+16 1.19E+15 2.24E+06 9.41E+09 6.33E+06 0.60

1.92E+20 1.06E+17 1.88E+17 8.61E+14 6.48E+16 2.64E+11 8.48E+15 1.30E+12 4.92E+11 1.64E+11 5.11E+11 6.22E+10 1.14E+11 8.30E+15 7.47E+16 1.82E+16 2.19E+17 5.69E+16 9.37E+16 2.63E+17 4.83E+16 7.60E+15 1.47E+16 6.59E+16 5.42E+16 1.45E+16 6.87E+14 2.66E+14 2.15E+15 6.76E+12 3.85E+15 8.52E+16 4.07E+15 5.95E+06 3.12E+10 2.14E+07 0.68

No.

Item

Units

Guizhou

Yunnan

Tibet

Shaanxi

Gansu

Qinghai

Ningxia

Xinjiang

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Sunlight Wind, kinetic energy Rain Earth cycle Net loss of topsoil Mechanical equipments Diesel Nitrogen fertilizer Phosphate fertilizer Potash fertilizer Compound fertilizer Pesticides Plastic mulch Labor Irrigating water Seeds Rice Wheat Corn Straw Chaff The other cereals Soybean Tubers Rape seed Peanut

J J J J J g J g g g g g g J J J J J J J J J J J J J

9.53E+19 5.29E+16 1.01E+17 4.27E+14 4.89E+16 1.45E+11 1.60E+15 4.65E+11 1.08E+11 7.65E+10 2.16E+11 1.29E+10 3.62E+10 3.04E+15 5.37E+16 7.72E+15 6.46E+16 3.30E+15 5.82E+16 6.38E+16 1.17E+16 4.06E+15 3.99E+15 2.46E+16 1.36E+16 1.81E+15

1.27E+20 7.03E+16 1.15E+17 5.68E+14 6.62E+16 2.02E+11 1.79E+16 9.75E+11 2.72E+11 1.79E+11 4.20E+11 4.62E+10 8.57E+10 4.19E+15 6.98E+16 1.02E+16 8.94E+16 6.12E+15 8.58E+16 8.99E+16 1.65E+16 3.86E+14 1.19E+16 2.45E+16 6.86E+15 1.65E+15

5.83E+18 3.24E+15 2.20E+15 2.62E+13 3.94E+15 3.17E+10 8.46E+14 1.92E+10 1.07E+10 4.40E+09 1.31E+10 1.04E+09 8.52E+08 1.80E+14 7.29E+15 8.56E+14 8.56E+13 3.23E+15 3.86E+14 3.37E+15 6.19E+14 9.62E+15 3.53E+14 4.94E+13 1.54E+15 5.78E+12

9.56E+19 5.31E+16 5.28E+16 4.29E+14 4.41E+16 1.68E+11 1.23E+16 8.77E+11 1.80E+11 2.00E+11 5.64E+11 1.24E+10 3.68E+10 2.43E+15 2.88E+16 1.02E+16 1.17E+16 5.37E+16 7.45E+16 6.58E+16 1.21E+16 3.96E+15 6.76E+15 1.10E+16 9.84E+15 2.12E+15

9.35E+19 5.19E+16 1.88E+16 4.19E+14 5.08E+16 1.66E+11 1.03E+16 3.79E+11 1.66E+11 6.09E+10 2.47E+11 4.46E+10 1.24E+11 1.76E+15 5.86E+16 1.02E+16 5.96E+14 3.34E+16 5.47E+16 3.46E+16 6.34E+15 1.46E+16 5.32E+15 2.61E+16 8.77E+15 3.99E+13

1.22E+19 6.75E+15 5.16E+15 5.46E+13 5.92E+15 3.53E+10 1.23E+15 3.53E+10 8.60E+09 3.40E+09 4.03E+10 2.06E+09 3.11E+09 3.31E+14 1.20E+16 1.97E+15 0.00E+00 4.96E+15 1.50E+15 5.06E+15 9.27E+14 1.41E+15 1.28E+15 5.15E+15 8.91E+15 0.00E+00

2.79E+19 1.55E+16 6.02E+15 1.25E+14 1.21E+16 6.11E+10 4.78E+15 1.77E+11 4.15E+10 2.18E+10 1.39E+11 2.64E+09 1.41E+10 3.87E+14 4.18E+16 2.85E+15 1.01E+16 9.35E+15 2.32E+16 1.90E+16 3.49E+15 6.53E+14 5.62E+14 5.99E+15 1.03E+13 1.49E+12

1.14E+20 6.30E+16 3.36E+16 5.10E+14 4.50E+16 1.38E+11 1.70E+16 7.87E+11 4.23E+11 1.03E+11 3.64E+11 1.82E+10 1.71E+11 1.44E+15 2.29E+17 1.35E+16 8.55E+15 8.29E+16 5.90E+16 9.26E+16 1.70E+16 2.81E+15 4.25E+15 2.89E+15 4.00E+15 4.11E+14

J. Tao et al. / Ecological Indicators 29 (2013) 325–338

337

Table B1 (Continued ) No.

Item

Units

Guizhou

Yunnan

Tibet

Shaanxi

Gansu

Qinghai

Ningxia

Xinjiang

27 28 29 30 31 32 33 34 35 36 37

The other oil-bearing Crops Cotton Sugarcane Sugar beet Tobacco Vegetable Fruit arable land area GDP Population Efficient production period

J J J J J J J ha $ person Year

4.00E+14 1.87E+13 1.20E+15 0.00E+00 6.14E+15 3.01E+16 1.78E+15 4.49E+06 8.87E+09 1.19E+07 0.45

4.77E+14 0.00E+00 4.03E+16 0.00E+00 1.56E+16 3.14E+16 1.86E+15 6.07E+06 1.40E+10 1.67E+07 0.44

0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.45E+15 4.04E+13 3.62E+05 6.97E+08 9.30E+05 0.34

3.79E+15 1.30E+15 4.64E+12 0.00E+00 1.06E+15 3.46E+16 7.85E+15 4.05E+06 1.67E+10 8.56E+06 0.50

1.18E+16 1.42E+15 0.00E+00 6.14E+14 1.92E+14 3.09E+16 6.24E+15 4.66E+06 1.14E+10 7.31E+06 0.43

2.51E+14 0.00E+00 0.00E+00 0.00E+00 1.17E+13 3.54E+15 7.92E+13 5.43E+05 1.39E+09 1.23E+06 0.48

8.03E+15 0.00E+00 0.00E+00 0.00E+00 3.69E+13 1.02E+16 5.41E+15 1.11E+06 2.95E+09 1.28E+06 0.53

1.92E+16 4.66E+16 0.00E+00 1.36E+16 0.00E+00 4.34E+16 1.44E+16 4.12E+06 2.08E+10 4.36E+06 0.58

a

i

Crop production requires appropriate climate, temperature, etc., so it is an intermittent production. This paper introduces the concept of efficient production period. It

can be calculated via this equation: P =

n

(Pi × (Ai /A)) × M, where P is the efficient production period of crop production system, Pi is the efficient production period of the

ith crop, n is the total number of crop types, Ai is the gross sown area of the ith crop, A is the total gross sown area of crop production system, and M is the multiple cropping index of crop production system. These items, including sunlight, wind, kinetic energy, rain, earth cycle and labor, have been revised by the efficient production period.

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