Environmental footprints of soybean production in China

doi:10.21203/rs.3.rs-220995/v1

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Environmental footprints of soybean production in China

https://doi.org/10.21203/rs.3.rs-220995/v1

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published 23 May, 2022

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The agriculture sector is both a significant consumer of energy and water and a major source of environmental pollution and greenhouse gases. Soybean production (Glycine max) has experienced a fast growth and it is the fourth most widely cultivated crop, leading to serious environmental concerns. This study evaluates the energy, carbon, and water footprints of China’s soybean production so that key environmental impacts can be identified. To provide reliable results for decision-making, uncertainty analysis is conducted based on the Monte Carlo model. Results show that the impact on climate change, fossils depletion, ecosystem quality, human health, and resource was 3.33×10³ kg CO₂ eq (GSD² = 1.87), 343.37 kg oil eq (GSD² = 1.60), 6.18×10^− 5 Species·yr (GSD² = 1.81), 3.26×10^− 3 DALY (GSD² = 1.81), and 89.22 $ (GSD² = 2.28), respectively. Freshwater ecotoxicity was the dominant contributor (77.69%) to the ecosystem quality category, while climate change (85.22%) was the dominant contributor to the human health category. Key factors analysis results show that diammonium phosphate and diesel, and on-site emissions from soybean production, were the major contributors to the overall environmental burden of soybean production. Several policy recommendations are proposed, focusing on trade structure optimization, technological improvements, and efficient resource use. Such policy recommendations provide valuable insights to those decision-makers so that they can prepare appropriate mitigation policies.

Environmental Policy

Water footprint

energy footprint

carbon footprint

life cycle assessment

soybeans

China

Soybean (Glycine max) is the fourth most widely cultivated crop and also a significant protein source for humans and animals (Fried et al., 2018). The global soybean production increased from 220.8 million tons in 2005 to 336.6 million tons in 2019, indicating a 52.4% increase. China was the world’s fourth largest soybean producer in 2019, with a yield of 17.1 million tons and accounting for 5.1% of the global production (SOPA, 2020). China is also the largest soybean importer in order to satisfy its soaring demand (Fuchs et al., 2019; Wu et al., 2020). Unfortunately, the recent trade skirmish between China and the United States has resulted in unstable soybean supply from the United States to China (Geng and Sarkis, 2018). In order to respond such a crisis, the Chinese government has increased its domestic soybean cultivation and increased its soybean production from 11.95 million tons in 2013 to 17.1 million tons in 2019 (SOPA, 2020). However, such large production is based upon a large amount of energy and water consumption and induced many environmental issues, such as soil contamination and greenhouse gases (GHGs) emission (Maciel et al., 2016; Taherzadeh and Caro, 2019). In addition, the outbreak of Covid-19 further influenced international trade globally. Consequently, it is critical to evaluate environmental impacts of soybean production so that appropriate mitigation policies can be prepared to ensure sustainable soybean production (Liu et al., 2019).

Academically, environmental footprint is one effective method to measure environmental impacts caused by human activities (Hoekstra and Wiedmann, 2014; Gu et al., 2016). It can quantify different environmental burdens on global sustainability caused by anthropogenic activities so that key problems can identified (Power, 2009; Čuček et al., 2012). Several carbon and energy footprints studies on soybean production have been conducted. For example, Maciel et al. (2016) and Raucci et al. (2015) measured the GHGs generated from soybean production in Brazil. Arrieta et al. (2018) accounted carbon and energy footprints of Argentina’s soybean production. But few studies have been conducted to calculate water footprint (WF) from soybean production. The WF of one product is a measure of the water consumed and polluted per unit of the product produced (Hoekstra et al., 2011; Haghighi et al., 2018). In the LCA framework, the assessment of the impact of the WF of one product is the process of quantifying the magnitude and significance of the potential environmental impacts related to water used and polluted in the studied product (ISO 14046, 2014). Different from volume-based WF, LCA-based WF method focuses on the impacts on water generated from the studied system rather than the water volume consumed and polluted by the investigated system (Berger and Finkbeiner, 2013). Since the publication of the ISO 14046 (2014), LCA-based WF studies on various aspects have been conducted worldwide, including several in the agricultural sector. For example, Zhai et al. (2019a; 2019b) used the LCA-based WF method to evaluate the WF of wheat production. Up to now, no LCA-based WF studies on soybean production have been documented. A life cycle inventory (LCI) is essential for LCA studies of down-stream products, such as soybean oil, biodiesel, and soybean meal. However, few LCA studies on China’s soybean production have been published, except the one written by Knudsen et al. (2010), in which they evaluated the environmental impacts of soybean production in Jilin province of China based on the 2006 data.

This study aims to fill these research gaps by evaluating China’s environmental footprints from soybean production. It is expected that a holistic picture of China’s environmental impacts from soybean production can be provided with a temporal and spacial analysis. Also, uncertainty analysis is performed so that more reliable results for decision-making could be offered. In addition, key factors contributing to these impacts are identified so that all the stakeholders can work together to prepare feasible policies to promote sustainable soybean production. The whole paper is organized as below: After this introduction section, section 2 details research methods and data sources. Then, section 3 presents research results and section 4 discusses policy recommendations. Finally, research conclusions are drawn in section 5.

2.1 Functional unit and system boundary

A functional unit is usually used as a reference unit as it enables all inputs and outputs of one investigated system to be normalized (Chen et al., 2015; ISO 14040, 2006). In this study, the functional unit is defined as one ton of soybean produced. The system boundary is set by using the “cradle-to-gate” approach, which means that the transportation and utilization of the soybean is not included in this study. The inputs and outputs associated with soybean production such as the seeds, chemical fertilizer, pesticides, irrigation water, energy, and direct emissions discharged to water are considered. In addition, the transport distances of various raw materials (i.e. seeds, chemical fertilizer, and pesticides) are assumed as 100 kilometers.

2.2 Methods

LCA is an ISO standardized method and has been widely used for assessing environmental impacts throughout the entire life cycle of one product, process, or activity (Ögmundarson et al., 2020). Figure 1 illustrates how to calculate the environmental footprints (i.e. water, energy, and carbon footprint) of China’s soybean production.

WF is one indicator to quantify the potential environmental impacts related to water and includes the availability and degradation of water (ISO 14046, 2016). In this study, six midpoint categories (i.e. water scarcity, aquatic eutrophication, acidification, freshwater ecotoxicity, carcinogens, and non-carcinogens) for WF are investigated. From water availability point of view (impact related to water quantity), water scarcity is measured by using the WAVE+ model (Berger et al., 2018) and regional information (e.g. growing cycle and planting time). In terms of water degradation (impacts related to water quality), aquatic eutrophication, acidification, freshwater ecotoxicity, carcinogens, and non-carcinogens are measured by multiplying the fate factor of each individual substance by the corresponding result factor (Eq. (1)). Detailed data of these characterization factors are listed in the supplementary information.

See equation 1 in the supplementary files.

where CF_impact_,i, FF_i, and RF_impact,i represents the characterization factor of individual substance i (all substances associated with water are considered, while substances emitted to air/soil/sediment by medium exchanges are excluded), fate factor of i in water (calculated by using the Mackay’s multi-media fugacity model), and result factor of i (calculated by using the exposure model and the dose-response model), respectively.

Energy footprint is one indicator for evaluating the demand for non-renewable energy resources of one product or process (Čuček et al., 2012). Data expressed in area units could increase uncertainties for a certain footprint estimate (Čuček et al., 2012). Energy footprint assessment is expressed in fossil energy with the unit of “kg oil” in this study. Under the framework of the ReCiPe model, energy footprint is calculated by using equation (2).

See equation 2 in the supplementary files.

where i, CF_i, M_i, CED_ref, CED_i, and IMPACT_FD represents one individual non-renewable energy resource input associated with the investigated system, the characterization factor of i (expressed in kg oil/unit), the amount of i, the cumulative energy demand indicator for reference oil resource (expressed in MJ/kg oil, usually 42 MJ/kg oil), the cumulative energy demand indicator of i (expressed in MJ/unit), and the total impact on fossils depletion category, respectively.

Carbon footprint represents the GHGs emitted by one product or process during its complete life cycle (Čuček et al., 2012; Maria et al., 2020). It has been widely used to quantify the impact of one process or product on climate change. Carbon footprint can be calculated by using equation (3).

See equation 3 in the supplementary files.

where EF_i,_j represents emission factor, namely, the GHG of i generated from the activity j, CF_GHG,_i represents global warming equivalent factor of i, and IMPACT_GW represents the impact on climate change (expressed in CO₂ equivalent), respectively.

For endpoint categories, the categories of human health, ecosystem quality, and resources are considered (see Figure 1). Detailed data on the coefficients of endpoint categories are listed in the supplemental information of this study. In addition, the Monte Carlo simulation is performed for uncertainty analysis (with 1000 trials) so that results obtained from this study can be more robust and reliable.

2.3 Data sources

Table 1 lists the life cycle inventories (LCI) of China’s soybean production. The inputs and outputs data related to soybean production (including seeds, fertilizers, and wastewater), were collected based on the functional unit and system boundary set in this study. Data on per unit area yield and gross output of soybean are obtained from the China Rural Statistical Yearbooks compiled by the National Bureau of Statistics (NBS, 2019). Data on annual inputs for soybean planting are obtained from public sources, such as the Compile of Cost-Benefit Data of Agricultural Products published by the National Development and Reform Commission (NDRC, 2019) and the China Rural Statistical Yearbooks published by the National Bureau of Statistics (NBS, 2019). Meteorological data. including wind velocity, precipitation, temperature, relative humidity, and sunshine hours are obtained from the China Meteorological Data Service Center (CMDC, 2016). The pollutants discharged to water from fertilizer and pesticide use are calculated on the basis of the national pollution survey (CAEP, 2003; CFPC, 2009a; CFPC, 2009b). The LCIs of soybean production for the period of 2009-2017 are listed in Table S4 in the supplementary information.

Background data (such as electricity generation and potash fertilizer production) for the LCI for soybean production (see Table 1) are taken from the China process-based LCI database (CPLCID), which are available in the supplemental information, except for the data of pesticides. European database which are available from the Ecoinvent centre (2015) is adopted for pesticide production due to the lack of Chinese data. During this process, the Chinese data such as electricity generation are used to replace those corresponding European data so that impacts from regional disparity can be minimized.

Table 1 Life cycle inventories from China’s soybean production (Functional unit: 1 metric ton of soybeans)

		Unit	Amount
Raw materials consumption	Irrigation water	kg	9.66×10⁵
	Effective rainfall	kg	1.76×10⁶
	Urea	kg	17.71
	Ammonia	kg	13.79
	Diammonium phosphate	kg	43.24
	Potash	kg	17.68
	Seed	kg	41.28
	Diesel	kg	35.44
	Electricity	kWh	24.43
	Pesticides	kg	1.56
Emissions to water	Ammonia, as N	kg	2.74×10^-2
	Total phosphorus	kg	3.77×10^-3
	Total nitrogen	kg	0.08
	Chemical Oxygen Demand	kg	79.34
	Chloropyrifos	kg	2.32×10^-4
	Acetochlor	kg	1.18×10^-3

3.1 Midpoint results

Table 2 lists environmental impacts generated by 1 metric ton soybean production at the midpoint level. This table also lists the results of uncertainty analysis so that more reliable results can be provided to different stakeholders. For example, the aquatic eutrophication WF generated by the production of 1 ton of soybeans is 1.21 kg PO₄^3- eq and the corresponding squared geometric standard deviation (GSD²) is 1.71. These results suggest that the impact of aquatic eutrophication varies from 0.71 PO₄^3- eq to 2.07 PO₄^3- eq, at the 95% confidence interval. Similarly, results for other categories could be obtained from Table 2.

Table 2 Midpoint results of China’s soybean production (Functional unit: 1 metric ton of soybeans)

Impact categories	Unit	Amount	GSD²
Aquatic eutrophication	kg PO₄^3- eq	1.21	1.71
Acidification	kg SO₂ eq	2.62	1.75
Global warming	kg CO₂ eq	3.33×10³	1.87
Carcinogens	Case	7.71×10^-6	2.66
Non-carcinogens	Case	3.48×10^-5	3.91
Freshwater ecotoxicity	PAF·m³·d	1.68×10⁴	1.88
Water scarcity	m³deprived	456.23	1.22
Fossil depletion	kg oil eq	343.37	1.60

GSD²: squared geometric standard deviation

3.2 Endpoint results

The environmental impacts of soybean production at the endpoint level are summarized in Table 3 and Figure S3. It is clear that the impact on ecosystem quality, human health, and resources is 6.18×10^-5 Species·yr, 3.26×10^-3 Disability-adjusted Life Years (DALY), and 89.22 $, respectively. The uncertainty analysis on the endpoint results indicates that the impact on ecosystem quality ranges from 3.41 ×10^-5 Species·yr to 1.12×10^-4 Species·yr (GSD² =1.81), the impact on human health ranges from 1.80×10^-3 DALY to 5.90×10^-3 DALY (GSD² =1.81), and the impact on resources ranges from 39.13 $ to 203.42 $ (GSD² =2.28).

The impact of freshwater ecotoxicity is the dominant contributor (77.69%) to the endpoint category of ecosystem quality, followed by the impacts of global warming (15.08%) and water scarcity (5.68%). For human health, the impact of global warming (85.22%) is the dominant contributor, followed by the impact of water scarcity (9.17%), while the dominant contributor to the category of resources is the oil depletion (see Figure S3).

Table 3 Endpoint results of China’s soybean production (Functional unit: 1 metric ton of soybeans)

Impact categories	Unit	Amount	GSD²
Ecosystem quality	Species·yr	6.18×10^-5	1.81
Human health	DALY	3.26×10^-3	1.81
Resources	$	81.51	2.28

GSD²: squared geometric standard deviation

3.3 Dominant contributors

3.3.1 Dominant contributors to midpoint results

Figure 2 illustrates the contributions of key substances and processes to the midpoint categories. Irrigation water, mainly used in the direct planting process and production of soybean seeds, is the dominant contributor to the water scarcity category. For acidification category, sulfur dioxide to air mainly generated from diesel and diammonium phosphate production is one dominant contributor, while it is Chemical Oxygen Demand (COD) to water for aquatic eutrophication category. For freshwater ecotoxicity category, direct chloropyrifos emission and iron emitted to soil are two dominant contributors, with copper emitted to air playing an additional contribution. The iron emitted to soil is mainly generated from the production of diesel and the copper to air is mainly generated from the production of diammonium phosphate and diesel. For global warming category, the contribution of carbon dioxide is mainly generated from the production of diesel and diammonium phosphate. For non-carcinogens, arsenic to air mainly caused by the production of diesel is the dominant contributor, while chromium to water and chromium to soil, mainly generated from the production of diesel and diammonium phosphate, are two dominant contributors for carcinogens. For fossils depletion category, the dominant contributors are coal in the ground mainly caused by diammonium phosphate and diesel production, as well as oil in the ground mainly caused by diesel production.

3.3.2 Dominant contributors to endpoint results

Figure 3 illustrates the dominant contributors to the endpoint results of the soybean production. The results indicate that the carbon dioxide to air is the dominant contributor (77.89%) to human health, with irrigation water (9.14%) and the methane to air (7.03%) making additional contributions. These results also indicate that the production of diesel and diammonium phosphate are the dominant sources of carbon dioxide to air and methane to air. For resources category, the dominant contributor is oil in the ground (86.10%), with coal in the ground (13.17%) making an additional contribution. These substances are mainly caused by the production of diesel and diammonium phosphate. For ecosystem quality, iron to soil (33.62%), carbon dioxide to air (13.78%), chloropyrifos to water (19.47%), and copper to air (9.87%) are the dominant contributors, with irrigation water (5.66%), copper to soil (4.99%), and strontium to water (2.77%) making additional contributions. These substances are mainly from the production of diammonium phosphate and diesel, as well as on-site emissions generated from soybean production.

3.4 Temporal and spatial analysis

Figure 4 illustrates the environmental impacts (midpoint level) of China’s soybean production from 2009 to 2018. Table S4 lists all the detailed results. It is clear that the impacts of aquatic eutrophication, acidification, global warming, fossil depletion, carcinogens, non-carcinogens, and freshwater ecotoxicity in 2016 are higher than those in other years. The main reason is that the diesel consumption in 2016 was much higher than other years, which is consistent with the results that diesel is a significant contributor to aquatic eutrophication, acidification, global warming, fossil depletion, carcinogens, non-carcinogens, and freshwater ecotoxicity (see Figure 2). The impact of water scarcity category fluctuated with the amount of irrigation water used for soybean production.

The spatial distribution of soybean production and corresponding productivity in 2018 are illustrated in Figure 5. It is clear that Heilongjiang and Inner Mongolia were the top two soybean producing provines in China in 2018 (see Figure 5(a)), with the yield of 6.58 million metric tons and 1.79 million metric tons, respectively. The total environmental impacts of soybean production in Heilongjiang and Inner Mongolia are also higher than other provinces due to their higher soybean yields. With regard to productivity, the top two provinces in China include Tibet and Hainan (see Figure 5(b)), with the productivity of 3.23 ton/ha and 3.18 ton/ha in 2018, respectively. The productivity of soybeans of Heilongjiang () and Inner Mongolia is 1.84 ton/ha and 1.64 ton/ha, respectively, uch lower than Tibet and Hainan. If their soybean productivity can reach 3.00 ton/ha, then the national soybean production would be increased by 35.16%.

3.5 Environmental impacts of soybean production

The total impact of China’s soybean production on aquatic eutrophication, acidification, global warming, fossil depletion, carcinogens, non-carcinogens, freshwater ecotoxicity, water scarcity, human health, ecosystem quality, and resources is 1.93×10⁷ kg PO₄^3- eq, 4.18×10⁷ kg SO₂ eq, 5.31×10¹⁰ kg CO₂ eq, 5.48×10⁹ kg oil eq, 123.15 Case, 555.17 Case, 2.69×10¹¹ PAF·m³·d, 7.28×10⁹ m³ deprived, 5.20×10⁴Species·yr, 986.79 DALY, and 1.30×10⁹$ for the year 2018, respectively, with the total soybean yield of 15.97 million metric tons (1.898 ton/ha) in the same year.

The environmental impacts of soybean production in the top three production provinces (i.e. Heilongjiang, Inner Mongolia, and Anhui) for year 2018 are listed in Table 4. It is clear that such impact in Anhui is lower than the national average, with the exception of the aquatic eutrophication category. However, such impact in Inner Mongolia is higher than the national average in all of the categories except for the fossils depletion category. For Heilongjiang, the impacts of acidification, global warming, fossil depletion, carcinogens, non-carcinogens, human health, and resources are higher than the national averages, while impacts of aquatic eutrophication, freshwater ecotoxicity, water scarcity, and ecosystem quality are lower than the national averages. These differences can be explained by region-specific soybean productivity, fertilization application, and diesel efficiency.

Table 4 Environmental impacts of soybean production at provincial level (Functional unit: 1 metric ton of soybeans)

Impact categories	Unit	Heilongjiang	Inner Mongolia	Anhui	Shandong	National average
Aquatic eutrophication	kg PO₄^3- eq	1.12	1.59	1.41	0.87	1.21
Acidification	kg SO₂ eq	3.04	2.59	1.46	1.34	2.62
Global warming	kg CO₂ eq	3.90×10³	3.52×10³	2.01×10³	1.49×10³	3.33×10³
Fossil depletion	kg oil eq	412.99	318.29	170.69	173.77	343.37
Carcinogens	Case	8.54×10^-6	9.97×10^-6	6.00×10^-6	2.94×10^-6	7.71×10^-6
Non-carcinogens	Case	3.74×10^-5	4.84×10^-5	3.02×10^-5	1.21×10^-5	3.48×10^-5
Freshwater ecotoxicity	PAF·m³·d	1.41×10⁴	4.42×10⁴	9.26×10³	4.86×10³	1.68×10⁴
Water scarcity	m³deprived	350.76	882.99	434.90	389.02	456.23
Ecosystem quality	Species·yr	3.68×10^-3	3.76×10^-3	2.11×10^-3	1.57×10^-3	6.18×10^-5
Human health	DALY	5.49×10^-5	1.44×10^-4	3.60×10^-5	2.15×10^-5	3.26×10^-3
Resources	$	89.54	106.19	65.69	30.95	81.51

Carbon and energy footprints of soybean production in various Chinese regions (with the latest available data for year 2018) are listed in Table 5. Such impact in Shanxi is the highest, followed by Hebei. Wu et al. (2020) also found that the environmental impacts caused by soybean production in Shanxi and Hebei were higher than the national average. The carbon and energy footprint of the production of 1 ton of soybeans in Jilin was 1.98×10³ kg CO₂ eq and 178.55 kg oil (7.50 GJ) in this study, higher than those in the previous published studies (Knudsen et al., 2010; Luo et al., 2011). By using data from questionnaires and interviews collected from 15 conventional farms in Jilin province of China for the year 2006, Knudsen et al. (2010) found that the GHG and non-renewable use from soybean production was 0.263 ton CO₂ eq/ton and 1.71 GJ/ton, respectively. Similarly, Luo et al. (2011) found that the GHG from soybean production in Jilin was 1.53 ton CO₂ eq/ton, in which their data were collected for the year 2006-2007. These results differences can be explained by the increase of diesel consumption caused by the improvement of agricultural mechanization. It is worth noting that the carbon footprint (0.38 ton CO₂ eq/ton soybeans) and energy footprint (2.30 GJ/ton soybeans) of soybean production in Chongqing was much lower than other Chinese regions (see Table 5) due to its less diesel consumption for soybean production (0.96 kg diesel/ton soybean). Different from Knudsen et al. (2010) and Luo et al. (2011), statistical data were used for evaluating the environmental impacts of soybean production in this study, which can better reflect regional disparities.

Table 5 China’s carbon and energy footprints from soybean production

	Chongqing	Shaanxi	Shanxi	Shandong	Inner Mongolia	Liaoning	Jilin	Heilongjiang	Henan	Hebei	Anhui
Productivity (ton/ha)	2.05	1.58	1.57	2.82	1.64	2.45	1.97	1.84	2.48	2.42	1.50
Global warming (kg CO₂ eq/ton soybeans)	381.23	3.74×10³	5.03×10³	1.49×10³	3.52×10³	3.04×10³	1.98×10³	3.90×10³	1.63×10³	4.01×10³	2.01×10³
Fossil depletion (kg oil eq/ton soybeans)	54.69	439.08	566.12	173.77	318.29	296.76	178.55	412.99	151.60	519.34	170.69

Soybean productivity is influenced by various factors, such as solar radiation, local precipitation, and cultivation technology (Guan, 2017). Soybean productivity in Heilongjiang (1.84 ton/ha), Inner Mongolia (1.64 ton/ha), and Anhui (1.50 ton/ha) was much lower than the national average level (NBS, 2019), while Tibet (3.23 ton/ha), Hainan (3.18 ton/ha), and Shandong (2.82 ton/ha) had the highest soybean productivity in 2018 (NBS, 2019). Also, the proportions of soybean planting area to the total agricultural area in the top three productivity regions (Tibet 1.66%, Hainan 0.84%, and Shandong Province 1.43%) are much lower than that of Heilongjiang (25.50%), Inner Mongolia (14.82%), and Anhui (7.41%). In addition, the impacts of soybean production in Shandong was much lower than the national average (see Table 4). Such findings indicate that sustainable soybean production policies should be made from a holistic perspective rather than only focusing on one factor. This is especially true for regions where soybean yield is low and correspnding environmental impact is high. Consequently, such policies should be made by considering the local endowments and the holistic agricultural development.

The results from this study provide valuable insights to all the stakeholders related with soybean production. Based upon the Chinese realities, the following policy recommendations are raised.4.1 Optimizing soybean trade structure

Figure 6 illustrates China’s major soybean trade partners based on the 2016 data. Brazil, the United States, and Argentina are the major soybean exporters (thick arrow in Figure 6). The eastern Chinese provinces are the major soybean importers, with Shandong being the largest one (darkest in cyan). For most Chinese provinces, the United States and Brazil are the major soybean trade partners, while it is Russia for Heilongjiang. China imported a total of 88.51 million tons of soybeans in 2019, mainly from Brazil (65.2%), the United States (19.1%), and Argentina (8.5%). However, the trade skirmishes between China and the United States has influenced such a soybean supply chain since 2017 (Geng and Sarkis, 2018), decreased from 34.17 million tons (40.72% of China’s total import) in 2016 to 16.94 million tons (19.1% of China’s total import) in 2019. This indicates the uncertainty of political factor and suggests China to diversity its soybean trade partners.

Initiated and promoted by the Chinese government, the Belt and Road initiative aims to promote broad cooperation among the countries locating in the Silk Road Economic Belt and the 21st Century Marine Silk Road (Chen et al., 2018). Many Belt and Road countries have large agricultural land and can plant more soybean to support their economy. However, they lack advanced cultivation technologies and feel reluctant to expand their soybean production. Therefore, the Chinese government should encourage technology transfer so that these countries can improve their soybean production efficiency and increase their soybean yields. Joint research activities and technical secondments are useful to facilitate such cooperation. For example, the maize-soybean strip intercropping technology has been successfully transferred from China to Pakistan, which is an active response to the Sustainable Development Goals (SDG 2 food), with its benefits of achieving food security and improving nutrition. It is essential for China to further share its advanced technologies with those Belt and Road countries so that these countries can increase their economic revenue. In addition, it is necessary to establish an open data platform so that all the involved countries can share relevant data on soybean production. In particular, such a platform can help identify the key environmental challenges from soybean production and seek possible mitigation pathways.

4.2 Improving fertilizer application efficiency

Results from this study (see Figure 2 and Figure 3) indicate that fertilizer is a key contributor to environmental impact generated from soybean production. China is the largest fertilizer consumer in the world (Chen et al., 2018). The overuse of fertilizer has resulted in several environmental issues, such as soil contamination, groundwater contamination, soil infertility, and eutrophication (Zhai et al., 2019a). Statistical data show that 66.98 kg of fertilizer was used for producing one metric ton soybean in 2018 (NBS, 2019). In Heilongjiang and Inner Mongolia, such figure is 76.31 kg and 83.82 kg respectively, higher than the national average (NBS, 2019). In particular, as one province with severe water shortage and tough weather conditions, the low quality cultivated land accounts for 87.34% of its total cultivated land in Inner Mongolia (Zheng 2018). Consequently, it is critical for such provinces to improve their fertilizer efficiency so that the overuse of fertilizer can be avoided or at least mitigated.

Returning straw to the fields is one effective way of reducing the use of fertilizer (Zhai et al., 2019a). Liu et al. (2019) found that returning straw to the fields increased the sustainable level of soybean production. If the consumption of diammonium phosphate is reduced by 10%, its environmental impact on acidification, global warming, fossil depletion, and human health can be reduced by 3.35%, 3.53%, 4.53%, and 3.05%, respectively. In addition, precision agriculture, such as testing the soil quality before the application of fertilizer, can not only improve the efficient use of fertilizer, but also increase economic and environmental benefits of soybean production (Huang et al., 2017; Zhai et al., 2019a). However, such application depends on financial support since the value of straw is so cheap and the collection and delivery of such straw is so difficult that most farmers do not like to engage in such efforts. It would be useful for the Chinese governments to provide financial subsidies to support the application of such innovation.

4.3 Supporting related research and capacity-building activities

China’s soybean productivity (1.89 ton/ha for 2019) is much lower than that in the United States (3.40 ton/ha for 2019), Brazil (3.26 ton/ha for 2019), and Argentina (3.33 ton/ha for 2019) (SOPA, 2020). Consequently, it is crucial for China to improve its soybean production technologies. The total production of the maize-soybean intercropping system is higher than monoculture of either maize or soybean (Yang et al., 2015). Thus, the maize-soybean strip intercropping system is one promising, efficient and sustainable planting system and should be promoted (Iqbal et al., 2019). The Ministry of Agriculture and Rural Affairs (MARA) of China released the national guidelines for promoting maize-soybean strip intercropping technology based on the local climate and soybean production features in different Chinese provinces (MARA, 2020), in which the detailed parameters, such as the space of the neighboring rows, seeding density, and the cultivation time are all provided (Yang et al., 2015). Capacity-building efforts are necessary so that all the local stakeholders can quickly learn and apply such new technologies. Especially, those western provinces such as Gansu, Yunnan, and Guizhou do not have adequate financial resources to apply such technologies. Under such a circumstance, those eastern Chinese provinces should support their western counterparts by providing financial and technological support.

Another finding from this study is that diesel is a significant contributor to the environmental impact from soybean production. For instance, the impact generated by diesel production accounted for 77.76% and 90.76% of the impact on carcinogens category and non-carcinogens category, respectively. Also, the diesel consumption for soybean production in Heilongjiang and Inner Mongolia was higher than the national average. Therefore, it is crucial for such provinces to improve their diesel efficiency for soybean production. In this regard, biodiesel is one renewable alternative and can address climate change (Hasan and Rahman, 2017). For example, waste cooking oil can be used to generate biodiesel, which has been widely promoted. However, the collection of waste cooking oil is difficult. Therefore, more innovative policies (such as preferable tax rates, financial subsidies, and low loan interests for such businesses) should be prepared to support the efficient collection of waste cooking oil.

In addition, green consumption behaviors should be promoted in China. Soybean has been consumed mainly for edible oil refinery and fodder (especially for pigs) in China. If low protein diet can be promoted across the whole country, then the total soybean consumption may be reduced. In fact, now there are more obesity patients in China due to rapid economic development and irrational diets. Especially, many Chinese children suffer from obesity. Therefore, it is necessary for the Chinese government to promote nutritious diets across the whole country so that the total pork consumption can be reduced. In this regard, education is key so that more Chinese citizens can better understand how to prepare appropriate diets. Regular workshops, TV and Internet promotions, pamphlets are useful measures to promote low carbon diets.

Soybean production is essential for supporting modern societies although it is based upon a large amount of energy and water consumption and causes serious environmental emissions. China is the largest soybean consumption country in the world. The aim of this study is to provide a combined analysis based on the assessment of different footprints from soybean production so that the corresponding environmental impacts could be quantified.

The results show that the impact of soybean production on aquatic eutrophication, acidification, global warming, fossil depletion, carcinogens, non-carcinogens, freshwater ecotoxicity, and water scarcity was 1.21 kg PO₄^3- eq (GSD²=1.71), 2.62 kg SO₂ eq (GSD²=1.75), 3.33×10³ kg CO₂ eq (GSD²=1.87), 343.37 kg oil eq (GSD²=1.60), 7.71×10^-6 Case (GSD²=2.66), 3.48×10^-5 Case (GSD²=3.91), 1.68×10⁴ PAF·m³·d (GSD²=1.88), and 456.23 m³deprived (GSD²=1.22), respectively. The key factors analysis results indicate that the production of diammonium phosphate and diesel, as well as the on-site emissions during soybean production were major contributors to the environmental impacts from soybean production. In addition, the impacts on acidification, global warming, fossil depletion, carcinogens, non-carcinogens, freshwater ecotoxicity, human health, ecosystem quality, and resources could be reduced by 4.41%, 5.36%, 4.11%, 7.62%, 9.05%, 5.45%, 5.03%, 5.10%, and 8.02%, respectively, if 10% diesel from the petroleum industry was replaced by biodiesel produced from waste cooking oil. Several policy recommendations are proposed by considering the Chinese realities, such as optimizing soybean trade structure, supporting technological development, improving resource efficiency, and promoting low carbon diets. These results can also expand the LCI database of China’s soybean production and provide useful insights for various stakeholders to improve the overall environmental performance of soybean production.

-Ethical Approval: This paper complies with the ethical standards of this journal.

-Consent to Participate: All the authors agree to participate in this study.

-Consent to Publish: All the authors agree to publish it in this journal.

-Authors Contributions: All the authors equally contribute to this paper.

-Funding: We gratefully acknowledge the financial support from the Research Project for Young Scholars of Shandong Normal University (Humanities & Social Science), National Key Research and Development Program of China (Grant No. 2017YFF0206702; 2017YFF0211605), National Natural Science Foundation of China (Grant No. 72088101; 71810107001; 71690241; 71671105; 71974113), Major Basic Research Projects of the Shandong Natural Science Foundation, China (ZR2018ZC2362), and the Fundamental Research Funds of Shandong University, China (2018JC049).

-Competing Interests: We declare that there are no competing interests in this study.

-Availability of data and materials: Al the data related with this study could be available if the readers raise such a request.

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Environmental footprints of soybean production in China

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