Quantifying and mapping bioenergy potentials in China
Zhang, Bingquan
DOI:
10.33612/diss.168012388
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Zhang, B. (2021). Quantifying and mapping bioenergy potentials in China: Spatiotemporal analysis of technical, economic and sustainable biomass supply potentials for optimal biofuel supply chains in China. University of Groningen. https://doi.org/10.33612/diss.168012388
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Chapter 4
Spatially explicit analyses of sustainable
agricultural residue potential for bioenergy in
China under various soil and land management
scenarios
Bingquan Zhang, Jialu Xu, Zhixian Lin, Tao Lin, André P.C. Faaij4
Abstract
Sustainability is critical for biomass feedstock supply and crop production. Most studies on agricultural residue estimations ignored the loss of soil organic carbon (SOC) and thus possibly overestimated its resource potential. This study estimated the resource potential of using agricultural residues for bioenergy in China, considering soil conservation, collection cost, and future changes in yield and management. This study carried out a spatial explicit assessment of sustainable agricultural residue potential and their on-farm collection costs. Rothamsted carbon model was used to quantify the grid-specific amount of residue to be retained in soil for sustainable purposes. The results showed that 226 Mt of residues could be collected annually to maintain the current SOC level, which ranges from 0.1% to 39.0% at a mean of 1.1% nationwide. To achieve SOC level above 2% over all arable land in China, the collectable residues would be reduced to 24 Mt. Future yield improvements and no-tillage would significantly increase the collectable residues to 117, 383, and 514 Mt in 2050 under SOC scenarios of above 2%, above 1%, and maintaining current level, respectively. Maintaining the current SOC level, 495 Mt of residues could be collected in 2050 with a cost ≤ 0.98 $⋅GJ-1, which equals 8.6 EJ of energy potential. From the view of high supply potentials and low collection costs, Shandong, Henan, and Jiangsu provinces are the preferred regions to develop residue-based bioenergy production. The results highlighted the differences of resource potential among various SOC scenarios and spatial heterogeneity of residue resource among regions.
Keywords
Agricultural residues, Biomass potential, Spatial analysis, Soil organic carbon, Sustainability, Economic potential
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4.1 Introduction
China has a huge potential of agricultural residue resources to offer, but the residues are not effectively used, especially in terms of bioenergy production [1]. Apart from using residues as livestock feed, rural household fuel, paper pulp, and substrate for mushroom cultivation, agricultural residue-based bioenergy production is now a popular practice in many countries, such as Denmark, Sweden, the UK, Spain, and China [2]. Agricultural residue-based bioenergy is likely to play an important role in future energy supply and carbon mitigation according to biomass energy deployment scenarios of the Intergovernmental Panel on Climate Change (IPCC), which argued that 15-70 EJ of bioenergy would contribute to long term global energy supply [3]. According to the “13th Five-Year Plan (2016-2021) of bioenergy” (National Energy Administration (NEA), 2016) released by the Chinese government, bioenergy goals, including production of 90 billion kWh of electricity, 8 billion m3 of biogas, 30 Mt of biomass pellet or briquette fuels, and 6 Mt of biomass liquid fuel, were proposed to be achieved by 2020. NEA set a goal of increasing the biomass combined heat and power (CHP) capacity to 12 GW by 2020 and 25 GW by 2035, and launched 136 county-level biomass CHP pilots in 2018 [4]. As one of various feedstocks, including agricultural residues, woody residues, energy crops, animal manure, organic municipal solid waste, and municipal sewage sludge for the production of bioenergy, agricultural residues will make a major contribution to the production of bioenergy, as they are less contentious, of low cost and low risk, with a large agricultural production base in China [3,5,6]. Therefore, it is necessary to answer the questions arising about the extent of the potential of available agricultural residues and their spatial distribution.
In recent decades, an increasing number of studies estimating the potential of biomass feedstocks were carried out at global, national, or regional scales by using different research methods, i.e., either statistical [2,7–15] or geographic information system (GIS)-based methods [1,6,16–20]. However, evaluated quantities of biomass residues available for bioenergy production vary greatly among different studies due to differences in the types of crop residues analysed, residue to product ratios, and constraining factors taken into account. Besides, only a few of them offered methodology for assessing the supply potential of agricultural residues, while taking into account the effects of sustainability criteria and agricultural managements. A literature review in terms of research objectives, methods,
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Sustainability criteria represent constraining factors that impact availability of agricultural residues, and can be applied to determine the quantity of residues required to be returned to the soil for maintenance of soil nutrients, organic carbon levels, and prevention of soil erosion. As shown in Table 4.1, many studies did not consider the sustainability criteria and consequently overestimate the availability of residues for bioenergy production. A few studies considered sustainability criteria of soil health by using a simple constant value as a factor to determine the amount of residues [21–25]. As the quantity of retentive residues for ensuring soil health depends on climate and soil conditions, sustainability criteria should be specific to the environmental conditions prevailing. Use of constant sustainability criteria cannot accurately represent the real conditions of an entire region with spatial differences. Only a limited number of studies applied multiple sustainability criteria with spatial differences rather than a constant value for an estimation of sustainable residues removal [2,17]. Sustainability criteria were determined using models based on soil and climate conditions of the region evaluated. However, no study has been carried out with spatial explicit modelling for soil organic carbon retention with regard to China so far. In addition, very few reports ever considered the below ground residues, which are important contributors to maintenance of soil carbon levels [26,27].
Availability of agricultural residues is also affected indirectly by agricultural managements, such as crop rotation and tillage. Appropriate crop rotation and conservation tillage could significantly reduce the decomposition rate of soil organic carbon (SOC), and prevent soil erosion, consequently reducing the volume of residues returning to soil [28]. Some studies have analysed the impact of agricultural managements on the availability of residues [2,19]. However, no such study has been conducted with regard to China yet.
It is critical to evaluate the cost of biomass supply chains that have an impact on economic feasibility of bioenergy production. Cosic et al. (2011) [25] assessed the economic bioenergy potential of crop residues in Croatia while considering sustainability criteria and the costs of the entire supply chain. Such assessments are helpful for policymakers and industries to identify economic feasibility of agricultural residues for bioenergy production. However, current studies regarding China have not calculated on-farm collection costs or the economic potential for residues collection based on the assessment of sustainable availability of agricultural residues at a national scale.
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Considering the state of the art and knowledge gaps in previous studies, the following research questions need to be answered: 1) what is the amount of agricultural residues that can be sustainably collected in China, taking into account soil health-related sustainability constraints and agricultural managements, 2) what is the spatial explicit distribution of the sustainable potential of agricultural residues, and 3) what are the on-farm collection costs of agricultural residues, and how much economic potential is held by agricultural residues in China? Not only was GIS-based spatial analysis implemented in this study, with arable land use data at 1 km × 1 km resolution and statistical data of crop production at the county level, but also multiple scenarios regarding various sustainability criteria, agricultural managements, and increased crop production in the future were created and combined to determine the sustainable availability of agricultural residues in China. Scenarios of sustainability criteria used varying SOC content levels to calculate location-dependent quantities of residues required for maintaining soil carbon balance based on explicit spatial soil and climatic data. Furthermore, on-farm collection costs and the economic potential of estimated sustainable residues in China were also calculated in this study.
This study was done by calculating the theoretical potential of residues first by using residue-to-product ratios (RPR) and production of crop. Then, the sustainable potential was estimated by using the Rothamsted carbon (RothC) model [29], by which the quantity of residues required for maintaining soil organic carbon levels was calculated, while at the same time ensuring protection against soil erosion. Finally, on-farm collection costs and economic potential were calculated using a cost calculation model and building cost-supply curves, respectively.
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Ta b le 4 .1 S ummar y of biomass re sidue studies in China and other
co un tries Au th or, year Co untr y (R egi o n) Feed stoc k Researc h ob jec tive Meth od ology an d mo del s Su stain ab le cr iteria Availab ility o f agric u ltu ral resid u es Timeframe Li et al ., 2005 [15] C h in a Agric u ltu ral res id u es, ani m al manur e, s av ed fu el w o o d , muni ci pal s o lid waste, wa ste water, b lac k liquo r Asse ssm en t of su stai n ab le en ergy po tenti al o f bi o m as s re si dues St ati sti cal metho d Co ns tant valu e 458 Mt, 638 Mt, 66 3 Mt 1997, 2005, 201 Liu et al ., 2008 [7] Ch in a Agric u ltu ral res id u es Asse ssm en t of u tiliza tion stru ct u re and po tenti al o f cr o p r es idues St ati sti cal metho d Co ns tant valu e 536 Mt 1995-2004 Chen et al ., 2009 [9] Chi n a A gr icul tur al r esi dues A ss es sm ent o f r enewabl e ener gy f ro m agric u ltu ral resid u es Statistical metho d N o 579-624 Mt 1995-2006
De Wit & Faaij, 2010 [10] European Union A gr icul tur al and f o re str y resid u es, en ergy c rop s A ss es sm ent o f co st and s uppl y po tenti al f o r bi o mass r es o ur ces Statistical metho d N o 3. 1-3. 9 EJ 2030 Liu et al ., 2012 [11] Chi n a ( Inner Mo ngo lia) Agric u ltu ral res id u es Sp atial-tem p ora l asse ssm en t of bi o ener gy po tenti al fr o m agr icul tur al re si dues Statistical metho d N o 14. 9 Mt 1980-2008 Jiang et al ., 2012 [16] Chi n a A gr icul tur al r esi dues A ss es sm ent o f bi o en er gy po tenti al from agric u ltu ral re si d u es G IS -bas ed metho d Co ns tant valu e 506 Mt 2000-2009 Muth et al ., 2013 [17] US A A gr icul tur al r esi dues A ss es sm ent o f s u st ainabl e agr icul tur al resid u es rem o val f o r b ioen ergy pr o ducti o n GI S-b ased m eth od ,
an integrated model incl
udi ng R U SL E2, WEPS , S CI mo del s Var io u s cr iteria 150 Mt, 207 Mt 2011, 203 Wang et al ., 2013 [12] C h in a Agric u ltu ral res id u es Estim ation of ava ilab ility of c rop re si dues f o r bi of uel pr o ducti o n Statistical metho d N o 750 Mt 2008-2009 Qiu et al ., 2014 [18] Chi n a A gr icul tur al r esi dues A ss es sm ent o f po tenti al f ro m agr icul tur al r es idues f o r co mmer ci al ener gy pr o ducti o n G IS -bas ed metho d Co ns tant valu e 609 Mt 2010 Yang et al ., 2015 [13] China (N o rth and N o rt heas t China) A gr icul tur al r esi dues A ss es sm ent o f po tenti al o f cr o p re si dues f o r bi of uel pr o ducti o n St ati sti cal metho d Co ns tant valu e 179-199 Mt 2008-2010
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Chi n a A gr icul tur al r esi dues A ss es sm ent o f po tenti al f ro m agr icul tur al r es idues f o r l iqui d bi o fu el pr o ducti o n St ati sti cal metho d Co ns tant valu e 711 Mt 2010 et al ., Chi n a A gr icul tur al and f o re str y re si dues Asse ssm en t of p o ten tial from agr icul tur al and f o restr y r es idues f o r bi o ener gy pr o ducti o n Statistical method, GCA M (G lo bal C h an ge Assessm en t Model) Yes 8. 4 EJ 2003-2008 ou et ., 2016 Glob al Agric u ltu ral an d fore stry re si dues P rojec tion s of th e av ailab ility an d c o st of resid u es fr om agri cu ltu re an d fore stry GI S-b ased m eth od , IMA G E mo del Co ns tant valu e 57-67 EJ 2010, 2030, 2050, 2080, 210 0 zirai ., 2016 So uth A fr ica A gr icul tur al r esi dues A ss es sment o f s us tainabl e r es idue rem o val an d c o st for large-sc ale bi o ener gy appl ic ati o nsStatistical method, Rothams
ted o rgani c car b o n (R o thC) mo del Var io u s cr iteria 5. 8-13. 1 Mt 2030 et ., 2017 China (Hei lo ngji ang) Agric u ltu ral res id u es Sp atial-tem p ora l an alysis o f th e av ai la bi lity o f agri cul tur al bi o m as s GIS-based metho d N o 32-77 Mt 2003-2013 et al ., C h in a Agric u ltu ral res id u es Sp atial-tem p ora l asse ssm en t of agric u ltu ral resid u es GIS-based metho d N o 471 Mt 2002-2009 et al ., C h in a Agric u ltu ral res id u es, w o o d la nd and gr as sl and re si dues , ani m al manur e, ener gy cr o p s, muni ci pal solid wa ste Sp atial asse ssm en t of u sab le b iom ass feeds to ck and tec hni cal bi o ener gy po tenti al G IS -bas ed metho d Co ns tant valu e 636 Mt 2015
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4.2 Methodology
Figure 4.1 presents a flowchart of the method used for assessment of the sustainable
potential of residues. Detailed steps are described in sections 4.2.1–4.2.4. Methods for calculating on-farm collection costs and economic potential are elaborated in sections 4.2.5 and 4.2.6.
Figure 4.1 Flowchart of the procedures, model, tool and data used for estimating the theoretical potential of
agricultural residues, quantity of residues required for maintaining soil health, and sustainable potential of agricultural residues.
4.2.1 Estimation of the theoretical potential of residues
Crop residue consists of aboveground and belowground residues. Therefore, the theoretical residues potential was defined as the production of aboveground residues at crop harvest. The amount of aboveground residue was calculated using RPR, which is the ratio of aboveground residue to crop product. The amount of belowground residue was calculated using root-shoot ratios (RSR), which describe the ratio of belowground residue to the entire aboveground biomass (i.e., aboveground residue and product). Calculations were done on a dry weight (DW) basis. In this study, six types of agricultural residues were selected, which were derived from corn, wheat, rice, beans, rapeseed, and cotton (Table 4.2).
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Table 4.2 The residue-to-product ratio, root-to-shoot ratio, and higher heating value of the agricultural residues
from six selected crops
a adapted from Bi et al. (2009) [30]; b adapted from Gao et al. (2016) [8]; c derived from Morison et al. (1984) [31];
d calculated based on ECN.TNO Phyllis2 database (https://www.ecn.nl/phyllis2/) [32].
Due to limited data availability, the crop production data used to calculate residue potential originated from 2009 (from the National Bureau of Statistics of China, 2010), which are the most recent production data available at the county level. Production of each crop in 2009 was calculated based on the county. Theoretical and belowground potentials of agricultural residues were determined using RPR and RSR, depending on the main crop types, which are shown in Table 4.2, and were calculated by Eqns (1-3).
𝐴𝑅 =∑ 𝑅𝑃𝑅 × 𝐶𝑃𝐶𝐴 , × 𝑃𝑒𝑟 1
𝐵𝑅 =∑ 𝑅𝑆𝑅 × 𝐶𝑃, × 1 + 𝑅𝑃𝑅 × 𝑃𝑒𝑟
𝐶𝐴 2
𝑇𝑃 = 𝐴𝑅 3
Where 𝑇𝑃 isthe theoretical potential of agricultural residues (t⋅km-2), 𝐴𝑅 represents the aboveground residue (t⋅km-2), 𝐵𝑅 is the belowground residue (t⋅km-2), 𝑅𝑃𝑅 represents the residue-to-product ratio, 𝑅𝑆𝑅 represents the root-shoot ratio, 𝐶𝑃, is total crop production (tonne), 𝐶𝐴 is the arable land area (km2), 𝑃𝑒𝑟 represents the percentage of arable land area per 1 km × 1 km grid cell, and c, e, and i stand for crop type, 1 km × 1 km grid cell, and county, respectively.
Total theoretical and belowground productions of residues were calculated for each county by integrating residue production for each crop type. Then, theoretical and belowground potentials of residues per 1 km × 1 km grid cell were calculated for the six crops by dividing
Crop Type of residues RPR (tresidue/dry tproduct) RSR (troot/dry tproduct) c HHV (GJ/DW t) d
Corn Stove 1.2 a 0.38 18.2
Cob 0.21 b 18.19
Rice Straw 0.9 a 0.37 14.79
Husk 0.21 b 15.55
Wheat Straw 1.1 a 0.31 18.09
Soybean Stalk and Leaves 1.5 b 0.44 16.47
Rapeseed Stalk and Leaves 1.5 a 0.32 18.52
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total theoretical and belowground potentials of residues of all crops for a given county by the areaof arable land. The latter was determined according to arable land cover data at 1 km2 resolution. Land cover data for China (2015), which included the percentage of arable land per 1 km × 1 km grid cell, were derived from the Data Centre for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC; http://www.resdc.cn). Regional differences of crop yield at the county level were not considered in this study. Although there are other crops growing on arable land, they were not included in this study due to their low production. Finally, the results from the last step were multiplied by the percentage of arable land area per 1 km × 1 km grid cell. Then, the spatial distribution of theoretical and belowground potentials of residues within each county were obtained at 1 km2 resolution using ArcGIS 10.3, and used as input for calculating the sustainable potential.
4.2.2 Estimation of the residue amount required for maintaining soil health
Residues play a significant role in maintaining soil health by providing sufficient soil organic matter (SOM) [24,33], generating a residue cover to prevent the soil from water and wind erosion [34], thus improving soil physical properties, such as water infiltration [35], evaporation rates of soil moisture, and preservation of biodiversity, which enable sustainable development in the agricultural system and ecosystem [36]. SOM is mainly derived from aboveground and belowground residues. Belowground residues make a greater contribution to SOM than aboveground residues, because the carbon in belowground residues is more efficiently converted into SOC [26,27]. In this study, it was assumed that all belowground residues remain in the soil to maintain SOC levels. Therefore, the sustainable potential of residues was defined as the collectable quantity of aboveground residues, while taking into account the residues required for maintaining soil health. Accordingly, the amount of residues required for maintaining healthy SOC levels and preventing soil erosion needs to be calculated. Taking into account the role of residues in maintaining SOC levels and preventing soil erosion, various sustainability criteria that limit the availability of these residues were imposed. In this study, three scenarios were set based on the SOC level, which are described in section 4.2.4 as sustainability criteria. The amount of residue-derived carbon required for maintaining SOC levels for various climate and soil conditions was calculated for each grid cell using the RothC model [29]. It is one of the best known carbon models, and has been validated and widely used for simulating various agro-ecological environments [2,37–42]. The estimated carbon
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input value for all grids in equilibrium was calculated based on the inverse approach of the RothC model version 26.3, which has been explained in detail already by Coleman and Jenkinson (2014) [43], The output of this model highly relies on the respective input parameters, including the SOC level targeted, soil clay content, monthly mean temperature, precipitation, evaporation, and whether or not covered monthly by manure and/or plants. For this study, it was assumed that there is no monthly input of manure, and that the soil is covered by plant all year. Then, the amount of input residue required for healthy soil was calculated by a factor of 0.45 t of carbon per t of dry residue [44].
Gridded soil clay content data used with the RothC model were derived from the Harmonised World Soil Database (HWSD) on a 0.00833-degree (approximately 1 km × 1 km) grid [45]. Gridded monthly spatial meteorological data, including mean temperature, precipitation, and evaporation, were derived from a gridded time-series CRU 4.1 TS dataset for China, documented in 2009 at 0.5-degree (approximately 50 km × 50 km) grid resolution [46]. Soil clay content data from HWSD were transformed from 0.00833-degree to 10 km × 10 km grid cell resolution, whereas climate data were converted from 0.5-degree to 10 km × 10 km grid cell resolution. Therefore, spatial modelling was carried out at 10 km × 10 km grid cell resolution, and the spatial distribution of residues required for maintaining the SOC level was generated using ArcGIS 10.3, and used as input for estimating the sustainable potential. In addition to the retention of residues to maintain the SOC level, those required for preventing soil erosion represented another sustainability criterion. The amount of residue required for soil erosion control is dependent on soil condition, climate, topography, and crop type [2,47]. Batidzirai et al. (2016) [2] and Daioglou et al. (2016) [19] used 2.0 and 2.5 t⋅ha-1 of residue cover for soil erosion control, respectively. For ensuring accuracy of the analyses performed in this study, an average value of 2.5 t⋅ha-1 of residue was used for the five dryland crops, and a value of 1.0 t⋅ha-1 of residue for paddy rice was used for soil erosion control. 4.2.3 Estimation of the sustainable potential of residues
The sustainable potential of agricultural residues was estimated based on the outputs reported in sections 4.2.1 and 4.2.2, and was calculated according to Eqns (4-7). Finally, the spatial distribution of the sustainable potential of agricultural residues was generated using ArcGIS 10.3.
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If 𝑆 ≥ 𝐸 : If 𝐵𝑅 < 𝑆 , then: 𝑆𝑃 = 𝑇𝑃 + 𝐵𝑅 − 𝑆 × 100 × 𝑃𝑒𝑟 (4) If 𝐵𝑅 ≥ 𝑆 , then: 𝑆𝑃 = 𝐴𝑅 (5) If 𝑆 < 𝐸 : If 𝐵𝑅 < 𝐸 , then: 𝑆𝑃 = 𝑇𝑃 + 𝐵𝑅 − 𝐸 × 100 × 𝑃𝑒𝑟 (6) If 𝐵𝑅 ≥ 𝐸 , then: 𝑆𝑃 = 𝐴𝑅 (7) Where 𝑆𝑃 (t⋅km-2)is the sustainable potential of agricultural residues, 𝑇𝑃 is thetheoretical potential of agricultural residues (t⋅km-2), 𝐴𝑅 represents the aboveground residue (t⋅km-2), 𝐵𝑅 is the belowground residue (t⋅km-2), 𝑆 represents the amount of residues required to maintain the SOC level (t⋅ha-1), 𝐸 is the amount of residues required to prevent soil erosion (t⋅ha-1), 𝑃𝑒𝑟 represents the percentage of arable land area per 1 km × 1 km grid cell, 100 is a unit conversion coefficient (km2 to ha), and e stands for 1 km × 1 km grid cell.4.2.4 Scenarios
Apart from the base case scenario, which estimates the residue potential under current conditions, this study also carried out evaluations on the agricultural residue potential under other scenarios, with regard to various SOC levels and increased residue supply by 2050. 4.2.4.1 SOC levels
The uncertainties of soil degradation and production abilities are huge concerns for future sustainable crop production. Lal (2008) [48] argued that an SOC level of 1% is the critical level for crop production, while Weil & Brady (2017) [49] insisted that an SOC level of 3% is ideal for arable soils. Batidzirai et al. (2016) [2] and Valk (2013) [39] used an intermediate SOC level of 2% in 20 cm of topsoil as criteria to calculate the amount of residues required to be returned to the soil. Based on the HWSD soil database, we found the SOC level in China’s arable land was low: the SOC contents ranges from 0.1% to 39.0% with a mean of 1.1%, and 43% of the grids have a low soil fertility with SOC < 1%. Although it is considered ideal for sustainable crop production if a 3% SOC level can be maintained in arable soil, this is unrealistic in China, according to statistical analyses. Based on these studies and the real soil condition in China,
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this study therefore proposed three scenarios regarding the preservation of different SOC levels in 20 cm of topsoil layer, which are referred to as High SOC Scenario (HSS), Medium SOC Scenario (MSS), and Maintaining Current SOC Scenario (MCSS) in the following. The defining criteria of these scenarios are depicted in Table 4.3. As an average bulk density for arable soils in China, 1.4 t⋅m-3 was assumed. In the top 20 cm of the soil, there is around 2800 t of soil per ha. Therefore, to provide an example, 56 t of carbon per ha should remain in the soil to maintain an SOC level of 2%.
Table 4.3 Defining characteristics of three soil organic carbon (SOC) and two improved scenarios
4.2.4.2 Improved land management and crop yields
The availability of agricultural residues is directly influenced by crop yield, RPR, and residues required for maintaining sustainable crop production. On the one hand, crop yield is likely to increase by improved agricultural management and crop productivities, which contribute to a higher residue yield. On the other hand, the volume of residues required for sustainable agriculture is affected by the target SOC level, soil type, climate, and agricultural management practice (e.g., crop rotation, conventional tillage, or no-till cultivation) [28,50,51]. This study included two improved scenarios in which the availability of agricultural residues increased due to a shift from conventional tillage to no-till cultivation and improved crop productivities. Improved scenarios were defined as shown in Table 4.3.
It was assumed that crop rotation or multiple cropping was implemented in both base and improved scenarios in this study, because these are common practices in the farming system of China. For example, winter wheat and summer maize rotation within one year is a typical rotation pattern in the north of China. Besides, two continuous rice seasons in one year, and
Scenarios Definition
High SOC Scenario (HSS) If the current SOC < 2%, then the target SOC should be set to 2%; if the current SOC ≥
2%, then the target SOC should maintain the current level.
Medium SOC Scenario (MSS) If the current SOC < 1%, then the target SOC should be set to 1%; if the current SOC ≥
1%, then the target SOC should maintain the current level. Maintaining Current SOC
Scenario (MCSS)
The target SOC should maintain the current SOC level.
No-till cultivation Scenario No-tillage cultivation is implemented to all the arable land in China to increase the SOC
level by 1.23 for tropical moisture climate, 1.17 for tropical dry climate, 1.16 for temperate moisture climate and 1.10 for temperate dry climate.
Increased yields Scenario Crop productivity increase by certain increase rate by 2050 as agricultural technology is
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three continuous rice seasons in two years are commonly employed in the south of China. Multiple cropping systems ensure that the soil is covered effectively by continuous crop culture every month of the year, thereby slowing the degradation of SOC.
No-till cultivation
Although conventional tillage has a positive effect on weed control in farming systems, numerous drawbacks, including loss of SOM, loss of nutrients, soil erosion, and death or disruption of microbe and macrofauna communities have been found in recent decades [33,52]. Compared with conventional tillage, conservation tillage, including no-till or reduced tillage, could significantly improve soil surface quality regarding biological, chemical, and physical properties. The benefits of conservation tillage result mainly from increased SOC storage, nutrient content, and water infiltration rate, as well as reduced soil erosion, and an enrichment of soil microbes and macrofauna at the soil surface [53–59]. It should be noticed that conversion from conventional to conserved tillage potentially results in some negative effects on crop production, such as higher occurrence of crop diseases, pests, and weeds, as well as soil compaction [60]. However, the benefits of conserved tillage outweigh its disadvantages by far. Generally, no-till cultivation with residue cover improves the concentration of SOC on the soil surface, and thus reduces the volume of residues required for maintaining a specific SOC level compared to conventional tillage [28,61]. Taking into account that conservation tillage has been implemented on 866.7 Mha arable land, which accounted for only 6.4% of total arable land area in China in 2015 [62], there is a huge potential to increase the SOC level by implementing no-till cultivation with residue cover. Therefore, no-till cultivation with residue cover was incorporated into the improved scenario, while conventional tillage was considered as the base case scenario.
In order to calculate the new sustainable SOC retention in soil under the no-till cultivation scenario, a response ratio, which represents the ratio of SOC content in no-till soil compared to conventional tillage soil, is required for making an estimation for improved scenarios [28]. This study used a response ratio of 1.23 for tropical moisture climate, 1.17 for tropical dry climate, 1.16 for temperate moisture climate, and 1.10 for temperate dry climate [28]. The carbon input required in the no-till scenario was then calculated by dividing the results obtained with the RothC model by the response ratio.
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Increased crop yields
Crop productivity increases as a result of the improvement of agricultural technology. Increased crop productivity contributes to an increased biomass yield, which results in higher residue availability [2]. This study implemented annual increase rates for the six crops in the improved scenario to take into account the effect of improved technology on crop yield, according to Jaggard et al. (2010) [63]. Annual increase rates (non-compounding) are shown
in Table 4.4. It should be noted that a possible climate change in the future was considered in
the study of Jaggard et al. (2010) [63]; therefore, the increase rate incorporates the impact of climate change on crop yields. Other studies on China also projected annual yield increase rates of 0.7% and 1.7% for rice and wheat, respectively, and global average rates of yield increase by 1.6%, 1.0%, 0.9%, and 1.3% for maize, wheat, rice, and soybean, respectively [64]. These scenarios, including both SOC and improved scenarios, were grouped into six scenarios, as shown in Table 4.5.
Table 4.4 Increase rates of crop yields between 2007 and 2050 [63]
a adapted from OECD/FAO, 2019 [65].
Table 4.5 Summary of elements included in each scenario
4.2.5 Calculation of on-farm costs for residues collection
Costs of agricultural residues at the farm gate are divided into direct and indirect costs. Direct
Crop Increase rate (%) Annual increase rate (%)
Maize 55 1.28 Wheat 73 1.70 Rice 41 0.95 Soybean 44 1.02 Rapeseed 61 1.42 Cotton a N/A 0.67
Scenarios Base SOC scenarios Improved scenarios
HSS MSS MCSS No-till cultivation Increased yields
HSS √ MSS √ MCSS √ IHSS √ √ √ IMSS √ √ √ IMCSS √ √ √
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maintenance costs for machineries, and labour costs. Indirect costs are compensation costs for farmers. Annual total on-farm costs of agricultural residues were calculated on a per-area (10 km × 10 km grid cell) basis using Eqn (8).
𝐶𝐶 = 𝐶𝑀 + 𝐶𝐿 + 𝑃𝑅 (8)
Where 𝐶𝐶 (Chinese Yuan (CNY) ⋅t-1) represents on-farm costs for collection of crop residues , 𝐶𝑀 (CNY⋅t-1) represents annualised costs for machines, 𝐶𝐿 (CNY⋅t-1) is annualised labour cost, 𝑃𝑅 (CNY⋅t-1) is compensation cost for farmers, and e represents the 10 km × 10 km grid cell. The calculation of all costs was based on the wet weight (WW) of sustainably collectable residues, and the moisture content of the crop residues is assumed to be 15% on farm fields.
4.2.5.1 Costs for machines
Total costs for using an agricultural machine include ownership (also called capital cost) and operating costs. The annualised costs for machines in each grid was calculated as Eqn (9):
𝐶𝑀 =𝐶𝑀 + 𝐶𝑀 ,
𝑆𝑃 (1 − 𝑀𝐶)⁄ × 𝑁 (9)
where 𝐶𝑀 (CNY⋅t-1) represents annualised costs for machines, 𝐶𝑀 (CNY) is the total annual ownership costs, 𝐶𝑀 , (CNY) is the total annualised operating costs, 𝑆𝑃 (tonne)is the sustainable potential of agricultural residues in one grid cell, 𝑀𝐶 (15% assumed) is the moisture content of the agricultural residues, 𝑁 is the number of machines required for collecting the residues in one grid cell, and e represents the 10 km × 10 km grid cell.
Ownership costs include those for depreciation, interest, and insurance. The total annual ownership costs (𝐶𝑀 ) were estimated as Eqn (10) by multiplying the purchase price of the machine by an annualised ownership cost percentage. The latter was calculated as Eqn (11) according to American Society of Agricultural Engineers (ASAE, 2000) standards [66]:
𝐶𝑀 = 𝑃𝑀 × 𝐶 (10)
𝐶 = + × 𝐼 + 𝐾 (11)
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ownership cost percentage, 𝑆 (decimal) is the salvage value of a machine at the end of machine life, 𝑇 is the lifetime of a machine (10 years assumed), 𝐼 (decimal) is the interest rate, and 𝐾 (decimal) is the insurance factor.
The total annualised operating costs consist of repair and maintenance costs, and fuel costs (Eqn (12)). Average annualised repair and maintenance costs were estimated as Eqn (13) according to ASAE standards (2000) [66]:
𝐶𝑀 , = 𝐶𝑀 + 𝐶, (12)
𝐶𝑀 = 𝑃𝑀 × 𝑅𝐹 × (ℎ 1000⁄ ) /𝑇 (13)
where 𝐶𝑀 (CNY) is the average annualised repair and maintenance costs for a given machine throughput its lifetime, 𝐶, (CNY) represents annualised fuels costs for a given machine in one grid cell, 𝑃𝑀 (CNY) is the purchase cost for a given machine, 𝑅𝐹 and 𝑅𝐹 are repair and maintenance factors, while ℎ (hours) is life-time accumulated use hours of a machine, 𝑇 is the lifetime of a machine, and e represents the 10 km × 10 km grid cell. The annualized fuels costs for a given machine were estimated by using Eqn (14):
𝐶, = 𝑄 × 𝑁 , × 𝑃 (14)
where 𝑄 (L⋅h-1) represents average fuel consumption per hour, 𝑁
, (h) represents actual working hours of a given machine per year, 𝑃 (CNY⋅L-1) refers to the fuel price in China, and
e represents the 10 km × 10 km grid cell.
Further, the 𝑄 could be estimated by Eqn (15) according to ASAE standards (2000) [66]. The 𝑁 and the number of machines required in each grid was calculated by using Eqn (16) and (17), respectively: 𝑄 = 0.305 × 0.73 × 𝑃 (15) 𝑁 , = 𝑆𝑃 (1 − 𝑀𝐶)⁄ 𝑊𝐸 × 𝑁 (16) 𝑁 = 𝐼𝑁𝑇 𝑆𝑃 (1 − 𝑀𝐶)⁄ 𝐶𝑎𝑝 + 1 (17)
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where 𝑃 (kW) is maximum PTO (power take off) power, 𝑆𝑃 (tonne)is the sustainable potential of agricultural residues in one grid cell, 𝑊𝐸 (t⋅h-1) is the working efficiency of a given machine, 𝑁 is the number of machines required for collecting the residues in one grid cell, 𝐶𝑎𝑝 (tonne) is the maximum quantity of residues processed by a given machine per year, and e represents the 10 km × 10 km grid cell. Apart from fuel consumption, total engine lubrication costs account for 15% of total fuel costs (ASAE, 2000) [66].
There are four types of machines required for residues collection. These machines include a square baler (New HollandBC5070, which can make square bales of 0.36 m × 0.46 m × 1.32 m at a wet weight of 45 kg) towed by a tractor (Lovol M804-B) with 80 horse power, a field transport tractor (Lovol M804-B) with 80 horse power towing a tractor trailer, and a bale fork loader (YTO TC4015), all of which form the set of collection equipment needed.
4.2.5.2 Labour costs
It was assumed that three persons are required to operate each set of collection equipment with two persons driving two tractors, and one operating the bale loader. It was also assumed that workers work eight hours a day.
The annualised labour cost was calculated by Eqn (18).
𝐶𝐿 = × ,
( )
⁄ × 𝑁 (18)
Where 𝐶𝐿 (CNY⋅t-1) stands for annualised labour costs; 𝑃 (CNY⋅h-1) is the daily wage, which is shown in Table 4.6, 𝑁 , (h) represents the working hours of a worker per year, 𝑁 is the number of workers which is equal to the number of machines required for collecting the residues in one grid cell, and e represents the 10 km ×10 km grid cell.
4.2.5.3 Compensation costs for farmers
The compensation costs for farmers (𝑃𝑅, CNY⋅t-1) include the costs for fertilisers required for compensating the loss of nutrients due to residues removal, and the incentive for farmers to harvest the residues [2,19]. Residues collection and machine parameters are listed in Table 4.6 and Table 4.7, respectively.
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Table 4.6 Parameters of residues collection
a was obtained by calculating the average price of diesel in 2018;
b adapted from Wang et al. 2017 [67];
c derived from Xu et al., 2014 [68].
Table 4.7 Parameters of agricultural machines for residues collection
a adapted from Wei, 2014 [69];
b derived from internet investigation (China Agricultural Machine http://china.agrimachine.cn/) [70];
c was calculated based on formula: ℎ 𝑇⁄ × 𝑊𝐸 ;
N/A, not available.
4.2.6 Estimation of the economic potential of agricultural residues
The economic potential was defined as the amount of theoretical energy content of residues that could be supplied under a certain on-farm collection cost in this study. It was calculated by constructing cost-supply curves that represent the relationship of aggregated energy supply as a function of on-farm collection costs. Cost-supply curves were constructed by ranking the grids that contain the values of energy content and corresponding collection costs in order of cost from small to large, and then aggregating the corresponding energy content grid by grid in the ascending order of cost. Finally, cost-supply curves were generated by
Parameters Value
Working hours per day (h⋅d-1) 8
Diesel price (Pf, CNY⋅L-1) a 6.8
Labour price (Pl, CNY⋅h-1) b 25
Compensation cost (PR, CNY/ t) c 20
Moisture content of residues (MC, %) 15
Machines Square baler
(New Holland BC5070) Tractor for baler (Lovol M804-B) Tractor for trailer (Lovol M804-B) Fork loader (YTO TC4015) Trailer
Salvage value factor (Sv) a 0.284 0.229 0.229 0.229 0.05
Life time (T, years) 10 10 10 10 10
Estimated use time (h, hours) a 2000 10000 10000 10000 10000
Interest rate (I) a 0.06 0.06 0.06 0.06 0.06
Insurance factor (K)a 0.006 0.009 0.009 0.009 0.009
RF1 a 0.23 0.007 0.007 0.007 0.003
RF2 a 1.8 2 2 2 2.2
PTO (Ppto, kW) b N/A 50.2 50.2 32.4 N/A
Purchase price with subsidy (PM, CNY) b 130000 56000 56000 85000 29000
Working efficiency (WE, t⋅h-1) a 5 N/A N/A 12 N/A
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creating a scatter plot to represent the aggregated energy supply that are likely to be collected at a certain on-farm collection cost. Theoretical energy contents of residues were calculated by multiplying the dry weight sustainable potential by the higher heating value (HHV) (as shown in Table 4.2).
4.3 Results and discussion
4.3.1 Sustainable agricultural residue potential
The sustainable agricultural residue potential was estimated to be 24 Mt for maintaining the SOC level above 2% (HSS scenario), 116 Mt for maintaining the SOC level above 1% (MSS scenario), and 226 Mt for the current SOC level (MCSS scenario) (Figure 4.2a). As the total theoretical availability was 642 Mt dry weight (Table A4.1 in Appendix A4), 65% of theoretical residues are needed for maintaining the current SOC level. This could be explained by the fact that the most crucial determinants of the estimated carbon input rate are the current SOC stock and target SOC level, whilst the current SOC content is relatively low in China. The average carbon input was calculated to be 6.24 t⋅ha-1, 4.76 t⋅ha-1, and 3.28 t⋅ha-1 for maintaining 2%, 1%, and the current level of SOC content, respectively. Spatial distributions of carbon input for different scenarios are presented in Figure A4.1 in Appendix A4. The results showed a large spatial variability of the carbon input required, depending on the soil clay content and climatic conditions. The results indicate that the estimated carbon input rate is positively correlated with the target SOC level or current SOC stock, and with monthly mean temperature and precipitation, but negatively with soil clay content. The higher the SOC stock, the higher the potential of SOC being lost, and, consequently, the higher the carbon input needed to maintain SOC balance [71]. Furthermore, higher temperature and precipitation result in more rapid decomposition of SOC, meaning higher carbon input is required to maintain a certain SOC balance in the south of China, where it is moister and warmer [72]. On the contrary, higher content of soil clay can help stabilise SOC by mineralogical protection [73]. Soil data reveal that 95% of arable land has the SOC content level below 2% currently, and 48% of arable land is less than 1%. If the target SOC level of above 2% is to be maintained, more agricultural residues will be required to be returned to soil. There are many reasons for the low SOC stock, including excessive use of chemical fertilisers instead of organic ones, directly burning residues in fields [74], and intensive cropping combined with rare fallow.
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and increased crop yields in the future, there are 117 Mt, 383 Mt, and 514 Mt of residues sustainably available for bioenergy production in IHSS, IMSS, and IMCSS scenarios, respectively. As shown in Figure 4.2a, availability of sustainable residues under improved scenarios can be significantly increased. From the perspective of the entire country, total production of sustainable residues of the top eight provinces account for more than 60% of total residues production in China under all scenarios. As shown in Figure 4.2b, the highest average yield of residues in China is 291 t⋅km-2, which is achieved under the IMCSS scenario, while the lowest average yield is 153 t⋅km-2, as discovered under the MSS scenario. Higher average yield was normally associated with lower target SOC level. Also, the collection area is larger under scenarios with lower target SOC levels. For intance, maintaining the current SOC level could achieve much higher collection area than maintaining the SOC level above 2%. Moreover, incresed crop yields and improved agricultural managements could contribute to increases in average yield and collection area of agricultural residues.
Figure 4.2 Total production and average yield of theoretical and sustainable residues under base and improved
scenarios (HSS, maintaining at least 2% of soil organic carbon level; IHSS, maintaining at least 2% of soil organic carbon level with no-till cultivation and increased crop yields in 2050; MSS, maintaining at least 1% of soil organic carbon level; IMSS, maintaining at least 1% of soil organic carbon level with no-till cultivation and increased crop yields in 2050; MCSS, maintaining the current soil organic carbon level; IMCSS, maintaining the current soil organic carbon level with no-till cultivation and increased crop yields in 2050) in China. (a) Total production breakdown by province; (b) average residue yield with collection area of China.
The top eight provinces, Henan, Heilongjiang, Shandong, Hebei, Jiangsu, Anhui, Hunan, and Jilin, contributed almost 60% of total production of residues theoretically available (Figure
4.2a). Figure 4.3illustrates the average yields of residues at the provincial level under different
0 150 300 450 600 750 900
Total production (Mt)
Henan Heilongjiang Shandong
Hebei Jiangsu Anhui
Hunan Jilin Other provinces
0 500 1000 1500 2000 2500 3000 3500 4000 0 50 100 150 200 250 300 350 400 Collec tion area ( K km 2) Average yield (t· km -2)
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scenarios. Shandong province has the highest average yields under almost all scenarios analysed, except for HSS and IHSS, where the highest average yields are achieved in Heilongjiang, followed by Henan.Figure 4.4presents the spatial distribution of the sustainable potential of agricultural residues at 1 km × 1 km grid resolution for different scenarios in China. As shown in the abovementioned figures, the extent of distribution and the density of sustainable residues are highest and lowest under IMCSS and HSS scenarios, respectively. Residues resources with high sustainable potential are primarily concentrated in the middle-east of China, where Shandong and Henan provinces are located, for all scenarios other than HSS and IHSS. Residues are mainly distributed in Heilongjiang and Jilin provinces in the north-east of China under HSS and IHSS scenarios. In contrast, there is relatively low potential of sustainable crop residues in the south of China. This result could be explained by the spatial distribution of required carbon input, which is shown in Figure A1. There are much higher amounts of residues required to remain in the soil in the south than in the north, especially under scenarios with high target SOC levels, due to climate conditions being more conducive to SOC decomposition in the south. This result has a profound impact on selecting the area of highest priority for developing bioenergy production strategies from agricultural residues according to the sustainability constraints deployed in China.
Figure 4.3 Average yields of theoretical and sustainable residues by the top eight provinces under different
scenarios. 0 100 200 300 400 500 600 700 Theoretical HSS MSS MCSS Improved theoretical
IHSS IMSS IMCSS
Average yield
(t·
km
-2)
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Figure 4.4 Spatial distributions of sustainable potentials of agricultural residues under different scenarios at 1
km × 1 km resolution in China. (a) HSS scenario; (b) IHSS; (c) MSS scenario; (d) IMSS scenario; (e) MCSS scenario; and (f) IMCSS scenario.
Spatial distributions and potentials of residues under improved scenarios are broader and higher than those under base scenarios. There is an increase of 394% in the sustainable potential of residues under the IHSS scenario compared to the HSS scenario, an increase of 229% under the MSS scenario compared to the IMSS scenario, and an increase of 128% under
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the MCSS scenario compared to the IMCSS scenario. This indicates that no-till cultivation combined with improved crop yields could significantly reduce the amount of residue input to soil, and increase residue yields, leading to a higher sustainable potential of these residues. 4.3.2 On-farm costs for residue collection
Average on-farm collection costs for agricultural residues under different scenarios were categorised by province, and are shown in Table A4.2 in Appendix A4. On average, sustainable residues in China are collected at a cost of 108.3 CNY⋅t-1 by wet weight, 106.7 CNY⋅t-1, 105.9 CNY⋅t-1, 106.2 CNY⋅t-1 , 104.7 CNY⋅t-1 , and 104.4 CNY⋅t-1 under HSS, MSS, MCSS, IHSS, IMSS, and IMCSS scenarios, respectively. The costs are approximately equal to 1.0-1.1 $⋅GJ-1 at a currency exchange rate of 1 US $ = 6.9 CNY. Jiangsu, Shandong, Henan, Anhui, Jilin, Hebei, Heilongjiang, and Hunan are the top eight provinces with low costs. As shown in Figure 4.5, Shandong province has the lowest average costs of 99.9 CNY⋅t-1, 97.4 CNY⋅t-1, and 96.1 CNY⋅t -1 under MSS, IMSS, and IMCSS scenarios, respectively. Low average costs are also found in Jiangsu (98.8 CNY⋅t-1) under the MCSS scenario, in Heilongjiang (104.7 CNY⋅t-1) under the HSS scenario, and in Jilin (99.3 CNY⋅t-1) under the IHSS scenario. The costs under improved scenarios are lower than those under base scenarios. This is caused by a higher production of residues under improved scenarios. Figure 4.5 also shows that the difference in average costs between provinces under HSS scenario is greater than the differences under other scenarios. This is because the average yields of agricultural residues in Hebei, Jiangsu, and Henan provinces are extremely low (19–29 t⋅km-2) under HSS scenario compared to their average yields (above 130 t⋅km-2) under other scenarios.
90 100 110 120 130 140
HSS MSS MCSS IHSS IMSS IMCSS
A ver age o n -f ar m co lle cti o n co st (CN Y/WW t)
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Figure 4.5 Average on-farm collection costs for the top eight provinces under different scenarios in China.
Spatial distributions of on-farm collection costs for residues collection are displayed at 10 km × 10 km resolution in Figure 4.6. As shown in this figure, most of the residues are collected on farms at a cost range of 93-100 CNY⋅t-1 (0.9-1.0 $⋅GJ-1). The distributions of low costs are consistent with those of high residue yields. This result could provide a reference for choosing areas with low collection costs for agricultural residues in planning bioenergy production.
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km × 10 km resolution in China. (a) HSS scenario; (b) IHSS scenario; (c) MSS scenario; (d) IMSS scenario; (e) MCSS scenario; and (f) IMCSS scenario.
On-farm collection costs of agricultural residues are more attractive when compared with farm-gate production costs of Miscanthus and switchgrass, which were 4.8 $⋅GJ-1 [75]. However, competitiveness of the costs for collecting agricultural residues could change with residue market and domestic economic development. For example, the compensation costs for farmers may increase if the residues are more commonly used for competing applications, and consequently cost-oriented supply would shift. Labour costs could also increase in the future with the development of economy, and vary in different regions. Costs of machines are affected by fuel price, interest rate, and insurance price. The current study assumed that the costs of items remain constant, no matter the space and time changes due to availability of cost data in different regions. In a future study, more efforts should be put into evaluating the uncertainties of inflation, government policy, and technology improvement.
4.3.3 Economic potential of agricultural residues
Cost-supply curves shown in Figure 4.7 were constructed to reflect the economic potential of agricultural residues according to on-farm collection costs of these residues. The economic potential was calculated to be 0.37 EJ⋅year-1 (90% of its total energy supply) under the HSS scenario, 1.84 EJ⋅year-1 (92% of its total energy supply) under the MSS scenario, 3.67 EJ⋅year-1 (94% of its total energy supply) under the MCSS scenario, 1.88 EJ⋅year-1 (94% of its total energy supply) under the IHSS scenario, 6.35 EJ⋅year-1 (96% of its total energy supply) under the IMSS scenario, and 8.57 EJ⋅year-1 (97% of its total energy supply) under the IMCSS scenario at collection costs of 6.8 CNY⋅GJ-1 (0.98 $⋅GJ-1) or less (Table 4.8). Around 98-99% of total energy could potentially be supplied at collection costs of 8.0 CNY⋅GJ-1 or less, under all scenarios presented. As shown in Figure 4.7, collection costs change very little from 6.4 CNY⋅GJ-1 (0.93 $⋅GJ-1) until reaching 90% of total energy supply. Then, costs increase dramatically from 6.8 CNY⋅GJ-1 (0.98 $⋅GJ-1) to 8.0 CNY⋅GJ-1 (1.16 $⋅GJ-1) with the accumulation of the last 10% of energy supply. This result indicates that most of the energy supply could be provided at low collection costs under all scenarios, making economical large-scale bioenergy production possible.
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Figure 4.7 Cost-supply curves of sustainable agricultural residues under different scenarios. Only displaying
economic potential at < 8 CNY⋅GJ-1
.
Table 4.8 Total energy supply, sustainable residue supply, and economic potential of sustainable residues under
different scenarios
a 100.0 CNY⋅t-1 by wet weight is converted from 6.8 CNY⋅GJ-1 by unit conversion; b 117.6 CNY⋅t-1 by wet weight is converted from 8.0 CNY⋅GJ-1 by unit conversion. 4.3.4 Competing use of agricultural residues
Competing use of crop residues, including animal feed, rural household fuel, industry materials, and mushroom cultivation were not taken into account in the spatial calculation due to a lack of competing use data with spatial distinction in different regions. Therefore, a correction was made by calculating the total available potential of crop residues in China by subtracting the total amount of competing use from the total sustainable potential of crop residues. Table 4.9shows the proportions of competing use of agricultural residues in China based on several reports. An average proportion of competing use was calculated according
6.8 6.0 6.5 7.0 7.5 8.0 0 2 4 6 8 10 On-fa rm co lle cti o n co st (CN Y· G J -1)
Energy supply (EJ·year-1)
HSS MSS MCSS IHSS
IMSS IMCSS Reference line
Scenario Total energy supply
(EJ⋅year-1)
Sustainable residue supply (Mt⋅year-1) Economic potential (EJ⋅year-1)
≤ 100.0 CNY⋅t-1a ≤ 117.6 CNY⋅t-1b ≤ 6.8 CNY⋅GJ-1 ≤ 8.0 CNY⋅GJ-1
HSS 0.41 21.4 23.1 0.37 0.4 MSS 2 106.4 113.9 1.84 1.97 MCSS 3.89 212.2 222.0 3.67 3.84 IHSS 2.01 108.7 114.5 1.88 1.98 IMSS 6.6 367.1 378.6 6.35 6.55 IMCSS 8.88 495.4 509.2 8.57 8.81
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to said literature. It was assumed that the residues used as fertilisers and rural household fuels are applied to maintain soil nutrients and produce bioenergy, respectively. It is also assumed that the proportions stay constant under different scenarios. Therefore, according to the table, there is a proportion of 38.4% of residues that is alternatively used as animal feed, industry materials, and in mushroom cultivation. Consequently, the available potential of crop residues in China was calculated to be 15 Mt⋅year-1 (0.25 EJ⋅year-1), 72 Mt⋅year-1 (1.23 EJ⋅year-1), 139 Mt⋅year-1 (2.39 EJ⋅year-1), 72 Mt⋅year-1 (1.24 EJ⋅year-1), 236 Mt⋅year-1 (4.07 EJ⋅year-1), and 317 Mt⋅year-1 (5.47 EJ⋅year-1) under HSS, MSS, MCSS, IHSS, IMSS, and IMCSS scenarios, respectively. It accounted for 0.2%, 0.9%, 1.8%, 0.9%, 3.0%, and 4.0% of total primary energy consumption (136 EJ) in China in 2018 under HSS, MSS, MCSS, IHSS, IMSS, and IMCSS scenarios, respectively. It should be noted that the demanding amount of crop residues for animal feed would be reduced by replacing crop residues with more advanced feed with higher protein content, providing a better diet structure and more feed space, or by improving digestibility and animal’s genetics [76]. Thus, this will lead to an increase in availability of crop residues for bioenergy production.
Table 4.9 Proportion (%) of competing use of agricultural residues in China
N/A, Not available.
4.3.5 Uncertainties in biomass supply
Most uncertainties in biomass supply comes from the projection on future crop yield. Yield estimation of this study was based on Jaggard et al. (2010), which comprehensively simulated possible yield changes with both climate change and technology improvements [63]. In addition to the impact of yield, temperatures and precipitation changes induced by climate
Literature Animal feed Fertilizer Industrial
material
Mushroom cultivation
Rural household fuel
Liu et al. (2008) [7] 23 N/A 4 N/A 37
Chen et al. (2009) [9] 28 15 3.29 N/A N/A
Liu et al. (2012) [11] 33.2 29.2 2.2 N/A N/A
Jiang et al., (2012) [16] 30.69 14.78 2.37 2.14 N/A
Wang et al., (2013) [12] 25 29 4 N/A 16
Yang et al. (2015) [13] 15.3 33.4 N/A N/A 28.7
Ji (2015) [14] 20.9 15 2.5 N/A 21.2
Gao et al. (2016) [8] 24.5 14.1-14.6 3.9 2.3 24.9-30.7
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change could also influence the SOC decomposition, and weather extremes might lead to soil erosion and degradation [72]. Though there are many uncertainties within climate change simulations [77], the uncertainties of climate are actually beyond the scope of this study, so they were not counted for that in the projection of residue supply. However, uncertainties of future climate and crop production in turn reflect the importance of mitigating carbon emission, which might be achieved by using residue-based bioenergy to replace fossil fuel [78]. Potential soil degradation and erosion under climate change also stress the importance of maintaining a healthy soil with a reliable SOC level, and implementing conservation tillage. Uncertainties also exist in the carbon modelling process due to data availability and limitations of the RothC model. For instance, irrigation could accelerate the organic matter decomposition and carbon turnover, while anaerobic environment of paddy soil could slow down the SOC decomposition [41,42]. Ignoring irrigation could lead to a general overestimation of the available residue resources in irrigated areas, while applying RothC model on paddy rice could underestimate the collectable residues in South China. The contribution of rhizodeposition to SOC [79] was not considered due to a lack of data. Moreover, the availability of crop residues for bioenergy production is affected by markets (e.g., residues price), social factors (e.g., farmer’s aspiration), and government policies (e.g., subsidies for residue collection and bioenergy production, as well as prohibiting straw burning in fields). On one hand, the government’s strategy to support the development of bioenergy will be accompanied by subsidies for bioenergy plants or farmers, which will direct more agricultural residues to bioenergy production. On the other hand, the government may encourage farmers to use more animal manure or conservative tillage out of the protection of soil sustainability, which could further increase the crop residues potential for bioenergy production.
4.3.6 Practical implications of this study
The results highlight the differences of resource potential among various SOC scenarios and spatial heterogeneity of residue resource among different regions. Carbon budget of the soil including residue input, turnover and emission should be always carefully calculated before removing all residues for energy use, otherwise the loss of SOC and soil degradation could offset any environmental benefit brought by using bioenergy. From the policymaker’s view, this study emphasizes the importance of region-specific planning on bioenergy resources
4
considering both biomass supply and soil conditions. From the farmer’s view, decisions on collecting and selling residues is not only on profit but also considering the long-term sustainability of their land. To supply more crop residues and maintain soil sustainability, management such as conservative tillage, applying organic fertilisers, and increasing the proportion of fallow can be introduced to the cropping systems. From the view of research, the grid-basis SOC modeling process developed by this study can serve as a guide for other large-scale studies on agricultural residues. From the industries’ view, high-resolution spatial explicit analysis could help enterprises to identify feasible regions with high resource densities and low collection costs for large-scale residue-based bioenergy production.
The results showed that provinces like Shandong, Henan and Jiangsu are favorable for crop residue based bioenergy development. Distribution of these hotspot provinces can well fill the spatial gaps left by other major renewable energy industries, e.g., the hydropower (southwestern China) and wind power (northern China). Local circular system with collection, conversion and consumption could be a direction for future development of crop residue-based bioenergy. Future studies can work on the supply chain optimization, particularly in the rural areas with high resource density and low collection cost of residues. Optimizing the energy conversion pathways provides another perspective for future studies. For instance, biomass cogeneration can provide heat for home heating in rural areas in northern China and alleviate the air quality problems induced by coal-burning in winter. Meanwhile, residue-based bioenergy may bring other socio-economic benefits, e.g., creating job opportunities in local areas to mitigate rural exodus.
4.4 Conclusions
In this contribution, the potential of collectable agricultural residues and their economic potential for bioenergy production have been estimated, taking into account sustainability criteria, transition of agricultural managements, and increased crop yields. RothC model combined with GIS-based technology was deployed to quantify the grid-specific amount of residue-derived carbon that is required to maintain a certain SOC level for sustainable agricultural production. High resolution of land use, soil, and climate data were used to quantify the spatial explicit resource potentials of agricultural residues. Results from different SOC scenarios show a trade-off between sustainable agriculture and bioenergy production. It was found that 226 Mt of residues could be collected annually to maintain the current SOC level, as compared to 24 Mt for maintaining at least 2% of the SOC level. With increased crop
4
yield and improved land management, the resource potential can be increased to 514 Mt and 117 Mt in 2050, respectively. A total of 8.6 EJ energy potential from agricultural residues could be supplied at a cost lower than 6.8 CNY⋅GJ-1 (0.98 $⋅GJ-1) in 2050. Five provinces, Shandong, Henan, Jiangsu, Heilongjiang, and Hebei, are suggested with high supply potential and low collection costs. This study provides a data basis for systems analysis of biomass supply chain in China. Systems optimization of biomass supply, transportation, storage, pre-treatment, and bioenergy production is planned for a follow-up study.
4.5 Acknowledgements
This study was partially supported by China Scholarship Council (CSC), the China National Key Research and Development Plan under Grant Number 2017YFD0700605, and National Natural Science Foundation of China under Grant Number 31701316. We thank Dr. Jingying Fu and Professor Dong Jiang from Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences for providing land use data.
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