Construction of Carbon Sequestration Estimation Model of Mulberry Garden Based on Statistical Data—A Case Study of Sichuan Province

doi:10.11924/j.issn.1000-6850.casb2021-0827

Abstract

Abstract:

In view of the current lack of basic data of mulberry orchards limits the evaluation of carbon sequestration, this study proposed a mulberry garden carbon sequestration estimation model (MCSD) based on statistical data. MCSD was based on relatively complete statistical cocoon production data, and all mulberry orchards in Sichuan were assumed to feed silkworms with mulberry leaves. Taking the shrub model of the forest ecosystem carbon storage measurement guide (SMFC) as a control, this paper compared the difference between the MCSD model and the SMFC model in measuring the contribution of carbon sink of mulberry orchards in Sichuan Province from 2000 to 2018. The results showed that: (1) the average annual carbon density of aboveground part of mulberry in mulberry orchards was estimated to be 10.13 t/hm², and the carbon reserve of Sichuan mulberry orchards in 2018 was 1.4239 million tons; (2) mulberry leaves and branches were the main sources of carbon sink in mulberry orchard, accounting for 44.44% and 26.65% of the total, respectively; (3) taking 2000 as the base year, MCSD model estimated that the cumulative carbon reserve of Sichuan mulberry orchards from 2000 to 2018 was 16.3625 million tons; (4) by 2018, the cumulative carbon sink contribution of mulberry leaves and mulberry branches accounted for 60.94% and 36.55% of the total, respectively; (5) the calculation result of the SMFC model was 10.73% lower than that of the MCSD model in this study, without considering the harvest year and yield of mulberry leaves, which could not fully reflect the cumulative carbon sink contribution of mulberry orchards. Mulberry carbon sink assessment is an indispensable part to achieve China’s goals of ‘emission peak’ and ‘carbon neutrality’. The results of this study could provide a basis for the future standardized assessment of the carbon sink of mulberry ecosystem in different regions and different utilization modes in China.

Key words: carbon pool, economic forest, carbon absorption, parameters, diversification

CLC Number:

F326.1

WANG Xie, DENG Qing, ZHOU Jing, TANG Tian, HU Lizhi, LI Rui, ZHANG Jianhua. Construction of Carbon Sequestration Estimation Model of Mulberry Garden Based on Statistical Data—A Case Study of Sichuan Province[J]. Chinese Agricultural Science Bulletin, 2022, 38(2): 31-37.

Figures/Tables 5

References 22

[1]	FANG J Y, KATO T, GAO Z D, et al. Evidence for environmentally enhanced forest growth[J]. PNAS, 2014, 111(26):9527-9532. doi: 10.1073/pnas.1402333111 URL
[2]	GUO Z D, HU H F, LI P, et al. Spatio-temporal changes in biomass carbon sinks in China's forests from 1977 to 2008[J]. Science China: life science, 2013, 56(7):661-671. doi: 10.1007/s11427-013-4492-2 URL
[3]	HU H F, WANG S P, GUO Z D, et al. The stage—classified matrix models project a significant increase in biomass carbon stocks in China's forests between 2005 and 2050[J]. Scientific reports, 2015: 11203.
[4]	IPCC. Climate change 2013: The physical scientific basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental[M]. Cambridge and New York: Cambridge University Press, 2013.
[5]	LIU Y C, YU G R, WANG Q F, et al. Carbon carry capacity and carbon sequestration potential in China based on an integrated analysis of mature forest biomass[J]. Science China: life sciences, 2014, 57(12):1218-1229. doi: 10.1007/s11427-014-4776-1 URL
[6]	于贵瑞, 何念鹏, 王秋凤. 中国生态系统碳收支及碳汇功能—理论基础与综合评估[M]. 北京: 科学出版社, 2013.
[7]	中国农业科学院蚕业研究所, 江苏科技大学. 中国桑树栽培学[M]. 上海: 上海科学技术出版社, 2020.
[8]	中国国际电子商务中心. 2019年中国茧丝绸行业发展报告[R]. 北京:商务部市场运行和消费促进司, 2020.
[9]	PAN Y, BIRDSEY R A, FANG J Y, et al. Alarge and persistent carbon sink in the world’s forests[J]. Science, 2011, 333(6045):988-993. doi: 10.1126/science.1201609 URL
[10]	田云, 张俊飚. 中国农业生产净碳效应分异研究[J]. 自然资源学报, 2013, 28(8):1298-1309.
[11]	张娟, 陈钦. 森林碳汇经济价值评估研究——以福建省为例[J]. 西南大学学报:自然科学版, 2021, 43(5):121-128.
[12]	王正淑, 王继军, 刘佳. 退耕地林草植被碳汇及与农业生态经济系统的关系——以陕西省县南沟流域为例[J]. 草地学报, 2016, 24(2):263-269.
[13]	吴仁儒. 实用桑树栽培学[M]. 成都: 四川科学技术出版社, 1989.
[14]	余茂德, 楼程富. 桑树学[M]. 北京: 高等教育出版社, 2016.
[15]	王谢, 唐甜, 张建华, 等. 桑树新生枝条和叶片中的养分分配格局研究[J]. 蚕业科学, 2017, 43(3):382-387.
[16]	张红爱. 广东8种主要乔木树种碳含量测定分析[J]. 林业资源管理, 2018(1):148-154.
[17]	LAMLOM S H, SAVIDGE R A. A reassessment of carbon content in wood: variation within and between 41 North American species[J]. Biomass & Bioenergy, 2003, 25(4):381-388.
[18]	THOMAS S C, MALCZEWSKI G. Wood carbon content of tree species in Eastern China: Interspecific variability and the importance of the volatile fraction[J]. Journal of environmental management, 2007, 85(3):659-662. doi: 10.1016/j.jenvman.2006.04.022 URL
[19]	王谢, 杨德乾, 郭海霞, 等. 我国桑园时空格局演变特征及驱动机制研究[J]. 西南农业学报, 2020, 33(2):381-388.
[20]	刘世荣, 王晖, 栾军伟. 中国森林土壤碳储量与土壤碳过程研究进展[J]. 生态学报, 2011, 31(19):5437-5448.
[21]	史军, 刘纪远, 高志强, 等. 造林对土壤碳储量影响的研究[J]. 生态学杂志2005, 24(4):410-416.
[22]	李长生. 土壤碳储量减少:中国农业之隐患——中美农业生态系统碳循环对比研究[J]. 第四纪研究, 2000, 20(4):345-350.

年份	四川省桑园面积/万hm²	蚕茧产量/万t	桑叶利用系数
2000	13.33	8.73	0.26
2001	10.67	9.22	0.35
2002	10.67	9.30	0.35
2003	10.67	9.29	0.35
2004	10.67	9.74	0.37
2005	10.67	9.80	0.37
2006	11.33	9.83	0.35
2007	11.33	10.68	0.38
2008	11.33	10.19	0.36
2009	11.33	10.15	0.36
2010	12.00	10.33	0.34
2011	12.00	10.27	0.34
2012	12.00	10.26	0.34
2013	12.00	9.99	0.33
2014	12.33	9.82	0.32
2015	12.33	9.55	0.31
2016	12.47	8.95	0.29
2017	13.00	9.08	0.28
2018	14.06	9.20	0.26

年份	四川省桑园面积/万hm²	蚕茧产量/万t	桑叶利用系数
2000	13.33	8.73	0.26
2001	10.67	9.22	0.35
2002	10.67	9.30	0.35
2003	10.67	9.29	0.35
2004	10.67	9.74	0.37
2005	10.67	9.80	0.37
2006	11.33	9.83	0.35
2007	11.33	10.68	0.38
2008	11.33	10.19	0.36
2009	11.33	10.15	0.36
2010	12.00	10.33	0.34
2011	12.00	10.27	0.34
2012	12.00	10.26	0.34
2013	12.00	9.99	0.33
2014	12.33	9.82	0.32
2015	12.33	9.55	0.31
2016	12.47	8.95	0.29
2017	13.00	9.08	0.28
2018	14.06	9.20	0.26

年份	对比模型计算的碳储量/万t	对比模型较累积碳汇量的低估比例/%	对比模型当年碳储量低估比例/%
2000	120.51	10.73	10.73
2001	96.41	41.78	10.73
2002	96.41	63.14	10.73
2003	96.41	71.51	10.73
2004	96.41	76.78	10.73
2005	96.41	80.4	10.73
2006	102.44	82.21	10.73
2007	102.44	84.42	10.73
2008	102.44	86.14	10.73
2009	102.44	87.51	10.73
2010	108.46	88.06	10.73
2011	108.46	89.1	10.73
2012	108.46	89.97	10.73
2013	108.46	90.71	10.73
2014	111.48	91.14	10.72
2015	111.48	91.72	10.72
2016	112.68	92.15	10.73
2017	117.52	92.33	10.73
2018	127.11	92.23	10.73

年份	对比模型计算的碳储量/万t	对比模型较累积碳汇量的低估比例/%	对比模型当年碳储量低估比例/%
2000	120.51	10.73	10.73
2001	96.41	41.78	10.73
2002	96.41	63.14	10.73
2003	96.41	71.51	10.73
2004	96.41	76.78	10.73
2005	96.41	80.4	10.73
2006	102.44	82.21	10.73
2007	102.44	84.42	10.73
2008	102.44	86.14	10.73
2009	102.44	87.51	10.73
2010	108.46	88.06	10.73
2011	108.46	89.1	10.73
2012	108.46	89.97	10.73
2013	108.46	90.71	10.73
2014	111.48	91.14	10.72
2015	111.48	91.72	10.72
2016	112.68	92.15	10.73
2017	117.52	92.33	10.73
2018	127.11	92.23	10.73