基于农户调查的长江流域双季稻生产碳、氮足迹分析——以江西和湖南为例

doi:10.16035/j.issn.1001-7283.2023.02.033

摘要/Abstract

摘要：

量化分析作物生产碳、氮足迹可以有效帮助减少农业温室气体和活性氮排放。基于足迹理论和方法，采用问卷调查方式定量研究长江流域水稻生产碳足迹和氮足迹大小及组成。研究表明，水稻单位产量碳足迹和氮足迹分别介于0.50~0.90kgCO₂-eq/kg和5.20~13.17gN-eq/kg，其中CH₄和NH₃排放为主要贡献因子。种植规模与碳足迹和氮足迹呈现显著负相关关系，与一般农户（低于0.7hm²）水稻种植相比，种粮大户（高于3.3hm²）碳足迹和氮足迹分别降低了33.8%和44.1%。在调研的稻田中发现，53.1%的地块肥料投入过量，54.9%的地块灌溉投入过量，且产量较低，存在着较大的节能减排潜力。提高农户的受教育水平和低碳绿色农业认知能够显著降低水稻生产碳、氮足迹。因此，发展双季稻节肥节水技术，提高农业机械运行效率，构建规模化农作种植模式，同时提升农户知识文化水平和加强低碳绿色农业知识的普及力度以及不同类型适应性行为的指导，健全低碳绿色农业技术创新与推广机制，有利于缓解中国长江流域双季稻种植区的气候变化和富营养化污染。

关键词: 碳足迹, 氮足迹, 双季稻, 生态评价, 生命周期

Abstract:

Quantifying the carbon footprint (CF) and nitrogen footprint (NF) of crop production can help identify key options to mitigate greenhouse gas and reactive nitrogen emissions from agriculture. Based on farmer's production survey data, the CF and NF of rice production in the Yangtze River were estimated. The results showed the CF and NF per unit yield for rice production in Yangtze River were 0.50-0.90kgCO₂-eq/kg and 5.20-13.17gN-eq/kg, respectively. The largest fraction of CF and NF of rice was the share of CH₄ emission and NH₃ volatilization, respectively. The scale of planting was negatively correlated with the CF and NF, and a decrease in the CF and NF were found in large sized farms (more than 3.3ha) by 33.8% and 44.1%, respectively, compared to smaller ones lower than 0.7ha. 53.1% of a field exposed to excessive fertilizer and 54.9% of a field exposed to excessive irrigation showed low yields. Improving farmers' education level and awareness of low-carbon and green agriculture could significantly reduce the CF and NF of rice production. Thus, developing technology that limits water and fertilizer consumption, as well as developing in farm machinery operation efficiencies and large scaled farms, strengthen the popularization of knowledge and culture level of farmers and low carbon green agriculture knowledge and guidance of different types of adaptive behavior; improve the innovation and promotion mechanism of low carbon green agriculture technology would be favorable toward mitigating climate change and eutrophication of double rice production in Yangtze River.

Key words: Key words Carbon footprint, Nitrogen footprint, Double rice cropping, Ecological assessment, Life cycle assessment

陈中督, 徐春春, 纪龙, 方福平. 基于农户调查的长江流域双季稻生产碳、氮足迹分析——以江西和湖南为例[J]. 作物杂志, 2023 (2): 229–237

Chen Zhongdu, Xu Chunchun, Ji Long, Fang Fuping. Carbon and Nitrogen Footprints of Double Rice Production in Yangtze River Based on Farm Survey Data——A Case Study of Jiangxi and Hunan[J]. Crops, 2023(2): 229–237

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项目 Item	碳排放系数 Carbon emission coefficient	氮排放系数 Nitrogen emission coefficient
柴油Diesel	0.89kgCO₂-eq/kg	0.56gN-eq/kg
柴油燃烧Diesel combustion	4.10kgCO₂-eq/kg	4.10gN-eq/kg
灌溉用电 Electricity for irrigation	0.82kgCO₂-eq/kW?h	0.76N-eq/kW?h
氮肥N fertilizer	1.53kgCO₂-eq/kg	0.47gN-eq/kg
磷肥P₂O₅	1.63kgCO₂-eq/kg	0.36gN-eq/kg
钾肥K₂O	0.65kgCO₂-eq/kg	0.03gN-eq/kg
农膜Flim	22.72kgCO₂-eq/kg	12.02gN-eq/kg
杀虫剂Insecticides	16.61kgCO₂-eq/kg	3.55gN-eq/kg
除草剂Herbicides	10.15kgCO₂-eq/kg	4.49gN-eq/kg
杀菌剂Fungicides	10.57kgCO₂-eq/kg	7.05gN-eq/kg
水稻种子Rice seed	1.84kgCO₂-eq/kg	0.76gN-eq/kg

种类Item	碳排放Carbon emission (kgCO₂-eq/hm²)				氮排放Nr emission (gN-eq/hm²)
	早稻 Early rice	晚稻 Late rice	双季稻 Double rice		早稻 Early rice	晚稻 Late rice	双季稻 Double rice
	早稻 Early rice	晚稻 Late rice	双季稻 Double rice	柴油Diesel	早稻 Early rice	晚稻 Late rice	双季稻 Double rice	635.9±157.9	608.8±147.6	1244.7±279.6	607.8±151.0	582.0±141.1	1189.8±267.3
氮肥N	383.4±82.6	423.5±97.0	806.9±165.1		77.0±16.6	85.1±19.5	162.1±33.2
磷肥P₂O₅	39.5±19.4	53.9±29.4	93.5±44.4		21.7±10.6	29.6±31.8	51.3±35.6
钾肥K₂O	66.2±28.0	72.1±28.6	138.2±52.2		3.0±1.3	3.3±1.3	6.3±2.4
灌溉Irrigation	21.5±13.4	29.4±16.2	50.8±27.6		19.9±12.4	27.2±15.0	47.1±25.6
农膜Film	164.4±33.1	－	164.4±33.1		87.0±17.5	－	87.0±17.5
种子Rice seed	115.5±57.9	85.7±47.7	201.1±86.3		47.7±23.9	35.4±19.7	83.1±35.6
除草剂Herbicides	2.0±0.8	3.5±2.1	5.4±2.2		0.9±0.4	1.5±0.9	2.4±1.0
杀虫剂Insecticides	8.1±3.7	11.4±6.7	19.5±8.2		1.7±0.8	2.4±1.4	4.2±1.7
杀菌剂Fungicides	8.6±3.9	8.1±3.9	16.6±7.0		5.7±2.6	5.4±2.6	11.1±4.7
总体Total	1445.0±201.2	1296.2±207.8	2741.2±379.6		872.9±160.8	771.8±157.3	1644.3±291.7

项目 Item	种植规模Cropping scope
项目 Item	种粮大户 Large-scale grain planter (23)	家庭农场 Family farm (13)	一般农户 General farmer (40)
碳足迹Carbon footprint (kgCO₂-eq/kg)	0.53±0.03c	0.62±0.05b	0.80±0.10a
氮足迹Nitrogen footprint (gN-eq/kg)	6.73±1.53c	9.67±1.11b	12.04±1.13a

解释变量 Explaining variable	碳足迹Carbon footprint		氮足迹Nitrogen footprint
解释变量 Explaining variable	回归系数 Coefficient of estimate	边际效应 Marginal effect	回归系数 Coefficient of estimate	边际效应 Marginal effect
性别Gender	0.115(0.058)	0.018(0.009)	0.216(0.108)	0.033(0.017)
年龄Age	0.061(0.031)^*	0.009(0.005)^*	0.044(0.022)^**	0.007(0.003)^**
教育Education	-0.330(-0.165)^*	-0.063(-0.032)^**	-0.345(-0.173)^*	-0.066(-0.033)^*
耕作年限Experience	-0.052(-0.026)	-0.008(-0.004)	-0.033(-0.017)	-0.005(-0.003)
耕作面积Farm size	-0.053(-0.027)^*	-0.008(-0.004)^*	-0.067(-0.034)^*	-0.010(-0.005)^*
气候变化认知Climate change awareness	-0.040(-0.020)	-0.006(-0.003)	-0.058(-0.029)	-0.009(-0.005)
低碳绿色认知Low carbon awareness	-0.336(-0.168)^*	-0.053(-0.027)^**	-0.412(-0.206)^*	-0.065(-0.033)^*
风险规避意识Risk aversion	-0.182(-0.091)	-0.028(-0.014)	-1.303(-0.652)	-0.201(-0.101)
农业收入比例Farm income ratio	0.162(0.081)	0.026(0.013)	0.173(0.087)	0.027(0.014)
机械拥有量Machinery ownership	-0.173(-0.087)^*	-0.028(-0.014)^*	-0.048(-0.024)^*	-0.008(-0.004)^*
土壤肥力状况Soil fertility deficiency	-0.229(-0.115)^*	-0.038(-0.019)^**	-0.121(-0.061)^*	-0.020(-0.011)^*
灌溉条件Sufficient water irrigation	-0.220(-0.110)^***	-0.037(-0.018)^**	-0.145(-0.073)^**	-0.024(-0.012)^**
信贷途径Credit access	0.113(0.057)	0.019(0.010)	0.159(0.080)	0.027(0.014)
技术支持Technical support	-0.350(-0.175)^***	-0.061(-0.031)^**	-0.341(-0.171)^***	-0.060(-0.031)^***