Carbon flow analysis of China's agro-ecosystem from 1980 to 2013: A perspective from substance flow analysis

Research on carbon cycling has attracted attention from both scientists and policy-makers. Based on material flow analysis, this study systematically budgets the carbon inputs, outputs and balance from 1980 to 2013 for China''s agro-ecosystem and its sub-systems, including agricultural land use, livestock breeding and rural life. The results show that from 1980 to 2013, both the carbon input and output were growing gradually, with the carbon input doubling from 1.6 Pg C/year in 1980 to 3.4 Pg C/year in 2013, while carbon output grew from 2.2 Pg C/year in 1980 to 3.8 Pg C/year in 2013. From 1980 to 2013, the crop production system in China has remained a carbon source, and the agricultural land uses were also almost all carbon sources instead of carbon sinks. As soil carbon stock plays a very important role in deciding the function of China''s agro-ecosystem as a carbon sink or source, practices that can promote carbon storage and sequestration will be an essential component of low carbon agriculture development in China.     

中国省域农业源非CO2温室气体排放的影响因素分析与峰值预测

运用IPCC清单方法核算了中国各省(直辖市、自治区)农业源非二氧化碳(非CO₂)温室气体(GHG)的排放,基于Tapio弹性脱钩理论和情景预测法、STIRPAT模型和向量自回归模型(VAR)预测了其达峰时间和规模,并结合对数平均迪氏指数(LMDI)模型、STIRPAT模型和固定效应模型识别了中国农业非CO₂ GHG排放的影响因素.结果表明,高情景和中情景下中国农业非CO₂ GHG排放量整体呈上升趋势,到2050年仍未达峰;2018—2050年低情景下GHG排放量整体呈下降趋势,其中,低情景下已于2018年达峰,峰值为0.73×10⁹ t (以CO₂-eq计,下同);北京市、上海市、江苏省、浙江省、福建省、广东省、海南省、重庆市、四川省和青海省农业生产与其农业非CO₂ GHG排放呈强脱钩状态,其余21个省(直辖市、自治区)呈弱脱钩状态;除天津市和黑龙江省以外的29个省(直辖市、自治区),经济和人口为农业非CO₂ GHG排放的促进因素,效率和结构为其抑制因素.     

不同氮水平下夏玉米农田土壤呼吸动态变化及碳平衡研究

为探讨氮肥对华北平原高产农田土壤呼吸动态变化及其碳平衡的影响,试验设计了习惯施氮量(N228,228 kg·hm-2)、2/3习惯施氮量(N152,152 kg·hm-2)和不施氮(N0)3个处理,采用密闭静态箱法研究了不同施氮水平下夏玉米生长季农田的土壤呼吸速率季节变化、土壤呼吸与地温等环境因素的关系以及农田系统的碳平衡。结果表明,夏玉米农田土壤呼吸速率均值和土壤呼吸释放的总碳量分别为C 98.8-115.9 mg·m-2.h-1和C 2 232.3-2 524.2 kg·hm-2。与处理N0相比,处理N152(N 152kg·hm-2)和N225(N 225 kg·hm-2)的土壤呼吸速率均值分别增加了10.2%和17.4%,土壤呼吸释放的总碳量分别增加了6.74%和13.1%。地温(5 cm)和土壤含水量(0-10 cm)分别与土壤呼吸速率呈指数和二次曲线关系,R2均达显著水平。其中地温(5 cm)解释了土壤呼吸季节变化的55.9%-67.0%,而土壤含水量(0-10 cm)可解释土壤呼吸季节变化的25.3%-59.3%。土壤呼吸的温度敏感系数Q10在2.05-2.23之间,且随着施氮水平的提高而增加。处理N0、N152(N 152 kg·hm-2)和N228(N 228kg·hm-2)的土壤含水量分别是22.5%、22.7%和23.3%时,土壤呼吸速率达最高值,超过此阈值,土壤呼吸速率均呈下降的趋势。夏玉米农田系统是大气二氧化碳(CO2)重要的汇,净初级生产力(NPP)固碳量和农田系统的净碳输入(NEP)分别为C 6 829.1-8 950.2 kg·hm-2和C 4 898.2-6 766.8 kg·hm-2。处理N152(N 152 kg·hm-2)和N228(N 228 kg·hm-2)与处理N0相比,NPP固碳量分别增加了24.8%和131.1%,NEP分别增加了31.9%和38.1%。     

Quantifying groundwater phosphorus flux to rivers in a typical agricultural watershed in eastern China

Environmental Science and Pollution Research - Increasing evidence indicates that groundwater can contain high dissolved phosphorus (P) concentrations, thereby contributing as a potential pollution...     

Farmers' perception on combined climatic and market risks and their adaptive behaviors: a case in Shandong Province of China

Environment, Development and Sustainability - This paper used original survey data in Shandong province of China to depict local farmers’ perceptions of combined climatic and market risks,...     

中国农业源甲烷和氧化亚氮排放的影响因素

基于清单方法对中国的甲烷（CH₄）及氧化亚氮（N₂O）等农业源非二氧化碳（CO₂）温室气体（GHG）排放情况进行核算研究,通过引入Tapio弹性脱钩理论和对数平均迪氏指数（LMDI）方法研究了中国农业源CH₄和N₂O排放的影响因素.结果表明：①1980—2018年中国农业源CH₄和N₂O排放量由0.56×10⁹ t CO₂-eq上升至0.73×10⁹ t CO₂-eq;②1980—2018年中国农业源CH₄和N₂O排放随着农业总产值增长而缓慢增加,整体呈弱脱钩状态,但其脱钩状态稳定性较差;③从LMDI因素分解角度看,经济因素和人口规模因素对中国农业源CH₄和N₂O排放呈正效应,经济因素影响最大;效率因素和结构因素均对中国农业源CH₄和N₂O排放有减缓作用,其中,效率因素为主要抑制因素;④结构因素、效率因素和人口规模因素对脱钩努力的影响程度为效率因素>结构因素>人口规模因素.     

利用DPeRS模型估算巢湖流域氨氮和化学需氧量的面源污染负荷

巢湖作为安徽省重要的饮用水源,其面源污染问题受到广泛关注.本文利用一种基于遥感分布式面源污染计算模型——DPeRS(Diffuse pollution estimation with remote sensing)模型,估算了巢湖流域2010年氨氮(NH+4-N)和化学需氧量(CODCr)面源污染物负荷,并进行污染特征解析,结果表明:1巢湖流域污染物以耗氧有机物为主,2010年产生NH+4-N 1562 t,进入水体800 t;CODCr9×104t,进入水体5×104t.22010年不同月份面源氨氮和CODCr污染负荷均有显著性差异,其中,7—8月氨氮和CODCr污染产生量较高.3空间分布上,氨氮和CODCr污染物主要集中在巢湖流域西北部地区;从区县角度来看,合肥市市辖区面源污染物产生量及入河排放量最大.4污染类型分析结果表明:城镇径流是氨氮最主要的面源污染源,且氨氮污染负荷与城镇人口密度的相关系数达到0.98,氨氮污染负荷与农田氮平衡的相关系数为0.65;而畜禽养殖是CODCr最主要的面源污染源,且CODCr污染产生负荷与畜禽养殖密度之间有显著的空间关联性,其相关系数达到0.91.     

Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay,China

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes (\({\updelta}^{15}\mathrm{N}-{\mathrm{NO}}_3^{\hbox{-} }\) and \({\updelta}^{18}\mathrm{O}-{\mathrm{NO}}_3^{\hbox{-} }\)) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine \({\mathrm{NO}}_3^{\hbox{-} }\) in the Cao’e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.