Knowledge Resource Center for Ecological Environment in Arid Area
| 基于多源数据的中国农作物生物量演变研究 | |
| 其他题名 | The Spatial and Temporal Evolution of Crop Biomass in China Based on the Multi-Sources Data |
| 王轶虹 | |
| 出版年 | 2016 |
| 学位类型 | 博士 |
| 导师 | 史学正 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 农田生态系统是陆地生态系统的重要组成部分，其碳源/汇功能对大气中温室气体变化具有直接影响，在全球变化及碳平衡中扮演着重要的角色。农田生态系统固定的碳（C）包括农作物植被碳和农田土壤有机碳（SOC）。农业植被碳库在分担大气碳库方面发挥着重要作用，大尺度农业植被碳库估算更能反映出农作物在生长-收获-消耗的整个生命周期中对大气碳库的分担作用。国内外大量研究表明农田土壤在固碳方面具有巨大潜力，农田土壤中的SOC的主要来源于有机肥料、农作物收获后还田的秸秆、农作物地下的根系及农作物生长过程中分泌和脱落的物质，因此准确估算各个来源的有机质数量对认识农田土壤有机碳的来源和模拟气候变化时土壤SOC的变化趋势有重要作用。据中国科学院碳专项项目组对全国农业生产的调查结果，当前中国的农业生产中只有少量有机肥施用于大田作物，农田土壤中新鲜有机质主要来自于农作物根系和还田的作物秸秆。许多研究表明对于农田土壤而言，地下生物量是农田有机质的主要来源，但是目前缺少对中国农作物地下生物量的多少、空间分布及变化趋势的研究，影响了农田土壤有机碳的变化原因、变化趋势的探索和国家尺度的碳汇估算。农作物秸秆一方面被认为是未来生物质能源的重要来源，在替代化石燃料方面具有重要意义，另一方面秸秆还田可以增加农田土壤有机碳的含量，因此，摸清中国农作物秸秆资源的数量和空间分布，是实现秸秆资源合理利用的基础。许多研究者估算了中国农作物的秸秆数量，但是目前的估算单元主要是省域尺度，估算单元较大，造成对秸秆量的空间分布不清楚等问题，影响了秸秆能源化和农田土壤固碳潜力的进一步研究。基于以上评述，开展了本研究。本文根据中国科学院碳专项项目组获得的实测作物生物量数据，建立了作物指标参数体系，评价了不同尺度指标参数体系对农作物生物量估算的影响。以中国县级农业统计数据为基础，结合建立的指标参数体系，运用比值法研究了中国农作物生物量的时空变异特征。并比较了基于农业统计数据与基于遥感MOD17A3数据获得的农作物NPP差异性，阐述了气候和化肥对农作物生物量的影响。主要研究结果如下：1. 运用作物指标参数结合比值法估算中国农作物生物量时，将全国分为9个农业区时估算结果最优。依据实测水稻、小麦、玉米、大豆、棉花和油菜的生物量，建立了主要作物的收获指数、根冠比和干燥系数的参数体系，并用建立的指标参数体系估算了农作物生物量。研究结果表明，采用指标参数的全国平均值估算农作物生物量时，引起的区域差异高达117%，将中国划分为9个一级农业区估算农作物生物量时得到的结果最优。2. 1980-2010年，中国农作物生物量的变化速率呈现先快后慢的增加趋势。1980-2010年，中国农田NPP年均值为486 Tg C yr-1，经济产量（CP）、秸秆部分(CS)和地下部分（CRE）分别占NPP总量的36%、46%和18%。从生物量密度看，中国农作物CP、CS和CRE密度年均值分别为1.52 Mg C ha-1yr-1、2.04Mg C ha-1yr-1、0.83 Mg C ha-1yr-1，年均增幅分别为0.04 Mg C ha-1yr-1、0.03 Mg C ha-1yr-1、0.03 Mg C ha-1yr-1。从1980年到2010年，中国农作物生物量密度整体呈增长趋势，但具有明显的阶段性。从不同阶段看，从1980s（1980-1990）到1990s（1990-2000），生物量密度增加速度较快，从1990s到2000s（2001-2010），生物量密度增加速度较慢，每个阶段CRE密度增加的最快。1980s和1990s及1980s与2000s之间的CP密度、CRE密度和NPP密度有显著性差异；1990s和2000s之间的CP密度、NPP密度无显著性差异，CRE密度有显著性差异。3. 中国不同区域农作物生物量分异大。1980-2010年，长江中下游农业区农作物年均生物量最高，其次为黄淮海农业区，两个农业区年均生物量相加占中国农作物生物量的57%。从农作物年均生物量密度看，生物量密度较高的区域主要分布在东北农业区、黄淮海平原、江苏浙江沿海平原、四川盆地、长江中下游平原和西北的宁夏平原和绿洲地带等粮食主产区。生物量密度较低的区域主要集中在在内蒙古及长城沿线区、黄土高原区和青藏区。从农作物生物量密度的时间演变来看，内蒙古及长城沿线区、黄土高原区和青藏区等县域生物量密度一直保持增长趋势，但是年际波动较大。东北和黄淮海等县域生物量密度一直保持增长趋势，且年际波动较小。长江中下游和华南地区生物量密度从1980s到1990s生物量密度是增加的，从1990s到2000s生物量密度有减小趋向，但年际波动较小。4. 不同数据源估算结果差异较大。基于统计数据得到的农作物NPP密度全国平均值比基于MOD17A3数据获得的值高21%，全国农田有51%的像元基于统计数据得到的农作物NPP密度大于基于MOD17A3数据的值。两种方法获得的NPP密度变化趋势在东北农业区、黄淮海地区和四川盆地差异亦较大。MOD17A3数据结果与统计数据结果的差异性，与计算时不区分作物类型，所有作物使用相同的光能利用率参数（LUE）以及数据空间分辨率较粗有关。5. 气候和化肥对农作物生物量密度都有影响，但气候的影响不如化肥影响明显。基于统计数据和基于MOD17A3数据的研究结果都表明大部分区域农作物NPP与气候因子未表现出显著相关关系，反映了农田生态系统受人类活动影响较大，降低了气候环境对农作物的影响。使用化肥可显著增加农作物的生物量，尤其是地下生物量和产量，但是化肥使用量的增加幅度明显大于生物量的增加幅度。随着时间推移，化肥对农作物生物量的贡献在减弱，出现明显的报酬递减效应。 |
| 英文摘要 | Agroecosystem is an important part of terrestrial ecosystems, because it has a direct influence on greenhouse gases (GHGs) concentrations in the atmosphere and plays an important role in the global carbon balance. There are two forms of carbon stocked in the farmland: agricultural vegetation and soil organic carbon (SOC). Agricultural vegetation plays an important role in abosorbing CO2, especially the large-scale agriculture vegetation estimates could better reflect the function of crop in the entire life cycle of grow - harvest - consumption. A number of researchers have proved that agricultural soil has great potential in terms of carbon sequestration. In agricultural soils, the organic fertilizer carbon (C) and the plant-derived input of C from above- and belowground harvest residues and rhizodeposition are major sources for soil organic matter formation. Thus, precise estimation of the C input is necessary to monitor the supply of soil organic matter in agricultural soils and model its future development under a changing climate.According to the findings of “Strategic Priority Research Program - Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences”, only a small amount of organic fertilizer were applied to field crops in current agricultural management and the soil organic matter mainly from above- and belowground harvest residues and rhizodeposition of crops. Many studies have shown that the belowground biomass is the main source of SOC in farmland. However, little research on China's crop belowground biomass estimation, spatial distribution, variation trend was reported, which hindered exploring the causes of SOC changes and development trend and increased difficulties for a further study on the potential of agricultural soil carbon sequestration and national scale carbon sequestration estimate. Crop straw is considered as an important source of future biomass energy which has an important value in replacement for fossil fuels. On the other side, straw can increase SOC in agricultural soils, so figuring out the amount of straw resources and its spatial distribution are the basis for the rational use of straw resources. Although many researchers have estimated the quantity of crop straw in China, current research focused more on the provincial scale and the large estimate unit results in the distribution of straw is still unclear, which affect further study of the straw energy potential and carbon sequestration potential in agricultural soils. Based on the above factors, we carried out our study.In this study, based on the data from “Strategic Priority Research Program - Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences” measured above- and belowground biomass across China cropland, we established different scale regional index system of HI (harvest index), R/S (ratio of root to shoot) and DC (dry coefficient) for staple corps and evaluated the influence of different scale index system on crop biomass estimation. Using the ratio method combined with county-level statistical data and established regional index system, we studied the temporal evolution and spatial variation of crop biomass across China. In order to illustrate the uncertainty of using model to estimate crop biomass, we compared the crop NPP estimated based on MOD17A3 with estimated based on statistics data. We discussed the influence of Climatic factors and fertilizer application on crop biomass. The main findings were summarized as follows:1. When using crop index combined with ratio method to estimate crop biomass in China, it is better to divide the China into nine agricultural regions. We collected above- and belowground biomass across China cropland, including rice, wheat, corn, soybean, cotton and oilseed, and established regional index system of HI (harvest index), R/S (ration of root to shoot ) and DC (dry coefficient) for six crops. Using the crop index and county-level statistical data, we estimated the crop biomass produced in China. The results showed that when adopting the average value of crop index to estimate the crop biomass, the maximum error is as high as 117% for regions and dividing China into 9 agricultural regions will obtain the optimum result.2. The Chinese crop biomass changing rate showed first increase fast then increase slowly from 1980 to 2010. The average NPP in China’s cropland is 486 Tg C yr-1 during 1980-2010. The crop yield (CP), straw biomass (CS) and belowground biomass (CRE) account for 36%, 46% and 18% of total NPP, respectively. The average values of CP, CS and CRE biomass density in China cropland are 1.52 Mg C ha-1yr-1, 2.04 Mg C ha-1yr-1 and 0.83 Mg C ha-1yr-1, respectively, with an average increase rate of 0.04 Mg C ha-1yr-1, 0.03 Mg C ha-1yr-1 and 0.03 Mg C ha-1yr-1 , respectively, from 1980 to 2010. From 1980 to 2010, the crop biomass density in China cropland presents an increasing trend and shows a characteristic of apparent phase. The curve characteristics of different periods showed that, biomass density increased rapidly from the 1980s (1980-1990) to the 1990s (1990-2000), and the increase of biomass density increases rate slowed down from the 1990s to the 2000s (2001-2010), in which the CRE density showed the most rapid increase in each period. There were significant differences in the CP, CRE and NPP densities from the 1980s, 1990s and 2000s. The CRE densities between 1990s and 2000s also varied differently.3. There exist large differences in crop biomass and their change trends among different agricultural regions. From 1980 to 2010, the highest crop NPP was found in the Middle-Lower Yangtze River, followed by the Huanghuaihai region, and the biomass values of the two regions account for 57% of total crop NPP in China. The counties with higher CP, CS and CRE densities mainly located in the primary grain producing areas, such as the Northeasten region, the Huanghuaihai Plain, the coastal plain of Jiangsu and Zhejiang, the Sichuan Basin, the Middle-Lower Yangtze River region, Ningxia Plain and oases belts in Xinjiang. The crop biomasses of counties with lower biomass density located in Inner Mongolia, Loess Plateau and Qinghai-Tibet Plateau has been maintained growth trend but with the higher inter-annual fluctuations from 1980 to 2010. The biomass density of counties located in Middle-Lower Yangtze River and Southern China is higher, while has less annual fluctuations. The biomass density of these regions shows increasing trend from 1980s to 1990s, while these values reduced from 1990s to 2000s.4. The estimate result has great difference between data sources. The average value of NPP density estimated from the statistical data is 21% higher than the result of MOD17A3. About 51% pixels of NPP in cropland calculated based on statistical data are greater than the estimates based on data MOD17A3. The change trend of NPP in 2001-2010 is also different, especially in Northeastern China region, Huanghuaihai region, and Sichuan Basin. The result obtained from MOD17A3 data is different from the result of the statistical data. The difference in NPP estimates may be caused by using one light use efficiency value to represent all crops and the coarser resolution (1 km) of MODIS. 5. Both climatic factors and chemical fertilizer have impact on crop biomass, but the influence of fertilizer is obvious than climate. Based on the findings of statistical data and MOD17A3 data both indicate that the crop NPP in most of China cropland showed no significant correlation with climatic factors, reflecting the agroecosystems is greatly affected by human activities. Using fertilizers can markedly increase crop biomass, especially for belowground biomass and crop yield, but the rate of fertilizer increase was significantly greater than the growth rate for the amount of biomass. As time goes on, the contribution of chemical fertilizers to the crop biomass is weakening, due to the obvious effect of diminishing returns. |
| 中文关键词 | 农作物生物量 ; 农业统计数据 ; 指标体系 ; 时空演变 |
| 英文关键词 | Crop biomass Statistical data index Temporal evolution Spatial variation |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 资源环境与遥感信息 |
| 来源机构 | 中国科学院南京土壤研究所 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287736 |
| 推荐引用方式 GB/T 7714 | 王轶虹. 基于多源数据的中国农作物生物量演变研究[D]. 中国科学院大学,2016. |
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