Net primary production of Chinese croplands from 1950 to 1999.

Considerable efforts have been made to assess the contribution of forest and grassland ecosystems to the global carbon budget, while less attention has been paid to agriculture. Net primary production (NPP) of Chinese croplands and driving factors are seldom taken into account in the regional carbon budget. We studied crop NPP by analyzing the documented crop yields from 1950 to 1999 on a provincial scale. Total NPP, including estimates of the aboveground and belowground components, was calculated from harvested yield data by (1) conversion from economic yield of the crop to aboveground mass using the ratio of aboveground residue production to the economic yield, (2) estimation of belowground mass as a function of aboveground mass, and (3) conversion from total dry mass to carbon mass. This approach was applied to 13 crops, representing 86.8% of the total harvested acreage of crops in China. Our results indicated that NPP in Chinese croplands increased markedly during this period. Averaging for each decade, the amount of NPP was 146 +/- 32, 159 +/- 34, 260 +/- 55, 394 +/- 85, and 513 +/- 111 Tg C/yr (mean +/- SD) in the 1950s, 1960s, 1970s, 1980s, and 1990s, respectively. This increase may be attributed to synthetic fertilizer application. A further investigation indicated that the climate parameters of temperature and precipitation determined the spatial variability in NPP. Spatiotemporal variability in NPP can be well described by the consumption of synthetic fertilizer and by climate parameters. In addition, the total amount of residue C and root C retained by the soils was estimated to be 618 Tg, with a range from 300 to 1040 Tg over the 50 years.     

中国农田作物植被碳储量研究进展

作物植被碳储量是全球陆地生态系统碳库的重要组成部分。中国农田作物植被碳储量的估算主要采用参数估算法、遥感资料反演法和环境参数模型法。通过对中国近几十年来全国和区域尺度作物植被碳储量的估算研究,获得了一些作物的经济系数、含碳率和作物收获部分水分系数等估算参数值,探讨了遥感反演和环境参数模型方法,并提出加强农田基本建设、改进农业生产技术与管理、调整作物结构和加强作物秸秆利用等固碳措施。目前对中国农田作物植被碳储量的估算仍存在较大的不确定性,获取的估算参数尚不充分,估算方法和模型有待完善,对作物植被碳储量变化的源/汇效应尚未取得统一认识。虽然在农田生态系统中土壤碳储量（密度）普遍大于作物植被碳储量（密度）,但作物植被碳储量仍然是一个数量可观、并有增加潜力和可能的碳库,其大小及秸秆利用情况直接影响着土壤碳库。因此,对农田作物植被碳储量应分时段和区域具体分析,才能认识其源/汇效应。今后应在以下几方面进一步加强作物植被碳储量的研究：进一步完善和改进估算方法;加强作物植被碳储量估算及固碳措施的区域个例研究,探索不同空间尺度作物植被碳储量的尺度转换;开展作物碳储量动态及固碳机理的综合研究。此外,还应就气候变化与作物植被碳储量的相互耦合关系进行探讨。     

基于MODIS数据的河南省冬小麦产量遥感估算模型

小麦是世界上最重要的粮食作物,小麦生产对中国的粮食保障起着十分重要的作用,及时、准确、大范围对小麦产量进行监测预报,对于农学经济发展和粮食政策制定具有极为重要的现实意义。对作物产量进行遥感监测的原理是建立在其遥感特征基础之上的,通过建立作物长势指标与遥感信息的定量关系,可实现对作物产量的监测预报。文章基于2009年MODIS遥感数据和气象数据,利用Arcgis和ENVI提取纯小麦像元,并提取纯小麦像元对应的NDVI、NPP和LAI,获取分县NDVI、NPP和LAI均值,利用统计软件对产量数据和分县遥感参数均值进行数据整理和分析,建立了河南省冬小麦产量估算模型。以往研究多采用遥感图像上某像元和地面调查点进行研究,具有很大的不确定性,文章以县为单位,对冬小麦平均单产和县域内冬小麦种植像元遥感参数的均值进行相关研究,提高了模型模拟精度。同时文章选用多种遥感参数和多项气象因子建立估产模型,避免了针对一个参数进行估产的局限性。在最佳时相的选择上,根据冯美辰（2010）以往的研究结果,从4月以后,5月8日和4月20Et植被指数和产量相关性最大,4月份之前冬小麦处于返青到拔节期,对产量来说还有很多不确定闪素,因此文章选用5月8El和4月20日进行冬小麦估产研究。结果表明,5月8日的估产模型优于4月20日,加入气象冈子的遥感气象估产模型优于只采用遥感参数进行估产的遥感模型。利用2010年产量数据对模型精度进行检验,遥感气象模型预测精度在70．2％N99．7％之间,平均精度为90．7％;遥感模型预测精度在68．1％到95．5％之间,平均精度为83．9％。表明遥感气象模型模拟精度更高,其精度可以满足大面积估产要求,可以对产量预报提供科学参考。     

Modeling net primary production of a fast-growing forest using a light use efficiency model

As interest grows in the quantification of global carbon cycles, Light Use Efficiency (LUE) model predictions of the forest net primary production (NPP) are being developed at an accelerating rate. Such models can provide useful predictions at large scales, but evaluating their performance has been difficult. In this study, a remote sensing-based LUE model was established to estimate forest NPP. Using the forest inventory data (FID) from the regional forest inventory survey in China and established allometric biomass equations, we calculated the biomass, the biomass increment, and the NPP of Eucalyptus urophylla (E. urophylla) plantation plots in the forestry jurisdiction of the Leizhou Forestry Bureau, Southern China. The FID-based NPP and the NPP from LUE model predictions were then compared to each other. Results show that the NPP from model predictions at a spatial resolution of 30 m × 30 m varied from 0 to 265 gC/(m² month) and showed regional differences. In addition, the stand age had variable effects on the average individual biomass of the E. urophylla plantation plots. The average individual biomass of the young and mid-age forests increased exponentially and logarithmically with the stand age (R² = 0.9178 and R² = 0.8683), respectively. For young and mid-age E. urophylla plantation plots, the LUE model-predicted NPP was fairly consistent with the FID-based NPP, but the model predictions of the NPP were higher than the estimates from FID. Through the analysis of the causes of uncertainty and the possible reasons for the discrepancy between the model-based NPP and FID-based NPP, the FID-derived estimates provided a foundation for model evaluation.     

区域及全球尺度的NPP过程模型和NPP对全球变化的响应

植被净第一性生产力（NPP）不仅是表征植被活动和生态过程的关键参数,而且是判定生态系统碳汇和反映生态系统对全球变化响应的主要因子。当前,模型模拟成为大尺度NPP研究的主要手段,而在众多NPP估算模型中,过程模型逐渐趋于主导地位。虽然目前有关NPP的研究有很多,但还没有关注于大尺度上应用的过程模型及其模拟的NPP对全球变化的响应。因此本文主要侧重于 NPP 过程模型在区域及全球尺度上的应用,具体包含以下内容,①进一步将区域及全球尺度的NPP过程模型分为静态植被模型和动态植被模型。②阐明这些模型间存在的区别与联系。③归纳出NPP过程模型在区域及全球尺度上应用的3大挑战：时空尺度转换、多源数据的获取与融合以及模型模拟结果的验证与评价,并根据其解决方案总结出通用的模型应用框架。④从气候变化、大气成分变化和土地利用/土地覆盖变化3个方面探讨NPP对全球变化的响应机制,以期找到NPP变化的规律与模式。最后根据NPP模型的发展对未来区域及全球尺度的NPP过程模型进行展望,认为未来模型的综合性将更高,机理性也将更强,同时与全球变化研究结合得更加紧密,且基于多个已有模型的混合模型也是未来NPP模型发展的一个重要方向。此外,本文认为对NPP模拟结果的尺度效应研究也是未来NPP研究的热点之一。     

Global potential net primary production predicted from vegetation class, precipitation, and temperature

Net primary production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (approximately 100-150 g C m(-2) yr(-1)) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be approximately 28 Pg and approximately 46 Pg C/yr, respectively. The NCEAS model estimated an approximately 13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns.     

Density derived estimates of standing crop and net primary production in the giant kelp <Emphasis Type="Italic">Macrocystis pyrifera</Emphasis>

Assemblages of macroalgae are believe to be among the most productive ecosystems in the world, yet difficulties in obtaining direct estimates of biomass and primary production have led to few macroalgal data sets from which the consequences of long-term change can be assessed. We evaluated the validity of using two easily measured population variables (frond density and plant density) to estimate the more difficult to measure variables of standing crop and net primary production (NPP) in the giant kelp Macrocystis pyrifera off southern California. Standing crop was much more strongly correlated to frond density than to plant density. Frond density data collected in summer were particularly useful for estimating annual NPP, explaining nearly 80% of the variation in the NPP from year to year. Data on frond densities also provided a relatively good estimate of seasonal NPP for the season that the data were collected. In contrast, estimates of seasonal and annual NPP derived from plant density data were less reliable. These results indicate that data on frond density collected at the proper time of year can make assessments of NPP by giant kelp more tractable. They also suggest that other easily measured variables that are strongly correlated with standing crop, such as surface canopy area, might serve as similarly useful proxies of NPP.     

Net primary production, growth, and standing crop of Macrocystis pyrifera in Southern California

Marine macroalgae are believed to be among the most productive autotrophs in the world. However, relatively little information exists about spatial and temporal variation in net primary production (NPP) by these organisms. The data presented here are being collected to investigate patterns and causes of variation in NPP by the giant kelp, Macrocystis pyrifera, which is believed to be one of the fastest growing autotrophs on earth. The standing crop and loss rates of M. pyrifera have been measured monthly in permanent plots at three sites in the Santa Barbara Channel, USA. Collection of these data began in June 2002 and is ongoing. Seasonal estimates of NPP and growth rate are made by combining the field data with a model of kelp dynamics. The purpose of this Data Paper is to make available a time series of M. pyrifera NPP, growth, and standing crop that is appropriate for examining seasonal and interannual patterns across multiple sites. Data on plant density in each plot and censuses of fronds on tagged plants at each site are also made available here. NPP, mass-specific growth rate, and standing crop are presented in four different metrics (wet mass, dry mass, carbon mass, and nitrogen mass) to facilitate comparisons with previous studies of M. pyrifera and with NPP measured in other ecosystems. Analyses of these data reveal seasonal cycles in growth and standing crop as well as substantial differences in M. pyrifera NPP among sites and years.     

A hierarchical Bayesian model for forecasting state-level corn yield

Historically, the National Agricultural Statistics Service crop forecasts and estimates have been determined by a group of commodity experts called the Agricultural Statistics Board (ASB). The corn yield forecasts for the “speculative region,” ten states that account for approximately 85 % of corn production, are based on two sets of monthly surveys, a farmer interview survey and a field measurement survey. The members of the ASB subjectively determine a forecast on the basis of a discussion of the survey data and auxiliary information about weather, average planting dates, and crop maturity. The ASB uses an iterative procedure, where initial state estimates are adjusted so that the weighted sum of the final state estimates is equal to a previously-determined estimate for the speculative region. Deficiencies of the highly subjective ASB process are lack of reproducibility and a measure of uncertainty. This paper describes the use of Bayesian methods to model the ASB process in a way that leads to objective forecasts and estimates of the corn yield. First, we use small area estimation techniques to obtain state-level forecasts. Second, we describe a way to adjust the state forecasts so that the weighted sum of the state forecasts is equal to a previously-determined regional forecast. We use several diagnostic techniques to assess the goodness of fit of various models and their competitors. We use Markov chain Monte Carlo methods to fit the models to both historic and current data from the two monthly surveys. Our results show that our methodology can provide reasonable and objective forecasts of corn yields for states in the speculative region.     

Sensitivity of a crop growth simulation model to variation in LAI and canopy nitrogen used for run-time calibration

Run-time calibration, i.e. adjusting simulation results for field observations of model driving variables during run-time, may allow correcting for deviations between complex mechanistic simulation model results and actual field conditions. Leaf area index (LAI) and canopy nitrogen contents (LeafNWt) are the most important driving variables for these models, as they govern light interception and photosynthetic production capacity of the crop. Remote sensing may provide (spatial) data from which such information can be estimated. How, when and at what frequency such additional information is integrated in the simulation process may have various effects on the simulations. The objective of this study was to quantify the effects of different run-time calibration scenarios for final grain yield (FGY) simulations in order to optimize remote sensing image (RS) acquisition. The PlantSys model was calibrated on LAI and LeafNWt for maize in France and used to simulate maize crop growth in the Argentina and the USA, for which non-destructive estimates of LAI and leaf chlorophyll contents were acquired by optical measurement techniques. Leaf chlorophyll data were used to estimate LeafNWt. Due to its structure, the PlantSys model was more susceptible to run-time calibration with LeafNWt than with LAI. Run-time calibration with LAI showed the largest effect on FGY before and around flowering, and could mainly be related to maintenance respiration costs. Run-time calibration with LeafNWt showed the largest effect on FGY at and after flowering and could mainly be related to the change in effective radiation interception due to change in leaf life. The accuracy of LAI estimates showed a major effect on FGY for underestimations but was small in absolute sense. The accuracy of LeafNWt estimates had significant impact at all crop development stages, but was the strongest after flowering where crop growth and nitrogen uptake are less able to recuperate from changes in LeafNWt. In absolute sense, the effect on FGY was as strong as the accuracy of the LeafNWt estimates when applied in the early reproductive stages. Based on these results it was concluded that remotely sensed in-field variability of LAI and LeafNWt is valuable information that can be used to spatially differentiate model simulations. Run-time calibration at sub-field level may lead to more accurate simulation results for whole fields.     

Improved estimates of net primary productivity from modis satellite data at regional and local scales.

We compared estimates of net primary production (NPP) from the MODIS satellite with estimates from a forest ecosystem process model (PnET-CN) and forest inventory and analysis (FIA) data for forest types of the mid-Atlantic region of the United States. The regional means were similar for the three methods and for the dominant oak-hickory forests in the region. However, MODIS underestimated NPP for less-dominant northern hardwood forests and overestimated NPP for coniferous forests. Causes of inaccurate estimates of NPP by MODIS were (1) an aggregated classification and parameterization of diverse deciduous forests in different climatic environments into a single class that averages different radiation conversion efficiencies; and (2) lack of soil water constraints on NPP for forests or areas that occur on thin or sandy, coarse-grained soil. We developed the "available soil water index" for adjusting the MODIS NPP estimates, which significantly improved NPP estimates for coniferous forests. The MODIS NPP estimates have many advantages such as globally continuous monitoring and remarkable accuracy for large scales. However, at regional or local scales, our study indicates that it is necessary to adjust estimates to specific vegetation types and soil water conditions.     

Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in global carbon cycling. Understanding the sensitivity of grassland to climate change and the effect of climate changes on the grassland ecosystems is a key issue in global carbon cycling. One of the goals of this study was to evaluate the three net primary productivity (NPP)–climate models, i.e. the Miami model, the Schuur model and the classification indices-based model. Results indicated that the classification indices-based model was the most effective model at estimating large-scale grassland NPP. In this research, changes in the spatial pattern of global potential grassland from recent past (1950–2000) to future (2001–2050) A2a scenario were analysed with the integrated orderly classification system of grassland (IOCSG) approach in a Geographic Information System (GIS) environment. NPP was evaluated with the classification indices-based model. Results indicate that under recent past climatic conditions, the main parts of global grassland are the savanna and tundra and alpine grassland and will be converted into the savanna, steppe and semi-desert grassland in A2a scenario. As a whole, areas of grassland will increase by 31.76 million hectares. The classification indices-based model estimated a 12.40% increase of total NPP in grassland from recent past to A2a scenario. It will impose a new issue for future grassland researches to support sustainable development and to provide action relevant knowledge to meet the challenge of climate change.     

温室气体排放与中国粮食生产

中国用占世界8％的土地养活了世界上1／5的人口，粮食生产已经基本满足了现有人口的需求。但是，生产粮食会向大气释放大最的温室气体。面对全球减少温室气体排放的压力，在粮食生产中，增加农田生态系统的碳固定能力及减少N2O和CH4的排放，直接关系着中国粮食生产的未来发展。文章分析全球和中国温室气体排放清单中粮食生产的作用和意义；利用生物地球化学模型DNDC和中国农业生产数据库，估算中国农田生态系统的土壤碳动态和N2O排放；通过情景分析(Scenario)，预测在不同方案下，中国农田生态系统碳动态和N2O的排放量；提出了我国未来粮食生产应该采取的对策和技术措施。     

土地利用/覆被变化对土地生态系统有机碳库的影响——以吉林省通榆县为例

土地利用/覆被变化对陆地碳循环影响显著.文章基于实地采集的土壤、植物样品的测试数据和1989、2004两年8月陆地卫星TM遥感影像数据,采用生态系统类型法分析吉林省通榆县1989-2004年耕地、林地、草地、盐碱地、沙地等11 种地类之间土地利用变化对土地生态系统有机碳库的影响.计算结果表明1989-2004年通榆县土地生态系统有机碳库共损失了3.18 TgC(1Tg=106t),年均损失约为0.265 Tgc.其中,湿地、草地有机碳库分别损失5.54 TgC和3.71 TgC,盐碱地面积的增加导致有机碳库损失4.75 TgC.林地面积增加和沙地面积减少分别使有机碳库增加了4.58 TgC和3.75 TgC.研究区总体上为一个碳失汇,草地退化、湿地萎缩、土地沙化和盐碱化造成了有机碳库的碳损失,而植树造林、草地植被的恢复和重建等活动则可以显著增加土壤有机碳储量.该研究对于评估自然环境与人为活动影响下,特别是大规模土地整治与生态修复对土壤有机碳的增汇潜力和固碳效应的影响具有重要的理论意义和应用价值.     

Simulating the yields of bioenergy and food crops with the crop modeling software BioSTAR: the carbon-based growth engine and the BioSTAR ET0 method

Background

With a growing production and use of agricultural substrates in biogas facilities, the competition between food and energy production, environmental issues, and sustainability goals has seen an increase in the last decade and poses a challenge to policy makers. Statistical yield data has a low spatial resolution and only covers standard crops and makes no statement in regard to yields under climate change. To support policy makers and regional planners in an improved allocation of agricultural land use, a new crop model (BioSTAR) has been developed.

Results

Simulations with weather and yield data from 7 years and four regions in Lower Saxony have rendered overall good modeling results with prediction errors (RMSE and percentage) ranging from 1.6 t and 9.8% for winter wheat to 2.1 t and 11.9% for maize. The model-generated ET₀ and ET_a values (mean of four locations) are lower than ET₀/ET_a values calculated with the Penman-Monteith method but appear more realistic when compared to field trial data from northern and eastern Germany.

Conclusions

The model has proven to be a functioning tool for modeling site-specific biomass potentials at the farm level, and because of its Access® database interface, the model can also be used for calculating biomass yields of larger areas, like administration districts or states. Out of the seven crops modeled in this study, only limited yield and test site data was available for winter barley, winter rye, sorghum, and sunflower. For further improvement of model performance and model calibration, more trial data and data testing are required for these crops.     

Inter-annual variability of ecosystem production in boreal jack pine forests (1975-2004) estimated from tree-ring data using CBM-CFS3

We describe and apply a method of using tree-ring data and an ecosystem model to reconstruct past annual rates of ecosystem production. Annual data on merchantable wood volume increment and mortality obtained by dendrochronological stand reconstruction were used as input to the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to estimate net ecosystem production (NEP), net primary production (NPP), and heterotrophic respiration (Rh) annually from 1975 to 2004 at 10 boreal jack pine (Pinus banksiana Lamb.) stands in Saskatchewan and Manitoba, Canada. From 1975 (when sites aged 41-60 years) to 2004 (when they aged 70-89 years), all sites were moderate C sinks except during some warmer than average years where estimated Rh increased. Across all sites and years, estimated annual NEP averaged 57 g Cm⁻² yr⁻¹ (range −31 to 176 g Cm⁻² yr⁻¹), NPP 244 g Cm⁻² yr⁻¹ (147-376 g Cm⁻² yr⁻¹), and Rh 187 g Cm⁻² yr⁻¹ (124-270 g Cm⁻² yr⁻¹). Across all sites, NPP was related to stand age and density, which are proxies for successional changes in leaf area. Regionally, warm spring temperature increased NPP and defoliation by jack pine budworm 1 year previously reduced NPP. Our estimates of NPP, Rh, and NEP were plausible when compared to regional eddy covariance and carbon stock measurements. Inter-annual variability in ecosystem productivity contributes uncertainty to inventory-based assessments of regional forest C budgets that use yield curves predicting averaged growth over time. Our method could expand the spatial and temporal coverage of annual forest productivity estimates, providing additional data for the development of empirical models accounting for factors not presently considered by these models.     

The calculation of equivalence factor for ecological footprints in China: a methodological note

The Ecological Footprint (EF), a physical indicator to measure the extent of humanity’s use of natural resources, has gained much attention since it was first used by Wackernagel and Rees in 1996. In order to appraise land area types with different levels of productivity, they introduced the concept of an equivalence factor. This relates to the average primary biomass productivities of different types of land (i.e. arable land, pasture, forest, water/fishery, built-up land and fossil energy land) to the regional average primary biomass productivity of all land types in a given year. Hence, the equivalence factor is an important parameter in the EF model and it directly affects the reliability of all results. Thus, this article calculates equivalence factors on the national and provincial levels in China based on Net Primary Production (NPP) from MODIS 1 km data in 2008. Firstly, based on the Light Utility Efficiency and CASA model, the NPP of different biologically productive lands of China and of different provinces was calculated. Secondly, China’s equivalence factor for 6 land area types was calculated based on NPP: arable land and built-up land has an equivalence factor of 1.71, forest and fossil energy land has a factor of 1.41, pasture has a factor of 0.44 and water/fishery 0.35; Finally, the equivalence factor of 6 land area types in different provinces was also calculated. The NPP of each ecosystem type varies along with the equivalence factor in different provinces. However, the ranking of the equivalence factors in different provinces remain the same, with that of arable land being the largest, and the water/fishery being the smallest.     

论全球生态经济系统的不可持续性——基于农业生态系统承载力视角

运用3种方法预测的作物产量潜力分别为：（1）利用作物历年单产回归拟合后进行趋势外推,得出多数作物的未来产量潜力极限大约将是现在单产的2～3倍;（2）运用＂国际应用系统研究所＂（IIASA）与＂联合国粮农组织＂（FAO）共同开发的＂农业生态区划＂（AEZ）模型计算我国主要粮油作物的区域单产最高潜力,得出水稻、小麦、玉米、马铃薯、油菜和大豆的单产潜力分别是它们2005年全国平均单产的1.2、2.2、2.2、2.9、2.0、1.9倍;（3）运用自然界中植物的最大光能利用率计算世界主要粮油作物单产的光合潜力,得出水稻、小麦、玉米、马铃薯、油菜、大豆产量的最大光合生产潜力大约分别是目前高产地区单产的1.4、2.5、1.2、1.8、1.9、2.2倍。据此：从作物产量潜力极限出发,阐述了农业生态系统的承载力;再从＂封闭＂系统特性出发,论述了全球生态经济系统的不可持续性。人类所能做的是尽力延缓＂终点＂的出现：行动越早,效果越好。     

A study on the spatial distribution of nitrogen and phosphorus balance, and regional nitrogen flow through crop production

The spatial distribution patterns of the nitrogen and phosphorus input/intake amounts in crop production within two small basins are examined, based upon a cropping unit distribution map that is obtained from remote sensing data analysis. Firstly, we examine the availability and suitability of approaches to the spatial distribution analysis of cultivation patterns classified from material flow characteristics of crop production using seasonal remote-sensing data. Secondly, material flow units in crop production are grouped according to the cultivation patterns obtained from the remote-sensing data analysis. Consequently, the spatial patterns of the amounts of both nitrogen and phosphorus inputs/intakes through crop production on farmland are examined and their spatial distribution maps are prepared according to the material flow units. In addition, we developed a nitrogen flow and runoff model and the model is simulated based on the examination of the results of spatial distribution patterns of the material flow units. The annual nitrogen runoff from small catchments, where various crops are cultivated, varies from 2.7 kg ha(-1) year(-1) to 108 kg ha(-1) year(-1) and the annual balanced losses of nitrogen in small catchments varied from -30 kg ha(-1) year(-1) to 101 kg ha(-1) year(-1). Also, the monthly changes in soil nitrogen of each material flow unit is estimated at -55 kg ha(-1) as a maximum decrease and 114 kg ha(-1) as a maximum increase. These results indicate that the spatial distribution patterns of nutrient input and intake through agricultural activities should be considered when analyzing the material flows and nutritient movement in soil-water systems in rural areas for watershed environmental control and regional agricultural management.     

1994—2008年河南省植被净第一性生产力及其时空变化

基于地理信息系统和卫星遥感技术,利用地面气象数据、遥感数据和土地分类等数据,根据河南省实际植被覆盖和自然地理情况,应用改进的CASA模型估算了1994—2008年间河南省植被净第一性生产力及其时空变化。结果表明：河南省植被净第一性生产力与年均温度的相关性好于植被年净第一性生产力与年降水量和年均太阳辐射之间的相关性,温度是限制河南省植被净第一性生产力的主要因子;15年来河南省植被净第一性生产力呈增加趋势,但不明显;河南省主要植被类型的净第一性生产力从大到小依次为林地、灌丛、耕地、草地、其它植被,它们随时间的变化趋势极为一致。