作物产量与土壤环境的关系

作物的产量与土壤元素中Ｎ、Ｐ、Ｋ、Ｎａ、Ｃａ、Ｍｇ、Ｓ、Ｆｅ、Ｍｎ、Ｃｕ、Ｚｎ、Ｂ、Ｍｏ、Ｖ、Ｃｏ、Ｎｉ、Ｃｒ、Ｐｂ、Ｃｄ、Ｈｇ、Ｓｅ、Ｆ、ＴＩ、Ｂａ、Ｔｅ、Ｔａ、Ｓｒ、Ｔｉ、Ｓｉ等元素及稀土、有机质、酸碱度和含水量、含盐量密切有关。     

北京延庆葡萄产地高氟地质环境特征研究

北京延庆葡萄产地北侧山区出露大面积花岗岩。岩浆活动频繁,构造发育,特殊地质条件造成土壤存在明显的高氟异常,全氟、水溶氟含量高,部分地下水氟化物含量超过相关标准,是导致当地居民因饮用地下水而患有氟中毒地方病的主要原因。尽管农作物含氟符合相关标准,但高氟地质环境对农作物的含氟量仍具有富集趋势。     

基于冠层温度的作物缺水研究进展

冠导温度信息可以很好地反映作物的水分状况。自20世纪70年代以来，基于冠层温度的作物缺水指标的研究经历了三个阶段，即单纯研究冠层温度本身变化特征的第一阶段、以冠层能量平衡原理为基础的作物水分胁迫指数的第二阶段和考虑冠层和土壤的复合温度的水分亏缺指数的第三阶段。指标的局长也由使用手持式红外辐射仪信息扩大到使用航空和卫星遥感信息。这一类指标在点和区域尺度上均可应用。加强这一类指标的研究对于我国北方地区农作物的有效灌溉和区域水资源的管理都有重要意义。     

干旱半干旱地区农田土壤NO3-N深层积累及其影响因素

以长期试验资料为基础,着重分析了干旱半干旱地区农田系统中施肥、作物、降水、耕作措施以及土壤类型和特性对产生土壤NO3-N深层积累的影响.分析发现,氮肥的过量施用和400～800 mm降水量偏低是导致干旱半干旱地区土壤NO3-N积累在100～300 cm土层的主要因素.随着氮肥用量的增加,NO3-N深层积累显著增加;氮磷配施有助于降低其积累量.不同作物对氮素的吸收利用效率也是影响NO3-N深层积累的因素,作物之间的轮作方式会有效降低NO3-N深层积累;休闲期种植合理植物可有效降低NO3-N深层积累.NO3-N深层积累主要产生在质地较重的土壤上,带正电荷粘土矿物对NO3-N吸附是导致热带土壤中NO3-N积累的主要因素.深入研究深层积累NO3-N的生物有效性、迁移变化机理、与作物根系之间的关系以及对土壤性状和环境的影响具有重要意义.     

A meta-analysis of global crop water productivity of three leading world crops (wheat,corn, and rice) in the irrigated areas over three decades

ABSTRACT

The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45?kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67?kg/m³) or 20–30° (0.94?kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75?kg/m³), medium (>0.75 to <1.10?kg/m³), and high CWP (≥1.10?kg/m³). For corn the global CWP categories were low (≤1.25?kg/m³), medium (>1.25 to ≤1.75?kg/m³), and high (>1.75?kg/m³). For rice the global CWP categories were low (≤0.70?kg/m³), medium (>0.70 to ≤1.25?kg/m³), and high (>1.25?kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India’s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100?m³ in volume that leads to many benefits such as acting as ‘water banks’ for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.     

遥感信息与作物生长模型的耦合应用研究进展

卫星遥感技术具有快速、宏观、准确、客观、及时、动态等特点,在大范围作物长势监测和产量预测等方面具有得天独厚的优势。但遥感监测常常受卫星遥感数据空间分辨率、时间分辨率等因素的影响,且遥感信息大多反映的是瞬间物理状况。作物生长模型是对作物生长、发育、产量形成过程中的一系列生理生化过程进行数学描述,是一种面向过程、机理性的动态模型。但是,当作物模拟从单点研究发展到区域应用时,由于随空间尺度的增大导致模型中一些宏观资料的获取和参数的区域化方面存在很多困难。
遥感信息与作物生长模型的耦合应用可以解决作物长势监测和产量预测等一系列农业问题,越来越受到相关研究人员的关注,已经逐渐成为一个重要的研究领域。因此,随着作物模型和遥感技术的迅速发展,如何将两者结合,进行互补性的研究是很有意义的。在查阅了相关资料的基础上,综述了遥感信息与作物生长模型的耦合应用以及发展历程,分别阐述了两种遥感数据与作物生长模型的结合方法——强迫法和同化法,总结了两类方法的应用情况。最后提出了该领域存在的问题,以及进一步解决的研究方向。     

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.     

Evaluation and use of Resourcesat-I data for agricultural applications

Resourcesat-1 satellite offers a unique opportunity of simultaneous observations at three different spatial scales through LISS-IV, LISS-III^* (improved LISS-III) and AWiFS sensors from a common platform. The sensors have enhanced capabilities in terms of spectral, spatial and radiometric resolution as compared to earlier Indian Remote sensing Satellite sensors. This paper summarizes the results of various studies such as evaluation of sensor characteristics, inter-sensor comparison studies, derivation and validation of surface reflectance measurements, quantification of improvements due to Resourcesat-1 sensors, and their use for various agricultural applications. The studies presented in this paper demonstrate that suit of sensors onboard Resourcesat-1 satellite provides better prospects for several agricultural applications like crop identification, discrimination and crop inventory for some major Indian crops, than its predecessors on IRS satellites.     

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment,Germany

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.     

植被指数-地面温度特征空间的生态学内涵及其应用

植被指数与地面温度是描述土地覆盖特征的重要参数 ,对两种数据的综合分析 ,可以衍生出更丰富、更清晰的地表信息 ,有助于更加准确、有效地认知土地覆盖 /土地利用的时空变化规律。本文探讨了植被指数与地面温度构成的二维向量空间的物理意义与生态学内涵 ,以基于 NOAA AVHRR的时间序列数据为本底 ,分析了不同土地覆盖类型在该特征空间上的时序变化规律 ,并以黄淮海地区主要农作物冬小麦为例 ,研究了植被指数-地面温度指标与干旱、半干旱地区农作物产量之间的响应关系。