<RECORD 1>

Accession number:20125115818535
Title:Spatial simulation of soil total potassium in regional scale for Loess Plateau Region
Authors:Liu, Zhipeng (1); Shao, Ming'an (2); Wang, Yunqiang (2)
Author affiliation:(1) State Key Laboratory of Soil Erosion and Dry-Land Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China; (2) Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (3) University of Chinese Academy of Sciences, Beijing 100049, China
Corresponding author:Shao, M.(shaoma@igsnrr.ac.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:28
Issue:22
Issue date:November 15, 2012
Publication year:2012
Pages:132-140
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering, Agricultural Exhibition Road South, Beijing, 100026, China
Abstract:To understand the spatial heterogeneity of soil total potassium in regional scale for Loess Plateau Region and its influencing factors, In the study, a total of 283 sampling sites were investigated in order to estimate the spatial variation of soil total potassium (STK) across the entire Loess Plateau (620, 000 km²). Spatial simulation and classical linear regression were used to quantify the relationships between STK and bulk density, clay and silt content, soil pH, precipitation, temperature, and elevation. The best state-space models explained more than 97% of the STK variation, while the best linear regression model explained less than 26% of the STK variation. The results showed that all the state-space models described the spatial variation of STK much better than that of the corresponding linear regression models. Spatial simulation is recommended as a useful tool for quantifying spatial relationships between soil properties and the other environmental factors in large-scale regions.
Number of references:31
Main heading:Linear regression
Controlled terms:Landforms - Potassium - Soils
Uncontrolled terms:Bulk density - Environmental factors - Large-scale - Linear regression models - Loess Plateau - Regional scale - Sampling site - Silt contents - Soil pH - Soil property  - Spatial heterogeneity - Spatial relationships - Spatial simulation - Spatial variations - State-space modeling - State-space models
Classification code:481.1 Geology - 483.1 Soils and Soil Mechanics - 549.1 Alkali Metals - 922.2 Mathematical Statistics
DOI:10.3969/j.issn.1002-6819.2012.22.020
Database:Compendex
Compilation and indexing terms, Copyright 2012 Elsevier Inc.

<RECORD 2>

Accession number:20125115818533
Title:Three-dimensional reconstruction of soil pore structure and prediction of soil hydraulic properties based on CT images
Authors:Cheng, Ya'nan (1); Liu, Jianli (1); L&#252;, Fei (1); Zhang, Jiabao (1)
Author affiliation:(1) Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; (2) University of Chinese Academy of Sciences, Beijing 100049, China
Corresponding author:Liu, J.(jlliu@issas.ac.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:28
Issue:22
Issue date:November 15, 2012
Publication year:2012
Pages:115-122
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering, Agricultural Exhibition Road South, Beijing, 100026, China
Abstract:In order to better understand the effects of soil pore structure on water movement, the three-dimensional structure of soil pore was constructed using CT images of fluvor-aquic soil in Huang-Huai-Hai Plain. Pore size distribution and connectivity parameters were determined by digital image analysis. A spatially-correlated network model was set up to describe the effects of pore-scale structure on water flow in soils, and predict the hydraulic properties of soil samples. The results showed that the predicted hydraulic properties agreed well with the measured values and the correlation coefficients were above 0.94. It indicated that correlated network model could simulate pore-scale water movement process, which provides reference for predicting the unsaturated hydraulic properties of soil.
Number of references:26
Main heading:Three dimensional
Controlled terms:Computerized tomography - Forecasting - Hydraulics - Nuclear magnetic resonance - Pore structure - Soils
Uncontrolled terms:Computed Tomography - Correlation coefficient - CT Image - Digital image analysis - Hydraulic properties - Network models - Soil hydraulic properties - Soil pores - Soil sample - Three-dimensional reconstruction  - Three-dimensional structure - Unsaturated hydraulics - Water flows - Water movements
Classification code:931.2 Physical Properties of Gases, Liquids and Solids - 921 Mathematics - 902.1 Engineering Graphics - 801 Chemistry - 632.1 Hydraulics - 531 Metallurgy and Metallography - 483.1 Soils and Soil Mechanics
DOI:10.3969/j.issn.1002-6819.2012.22.018
Database:Compendex
Compilation and indexing terms, Copyright 2012 Elsevier Inc.

<RECORD 3>

Accession number:20125115818541
Title:Comprehensive monitoring model for agricultural drought and its application based on spatial information
Authors:Li, Hailiang (1); Dai, Shengpei (1); Hu, Shenghong (2); Tian, Guanghui (3); Luo, Hongxia (1)
Author affiliation:(1) Key Laboratory of Practical Research on Tropical Crops Information Technology in Hainan, Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences, Danzhou 571737, China; (2) Environment and Plant Protection Research Institute, Chinese Academy of Tropical Agricultural Sciences, Danzhou 571737, China; (3) Hainan Institute of Meteorological Sciences, Haikou 570203, China
Corresponding author:Li, H.(fondgis@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:28
Issue:22
Issue date:November 15, 2012
Publication year:2012
Pages:181-188
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering, Agricultural Exhibition Road South, Beijing, 100026, China
Abstract:To improve the accuracy of the agricultural drought monitoring, the comprehensive monitoring model coupled with the remote sensing data and meteorological data was used. This model was established based on the relationship between the standardization vegetation supply water index (VSWI<inf>S</inf>), the comprehensive precipitation index (CPI) and the synchronous soil moisture data measured in the study area. The VSWI<inf>S</inf> was suitable for monitoring drought at high density vegetation area, and the CPI was suitable for monitoring drought at tropical area. The root mean square error (RMSE) of the model was 4.65% and the relative root mean square error (RMSE<inf>r</inf>) was 19.28%. Furthermore, the model was used to evaluate the change of the agricultural drought in Hainan island from October 2004 to January 2005. The results indicated that the drought in the study area were significantly different. Overall, the drought in the west and north were more serious than that in the east and south of the island respectively, and the drought in the plain was more serious than that in the mountain area. In terms of the time changes, the drought approached maximum in early December 2004. Until late January 2005, the drought was still serious. The heavy drought area of paddy, upland and special woodland accounted for 59%, 61% and 20% respectively. Crop growing was constrained obviously by this drought. Compared with last year, the value of the accumulated normalized difference vegetation index (NDVI) was reduced by 6.34%, and yield of the natural rubber was reduced about 1.16&times;10⁴t from October to December in 2004. The research provides a reference for monitoring agricultural drought.
Number of references:30
Main heading:Drought
Controlled terms:Agriculture - Mean square error - Meteorology - Monitoring - Remote sensing - Soil moisture - Vegetation
Uncontrolled terms:Agricultural drought - Hainan island - High density - Meteorological data - Monitoring models - Normalized difference vegetation index - Remote sensing data - Root mean square errors - Spatial informations - Study areas  - Time change - Water index
Classification code:944 Moisture, Pressure and Temperature, and Radiation Measuring Instruments - 943 Mechanical and Miscellaneous Measuring Instruments - 942 Electric and Electronic Measuring Instruments - 941 Acoustical and Optical Measuring Instruments - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 731.1 Control Sys