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Spatial variability of soil properties in red soil and its implications for site-specific fertilizer management
Authors:SONG Fang-fang  XU Ming-gang  DUAN Ying-hua  CAI Ze-jiang  WEN Shi-lin  CHEN Xian-ni  SHI Wei-qi  Gilles COLINET
Affiliation:1. National Engineering Laboratory for Improving Quality of Arable Land, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China;2. Gembloux Agro-Bio Tech, University of Liege, Gembloux 5030, Belgium;3. South Subtropical Crop Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang 524091, P.R.China;4. College of Agriculture, Henan University of Science & Technology, Luoyang 471003, P.R.China
Abstract:Assessing spatial variability and mapping of soil properties constitute important prerequisites for soil and crop management in agricultural areas. To explore the relationship between soil spatial variability and land management, 256 samples were randomly collected at two depths (surface layer 0–20 cm and subsurface layer 20–40 cm) under different land use types and soil parent materials in Yujiang County, Jiangxi Province, a red soil region of China. The pH, soil organic matter (SOM), total nitrogen (TN), cation exchange capacity (CEC), and base saturation (BS) of the soil samples were examined and mapped. The results indicated that soils in Yujiang were acidified, with an average pH of 4.87 (4.03–6.46) in the surface layer and 4.99 (4.03–6.24) in the subsurface layer. SOM and TN were significantly higher in the surface layer (27.6 and 1.50 g kg−1, respectively) than in the subsurface layer (12.1 and 0.70 g kg−1, respectively), while both CEC and BS were low (9.0 and 8.0 cmol kg−1, 29 and 38% for surface and subsurface layers, respectively). Paddy soil had higher pH (mean 4.99) than upland and forest soils, while soil derived from river alluvial deposits (RAD) had higher pH (mean 5.05) than the other three parent materials in both layers. Geostatistical analysis revealed that the best fit models were exponential for pH and TN, and spherical for BS in both layers, while spherical and Gaussian were the best fitted for SOM and CEC in the surface and subsurface layers. Spatial dependency varied from weak to strong for the different soil properties in both soil layers. The maps produced by selecting the best predictive variables showed that SOM, TN, and CEC had moderate levels in most parts of the study area. This study highlights the importance of site-specific agricultural management and suggests guidelines for appropriate land management decisions.
Keywords:spatial variability  soil pH  CEC  BS  site-specific fertilizer management
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