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Establishing a minimum data set of soil quality assessment for cold-waterlogged paddy field in Fujian Province, China.

WANG Fei, LI Qing-hua, LIN Cheng, HE Chun-mei, ZHONG Shao-jie, LI Yu, LIN Xin-jian   

  1. (Institute of Soil and Fertilizer, Fujian Academy of Agricultural Sciences, Fuzhou 350013, China)
  • Online:2015-05-18 Published:2015-05-18

Abstract:

The yields of coldwaterlogged (CW) paddy fields widely spreading in Jiangnan mountainous areas are moderate or low but have a high potential to be increased. Based on data including 41 soil characteristics of 17 pairs of typical surface soils of cold-waterlogged paddy field and non coldwaterlogged (NCW) paddy field at a neighboring landscape unit in Fujian Province, various index differences of soil properties and causes between CW paddy field and NCW paddy field were systematically studied, and a minimum data set (MDS) of soil quality assessment for CW paddy field was established by principal component analysis. By pair analysis, soil characteristics of CW paddy field showed that the content of organic matter increased by 31.7%, but the microbial biomass C decreased by 37.8%, which belonged to active soil organic matter component. The content of ferrous iron (Fe2+) increased by 177.0%, but the available phosphorus (P) and potassium (K) decreased by 52.3% and 22.8%, respectively. Catalase and invertase activities increased by 58.3% and 22.1%, but phosphatase, nitrate reductase activities and microflora decreased by 47.8%, 66.6% and 29.8%-46.0%, respectively. The sand content increased about 8.0%, but the water immersed bulk density decreased by 25.8%. There were significant differences of indices for 28 of all 41 soil characteristics. Five principal components cumulatively exhibiting about 78.5% contribution were concluded from the 28 soil characteristics to reflect characteristics related to soil biochemistry, active organic nitrogen, reducing barriers, physical and chemical nutrients, respectively. Eventually, correlation analysis combined with expert experience method were applied to optimize MDS containing six factors for soil quality assessments, including C/N, bacteria, microbial biomass N, total reducing agents, physical sand and total P.