福建冷浸田土壤质量评价因子的最小数据集

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

江南山区中低产冷浸田分布广泛,其改良利用对促进粮食增产意义重大.本研究通过比较福建省17对典型冷浸田与同一微地貌单元内非冷浸田表层土壤的41项物理、化学与生物指标,系统分析了冷浸田与非冷浸田之间各指标差异及其产生的原因,并利用主成分分析等方法构建冷浸田土壤质量评价因子最小数据集.结果表明：与非冷浸田相比,冷浸田土壤总有机质高31.7%,表征活性有机质的微生物生物量C降低37.8%;Fe2＋高177.0%,速效磷、钾分别降低52.3%和22.8%;过氧化氢酶和转化酶分别高58.3%和22.1%,磷酸酶、硝酸还原酶分别降低47.8%和66.6%,微生物区系数量降低29.8%~46.0%;物理性砂粒含量高8.0%,浸水容重降低25.8%.冷浸田与非冷浸田之间表土有28项属性指标呈现显著差异.用因子分析方法从28项有显著差异的指标中归纳出累计贡献率达78.5%并能分别反映土壤生化、活性有机N、还原性障碍、物理与化学养分特征的5个主成分,结合相关分析模型和专家经验法建立了包括C/N、细菌、微生物生物量N、还原性物质总量、物理性砂粒、全磷6项因子的冷浸田土壤质量评价因子最小数据集.

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 including41 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.