期刊
REVISTA BRASILEIRA DE CIENCIA DO SOLO
卷 37, 期 4, 页码 889-910出版社
SOC BRASILEIRA DE CIENCIA DO SOLO
DOI: 10.1590/S0100-06832013000400007
关键词
field capacity; permanent wilting point; plant available water; correlation analysis; path analysis
类别
资金
- Universidade do Estado de Santa Catarina
- National Council for Scientific and Technological Development (CNPq)
- Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES)
- Fundacao de Apoio a Pesquisa Cientifica e Tecnologica do Estado de Santa Catarina (FAPESC)
Studies on water retention and availability are scarce for subtropical or humid temperate climate regions of the southern hemisphere. The aims of this study were to evaluate the relations of the soil physical, chemical, and mineralogical properties with water retention and availability for the generation and validation of continuous point pedotransfer functions (PTFs) for soils of the State of Santa Catarina (SC) in the South of Brazil. Horizons of 44 profiles were sampled in areas under different cover crops and regions of SC, to determine: field capacity (FC, 10 kPa), permanent wilting point (PWP, 1,500 kPa), available water content (AW, by difference), saturated hydraulic conductivity, bulk density, aggregate stability, particle size distribution (seven classes), organic matter content, and particle density. Chemical and mineralogical properties were obtained from the literature. Spearman's rank correlation analysis and path analysis were used in the statistical analyses. The point PTFs for estimation of FC, PWP and AW were generated for the soil surface and subsurface through multiple regression analysis, followed by robust regression analysis, using two sets of predictive variables. Soils with finer texture and/or greater organic matter content retain more moisture, and organic matter is the property that mainly controls the water availability to plants in soil surface horizons. Path analysis was useful in understanding the relationships between soil properties for FC, PWP and AW. The predictive power of the generated PTFs to estimate FC and PWP was good for all horizons, while AW was best estimated by more complex models with better prediction for the surface horizons of soils in Santa Catarina.
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