Estimating soil water‐holding capacities by linking the Food and Agriculture Organization Soil map of the world with global pedon databases and continuous pedotransfer functions
書誌事項
- 公開日
- 2000-12
- 権利情報
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- http://onlinelibrary.wiley.com/termsAndConditions#vor
- DOI
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- 10.1029/2000wr900130
- 公開者
- American Geophysical Union (AGU)
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説明
<jats:p>Spatial soil water‐holding capacities were estimated for the Food and Agriculture Organization (FAO) digital Soil Map of the World (SMW) by employing continuous pedotransfer functions (PTF) within global pedon databases and linking these results to the SMW. The procedure first estimated representative soil properties for the FAO soil units by statistical analyses and taxotransfer depth algorithms [<jats:italic>Food and Agriculture Organization (FAO)</jats:italic>, 1996]. The representative soil properties estimated for two layers of depths (0–30 and 30–100 cm) included particle‐size distribution, dominant soil texture, organic carbon content, coarse fragments, bulk density, and porosity. After representative soil properties for the FAO soil units were estimated, these values were substituted into three different pedotransfer functions (PTF) models by <jats:italic>Rawls et al.</jats:italic> [1982], <jats:italic>Saxton et al.</jats:italic> [1986], and <jats:italic>Batjes</jats:italic> [1996a]. The Saxton PTF model was finally selected to calculate available water content because it only required particle‐size distribution data and results closely agreed with the Rawls and Batjes PTF models that used both particle‐size distribution and organic matter data. Soil water‐holding capacities were then estimated by multiplying the available water content by the soil layer thickness and integrating over an effective crop root depth of 1 m or less (i.e., encountered shallow impermeable layers) and another soil depth data layer of 2.5 m or less.</jats:p>
収録刊行物
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- Water Resources Research
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Water Resources Research 36 (12), 3653-3662, 2000-12
American Geophysical Union (AGU)
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詳細情報 詳細情報について
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- CRID
- 1361137044088017152
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- NII論文ID
- 30013356271
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- ISSN
- 19447973
- 00431397
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- データソース種別
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- CiNii Articles