『Abstract
　In order to find common distribution and relationship patterns between soil properties a statistic methodology is applied and discussed; the aim is to validate conceptual distribution models of soil constituents. Solum (A and Bt horizons) data of five profiles located in the Chacopampeana Plain of northwest Argentina were studied; the variables under analysis were the sampling depth (SD) and the content of organic carbon (OC), clay (CLAY), sand (SAND) and total phosphorus (TP).
　The method was founded on the construction of variables representing the relative variation of contents and sampling depth inside sola (RX variables); RX variables were calculated from the original variables as follows: an “rx” value was defined as the deviation of a value (x) regarding the profile mean (x（頭に-）) expressed in standard deviation (S) units of that particular profile.
　A quadratic regression model of ROC on RSD and RCLAY on RSD explained 97％ and 87％ of ROC and RCLAY variations respectively, whereas a cubic model of RSAND on RSD explained 90％ of RSAND variations; in all models, the determination coefficient (R²) was highly significant. These models were consistent with a process of organic matter addition on the top of the soils and a clay eluvial-illuvial process, which originated an eluvial horizon of maximum SAND content, overlying an illuvial CLAY enriched Bt horizon. Regarding TP distribution, the best fitted regression model for RTP on RSD was quadratic, with R² 0.77. Besides, a multiple regression model for RTP on RCLAY and ROC explained 83％ of the observed variability. The aforementioned models were compatible with a combined TP redistribution process: an upward translocation toward the top horizon carried out by biological transport and a downward translocation related to clay eluviation-illuviation; hence, both processes produced a TP impoverishment of the eluvial horizon.

Keywords: Modeling; Regression analysis; Vertical distribution; Phosphorus; Organic carbon; Clay; Sand』

1. Introduction
2. Materials and methods
　2.1. Area and soils under study
　2.2. Analyzed variables
　2.3. Basic statistical method
　　2.3.1. Nested regression analysis
　2.4. Method to compute relative intra-profile variations (RX variables)
3. Results and discussion
4. Conclusions
Acknowledgements
References