Evaluating Soil Moisture and Textural Relationships Using Regression Analysis,

Abstract

Soil moisture and textural conditions are described for 179 soil samples from an arid to semiarid climate. Stepwise multiple regression analysis of these data produced four regression equations that related (1) the percent sand and clay and (2) the percent fines, with the percent soil water held at 0.33 bar (FC) and the 15 bar (WP) potentials. Evaluation of these equations showed no differences between the estimates at the 0.33 bar potential using either the percent sand and clay or the percent fines. Better estimates for the WP were obtained when the percent sand and clay were used instead of the percent fines. The differences between the estimated soil moisture at FC or WP varied less than 30 percent from the measured soil moisture values for 161 (90 percent) of the 179 soil samples. The differences between the estimated and the measured soil moisture values were not significant at the 95 percent level of confidence. The regression equations provide a method by which the potential percent soil water held at the FC or WP can be estimated from soil textural data. The accuracy and precision of the results of applying these equations to soils of other areas has not been determined. It would seem, however, that they would be applicable in those instances where only general working estimates are needed.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1980
Accession Number
ADA087371

Entities

People

  • Melvin B. Satterwhite

Organizations

  • Geospatial Research Laboratory

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Classification
  • Computer Programs
  • Electrical Conductivity
  • Geographic Regions
  • Handbooks
  • Materials
  • Measurement
  • Moisture
  • New York
  • Particle Size
  • Physical Properties
  • Regression Analysis
  • Remote Sensing
  • Soil Surveys
  • Soil Tests
  • Statistics
  • Surveys

Fields of Study

  • Agricultural and Food sciences

Readers

  • Agricultural Chemistry/Soil Science
  • Mathematics or Statistics
  • Regression Analysis.