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Prediction of soil moisture from selected climatic data

Climatic variables have been used to calculate evapotranspiration loss by many researchers. Evapotranspiration formulas have been developed for local use in predicting soil moisture in many parts of the United States. The objective of this study was to develop a method to estimate soil moisture in Virginia using data collected by the Agricultural Engineering Department, Virginia Polytechnic Institute, Blacksburg, Virginia.

The first step in the study was to determine the relationship between soil moisture and loss pan evaporation. This relationship was found on a seasonal cumulated basis. For shorter periods, the results were poor. No workable relation could be found that would satisfactorily estimate soil moisture from any of these procedures.

A soil moisture accounting system based on the soil moisture balance principle was then devised in which daily evapotranspiration loss was calculated by Van Bavel’s nomogram method. Daily soil moisture and measure soil moisture were compared by the student t-test for paired observations. The results of the statistical analysis indicated it could be used to estimate soil moisture in local conditions. Finally, the correlation between daily evapotranspiration and pan evaporation was determined. A high correlation was found.

The problem encountered in analyzing the data available for this study indicated the need for a detailed statistically controlled experiment. Hence an experimental design has been formulated that, hopefully, will allow an systematic investigation and evaluation of the problem. / Master of Science

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/74614
Date January 1966
CreatorsChang, Andrew Chia-Shing
ContributorsAgricultural Engineering
PublisherVirginia Polytechnic Institute
Source SetsVirginia Tech Theses and Dissertation
Languageen_US
Detected LanguageEnglish
TypeThesis, Text
Format1 v. (various pagings), application/pdf, application/pdf
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/
RelationOCLC# 20623993

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