Date of Award:

5-1990

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Plants, Soils, and Climate

Department name when degree awarded

Plants, Soils and Biometeorology

Committee Chair(s)

R.J. Hanks

Committee

R.J. Hanks

Committee

V.P. Rasmussen

Committee

D.S. Bowles

Committee

J.E. Keith

Committee

R.W. Jeppson

Abstract

The increase in multipurpose use of water resources, dwindling xii water supplies, and a decline in the economic viability of many agricultural operations make efficient irrigation management imperative. Soil variability and climatic uncertainty are major obstacles to efficient irrigation management due to a lack of reliable information for decision making. This study was aimed at identifying sources of uncertainty and examining methods for reducing the effects of uncertainty on irrigation scheduling schemes.

A method for spatial-temporal estimation that combines spatial information from few measurements with a physically based model of the system dynamics is proposed. The spatial phase of the proposed method is facilitated by the Conditional Multivariate Normal (CMVN) method, and the temporal phase employs the Kalman filter (KF) for dynamic estimation. CMVN estimates with their estimation errors were supplied to the KF. The KF then combined the CMVN spatial estimates as "measurements" with information from a model of the system dynamics to obtain improved estimates.

Soil spatial variability was included in the irrigation scheduling scheme by grouping similar properties into decision units. The relative contribution to the total yield of each decision unit was weighed by its relative area. Linear programming was used to optimize the irrigation schedule of a limited amount of water.

A special irrigation system was designed with spatially variable application amounts determined by drippers with different flow rates. The system was used to induce a similar pattern of soil water spatial variability on six plots in a field with uniform and homogeneous soil.

The optimized irrigation schedule improved corn (zea mays L.) biomass on three plots by about 12% over a pre-dated schedule that was given to another three plots. Plant height and crop biomass spatial structures were correlated with the spatial distribution of drippers. The proposed spatial-temporal estimation method provided better estimates of soil water than methods based only on spatial information.

Download

Share

COinS

Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .