Persistent Link:
http://hdl.handle.net/10150/288770
Title:
Irrigation scheduling decision support
Author:
Fox, Fred Andrew, 1956-
Issue Date:
1997
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Irrigation scheduling using the soil water balance approach has been recommended to irrigators for many years. Reasonably good results are normally obtained by researchers using carefully quantified inputs. Irrigators in production agriculture may estimate inputs and then question the validity of the method when the irrigation recommendations conflict with present irrigation schedules. By associating each input with an interval representing possible bias based on the way the input was estimated, and solving the irrigation scheduling model using the intervals as inputs, the output was associated with an interval representing possible bias. This method was also used to evaluate possible bias associated with growing degree day based crop coefficient curves developed from Arizona crop consumptive use measurements. For comparison purposes, roughly estimated inputs based on irrigation system type, soil type, area weather data and available crop coefficient curves were used as default intervals. Improved input intervals consisted of observed irrigation system performance, soil property measurements, local weather data and theoretical improvements in crop coefficient curves. For surface irrigation, field observation of plant stress and soil water content showed the greatest potential to improve irrigation date predictions. For buried drip under a row crop, accuracy of the predicted daily irrigation rate was most improved by a better estimate of irrigation efficacy.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Agricultural.; Engineering, System Science.; Operations Research.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Agricultural and Biosystems Engineering
Degree Grantor:
University of Arizona
Advisor:
Slack, Donald C.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleIrrigation scheduling decision supporten_US
dc.creatorFox, Fred Andrew, 1956-en_US
dc.contributor.authorFox, Fred Andrew, 1956-en_US
dc.date.issued1997en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractIrrigation scheduling using the soil water balance approach has been recommended to irrigators for many years. Reasonably good results are normally obtained by researchers using carefully quantified inputs. Irrigators in production agriculture may estimate inputs and then question the validity of the method when the irrigation recommendations conflict with present irrigation schedules. By associating each input with an interval representing possible bias based on the way the input was estimated, and solving the irrigation scheduling model using the intervals as inputs, the output was associated with an interval representing possible bias. This method was also used to evaluate possible bias associated with growing degree day based crop coefficient curves developed from Arizona crop consumptive use measurements. For comparison purposes, roughly estimated inputs based on irrigation system type, soil type, area weather data and available crop coefficient curves were used as default intervals. Improved input intervals consisted of observed irrigation system performance, soil property measurements, local weather data and theoretical improvements in crop coefficient curves. For surface irrigation, field observation of plant stress and soil water content showed the greatest potential to improve irrigation date predictions. For buried drip under a row crop, accuracy of the predicted daily irrigation rate was most improved by a better estimate of irrigation efficacy.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Agricultural.en_US
dc.subjectEngineering, System Science.en_US
dc.subjectOperations Research.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineAgricultural and Biosystems Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSlack, Donald C.en_US
dc.identifier.proquest9817330en_US
dc.identifier.bibrecord.b38268267en_US
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