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Dept of Agricultural & Biosystems Engineering University of Arizona 1177 E. Fourth Street Shantz Bldg #38, Room 403 Tucson, AZ 85721-0038 Phone: (520) 621-1753 Fax: (520) 621-3963 Email: abe@arizona.edu Maps: Campus | Tucson |
Home Research & Projects Projects Recently Completed Projects Systems & Technology
Recently Completed Systems & Technology Research Projects
Eaton, Ed -- Ph.D. 2001
Generation of Predetermined Soil Profiles in a Soil Bin
Boonmung, Suwanee -- M.S. 1999
Position indicator for laboratory studies in a soil bin
Didan, Kamel -- Ph.D. 1999
Prototype geographic information system for agricultural water quality management
Reynolds, Curt -- Ph.D. 1998
Estimating
crop yields by integrating the fao crop specific water balance model
with real time satellite data and ground-based ancillary data
Fox, Fred -- Ph.D. 1997
Irrigation scheduling decision support
Eaton, Ed -- Ph.D. 2001
Advisor: Dr. Kenneth Jordan
Generation of Predetermined Soil Profiles in a Soil Bin
ABSTRACT:
The soil preparation in
soil bins must be capable of generating any desired soil density
profile varying from uniform to heavily compacted layers lying below
more friable soil. A subsurface rotating square rod firmed the soil
beneath the rod and repetitious operations at incrementally decreasing
depths produced the desired profile. Retrofitting the existing
instrumentation with virtual instrumentation methods resulted in more
precise measurements and improved repeatability. A wheatstone bridge
force transducer measured cone penetrometer forces as a function of
soil penetration depth. Soil surface elevation and implement depths
were located with ratiometric linear transducers. Speed and distance
were found with an optical encoder, and prime mover hydraulic oil
temperatures and pressures were gauged with current loop sensors.
Boonmung, Suwanee -- M.S. 1999
Advisor: Dr. Kenneth Jordan
Position indicator for laboratory studies in a soil bin
ABSTRACT:
A positioning device
was developed to indicate the linear displacement of a soil bin's
carriage utilizing an incremental optical encoder. An off-line
positioning device was provided for visualization by operators, and a
computer-based system was developed to electronically record the
position data for processing. The incremental encoder with an output of
1000 pulses per revolution was mounted on a rubber-lagged wheel, 304.8
mm foot) in circumference, to produce an input resolution of
0.3048mm. In the off-line display only the high-order four of
five digits were displayed. The accuracy of the microcomputer
instrumentation system and the off-line device was 10 and 15 counts
respectively.
Didan. Kamel -- Ph.D. 1999
Advisor: Dr. Muluneh Yitayew
Prototype geographic information system for agricultural water quality management
ABSTRACT:
A prototype raster
geographic information system (GIS) for agricultural water quality
analysis was developed considering the farm as an aggregation of
spatial units with homogeneous physical and management characteristics.
A crop model that simulates the farm and environment response to
different management scenarios was integrated with the GIS. The
integrated GIS-model is then run on each homogeneous area. The results
of crop yield and chemical leaching are geographically referenced for
further display and analysis, and to serve as an input to the decision
Model. A decision model based on maximization of expected utility (MEU)
was also integrated to help assess and evaluate the impacts of
fertilizer application on the farm system and the environment. By using
utilities for both crop yield and chemical leaching the model
circumvents the issue of assigning a monetary value to the environment.
Accommodating both the farmers' goals, in terms of higher yield and the
well being of the environment, in terms of lower chemical leaching, the
model computes the expected utility of each management scenario. The
management practice with the maximum expected utility is then
recommended. The integrated model was tested with an example of lettuce
production in Arizona. Results were compared to published field
reports, the model recommendation matched well with the field results.
The prototype model was simple to use, and very well integrated, which
makes it an alternative to the more complex and expensive coupling of
commercial GIS and simulation models
Reynolds, Curt -- Ph.D. 1998
Advisor: Dr. Muluneh Yitayew
Estimating
crop yields by integrating the fao crop specific water balance model
with real time satellite data and ground-based ancillary data
ABSTRACT:
The broad objective of
this research was to develop a spatial model which provides both timely
and quantitative regional maize yield estimates for real-time Early
Warning Systems (EWS) by integrating satellite data with ground-based
ancillary data. The Food and Agriculture Organization (FAO) Crop
Specific Water Balance (CSWB) model was modified by using the real-time
spatial data that include: dekad (ten-day) estimated rainfall (RFE) and
Normalized Difference Vegetation Index (NDVI) composites derived from
the METEOSAT and NOAA-AVHRR satellites, respectively; ground-based
dekad potential evapo-transpiration (PET) data and seasonal estimated
area-planted data provided by the Government of a Geographical
Information System (GIS) software was utilized to: drive the crop yield
model; manage the spatial and temporal variability of the satellite
images; interpolate between ground-based potential evapotranspiration
and rainfall measurements; and import ancillary data such as soil maps,
administrative boundaries, etc.. In addition, agro-ecological zones,
length of growing season, and crop production functions, as defined by
the Kenya (GoK). The GIS-based CSWB model was developed for three
different resolutions: agro-ecological zone (AEZ) polygons;
7.6-kilometer pixels; and 1.1-kilometer pixels. The model was validated
by comparing model production estimates from archived satellite and
agro-meteorological data to historical district maize production
reports from two Kenya government agencies, the Ministry of Agriculture
(MoA) and the Department of Resource Surveys and Remote Sensing
(DRSRS). For the AEZ analysis, comparison of model district maize
production results and district maize production estimates from the MoA
(1989-1997) and the DRSRS (1989-1993) revealed correlation coefficients
of 0.94 and 0.93, respectively. The comparison for the 7.6-kilometer
analysis showed correlation coefficients of 0.95 and 0.94,respectively.
Comparison of results from the 1.1-kilometer model with district maize
production data from the MoA (1993-1997) gave a correlation coefficient
of 0.94. These results indicate the 7.6-kilometer pixel-by-pixel
analysis is the most favorable method. Recommendations to improve the
model are finer resolution images for area planted, soil moisture
storage, and RFE maps; and measuring the actual length of growing
season from a satellite-derived Growing Degree Day product.
Fox, Fred -- Ph.D. 1997
Advisor: Dr. Donald Slack
Irrigation scheduling decision support
ABSTRACT:
Irrigation scheduling
using the soil water balance approach has been recommended to
irrigators for many years. Reasonably researchers using carefully
quantified inputs normally obtain good results. 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.
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