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Development of CornSoyWater, a web-based irrigation app for corn and soybean

Author: James Chengchou Han; Haishun Yang; University of Nebraska--Lincoln,
Publisher: Lincoln, Nebraska : University of Nebraska-Lincoln, 2016
Dissertation: Ph. D. University of Nebraska-Lincoln 2016
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
Irrigation decision making is critical for crop producers in the Midwestern United States because of the high demand for water during the peak of growing season of corn and soybean fields. Agronomists try to use agricultural-related data to optimize irrigation decision making. The biggest obstacle is the gap of transforming data to usable information which producers can access and take corresponding actions
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Details

Genre/Form: Academic theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: James Chengchou Han; Haishun Yang; University of Nebraska--Lincoln,
ISBN: 9781369408553 1369408552
OCLC Number: 1035635795
Language Note: English
Notes: UMI number: 10247096
Under the supervision of: Haishun Yang.
Description: 1 electronic resource (163 pages)
Responsibility: James Chengchou Han.

Abstract:

Irrigation decision making is critical for crop producers in the Midwestern United States because of the high demand for water during the peak of growing season of corn and soybean fields. Agronomists try to use agricultural-related data to optimize irrigation decision making. The biggest obstacle is the gap of transforming data to usable information which producers can access and take corresponding actions regarding when to irrigate their fields.

We developed CornSoyWater (http://cornsoywater.unl.edu), a web-based app that can be used in a web browser of any desktop computers or mobile devices. The goal is to use state-of-the-art quantitative agronomic sciences and information technologies, and in-season real-time weather data with field-specific crop management information to predict crop development and growth, crop water use and soil water balance to aid producers' irrigation decision making.

For practical use of the app, the corn crop model (Hybrid-Maize model) which runs inside of the app needed to be tested for its accuracy. We used a 5-year field dataset to test the performance of Hybrid-Maize model on estimating soil water balance near Mead, NE. We conducted a 2-year field experiment to test the performance of Hybrid-Maize model on maize growth and crop water use under a range of irrigation treatments including 100% (recharge top 30 cm soil to field capacity), 75% and 50% of the 100%, and 0% (rainfed) in Lincoln, Nebraska. The results showed that the Hybrid-Maize model simulated soil water balance well for the entire root zone, but underestimated the soil water balance at 0-30 cm and 60 cm to maximum rooting depth, respectively. For the fields at Mead, Hybrid-Maize model can reduce irrigation pumping by 93 mm during the season compared to actual irrigation scheduling by delaying the first irrigation and reducing the overall number of irrigation events. The Hybrid-Maize model performed well in a relatively wet year for biomass and grain yield simulation.

The test results indicated that producers can utilize this app for irrigation decision making. A business plan was proposed on how a startup can commercialize this type of agricultural-related apps or technologies to benefit producers.

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Linked Data


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