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Integrating Growth Stage Deficit Irrigation into a Process Based Crop ModelCurrent rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in water restricted scenarios. For example, the mean WP across 38 years of maize production was 1.1 kg/cu m for non-optimized irrigation schedules with 50 mm of seasonal available water and 2.1 kg/cu m optimized ion schedules, a 91% improvement in WP with optimized irrigation schedules. The framework described in this work could be used to estimate WP for regional to global assessments, as well as derive location specific irrigation guidance.
Document ID
20170007351
Document Type
Reprint (Version printed in journal)
Authors
Lopez, Jose R.
(Dartmouth Coll. Hanover, NH, United States)
Winter, Jonathan M.
(Dartmouth Coll. Hanover, NH, United States)
Elliott, Joshua
(Columbia Univ. New York, NY, United States)
Ruane, Alex C.
(NASA Goddard Inst. for Space Studies New York, NY United States)
Porter, Cheryl
(Florida Univ. Gainesville, FL, United States)
Hoogenboom, Gerrit
(Florida Univ. Gainesville, FL, United States)
Date Acquired
August 3, 2017
Publication Date
May 24, 2017
Publication Information
Publication: Agricultural and Forest Meteorology
Volume: 243
ISSN: 0168-1923
Subject Category
Meteorology And Climatology
Report/Patent Number
GSFC-E-DAA-TN44930
Funding Number(s)
WBS: WBS 389018.02.10.02.18
Distribution Limits
Public
Copyright
Other
Keywords
crop model
rainfed
schedule
decision support
water use
deficit irrigation
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