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Title: Multi-model generation of corn production functions with associated uncertainty in high plains

Author
item KISEKKA, ISAYA - Kansas State University
item ARAYA, A - Kansas State University
item VARA PRASAD, P - Kansas State University
item Gowda, Prasanna
item ANDALES, ALLAN - Colorado State University

Submitted to: Miscellaneous Publishing Information Bulletin
Publication Type: Abstract Only
Publication Acceptance Date: 2/2/2017
Publication Date: 6/13/2017
Citation: Kisekka, I., Araya, A., Vara Prasad, P.V., Gowda, P.H., Andales, A. 2017. Multi-model generation of corn production functions with associated uncertainty in high plains[abstract]. 2017 UCOWR/NIWR Conference, June 13-15, 2018, Fort Collins, Colorado. Session 21, p.53.

Interpretive Summary: Abstract only.

Technical Abstract: As irrigation capacities continue to decline in the southern and central High Plains region, to optimize net returns producers have to strategically allocate limited water to appropriate mixture of crops. Production functions have been widely used by agronomists, engineers and economists to quantify crop yield response to water with the goal to optimize water allocation. However, there is a great deal of uncertainty in both the estimated coefficients and functional forms of the production functions even at the same location for the same crop because processes that affect crop yield response to water are influenced by a number of factors that vary both in time and space. There is a scarcity of high-quality, long-term field studies to characterize uncertainty in production functions for major irrigated crops, particularly corn. In this study, a multi-modeling approach (using DSSAT-CSM, APSIM, AquaCrop and RZWQM) coupled with short-term experimental data and long-term climate data was used to generate production functions for corn that accounted for differences in irrigation management, cultural practices and climatic conditions. Uncertainty in yield response to water was quantified from ensemble of production functions generated by different crop simulation models. A robust output data set from this study will be useful for economists to conduct economic analysis of different scenarios to identify management practices that optimize net returns under limited water conditions in the target region.