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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #404877
Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Title: Quantifying crop behavior with observations and models: Constraining simulations at LIRF

Author
item BAKER, IAN - Colorado State University
item SCHUH, ANDREW - Colorado State University
item ALTENHOFEN, JOHN - Northern Water
item Zhang, Huihui
item Comas, Louise
item Barnard, David
item CHÁVEZ, JOSÉ - Colorado State University
item COSTA-FILHO, EDSON - Colorado State University
item MAGENY, TROY - University Of California, Davis
item ULEP, FRANCIS - University Of California, Davis

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/19/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary: N/A

Technical Abstract: We have an unprecedented opportunity to observe and simulate corn grown under unstressed and stressed conditions at the USDA Limited Irrigation Research Facility (LIRF) near Greeley Colorado. Here, corn is grown under full irrigation at 85% of potential evapotranspiration, as well as under conditions of deficit irrigation at 65% of potential ET. The site is heavily instrumented, with observations of canopy temperature, sap flux, eddy covariance measurement of water, energy, and carbon flux, soil moisture, LAI, and yield in both full and deficit treatments. We simulate both treatments at LIRF using a Terrestrial Biosphere Model with a fully prognostic carbon cycle (SiB4). However, when confronted with the multiple observational datasets, a holistic and self-consistent picture of model performance is elusive. Comparison with some datasets are contradictory, as in the fact that simulated LAI is high when compared to observations, yet model yield is low. Simulated ET compares well with Eddy Covariance measurements, yet are low when compared to sap flux. Nonetheless, we find some overarching behavior in the simulations; stress as simulated in SiB4 is not as severe as what is experienced in the field, and the contradictions in LAI and yield may inform model allocation of assimilated carbon to leaves, stems, roots, and product. By adding observation-based empirical models of crop behavior to the analysis, we continue to refine our understanding of crop ecosystems across temporal scales from the diurnal to synoptic and seasonal.