Quantifying Crop Behavior Using Observations and Models
Ian Baker, Colorado State University, baker@atmos.colostate.edu (Presenter)
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.
Associated Project(s):
Poster Location ID: 2-1
Presentation Type: Poster
Session: Poster Session 2
Session Date: Wed (May 10) 5:15-7:15 PM
CCE Program: TE