|BULLIED, W - University Of Guelph|
|BULLOCK, PAUL - University Of Manitoba|
|VAN ACKER, RENE - University Of Guelph|
Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/23/2013
Publication Date: 3/3/2014
Citation: Bullied, W.J., Flerchinger, G.N., Bullock, P.R., Van Acker, R.C. 2014. Process-based modeling of temperature and water profiles in the seedling recruitment zone: Part I. Model validation. Agricultural and Forest Meteorology. 188:89-103. DOI: 10.1016/j.agrformet.2013.11.012
Interpretive Summary: Knowledge of temperature and moisture conditions near the soil surface can be used to predict seedling emergence and weed recruitment. Measurement of these conditions is not always cost-effective or practical, particularly when varying slope conditions and soil properties create varying soil temperature and moisture conditions across a field. The accuracy of the Simultaneous Heat and Water (SHAW) model in predicting soil temperature and moisture conditions was investigated by applying the model for varying conditions across the ridge of a hilltop. The model was shown to provide representative simulations of the soil environment that may potentially be used in conjunction with a germination model to predict time of seedling emergence. By using process-based modeling to predict timing of weed emergence, pre-emergent pesticides can be used to better target weeds.
Technical Abstract: Process-based modeling provides detailed spatial and temporal information of the soil environment in the shallow seedling recruitment zone across field topography where measurements of soil temperature and water may not sufficiently describe the zone. Hourly temperature and water profiles within the 75 mm recruitment zone for 75 days after seeding were simulated for Canadian Prairie conditions from the process-based simultaneous heat and water (SHAW) model using local and non-local microclimatic data. Measured and modeled soil cover and spring wheat vegetative cover were used to parameterize the model. Heat and water transfer was simulated through surface residue, early vegetation and soil. Simulations were evaluated using model efficiency, root mean square deviation, and components of mean squared error. The greatest amount of error in simulated soil temperature was lack of correlation in the fluctuation pattern over time, followed by bias of the simulation. Soil temperature simulations had model efficiency of 0.87, overestimation of 0.4ºC, and a RMSD of 2.1ºC averaged across all topographical factors and soil depths. Simulations of soil water had low model efficiency and RMSD of 0.55 MPa. Average absolute bias for soil water was 0.27 MPa which reflected predominantly positive bias at the soil surface and 0–25 mm soil layer and negative bias in the 25–50 mm and 50–75 mm soil layers. Process-based modeling using microclimatic information was shown to provide representative simulations of the soil environment for all depths of the seedling recruitment zone; however, the level of temperature and water accuracy in the seedling recruitment zone achieved by the simulation needs to be assessed for the ability to predict the timing of seedling emergence.