I am using the CERES rice model in DSSAT to model CA rice production. The model is unable to predict lodging and damage from pests and diseases. I am modeling a cultivar that has been fairly extensively studied in California. The wealth of field trial data available for this cultivar allows a fairly accurate sense of losses due to lodging and disease. It seems logical that I could apply these loss rates to the total yield predicted by the model to get a more accurate yield output. However, I'm struggling to determine whether there is precedence for this kind of post-model adjustment?
Dear Cara,
As the model is unable to simulating lodging and disease effects, I am wondering how did you calibrate your model for further simulation? Assuming that you are using a pre-calibrated model with independent data under potential conditions ( no lodging and disease stress). The solution I can think about: Run the model under potential condition then adjust the grain yield by the using function that you would derive from your grain yield data affected by lodging and diseases under field conditions. Maybe a simple regression or multi-regression model. There is a caution to use this approach though; you will be able to adjust only the grain yield as this adjustment will assume that you have similar plant parts ( leaf number, leaf weight, stem number, stem weight etc.) as in potential conditions. But the reality is different, the sink numbers and sizes change under stress conditions.
Hope this helps.
Regards,
Uttam
I did not understand your subject well but for better modeling, I propose to use the arficial neural networks that are very effective in this type of tasks.
Can you explain more about CERES rice model in DSSAT