Daisy usually has many ways to match measurements for a specific field experiment, so you need to limit the number of free parameters. Otherwise, you will get "overfitting", a set of parameters that fit the optimization data very nicely but has very little predictive power when used on an independent dataset.
I haven't worked on grass-clover myself, but intuitively I would "trust" the grass parameterization more, as it is more widely used, and initially limit the optimization to clover parameters.
Kindly,
Per A.