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Using a random utility consistent discrete-continuous model, we analyze decisions by a sample of Australian farmers on the mix of herbicides and their intensity of use. The proposed model is flexible and even accommodates decisions concerning a single herbicide. We use a sequential quadratic programming-based forecasting approach to predict optimal herbicide types and allocations. Structural estimates are used to forecast how price changes affect herbicide demand. The forecasting approach allows for calibration of alternative specific constants to reproduce existing brand market shares.