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 accommodates even a single herbicide type discrete-continuous decision. We use structural estimates to forecast how the demand for herbicide brands changes as their prices change. To do so, we develop a forecasting algorithm that forecasts optimal herbicide types and allocations via a Sequential Quadratic Programming approach. This novel forecasting approach allows for calibration of alternative specific constants so as to reproduce brand market shares.

Multiple herbicide use in cropland: A discrete-continuous model for stated choice data

Scarpa Riccardo
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Abstract

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 accommodates even a single herbicide type discrete-continuous decision. We use structural estimates to forecast how the demand for herbicide brands changes as their prices change. To do so, we develop a forecasting algorithm that forecasts optimal herbicide types and allocations via a Sequential Quadratic Programming approach. This novel forecasting approach allows for calibration of alternative specific constants so as to reproduce brand market shares.
Crop farmers decisions, Herbicides’ attributes, Multiple Discrete Continuous decisions, Forecasting procedure
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/1054497
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