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An examination of factors influencing producer adoption of HT canola

Date

2004-08-19

Journal Title

Journal ISSN

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Publisher

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Type

Degree Level

Masters

Abstract

This thesis develops a conceptual framework to determine the probability of adopting HT canola when producers are assumed heterogeneous. The model is based on the framework developed by Fulton and Keyowski (1999), but is modified from a deterministic model to a probabilistic model. The study also considers the gross returns from adopting HT canola. Canola production in Manitoba, Canada is chosen as the region of analysis for the empirical component of the study. In 2002, 74 per cent of total canola acres in Manitoba were devoted to HT canola production. Factors such as soil type, producer risk profile, experience, productivity, and management ability are considered as potential determining factors which distinguish adopters of HT technology from non-adopters. Based on an initial assessment of Manitoba canola data, which shows the incomplete adoption of HT technology in Manitoba, a model is developed which considers adoption of a new technology as a function of the characteristics of the adopters. The conceptual model is tested empirically in two-stages. The first stage employs Ordinary Least Squares analysis to estimate the expected yield of different canola varieties to determine whether producers realize a benefit from the adoption of HT varieties. A logit analysis is conducted in the second stage, and considers different attributes of producers – such as risk aversion, management ability, productivity and expected yields – to determine the probability of producers adopting HT technology. The results show two primary findings. First, certain HT varieties can be shown to give producers higher returns. Second, differentiating characteristics of producers are key in determining the likely adoption of HT canola.

Description

Keywords

adoption, binary choice models, non-competitive pricing, innovation diffusion, threshold models

Citation

Degree

Master of Science (M.Sc.)

Department

Agricultural Economics

Program

Agricultural Economics

Citation

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DOI

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