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Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance *
Author(s) -
Ritchie John W.,
Abawi G. Yahya,
Dutta Sunil C.,
Harris Trevor R.,
Bange Michael
Publication year - 2004
Publication title -
australian journal of agricultural and resource economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.683
H-Index - 49
eISSN - 1467-8489
pISSN - 1364-985X
DOI - 10.1111/j.1467-8489.2004.00236.x
Subject(s) - stochastic dominance , gross margin , agriculture , environmental science , irrigated agriculture , limiting , dominance (genetics) , irrigation , risk management , margin (machine learning) , agricultural economics , water resource management , geography , economics , agronomy , engineering , econometrics , archaeology , biology , mechanical engineering , biochemistry , chemistry , management , machine learning , computer science , gene
Decision‐making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream‐flows in north‐eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators’ attitude towards risk.