Abstract
Predictability of seasonal climate variability associated with the El Niño Southern Oscillation (ENSO) suggests a potential to reduce farm risk by selecting crop insurance products with the purpose of increasing farm income stability. A hypothetical 50% peanut, 50% cotton, non-irrigated, 40 ha (100 ac) north Florida farm was used to study the interactions of different crop insurance products with ENSO-based climate information and levels of risk aversion under uncertain conditions of climate and prices. Crop yields simulated by the DSSAT suite of crop models using multiyear weather data combined with historical series of prices were used to generate long series of stochastic income distributions in a whole-farm model portfolio. The farm model optimized planting dates and simulated uncertain incomes for 50 alternative crop insurance combinations for different levels of risk aversion under different planning horizons. Results suggested that incomes are greatest and most stable for low risk-averse farmers when catastrophic (CAT) insurance for cotton and 70% or 75% actual production history (APH) for peanut are selected in all ENSO phases. For high risk-averse farmers, the best strategy depends on the ENSO phase: (1) 70% crop revenue coverage (CRC) or CAT for cotton and 65% APH for peanut during EL Niño years; (2) CAT for cotton and 65%, 70%, or 75% APH for peanut during neutral years; and (3) 65% to 70% APH, or CAT for cotton and 70% APH for peanut during La Niña years. Optimal planting dates varied for all ENSO phases, risk aversion levels, and selected crop insurance products.
Original language | English (US) |
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Pages (from-to) | 1223-1233 |
Number of pages | 11 |
Journal | Transactions of the ASABE |
Volume | 49 |
Issue number | 4 |
State | Published - Jul 2006 |
Keywords
- Coefficient of relative risk aversion
- ENSO
- El Niño Southern Oscillation
- Farm programs
- Government intervention
- Value of climate information
ASJC Scopus subject areas
- Forestry
- Food Science
- Biomedical Engineering
- Agronomy and Crop Science
- Soil Science