000022160 001__ 22160
000022160 005__ 20180122202220.0
000022160 037__ $$a376-2016-20240
000022160 041__ $$aen
000022160 260__ $$c2003
000022160 269__ $$a2003
000022160 270__ $$mshepardson@hotmail.com$$pRubas,   Debra J.
000022160 270__ $$mj-mjelde@tamu.edu$$pMjelde,   James W.
000022160 270__ $$malove@tamu.edu$$pLove,   H. Alan
000022160 300__ $$a27
000022160 336__ $$aConference Paper/ Presentation
000022160 446__ $$aEnglish
000022160 490__ $$aSelected Paper
000022160 520__ $$aPrevious climate information studies have used static models to estimate the benefits of using seasonal forecasts.  Technology adoption studies, on the other hand, have used dynamic models but have examined the benefits of adoption ex post.  The objective of this study is to examine ex ante the effects of climate forecast adoption on the international wheat market over time.  Two general sets of scenarios are analyzed.  The first set assumes all wheat-producers within a country or set of countries adopt the forecasts, while producers in the remaining country (ies) do not.  Next, producers adopt sequentially over time based on S-shaped adoption curves, whose rates vary by country.  Welfare implications are examined and compared in the different scenarios.
The model simulates wheat production and trade for the three major exporting countries:  United States, Canada, and Australia over 20 years using Monte Carlo techniques.  Climate forecasts are based on the five phases of the Southern Oscillation Index.  Results for each scenario are based on 1000 simulations.
This paper illustrates that when producers in one area of the world adopt a new technology, it has worldwide ramifications.  In the case of wheat, producers are better off, on average, if everybody adopts climate forecasts.  The gains are not distributed evenly, however, and the variability can be quite large.  Adoption can, in some years, lead to substantial losses.  Results indicate a great advantage to those who adopt first, though this advantage declines rapidly as more producers adopt.  Understanding ex ante the likely consequences of using ENSO-based forecasts allows decision-makers to make better choices.
000022160 650__ $$aCrop Production/Industries
000022160 700__ $$aRubas, Debra J.
000022160 700__ $$aMjelde, James W.
000022160 700__ $$aLove, H. Alan
000022160 8564_ $$s517667$$uhttp://ageconsearch.umn.edu/record/22160/files/sp03ru01.pdf
000022160 887__ $$ahttp://purl.umn.edu/22160
000022160 909CO $$ooai:ageconsearch.umn.edu:22160$$pGLOBAL_SET
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  Previous issue date: 2003
000022160 982__ $$gAmerican Agricultural Economics Association>2003 Annual meeting, July 27-30, Montreal, Canada
000022160 980__ $$a376