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Assessing the Value of SCFs on Farm-level Corn Production through Simulation Modeling

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Author Info

  • Celia M. Reyes

    (PIDS)

  • Kathrina G. Gonzales
  • Canesio D. Predo
  • Rosalina G. de Guzman
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    Abstract

    Rainfall variability greatly influences corn production. Thus, an accurate forecast is potentially of value to the farmers because it could help them decide whether to grow their corn now or to delay it for the next cropping opportunity. A decision tree analysis was applied in estimating the value of seasonal climate forecast (SCF) information for corn farmers in Isabela. The study aims to estimate the value of SCF to agricultural decision makers under climate uncertainty. Historical climatic data of Isabela from 1951 to 2006 from PAGASA and crop management practices of farmers were used in the Decision Support System for Agrotechnology Transfer (DSSAT) to test the potential impact of climate change on corn. The approach is developed for a more accurate SCF and to be able to simulate corn yields for wet and dry seasons under different climatic conditions -- El Nio (poor year), La Nia (good year) and Neutral (neutral year) conditions. In order for the forecast to have value, the with forecast scenario should lead to better decision making for farmers to eventually get increase production over the without forecast scenario. While SCF may potentially affect a number of decisions including crop management practices, fertilizer inputs, and variety selection, the focus of the study was on the effect of climate on corn production. Improving SCF will enhance rainfed corn farmers decisionmaking capacity to minimize losses brought about by variable climate conditions.

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    Bibliographic Info

    Paper provided by East Asian Bureau of Economic Research in its series Development Economics Working Papers with number 22688.

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    Date of creation: Jan 2009
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    Handle: RePEc:eab:develo:22688

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    Related research

    Keywords: decision tree analysis; seasonal climate forecast (SCF); climate uncertainty; Decision Support System for Agrotechnology Transfer (DSSAT);

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