Towards Enhanced Resilience: Monthly Updated Seasonal Rainfall “Scenarios” as Climate Predictions for Farmers in Indonesia

Kees Cornelis Johan Stigter, Yunita T. Winarto, Muki Wicaksono

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

This paper exemplifies how agricultural production suffers from climate change and how short term seasonal rainfall prediction improves its resilience under tropical lowland rice production in Indonesia. An introduction to and explanations on climate change and long term climate predictions in agriculture form the first parts of this paper. Two problems haunt seasonal climate predictions for farmers to increase their resilience. These are the skill of predictions and the terminology chosen for monthly updated seasonal rainfall predictions. These “scenarios” are part of climate change adaptation attempts on the islands of Java and Lombok, Indonesia. Originally, NOAA and subsequently NOAA/IRI monthly ENSO predictions for a period of 3 months were chosen to build planting season “scenarios”, because they often explicitly mention Indonesia in their predictions, including recent higher atmosphere convection situations. More recently a check was added on these ENSO predictions, by reading IRI prediction maps for Asia, provided each third Thursday of the month. These maps are more detailed than the written predictions but not more accurate. However, these maps make it possible to separate Java from Nusa Tenggara (region of the islands east of Bali, like Lombok). The wordings chosen for the monthly SMS messages on seasonal rainfall “scenarios” to farmers use terminology of probabilities as common in daily life. Replies to February 2015 questionnaires show how satisfied farmers are but also how they must get used to this wording.

Original languageEnglish
Title of host publicationClimate Change Management
PublisherSpringer
Pages289-303
Number of pages15
DOIs
Publication statusPublished - 1 Jan 2016

Publication series

NameClimate Change Management
ISSN (Print)1610-2002
ISSN (Electronic)1610-2010

Keywords

  • Adaptation
  • Climate prediction
  • Indonesia
  • Rainfall scenarios
  • Resilience
  • Skill

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