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Article

Agricultural Drought-Triggering for Anticipatory Action in Papua New Guinea

1
Regional Office for Asia and the Pacific, Food and Agriculture Organization of the United Nations, Bangkok 10200, Thailand
2
PNG National Weather Service, Port Moresby P. O. Box 1240, Papua New Guinea
3
Papua New Guinea Country Office, Food and Agriculture Organization of the United Nations, Port Moresby P. O. Box 545, Papua New Guinea
4
Mountain Societies Research Institute, University of Central Asia, Bishkek 720001, Kyrgyzstan
5
Yamano Bosai, 150-3 Tateya, Akiruno City, Tokyo 190-0163, Japan
*
Author to whom correspondence should be addressed.
Water 2024, 16(14), 2009; https://doi.org/10.3390/w16142009
Submission received: 27 May 2024 / Revised: 7 June 2024 / Accepted: 16 June 2024 / Published: 15 July 2024
(This article belongs to the Special Issue Drought Monitoring and Risk Assessment)

Abstract

Throughout its history, Papua New Guinea (PNG) has faced recurrent agricultural droughts, imposing considerable strain on both livelihoods and the economy. Particularly severe droughts have been associated with El Niño climate patterns. During these episodes, PNG becomes especially vulnerable to extended periods of aridity and diminished precipitation. Historically, humanitarian assistance for these events has primarily focused on responding to emergencies after an agricultural drought has been declared and communities have already been impacted. Here, we developed a proactive agricultural drought-triggering method for anticipatory action (AA) in PNG to offer a more sustainable and cost-effective approach to address this hazard. Our AA uses weather forecasts and risk data to identify and implement mitigative actions before a disaster occurs. The research details a step-by-step guide from early warning to action implemented by the Food and Agricultural Organization of the United Nations and the Government of Papua New Guinea. This preemptive disaster risk management initiative integrates a combined drought index (CDI) with specific thresholds and tailored anticipatory actions based on crop calendars. Moreover, the developed CDI provides a 3-month lead time for implementing AA to reduce the impact of the agricultural drought. During the El Niño-induced drought event that began in 2023, the CDI was tested and the AA was piloted for the first time.
Keywords: early warning; artificial intelligence; machine learning; early action; forecast-based financing; food security; agricultural drought; Papua New Guinea early warning; artificial intelligence; machine learning; early action; forecast-based financing; food security; agricultural drought; Papua New Guinea

Share and Cite

MDPI and ACS Style

Isaev, E.; Yuave, N.; Inape, K.; Jones, C.; Dawa, L.; Sidle, R.C. Agricultural Drought-Triggering for Anticipatory Action in Papua New Guinea. Water 2024, 16, 2009. https://doi.org/10.3390/w16142009

AMA Style

Isaev E, Yuave N, Inape K, Jones C, Dawa L, Sidle RC. Agricultural Drought-Triggering for Anticipatory Action in Papua New Guinea. Water. 2024; 16(14):2009. https://doi.org/10.3390/w16142009

Chicago/Turabian Style

Isaev, Erkin, Nathan Yuave, Kasis Inape, Catherine Jones, Lazarus Dawa, and Roy C. Sidle. 2024. "Agricultural Drought-Triggering for Anticipatory Action in Papua New Guinea" Water 16, no. 14: 2009. https://doi.org/10.3390/w16142009

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