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Early detection of drought impact on rice paddies in Indonesia by means of Niño 3.4 index
Surmaini E.a, Hadi T.W.b, Subagyono K.c, Puspito N.T.b
a Indonesian Agro-climate and Hydrology Research Institute, Bogor, Indonesia
b Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Bandung, Indonesia
c Indonesian Agency for Agricultural Research Development, Jakarta, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2014, Springer-Verlag Wien.El Niño events have been frequently marked by drought occurrences with severe consequences for agricultural production in Indonesia. Paddy drought occurs almost every year and extends during El Niño phenomena. The Niño 3.4 index is commonly used as an important tool for managing a food security policy. However, there are no details regarding the impact of El Niño on drought-induced paddy damage. We developed the Paddy Drought Impact Index (PDII), which is the ratio of drought-induced paddy damaged area to the total paddy area planted in order to investigate the impact of drought on paddies among 335 districts in Indonesia. Unlike other agricultural drought indices, this index represents real-life percentage of drought-induced paddy damage to indicate each district’s relative severity to drought, which can be easily understood by practical users. The connection between the Niño 3.4 index and PDII was assessed using cross correlation analysis. Scatter plots of best lag time Niño 3.4 index against PDII were examined. The findings show that with 2 months lag of Niño 3.4 prior to PDII, March and June Niño 3.4 indices can be used to predict May–July and August–October PDII, respectively. Critical thresholds of the March Niño 3.4 index were found to range from 0.0 to 0.5 °C, which is associated with a 0.57 probability of weak El Niño occurrence during the subsequent 5 months. On the other hand, a higher probability of 0.67 for occurrences of moderate El Niño is associated with the critical thresholds of June Niño 3.4 index, which ranges from 0.5–1.0 °C. This study has found that the potential impact of drought due to the weak and moderate El Niño occurrences in Indonesia is such that yields are reduced by about 40 % in average. We also found that the most drought-prone areas are located in West Java for both May–July and August–October and in South Sulawesi for August–October.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]First of all, we would like to acknowledge the invaluable funding support given by the Indonesian Agency of Agriculture Research Development. We are also beyond gratefulness to the anonymous reviewer for their insightful comments with regards to improving the quality of this paper. We would also like to express our gratitude for the many constructive comments as given by Dr. Dewi Kirono, without whom this paper would never be of such a standard. Our thanks also go to the Australian Leadership Award Fellowship held by Monash University for helping us with the preparation of this paper. Additionally, the authors appreciate the technical assistance given by Ridho Syahputra from the Weather and Climate Prediction Laboratory, Bandung Institute of Technology.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1007/s00704-014-1258-0[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]