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Exploring Standardized Precipitation Index for predicting drought on rice paddies in Indonesia
Surmaini E.a, Susanti E.a, Syahputra M.R.b, Hadi T.W.b
a Indonesia Agency for Agro-climate and Hydrology Research Institute, West-Java, 16111, Indonesia
b Faculty of Earth Sciences and Technology, Bandung Institute of Technology, 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]© 2019 IOP Publishing Ltd. All rights reserved.Droughts have severe consequences for rice crops in Indonesia, as these occur annually and increase during El Ni?o phenomena. Accordingly, paddy drought assessment is necessary for developing adaptation strategies for successful crop production. We conducted a detailed assessment of paddy drought-climate indices in Indonesia. This was done by looking at the onset and trends of the Standardized Precipitation Index (SPI) in a three-month time scale (SPI-3) and exploring their relationship with paddy drought-affected areas. The Cartesian quadrant was used to illustrate the combination of SPI-3 onset and trend on the paddy drought affected area. This gave four different drought risk levels: low, moderate, high, and very high. The hit rate (HR) as the proportion of drought occurrences were correctly hindcast and percent of correct (PC) as the total number of correct hindcast divided by the total number of hindcast was used to verify the accuracy of drought effect prediction on paddy rice. The results demonstrate that the highest accuracy of paddy drought predictions occurred in the peak of dry season in July, while the accuracy of drought and non-drought occurrences (PC) was higher in the non-peak months, April through September, excluding July.[/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]Adaptation strategies,Affected area,Climate index,Crop production,Drought risks,paddies,Standardized precipitation index,Time-scales[/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]drought,paddies,rainfall,SPI[/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]This work is funded by the Indonesian Agency for Agricultural Research and Development under Grant No 83.3/PL.040/I.1/04/2016.K.[/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/10.1088/1755-1315/303/1/012027[/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]