Daily extreme rainfall indices and their impact on rice yield in parts of North-central States of Nigeria

Abstract

ABSTRACT Recurrent extreme rainfall events create a severe threat to crop production across the globe. The study assessed daily extreme rainfall indices and their impact on rice yield in parts of North-central States of Nigeria.Thirty (30) years daily rainfall data were obtained from the Climatic Prediction Center Merged Analysis of Precipitation (CMAP) and Rice yield data from the Agricultural Development Project (ADP) Offices of four states and Federal Capital Territory (FCT) in Nigeria. Nine (9) extreme rainfall indices developed by Expert Team on Climate Change Detection and Indices (ETCCDI) under the World Meteorological Organization (WMO) were generated using the RClimDex Software. Mann-Kendall test for trend detection was used to determine a trend in the occurrence of the extreme rainfall indices during the study period. The Spearman Rho Rank Correlation Coefficient obtained indicates a non significant correlation between Consecutive Dry Day (CDD) and rice yield across the stations at the same time, Consecutive Wet Day (CWD) shows significant negative correlation with Rice yield at (P< 0.01) and (P< 0.05) at Abuja and Ilorin stations respectively. The study revealed that maximum 1-day rainfall (R1D) and maximum 5-day rainfall (R5D) has no significant correlation with rice yield across the stations. Further, the study established that very wet day rainfall (R95T) has more impact on rice yield as it shows significant positive correlation at (P< 0.05) in Lafia while other stations depict positive but non-significant correlation during the study period. Generally, the study shows that optimal growth of rice crop in the study area requires moderate and continuous rainfall. The research also shows that correlation was more pronounced between extreme rainfall indices and rice yield at Minna, Abuja and Lafia stations compared to Lokoja and Ilorin stations during the study period. We recommend that continuous monitoring of rainfall distribution of the study area is essential in putting in place an informed climate change adaptation measures.