Assessment of the Impacts of Climate Variability and Change on Rice Production in Bong County, Liberia

Abstract

Liberia’s economy and food security are heavily dependent upon agriculture, specifically, rice farming. However, in Liberia and specifically Bong County, climate variability and change are having negative impacts on production thereby leading to decline in yields. Therefore, this study seeks to analyze the impact of climate variability and change on rice production in Bong County of Liberia. Data used in this study comprises of daily weather data for rainfall, maximum temperature, and minimum temperature, and downscaled crop productivity data for rice yields (Kg/ha). Rainfall data was obtained from Climate Hazards Group InfraRed Precipitation Station (CHIRPS) and maximum and minimum temperature from ERA5 by downloading them using the KNMI Climate Explorer website for the base period/historical data covering 1981-2011, and Projected climate datasets (SSPs 4.5 & 8.5) for the period 2021-2080 covering near (2021-2050) and medium (2051-2080) terms representing moderate and high emission scenarios were obtained from the Earth System Grid Federation (ESGF) website for CMIP6 GCMs (MPI-ESM1-2-HR, MIROC6 and CNRM-CM6-1), selected for their representation of a range of climate sensitivities. Modelling approaches and Crop data for rice were obtained from FAOSTAT. Data quality checks were done using homogeneity test by means of the single mass curve technique. The methodology used in this study included trend analysis using Mann-Kendall trend tests; the coefficient of variation for analysis of climate variability and rice yields variability. The Pearson’s correlation coefficient was employed to assess the degree of connection between climatic parameters and rice yields. The significance of the correlation coefficients was established through the use the student’s t-test. Multiple Linear regression was employed to determine the variance between rice yields and climatic parameters that had higher statistically significant correlation coefficient. Climate variability and change impact on rice productivity was determined using the AquaCrop model based on SSP2-4.5 and SSP5-8.5 emission scenarios for the prediction of rice yields in the future...