Arima and Arimax Analysis on the Effect of Variability of Rainfall, Temperature, Humidity on Some Selected Crops in Nasarawa State

Salisu Auta Musa, Baba Usman Dr. Yahaya, Ganaka Kubi Musa, Nafisatu Tanko Ahmad and Ibrahim Baba
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Salisu Auta Musa: Department of Mathematics and Statistics, Federal Polytechnic, Nasarawa, Nasarawa State, Nigeria
Baba Usman Dr. Yahaya: Department of Mathematics and Statistics, Federal Polytechnic, Nasarawa, Nasarawa State, Nigeria
Ganaka Kubi Musa: Department of Mathematics and Statistics, Federal Polytechnic, Nasarawa, Nasarawa State, Nigeria
Nafisatu Tanko Ahmad: Department of Mathematics and Statistics, Federal Polytechnic, Nasarawa, Nasarawa State, Nigeria
Ibrahim Baba: Department of Mathematics and Statistics, Federal Polytechnic, Nasarawa, Nasarawa State, Nigeria

International Journal of Research and Innovation in Applied Science, 2021, vol. 6, issue 9, 19-27

Abstract: Crops production are highly sensitive to climate change. They are affected by long-term trends in average rainfall temperature and humidity. This study examines the effects of the variability of rainfall, temperature and humidity on some selected crops (rice and yam) in Nasarawa using autoregressive integrated moving averages (ARIMA) and autoregressive integrated moving averages with exogenous variables (ARIMAX). This research compare ARIMA modeling method which make forecast in univariate data and ARIMAX as multivariate method which include independent variables such as rainfall, temperature and humidity. The data for the study were collected from the Nasarawa Agricultural Development programme (NADP) for the period of twenty-three years from (1998 – 2020). The data collected were analyzed using ARIMA and ARIMAX models. The results from the analysis indicates that rainfall and humidity has negative and significant effect on yam production. However, rainfall and humidity has insignificant effect on rice production. Also, the forecast performance evaluation revealed that ARIMAX model performed better in modelling production of yam while the ARIMA model performed better in modelling production of rice in the study area.

Date: 2021
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