अमूर्त

Regression Analysis and Spatial Distribution of Drought Indices on Maize Production in the Northern Part of Nigeria

A Adedayo Adepoju, Grace O Adenuga, Tayo P Ogundunmade

Drought is a key abiotic stress affecting maize yield and production in Sub Saharan Africa contributing between 44% to 58% grain yield decline in West and Central Africa. For the detection, classification, and control of drought conditions, drought indices are used. This paper presents the application of a multiple linear regression model and spatial distribution to assess the performance of drought indices on maize production in the Northern part of Nigeria. In this research, observed annual data of drought indices, RDI and the palmer drought indices which includes SCPDSI, SCPHDI and SCWLPM, maize yield (measured in tonnes) in Northern states of Nigeria were obtained from 1993 to 2018. The multiple linear regression was carried out at different training sets: 70%, 80% and 90%. Results from the multiple linear regression showed that in the North-Central states, FCT has the lowest MSE (0.7788234) at 90% training level. In North-Eastern states, Borno state has the lowest MSE (0.7240276) at 80% training sets. In North-Western states, Kebbi state has the lowest MSE (0.8029484) at 70% training set. Results from the spatial distribution revealed that Yobe state has the lowest maize yield in the Northern states.