Assessment of Rainfall and Temperature Variability on Maize Production in Kaduna State

Abstract

Rainfall and temperature are important requirements in crop production. Any variation in the distribution of these elements have marked influence on the productivity of most crops including maize. This study assessed rainfall and temperature variability on maize production from 1999 to 2016 in Kaduna State, Nigeria. Data for this study were obtained mostly from secondary sources. These data include temperature, rainfall and maize production which were collected from Nigerian Meteorological Agency (NiMet), National Bureau of Statistic (NBS) and the Kaduna State Agricultural Development Projects (KADP). Results are presented and discussed by the use of mean, mean departures, regression and correlation. To ascertain the annual trend in the meteorological parameters, a statistical t-test was carried out at the 1 and 5% levels of significance. An increasing trend in rainfall and temperature was observed with early onset and cessation dates although only temperature was significant at the 1 and 5% level. To ensure uniformity in the analysis, the average annual yield per 100,000 hectares for each of the years were determined. Thereafter a multiple linear regression (MLR) model, that include maize yield as the dependent variable and the meteorological parameters, such as annual rainfall, average annual temperature, onset and cessation of rainfall as the dependent variables was developed using least square approach. Its overall significance was tested using the statistical f-test at the 1, 5 and 10% levels of significance and found to be significant. On that basis, each of the climatic parameter was tested and temperature and rainfall were found to be significant contributors to maize production. A model for predicting maize production was then developed and it was significant at 95 and 99 % confidence level. The computed co-efficient of determination and linear correlation were 74.2% and 0.86. The study recommends the use of the model developed to determine maize yield before the production season so as to equip farmers with fore knowledge of the expected yield and make alternative adjustments were necessary.