Modelling relationship between rainfall variability and millet (<i>Pennisetum americanum</i> L.) and sorghum (<i>Sorghum bicolor</i> L. Moench.) yields in the Sudan savanna ecological zone of Nigeria

Abstract

This study models the relationship between rainfall variability parameters and millet (Pennisetum americanum L.) and sorghum (Sorghum bicolor L. Moench.) yields in the Sudan Savanna ecological zone of Nigeria. Daily rainfall data recorded by the nearby stations for the period between 1981 and 2010 as well as millet and sorghum yield data were used as inputs in the model. It attempts to develop model for predicting millet and sorghum yields based on rainfall variables. The analytical tools used in developing and testing the model performance include ogive of cumulative pentad rainfall, product-moment correlation coefficient (r), stepwise multiple regression analysis and coefficient of determination (R 2 ). The study produced four yield forecast models; three for millet at Kano, Katsina and Potiskum and the last for sorghum at Potiskum. Model accounted for 61.7% of variation in millet yield due to total annual rainfall and 68.1% in sorghum yield due to total rainfall in may assume the best-fitted yield forecast models. The unaccounted variation of 38.3% in millet yield and 31.9% in sorghum indicate other factors could influence the yield variability of the two crops. It is recommended that based on holding annual and may totals rainfall constant, the two best-fitted crop yield models should be used for planning and forecasting the yield of millet and sorghum in the study area.

Impact of climate variability on the yield of staple grain crops in Wudil local government area, Kano State, Nigeria

“…(Tunde, 2011), this areal most similar finding with that of (Sokoto et al, 2016). Standardization of the coefficient is usually done to answer the question of which of the independent variables have a greater effects/impacts on the dependent variable in a multiple regression analysis, when the variables are measured in different units of measurement, (Mcculloch, 2008).This is also supported by the findings of (Govinda, 2013;Sokoto et al, 2016;Ram, 2016;Samuel, 2016;Haruna et al, 2017) also found a strong impact of independents variables on millet. It also indicated that independents variables have moderate impacts on Maize yield in the study area.…”

Determination of Green Tea Recovery Percentage and Its Relation to Different Weather Parameters

“…Rainfall also a has a strong relation maize yield [20] and rainfall variability parameters could be used to develop yield forecast models for millet and sorghum in the Sudan Savanna zone of Nigeria [21]. To predict the response of recovery percentage against rainfall, regression graph was made (Fig.…”

“…Value was 0.000146 (Table 5) which was significant at p<0.05, hence, the null hypothesis (Ho) was rejected, can be explained ainfall (mm) had statistically significant relation on recovery percentage. The R Square (Table 6), that means 88.76% can be explained by also a has a strong relation maize yield [20] and rainfall variability parameters could be used to develop yield forecast models for millet and sorghum in the Sudan Savanna ecological zone of Nigeria [21]. To predict the response of recovery percentage against rainfall, the linear regression graph was made (Fig.…”