Application of Predictive Model for Efficient Cassava (ManihotEsculanta) Yield in the Face of Climate Variability in Enugu State, Nigeria

Climate variability as occasioned by conditions such as rainfall cessation, irregular and over stretched temperatures have considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (manihotesculanta) amidst climate variability. This study utilized data of climate variables and tonnage of cassava yield as well as information from a questionnaire and focus group discussion with farmers. Regression analysis was employed to develop the predictive model for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall seasonwas also accessed.The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939; P = 0.00514; Cassava and key climatic variables: R2 = 0.560; P = 0.007. The result of the predictive model equation is y = − 264637 + 0.237–1.073 + 077–4.402 + 2.636 + 5.250–8.658 + 2.054 + 1.956–4.831 + 0.978–0.591 + 11402.6. The result of the predictive model equation indicates that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by rainfall and temperature anomalies which identified that rainfall anomalies range from − 478.5 to 517.8mm while temperature anomalies range from − 1.2 to 2.3 0 C over the years. The questionnaire and focus group identified that farmers experienced more late onset and early onset of rain and rainfall cessation over the years. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers.