Modeling Eects of Climatic Variables on Tea Production in Kenya Using Linear Regression Model with Serially Correlated Errors

Abstract

Aims/ Objectives: To formulated a linear regression model to capture the relationship between tea production and climatic variables in terms of ARIMA.Place and Duration of Study: Department of Mathematics and Actuarial Science, Catholic University of Eastern Africa, Nairobi, Kenya, between June 2019 and April 2021.Methodology: The study used time-series data for mean annual temperature, mean annual rainfall, humidity, solar radiation, and NDVI, collected from six counties, namely Embu, Kakamega, Kisii, Kericho, Meru, and Nyeri.Results: The study ndings noted that there is a presence of trend and seasonality for all the data. The scatter plot matrix for all the climatic variables for all the counties under the study indicated that tea production has a linear relationship with most climatic variables. Model t of the data indicated statistical signicance when tea production data is dierenced. A second linear model with tea production data deseasoned has mixed results in terms of a signicancetest. The variation of independent variables with tea production yielded very low values, suggesting that the data used has many variabilities.Conclusion: The study ndings show the climatic variables can be used to forecast tea production.Recommendation: Future studies may combine the analysis with other statistical modeling procedures such as the GARCH models.

Econometric Modeling for High Impact Sustainable Organic Tea Production: The Box-Jenkins Approach

“…The ARIMA model is mainly expressed by the following 3 terms: p, d, q. Box and Jenkins, [31,[32][33][34][35][36][37][38][39][40][41][42][43]; Gujarati et al, [27].…”