Modeling Eects of Climatic Variables on Tea Production in Kenya Using Linear Regression Model with Serially Correlated Errors
Consolata A. Muganda *
Department of Mathematics and Actuarial Science, The Catholic University of Eastern Africa, Kenya.
Sewe Stanley
Department of Mathematics and Actuarial Science, The Catholic University of Eastern Africa, Kenya.
Winnie Onsongo
Department of Statistics and Actuarial Science, University of Ghana, Ghana.
*Author to whom correspondence should be addressed.
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 signicance
test. 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.
Keywords: Climatic variability, Time-Series, ARIMA.