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Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. The objectives of this work were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques were first used to obtain the yield function. A multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets because they can adjust for those underlying variables and factor effects that are not measured or not well understood.
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The author received: PhD in Agronomy from Kansas State University, USA in 2010. M.Sc degree in Soil Science (Soil quality) from Wageningen, the Netherlands in 2006. B.Sc in plant Science from Alemaya University, Ethiopia in 2000.
