Time Series Analysis

Weather data during a growing season cannot be used for obtaining the long-term estimates simply because the long-term estimates are required before crops are even sown. As the yield is known to be influenced most by weather conditions during the growing season, it is a common practice to estimate yield using weather data. Attempts to obtain long-term estimates that do not employ weather data are limited.

As an alternative to weather data, annual time series of yield data is used to obtain the long-term yield estimates by modeling the series. In a time series analysis, a variable to be forecasted (yield, in the present case) is modeled as a function of time:

where Yt is yield for year t, f (t) is a function of time t, and st refers to error (i.e., the difference between observed yield and forecasted yield for year t). Once a functional relationship between yield and time is developed, yield for the year ahead can be forecasted. A few techniques (e.g., linear trend, quadratic trend, simple exponential smoothing, double exponential smoothing, simple moving averaging, and double moving averaging) can be used to model an yield series (Boken, 2000).

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