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WHEAT

Forecasting Spring Wheat Yield Using Time Series Analysis

A Case Study for the Canadian Prairies

Dep. of Geography, Southwest Texas State Univ., San Marcos, TX 78666-4616 USA

vijendra_boken{at}und.nodak.edu

Techniques commonly used for wheat (Triticum aestivum L.) yield estimation employ weather data over the growing season. However, yield estimates are also required before wheat is sown—particularly by the grain-exporting agencies to help them determine, in advance, wheat-export targets. In that case, time series techniques relying on past yield data can be used for yield forecasting. In this paper, a procedure for applying time series analysis to forecast yield is described. A few techniques (linear trend, quadratic trend, simple exponential smoothing, double exponential smoothing, simple moving averaging, and double moving averaging) were tested to model the average spring wheat yield series for Saskatchewan, Canada. Using 1975–1993, 1975–1994, and 1975–1995 spring wheat yield data, yields were forecasted for 1994, 1995, and 1996, respectively. Based on a deterministic measure (i.e., mean squared error, MSE), it was found that the quadratic model produced the most accurate forecast during the model development periods (1975–1993, 1975–1994, and 1975–1995) and model testing periods (1994, 1995, and 1996). Further, a discussion is provided on improving the forecast by forecasting the yield for the homogeneous subareas (within Saskatchewan) instead for the entire Saskatchewan as a unit. The subareas could be constructed on the basis of soil-climatic conditions or yield fluctuation, using a geographic information system.

Abbreviations: AR, autoregressive • ARMA, autoregressive moving average • ARIMA, autoregressive integrated moving average • CWB, Canadian Wheat Board • MSE, mean squared error • GIS, geographic information system

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