Forecasting Rice Yields Based ofMarkov Chain theory - Ma. Divina S. Paraguas; Ferdinand J. Paraguas; Anton Abdulbasah Kamil (STATISTIKA: Forum Teori dan Aplikasi Statistika, 2005)
The study explores the application of Markov Chain theory for rice yield forecasting. Yieldforecasts are based on the eco-physiological process of rice growth given measurable rice cropcharacteristics and weather data at intermediate times in the growing season of the rice crop. TheORYZA1 model is used to simulate a database containing rice yields and rice crop conditions atspecified times during the growing season. The model was ran on 32 years of historical weather data(1959 - 1990) from the meteorological station at the International Rice Research Institute (IRRI), LosBaños(121 15 E Latitude: 14 11 N Altitude: 21.0m), Laguna, Philippines. As input to the model, thestudy adopted the parameters on one of the representative yield potential field experiments at IRRIduring the 1992 dry season for the IR72 variety planted on a 15x15 m2 plot. Based on the output ofORYZA1, a Markov Chain (matrix of transition probabilities) was constructed to provide forecastdistributions of rice yield for various rice condition classes at different rice phenological stages priorto harvest. This Markov Chain can provide several statistics of interests. This ranges from mean,percentile (median) and standard error of the forecasts to probability interval forecasts and predictedprobabilities of exceeding (or falling bellow) target yields. The simulated rice yield obtained fromORYZA1 model for 32 years ranged from 8.33 to 10.88 ton ha-1 with an average of 9.57 ton ha-1 anda standard deviation of 0.60 ton ha-1. Forecasted yields from the matrix of transition probabilitiesranged from 8.58 to 9.45 ton ha-1 and standard deviations ranging from 0.39 to 0.60 ton ha-1.Results also showed that forecasted yields are more consistent when forecasting starts when the riceplants are more mature.