Forecasting Rice Yields Based ofMarkov Chain theory
The study explores the application of Markov Chain theory for rice yield forecasting. Yield forecasts are based on the eco-physiological process of rice growth given measurable rice crop characteristics and weather data at intermediate times in the growing season of the rice crop. The ORYZA1 model i...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
Universitas Islam Bandung
2014-10-01
|
| Series: | Statistika |
| Online Access: | http://ejournal.unisba.ac.id/index.php/statistika/article/view/922 |
| _version_ | 1818929934688583680 |
|---|---|
| author | Ma. Divina S. Paraguas Ferdinand J. Paraguas Anton Abdulbasah Kamil |
| author_facet | Ma. Divina S. Paraguas Ferdinand J. Paraguas Anton Abdulbasah Kamil |
| author_sort | Ma. Divina S. Paraguas |
| collection | DOAJ |
| description | The study explores the application of Markov Chain theory for rice yield forecasting. Yield
forecasts are based on the eco-physiological process of rice growth given measurable rice crop
characteristics and weather data at intermediate times in the growing season of the rice crop. The
ORYZA1 model is used to simulate a database containing rice yields and rice crop conditions at
specified 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), Los
Baños(121 15 E Latitude: 14 11 N Altitude: 21.0m), Laguna, Philippines. As input to the model, the
study adopted the parameters on one of the representative yield potential field experiments at IRRI
during the 1992 dry season for the IR72 variety planted on a 15x15 m2 plot. Based on the output of
ORYZA1, a Markov Chain (matrix of transition probabilities) was constructed to provide forecast
distributions of rice yield for various rice condition classes at different rice phenological stages prior
to 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 predicted
probabilities of exceeding (or falling bellow) target yields. The simulated rice yield obtained from
ORYZA1 model for 32 years ranged from 8.33 to 10.88 ton ha-1 with an average of 9.57 ton ha-1 and
a standard deviation of 0.60 ton ha-1. Forecasted yields from the matrix of transition probabilities
ranged 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 rice
plants are more mature. |
| first_indexed | 2024-12-20T03:52:42Z |
| format | Article |
| id | doaj.art-71a82d7ea73140c9a78299e8d92c6805 |
| institution | Directory Open Access Journal |
| issn | 1411-5891 |
| language | Indonesian |
| last_indexed | 2024-12-20T03:52:42Z |
| publishDate | 2014-10-01 |
| publisher | Universitas Islam Bandung |
| record_format | Article |
| series | Statistika |
| spelling | doaj.art-71a82d7ea73140c9a78299e8d92c68052022-12-21T19:54:26ZindUniversitas Islam BandungStatistika1411-58912014-10-0152718Forecasting Rice Yields Based ofMarkov Chain theoryMa. Divina S. ParaguasFerdinand J. ParaguasAnton Abdulbasah KamilThe study explores the application of Markov Chain theory for rice yield forecasting. Yield forecasts are based on the eco-physiological process of rice growth given measurable rice crop characteristics and weather data at intermediate times in the growing season of the rice crop. The ORYZA1 model is used to simulate a database containing rice yields and rice crop conditions at specified 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), Los Baños(121 15 E Latitude: 14 11 N Altitude: 21.0m), Laguna, Philippines. As input to the model, the study adopted the parameters on one of the representative yield potential field experiments at IRRI during the 1992 dry season for the IR72 variety planted on a 15x15 m2 plot. Based on the output of ORYZA1, a Markov Chain (matrix of transition probabilities) was constructed to provide forecast distributions of rice yield for various rice condition classes at different rice phenological stages prior to 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 predicted probabilities of exceeding (or falling bellow) target yields. The simulated rice yield obtained from ORYZA1 model for 32 years ranged from 8.33 to 10.88 ton ha-1 with an average of 9.57 ton ha-1 and a standard deviation of 0.60 ton ha-1. Forecasted yields from the matrix of transition probabilities ranged 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 rice plants are more mature.http://ejournal.unisba.ac.id/index.php/statistika/article/view/922 |
| spellingShingle | Ma. Divina S. Paraguas Ferdinand J. Paraguas Anton Abdulbasah Kamil Forecasting Rice Yields Based ofMarkov Chain theory Statistika |
| title | Forecasting Rice Yields Based ofMarkov Chain theory |
| title_full | Forecasting Rice Yields Based ofMarkov Chain theory |
| title_fullStr | Forecasting Rice Yields Based ofMarkov Chain theory |
| title_full_unstemmed | Forecasting Rice Yields Based ofMarkov Chain theory |
| title_short | Forecasting Rice Yields Based ofMarkov Chain theory |
| title_sort | forecasting rice yields based ofmarkov chain theory |
| url | http://ejournal.unisba.ac.id/index.php/statistika/article/view/922 |
| work_keys_str_mv | AT madivinasparaguas forecastingriceyieldsbasedofmarkovchaintheory AT ferdinandjparaguas forecastingriceyieldsbasedofmarkovchaintheory AT antonabdulbasahkamil forecastingriceyieldsbasedofmarkovchaintheory |