Predicting fresh fruit bunch yield of oil palm
This study aimed to develop the simulation model for predicting fresh fruit bunch (FFB) yield of oil palm through multiple linear regression analysis. Two experiments were conducted at the oil palm plantation of Agricultural and Technology College, Krabi province. Six-year-old Tenera hybrid palms we...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Prince of Songkla University
2001-11-01
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| Series: | Songklanakarin Journal of Science and Technology (SJST) |
| Subjects: | |
| Online Access: | http://rdo.psu.ac.th/sjstweb/journal/23-Suppl-1/23-S1-2001-717-726.pdf |
| _version_ | 1818153772512182272 |
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| author | Nilnond, C. Tongkum, P. Juntaraniyom, T. Eksomtramage, T. |
| author_facet | Nilnond, C. Tongkum, P. Juntaraniyom, T. Eksomtramage, T. |
| author_sort | Nilnond, C. |
| collection | DOAJ |
| description | This study aimed to develop the simulation model for predicting fresh fruit bunch (FFB) yield of oil palm through multiple linear regression analysis. Two experiments were conducted at the oil palm plantation of Agricultural and Technology College, Krabi province. Six-year-old Tenera hybrid palms were used for the experiments. These palms were planted in Tha-sae soil series (Typic Paleudults; Fine loamy mixed)with spacing of 9x9x9 m. In the first experiment, 151 Tenera palms were selected and marked randomly throughout an area of plantation about 16 ha. For each selected palm, FFB yield and yield component characters (FFB number and bunch weight) were recorded at every harvesting time for four consecutive years (June 1993 to May 1997). The results showed that the FFB number and bunch weight could be used to predict the FFB oil palm yield. In the second experiment, nine plots of Tenera hybrid palms were arranged. The plot size was 0.48 ha and had twenty palms per plot for data collection for three consecutive years (January 1994 to December 1996). These data included leaf nutrient (N, P, K, Mg and B) contents in the 17th frond, the fresh fruit bunch (FFB) yield and the amount of rainfall. The results showed that N, P, K, Mg and B contents in the leaves, the amount of rainfall and FFB yield in the previous year, together with the N, P, K, Mg and B contents in the leaves (in the predicting year) could be used to predict the FFB oil palm yield. |
| first_indexed | 2024-12-11T14:15:56Z |
| format | Article |
| id | doaj.art-89d5591cb6324ea6b9cc719c81c27da9 |
| institution | Directory Open Access Journal |
| issn | 0125-3395 |
| language | English |
| last_indexed | 2024-12-11T14:15:56Z |
| publishDate | 2001-11-01 |
| publisher | Prince of Songkla University |
| record_format | Article |
| series | Songklanakarin Journal of Science and Technology (SJST) |
| spelling | doaj.art-89d5591cb6324ea6b9cc719c81c27da92022-12-22T01:03:08ZengPrince of Songkla UniversitySongklanakarin Journal of Science and Technology (SJST)0125-33952001-11-0123Suppl.717726Predicting fresh fruit bunch yield of oil palmNilnond, C.Tongkum, P.Juntaraniyom, T.Eksomtramage, T.This study aimed to develop the simulation model for predicting fresh fruit bunch (FFB) yield of oil palm through multiple linear regression analysis. Two experiments were conducted at the oil palm plantation of Agricultural and Technology College, Krabi province. Six-year-old Tenera hybrid palms were used for the experiments. These palms were planted in Tha-sae soil series (Typic Paleudults; Fine loamy mixed)with spacing of 9x9x9 m. In the first experiment, 151 Tenera palms were selected and marked randomly throughout an area of plantation about 16 ha. For each selected palm, FFB yield and yield component characters (FFB number and bunch weight) were recorded at every harvesting time for four consecutive years (June 1993 to May 1997). The results showed that the FFB number and bunch weight could be used to predict the FFB oil palm yield. In the second experiment, nine plots of Tenera hybrid palms were arranged. The plot size was 0.48 ha and had twenty palms per plot for data collection for three consecutive years (January 1994 to December 1996). These data included leaf nutrient (N, P, K, Mg and B) contents in the 17th frond, the fresh fruit bunch (FFB) yield and the amount of rainfall. The results showed that N, P, K, Mg and B contents in the leaves, the amount of rainfall and FFB yield in the previous year, together with the N, P, K, Mg and B contents in the leaves (in the predicting year) could be used to predict the FFB oil palm yield.http://rdo.psu.ac.th/sjstweb/journal/23-Suppl-1/23-S1-2001-717-726.pdfoil palmElaeis guineensispredicting fresh fruit bunch yieldmultiple linear regression |
| spellingShingle | Nilnond, C. Tongkum, P. Juntaraniyom, T. Eksomtramage, T. Predicting fresh fruit bunch yield of oil palm Songklanakarin Journal of Science and Technology (SJST) oil palm Elaeis guineensis predicting fresh fruit bunch yield multiple linear regression |
| title | Predicting fresh fruit bunch yield of oil palm |
| title_full | Predicting fresh fruit bunch yield of oil palm |
| title_fullStr | Predicting fresh fruit bunch yield of oil palm |
| title_full_unstemmed | Predicting fresh fruit bunch yield of oil palm |
| title_short | Predicting fresh fruit bunch yield of oil palm |
| title_sort | predicting fresh fruit bunch yield of oil palm |
| topic | oil palm Elaeis guineensis predicting fresh fruit bunch yield multiple linear regression |
| url | http://rdo.psu.ac.th/sjstweb/journal/23-Suppl-1/23-S1-2001-717-726.pdf |
| work_keys_str_mv | AT nilnondc predictingfreshfruitbunchyieldofoilpalm AT tongkump predictingfreshfruitbunchyieldofoilpalm AT juntaraniyomt predictingfreshfruitbunchyieldofoilpalm AT eksomtramaget predictingfreshfruitbunchyieldofoilpalm |