A model for predicting flower development in Elaeis guineensis Jacq.

Chai Sook-Keat, . and Kok Sau-Yee, . and Azimi Nuraziyan, . and Ishak Zamzuri, . and Norashikin Sarpan, . and Meilina Ong-Abdullah, . and Anwar Fitrianto, . and Ooi Siew-Eng, . A model for predicting flower development in Elaeis guineensis Jacq. pp. 315-325. ISSN 1511-2780

The proper development of oil palm fruit is important as the source of oil is the fruit mesocarp and kernel. Prior to fruit formation the development of flowers is therefore also important. Determination of the flower development stages in oil palm generally involves tedious histological analyses of each sampled inflorescence making it a costly and inefficient way of gauging the developmental state. In this study a statistical model was established from the association of physical or macroscopic measurement data to flower development which was determined via histological analyses. The final reduced ordinal logistic regression model is a partial proportional odds model that uses inflorescence length and palm age as predictors to predict the flower development stage. The likelihood-ratio X2 test suggested the model adequately fits the data (p 0.01). The model with a prediction accuracy of 78.5 can be used for selecting inflorescences of specific development stages from palms aged three to 10 years of field-planting. These stages can be further verified by histological analyses. This lowers the overall costs and time by reducing the number of samples requiring histological analysis prior to downstream studies.

Full text available from: Official URL: http://jopr.mpob.gov.my/a-model-for-predicting-flo...

Abstract

The proper development of oil palm fruit is important as the source of oil is the fruit mesocarp and kernel. Prior to fruit formation the development of flowers is therefore also important. Determination of the flower development stages in oil palm generally involves tedious histological analyses of each sampled inflorescence making it a costly and inefficient way of gauging the developmental state. In this study a statistical model was established from the association of physical or macroscopic measurement data to flower development which was determined via histological analyses. The final reduced ordinal logistic regression model is a partial proportional odds model that uses inflorescence length and palm age as predictors to predict the flower development stage. The likelihood-ratio X2 test suggested the model adequately fits the data (p 0.01). The model with a prediction accuracy of 78.5 can be used for selecting inflorescences of specific development stages from palms aged three to 10 years of field-planting. These stages can be further verified by histological analyses. This lowers the overall costs and time by reducing the number of samples requiring histological analysis prior to downstream studies.

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Item Type:	Article
AGROVOC Term:	Elaeis guineensis
AGROVOC Term:	Oil palms
AGROVOC Term:	Flowers
AGROVOC Term:	Distilled water
AGROVOC Term:	Sampling
AGROVOC Term:	Mathematical models
AGROVOC Term:	Inflorescences
AGROVOC Term:	Length
AGROVOC Term:	Prediction
AGROVOC Term:	Plant developmental stages
Geographical Term:	Malaysia
Depositing User:	Ms. Suzila Mohamad Kasim
Last Modified:	28 Apr 2025 07:23
URI:	http://webagris.upm.edu.my/id/eprint/24332

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