Modelling the yield loss of oil palm due to Ganoderma Basal Stem Rot disease
Oil palm or scientifically known as Elaeis guineensis Jacq. is the most efficient oilseed crop in the world. This commodity crop is considered as the golden crop in Malaysia. This is due to the contribution of the oil palm industry to the country's overall economy, providing both employment...
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Format: | Thesis |
Language: | English |
Published: |
2016
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Subjects: | |
Online Access: | https://eprints.ums.edu.my/id/eprint/17733/1/Modelling%20the%20yield%20loss.pdf |
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Summary: | Oil palm or scientifically known as Elaeis guineensis Jacq. is the most efficient
oilseed crop in the world. This commodity crop is considered as the golden crop in
Malaysia. This is due to the contribution of the oil palm industry to the country's
overall economy, providing both employment and income from exports. The efforts
of the country to strengthen the industry are being interrupted by a fatal disease
which is called as Ganoderma Basal Stem Rot (BSR) disease. This disease can
cause a significant economic loss to the industry. To date, there is still no effective
control of the disease at the commercial fields' level. The existing control measures
can only prolong the productive life of the infected palms. It is very crucial to the
planters to estimate the yield loss due to the disease. Currently, there is no existing
mathematical model that can be used for that purpose. Therefore, this empirical
study was conducted to build a mathematical model which can be used for yield
loss estimation due to the disease. For the purpose of data collection, three
commercial oil palm plots with different production phase (i. e. step ascent phase,
plateau phase, and declining phase) were selected as the study sites. The yield and
disease severity of the selected palms in the three study sites were recorded for the
duration of twelve months. Before building the yield loss model, a data screening
was performed in order to remove palms with extreme yield values. The
identification of the main sources of multicollinearity was also performed based on
correlation-based test and also variance-based test. All the remaining data set was
splitted into model building data set and validation data set. Two model building
approaches were applied, which are estimation-post-selection and Bayesian model
averaging (BMA). For estimation-post-selection approach, there were two subset
selection algorithms were applied, namely backward stepwise subset selection and
best-subset selection. The best single model from the best-subset selection
algorithm was chosen based on eight criteria, namely Akaike Information Criterion
(AIC), Finite Prediction Error (FPE), Generalised Cross Validation (GCV), Hannan-
Quinn (HQ), RICE, SCHWARZ, sigma square (SGMASQ) and SHIBATA. The
predictive performance of the three best models which represent three different
model building algorithms were assessed and compared. Based on mean square
error (MSE), root mean square error (RMSE), and mean absolute error (MAE), BMA
model has the lowest values, thus selected as the best model for oil palm yield loss.
This best model (i. e. estimated loss of total bunch weight in 12 months = -24.632
+ (-18.307*R2) + (13.456*R3) + (21.531*R4) + (2.346*AUDPC) +
(0.551*NEIGHBOUR) + (35.113*PT) + (0.014*AUDPC*NEIGH BOUR) + (-
0.011*AUDPC*PT)) revealed that planting technique as the most important
predictors of oil palm yield loss and followed by disease progress (AUDPC), disease
severity (mild, medium, and severe), number of infected neighbouring palms, and
two interaction variables. The economic loss was then estimated by using the best
model. The estimated economic loss showed that the loss can be up to 68 percent
as compared to the attainable yields of all the infected palms. In conclusion, the
yield loss model built in this study can potentially be used by the oil palm planters
in helping them to estimate the yield loss as well as economic loss due to
Ganoderma BSR disease if no treatment is applied. |
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