The autopilot-automated steering system is one of today’s modern technologies in a
tractor’s driving system for conducting the operations in farmland. However, further study
on the suitability of this steering technology on a particular geographic region is still a
necessity. This study evaluated the precision of tractor operations with soil sensor
implement using manual and autopilot-automated steering systems on oil palm plantation
terrain in Malaysia. A New Holland TD5.75 tractor with 75 hp engine size equipped with
a Trimble autopilot-automated steering system pulling a Veris 3100 soil electrical
conductivity (EC) sensor was tested in this study. The findings showed that each steering
system generated a little different pattern of spatial variability in interpolated soil EC
maps. Apart from that, autopilot-automated steering system offered better performances
by saving energy expenditure of operator and improving the field capacity of operation.
Conclusively, tractor with autopilot-automated steering presented a great suitability for the
use in agricultural operations in Malaysia.
Excessive grain loss is still a persistence problem in mechanised rice harvesting using combine harvester in Malaysian paddy fields. Therefore, this study was conducted to develop a model for predicting the effect of some operational parameters on grain loss during mechanised harvesting operation. Three operational parameters that always associated with grain loss were measured through direct field measurements on the daily harvesting operations in the sampled rice granaries. The measured parameters included field speed of combine harvester, grain moisture, and soil moisture contents. Regression and correlation analyses were employed to develop the model estimation. The results of the predictive model exposed the fitted model with a strong 24 N.A. A. Johari et al. relationship between associated variables and established the measured parameters to determine the grain loss. The findings would be beneficial in assisting the rice farmers to predict grain loss during harvesting and minimise excessive grain loss in mechanised harvesting operations in Malaysia.
This study was conducted to identify and visualise spatially the Nitrogen (N) status on immature oil palm area with an autopilot tractor-mounted active light sensor (ALS) in a Malaysian oil palm plantation. All the measurements taken by the ALS were assessed 'on-the-go' at every second while the tractor was moving on the field with autopilot steering mode. The N status was analysed based on 46% of the N content in urea and 40 kg ha -1 N application rate for a standard fertiliser requirement for immature oil palm. The ordinary kriging method was used to produce the interpolated maps of the N status by means of the ArcGIS 10.3 software. It was found that mean N-as applied rate per hectare read by the sensor was 1.62% lower than the recommended one. By showing such very small difference in mean rates, generally, the system showed its effectiveness in monitoring N status on immature oil palm. The interpolated maps also successfully displayed spatial variability of the N status on immature oil palm area, which are useable for reference in applying variable rate application (VRA) to economise the use of fertiliser on the said crop.
Excessive grain loss is still a persistence problem in mechanised rice harvesting using combine harvester in Malaysian paddy fields. Therefore, this study was conducted to develop a model for predicting the effect of some operational parameters on grain loss during mechanised harvesting operation. Three operational parameters that always associated with grain loss were measured through direct field measurements on the daily harvesting operations in the sampled rice granaries. The measured parameters included field speed of combine harvester, grain moisture, and soil moisture contents. Regression and correlation analyses were employed to develop the model estimation. The results of the predictive model exposed the fitted model with a strong 24 N.A. A. Johari et al. relationship between associated variables and established the measured parameters to determine the grain loss. The findings would be beneficial in assisting the rice farmers to predict grain loss during harvesting and minimise excessive grain loss in mechanised harvesting operations in Malaysia.
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