A mid-size combine harvester with 2.76 m reaping width and 103.53 hp engine output has been employed in grain corn production, especially by small-scale grain corn farmers. This study attempted to determine field performances of a typical mid-size combine harvester by measuring its effective field capacity (EFC), field efficiency (FE), fuel consumption (FC) and field machine index (FMI). Different types of energy inputs such as fuel, machinery, human, included direct, indirect, renewable and non-renewable energy involved in grain corn harvesting were also measured. The field measurements were carried out in 3 ha of grain corn farm, under similar field conditions using a typical mid-size combine harvester. The average values of EFC, FE, FC and FMI for the mid-size combine harvester were found to be 0.23 ha/h, 34.97%, 37.25 lit/ha and 0.91, respectively. The average equivalent energy values of fuel, machinery and human energy were 1780.70 MJ/ha, 587.73 MJ/ha and 8.53 MJ/ha, respectively. The average values of the direct and indirect energy were 1789.23 MJ/ha and 587.73 MJ/ha, respectively. The average values of renewable and non-renewable energy were recorded at 8.53 MJ/ha and 2368.42 MJ/ha, respectively. The mid-size combine harvester investigated in this study exhibited good field performance characteristic using a reasonable amount of energy consumption as compared to harvesting operation for other grain crops. From the results, it can be concluded that good practice in harvesting operation could improve field performance, and minimise operational costs and energy consumption.
Precision agriculture with regard to crop science was introduced to apply only the required and optimal amount of fertiliser, which inspired the present study of nutrient prediction for oil palm using spectroradiometer with wavelengths ranging from 350 to 2500 nm. Partial least square (PLS) method was used to develop a statistical model to interpret spectral data for nutrient deficiency of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca) and boron (B) of oil palm. Prior to the development of the PLS model, pre-processing was conducted to ensure only the smooth and best signals were studied, which includes the multiplicative scatter correction (MSC), first and second derivatives and standard normal variate (SNV), Gaussian filter and Savitzky-Golay smoothing. The MSC technique was the optimal overall pre-treatment method for nutrients in this study, with highest prediction R2 of 0.91 for N and lowest RMSEP value of 0.00 for P.