| Precision agriculture (PA) is changing the way people manage crops and soils. As an information-driven system, PA possesses fine-scale diagnostic capabilities that facilitate the application of production inputs so as to match varying crop, soil and other field attributes. Conceptually, this offers increased potential for crop/soil productivity and quality. The oil palm, a tropical tree perennial, makes a good candidate for PA given its high economic value and high fertilizer requirements. This 3-part research explored the feasibility of precision oil palm management (POPM) as a means to increase agronomic efficiency and quality, with emphasis on nutrient management.; In part-1, the spatial variability of yield-influencing soil fertility parameters were characterized at different topographic positions. Spatial characterization was done using classical and geo-spatial statistics. Results showed clear yield and fertility gradients across topography. The spatial variability of yield-influencing variables (YIVs) also differed across topography. Among the YIVs, potassium demonstrated the highest potential for variable rate management.; Part-2 aimed at determining suitable rates of N, P, and K for site-specific nutrient management using information about spatial variability. Results showed that yield, growth and fertility responses to treatments were insignificant in most cases. High inherent nutrient concentrations and within-block spatial trends were believed to have masked the responses. Growth variables exhibited significant spatial structures. To account for spatial effects, an iterated nearest-neighbor analysis (NNA) was employed. The NNA-adjustment on growth produced significant treatment effects and improved model precision. Recomputed growth curves indicated that lower N, P and K rates resulted in higher growth increments. Yield and adjusted-growth curves were comparable for N and P but contrasted for K. As such, lower rates are recommended for N and P while higher rates are justified for K.; Part-3 investigated the relationship between oil yield and external fruit characteristics with an aim of developing a simple protocol to facilitate bunch quality determination, ripeness and grading assessment, and yield mapping for site-specific management. Results showed that oil yield could be predicted reliably (R2 = 0.76–0.78) based on fruit color. Validation of the calibrated models afforded 54–56% accuracy.; Overall, this research demonstrated feasible and relevant strategies for POPM. |
