Digital Methods for Soil Mapping and Fertilizer Management in Oil Palm

Oil palm (Elaeis guineensis Jacq.) is the world’s leading source of vegetable oil and an important driver of rural economic activity in Southeast Asia, West Africa, and the equatorial region of Latin America. In the Llanos region of Colombia, palm oil production is additionally an important vehicle for legal employment and social stability in a region deeply affected by the country’s longstanding and recently-concluded armed conflict. The economic viability of palm oil production is thus of great interest to both those directly employed in the industry and to the larger society around them, and yet oil palm remains a relatively understudied cropping system.

Spending on fertilizer is one of the largest costs in palm oil production, and plantations face considerable pressure to apply fertilizer as efficiently as possible in order to maintain the profitability of their operations. However, developing strategies for optimizing fertilizer applications in oil palm can be considerably challenging given the particular characteristics of palm oil production systems. Oil palm has a typical life-cycle of 25 years, with harvesting done manually approximately every fifteen days for the duration of the palms productive life-cycle. The morphology of oil palm’s reproductive system makes it possible for environmental changes to affect yield in irregular ways, with the same soil or climate-related stressors having the potential to affect yields either immediately or multiple years after the event. It can therefore be difficult for plantations to link changes in yield patterns to individual management changes or environmental factors. Additionally, since unlike all other major oilseeds oil palms must be harvested manually, plantation managers do not have access to the kind of detailed yield data made possible by mechanized harvesting equipment, but must rely on much more irregular and coarser-resolution information to examine yield variability within plantations. Understanding how the particular soil conditions and fertilizer management history of an individual oil palm plantation drive variability in yields requires employing innovative approaches to maximize the insights to be learned from the available data.

For this study, we worked with a 5,220 hectare oil palm plantation in the Colombian Llanos, in the municipality of Villanueva, Casanare. Despite uniform fertilizer applications and management practices, along with uniform climatic conditions within the plantation, significant yield variability existed within the plantation, with plantation managers initially unable to determine the underlying causes. We proposed and evaluated a methodology for using digital terrain and soil mapping for generating continuous soil data within an oil palm production system, based on Functional Soil Mapping (FSM) methods using the SRTM Global Digital Elevation model and geo-referenced soil sampling, with the goal of identifying soil physical, chemical and hydrological properties that could be directly linked to different yield responses to fertilizer application at the field scale. Furthermore, the economic implications for the plantation of infield variability in yield response to fertilizer arising from variation in soil properties were examined.

The perennial nature and particularities in reproductive morphology of oil palm, including an approximately 8-10 year growth period before mature yields are reached, mean that developing site-specific yield response curves to different nutrient application levels in oil palm requires extensive time and resources. The PORIM model, developed by the Malaysian Palm Oil Board (MPOB) across multiple decades of extensive and continuous field testing, is one of the most commonly used methods by which plantations can estimate yield response at different levels of fertilizer application. Traditionally, the PORIM model is run by using site-specific low-resolution vector-layer soil analysis to adjust various parameters in multiple equation systems developed using statistical methods and many decades worth of field tests by the MPOB. In this study, the PORIM model is used as the basis for a methodology to employ a precision approach to fertilizer management in oil palm using high-resolution raster-layer soil property maps and a constrained-optimization model programmed in the General Algebraic Modeling System (GAMS).