Location: Dale Bumpers Small Farms Research CenterTitle: Using terrain algorithms on a digital elevation model to evaluate yield potential in oil palm
|MARTINEZ, ALBERTO - Purdue University
|CAMBERATO, JAMES - Purdue University
|ASHTEKAR, JENETTE - Purdue University
Submitted to: Journal of Oil Palm Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2020
Publication Date: 10/20/2020
Citation: Martinez, A., Camberato, J., Owens, P.R., Ashtekar, J.M. 2020. Using terrain algorithms on a digital elevation model to evaluate yield potential in oil palm. Journal of Oil Palm Research. https://doi.org/10.21894/jopr.2020.0092.
Interpretive Summary: Oil palm is an important crop in tropical regions where fertility is a major issue limiting production. A digital soil mapping method that was developed to improve fertilizer efficiency for corn and soybean was applied to the oil palm plantation. There results showed positive response of yield with topography since topography is controlling water redistribution. By understanding the soil moisture and nutrient balance, producers can apply nutrient s more efficiently according to this study.
Technical Abstract: Oil palm (Elaeis guineensis Jacq.) plantations face strong pressure to improve fertilizer-use efficiency. Digital soil mapping methods based on topographic analysis using globally-available Digital Elevation Models (DEM’s) provide an efficient means of quantifying topography-driven variability of soil properties within oil palm plantations. The Shutter Radar Topography Mission (SRTM) Global Digital Elevation Model (GDEM) was used as the basis for modeling topography across an individual oil palm plantation. Terrain algorithms were used to model terrain attributes and generate continuous soil property maps along topographic soil classes in conjunction with georeferenced soil samples as model inputs. The resulting raster layers of soil property values were evaluated for mean error and their correlation to yield variability across the plantation. Modified Catchment Area (MCA), an iterative measure of a landscape position represented by a grid cell’s propensity to lose or gain soil water, was found to have a strong effect on yield, suggesting that soil moisture distribution was an important driver of yield variability in this system.