Abstract
Societal Impact StatementOil palm is the first oil‐producing crop globally, representing nearly 20 million ha. In the recent past, oil palm cultivation has been controversial not only because of land utilisation at the expense of primary tropical forests or health concerns associated with palm oil, but also pollution caused by fertilization (including CO2 produced to synthesise fertilizers). Oil palm fields are heavily fertilized with potassium (K), and thus finding better, more parsimonious methods to monitor K nutrition is more important than ever. Here, we suggest that metabolomics and subsequent machine learning of metabolic signatures represent a promising tool to probe K requirements, opening avenues for precision agriculture in oil palm industry.
Highlights
Optimal leaflet K elemental content is quite high (≈1%) while N is about 3% (Foster, 2003; Ochs, 1965; Ollagnier et al, 1987) there are some variations with seasons, locations and oil palm crosses (Foster & Chang, 1977; Ollagnier & Ochs, 1981)
This agrees with the fact that the K content co-varied with growth but was determined by other mechanisms independent of growth and age (Figure 1d). Such mechanisms manifested themselves by an increase in some metabolites and a decrease in others when the K content increased. This response is consistent with documented effects of K availability on metabolic pathways in oil palm (Cui et al, 2019; Mirande-Ney et al, 2020)
Our study shows that leaflet metabolome seems to be an excellent tool to monitor K nutrition in oil palm, allowing proper diagnostic in the growth-K space, which is essential to take fertilization decisions
Summary
Optimal leaflet K elemental content is quite high (≈1%) while N is about 3% (Foster, 2003; Ochs, 1965; Ollagnier et al, 1987) there are some variations with seasons, locations and oil palm crosses (Foster & Chang, 1977; Ollagnier & Ochs, 1981). The description of metabolic effects of K availability offers an excellent perspective, to understand how K nutrition controls oil palm physiology and to find typical metabolic signatures that can be implemented via machine-learning to determine how much fertilization palm trees require.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
