Recent advances in oil-palm breeding and agronomic practices may have influenced the dry-matter production and nutrient demand of tenera oil palm since the last comprehensive studies published some three decades ago. This has raised concerns, since updated knowledge of nutrient requirements at various stages of oil palm growth is essential for formulating fertilizer recommendations that meet agronomic, economic, and environmental objectives. We therefore filled in this knowledge gap with the objective to quantify the standing biomass, dry-matter production, and nutrient demand of tenera oil palm of different ages, grown under current agronomic practices. Tenera palms were sampled at 12, 29, 48, 88, 133, 209, and 238 months since nursery planting and separated into spear leaves, leaflets, rachis, petiole, leaf bases, cabbage, trunk, bole, and roots attached to bole for determination of dry weights and nutrient contents. Fruit bunches were harvested, with dry weights and nutrient contents of ripe bunches determined. Annual dry-matter production was calculated from dry weights of different plant parts, and together with nutrient concentrations determined in these plant parts and fruit bunches, annual nutrient demand was calculated. Excluding roots, the total standing vegetative biomass increased almost linearly from 1.3 kg palm⁻¹ at 12 months old to 808.6 kg palm⁻¹ at 238 months old. Whole-palm dry-matter production increased with palm age but at a decreasing rate, from 1.5 kg palm⁻¹ year⁻¹ at 12 months old to 285.5 kg palm⁻¹ year⁻¹ after 133 months, with little increase thereafter. The maximum rates of nutrient demand occurred after 209 months, coinciding with peak production of fruit bunches—except for K, which occurred at month 88 when vegetative growth demand for K peaked. Annual gross amounts of nutrients required to produce 25 t fresh fruit bunches ha⁻¹ were 1.99 kg N, 0.28 kg P, 3.94 kg K, 0.42 kg Mg, 0.99 kg Ca, and 2.5 g B per palm. The net amounts of nutrients required to meet similar production level of fruit bunches were 1.04 kg N, 0.16 kg P, 1.85 kg K, 0.26 kg Mg, 0.47 kg Ca, and 1.3 g B per palm per year if all nutrients contained in the pruned leaves were recycled. These results provide much-needed updated data on the dry-matter production and nutrient demand of tenera oil palm grown under current agronomic practices and also serve as a general yardstick for practitioners to further refine fertilizer recommendations.

A semi-mechanistic oil palm growth and yield model called Sawit.jl was developed to account for a wide range of planting densities and soil textures under Malaysia's climate conditions. The model comprises components related to meteorology, photosynthesis, energy balance, soil water content, and crop growth. The model simulates instantaneous meteorological properties using daily weather data, calculates simultaneous evaporation from crop and soil with the Shuttleworth-Wallace model, determines soil water content through Darcy's law, and adapts a biochemical C3 model for photosynthesis. The model is also parameterized using updated measurements from the newer tenera oil palm, including temperature-dependent Rubisco kinetics, specific leaf area, and the partitioning of nutrients and dry matter between various tree parts. Sawit.jl was validated using historical field measurement data from seven Malaysian oil palm sites, encompassing palm ages spanning 1-23 years. These seven sites differed in soil type (Inceptisols and Ultisols), planting density (82-299 palms ha^-1), soil texture (27-59 % clay and 7-67 % sand), and rainfall (1800-2800 mm yr^-1). The model showed overall good accuracy in simulating oil palm parameters (except for trunk weight) across diverse conditions, with model agreement metrics ranging from 6 to 27 % for model absolute errors, -22 to +17 % for model bias, and 0.38 to 0.98 for the Kling-Gupta Efficiency index. The model also predicted the response of oil palm yield to abrupt rainfall changes, such as those during El Niño and La Niña events, while accounting for how soil texture, rainfall, and other meteorological factors influence water deficits and crop photosynthesis. However, model accuracy varied by site, planting density, and oil palm parameter. Model accuracy can be increased by more accurately representing the oil palm microclimate, incorporating fruiting activity, and refining the dry matter partitioning mechanism for the trunk.

Oil palm crops are essential for ensuring sustainable edible oil production, in which production is highly dependent on fertilizer applications. Using Landsat-8 imageries, the feasibility of macronutrient level classification with Machine Learning (ML) was studied. Variable rates of compost and inorganic fertilizer were applied to experimental plots and the following nutrients were studied: nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca). By applying image filters, separability metrics, vegetation indices (VI) and feature selection, spectral features for each plot were acquired and used with ML models to classify macronutrient levels of palm stands from chemical foliar analysis of their 17th frond. The models were calibrated and validated with 30 repetitions, with the best mean overall accuracy reported for N and K at 79.7 ± 4.3% and 76.6 ± 4.1% respectively, while accuracies for P, Mg and Ca could not be accurately classified due to the limitations of the dataset used. The study highlighted the effectiveness of separability metrics in quantifying class separability, the importance of indices for N and K level classification, and the effects of filter and feature selection on model performance, as well as concluding RF or SVM models for excessive N and K level detection. Future improvements should focus on further model validation and the use of higher-resolution imaging.

Malaysia's Journal of Oil Palm Research Website

Photosynthetica covers wide range of photosynthesis research or research including photosynthetic parameters of both experimental and theoretical nature.