Modeling oil palm pollinator dynamics using deterministic and agent-based approaches. Applications on fruit set estimates. Some preliminary results - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Mathematical Methods in the Applied Sciences Year : 2018

Modeling oil palm pollinator dynamics using deterministic and agent-based approaches. Applications on fruit set estimates. Some preliminary results

Abstract

Oil palm production is of economical importance in several southern countries. The increasing demand of oil palm put a lot of pressure in several places where the rain forest and thus the tropical diversity are in danger due to deforestation. With the land already cultivated, we need to improve the yields, which means to increase the production of fruits in plots. One way is to increase the pollination or the fruit set through pollination. Palm tree has a specific entomophilous pollinator, a weevil, Elaeidobius spp, that absolutely needs male inflorescence to complete its life cycle. In young plots (3‐7 years old), mainly female inflorescences are produced, and thus, the pollinator population cannot maintain, resulting in a bad fruit set, and, thus, a bad production. That is why several questions arise: What is the mean number of male inflorescences (per hectare) needed to maintain the pollinators population above a certain threshold? And, in terms of yield, what is the optimal size of the population to reach an optimal fruit set? We propose, compare and discuss 2 different modeling approaches to develop preliminary models to study the dynamics of the pollinator population, and obtain some rough estimates of the fruit set. We derive some simulations and discuss these preliminary results.

Dates and versions

lirmm-01799376 , version 1 (24-05-2018)

Identifiers

Cite

Yves Dumont, Jean-Christophe Soulié, Fabien Michel. Modeling oil palm pollinator dynamics using deterministic and agent-based approaches. Applications on fruit set estimates. Some preliminary results. Mathematical Methods in the Applied Sciences, 2018, Special Issue: Biomathematics/Advanced Analysis in Pure & Applied Sciences, 41 (18), pp.8545-8564. ⟨10.1002/mma.4858⟩. ⟨lirmm-01799376⟩
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