Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/18371
Title: Using Active Optical Sensing for Determining Pasture Growth Rate Using a Light Use Efficiency Model
Contributor(s): Rahman, Muhammad Moshiur (author)orcid ; Lamb, David (supervisor); Guppy, Christopher (supervisor)orcid ; Stanley, John (supervisor)
Conferred Date: 2015
Copyright Date: 2015
Open Access: No
Handle Link: https://hdl.handle.net/1959.11/18371
Abstract: The ability to quantify pasture biomass and growth rate is of prime importance to the sustainability and profitability of extensive livestock industries, specifically as it relates to provide information for better farm management decisions. Assessment of pasture growth rate (PGR, kg/ha.day) using remote sensing has gained considerable interest to the farm managers for livestock grazing management. The context of this research is to investigate the use of in situ sensors and a light use efficiency (LUE) model to estimate PGR. A key parameter in this model is the light interception by the canopy, or fAPAR. Measuring fAPAR using active optical sensors (AOS) introduces new challenges hitherto not appreciated using traditional passive optical sensors and so a considerable portion of this work focusses on the derivation of fAPAR from a widely used optical reflectance index, the normalized difference vegetation index (NDVI). Therefore this research project comprises of two main components: (i) investigating an AOS to infer the fraction of absorbed photosynthetically active radiation (fAPAR) by the plant, a key variable in LUE model; and (ii) evaluating the LUE model using in situ sensors for estimating of PGR (kg/ha.day) at the sub field scale.
Publication Type: Thesis Doctoral
Field of Research Codes: 070302 Agronomy
070104 Agricultural Spatial Analysis and Modelling
070101 Agricultural Land Management
Rights Statement: Copyright 2015 - Muhammad Moshiur Rahman
Open Access Embargo: 2017-10-23
HERDC Category Description: T2 Thesis - Doctorate by Research
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Appears in Collections:Thesis Doctoral

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