Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/57252
Title: Extracting Pasture Evapotranspiration Parameters from Proximal Sensing and Mathematical Modelling
Contributor(s): Alam, Muhammad Shahinur  (author); Lamb, David  (supervisor)orcid ; Mccarthy, Cheryl (supervisor); Rahman, Muhammad  (supervisor)orcid ; Warwick, Nigel William  (supervisor)orcid 
Conferred Date: 2020-02-07
Copyright Date: 2019-10
Thesis Restriction Date until: 2120-02-07
Handle Link: https://hdl.handle.net/1959.11/57252
Related DOI: 10.1016/j.compag.2018.02.008
10.1016/j.agwat.2018.10.042
Related Research Outputs: https://hdl.handle.net/1959.11/57253
Abstract: 

Knowledge of crop evapotranspiration is crucial for irrigation decision making. An appropriate, user-friendly and time-efficient means of inferring such information is therefore essential. In this study, a closed hemispherical chamber was instrumented, calibrated and deployed in the field for measuring actual evapotranspiration of a vital pasture species, Tall Fescue (Festuca arundinacea). The pasture crop coefficient (Kc) was calculated from the measured instantaneous evapotranspiration and reference crop evapotranspiration (ETo) for a range of growth stages. Also the relationship between Kc and Normalized Difference Vegetation Index (NDVI) as measured using an active optical sensor was established. Using the FAO dual crop coefficient approach and the hemispherical chamber, a technique for partitioning evapotranspiration components was developed. The components of evapotranspiration in terms of basal crop coefficient (Kcb) and soil evaporation coefficient (Ke) were expressed relative to canopy NDVI and Leaf Area Index (LAI). A theoretical model for estimating transpiration was also developed by scaling up stomatal conductance to canopy level in a controlled glasshouse environment. The model was validated against the measured transpiration.

Publication Type: Thesis Doctoral
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
300405 Crop and pasture biomass and bioproducts
460306 Image processing
Socio-Economic Objective (SEO) 2020: 100503 Native and residual pastures
100505 Sown pastures (excl. lucerne)
180601 Assessment and management of terrestrial ecosystems
HERDC Category Description: T2 Thesis - Doctorate by Research
Description: 

Please contact rune@une.edu.au if you require access to this thesis for the purpose of research or study.

Awarded the Chancellor's Doctoral Research Medal

Appears in Collections:School of Environmental and Rural Science
School of Science and Technology
Thesis Doctoral

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