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Modelling seasonal pasture growth and botanical composition at the paddock scale with satellite imagery


Ara, I and Harrison, MT and Whitehead, J and Waldner, F and Bridle, K and Gilfedder, L and da Silva, JM and Marques, F and Rawnsley, R, Modelling seasonal pasture growth and botanical composition at the paddock scale with satellite imagery, In Silico Plants Article diaa013. ISSN 2517-5025 (2020) [Refereed Article]

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DOI: doi:10.1093/insilicoplants/diaa013


Seasonal pasture monitoring can increase the efficiency of pasture utilization in livestock grazing enterprises. However, manual monitoring of pasture over large areas is often infeasible due to time and financial constraints. Here, we monitor changes in botanical composition in Tasmania, Australia, through application of supervised learning using satellite imagery (Sentinel-2). In the field, we measured ground cover and botanical composition over a twelve-month period to develop a supervised classification approach used to identify pasture classes. Across seasons and paddocks, the approach predicted pasture classes with 75-81% accuracy. Botanical composition varied seasonally in response to biophysical factors (primarily climate) and grazing behaviour, with seasonal highs in spring and troughs in autumn. Overall, we demonstrated that 10 m multispectral imagery can be reliably used to distinguish between pasture species as well as seasonal changes in botanical composition. Our results suggest that farmers and land managers should aim to quantify within-paddock variability rather than paddock average cover, because the extent and duration of very low ground cover puts the paddock/field at risk of adverse grazing outcomes, such as soil erosion and loss of pasture biomass, soil carbon and biodiversity. Our results indicate that satellite imagery can be used to support grazing management decisions for the benefit of pasture production and the improvement of environmental sustainability.

Item Details

Item Type:Refereed Article
Keywords:pasture management, supervised classification, Sentinel-2, grazing system modelling, ecosystem, dry matter digestibility, pasture quality, grazing preference
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Agriculture, land and farm management
Research Field:Agricultural production systems simulation
Objective Division:Animal Production and Animal Primary Products
Objective Group:Livestock raising
Objective Field:Sheep for wool
UTAS Author:Ara, I (Dr Iffat Ara)
UTAS Author:Harrison, MT (Associate Professor Matthew Harrison)
UTAS Author:Bridle, K (Dr Kerry Bridle)
UTAS Author:Gilfedder, L (Ms Louise Gilfedder)
UTAS Author:Rawnsley, R (Dr Richard Rawnsley)
ID Code:142122
Year Published:2020
Deposited By:TIA - Research Institute
Deposited On:2020-12-14
Last Modified:2020-12-15

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