Abstract
Abstract 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 12-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.
Highlights
Pasture botanical composition is directly linked with overall sward productivity, with productivity varying seasonally according to pasture ecotype, resource availability and phenology
Pasture quanta and botanical composition of intensively grazed pastures in r southern Australia have been assessed visually, with farm managers often relying on knowledge c gained through their own heuristics or professional networks to distinguish between pasture species. s ground-based methods are useful for pasture monitoring over small scales, such methods u are often laborious, time consuming and sometimes expensive (Alcock et al, 2015; Harrison, Jackson, et al, 2014; Xu et al, 2008)
3.1 Seasonal paddock level pasture cover t Paddock average pasture cover was generally greatest in spring and least in autumn (Figure 5) in line ip with growth rates typically seen for temperate pastures (Alcock et al, 2015; Harrison et al, 2017; r Harrison, Jackson, et al, 2014)
Summary
Pasture botanical composition is directly linked with overall sward productivity, with productivity varying seasonally according to pasture ecotype, resource availability and phenology. Schaefer and Lamb (2016) used vehicle-mounted light detection and ranging (LiDAR) measurements to improve estimation of pasture biomass and found that combined LiDAR and active optical reflectance sensors contributed to better estimation of pasture biomass. Many of these approaches do not have either the temporal (sub-weekly) or spatial (< 20 m2) resolution necessary to enable pasture species cover or botanical composition quantification to provide information of sufficient resolution to facilitate grazing management decision making at the paddock level
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