Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices

Gustavo Togeiro De Alckmin,Lammert Kooistra,Richard Rawnsley,Arko Lucieer

doi:10.1007/s11119-020-09737-z

Gustavo Togeiro De Alckmin, Lammert Kooistra + Show 2 more

Open Access

https://doi.org/10.1007/s11119-020-09737-z

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Abstract

Pasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

Highlights

Efficient pasture production and utilisation is often the most critical component in a pasture-based dairy operation (García et al 2014)
Pasture biomasses ranged from 164 kg DM ha−1 to 4663 kg DM h a−1 and a mean of 1633 kg DM h a−1
The approach of an exhaustive-search of an optimised normalized ratio indices (NRI) identified a band combination that, both in terms of accuracy and precision, is equivalent to the rising plate meter (RPM) (Table 5). The performance of this particular vegetation indices (VI) is in-line with the results reported by Mutanga and Skidmore (2004)

Summary

Introduction

Efficient pasture production and utilisation is often the most critical component in a pasture-based dairy operation (García et al 2014). In a farm scenario, such coordination can be achieved through weekly measurements of pasture biomass (García et al 2014) Common methods, such as the rising plate meter (RPM), rely on linear relationships between canopy height (Allen et al 2011) and biomass. These relationships are limited in their accuracy and biased due to plantdevelopment stages, canopy architecture (erectophile or plagiophile) or canopy density (Nakagami 2016). Such a method requires a trained observer constantly monitoring and sampling paddocks, which can lead to inconsistencies due to observer-bias (Thomson et al 1997) and requires extensive time investment (Hall et al 2019)

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