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Abstract
Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha−1. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation.
Highlights
Crop dry biomass is an important parameter for nutrient management
The results in this study indicate the potential for the use of this technology for crop biomass mapping in the context of precision agriculture and site-specific nutrient management
A ground-based mobile light detection and ranging (LiDAR) system was successfully deployed for mapping crop biomass in a commercial broadacre wheat field in South Australia
Summary
Crop dry biomass is an important parameter for nutrient management. If biomass information is accessible, growers can set a target plant N concentration for mid-season fertilisation using pre-established N dilution models [3]. Because of the necessity for destructive sampling and laboratory analysis, the high cost and labour required for the assessment of this crop parameter at the field level are impediments for the use of biomass information as a guide for N fertilisation underpinned by an N dilution framework [4]. Alternative approaches for N management are often preferred by farmers, such as the use of empirical N response functions, or estimation of N demand (the final N uptake prediction) and supply for recommendations based on mass balance.
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