Abstract
Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model’s prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha−1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha−1). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading.
Highlights
Assessment of feed quality parameters is necessary for optimal management of pasture-based dairy systems
Visible and near-infrared (VIS-NIR, 400–1100 nm) instruments, mostly Silicon-based (SI) semiconductors, mass-produced for off-the-shelf consumer cameras, are less expensive than shortwave infrared (SWIR, 1100–2500 nm) instruments, which are mostly based on indium gallium arsenide (InGaAs) or lead sulfide (PbS). Discussing this issue, Starks et al [24] reported that crude protein (CP) (%CP and CPm) could be best estimated using the VIS-NIR rather than the SWIR portion of the spectrum. By joining these methods [19,24], this study aims to assess model performances coupled with routines of feature selection within different spectral regions (i.e., VIS-NIR or SWIR) or the full spectrum (FS) range
Following the removal of high biomass observations and the outlier detection protocol, the final number of samples used for model development was reduced to 231
Summary
Assessment of feed quality parameters is necessary for optimal management of pasture-based dairy (livestock) systems. Such monitoring can instruct well-informed decisions in grazing (feeding) management according to whichever nutritional goals are established [1]. These parameters are usually estimated through laboratory analysis, involving time-consuming sampling procedures and complex logistical operations, and are subject to the availability of a service provider [2,3]. Feed quality assessment is based on wet-chemistry methods, which are usually expensive, complex, and time consuming. Employing non-destructive spectral analysis in field conditions would be advantageous to farmers, as accurate real-time information allows precise management of a herd’s diet and understanding of short- and long-term effects of different strategies for grazing (e.g., grazing interval and pressure) and pasture management (e.g., timing and application rates of inputs)
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