Using LIDAR for Forage Yield Measurement of Perennial Ryegrass (Lolium perenne L.) Field Plots

Ghamkhar, Kioumars; Irie, Kenji; Hagedorn, M.; Hsiao, Jeffrey; Fourie, Jaco; Gebbie, Steve; Flay, Casey; Barrett, Brent; Stewart, Alan V.; Werner, A.

doi:10.1007/978-3-319-89578-9_37

Cited by 9 publications

(12 citation statements)

References 2 publications

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“…Overall, the accuracy of the LiDAR in estimating both FW and DM yield compares favourably with prior reports for this LiDAR system Ghamkhar et al 2018) and is a substantial advance on traditional methods (Smith et al 2001). The level of accuracy achieved here, ranging from 0.73 to 0.87 compares favourably with the R 2 = 0.76 for pasture grass assessment using a more complex system relying on LiDAR augmented with an additional sensor providing normalised difference vegetation index data (Schaefer & Lamb 2016).…”

Section: Discussionsupporting

confidence: 67%

“…Building on recent progress in Canterbury developing a mobile precision phenomics platform using LiDAR to measure seasonal DM in the field Ghamkhar et al 2018), the aim of this study was to evaluate this platform for the accuracy of nondestructive, real-time estimation of FW and DM yield in a perennial ryegrass breeding trial in the Waikato.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass

George¹,

Barrett

Ghamkhar

2019

Journal of NZ Grasslands

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mproving pasture yields is a primary goal for plant breeders. However, measuring and selecting for yield is a major bottleneck in breeding, requiring methods that are laborious, destructive, and/or imprecise. A computerised scanner developed in Canterbury using LiDAR (light detection and ranging) technology was evaluated in the Waikato on perennial ryegrass paired-row breeding plots. At eight timepoints, all plots were scanned prior to mechanical defoliation and recording of fresh weight (FW) and dry matter (DM) yield on a random subset of plots. Yield data on 1206 FW and 504 DM samples were compared with LiDAR scan results on a seasonal basis by regression. Winter, spring, summer and autumn correlation with FW were R2 = 0.81, 0.92, 0.94 and 0.90, respectively, and with DM yield R2 = 0.87, 0.73, 0.87 and 0.79, respectively. These results indicate LiDAR estimation of DM yield was accurate within seasons for the paired-row breeding plots, although it was sensitive to large changes in dry matter content (%) among seasons, which may require seasonal algorithms to correct for this variation if this technology is to be adopted. In conclusion, the scanner could be useful in removing a major bottleneck in perennial ryegrass breeding and may have application for agronomy and farm management in cases where precise non-destructive real-time estimation of DM yield are of value.

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Section: Discussionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass

George¹,

Barrett

Ghamkhar

2019

Journal of NZ Grasslands

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“…There are many new options available to forage breeders including genomic selection (Faville et al, 2018), phenomics (Ghamkhar et al, 2018), wide hybridization (Nichols et al, 2014), novel traits (Hancock et al, 2012), and breeding strategy per se (Hoyos-Villegas et al, 2018), which may influence the rate of gain. As such, historical genetic gain rates can serve as a benchmark for realized rates of gain to compare with alternative breeding strategies.…”

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confidence: 99%

Rate of Genetic Gain for Persistence to Grazing and Dry Matter Yield in White Clover across 90 Years of Cultivar Development

et al. 2019

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537 RESEARCHW hite clover (Trifolium repens L.) is a major contributor of productivity and ecological services in pastures and rangelands worldwide. The advantages of including and maintaining white clover in a pasture are well documented (Chapman et al., 1993). In New Zealand alone, it is estimated that white clover adds approximately NZ$3 billion attributable to the seed industry, N fixation, and honey production . In grazed swards, white clover provides up to 400 kg N ha −1 via biological N fixation (Crush, 1987). In terms of area, white clover is the predominant temperate forage legume serving as a companion to temperate grasses in grazing systems (Laidlaw and Teuber, 2001). White clover in mixed sward pastures is estimated to occupy ?15 million ha in Australasia and 5 million ha in the United States. Globally, renewal of white clover is estimated at 3 to 4 million ha yr −1 (Mather et al., 1996).White clover breeding is important worldwide and is recognized as a priority in many countries with a pastoral-based livestock productivity system ( Jahufer et al., 2012). For example, the last checklist of white clover varieties (Caradus and Woodfield, 1997) described a total of 326 cultivars in relation to their maintainer institution, origin and breeding procedures used, agronomic performance, and disease susceptibility, among other characteristics, depending on the information available. This checklist comprises information collected from cultivars from 32 ABSTRACT White clover (Trifolium repens L.) is a major contributor of productivity and ecological services in pastures and rangelands worldwide. White clover breeding is recognized as a priority in many countries with a pastoral-based livestock productivity system. The objective of this study was to provide an updated estimate of the rate of change in genotypic value attributable to population improvement in white clover, using a set of 80 cultivars released between 1920 and 2010 by public and private plant breeding programs across 17 countries, in an experiment to evaluate forage yield across three locations in New Zealand. Overall, some New Zealand cultivars released in the 2000s were highly adapted to more stable testing environments. Geographic origin resulted in differential rates of gain. A segmental regression comparing the pre-1965 and post-1965 rates of gain was performed. The pre-1965 regression line for white clover dry matter yield resulted in an absolute rate of increase of 0.031 g m −2 yr −1 decade −1 or 0.087% decade −1 , whereas the post-1965 line resulted in an absolute rate of increase of 0.058 g m −2 yr −1 decade −1 or 0.162% decade −1 . White clover content resulted in a pre-1965 absolute rate of increase of 0.011% content decade −1 or 0.032% decade −1 on average, and the post-1965 line indicated an absolute rate of increase of 0.04% content decade −1 or 0.121% decade −1 on average. The comparisons between pre-1965 and post-1965 indicated a twofold increase for white clover dry matter yield and a fourfold increase in rates of gain for white c...

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“…Lidar has demonstrated accurate predictions (R 2 > 0.80) for crop density in wheat (Triticum aestivum L.; Saeys et al, 2009;Hosoi and Omasa, 2009) Zhang and Grift, 2012), rice (Oryza sativa L.; Tilly et al, 2014), and maize (Zea mays L.; Luo et al, 2016); canopy volume in tree species (Rosell et al, 2009); and biomass in maize (Luo et al, 2016), alfalfa (Medicago sativa L.; Noland et al, 2018), and wheat (Jimenez-Berni et al, 2018). Numerous other forage crops have exhibited moderate to high correlations between lidar and biomass (Freeman et al, 2007;Schaefer and Lamb, 2016;Ghamkhar et al, 2018).…”

Section: Hairy Vetch (Vicia Villosamentioning

confidence: 99%

Lidar and RGB Image Analysis to Predict Hairy Vetch Biomass in Breeding Nurseries

Wiering

Ehlke

2019

The Plant Phenome Journal

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Core Ideas Early‐season biomass is conventionally phenotyped by subjective visual estimates. RGB image data are highly predictive of biomass in hairy vetch breeding plots. Lidar and RGB image data can be combined to accurately predict sward biomass. Remote sensing could increase genetic gain potential for biomass in cover crops. Hairy vetch (Vicia villosa Roth) is an annual legume grown as a forage and cover crop. To improve cover crop function, traits such as biomass production are of high interest for cover crop breeders. However, direct phenotypic methods for biomass production are destructive. Breeders have thus relied on subjective, visual scoring of biomass, which is generally correlative but not quantitative or absolute. We evaluated two low‐cost remote sensing tools, lidar and red–green–blue (RGB) image analysis, for their potential to predict biomass in vivo. We evaluated these tools in two common forage breeding scenarios, spaced‐plant and sward‐plot nurseries, at three Minnesota locations following the winter of 2016–2017. Ground cover from RGB image binarization had a significant and linear relationship with aboveground biomass in spaced plants (R2 = 0.93) and sward plots (R2 = 0.89). However, once the image area from sward plots became saturated with vegetative pixels, a near‐exponential relationship would occur. Because of the prostrate growth habit of hairy vetch, RGB image analysis was more appropriate at lower plant densities, such as spaced‐plant nurseries. Conversely, the dimensionality of lidar sensing gave it greater predictive ability at higher plant densities where RGB analysis could not detect vertical increases in biomass. Lidar measures of sward‐plot height were also linearly and strongly related to dry‐matter biomass in sward plots (R2 = 0.80). When we combined RGB and lidar data to predict sward‐plot biomass in a multiple mixed‐effect regression model, we were able to explain more biomass variation than with the use of either phenotypic tool as a single predictor (R2 = 0.94).

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Using LIDAR for Forage Yield Measurement of Perennial Ryegrass (Lolium perenne L.) Field Plots

Cited by 9 publications

References 2 publications

Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass

Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass

Rate of Genetic Gain for Persistence to Grazing and Dry Matter Yield in White Clover across 90 Years of Cultivar Development

Lidar and RGB Image Analysis to Predict Hairy Vetch Biomass in Breeding Nurseries

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