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Key message <p>Enhancing the efficiency and precision of breeding programs necessitates the implementation of “high-throughput” phenotyping. By employing various sensors for rapid and frequent measurements, we can gather extensive datasets crucial for conventional breeding efforts. This approach not only holds promise for improving forest production but also for evaluating emerging challenges such as fungal infestations and drought damage. Our research demonstrates the efficiency of utilizing height data derived from LiDAR analysis to identify superior genotypes within the Scots pine breeding program, aimed at enhancing volume production.</p> Context <p>Cost-effective ‘high-throughput’ phenotyping methods would be highly valuable in both conventional and advanced molecular tree breeding programs. Light Detection and Ranging (LiDAR) systems installed on unmanned aerial vehicles (UAVs, drones) have highly promising potential for such purposes as they enable rapid acquisition of relevant data.</p> Aims <p>To assess their current capacity, we have compared heights from conventional and LiDAR-based measurements in a Scots pine clonal/progeny trial (9&#xa0;years old) in central Sweden. We have also compared effects of using them to obtain relationships between phenotypic and genetic parameters, and for selection.</p> Methods <p>The study was done in&#xa0;a Scots pine genetic field trial that included clones and seedlings. Mean values and estimation of genetic parameters for height were compared between datasets obtained by conventional measurements and by analysis of LiDAR objects obtained by a drone. The potential influence of the&#xa0;measurement method on genetic selection was quantified.</p> Results <p>The phenotypic correlations between heights obtained with the two methods were very high (≥ 0.9) and so were both the genetic correlations and estimated heritabilities. Selections of the best clones within tested families using the two sets of measurements matched almost perfectly. A wrong clone with a difference in rank of more than one was selected for just one family (of 47). The findings highlight the great potential of the approach for use in breeding practices, as it will allow the collection of vast amounts of accurate data much cheaper than conventional measurements.</p>

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LiDAR-estimated height in a young Scots pine (Pinus sylvestris L.) genetic trial supports high-accuracy early selection for height

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  • 发表日期：

    2025年3月17日

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  • Mateusz Liziniewicz,
  • Curt Almqvist,
  • Andreas Helmersson,
  • Anton Holmström,
  • Liviu Theodor Ene

摘要

Key message

Enhancing the efficiency and precision of breeding programs necessitates the implementation of “high-throughput” phenotyping. By employing various sensors for rapid and frequent measurements, we can gather extensive datasets crucial for conventional breeding efforts. This approach not only holds promise for improving forest production but also for evaluating emerging challenges such as fungal infestations and drought damage. Our research demonstrates the efficiency of utilizing height data derived from LiDAR analysis to identify superior genotypes within the Scots pine breeding program, aimed at enhancing volume production.

Context

Cost-effective ‘high-throughput’ phenotyping methods would be highly valuable in both conventional and advanced molecular tree breeding programs. Light Detection and Ranging (LiDAR) systems installed on unmanned aerial vehicles (UAVs, drones) have highly promising potential for such purposes as they enable rapid acquisition of relevant data.

Aims

To assess their current capacity, we have compared heights from conventional and LiDAR-based measurements in a Scots pine clonal/progeny trial (9 years old) in central Sweden. We have also compared effects of using them to obtain relationships between phenotypic and genetic parameters, and for selection.

Methods

The study was done in a Scots pine genetic field trial that included clones and seedlings. Mean values and estimation of genetic parameters for height were compared between datasets obtained by conventional measurements and by analysis of LiDAR objects obtained by a drone. The potential influence of the measurement method on genetic selection was quantified.

Results

The phenotypic correlations between heights obtained with the two methods were very high (≥ 0.9) and so were both the genetic correlations and estimated heritabilities. Selections of the best clones within tested families using the two sets of measurements matched almost perfectly. A wrong clone with a difference in rank of more than one was selected for just one family (of 47). The findings highlight the great potential of the approach for use in breeding practices, as it will allow the collection of vast amounts of accurate data much cheaper than conventional measurements.