Inamori, M. | bioRxiv Science

bioRxiv2024

Quantifying Forest Biomass and Genetic Contribution using Light Detection and Ranging

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selections based on these traits. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selections based on these traits using 60-year-old larch as the study material. Unmanned aerial vehicle (UAV) and backpack LiDAR were compared against hand-measured values. The accuracy of DBH estimations using backpack LiDAR resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with UAV LiDAR was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was found to be higher for backpack LiDAR than for UAV LiDAR and even exceeded that of hand measurements. Comparisons of the accuracy of timber volume selections based on the measured traits demonstrated comparable performances between the backpack and UAV LiDAR. Overall, these findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements.

genetics↗

bioRxiv2024

Cross Potential Selection: A Proposal for Optimizing Crossing Combinations in Recurrent Selection Based on the Ability of Future Inbred Lines

In plant breeding programs, rapid production of novel varieties is highly desirable. Genomic selection allows the selection of superior individuals based on genomic estimated breeding values. However, it is worth noting that superior individuals may not always be superior parents. The choice of the crossing pair significantly influences the genotypic value of the resulting progeny. This study introduced a new strategy for selecting crossing pairs, termed Cross Potential Selection (CPS), designed to expedite the production of novel varieties. The CPS assesses the potential of each crossing pair to generate a novel variety. It considers the segregation of each crossing pair and computes the expected genotypic values of the topperforming individuals, assuming that the progeny distribution of genotypic values follows a normal distribution. We simulated a 10-year breeding program to compare CPS with three other selection strategies. CPS consistently demonstrated the highest genetic improvements among the four strategies in early cycles. In particular, during the middle cycles of the breeding program, CPS exhibited the highest genetic improvement of 73% of the 300 independent breeding simulations. In a long-term breeding scheme, some progeny distributions of genotypic values may deviate from normal distribution, affecting the efficiency of CPS. Nevertheless, compared with the other three strategies, CPS achieved significant short-term genetic improvements. In conclusion, CPS holds substantial promise for enhancing the efficiency of plant breeding programs. Article SummaryThis study introduces a novel plant breeding strategy termed Cross Potential Selection (CPS), which was designed to expedite the production of novel varieties. The CPS evaluates the potential of each crossing pair for the target generation. Through comparative breeding simulations, CPS demonstrated superior performance over the three alternative breeding strategies, particularly in the early cycles. These findings suggest that CPS holds significant promise for enhancing plant breeding efficiency.

genetics↗