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 has introduced a new crossing strategy, termed cross potential selection (CPS), designed to expedite the production of novel varieties of inbred crops. CPS integrates fast recurrent selection and usefulness criterion to generate novel varieties. It considers the segregation of each crossing pair and computes the expected genotypic values of the top-performing individuals, assuming that the progeny distribution of genotypic values follows a normal distribution. It does not consider genetic diversity and focuses only on producing a novel variety as soon as possible. We simulated a 30-year breeding program in two scenarios, low heritability (h² = 0.3) and high heritability (h² = 0.6), to compare CPS with two other selection strategies. CPS consistently demonstrated the highest genetic gains among the three strategies in early cycles. In the 3^rd year of the breeding program with a high heritability (h² = 0.6), CPS exhibited the highest genetic gains, 138 times that of 300 independent breeding simulations. Regarding long-term improvement, the other selection strategies outperformed CPS. Nevertheless, compared with the other two strategies, CPS achieved significant short-term genetic improvements. CPS is a suitable breeding strategy for the rapid production of varieties within limited time and cost.

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 the 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 selection based on them. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selection based on these traits, using 60-year-old larch as the study material. Unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) were compared against hand-measured values. The accuracy of DBH estimations using BLS 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 ULS was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was higher with BLS than with ULS and even exceeded that of hand measurements. Comparisons of timber volume selection accuracy based on the measured traits demonstrated comparable performance between BLS and ULS. 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.