Plant Methods最新文献

{"title":"Vapor pressure deficit control and mechanical vibration techniques to induce self-pollination in strawberry flowers.","authors":"Hyein Lee, Meiyan Cui, Byungkwan Lee, Jeesang Myung, Jaewook Shin, Changhoo Chun","doi":"10.1186/s13007-025-01343-2","DOIUrl":"10.1186/s13007-025-01343-2","url":null,"abstract":"<p><strong>Background: </strong>Pollination strategies to supplement or replace insect pollinators are needed to produce marketable strawberry fruits in indoor vertical farms. To ensure the self-pollination of strawberry flowers, anther dehiscence, and pollen attachment were investigated under different vapor pressure deficit (VPD) conditions and external mechanical wave vibrations.</p><p><strong>Results: </strong>The proportion of dehisced anthers was examined under VPDs of 2.06, 1.58, and 0.33 kPa, and the projected area of pollen clumps was assessed under VPDs of 2.06 and 0.33 kPa. After exposing flowers to a VPD of 2.06 kPa, vibrations with various frequency (Hz) and root mean square acceleration (m s^-2) combinations were used to evaluate pollination effectiveness. The anthers underwent complete dehiscence at VPDs of 2.06, 1.58, and 0.33 kPa. The pollen clump ejection index was highest at a VPD of 2.06 kPa. Pollen clump detachment was effective at 800 Hz with 40 m s^-2, while pollen attachment to the stigma was most effective at 100 Hz with 30 and 40 m s^-2.</p><p><strong>Conclusions: </strong>These findings demonstrate that high VPD promotes anther dehiscence timing and facilitates pollen clump formation, while specific vibration frequencies with high acceleration optimize pollen detachment and stigma attachment, offering an effective strategy for controlled strawberry pollination in vertical farming.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"28"},"PeriodicalIF":4.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143503398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method for phenotyping lettuce volume and structure from 3D images.","authors":"Victor Bloch, Alexey Shapiguzov, Titta Kotilainen, Matti Pastell","doi":"10.1186/s13007-025-01347-y","DOIUrl":"10.1186/s13007-025-01347-y","url":null,"abstract":"<p><p>Monitoring plant growth is crucial for effective crop management, and using color and depth (RGBD) cameras to model lettuce has emerged as one of the most convenient and non-invasive methods. In recent years, deep learning techniques, particularly neural networks, have become popular for estimating lettuce fresh weight. However, these models are typically specific to particular datasets, lack domain adaptation, and are often limited by the availability of open-access datasets. In this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce. This new approach was compared to existing methods that reconstruct surfaces from point clouds, such as Ball Pivoting and Alpha Shapes. The proposed method creates a tight hull around the plant's point cloud, preserving high detail of the rosette structure while filling in surface holes in areas not visible to 3D cameras. Using a linear regression model, we estimated fresh weight for this dataset, achieving a root mean square error (RMSE) of 18.2 g when using only the estimated plant volume, and 17.3 g when both volume and geometric features were included. Additionally, we introduced new geometric features that characterize leaf density, which could be useful for breeding applications. A dataset of 402 point clouds of lettuce plants, captured before harvest, was compiled using one top-down and three side-view 3D cameras.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"27"},"PeriodicalIF":4.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of chlorophyll content in rice canopy leaves using 3D radiative transfer modeling and unmanned aerial hyperspectral images.","authors":"Honggang Zhang, Dan Zhao, Zhonghui Guo, Sien Guo, Quchi Bai, Huini Cao, Shuai Feng, Fenghua Yu, Tongyu Xu","doi":"10.1186/s13007-025-01346-z","DOIUrl":"10.1186/s13007-025-01346-z","url":null,"abstract":"<p><strong>Background: </strong>The chlorophyll content has a strong influence on plant photosynthesis and crop growth and is a key factor for understanding the functioning of farming systems. Therefore, the accurate estimation of chlorophyll content (Cab) is important in precision agriculture. In this study, the three-dimensional radiative transfer model (3DRTM) was used to calculate the radiative transfer and simulate the canopy hyperspectral image of a rice field. Then, a physically based joint inversion model was developed using an iterative optimization approach with penalty function and a priori information constraints to estimate chlorophyll content efficiently and accurately from the hyperspectral curve of a rice canopy.</p><p><strong>Results: </strong>The inversion model demonstrates that the sparrow search algorithm (SSA) can estimate rice Cab, providing relatively satisfactory Cab estimation outcomes. In addition, the inversion of the SSA method with or without carotenoids content (Car) constraints was compared, and compared to the inversion of Cab without Car constraints [coefficient of determination (R²) = 0.690, root mean square error (RMSE) = 7.677 µg/cm²)], the SSA with constraints was more accurate (R² = 0.812, RMSE = 5.413 µg/cm²).</p><p><strong>Conclusions: </strong>The Large-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes (LESS) exhibited higher accuracy in estimating the rice Cab compared to the 1DRTM PROSAIL model, which is constituted by coupling the Leaf Optical Properties Spectra (PROSPECT) model and the Scattering by Arbitrarily Inclined Leaves (SAIL) model. The 3DRTM is conducive to precisely estimating Cab from the hyperspectral data of the rice canopy, thereby holding great potential for precise nutrient management in rice cultivation.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"26"},"PeriodicalIF":4.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11849381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the potential of microscopic hyperspectral, Raman, and LIBS for nondestructive quality assessment of diverse rice samples.","authors":"Jing Guo, Sijia Jiang, Bingjie Lu, Wei Zhang, Yinyin Zhang, Xiao Hu, Wanneng Yang, Hui Feng, Liang Xu","doi":"10.1186/s13007-025-01345-0","DOIUrl":"10.1186/s13007-025-01345-0","url":null,"abstract":"<p><p>The enhancement of rice quality stands as a pivotal focus in crop breeding research, with spectral analysis-based non-destructive quality assessment emerging as a widely adopted tool in agriculture. A prevalent trend in this field prioritizes the assessment of effectiveness of individual spectral technologies while overlooking the influence of sample type on spectral quality testing outcomes. Thus, the present study employed Microscopic Hyperspectral Imaging, Raman, and Laser-Induced Breakdown Spectroscopy (LIBS) to acquire spectral data from paddy rice, brown rice, polished rice, and rice flour. The data were then modeled and analyzed with respect to the amylopectin and protein contents of the rice samples via regression methods. Correlation analysis revealed varying degrees of correlation, both positive and negative, among the three spectral techniques and the analytes of interest. LIBS and Raman spectroscopy demonstrated stronger correlations with the analytes compared to microscopic hyperspectral imaging. Based on the selected correlation variables, feature screening and regression modeling were conducted. The modeling results indicated that microscopic hyperspectral data modeling yielded the lowest coefficient of determination of R² = 0.2, followed by Raman data modeling result was higher than it, which was about 0.5. The modeling effect of polished rice is the best. LIBS data modeling performed best, with a coefficient of determination of 0.6. The influence of different sample types on the modeling results was less than that of Raman spectroscopy, and modeling results of grains were better. The feature matching analysis of Raman and libs spectroscopy techniques showed that there were spectral variables that could match amylopectin and protein in the features obtained by multiple modeling statistics, but some modeling variables failed to match. LIBS matched more variables than Raman. These findings provide valuable insights into the application effectiveness of different spectral techniques in detecting rice contents across diverse sample types.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"25"},"PeriodicalIF":4.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Production of HSVd- and PPV-free apricot cultivars by in vitro thermotherapy followed by meristem culture.","authors":"C Pérez-Caselles, L Burgos, E Yelo, L Faize, N Alburquerque","doi":"10.1186/s13007-025-01344-1","DOIUrl":"10.1186/s13007-025-01344-1","url":null,"abstract":"<p><strong>Background: </strong>The production of virus-free apricots (Prunus armeniaca L.) is essential for controlling viral diseases, exchanging breeding materials without the risk of spreading new diseases, and preserving plant germplasm. Plum pox virus (PPV) is the most devastating disease of the Prunus genus and Hop stunt viroid (HSVd) is prevalent in most apricot-growing regions. It was evaluated whether thermotherapy, etiolation, or a combination of both followed by meristem culture could effectively eliminate PPV and HSVd from 'Canino' and 'Mirlo Rojo' apricot cultivars in vitro.</p><p><strong>Results: </strong>In the thermotherapy treatments, shoots were exposed to 38ºC and 32ºC, alternating every four hours, for 30, 35, 40, and 45 days. Before this, shoots were acclimated to heat for one day at 28ºC and two days at 30ºC. Etiolation experiments consisted of eight weeks of culture in dark conditions. A combination of 45 days of thermotherapy, as described previously, and etiolation was also performed. At the end of each treatment, 1.5 mm meristems were cultured, and developed as potential independent pathogen-free lines. The presence or absence of pathogens was analysed by RT-PCR. The 45 days of thermotherapy and the combined thermotherapy and etiolation treatments resulted in the highest percentages of PPV-free plants (66.7 and 75.0%, respectively). At least 40 days of thermotherapy were required to obtain HSVd-free plants, although the best efficiency was achieved at 45 days (22.7%).</p><p><strong>Conclusions: </strong>In this study, we have developed an effective in vitro thermotherapy protocol that eliminates PPV and HSVd from apricot cultivars. This is the first report where a thermotherapy protocol eliminates HSVd in Prunus species.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"23"},"PeriodicalIF":4.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11843746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-fold red excess (TREx): a simple and novel digital color index that enables non-invasive real-time monitoring of green-leaved as well as anthocyanin-rich crops.","authors":"Avinash Agarwal, Filipe de Jesus Colwell, Viviana Andrea Correa Galvis, Tom R Hill, Neil Boonham, Ankush Prashar","doi":"10.1186/s13007-025-01339-y","DOIUrl":"10.1186/s13007-025-01339-y","url":null,"abstract":"<p><strong>Background: </strong>Digital color indices provide a reliable means for assessing plant status by enabling real-time estimation of chlorophyll (Chl) content, and are thus adopted widely for crop monitoring. However, as all prevalent leaf color indices used for this purpose have been developed using green-leaved plants, they do not perform reliably for anthocyanin (Anth)-rich red-leaved varieties. Hence, the present study investigates digital color indices for six types of leafy vegetables with different levels of Anth to identify congruent trends that could be implemented universally for non-invasive crop monitoring irrespective of species and leaf Anth content. For this, datasets from three digital color spaces, viz., RGB (Red, Green, Blue), HSV (Hue, Saturation, Value), and L*a*b* (Lightness, Redness-greenness, Yellowness-blueness), as well as various derived plant color indices were compared with Anth/Chl ratio and SPAD Chl meter readings of n = 320 leaf samples.</p><p><strong>Results: </strong>Logarithmic decline of G/R, G-minus-R, and Augmented Green-Red Index (AGRI) with increasing Anth/Chl ratio (R² > 0.8) revealed that relative Anth content affected digital color profile markedly by shifting the greenness-redness balance until the Anth/Chl ratio reached a certain threshold. Further, while most digital color features and indices presented abrupt shifts between Anth-rich and green-leaved samples, the proposed color index Two-fold Red Excess (TREx) did not exhibit any deviation due to leaf Anth content and showed better correlation with SPAD readings (R² = 0.855) than all other color features and vegetation indices.</p><p><strong>Conclusion: </strong>The present study provides the first in-depth assessment of variations in RGB-based digital color indices due to high leaf Anth contents, and uses the data for Anth-rich as well as green-leaved crops belonging to different species to formulate a universal digital color index TREx that can be used as a reliable alternative to handheld Chl meters for rapid high-throughput monitoring of green-leaved as well as red-leaved crops.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"24"},"PeriodicalIF":4.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11843946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks.","authors":"Joshua Larsen, Jeffrey Dunne, Robert Austin, Cassondra Newman, Michael Kudenov","doi":"10.1186/s13007-024-01316-x","DOIUrl":"10.1186/s13007-024-01316-x","url":null,"abstract":"<p><strong>Background: </strong>Late and early leaf spot in peanuts is a foliar disease contributing to a significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity of leaf spot infection. The objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field. This was accomplished using image capture protocols and segmentation neural networks that extracted lesion areas from plot-level images to determine an appropriate rating for infection severity.</p><p><strong>Results: </strong>The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1-9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. We were able to demonstrate automated scoring, as compared to expert visual scoring, with a root mean square error of 0.996 visual scores, on individual images (one image per plot), and 0.800 visual scores when three images were captured of each plot.</p><p><strong>Conclusion: </strong>Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"22"},"PeriodicalIF":4.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11841321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAS-based MT-YOLO model for detecting missed tassels in hybrid maize detasseling.","authors":"Jiangtao Qi, Chenchen Ding, Ruirui Zhang, Yuxin Xie, Longlong Li, Weirong Zhang, Liping Chen","doi":"10.1186/s13007-025-01341-4","DOIUrl":"10.1186/s13007-025-01341-4","url":null,"abstract":"<p><p>Accurate detection of missed tassels is crucial for maintaining the purity of hybrid maize seed production. This study introduces the MT-YOLO model, designed to replace or assist manual detection by leveraging deep learning and unmanned aerial systems (UASs). A comprehensive dataset was constructed, informed by an analysis of the agronomic characteristics of missed tassels during the detasseling period, including factors such as tassel visibility, plant height variability, and tassel development stages. The dataset captures diverse tassel images under varying lighting conditions, planting densities, and growth stages, with special attention to early tasseling stages when tassels are partially wrapped in leaves-a critical yet underexplored challenge for accurate detasseling. The MT-YOLO model demonstrates significant improvements in detection metrics, achieving an average precision (AP) of 93.1%, precision of 93.3%, recall of 91.6%, and an F1-score of 92.4%, outperforming Faster R-CNN, SSD, and various YOLO models. Compared to the baseline YOLO v5s, the MT-YOLO model increased recall by 1.1%, precision by 4.9%, and F1-score by 3.0%, while maintaining a detection speed of 124 fps. Field tests further validated its robustness, achieving a mean missed rate of 9.1%. These results highlight the potential of MT-YOLO as a reliable and efficient solution for enhancing detasseling efficiency in hybrid maize seed production.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"21"},"PeriodicalIF":4.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of YOLO-based sorghum spike identification detection models and monitoring at the flowering stage.","authors":"Song Zhang, Yehua Yang, Lei Tu, Tianling Fu, Shenxi Chen, Fulang Cen, Sanwei Yang, Quanzhi Zhao, Zhenran Gao, Tengbing He","doi":"10.1186/s13007-025-01338-z","DOIUrl":"10.1186/s13007-025-01338-z","url":null,"abstract":"<p><p>Monitoring sorghum during the flowering stage is essential for effective fertilization management and improving yield quality, with spike identification serving as the core component of this process. Factors such as varying heights and weather conditions significantly influence the accuracy of sorghum spike detection models, and few comparative studies exist on model performance under different conditions. YOLO (You Only Look Once) is a deep learning object detection algorithm. In this research, images of sorghum during the flowering stage were captured at two heights (15 m and 30 m) in 2023 via a UAV and utilized to train and evaluate variants of YOLOv5, YOLOv8, YOLOv9, and YOLOv10. This investigation aimed to assess the impact of dataset size on model accuracy and predict sorghum flowering stages. The results indicated that YOLOv5, YOLOv8, YOLOv9, and YOLOv10 achieved mAP@50 values of 0.971, 0.968, 0.967, and 0.965, respectively, with dataset sizes ranging from 200 to 350. YOLOv8m performed best on 15_sunny and 15_cloudy clouds and, overall, exhibited superior adaptability and generalizability. The predictions of the flowering stage using YOLOv8m were more accurate at heights between 12 and 15 m, with R² values ranging from 0.88 to 0.957 and rRMSE values between 0.111 and 0.396. This research addresses a significant gap in the comparative evaluation of models for sorghum spike detection, identifies YOLOv8m as the most effective model, and advances flowering stage monitoring. These findings provide theoretical and technical foundations for the application of YOLO models in sorghum spike detection and flowering stage monitoring. These findings provide a technical means for the timely and efficient management of sorghum flowering.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"20"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11834282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating eucalypt clone selection pipeline via cloned progeny trials and molecular data.","authors":"Thiago Romanos Benatti, Filipe Manoel Ferreira, Rodolfo Manoel Lemes da Costa, Mario Luiz Teixeira de Moraes, Aurélio Mendes Aguiar, Donizete da Costa Dias, José Wilacildo de Matos, Aline Cristina Miranda Fernandes, Mateus Chagas Andrade, Leandro de Siqueira, Itaraju Junior Baracuhy Brum, André Vieira do Nascimento, Yuri Tani Utsunomiya, José Fernando Garcia, Evandro Vagner Tambarussi","doi":"10.1186/s13007-025-01342-3","DOIUrl":"10.1186/s13007-025-01342-3","url":null,"abstract":"<p><p>The high productivity of Eucalyptus spp. forest plantations is mainly due to advances in silvicultural techniques and genetic improvement associated with the potential that many species of the genus have for vegetative propagation. However, long reproduction cycles for forest species pose significant challenges for genetic progress via traditional breeding programs. Furthermore, there is often poor correlation between individual (seedling) performance in initial (progeny trials) and final (clonal trials) stages of the breeding program. In this scenario, cloned progeny trials (CPT) offer an alternative to accelerate the eucalypt clone selection pipeline, combining progeny and clonal trials in a single experiment. CPT has the potential to speed up the evaluation process and increase its efficiency by developing new commercial genotypes that were tested as clones from the initial stage of the breeding program. Thus, this study aims to assess the potential of CPT to accelerate eucalypt clone selection programs by estimating the genetic parameters, analyzing responses to selection, and predicting the adequate number of ramets to be used in CPT of Eucalyptus urophylla x Eucalyptus grandis. The results show that when the number of ramets per progeny was decreased from five to one there was a reduction in the estimates of broad-sense heritability and accuracy. However, three ramets/progeny can be used without significant reductions in these estimates. CPT accelerates clonal selection by combining progeny and clonal trial methodologies, enabling an evaluation of performance as both progeny and clone. This capacity is very important for vegetatively propagated crop species such as Eucalyptus. Integrating CPT with SNP markers can offer an alternative to shorten the tree clone selection pipeline, better estimate and decompose the genetic variance components, and improve the correlation between initial and final performance for selected genotypes. This study confirms the potential of CPT to improve selection processes and accelerate genetic gains in the eucalypt clone selection pipeline.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":"21 1","pages":"19"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}