Hovenia acerba is a precious medicinal and edible tree. We assessed the genetic variation of H. acerba quality traits and conducted a comprehensive germplasm resource evaluation to provide a theoretical basis for breeding edible, medicinal, and edible/medicine combination varieties. We evaluated 31 H. acerba germplasm resources, including 12 infructescence and 8 fruit quality traits using correlation, principal component, and cluster analyses. The results showed that there were significant differences in all quality traits, with an average coefficient of variation greater than 0.20, an average genetic diversity greater than 1.80, and an average repeatability greater than 0.90. The average genetic variation and repeatability of quality traits in infructescence were higher than fruit. Infructescence K, Ca, Mn, Mg, and reducing sugar contents are important indicators in evaluating infructescence and fruit quality traits, and infructescence K, Mg, and reducing sugar contents are also quality innovation indices of H. acerba germplasms. Tannin, protein, and soluble sugar were the most suitable quality components for screening, followed by reducing sugar, starch, fat, total saponins, and total flavones. According to principal component factor scores and cluster analysis results, specific genotypes were selected as breeding materials for infructescence protein, tannin, flavone, reductive sugar, fruit tannin, fat, flavonoid, saponin, protein, and starch. The correlation analysis with environmental factors showed that the total amount of applied water could influence H. acerba infructescence and fruit quality. In conclusion, the variability of H. acerba germplasm resources was rich, and selection potential is large, which is beneficial to germplasm quality innovation and breeding.

Plant aging and senescence are key determinants of lifespan, yet the coordinated changes in endogenous polyamines and hormones during long-lived tree aging remain largely unclear. Cinnamomum camphora exhibits sequential senescence from leaves to trunk, with leaf physiology shifting toward senescence around 450 years. This study aimed to clarify the patterns of polyamines and hormones across a wide age gradient (10–810 years) and their associations with aging in ancient C. camphora. Newly expanded leaves were analyzed using curve fitting, correlation, regression, and path analysis. Tree age significantly influenced most polyamine and hormone indices, except for indole-3-acetic acid (IAA), abscisic acid (ABA), and salicylate glucoside (SAG). Spermidine (Spd) and gibberellic acid (GA) were negatively correlated with aging, and cytokinin (CK) and cadaverine (Cad) were positively correlated with aging. Free salicylic acid (SAF) was closely related to the senescence transition point. Polyamines and hormones interacted strongly. Cad was positively correlated with CK, and Spd was positively correlated with GA. A model combining Spd, GA/ABA, and CK/GA reliably predicted ancient tree age. Overall, Spd and CK exhibited the strongest negative and positive correlations with aging, respectively, providing insights into the physiological regulation of longevity in ancient trees.

Genotype-to-phenotype (G2P) prediction is the central mission of modern plant breeding. Genomic data accumulation has outpaced the ability of current frameworks to predict field performance across variable environments. Statistical genomic models deliver computationally efficient breeding values but lose accuracy when applied outside training environments. Artificial intelligence (AI) extracts high-dimensional patterns at very high throughput but extrapolates unreliably under novel stress conditions. Conventional crop models encode physiological rules across environments but treat the canopy as a homogeneous layer and cannot resolve architectural variation. Functional–structural plant models simulate three-dimensional organ-level morphogenesis at the specific biological scale where genetic variation in plant architecture shapes resource capture and yield. The four paradigms address distinct layers of the G2P problem and are therefore complementary rather than substitutable. Here we propose a plant model-centred integration in which functional–structural plant models are the bidirectional hub linking upstream genomic parameter estimation, lateral multi-omics constraints, AI-based pattern recognition, and downstream canopy and field upscaling. We outline the structural rationale, review graded empirical evidence, and identify the technical conditions and benchmark trial needed to advance this framework towards operational precision breeding.