Chinese Academy of Forestry

Macadamia is a high-quality forest-food tree species and has now become one of the pillar industries in economic forestry in Southwest China. Understanding the characteristics of incompatibility in pairwise combinations of female and male parents, and further enriching the research cases of cultivars will help screen more high-quality parental hybrid combinations, thereby providing a basis for increasing yields. In our study, artificial pollination was performed on 12 reciprocal cross combinations of four cultivars: HAES 344, Own Choice, Hinde, and Guang 11. Fruit set, pollen germination, pollen tube growth, and embryo development were investigated. We found that the average number of fruit per inflorescence of the pollination combination ‘HAES 344’ ♂ × ‘Own Choice’ ♀ at the fruit maturity stage was 5.13 ± 1.88, which was significantly greater than that of other pollination combinations. In contrast, ‘Own Choice’ ♂ × ‘HAES 344’ ♀, ‘Own Choice’ ♂ × Guang 11’ ♀, and the reciprocal crosses between ‘Hinde’ and ‘Guang 11’ exhibited no effective fruit set under the tested sample size. Moreover, macadamia intercultivar incompatibility involves both gametophytic incompatibility and sporophytic incompatibility stages, manifested as pollen tube growth inhibition and abnormal endosperm development, leading to seed abortion. For the attainment of high yields, it is advisable to avoid random or indiscriminate intercultivar combinations in cultivation.

This study evaluated the effects of red LED (RL), blue LED (BL), and white LED (WL) on the growth, physiological responses, and hormonal regulation of Aquilaria crassna tissue-cultured plantlets. Morphological assessment revealed that both RL and BL treatments reduced growth variation, with RL significantly promoting shoot elongation and secondary root development. Compared to WL, RL also enhanced the rooting rate and aboveground biomass. Analysis of hormones and physiological indicators indicated that RL and BL treatments decreased abscisic acid (ABA), cytokinin (CTK), and malondialdehyde (MDA) contents, while increasing indole-3-acetic acid (IAA), gibberellic acid (GA), soluble sugar levels, and superoxide dismutase (SOD) and catalase (CAT) activities, thereby altering hormone balance and antioxidant system stability. Correlation analysis revealed that light quality was significantly negatively correlated with ABA content, while root development was closely associated with hormonal balance and antioxidant capacity. A comprehensive evaluation using the entropy-weighted TOPSIS method ranked RL as the most favorable light condition for overall growth and development, with a closeness coefficient of 0.71. These findings provide a scientific basis for optimizing light quality management to improve the efficiency and quality of A. crassna tissue culture systems.

The miRNA172-APETALA2 (AP2) regulatory module is a conserved mechanism governing floral development in plants. Disruption of the miR172 target sites in AP2 genes has been shown to be key to the domestication of double flowers in ornamental species. Camellia japonica, a woody ornamental plant with diverse floral forms, serves as an important model for studying double-flower formation. In this study, we characterized two AP2-like transcription factors, CjAP2-1 and CjAP2-2, which possess evolutionarily conserved miR172-binding sites and exhibit broad expression across floral tissues. To investigate the role of the miR172-AP2 module in C. japonica, we identified four members of the miR172 family and demonstrated that miR172 is directly involved in the cleavage of CjAP2-1 and CjAP2-2 transcripts. Through bulked amplicon sequencing of cultivars with diverse floral forms, we uncovered natural variations at the miR172-binding sites of CjAP2-1 and CjAP2-2, which can potentially disrupt miR172-mediated mRNA cleavage. We showed that two dinucleotide mutations (CjAP2-1-mut5 and CjAP2-1-mut9) significantly reduced the miR172-mediated repression of CjAP2-1 transcripts. Functional analysis in Arabidopsis revealed that overexpression of the CjAP2-1-mut5 variant caused significant floral abnormalities, including ectopic formation of reproductive organs, loss of floral determinacy, and fusion of floral organs. Further analysis of downstream genes indicated that key regulators of floral homeotic and meristem activity were markedly altered in the transgenic plants. Our findings demonstrate that perturbations in the miR172-AP2 regulatory relationship underlie the formation of double flowers in C. japonica by altering floral meristem determinacy and organ identity.

This paper provides a quantitative forecast of the dynamic impact of the European Union Deforestation Regulation on forest product exports from nine major Asia-Pacific countries. Employing a novel two-stage forecasting framework, we first utilize a Least Absolute Shrinkage and Selection Operator regression model on a high-dimensional dataset of 46 macroeconomic and trade-related predictors to perform automated variable selection for multiple forecast horizons. The selected variables are then used in an Ordinary Least Squares model to generate dynamic forecasts from October 2025 to December 2027. A forward-looking policy shock is simulated by combining a historical proxy based on the European Union Timber Regulation with a calibrated “intensity multiplier” differentiated by country-risk classification. Our projections reveal a multi-phase adjustment process: a sharp, region-wide contraction in the fourth quarter of 2025, followed by a period of significant volatility and supply chain disruption in 2026, and an uneven recovery in 2027. The findings highlight significant heterogeneity. Standard-risk countries (Indonesia, Malaysia) are projected to face greater volatility and suppressed growth trajectories, while some smaller, low-risk nations (the Philippines) may benefit from a substitution effect. The findings provide crucial, evidence-based insights for policymakers and industry stakeholders navigating the implementation of this landmark regulation.

The practical use of agricultural hydrogels remains limited by poor mechanical strength, incompatibility with hydrophobic agrochemicals, and weak environmental responsiveness, although they have advanced in retaining water and delivering nutrients. In this study, a thermoresponsive hydrogel was synthesized, with urea incorporated as a nutrient source, by copolymerizing the hydrophobic monomer N-vinylcaprolactam (NVCL) with the fungicide azoxystrobin (Azo), using amphiphilic disproportionated rosin acid soap (DRAS) as both an emulsifier and a structural modifier. This dual role of DRAS enabled it to not only disperse the hydrophobic components but also regulate the hydrogel’s network architecture, thereby enhancing its mechanical strength, swelling behavior, and controlled release kinetics. Below the lower critical solution temperature (LCST), the hydrogel demonstrated effective soil moisture retention and reduced water evaporation. Upon heating above the LCST, it facilitated the controlled release of urea and Azo compounds. This temperature-responsive behavior allows for the on-demand coordination of water, fertilizer, and pesticide delivery in alignment with crop growth stages and disease prevention needs. Biosafety evaluation revealed that the unloaded hydrogel exhibited low toxicity toward earthworms. In antifungal tests, the drug-loaded hydrogel showed strong inhibitory activity against Fusarium oxysporum f. sp. niveum (FON). Pot experiments further confirmed that the drug-loaded hydrogel effectively suppressed FON infection, increased watermelon biomass, and improved fruit quality. These results highlight the potential of this multifunctional hydrogel as an environmentally sustainable and stimulus-responsive carrier for smart agrochemical delivery in modern agriculture.

IntroductionAccurate estimation of individual tree aboveground biomass (AGB) is essential for tree species selection, carbon accounting, and precision forestry. Unmanned aerial vehicle (UAV) LiDAR provides rapid access to detailed tree structural information, offering a promising tool for high-frequency biomass assessment.MethodsIn this study, a nonlinear mixed-effects (NLME) model integrating UAV LiDAR and field measurements was developed to quantify the influence of genetic heterogeneity and environmental factors on AGB estimation of Catalpa bungei. Data from 2,941 trees across 79 genotypes were collected in Henan Province, including LiDAR-derived tree height (LH), LiDAR-derived crown diameter (LCD), and AGB. By incorporating genotype as a random effect and planting density as a dummy variable, the NLME model significantly outperformed traditional dummy-variable models.ResultsGenotype effects explained significant AGB variation, achieving high accuracy (R²=0.7916, RMSE = 3.7095) and reducing TRE by 23.29% compared to the basic power function model. Leave-one-genotype-out cross-validation confirmed robustness. Calibration with the four largest trees yielded the best performance (TRE = 13.09%), while a simplified scheme using only two trees per genotype maintained high accuracy (TRE = 13.24%), markedly reducing field effort.DiscussionThese results highlight the superiority of NLME AGB models over linear approaches and demonstrate that accounting for genotype effects is critical for reliable biomass estimation. The proposed framework provides an efficient and cost-effective solution for biomass monitoring, tree breeding, carbon sink assessment, and precision forestry.