Red walnuts have been widely studied because of their strong antioxidant activity and ornamental value. However, research on the mechanism of anthocyanin biosynthesis in walnuts remains in the initial stage. The regulatory mechanism of TT2-type R2R3-MYB transcription factors in anthocyanin biosynthesis in walnuts is also unclear. Therefore, this study used ‘D2-1’ and ‘Jinghong 1’ walnuts as plant materials. The testa of ‘Jinghong 1’ was red, and its anthocyanin content was significantly higher than that of ‘D2-1’, mainly composed of cyanidin-3-O-arabinoside, cyanidin-3-O-galactoside, and cyanidin-3-O-glucoside. Differentially expressed genes between ‘D2-1’ and ‘Jinghong 1’ testa were enriched in phenylpropanoid biosynthesis and flavonoid biosynthesis. Next, this study identified a TT2-type R2R3-MYB transcription factor JrMYB1L, which was involved in regulating the anthocyanin biosynthesis in the testa of ‘Jinghong 1’. The overexpression of JrMYB1L could promote anthocyanin accumulation in walnut leaves and activate the expression of JrCHS, JrCHI, JrF3H, JrDFR, JrANS, JrUFGT, JrLAR, and JrANR. In addition, yeast two-hybrid results proved that JrMYB1L, JrbHLH42, and JrWD40 proteins could interact with each other. The results of yeast one-hybrid and dual-luciferase assays indicated that JrMYB1L could activate the expression of JrCHS and JrUFGT by binding to their promoters. Based on the above results, this study proposed a possible regulatory mechanism. JrMYB1L activated the expression of JrCHS and JrUFGT in the form of JrMYB1L-JrbHLH42-JrWD40 complex, thereby promoting anthocyanin accumulation in the testa of ‘Jinghong 1’. In summary, this study lays a theoretical foundation for revealing the regulatory mechanism of anthocyanin biosynthesis in red walnut and contributes to the breeding of new varieties of red walnuts with more edible and ornamental value.

Walnut is one of four major nuts in the world. China has abundant walnut germplasm resources, but there are still shortcomings in the identification of germplasm resources. This study used different walnut varieties as materials and developed 14 high-quality SSR molecular markers from 60 pairs of primers based on genome re-sequencing results. This study analyzed the genetic diversity of Chinese walnut genetic resources using 14 selected SSR markers. A total of 64 alleles were detected in 47 walnut resources, with an average of 4.571 alleles per locus. The variation range of polymorphism information content was 0.096~0.711, with an average value of 0.422. Cluster analysis, population genetic structure, and principal coordinate analysis divided 47 walnut resources into ordinary walnuts, Juglans hopeiensis, and Liaoyi 1. In addition, core SSR markers (Jr45, Jr40, Jr29, Jr35, and Jr11) were selected from 14 SSR markers, which were sufficient to distinguish 47 walnut resources. At the same time, 47 unique molecular fingerprints of walnuts were constructed using these core SSR markers. This study provides strong scientific support for rapid and efficient identification, germplasm innovation, and a variety of property protection of walnut germplasm.

Worldwide, a huge amount of solid fermented waste is produced every year during mushroom production. The rational utilization of spent mushroom substrate (SMS) is conducive to environmental protection and the sustainable development of agriculture. The aims of this work were to analyze the effects of the SMS mulching thickness on the soil environment, weed suppression, leaf nutrients, and nut traits in a hazelnut plantation and provide a theoretical basis for the scientific and reasonable utilization of SMS. An SMS mulching experiment with four mulching thickness treatments (MT1: 5 cm, MT2: 10 cm, MT3: 15 cm, and CK: 0 cm) was conducted in a semi-arid area of North China in 2019. The soil properties, weed control efficiency, leaf nutrient content, and nut yield and quality characteristics of these treatments were all measured and analyzed. The results showed that (1) the average soil temperatures of MT2 and MT3 at different soil depths were 0.73–1.78 °C and 1.18–2.50 °C lower than when no mulch was used in warm weather and 0.67–1.05 °C and 0.99–1.56 °C higher than when no mulch was used in cold weather. The average soil moisture content of MT1, MT2, and MT3 at different soil depths was 6.27–8.13%, 10.66–17.23%, and 7.26–12.81% higher than that of CK, respectively. There were no significant differences in the soil bulk density or porosity among the four treatments. The average soil nutrient contents (soil organic matter and available N, P, and K) of MT1, MT2, and MT3 were 7.05–15.23%, 14.90–38.93%, and 17.42–40.11% higher than those of CK, respectively. Soil enzymatic activities of these treatments followed the order MT3 ≈ MT2 > MT1 > CK. (2) MT2 and MT3 both had high levels of weed control efficiency: 55.66–92.93% and 70.43–97.90%, respectively. (3) The leaf N, P, K, and SPAD increased significantly under MT2 and MT3 by about 10% compared with the CK. (4) MT2 effectively increased the nut size, nut and kernel mass, and crude fat content of the nut (CFC). In general, the short-term field experiment clearly showed that SMS is a superior mulching material for improving the soil environment and plant growth, and 10 cm thickness is suitable for mulching practice.

To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang ‘Wen 185’ walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R² and RPD compared to the other individual models under the present dataset, achieving a validation R² of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R² of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction.

Walnut (Juglans regia) is an economically significant woody oil tree species widely cultivated in China. However, its production is increasingly threatened by extreme low-temperature events, such as unseasonal frosts and late-spring cold. Salicylic acid (SA) is a key phytohormone known to enhance cold tolerance in plants, yet its underlying mechanism in walnut remains unclear. In this study, we present the first integrated analysis combining physiological measurements, transcriptomics, and metabolomics to investigate how exogenous SA improves cold tolerance in walnut leaves. Our results showed that SA treatment significantly increased the accumulation of soluble sugars, chlorophyll, and proline, enhanced peroxidase (POD) activity, and reduced malondialdehyde (MDA) levels under cold stress. Multi-omics analysis revealed that SA modulated the expression of genes involved in multiple hormone signaling pathways, including those of SA, auxin, jasmonic acid, and abscisic acid, and altered corresponding hormone levels. Notably, carbohydrate metabolism emerged as a central pathway mediating SA-induced cold adaptation. Weighted gene co-expression network analysis (WGCNA) further identified several core candidate genes, such as JrTGA, JrPP2C, JrTPS, and JrBAM, which may play key roles in this process. Collectively, this study provides the first multi-omics perspective on the regulatory network underlying SA-enhanced cold tolerance in walnut. These findings offer both a theoretical and technical foundation for applying SA in cold-resistant walnut cultivation and contribute to the development of stress-resilient production strategies.