This study aimed to investigate the effects of applying arbuscular mycorrhizal fungi (AMF) on maize root growth and yield formation under different soil conditions. This study was conducted under sandy soil (S) and saline–alkali soil (Y), with treatments of AMF application (AM) and no AMF application (CK). The root characteristics, yield, and quality of maize were measured. High-throughput sequencing technology was employed to assess the impact of AMF on the soil microbial community structure, and the correlation between soil microbes and soil physicochemical properties was elucidated. The results show that under both sandy and saline–alkali soil conditions, AMF application significantly enhanced maize root growth, yield, grain quality, and soil available nitrogen (AN), available phosphorus (AP), and available potassium (AK) contents compared to the CK treatment. Soil microbial Alpha diversity analysis indicated that AMF application effectively increased soil microbial diversity and richness. Principal coordinate analysis (PCoA) and microbial community structure analysis revealed significant differences in bacterial communities between AM treatment in sandy soil (SAM) and CK in sandy soil (SCK), and significant differences in both bacterial and fungal communities between AM treatment in saline–alkali soil (YAM) and CK in saline–alkali soil (YCK). Furthermore, significant correlations between microbial communities and soil physicochemical properties were found, such as AN, AP, AK, soil salinity (SS), and organic matter (OM) content. AMF application had a greater impact on bacterial communities than on fungal communities. This study demonstrated that the use of AMF as a bio-fungal fertilizer was effective in improving spring maize yields, especially in terms of yield increase and quality stability in sandy and saline soils, thereby contributing to safe and sustainable cropping practices.

In a study aimed at developing a rapid and nondestructive method for testing the quality of strawberries, spectral data from four strawberry varieties at different ripening stages were collected using a geophysical spectrometer, primarily focusing on the 350–1800 nm band. The spectra were preprocessed using Savitzky–Golay (SG) filtering, and characteristic bands were extracted using Pearson correlation coefficient (PCC) analysis. Models for predicting strawberry quality were built using random forest (RF), support vector machine (SVM), partial least squares (PLS), and Gaussian regression (GPR). The results indicated that the SVM model exhibited relatively high accuracy in predicting anthocyanin, hardness, and soluble solids content in strawberries. For the test set, the SVM model achieved R² and RMSE values of 0.81, 0.87, and 0.89, and 0.04 mg/g, 0.33 kg/cm², and 0.72%, respectively. Additionally, the PLS model demonstrated relatively high accuracy in predicting the titratable acid content of strawberries, achieving R² and RMSE values of 0.85 and 0.03%, respectively, for the test set. These findings provided a solid foundation for strawberry quality modeling and a veritable guide for non-destructive assessment of strawberry quality.

Wine grape quality is influenced by the variety and growing environment, and the quality of the grapes has a significant impact on the quality of the wine. Tannins are a crucial indicator of wine grape quality, and, therefore, rapid and non-destructive methods for detecting tannin content are necessary. This study collected spectral data of Pinot Noir and Chardonnay using a geophysical spectrometer, with a focus on the 500–1800 nm spectrum. The spectra were preprocessed using Savitzky–Golay (SG), first-order differential (1D), standard normal transform (SNV), and their respective combinations. Characteristic bands were extracted through correlation analysis (PCC). Models such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and one-dimensional neural network (1DCNN) were used to model tannin content. The study found that preprocessing the raw spectra improved the models’ predictive capacity. The SVM–RF model was the most effective in predicting grape tannin content, with a test set R² of 0.78, an RMSE of 0.31, and an RE of 10.71%. These results provide a theoretical basis for non-destructive testing of wine grape tannin content.

Several studies have been devoted to seeking some beneficial plant-related microorganisms for a long time, and on this basis, it has been found that arbuscular mycorrhizal fungi (AMF) have a considerable positive impact on plant health as a biological fungal agent. In this study, we focused on the effects of different AMF on the growth dynamics and root configuration of licorice under saline and alkali conditions. The metabolites of licorice under different AMF were assessed using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Funneliformis mosseae (Fm) and Rhizophagus intraradices (Ri) were added as different AMF treatments, while the sterilized saline–alkali soil was treated as a control. Samples were taken in the R1 period (15 d after AMF treatment) and the R2 period (45 d after AMF treatment). The results showed that the application of AMF significantly increased the root growth of licorice and significantly increased the biomass of both shoot and root. A total of 978 metabolites were detected and divided into 12 groups including lipids, which accounted for 15.44%; organic acids and their derivatives, at 5.83%; benzene compounds and organic heterocyclic compounds, at 5.42%; organic oxides, at 3.78%; and ketones, accounting for 3.17%. Compared with the control, there were significant changes in the differential metabolites with treatment inoculated with AMF; the metabolic pathways and biosynthesis of secondary metabolites were the main differential metabolite enrichment pathways in the R1 period, and those in the R2 period were microbial metabolism in diverse environments and the degradation of aromatic compounds. In conclusion, the use of AMF as biofertilizer can effectively improve the growth of licorice, especially in terms of the root development and metabolites, in saline–alkali soil conditions.

Boron (B) deficiency is detrimental to Glycine max (L.) Merr., which is one of the leading oil crops. In this study, physiological analysis of soybean seedlings under B deficiency and control after 12 h, 24 h, 72 h and 8 days was carried out, and the roots were subjected to transcriptome sequencing analysis. The results showed that under B deficiency, the plant height, SPAD and chlorophyll fluorescence value of soybean seedlings decreased significantly, indicating that B stress significantly inhibited plant growth and photosynthesis. RNA-seq revealed a total of 5126 DEGs (Differentially Expressed Genes), and nine DEGs co-existed at the four-time points, among which GLYMA_03G130600 regulates transcription factor ORG3, which is related to plant growth. GO analysis revealed a total of annotated 4018 DEGs, among which the terms plasma membrane, extracellular, enzyme activity, ion transport metabolic process and oxidoreductase activity were significantly enriched at the four time points, which might hinder the growth of soybean and accelerate senescence. KEGG analysis showed that at level 1, the DEGs were mainly enriched in photosynthesis-related, phenylpropanoid biosynthesis, nitrogen metabolism and plant hormone signal transduction. At level 2 of secondary metabolism, the DEGs were mainly enriched in energy metabolism and amino acid metabolism pathways, which indicates that B stresses mainly affect photosynthesis, hormone regulation and amino acid metabolism of soybean, thus affecting plant growth. The above results give deeper insight into the soybean response to B deficiency and lay the foundation for further studies of the molecular mechanism of soybean response to boron deficiency.

This study aimed to screen genes associated with drought adaptation in Cerasus humilis and analyze their sequence characteristics and evolutionary relationships. The results could provide a theoretical basis for exploring the molecular basis underlying drought adaptation of Cerasus humilis. Two-year-old wild Cerasus humilis plants were cultivated in a greenhouse, and irrigation was completely withheld for 20 consecutive days. Leaves were sampled and a cDNA library of wild Cerasus humilis was constructed. By utilizing high-throughput screening in yeast combined with mannitol-induced simulated drought stress, genes associated with drought adaptation were isolated and identified. Additionally, core candidate genes were cloned and subjected to systematic bioinformatic analysis. The results showed that: (1) the drought adaptation-associated genes ChU40rps27a and ChTp258 were obtained through high-throughput screening of yeast and mannitol-simulated drought resistance test; (2) gene cloning revealed that the open reading frame of ChU40rps27a is 471 bp long, encoding 157 amino acids, and the open reading frame of ChTp258 is 231 bp long, encoding 77 amino acids; (3) multiple sequence alignment of the genes indicated that ChU40rps27a shares 88.5% identity with apple 40S (XM_050288755), and ChTp258 shares 94.4% identity with peach (XM_020567676) in terms of nucleotide sequence; (4) the phylogenetic tree analysis revealed that ChU40rps27a is closely related to Citrus sinensisU40rps27a (XM_006487968); and ChTp258 is closely related to peach (Prunus persica, XM_020567676), apricot (Prunus dulcis, XM_034370534), plum (Prunus mume, XM_008244122), and sweet cherry (Prunus avium, XM_021951344) in the Rosaceae family, clustering in the same clade. This study identified two candidate genes involved in long-term drought adaptation rather than short-term stress responses. These findings clarify their sequence characteristics, sequence conservation and evolutionary status, providing important gene resources and theoretical support for further exploring drought adaptation related molecular processes in Cerasus humilis and developing drought-adapted molecular breeding.