Sugarcane is an important sugar and energy crop that is widely planted in the world. Among the environmental stresses, the water-deficit stress is the most limiting to plant productivity. Some groups have used PCR-based and microarray technologies to investigate the gene expression changes of multiple sugarcane cultivars under water stress. Our knowledge about sugarcane genes in response to water deficit is still poor. A wild sugarcane type, Saccharum narenga, was selected and treated with drought stress for 22 days. Leaves from drought treated (DTS) and control (CK) plants were obtained for deep sequencing. Paired-end sequencing enabled us to assemble 104,644 genes (N50 = 1605 bp), of which 38,721 were aligned to other databases, such as UniProt, NR, GO, KEGG and Pfam. Single-end and paired-end sequencing identified 30,297 genes (> 5 TPM) in all samples. Compared to CK, 3389 differentially expressed genes (DEGs) were identified in DTS samples, comprising 1772 up-regulated and 1617 down-regulated genes. Functional analysis showed that the DEGs were involved in biological pathways like response to blue light, metabolic pathways and plant hormone signal transduction. We further observed the expression patterns of several important gene families, including aquaporins, late embryogenesis abundant proteins, auxin related proteins, transcription factors (TFs), heat shock proteins, light harvesting chlorophyll a-b binding proteins, disease resistance proteins, and ribosomal proteins. Interestingly, the regulation of genes varied among different subfamilies of aquaporin and ribosomal proteins. In addition, DIVARICATA and heat stress TFs were first reported in sugarcane leaves in response to water deficit. Further, we showed potential miRNAs that might be involved in the regulation of gene changes in sugarcane leaves under the water-deficit stress. This is the first transcriptome study of Saccharum narenga and the assembled genes are a valuable resource for future research. Our findings will improve the understanding of the mechanism of gene regulation in sugarcane leaves under the water-deficit stress. The output of this study will also contribute to the sugarcane breeding program.

Heavy metal pollution of farmland is worsened by the excessive introduction of heavy metal elements into soil systems, posing a substantial threat for global food security and human health. The traditional laboratory-based methods for monitoring soil heavy metals are limited for large-scale applications, while hyperspectral imagery data-based methods still face accuracy challenges. Therefore, a fusion XGBoost model based on the superposition of ensemble learning and packaging methods is proposed for large-scale monitoring with high accuracy of soil heavy metal using hyperspectral imagery. We took Xiong’an New Area, Hebei Province, as the study area, and acquired heavy metal content using chemical analysis. The XGB-Boruta-PCC algorithm was used for precise feature selection to obtain the final modeled spectral response features. On this basis, the performance indicators of the Optuna-optimized XGBoost model were compared with traditional linear and nonlinear models. The optimal model was extended to the entire region for drawing the spatial distribution map of soil heavy metal content. The results suggested that the XGB-Boruta-PCC method effectively achieved double dimensionality reduction of high-dimensional hyperspectral data, extracting spectral response features with a high contribution, which, combined with the XGBoost model, exhibited greater general estimation accuracies for heavy metal (Pb) in farmland (i.e., Pb: R² = 0.82, RMSE = 11.58, MAE = 9.89). The results of the mapping indicated that there were exceedances for the southwest and parts of the west over the research region. Factories and human activities were the potential causes of heavy metal Pb contamination in farmland. In conclusion, this innovative method can quickly and accurately achieve monitoring large-scale soil heavy metal contamination in farmland, with ZY-1-02E spaceborne hyperspectral imagery proving to be a reliable tool for mapping soil heavy metal in farmland.

In order to maintain soil stability, improve soil productivity and optimize planting patterns, the distribution characteristics of soil aggregates in sugarcane fields under different intercropping methods were studied. We investigated the differences in organic carbon content of different particle size aggregates and the effects of intercropping on soil organic carbon, aggregates stability, sugarcane water content and nutrients content. The results showed that intercropping could effectively improve soil physicochemical properties, increase soil organic carbon (SOC) content, but such an approach had little effect on soil stability. After intercropping, the content of SOC, readily oxidized organic carbon (ROC), dissolved organic carbon (DOC) in soil increased significantly. The content of SOC increased with the decrease in aggregate size, and the organic carbon is more stable in the aggregates with the particle size of R>2 mm and R<0.154 mm. The selection of soybean and mucuna pruriens as intercropping crops in both OS//M and OM treatments was less damaging to soil stability and soil performance was significantly improved than the other groups, making them more suitable for intercropping in sugarcane fields. Intercropping increased the water content and N, P and K content of sugarcane plants.

To explore and utilize the abundant soil microorganisms and their beneficial functions, high-throughput sequencing technology was used to analyze soil microbial compositions in the rhizosphere of red and green amaranth varieties. The results showed that significant differences in soil microbial composition could be found in the rhizosphere of amaranth plants with different color phenotypes. Firstly, soil bacterial compositions in the rhizosphere were significantly different between red and green amaranths. Among them, Streptomyces, Pseudonocardia, Pseudolabrys, Acidibacter, norank_ f_ Micropepsaceae, Bradyrhizobium, and Nocardioides were the unique dominant soil bacterial genera in the rhizosphere of red amaranth. In contrast, Conexibacter, norank_f_norank_o_norank_c_TK10, and norank_f_ norank_o_ norank_ c_AD3 were the special dominant soil bacterial genera in the rhizosphere of green amaranth. Additionally, even though the soil fungal compositions in the rhizosphere were not significantly different between red and green amaranths, the abundance of the dominant soil fungal genera in the rhizosphere showed significant differences between red and green amaranths. For example, unclassified_k__Fungi, Fusarium, Cladophialophora, unclassified_c__Sordariomycetes and unclassified_p__Chytridiomycota significantly enriched as the dominant soil fungal genera in the rhizosphere of the red amaranth. In contrast, Aspergillues only significantly enriched as the dominant soil fungal genus in the rhizosphere of green amaranth. All of the above results indicated that amaranth with various color phenotypes exactly recruited different microorganisms in rhizosphere, and the enrichments of soil microorganisms in the rhizosphere could be speculated in contributing to amaranth color formations.

Sugarcane is an important industrial crop with a high sugar yield that has become a leading energy crop worldwide. It is widely cultivated in tropical and subtropical regions. Various diseases beset the cultivation of sugarcane. The molecular study of disease resistance in sugarcane is limited by its complicated genome. In our study, RNA-seq was employed to detect the mechanism of twisted leaf disease tolerance in modern cultivar sugarcane, which derived from Narenga porphyrocoma. We completed high-throughput transcriptomic sequencing of 12 samples, including three stages of a susceptible (NSBC1_T “H3–8”) and an unsusceptible cultivar (NSBC1_CK “H-19”) with two biological repeats, respectively. Using the Saccharum spontaneum genome as reference, the average mapping ratio of the clean data was over 70%. Among the differentially expressed genes between H3–8 and H3–19, we focused on the analysis of hormone pathways and resistance (R) genes. The results showed that twisted leaf disease triggers hormone networks and around 40% of R genes conditioned lower expression in the susceptible cultivar. One of the possible reasons for H3–8 being susceptible to twisted leaf disease might be the null/retarded response of R genes, especially in pre-onset stage (46% down-regulated) of pathogens infection.