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Environmental variability induces fluctuating drought stress, significantly affecting crop growth and productivity. However, the overall changes in both shoot and root systems caused by different drought severities and crop types remain unclear. Here, we analyzed the available information on the responses of crop shoot and root systems to different drought stresses. A meta-analysis was conducted using published papers from 1985 to 2023, examining the changes in eight shoot and six root system-related traits in response to different types of droughts. Distinct response patterns were observed under prolonged drought and drying-rewetting cycles. Taproot and fibrous-root crops exhibit a return to near-normal growth after alternating wet and dry conditions. After drought, aboveground growth is dominated by stomatal regulation, reducing aboveground growth to preserve energy. Crops often exhibited rapid growth after drought rewatering to compensate for stress-induced losses and adapt to environmental changes. Root biomass interacted with root morphology to redistribute root biomass in the soil, enhancing plant adaptation to drought stress. This meta-analysis evaluated how crop and drought types influence crop responses to drought, emphasizing the effects of various drought conditions on shoot traits and root plasticity. The findings offer valuable insights for selecting and breeding crops with optimal shoot and root phenotypes, developing drought-tolerant varieties with improved food production potential and promoting efficient water use and informing rational irrigation practices.

Drought stress is a critical environmental factor affecting sugarcane yield, and the adaptability of the sugarcane rhizosphere bacterial community is essential for drought tolerance. This review examines the adaptive responses of sugarcane rhizosphere bacterial communities to water stress and explores their significant role in enhancing sugarcane drought tolerance. Under drought conditions, the sugarcane rhizosphere bacterial community undergoes structural and functional shifts, particularly the enrichment of beneficial bacteria, including Streptomycetales and Rhizobiales. These bacteria enhance sugarcane resilience to drought through various means, including nutrient acquisition and phytohormone synthesis. Furthermore, changes in the rhizosphere bacterial community were closely associated with the composition and levels of soil metabolites, which significantly influenced the physiological and biochemical processes of sugarcane during drought stress. This study deepens our understanding of rhizosphere bacterial communities and their interactions with sugarcane, laying a scientific foundation for developing drought-resistant sugarcane varieties, optimizing agricultural practices, and opening new avenues for agricultural applications.

ABSTRACT Sugarcane is highly sensitive to changes in moisture, and increased drought severely restricts its growth and productivity. Recent studies have shown that plant growth-promoting microorganisms are essential to reduce the adverse effects of environmental stresses, especially drought. However, our knowledge about the dynamics of rhizosphere microbial community structure in sugarcane under varying degrees of drought stress is limited. We analyzed the effects of different degrees of drought stress on the rhizosphere microbial communities of Zhongzhe 1(ZZ1) and Zhongzhe 6(ZZ6) with differences in drought resistance, by combining soil enzyme activity, nutrient content, and physiological and morphological characteristics of sugarcane roots. The results showed that rhizosphere bacterial community began to change at a field capacity of 50%, enriching the sugarcane rhizosphere with drought-resistant bacteria. The core strains of ZZ1 and ZZ6 rhizosphere enrichment were mainly Streptomycetales, Sphingomonadales, and Rhizobiales. However, compared to ZZ1, the changes in rhizosphere bacterial abundance in ZZ6 were primarily associated with the abundance of Streptomycetales as drought levels increased. Rhizobiales and Streptomycetales, enriched in the rhizosphere of ZZ6 under drought, were positively correlated with root tip number and total root length (TRL), increasing the distribution area of roots and, thus, improving water and nutrient uptake by the roots thereby enhancing the resistance of sugarcane to drought stress. This research enhances our understanding of the composition of the rhizosphere microbial community in sugarcane under different levels of drought stress and its interaction with the roots, thereby providing valuable insights for enhancing drought resistance in sugarcane. Importance Drought stress is expected to further increase in intensity, frequency, and duration, causing substantial losses in sugarcane yields. Here, we exposed sugarcane to varying degrees of drought treatment during growth and quantified the eventual composition of the resulting sugarcane rhizosphere bacterial community groups. We found that sugarcane rhizosphere under mild drought began to recruit specific bacterial communities to resist drought stress and used the interactions of root tip number, total root length, and drought-resistant strains to improve sugarcane survival under drought. This research provides a theoretical basis for the rhizosphere microbiome to help sugarcane improve its resistance under different levels of drought stress.

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources.

Rhizosphere bacteria, the main functional microorganisms inhabiting the roots of terrestrial plants, play important roles in regulating plant growth and environmental stress resistance. However, limited information is available regarding changes occurring within the structure of the root microbial community and the response mechanisms of host plants that improve adaptability to drought stress. In this study, we conducted an experiment on two sugarcane varieties with different drought tolerance levels under drought and control treatments and analyzed the rhizosphere bacterial communities using 16S rRNA high-throughput sequencing. Correlation analysis results clarified the influence of various factors on the rhizosphere bacterial community structure. Drought stress reduced the diversity of the bacterial community in the rhizosphere of sugarcane. Interestingly, the bacterial community of the drought-sensitive sugarcane cultivar GT39 changed more than that of the drought-tolerant cultivar ZZ9. In addition, ZZ9 had a high abundance of drought-resistant bacteria in the rhizosphere under optimal soil water conditions, whereas GT39 accumulated a large number of drought-resistant bacteria only under drought stress. GT39 mainly relied on Actinobacteria in its response to drought stress, and the abundance of this phylum was positively correlated with soil acid phosphatase and protease levels. In contrast, ZZ9 mainly relied on Bacilli in its response to drought stress, and the abundance of this class was positively correlated with only soil acid phosphatase levels. In conclusion, drought stress can significantly reduce the bacterial diversity and increase the abundance of drought-resistant bacteria in the sugarcane rhizosphere. The high abundance of drought-resistant bacteria in the rhizosphere of drought-tolerant cultivars under non-drought conditions is an important factor contributing to the high drought adaptability of these cultivars. Moreover, the core drought-resistant bacteria of the sugarcane rhizosphere and root exudates jointly affect the resistance of sugarcane to drought.

Sugarcane is an important sugar and energy crop, and its yield is greatly affected by drought. Although a large number of studies have shown that rhizosphere microorganisms can help improve the adaptability of plants to biotic or abiotic stresses, there is a lack of studies on the adaptability of sugarcane rhizosphere microbial communities to host plants. Therefore, we conducted drought stress treatment and normal irrigation treatment on three sugarcane varieties GT21, GT31, and GT42 widely cultivated in Guangxi. Using 16S rDNA sequencing technology to analyze the changes in abundance of the sugarcane rhizosphere bacterial community under different treatments, combined with the determination of soil enzyme activity, soil nutrient content, and sugarcane physiological characteristics, we explored the sugarcane rhizosphere bacterial community response to drought stress. In addition, we used the structural equation model to verify the response path of sugarcane rhizosphere bacteria. The results show that the bacterial community structure in the rhizosphere of sugarcane is stable under normal water conditions. The change in the bacterial community structure under drought stress has a 25.2% correlation with the drought adaptability of sugarcane, but the correlation with drought stress is as high as 42.17%. The changes in abundance of rhizosphere bacteria under drought stress are mainly concentrated in the phylum Rhizobiales and Streptomycetales. This change is directly related to the physiological state of the host plant under drought stress, soil available phosphorus, soil urease and soil acid protease. We investigated the response species of rhizosphere microorganisms and their response pathways under drought stress, providing a scientific basis for rhizosphere microorganisms to assist host plants to improve drought adaptability.

The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase remote sensing images. Although synthetic aperture radar (SAR) data have strong potential for application in forest change detection tasks, most existing deep learning-based models have been designed for optical imagery. Therefore, to effectively combine optical and SAR data in forest change detection, this paper proposes a double Siamese branch-based change detection network called DSNUNet. DSNUNet uses two sets of feature branches to extract features from dual-phase optical and SAR images and employs shared weights to combine features into groups. In the proposed DSNUNet, different feature extraction branch widths were used to compensate for a difference in the amount of information between optical and SAR images. The proposed DSNUNet was validated by experiments on the manually annotated forest change detection dataset. According to the obtained results, the proposed method outperformed other change detection methods, achieving an F1-score of 76.40%. In addition, different combinations of width between feature extraction branches were analyzed in this study. The results revealed an optimal performance of the model at initial channel numbers of the optical imaging branch and SAR image branch of 32 and 8, respectively. The prediction results demonstrated the effectiveness of the proposed method in accurately predicting forest changes and suppressing cloud interferences to some extent.