Salt and drought stress-mitigating approaches in sugar beet (Beta vulgaris L.) to improve its performance and yield

Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.

Research on phytotoxicity assessment and photosynthetic characteristics of nicosulfuron residues on Beta vulgaris L

Nicosulfuron is a common herbicide used to control weeds in maize fields. In northeast China, sugar beet is often grown as a subsequent crop after maize, and its frequently suffers from soil nicosulfuron residue damage, but the related toxicity evaluation and photosynthetic physiological mechanisms are not clear. Therefore, we experimented to evaluate the impacts of nicosulfuron residues on beet growth, photochemical properties, and antioxidant defense system. The results showed that when the nicosulfuron residue content reached 0.3 μg kg-1, it inhibited the growth of sugar beet. When it reached 36 μg kg-1 (GR50), the growth stagnated. Compared to the control group, a nicosulfuron residue of 36 μg kg-1 significantly decreased beet plant height (70.93 %), leaf area (91.85 %), dry weights of shoot (70.34 %) and root (32.70 %). It also notably reduced the potential photochemical activity (Fv/Fo) by 12.41 %, the light energy absorption performance index (PIabs) by 46.09 %, and light energy absorption (ABS/CSm) by 6.56 %. It decreased the capture (TRo/CSm) by 9.30 % and transferred energy (ETo/CSm) by 16.13 % per unit leaf cross-section while increasing the energy flux of heat dissipation (DIo/CSm) by 22.85 %. This ultimately impaired the photochemical capabilities of PSI and PSII, leading to a reduction in photosynthetic performance. Furthermore, nicosulfuron increased malondialdehyde (MDA) content while decreasing superoxide dismutase (SOD) and catalase (CAT) activities. In conclusion, this research clarified the toxicity risk level, lethal dose, and harm mechanism of the herbicide nicosulfuron residue. It provides a theoretical foundation for the rational use of herbicides in agricultural production and sugar beet planting management.

Role of Microbes in Alleviating Crop Drought Stress: A Review

Drought stress is an annual global phenomenon that has devastating effects on crop production, so numerous studies have been conducted to improve crop drought resistance. Plant-associated microbiota play a crucial role in crop health and growth; however, we have a limited understanding of the key processes involved in microbiome-induced crop adaptation to drought stress. In this review, we summarize the adverse effects of drought stress on crop growth in terms of germination, photosynthesis, nutrient uptake, biomass, and yield, with a focus on the response of soil microbial communities to drought stress and plant-microbe interactions under drought stress. Moreover, we review the morpho-physiological, biochemical, and molecular mechanisms underlying the mitigation effect of microbes on crop drought stress. Finally, we highlight future research directions, including the characterization of specific rhizosphere microbiome species with corresponding root exudates and the efficiency of rhizobacteria inoculants under drought conditions. Such research will advance our understanding of the complex interactions between crops and microbes and improve crop resistance to drought stress through the application of beneficial drought-adaptive microbes.

Genome-Wide Analysis of Q-Type C2H2 ZFP Genes in Response to Biotic and Abiotic Stresses in Sugar Beet

A plant's Q-type C2H2-type ZFP plays key roles in plant growth and development and responses to biotic and abiotic stresses. Sugar beet (Beta vulgaris L.) is an important crop for sugar production. Salt stress and viral infection significantly reduce the root yield and sugar content of sugar beet. However, there is a lack of comprehensive genome-wide analyses of Q-type C2H2 ZFPs and their expression patterns in sugar beet under stress. In this study, 35 sugar beet Q-type C2H2 ZFPs (BvZFPs) containing at least one conserved "QALGGH" motif were identified via bioinformatics techniques using TBtools software. According to their evolutionary relationship, the BvZFPs were classified into five subclasses. Within each subclass, the physicochemical properties and motif compositions showed strong similarities. A Ka/Ks analysis indicated that the BvZFPs were conserved during evolution. Promoter cis-element analysis revealed that most BvZFPs are associated with elements related to phytohormone, biotic or abiotic stress, and plant development. The expression data showed that the BvZFPs in sugar beet are predominantly expressed in the root. In addition, BvZFPs are involved in the response to abiotic and biotic stresses, including salt stress and viral infection. Overall, these results will extend our understanding of the Q-type C2H2 gene family and provide valuable information for the biological breeding of sugar beet against abiotic and biotic stresses in the future.