Novel melatonin-producing Bacillus safensis EH143 mitigates salt and cadmium stress in soybean.

Recently, microorganism and exogenous melatonin application has been recognized as an efficient biological tool for enhancing salt tolerance and heavy metal detoxification in agriculture crops. Thus, the goal of this study was to isolate and evaluate a novel melatonin-producing plant growth promoting bacterium. With high-throughput whole genome sequencing, phytohormone measurements, expression profiling, and biochemical analysis, we can identify a novel PGPB that produces melatonin and unravel how it promotes soybean growth and development and protects against salt and Cd stress. We identify the melatonin synthesis pathway (tryptophan→tryptamine→serotonin melatonin) of the halotolerant (NaCl > 800 mM) and heavy metal-resistant (Cd >3 mM) rhizobacterium Bacillus safensis EH143 and use it to treat soybean plants subjected to Cd and NaCl stresses. Results show that EH143 will highly bioaccumulate heavy metals and significantly improve P and Ca2+ uptake and the K+/Na+ (93%↑under salt stress) ratio while reducing Cd uptake (49% under Cd stress) in shoots. This activity was supported by the expression of the ion regulator HKT1, MYPB67, and the calcium sensors CDPK5 and CaMK1 which ultimately led to increased plant growth. EH143 significantly decreased ABA content in shoots by 13%, 20%, and 34% and increased SA biosynthesis in shoots by 14.8%, 31%, and 48.2% in control, salt, and Cd-treated plants, upregulating CYP707A1 and CYP707A2 and PAL1 and ICS, respectively. The melatonin content significantly decreased along with a reduced expression of ASMT3 following treatment with EH143; moreover, reduced expression of peroxidase (POD) and superoxide dismutase (SOD) by 134.5% and 39% under salt+Cd stress, respectively and increased level of total amino acids were observed. Whole-genome sequencing and annotation of EH143 revealed the presence of the melatonin precursor tryptophan synthase (trpA, trpB, trpS), metal and other ion regulators (Cd: cadA, potassium: KtrA and KtrB, phosphate: glpT, calcium: yloB, the sodium/glucose cotransporter: sgIT, and the magnesium transporter: mgtE), and enzyme activators (including the siderophore transport proteins yfiZ and yfhA, the SOD sodA, the catalase katA1, and the glutathione regulator KefG) that may be involved in programming the plant metabolic system. As a consequence, EH143 treatment significantly reduced the contents of lipid peroxidation (O2-, MDA, and H2O2) up to 69%, 46%, and 29% in plants under salt+Cd stress, respectively. These findings suggest that EH143 could be a potent biofertilizer to alleviate NaCl and Cd toxicity in crops and serve as an alternative substitute for exogenous melatonin application.

Identification of Quantitative Trait Loci Controlling Root Morphological Traits in an Interspecific Soybean Population Using 2D Imagery Data.

Roots are the hidden and most important part of plants. They serve as stabilizers and channels for uptaking water and nutrients and play a crucial role in the growth and development of plants. Here, two-dimensional image data were used to identify quantitative trait loci (QTL) controlling root traits in an interspecific mapping population derived from a cross between wild soybean 'PI366121' and cultivar 'Williams 82'. A total of 2830 single-nucleotide polymorphisms were used for genotyping, constructing genetic linkage maps, and analyzing QTLs. Forty-two QTLs were identified on twelve chromosomes, twelve of which were identified as major QTLs, with a phenotypic variation range of 36.12% to 39.11% and a logarithm of odds value range of 12.01 to 17.35. Two significant QTL regions for the average diameter, root volume, and link average diameter root traits were detected on chromosomes 3 and 13, and both wild and cultivated soybeans contributed positive alleles. Six candidate genes, Glyma.03G027500 (transketolase/glycoaldehyde transferase), Glyma.03G014500 (dehydrogenases), Glyma.13G341500 (leucine-rich repeat receptor-like protein kinase), Glyma.13G341400 (AGC kinase family protein), Glyma.13G331900 (60S ribosomal protein), and Glyma.13G333100 (aquaporin transporter) showed higher expression in root tissues based on publicly available transcriptome data. These results will help breeders improve soybean genetic components and enhance soybean root morphological traits using desirable alleles from wild soybeans.

Evaluating the Effectiveness of Calcium Silicate in Enhancing Soybean Growth and Yield.

The application of silicon (Si) fertilizer positively impacts crop health, yield, and seed quality worldwide. Si is a "quasi-essential" element that is crucial for plant nutrition and stress response but is less associated with growth. This study aimed to investigate the effect of Si on the yield of cultivated soybean (Glycine max L). Two locations, Gyeongsan and Gunwi, in the Republic of Korea were selected, and a land suitability analysis was performed using QGIS version 3.28.1. The experiments at both locations consisted of three treatments: the control, Si fertilizer application at 2.3 kg per plot (9 m × 9 m) (T1), and Si fertilizer application at 4.6 kg per plot (9 m × 9 m) (T2). The agronomic, root, and yield traits, as well as vegetative indices, were analyzed to evaluate the overall impact of Si. The results demonstrated that Si had consistently significant effects on most root and shoot parameters in the two experimental fields, which led to significantly increased crop yield when compared with the control, with T2 (22.8% and 25.6%, representing an output of 2.19 and 2.24 t ha-1 at Gyeongsan and Gunwi, respectively) showing a higher yield than T1 (11% and 14.2%, representing 1.98 and 2.04 t ha-1 at Gyeongsan and Gunwi, respectively). These results demonstrate the positive impact of exogenous Si application on the overall growth, morphological and physiological traits, and yield output of soybeans. However, the application of the optimal concentration of Si according to the crop requirement, soil status, and environmental conditions requires further studies.

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging.

Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible.

Silicon as a Smart Fertilizer for Sustainability and Crop Improvement.

Silicon (Si), despite being abundant in nature, is still not considered a necessary element for plants. Si supplementation in plants has been extensively studied over the last two decades, and the role of Si in alleviating biotic and abiotic stress has been well documented. Owing to the noncorrosive nature and sustainability of elemental Si, Si fertilization in agricultural practices has gained more attention. In this review, we provide an overview of different smart fertilizer types, application of Si fertilizers in agriculture, availability of Si fertilizers, and experiments conducted in greenhouses, growth chambers, and open fields. We also discuss the prospects of promoting Si as a smart fertilizer among farmers and the research community for sustainable agriculture and yield improvement. Literature review and empirical studies have suggested that the application of Si-based fertilizers is expected to increase in the future. With the potential of nanotechnology, new nanoSi (NSi) fertilizer applications may further increase the use and efficiency of Si fertilizers. However, the general awareness and scientific investigation of NSi need to be thoughtfully considered. Thus, we believe this review can provide insight for further research into Si fertilizers as well as promote Si as a smart fertilizer for sustainability and crop improvement.

Utilization of Spectral Indices for High-Throughput Phenotyping.

The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants' agronomic traits and data-driven HTP resolutions for precision breeding.