Investigating the Impact of Biostimulants on the Row Crops Corn and Soybean Using High-Efficiency Phenotyping and Next Generation Sequencing

Biostimulants and environmental stress mitigation in crops: A novel and emerging approach for agricultural sustainability under climate change

Pesticide and fertilizer usage is at the center of agricultural production to meet the demands of an ever-increasing global population. However, rising levels of chemicals impose a serious threat to the health of humans, animals, plants, and even the entire biosphere because of their toxic effects. Biostimulants offer the opportunity to reduce the agricultural chemical footprint owing their multilevel, beneficial properties helping to make agriculture more sustainable and resilient. When applied to plants or to the soil an increased absorption and distribution of nutrients, tolerance to environmental stress, and improved quality of plant products explain the mechanisms by which these probiotics are useful. In recent years, the use of plant biostimulants has received widespread attention across the globe as an ecologically acceptable alternative to sustainable agricultural production. As a result, their worldwide market continues to grow, and further research will be conducted to broaden the range of the products now available. Through this review, we present a current understanding of biostimulants, their mode of action and their involvement in modulating abiotic stress responses, including omics research, which may provide a comprehensive assessment of the crop's response by correlating molecular changes to physiological pathways activated under stress conditions aggravated by climate change.

Brown Seaweed Extract (BSE) Application Influences Auxin- and ABA-Related Gene Expression, Root Development, and Sugar Yield in Beta vulgaris L

The molecular and phenotypic effects of a brown seaweed extract (BSE) were assessed in sugar beet (Beta vulgaris L.). Transcript levels of BSE-treated and untreated plants were studied by RNA-seq and validated by quantitative real-time PCR analysis (RT-qPCR). Root morphology, sugar yield, and processing quality traits were also analyzed to better elucidate the treatment effects. RNA-seq revealed 1019 differentially expressed genes (DEGs) between the BSE-treated and untreated plants. An adjusted p-value < 0.1 and an absolute value of log2 (fold change) greater than one was used as criteria to select the DEGs. Gene ontology (GO) identified hormone pathways as an enriched biological process. Six DEGs involved in auxin and ABA pathways were validated using RT-qPCR. The phenotypic characterization indicated that BSE treatment led to a significant increase (p < 0.05) in total root length and the length of fine roots of plants grown under hydroponics conditions. The sugar yield of plants grown under field conditions was higher (p < 0.05) in the treated field plots compared with the control treatment, without impacting the processing quality. Our study unveiled the relevant effects of BSE application in regulating auxin- and ABA-related gene expression and critical traits related to sugar beet development and yield.

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

Presence and future of plant phenotyping approaches in biostimulant research and development

Commercial interest in biostimulants as a tool for sustainable green economics and agriculture concepts is on a steep rise, being followed by increasing demand to employ efficient scientific methods to develop new products and understand their mechanisms of action. Biostimulants represent a highly diverse group of agents derived from various natural sources. Regardless of their nutrition content and composition, they are classified by their ability to improve crop performance through enhanced nutrient use efficiency, abiotic stress tolerance, and quality of crops. Numerous reports have described modern, non-invasive sensor-based phenotyping methods in plant research. This review focuses on applying phenotyping approaches in biostimulant research and development, and maps the evolution of interaction of these two intensively growing domains. How phenotyping served to identify new biostimulants, the description of their biological activity, and the mechanism/mode of action are summarized. Special attention is dedicated to the indoor high-throughput methods using model plants suitable for biostimulant screening and developmental pipelines, and high-precision approaches used to determine biostimulant activity. The need for a complex method of testing biostimulants as multicomponent products through integrating other -omic approaches followed by advanced statistical/mathematical tools is emphasized.

Spatial correlation among cultivated land intensive use and carbon emission efficiency: A case study in the Yellow River Basin, China

Considering the current global goal of carbon neutrality, the relationship between cultivated land intensive use (CLIU) and carbon emission efficiency (CEE) should be explored to address the global climate crisis and move toward a low-carbon future. However, previous work in this has been conducted at provincial/regional scales and few have identified the spatial correlation between CLIU and CEE at the scale of large river basins. Therefore, this study explored the spatiotemporal characteristics of CLIU, cultivated land carbon emissions (CLCE), and CEE, as well as the spatial correlation between CLIU and CEE in the Yellow River Basin (YRB), China. A comprehensive evaluation model, the Intergovernmental Panel on Climate Change (IPCC) coefficient methodology, existing data envelopment analysis model, and bivariate spatial autocorrelation models were used to analyze statistical data from 2005 to 2017. We found that the overall CLIU and CLCE values in the YRB exhibited a continuous increase; the average carbon emission total efficiency and carbon emission scale efficiency first decreased and then increased, and the average carbon emission pure technical efficiency gradually decreased. Areas of high CLCE were concentrated in eastern areas of the YRB, whereas those of high CLIU, carbon emission total efficiency, carbon emission scale efficiency, and carbon emission pure technical efficiency predominantly appeared in the eastern areas, followed by central and western areas of the YRB. Spatial analysis revealed a significant spatial dependence of CLIU on CEE. From a global perspective, the spatial correlations between CLIU and CEE changed from positive to negative with time. Moreover, the aggregation degree between CLIU and CEE gradually decreases with time, while the dispersion degree increases with time, and the spatial correlation gradually weakens. The local spatial autocorrelation further demonstrates that the number of high-low and low-high clusters between CLIU and CEE gradually increases over time, while the number of high-high and low-low clusters gradually decreased over time. Collectively, these findings can help policymakers formulate feasible low-carbon and efficient CLIU policies to promote win-win cooperation among regions.

Dysbiosis in the Rhizosphere Microbiome of Standing Dead Korean Fir (Abies koreana)

The Korean fir (Abies koreana), a native coniferous tree species mainly found on Mt. Halla in Jeju, South Korea, is suffering from continuous population decline and has been declared an endangered species. Research efforts have focused on the possible abiotic causes behind this worrying decline. However, the potential link between tree vitality and the rhizosphere microbiome remains unclear. In this study, a comparative metagenomic 16S rRNA sequence analysis was used to investigate the composition of the rhizosphere microbiota of samples collected from healthy and die-back-affected trees on Mt. Halla. The results indicated a significant reduction in the richness and diversity of microbiota in the rhizosphere of die-back-affected trees. Moreover, the relative abundance of Proteobacteria, Actinobacteria, and Bacteroidetes were significantly higher in healthy trees than in standing dead trees. Many bacterial genera were significantly more abundant in the rhizosphere of healthy trees, including those known for promoting plant growth and tolerance to biotic and abiotic stresses (e.g., Bradyrhizobium, Rhizomicrobium, Caulobacter, Nitrosospira, Rhizobacter, Paraburkholderia, Rhizobium, Devosia, Caballeronia, Niveispirillum, Dyella, Herbaspirillum, Frankia, Streptomyces, Actinoallomurus, Lysobacter, Luteibacter, Mucilaginibacter, and Variovorax). To our knowledge, this is the first report on rhizosphere bacterial microbiome dysbiosis in die-back-affected Korean fir trees, suggesting that the influence of rhizosphere microbiota should be considered to save this endangered species by investigating possible intervention strategies in future work.

Biostimulants on Crops: Their Impact under Abiotic Stress Conditions

Biostimulants are agronomic tools that have been gaining importance in the reduction of fertilizer applications. They can improve the yield of cropping systems or preventing crop yield losses under abiotic stresses. Biostimulants can be composed of organic and inorganic materials and most of the components are still unknown. The characterization of the molecular mechanism of action of biostimulants can be obtained using the omics approach, which includes the determination of transcriptomic, proteomic, and metabolomic changes in treated plants. This review reports an overview of the biostimulants, taking stock on the recent molecular studies that are contributing to clarify their action mechanisms. The omics studies can provide an overall evaluation of a crop’s response, connecting the molecular changes with the physiological pathways activated and the performance with or without stress conditions. The multiple responses of plants treated with biostimulants must be correlated with the phenotype changes. In this context, it is also crucial to design an adequate experimental plan and statistical data analysis, in order to find robust correlations between biostimulant treatments and crop performance.

Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity

Plant phenomics is becoming a common tool employed to characterize the mode of action of biostimulants. A combination of this technique with other omics such as metabolomics can offer a deeper understanding of a biostimulant effect in planta. However, the most challenging part then is the data analysis and the interpretation of the omics datasets. In this work, we present an example of how different tools, based on multivariate statistical analysis, can help to simplify the omics data and extract the relevant information. We demonstrate this by studying the effect of protein hydrolysate (PH)-based biostimulants derived from different natural sources in lettuce and tomato plants grown in controlled conditions and under salinity. The biostimulants induced different phenotypic and metabolomic responses in both crops. In general, they improved growth and photosynthesis performance under control and salt stress conditions, with better performance in lettuce. To identify the most significant traits for each treatment, a random forest classifier was used. Using this approach, we found out that, in lettuce, biomass-related parameters were the most relevant traits to evaluate the biostimulant mode of action, with a better response mainly connected to plant hormone regulation. However, in tomatoes, the relevant traits were related to chlorophyll fluorescence parameters in combination with certain antistress metabolites that benefit the electron transport chain, such as 4-hydroxycoumarin and vitamin K1 (phylloquinone). Altogether, we show that to go further in the understanding of the use of biostimulants as plant growth promotors and/or stress alleviators, it is highly beneficial to integrate more advanced statistical tools to deal with the huge datasets obtained from the -omics to extract the relevant information.

Harnessing Synergistic Biostimulatory Processes: A Plausible Approach for Enhanced Crop Growth and Resilience in Organic Farming

Simple Summary

Demand for organically grown crops has risen globally due to its healthier and safer food products. From a sustainability perspective, organic farming offers an eco-friendly cultivation system that minimizes agrochemicals and producing food with little or no environmental footprint. However, organic agriculture’s biggest drawback is the generally lower and variable yield in contrast to conventional farming. Compatible with organic farming, the selective use of biostimulants can close the apparent yield gap between organic and conventional cultivation systems. A biostimulant is defined as natural microorganisms (bacteria, fungi) or biologically active substances that are able to improve plant growth and yield through several processes. Biostimulants are derived from a range of natural resources including organic materials (composts, seaweeds), manures (earthworms, fish, insects) and extracts derived from microbes, plant, insect or animal origin. The current trend is indicative that a mixture of biostimulants is generally delivering better growth, yield and quality rather than applying biostimulant individually. When used correctly, biostimulants are known to help plants cope with stressful situations like drought, salinity, extreme temperatures and even certain diseases. More research is needed to understand the different biostimulants, key components, and also to adjust the formulations to improve their reliability in the field.

Abstract

Demand for organically grown food crops is rising substantially annually owing to their contributions to human health. However, organic farm production is still generally lower compared to conventional farming. Nutrient availability, content consistency, uptake, assimilation, and crop responses to various stresses were reported as critical yield-limiting factors in many organic farming systems. In recent years, plant biostimulants (BSs) have gained much interest from researchers and growers, and with the objective of integrating these products to enhance nutrient use efficiency (NUE), crop performance, and delivering better stress resilience in organic-related farming. This review gave an overview of direct and indirect mechanisms of microbial and non-microbial BSs in enhancing plant nutrient uptake, physiological status, productivity, resilience to various stressors, and soil-microbe-plant interactions. BSs offer a promising, innovative and sustainable strategy to supplement and replace agrochemicals in the near future. With greater mechanistic clarity, designing purposeful combinations of microbial and non-microbial BSs that would interact synergistically and deliver desired outcomes in terms of acceptable yield and high-quality products sustainably will be pivotal. Understanding these mechanisms will improve the next generation of novel and well-characterized BSs, combining microbial and non-microbial BSs strategically with specific desired synergistic bio-stimulatory action, to deliver enhanced plant growth, yield, quality, and resilience consistently in organic-related cultivation.

Plant Material as a Novel Tool in Designing and Formulating Modern Biostimulants-Analysis of Botanical Extract from Linum usitatissimum L

Nowadays, researchers are looking into next-generation biostimulants that can be designed as a dedicated agronomic tool based on plant materials. The aim of the present study was to develop a novel biostimulating product, based on plant material in the form of linseed aqueous extracts. The scope of the research included the physicochemical characterization of the product and identification of its biostimulating potential. The study has confirmed that the plant biostimulant derived from L. usitatissimum can be used as a viable agronomic tool for growing soybean. The designed and produced biostimulant is rich in bioactive compounds, including amino acids, free fatty acids, carbohydrates, and micro- and macroelements. The tested biostimulant showed significantly lower values of surface tension in relation to water and a commercial biostimulant. The soybean crops responded to the application of the preparation by improvements in agronomic and morphological levels. The linseed macerates were effective in terms of soybean yields and profitability. Our findings serve as preliminary evidence for the viability of designing and developing novel biostimulants derived from plant materials. This comprehensive approach to designing and formulating novel bioproducts necessitates more extensive and targeted research to fully explain the mechanisms behind the improvements observed in the soybean cultivation.

Seed Priming With Protein Hydrolysates Improves Arabidopsis Growth and Stress Tolerance to Abiotic Stresses

The use of plant biostimulants contributes to more sustainable and environmentally friendly farming techniques and offers a sustainable alternative to mitigate the adverse effects of stress. Protein hydrolysate-based biostimulants have been described to promote plant growth and reduce the negative effect of abiotic stresses in different crops. However, limited information is available about their mechanism of action, how plants perceive their application, and which metabolic pathways are activating. Here we used a multi-trait high-throughput screening approach based on simple RGB imaging and combined with untargeted metabolomics to screen and unravel the mode of action/mechanism of protein hydrolysates in Arabidopsis plants grown in optimal and in salt-stress conditions (0, 75, or 150 mM NaCl). Eleven protein hydrolysates from different protein sources were used as priming agents in Arabidopsis seeds in three different concentrations (0.001, 0.01, or 0.1 μl ml-1). Growth and development-related traits as early seedling establishment, growth response under stress and photosynthetic performance of the plants were dynamically scored throughout and at the end of the growth period. To effectively classify the functional properties of the 11 products a Plant Biostimulant Characterization (PBC) index was used, which helped to characterize the activity of a protein hydrolysate based on its ability to promote plant growth and mitigate stress, and to categorize the products as plant growth promoters, growth inhibitors and/or stress alleviator. Out of 11 products, two were identified as highly effective growth regulators and stress alleviators because they showed a PBC index always above 0.51. Using the untargeted metabolomics approach, we showed that plants primed with these best performing biostimulants had reduced contents of stress-related molecules (such as flavonoids and terpenoids, and some degradation/conjugation compounds of phytohormones such as cytokinins, auxins, gibberellins, etc.), which alleviated the salt stress response-related growth inhibition.

Ascophyllum nodosum Extract (SealicitTM) Boosts Soybean Yield Through Reduction of Pod Shattering-Related Seed Loss and Enhanced Seed Production

Soybean is one of the most valuable commercial crops because of its high protein, carbohydrate, and oil content. The land area cultivated with soybean in subtropical regions, such as Brazil, is continuously expanding, in some instances at the expense of carbon storing natural habitats. Strategies to decrease yield/seed losses and increase production efficiency are urgently required to meet global demand for soybean in a sustainable manner. Here, we evaluated the effectiveness of an Ascophyllum nodosum extract (ANE), Sealicit^TM, in increasing yields of different soybean varieties, in two geographical regions (Canada and Brazil). In addition, we investigated the potential of Sealicit^TM to reduce pod shattering at the trials in Brazil. Three different concentrations of Sealicit^TM were applied to pod shatter-susceptible (SS) UFUS 6901 and shatter-resistant (SR) UFUS 7415 varieties to assess their impact on pod firmness. SS variety demonstrated a significant decrease in pod shattering, which coincided with deregulation of GmPDH1.1 and GmSHAT1-5 expression, genes that determine pod dehiscence, and higher seed weight per pod. Sealicit^TM application to the SR variety did not significantly alter its inherent pod shatter resistance, but provided higher increases in seed yield at harvest. This yield increase maybe associated with to other yield components stimulated by the biostimulant. This work demonstrates that Sealicit^TM, which has previously been shown to improve pod firmness in Arabidopsis and selected commercial oilseed rape varieties through IND gene down-regulation, also has the potential to improve pod resistance and seed productivity in soybean, a member of the legume family sharing a similar strategy for seed dispersal.

Transcriptional and Physiological Analyses to Assess the Effects of a Novel Biostimulant in Tomato

This work aimed to study the effects in tomato (Solanum lycopersicum L.) of foliar applications of a novel calcium-based biostimulant (SOB01) using an omics approach involving transcriptomics and physiological profiling. A calcium-chloride fertilizer (SOB02) was used as a product reference standard. Plants were grown under well-watered (WW) and water stress (WS) conditions in a growth chamber. We firstly compared the transcriptome profile of treated and untreated tomato plants using the software RStudio. Totally, 968 and 1,657 differentially expressed genes (DEGs) (adj-p-value < 0.1 and |log2(fold change)| ≥ 1) were identified after SOB01 and SOB02 leaf treatments, respectively. Expression patterns of 9 DEGs involved in nutrient metabolism and osmotic stress tolerance were validated by real-time quantitative reverse transcription PCR (RT-qPCR) analysis. Principal component analysis (PCA) on RT-qPCR results highlighted that the gene expression profiles after SOB01 treatment in different water regimes were clustering together, suggesting that the expression pattern of the analyzed genes in well water and water stress plants was similar in the presence of SOB01 treatment. Physiological analyses demonstrated that the biostimulant application increased the photosynthetic rate and the chlorophyll content under water deficiency compared to the standard fertilizer and led to a higher yield in terms of fruit dry matter and a reduction in the number of cracked fruits. In conclusion, transcriptome and physiological profiling provided comprehensive information on the biostimulant effects highlighting that SOB01 applications improved the ability of the tomato plants to mitigate the negative effects of water stress.

Biostimulants for Plant Growth and Mitigation of Abiotic Stresses: A Metabolomics Perspective

Adverse environmental conditions due to climate change, combined with declining soil fertility, threaten food security. Modern agriculture is facing a pressing situation where novel strategies must be developed for sustainable food production and security. Biostimulants, conceptually defined as non-nutrient substances or microorganisms with the ability to promote plant growth and health, represent the potential to provide sustainable and economically favorable solutions that could introduce novel approaches to improve agricultural practices and crop productivity. Current knowledge and phenotypic observations suggest that biostimulants potentially function in regulating and modifying physiological processes in plants to promote growth, alleviate stresses, and improve quality and yield. However, to successfully develop novel biostimulant-based formulations and programs, understanding biostimulant-plant interactions, at molecular, cellular and physiological levels, is a prerequisite. Metabolomics, a multidisciplinary omics science, offers unique opportunities to predictively decode the mode of action of biostimulants on crop plants, and identify signatory markers of biostimulant action. Thus, this review intends to highlight the current scientific efforts and knowledge gaps in biostimulant research and industry, in context of plant growth promotion and stress responses. The review firstly revisits models that have been elucidated to describe the molecular machinery employed by plants in coping with environmental stresses. Furthermore, current definitions, claims and applications of plant biostimulants are pointed out, also indicating the lack of biological basis to accurately postulate the mechanisms of action of plant biostimulants. The review articulates briefly key aspects in the metabolomics workflow and the (potential) applications of this multidisciplinary omics science in the biostimulant industry.

The Quality of Carrot after Field Biostimulant Application and after Storage

The carrot (Daucus carota L.) is a staple vegetable in human nutrition in Europe. In recent years, the use of biostimulants in vegetable crops has become a way to affect the quantity and quality of yields. The aim of this study was to assess the effect of the type and methods of biostimulant (natural seaweed extract Kelpak and synthetic Asahi) application on the nitrates and nitrites content in carrot roots after harvest and storage. The study was based on a strict field experiment with carrot cv. ′Karotan′, conducted in Poland (53°13′N; 17°51′E) in three successive growing seasons and after six months of storage (RH 95%, and air temperature +1 °C). The biostimulants were applied during the growing season in a foliar form. The content of NO3ˉ and NO2ˉ in carrot after harvest depended on the dose and the date of biostimulant application. The single application of biostimulant Kelpak as well as two times of Asahi had no effect on the nitrate and nitrite content, while the application of Kelpak in a total dose of 6 or 7 dm3 ha−1 increased them. The maximum intake of nitrates and nitrites following the harvest and storage was, respectively, 7.1, 2.3% and 6.7, 2.1% of the ADI.