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2024 Volume 4
Article Contents
Abstract
Introduction
Material and methods
Plant materials, plastome assembly, and genome annotation
Codon usage and repeat element patterns
Phylogenetic analysis
Haplotype and genetic diversity analyses
Results
General features of the pea pan-plastome
Codon usage and simple sequence repeats (SSRs) patterns in peas
Phylogenetic analysis
Nucleotide variation in the pea pan-plastome
Discussion
Genomic structure
Evolutionary history
Conclusions
Author contributions
Data availability
Acknowledgments
Conflict of interest
Rights and permissions
References
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ARTICLE   Open Access    

Optimization of a new organic approach to natural biostimulant (Jeevamrutha) for yield and quality management in Senna (Cassia angustifolia Vahl.): an agriculturally highly export-oriented crop

  • 1.

    Crop Production & Protection Divisional Unit, CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Boduppal, Hyderabad, Telangana State, 500092, India

  • 2.

    CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, U.P. State, 226015, India

More Information
  • Senna is a leguminous and industrial crop that produces high-quality glycosides (sennosides) in its leaves and pods, which have substantial therapeutic effects for alleviating constipation worldwide. However, further research on employing Jeevamrutha in Senna is required. As a result, the experiment was carried out at CSIR-CIMAP in Hyderabad for two consecutive years, in the years 2020–2021 and 2021–2022. The main aim is to identify the optimum dose of Jeevamrutha for higher growth, yield, and quality in Senna. The study used a randomized complete block design (RCBD) with seven treatments repeated three times. From the obtained result, it was observed that the application of 150 L of Jeevamrutha per acre observed significantly high leaf yields (1,085.2 kg·ha−1) and pod (318.7 kg·ha−1) equivalent to T2 in comparison to other treatments, i.e., application of 125 L of Jeevamrutha per acre (1,022.5 kg·ha−1, 312.1 kg·ha−1), and was succeeded by T3, i.e., application of 100 L of Jeevamrutha per acre (998.5 kg·ha−1, 288.5 kg·ha−1, respectively). Lower leaf yield (700.2 kg·ha−1) and pod yield (487 kg·ha−1) were observed in the control (T7). Similarly, the application of 150 L of Jeevamrutha per acre recorded significantly higher sennoside content in leaves (2.01%) and pods (3.11%), in comparison to other treatments, and was followed by T2 (1.98%, 3.09%) and T3 (1.89%, 2.97%). A similar trend was noticed in returns, i.e., the application of 150 L of Jeevamrutha per acre recorded significantly higher gross returns (USD$1,495 ha−1) and net returns (USD$1,066.4 ha−1).

  • Introduction

    The Fabaceae family, the third largest family in angiosperms, contains about 24,480 species (WFO, https://wfoplantlist.org), and has been a historically important source of food crops[1−4]. Peas (formerly Pisum sativa L. renamed to Lathyrus oleraceus – Pisum spp. will be used hereafter due to historical references to varietal names and subspecies that may not have been fully synonymized), are a member of the Fabaceae family and is among one of the oldest domesticated food crops with ongoing importance in feeding humans and stock. Peas originated in Western Asia and the Mediterranean basin where early finds from Egypt have been dated to ~4500 BCE and further east in Afghanistan from ~2000 BCE[5], and have since been extensively cultivated worldwide[6,7]. Given that peas are rich in protein, dietary fiber, vitamins, and minerals, have become an important part of people's diets globally[8−10].

    Domesticated peas are the result of long-term human selection and cultivation, and in comparison to wild peas, domesticated peas have undergone significant changes in morphology, growth habits, and yield[11−13]. From the long period of domestication starting in and around Mesopotamia many diverse lineages of peas have been cultivated and translocated to other parts of the world[14,15]. The subspecies, Pisum sativum subsp. sativum is the lineage from which most cultivars have been selected and is known for possessing large, round, or oval-shaped seeds[16,17]. In contrast, the subspecies, P. sativum subsp. elatius, is a cultivated pea which more closely resembles wild peas and is mainly found in grasslands and desert areas in Europe, Western Asia, and North Africa. Pisum fulvum is native to the Mediterranean basin and the Balkan Peninsula[15,18], and is resistant to pea rust caused by the fungal pathogen Uromyces pisi. Due to its resistance to pea rust, P. fulvum has been cross-bred with cultivated peas in the development of disease-resistant strains[19]. These examples demonstrate the diverse history of the domesticated pea and why further study of the pea pan-plastome could be employed for crop improvement. While studies based on the nuclear genome have been used to explore the domestication history of pea, these approaches do not account for certain factors, such as maternal inheritance. Maternal lineages, which are inherited through plastomes, play a critical role in understanding the full domestication process. A pan-plastome-based approach will no doubt allow us to investigate the maternal genetic contributions and explore evolutionary patterns that nuclear genome studies may overlook. Besides, pan-plastome analysis enables researchers to systematically compare plastome diversity across wild and cultivated species, identifying specific regions of the plastome that contribute to desirable traits. These plastid traits can then be transferred to cultivated crops through introgression breeding or genetic engineering, leading to varieties with improved resistance to disease, environmental stress, and enhanced agricultural performance.

    Plastids are organelles present in plant cells and are the sites in which several vital biological processes take place, such as photosynthesis in chloroplasts[20−24]. Because the origin of plastids is the result of an ancient endosymbiotic event, extant plastids retain a genome (albeit much reduced) from the free-living ancestor[25]. With the advancement of high-throughput DNA sequencing technology, over 13,000 plastid genomes or plastomes have been published in public databases by the autumn of 2023[24]. Large-scale comparison of plastomic data at multiple taxonomic levels has shown that plastomic data can provide valuable insights into evolution, interspecies relationships, and population genetic structure. The plastome, in most cases, displays a conserved quadripartite circular genomic architecture with two inverted repeat (IR) regions and two single copy (SC) regions, referred to as the large single-copy (LSC) and small single-copy (SSC) regions. However, some species have lost one copy of the inverted repeated regions, such as those in Erodium (Geraniaceae family)[26,27] and Medicago (Fabaceae)[28,29]. Compared to previous plastomic studies based on a limited number of plastomes, the construction of pan-plastomes attempts to describe all nucleotide variants present in a lineage through intensive sampling and comparisons. Such datasets can provide detailed insights into the maternal history of a species and help to better understand applied aspects such as domestication history or asymmetries in maternal inheritance, which can help guide future breeding programs. Such pan-plastomes have recently been constructed for several agriculturally important species. A recent study focuses on the genus Gossypium[20], using plastome data at the population level to construct a robust map of plastome variation. It explored plastome diversity and population structure relationships within the genus while uncovering genetic variations and potential molecular marker loci in the plastome. Besides, 65 samples were combined to build the pan-plastome of Hemerocallis citrina[30] , and 322 samples for the Prunus mume pan-plastome[31]. Before these recent efforts, similar pan-plastomes were also completed for Beta vulgaris[32], and Nelumbo nucifera[33]. However, despite the agricultural importance of peas, no such pan-plastome has been completed.

    In this study, 103 complete pea plastomes were assembled and combined another 42 published plastomes to construct the pan-plastome. Using these data, the following analyses were conducted to better understand the evolution and domestication history of pea: (1) genome structural comparisons, (2) codon usage bias, (3) simple sequence repeat patterns, (4) phylogenetic analysis, and (5) nucleotide variation of plastomes in peas.

    Material and methods

    Plant materials, plastome assembly, and genome annotation

    One hundred and three complete pea plastomes were de novo assembled from public whole-genome sequencing data[34]. For data quality control, FastQC v0.11.5 (www.bioinformatics.babraham.ac.uk/projects/fastqc/) was utilized to assess the quality of the reads and ensure that the data was suitable for assembly. The clean reads were then mapped to a published pea plastome (MW308610) plastome from the GenBank database (www.ncbi.nlm.nih.gov/genbank) as the reference using BWA v0.7.17[35] and SAMtools v1.9[36] to isolate plastome-specific reads from the resequencing data. Finally, these plastome-specific reads were assembled de novo using SPAdes v3.15.2[37]. The genome annotation was conducted by Geseq online program (https://chlorobox.mpimp-golm.mpg.de/geseq.html). Finally, the OGDRAW v1.3.1[38] program was utilized to visualize the circular plastome maps with default settings. To better resolve the pan-plastome for peas, 42 complete published pea plastomes were also downloaded from NCBI and combined them with the de novo data (Supplementary Table S1).

    Codon usage and repeat element patterns

    To investigate the codon usage in the pan-plastome of pea, we utilized CodonW v.1.4.2 (http://codonw.sourceforge.net) to calculate the Relative Synonymous Codon Usage (RSCU) value of the protein-coding genes (PCGs) longer than 300 bp, excluding stop codons. The RSCU is a calculated metric used to evaluate the relative frequency of usage among synonymous codons encoding the same amino acid. An RSCU value above 1 suggests that the codon is utilized more frequently than the average for a synonymous codon. Conversely, a value below 1 indicates a lower-than-average usage frequency. Besides, the Effective Number of Codons (ENC) and the G + C content at the third position of synonymous codons (GC3s) were also calculated in CodonW v.1.4.2. The ENC value and GC3s value were utilized for generating the ENC-GC3s plot, with the expected ENC values (standard curve), are calculated according to formula: ENC = 2 + GC3s + 29 / [GC3s2 + (1 – GC3s)2][39].

    The MISA program[40] was utilized to detect simple sequence repeats (SSRs), setting the minimum threshold for repeat units at 10 for mono-motifs, 6 for di-motifs, and 5 for tri-, tetra-, penta-, and hexa-motif microsatellites, respectively.

    Phylogenetic analysis

    The 145 complete pea plastomes were aligned using MAFFT v 7.487[41]. Single nucleotide variants (SNVs)-sites were used to derive an SNV only dataset from the entire-plastome alignment[42]. A total of 959 SNVs were analyzed using IQ-TREE v2.1[43] with a TVMe + ASC + R2 substitution model, determined by ModelTest-NG[44] based on BIC, and clade support was assessed with 1,000 bootstrap replicates. Vavilovia formosa (MK604478) was chosen as an outgroup. The principal coordinates analysis (PCA) was conducted in TASSEL 5.0[45].

    Haplotype and genetic diversity analyses

    DnaSP v6[46] was utilized to identify different haplotypes among the plastomes, with gaps and missing data excluded. Haplotype networks were constructed in Popart v1.7[47] using the median-joining algorithm. Haplotype diversity (Hd) for each group was calculated by DnaSP v6[46], and the evolutionary distances based on the Tamura-Nei distance model were computed based on the population differentiation index (FST) between different groups with the plastomic SNVs.

    Results

    General features of the pea pan-plastome

    In this study, the pan-plastome structure of peas was elucidated (Fig. 1). The length of these plastomes ranged from 120,826 to 122,547 bp. And the overall GC content varied from 34.74% to 34.87%. In contrast to typical plastomes characterized by a tetrad structure, the plastomes of peas contained a single IR copy. The average GC content among all pea plastomes was 34.8%, with the highest amount being 34.84% and the lowest 34.74%, with minimal variation among the pea plastomes.

    Figure 1.  Pea pan-plastome annotation map. Indicated by arrows, genes listed inside and outside the circle are transcribed clockwise and counterclockwise, respectively. Genes are color-coded by their functional classification. The GC content is displayed as black bars in the second inner circle. SNVs, InDels, block substitutions and mixed variants are represented with purple, green, red, and black lines, respectively. Single nucleotide variants (SNVs), block substitutions (BS, two or more consecutive nucleotide variants), nucleotide insertions or deletions (InDels), and mixed sites (which comprise two or more of the preceding three variants at a particular site) are the four categories into which variants are divided.
    DownLoad: Full-Size Img PowerPoint

    A total of 110 unique genes were annotated (Supplementary Table S2), of which 76 genes were PCGs, 30 were transfer RNA (tRNA) genes and four were ribosomal RNA (rRNA) genes. Genes containing a single intron, include nine protein-coding genes (rpl16, rpl2, ndhB, ndhA, petB, petD, rpoC1, clpP, atpF) and six tRNA genes (trnK-UUU, trnV-UAC, trnL-UAA, trnA-UGC, trnI-GAU, trnG-UCC). Additionally, two protein-coding genes ycf3 and rps12 were found to contain two introns.

    Codon usage and simple sequence repeats (SSRs) patterns in peas

    The codon usage frequency in pea plastome genes is shown in Fig. 2a. The analysis of codon usage in the pea plastome indicated significant biases for specific codons across various amino acids. Here a nearly average usage in some amino acids was observed, such as Alanine (Ala) and Valine (Val). For most amino acids, the usage of different synonymous codons was not evenly distributed. Regarding stop codons, a nearly even usage was found, with 37.0% for TAA, 32.2% for TAG and 30.8% for TGA.

    Figure 2.  (a) The overall codon usage frequency in 51 CDSs (length > 300 bp) from the pea pan-plastome. (b) The heatmap of RSCU values in 51 CDSs (length > 300 bp) from the pea pan-plastome. The x-axis represents different codons and the y-axis represents different CDSs. The tree at the top was constructed based on a Neighbor-Joining algorithm.
    DownLoad: Full-Size Img PowerPoint

    The RSCU heatmap (Fig. 2b) showed different RSCU values for all codons in plastomic CDSs. In general, a usage bias for A/T in the third position of codons was found among CDSs in the pea pan-plastome. The RSCU values among these CDSs ranged from 0 to 4.8. The highest RSCU value (4.8) was found with the CGT codon in the cemA gene, where six synonymous codons exist for Arg but only CGT (4.8) and AGG (1.2) were used in this gene. This explained in large part the extreme RSCU value for CGT, resulting in an extreme codon usage bias in this amino acid.

    In the ENC-GC3s plot (Fig. 3), 31 PCGs were shown below the standard curve, while 20 PCGs were above. Besides, around 12 PCGs were near the curve, which meant these PCGs were under the average natural selection and mutation pressure. This plot displayed that the codon usage preferences in pea pan-plastomes were mostly influenced by natural selection. Five genes were shown an extreme influence with natural selection for its extreme ΔENC (ENCexpected – ENC) higher than 5, regarding as petB (ΔENC = 5.18), psbA (ΔENC = 8.96), rpl16 (ΔENC = 5.62), rps14 (ΔENC = 14.29), rps18 (ΔENC = 6.46) (Supplementary Table S3).

    Figure 3.  The ENC-GC3s plot for pea pan-plastome, with GC3s as the x-axis and ENC as the y-axis. The expected ENC values (standard curve) are calculated according to formula: ENC = 2 + GC3s + 29 / [GC3s2 + (1 − GC3s)2].
    DownLoad: Full-Size Img PowerPoint

    For SSR detection (Fig. 4), mononucleotide, dinucleotide, and trinucleotide repeats were identified in the pea pan-plastome including A/T, AT/TA, and AAT/ATT. The majority of these SSRs were mononucleotides (A/T), accounting for over 90% of all identified repeats. Additionally, we observed that A/T and AT/TA repeats were present in all pea accessions, whereas only about half of the plastomes contained AAT/ATT repeats. It was also found that the number of A/T repeats exhibited the greatest diversity, while the number of AAT/ATT repeats showed convergence in all plastomes that possessed this repeat.

    Figure 4.  Simple sequence repeats (SSRs) in the pea pan-plastome. The x-axis represents different samples of pea and the y-axis represents the number of repeats in this sample. (a) The number of A/T repeats in the peapan-plastome. (b) The number of AT/TA and AAT/ATT repeats of pea pan-plastomes.
    DownLoad: Full-Size Img PowerPoint

    Phylogenetic analysis

    To better understand the phylogenetic relationships and evolutionary history of peas, a phylogenetic tree was reconstructed using maximum likelihood for 145 pea accessions utilizing the whole plastome sequences (Fig. 5a). The 145 pea accessions were grouped into seven clades with high confidence. These groups were named the 'PF group', 'PSeI-a group', 'PSeI-b group', 'PA group', 'PSeII group', 'PSeIII group', and the 'PS group'. The naming convention for these groups relates to the majority species names for accessions in each group, where P. fulvum makes up the 'PF group', P. sativum subsp. elatius in the 'PSeI-a group', 'PSeI-b group', 'PSeII group', and 'PSeIII group', P. abyssinicum in the 'PA group', and P. sativum in the 'PS group'. From this phylogenetic tree, it was observed that the 'PSeI-a group' and the 'PSeI-b group' had a close phylogenetic relationship and nearly all accessions in these two groups (except DCG0709 accession was P. sativum) were identified as P. sativum subsp. elatius. In addition to the P. sativum subsp. elatius found in PSeI, seven accessions from the PS group were identified as P. sativum subsp. elatius.

    The PCA results (Fig. 5b) also confirmed that domesticated varieties P. abyssinicum were closer to cultivated varieties PSeI and PSeII, while PSeIII was more closely clustered with cultivated varieties of P. sativum. A previous study has indicated that P. sativum subsp. sativum and P. abyssinicum were independently domesticated from different P. sativum subsp. elatius populations[34].

    The complete plastome sequences were utilized for haplotype analysis using TCS and median-joining network methods (Fig. 5c). A total of 76 haplotypes were identified in the analysis. The TCS network resolved a similar pattern as the other analyses in that six genetic clusters were resolved with genetic clusters PS and PSeIII being very closely related. The genetic cluster containing P. fulvum exhibits greater genetic distance from other genetic clusters. The genetic clusters containing P. abyssinicum (PA) and P. sativum (PS) had lower levels of intracluster differentiation. In the TCS network, Hap30 and Hap31 formed distinct clusters from other haplotypes, such as Hap27, which may account for the genetic difference between the 'PSeI-a group' and 'PSeI-b group'. The network analysis results were consistent with the findings of the phylogenetic tree and principal component analyses results in this study.

    Figure 5.  (a) An ML tree resolved from 145 pea plastomes. (b) PCA analysis showing the first two components. (c) Haplotype network of pea plastomes. The size of each circle is proportional to the number of accessions with the same haplotype. (d) Genetic diversity and differentiation of six clades of peas. Pairwise FST between the corresponding genetic clusters is represented by the numbers above the lines joining two bubbles.
    DownLoad: Full-Size Img PowerPoint

    Among the six genetic clusters, the highest haplotype diversity (Hd) was observed in PSeIII (Hd = 0.99, π = 0.22 × 10−3), followed by PSeII (Hd = 0.96, π = 0.43 × 10−3), PSeI (Hd = 0.96, π = 0.94 × 10−3), PF (Hd = 0.94, π = 0.6 × 10−4), PS (Hd = 0.88, π = 0.3 × 10−4), and PA (Hd = 0.70, π = 0.2 × 10−4). Genetic differentiation was evaluated between each genetic cluster by calculating FST values. As shown in Fig. 5d, except for the relatively lower population differentiation between PS and PSeIII (FST = 0.54), and between PSeI and PSeII (FST = 0.59), the FST values between the remaining clades ranged from 0.7 to 0.9. The highest population differentiation was observed between PF and PA (FST = 0.98). The FST values between PSeI and different genetic clusters were relatively low, including PSeI and PF (FST = 0.80), PSeI and PS (FST = 0.77), PSeI and PSeIII (FST = 0.72), PSeI and PSeII (FST = 0.59), and PSeI and PA (FST = 0.72).

    Nucleotide variation in the pea pan-plastome

    To further determine the nucleotide variations in the pea pan-plastome, 145 plastomes were aligned and nucleotide differences analyzed across the dataset. A total of 1,579 variations were identified from the dataset (Table 1), including 965 SNVs, 24 Block Substitutions, 426 InDels, and 160 mixed variations of these three types. Among the SNVs, transitions were more frequent than transversions, with 710 transitions and 247 transversions. In transitions, T to G and A to C had 148 and 139 occurrences, respectively, while in transversions, G to A and C to T had 91 and 77 occurrences, respectively.

    Table 1.  Nucleotide variation in the pan-plastome of peas.
    Variant Total SNV Substitution InDel Mix
    (InDel, SNV)    		 Mix
    (InDel, SUB)
    Total 1,576 965 24 426 156 4
    CDS 734 445 6 176 103 4
    Intron 147 110 8 29 0 0
    tRNA 20 15 1 4 0 0
    rRNA 11 3 0 6 2 0
    IGS 663 392 9 211 51 0
     | Show Table
    DownLoad: CSV

    When analyzing variants by their position to a gene (Fig. 6), there were 731 variations in CDSs, accounting for 46.3% of the total variations, including 443 SNVs (60.6%), six block substitutions (0.83%), 175 InDels (23.94%), and four mixed variations (14.64%). There were 104 variants in introns, accounting for 6.59% of the total variations, including 78 SNVs (75%), seven block substitutions (6.73%), and 19 InDels (18.27%). IGS (Intergenic spacers) contained 660 variations, accounting for 41.8% of the total variations, including 394 SNVs (59.7%), nine block substitutions (1.36%), 207 InDels (31.36%), and 50 mixed variations (7.58%). The tRNA regions contained 63 variants, accounting for 3.99% of the total variations, including 47 SNVs (74.6%) and 14 InDels (22.2%). The highest number of variants were detected in the IGS regions, while the lowest were found in introns. Among CDSs, accD (183) had the highest number of variations. In introns, rpL16 (18) and ndhA (16) had the most variants. In the IGS regions, ndhD-trnI-CAU (73), and trnL-UAA-trnT-UGU (44) possessed the greatest number of variants.

    Figure 6.  Variant locations within the pea pan-plastome categorized by genic position (Introns, CDS, and IGS).
    DownLoad: Full-Size Img PowerPoint

    Finally, examples of some genes with typical variants were provided to better illustrate the sequence differences between clades (Fig. 7). For example, the present analysis revealed that the ycf1 gene exhibited a high number of variant loci, which included unique single nucleotide variants (SNVs) specific to the P. abyssinicum clade. Additionally, a unique InDel variant belonging to P. abyssinicum was identified. Similar unique SNVs and InDels were also found in other genes, such as matK and rpoC2, distinguishing the P. fulvum clade from others. These unique SNVs and InDels could serve as DNA barcodes to distinguish different maternal lineages of peas.

    Figure 7.  Examples of variant sites.
    DownLoad: Full-Size Img PowerPoint

    Discussion

    Genomic structure

    The present research combined 145 pea plastomes to construct a pan-plastome of peas. Compared to single plastomic studies, pan-plastome analyses across a species or genus provide a higher-resolution understanding of phylogenetic relationships and domestication history. Most plastomes in plants possess a quadripartite circular structure with two inverted repeat (IR) regions and two single copy regions (LSC and SSC)[20−24]. However, the complete loss of one of the IR regions in the pea plastome was observed which is well-known among the inverted repeat-lacking clade (IRLC) species in Fabaceae. The loss of IRs has been documented in detail from other genera such as Erodium (Geraniaceae family)[26,27] and Medicago (Fabaceae family)[28,29]. This phenomenon although not commonly observed, constitutes a significant event in the evolutionary trajectories of certain plant lineages[26]. Such large-scale changes in plastome architecture are likely driven in part by a combination of selective pressures and genetic drift[48]. In the pea pan-plastome, it was also found that, compared to some plants with IR regions, the length of the plastomes was much shorter, and the overall GC content was lower. This phenomenon was due to the loss of one IR with high GC content.

    Repetitive sequences are an important part of the evolution of plastomes and can be used to reconstruct genealogical relationships. Mononucleotide SSRs are consistently abundant in plastomes, with many studies identifying them as the most common type of SSR[49−52]. Among these, while C/G-type SSRs may dominate in certain species[53,54], A/T types are more frequently observed in land plants. The present research was consistent with these previous conclusions, showing an A/T proportion exceeding 90% (Fig. 4). Due to their high rates of mutation, SSRs are widely used to study phylogenetic relationships and genetic variation[55,56]. Additionally, like other plants, pea plastome genes have a high frequency of A/Ts in the third codon position. This preference is related to the higher AT content common among most plant plastomes and Fabaceae plastomes in particular with their single IRs[57,58]. The AT-rich regions are often associated with easier unwinding of DNA during transcription and potentially more efficient and accurate translation processes[59]. The preference for A/T in third codon positions may also be influenced by tRNA availability, as the abundance of specific tRNAs that recognize these codons can enhance the efficiency of protein synthesis[60,61]. However, not all organisms exhibit this preference for A/T-ending codons. For instance, many bacteria have GC-rich genomes and thus show a preference for G/C-ending codons[62−64]. This variation in codon usage bias reflects the differences in genomic composition and the evolutionary pressures unique to different lineages.

    This study also comprehensively examined the variant loci of the pea pan-plastome. Among these variant sites, some could potentially serve as DNA barcode sites for specific lineages of peas, such as ycf1, rpoC2, and matK. Both ycf1 and matK have been widely used as DNA barcodes in many species[65−68], as they are hypervariable. Researchers now have a much deeper understanding of the crucial role plastomes have played in plant evolution[69−71]. By generating a comprehensive map of variant sites, future researchers can now more effectively trace differences in plastotypes to physiological and metabolic traits for use in breeding elite cultivars.

    Evolutionary history

    The development of a pan-plastome for peas provides new insights into the maternal domestication history of this important food crop. Based on the phylogenetic analysis in this study, we observed a clear differentiation between wild and cultivated peas, with P. fulvum being the earliest diverging lineage, and was consistent with former research[34]. The ML tree (Fig. 5a) indicated that cultivated peas had undergone at least two independent domestications, namely from the PA and PS groups, which is consistent with former research[34]. However, as the present study added several accessions over the previous study and plastomic data was utilized, several differences were also found[34], such as the resolution of the two groups, referred to as PSeI-a group and PSeI-b group which branched between the PA group and PF group. Previous research based on nuclear data[34] only and with fewer accessions showed that the PA group and PF group were closely related in phylogeny, with no PSeI group appearing between them. One possible explanation is that the PSeI-a and PSeI-b lineages represents the capture and retention of a plastome from a now-extinct lineage while backcrossing to modern cultivars has obscured this signal in the nuclear genomic datasets. However, procedural explanations such as incorrectly identified accessions might have also resulted in such patterns. In either case, the presence of these plastomes in the cultivated pea gene pool should be explored for possible associations with traits such as disease resistance and hybrid incompatibility. This finding underscores the complexity of the domestication process and highlights the role of hybridization and selection in shaping the genetic landscape of cultivated peas. As such, future studies integrating data from the nuclear genome, mitogenome, and plastome will undoubtedly provide deeper insights into the phylogeny and domestication of peas. This pan-plastome research, encompassing a variety of cultivated taxa, will also support the development of elite varieties in the future.

    Conclusions

    This study newly assembled 103 complete pea plastomes. These plastomes were combined with 42 published pea plastomes to construct the first pan-plastome of peas. The length of pea plastomes ranged from 120,826 to 122,547 bp, with the GC content varying from 34.74% to 34.87%. The codon usage pattern in the pea pan-plastome displayed a strong bias for A/T in the third codon position. Besides, the codon usage of petB, psbA, rpl16, rps14, and rps18 were shown extremely influenced by natural selection. Three types of SSRs were detected in the pea pan-plastome, including A/T, AT/TA, and AAT/ATT. From phylogenetic analysis, seven well-supported clades were resolved from the pea pan-plastome. The genes ycf1, rpoC2, and matK were found to be suitable for DNA barcoding due to their hypervariability. The pea pan-plastome provides a valuable supportive resource in future breeding and selection research considering the central role chloroplasts play in plant metabolism as well as the association of plastotype to important agronomic traits such as disease resistance and interspecific compatibility.

  • The authors confirm contribution to the paper as follows: study conception and design: Wang J; data collection: Kan J; analysis and interpretation of results: Kan J, Wang J; draft manuscript preparation: Kan J, Wang J, Nie L; project organization and supervision: Tiwari R, Wang M, Tembrock L. All authors reviewed the results and approved the final version of the manuscript.

  • The annotation files of newly assembled pea plastomes were uploaded to the Figshare website (https://figshare.com/, doi: 10.6084/m9.figshare.26390824).

    • Acknowledgments

    This study was funded by the Guangdong Pearl River Talent Program (Grant No. 2021QN02N792) and the Shenzhen Fundamental Research Program (Grant No. JCYJ20220818103212025). This work was also funded by the Science Technology and Innovation Commission of Shenzhen Municipality (Grant No. RCYX20200714114538196) and the Innovation Program of Chinese Academy of Agricultural Sciences. We are also particularly grateful for the services of the High-Performance Computing Cluster in the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences.

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Jnanesha AC, Venugopal S, Kumar SR, Kumar A, Bisht D, et al. 2024. Optimization of a new organic approach to natural biostimulant (Jeevamrutha) for yield and quality management in Senna (Cassia angustifolia Vahl.): an agriculturally highly export-oriented crop. Technology in Horticulture 4: e009 doi: 10.48130/tihort-0024-0006
    Jnanesha AC, Venugopal S, Kumar SR, Kumar A, Bisht D, et al. 2024. Optimization of a new organic approach to natural biostimulant (Jeevamrutha) for yield and quality management in Senna (Cassia angustifolia Vahl.): an agriculturally highly export-oriented crop. Technology in Horticulture 4: e009 doi: 10.48130/tihort-0024-0006
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Optimization of a new organic approach to natural biostimulant (Jeevamrutha) for yield and quality management in Senna (Cassia angustifolia Vahl.): an agriculturally highly export-oriented crop

  • 1.

    Crop Production & Protection Divisional Unit, CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Boduppal, Hyderabad, Telangana State, 500092, India

  • 2.

    CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, U.P. State, 226015, India

  • Received: 20 June 2023
  • Revised: 25 December 2023
  • Accepted: 01 March 2024
  • Published online: 02 April 2024
Technology in Horticulture  4,  Article number: e009  (2024)  |  Cite this article

Abstract: Senna is a leguminous and industrial crop that produces high-quality glycosides (sennosides) in its leaves and pods, which have substantial therapeutic effects for alleviating constipation worldwide. However, further research on employing Jeevamrutha in Senna is required. As a result, the experiment was carried out at CSIR-CIMAP in Hyderabad for two consecutive years, in the years 2020–2021 and 2021–2022. The main aim is to identify the optimum dose of Jeevamrutha for higher growth, yield, and quality in Senna. The study used a randomized complete block design (RCBD) with seven treatments repeated three times. From the obtained result, it was observed that the application of 150 L of Jeevamrutha per acre observed significantly high leaf yields (1,085.2 kg·ha−1) and pod (318.7 kg·ha−1) equivalent to T2 in comparison to other treatments, i.e., application of 125 L of Jeevamrutha per acre (1,022.5 kg·ha−1, 312.1 kg·ha−1), and was succeeded by T3, i.e., application of 100 L of Jeevamrutha per acre (998.5 kg·ha−1, 288.5 kg·ha−1, respectively). Lower leaf yield (700.2 kg·ha−1) and pod yield (487 kg·ha−1) were observed in the control (T7). Similarly, the application of 150 L of Jeevamrutha per acre recorded significantly higher sennoside content in leaves (2.01%) and pods (3.11%), in comparison to other treatments, and was followed by T2 (1.98%, 3.09%) and T3 (1.89%, 2.97%). A similar trend was noticed in returns, i.e., the application of 150 L of Jeevamrutha per acre recorded significantly higher gross returns (USD$1,495 ha−1) and net returns (USD$1,066.4 ha−1).

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    Introduction

    • Cassia angustifolia (Caesalpinaceae), known as Tinnevelly or Indian Senna, is cultivated for its leaves and immature pods. Dianthrone glucosides and sennosides A and B in the leaves and pods have potent laxative properties[1,2]. Sennosides primarily operate on the lower colon and are notably beneficial in cases of chronic constipation[1,3]. The glycosides are absorbed from the intestinal system; they stimulate the peristaltic movements of the colon, causing it to move. Long-term usage of the leaves may induce colon problems and produce grip if not paired with carminatives. The National Medicinal Plant Board (NMPB) of India has identified 32 plants for scaling up, and Senna is one of them. Senna is the second-largest earner of foreign exchange through exports. Its leaves and pods are regarded as reliable sennoside sources in global trade[4]. However, Indian Senna should compete with Alexandrian Senna regarding cost-effectiveness and quality. Alexandrian senna natural collections cannot supply the growing demand for Senna commodities. India has a tremendous opportunity to expand its manufacturing, commerce, and export opportunities. Tinnevelly Senna (C. angustifolia) is grown in India's southern and central parts[5]. Senna herbage production is estimated to be around 7,500 tonnes per year. The pods and leaves of a few other senna species, the most important of which is Alexandrian Senna, have laxative properties similar to those of Cassia angustifolia. Alexandrian Senna grows naturally in North African countries such as Ethiopia and Sudan[1,2].

      The swiftly increasing global population and continuously expanding geographical boundaries of the global agricultural system are extending agricultural activities on marginal soils unsuited for growing. On such terrain, crop options are limited, especially in an arid macroregion. Senna is a tropical medicinal plant that could be a dry-land crop for barren land. Areas with inadequate irrigation facilities (arid or semi-arid) are ideal for Senna cultivation, while regions with heavy rainfall, high humidity, and poor drainage are not perfect[6−8]. Senna grows as a perennial shrub in dry areas of Africa and neighboring countries. The Senna crop is commercially grown in all sub-tropical regions of India and spread in semi-arid parts of southern India; it is marketed under the brand name 'Tirunelveli Senna' (C. angustifolia)[3,9,10]. Tuticorin has many exporters, shipping 7,500 to 9,000 tonnes of Senna leaves each year and earning Rs 35 to 60 crore in forex 'depending on the current market price'[9].

      Modern agriculture relies heavily on chemical fertilizers to cope with the demands of a growing population. The continued use of inorganic fertilizers endangers soil health. The beneficial microorganisms decline, and natural nutrition restoration in the soil ceases, causing the soil to become unfertile[9,10]. As a result, the use of organic manure and proportionate inorganic fertilizers needs to be reduced to improve the quality and productivity of the crop's food grain, oilseed, or medicinal crop. This gradually results in a significant need for integrated nutrient management (INM), which will boost soil productivity continuously over time through the appropriate use of fertilizers and liquid organic manure[11,12].

      Organic farming has recently risen in popularity because of its inherent benefits. It contributes to crop production sustainability, complex soil nutrient status, and a clean environment[11,12]. Using fermented liquid organic manure or bio-enhancers like Jeevamrutha is a less expensive and eco-friendly preparation made from cow products. A natural biostimulant (Jeevamrutha) is a plant growth stimulant that increases crop biological efficiency[13]. It aids in accelerating soil, protects plants from diseases, and enhances the nutritional content of fruits and vegetables. It has been utilized in seedling treatment, soil application with irrigation water, foliar spraying, and much more.

      The application of liquid manure boosts microbial activity and biomass in the soil. The use of liquid organic inputs like Jeevamrutha boosts the population of beneficial bacteria and has a substantial impact on soil enzyme activity. As a result, they promote crop growth and help to maintain a safe environment and production of crops. Given the foregoing, the experiment was conducted at CSIR-CIMAP, RC, Hyderabad, with the aim of establishing the optimal doses of Jeevamrutha for increasing Senna quality and production.

    Materials and methods

      Experimental field and statistical design

    • A trial was undertaken in the CSIR-CIMAP R.C. in Hyderabad, India, for two consecutive years, 2020−2021 and 2021−2022 in the Rabi season (September to January). The experimental site's latitude, longitude, and altitude were 17°25' N, 78°33' E, and 582 m above mean sea level. Table 1 lists further information, including the climatic conditions. The experiment was laid out in a randomized complete block design (RCBD) with three replications on well-drained, red sandy soil (Table 1).

      Table 1.  Location, climate and soil of CSIR-CIMAP R.C. at Boduppal, Hyderabad, Telangana State, PIN: 500 092, India and chemical composition of bio stimulant.

      GPS coordinates, soil and climateEstimated parameters of bio stimulant (Jeevamrutha)
      Latitude17°25' N
      Longitudes78º33' EpH7.08
      Mean sea level582 m aboveEC (dS·m−1)2.98
      ClimateSemi-arid tropicalTotal nitrogen (ppm)67
      Average annual rainfall764 mmTotal phosphorus (ppm)154
      SoilRed sandy soil (79.2% sand, 9.8% silt, 6.8% clay)
      		Total potassium (ppm)112
      pH7.7Total zinc (ppm)3.52
      EC0.77 dS·m−1Total copper1.32
      Organic carbon0.29%Total iron (ppm)12.4
      Available N162.4kg·ha−1Total manganese (ppm)7.4
      Available P9.2 kg·ha−1IAA (ppm)5.9
      Available K272.6 kg−1GA3 (ppm)3.1

      Preparation of biostimulant/ Jeevamrutha

    • The method of Palekar was used to prepare the organic liquid formulation Jeevamrutha[14]. The following were the ingredients: 10 kg cow dung, 10 L of cow urine of Gir cow breeds, 2 kg jaggery, 2 kg gram/chickpea (pulse) flour, a handful of rhizospheric soil, and 200 L of water were well combined in a stainless steel container with the help of a wooden stick. The cow dung and urine source was a local dairy farm located at Boduppal, Hyderabad, Telangana State, 500092, India. The mixture was mixed twice daily and fermented for 5–7 d. The prepared liquid formulation was used for soil application by applying irrigation water. In the Department of Soil Chemistry Laboratory at the Council of Scientific Research-Central Institute of Medicinal and Aromatic Plants, Boduppal, Hyderabad, Telangana State, 500092, India, the chemical composition of the biostimulant (Jeevamrutha) was determined. The results are presented in Table 1.

      Treatments

    • The treatments were comprised of seven treatments with three replications, viz., T1: application of 150 L of Jeevamrutha per acre, T2: application of 125 L of Jeevamrutha per acre, T3: application of 100 L of Jeevamrutha per acre, T4: application of 75 L of Jeevamrutha per acre, T5: application of 50 L of Jeevamrutha per acre, T6: application of 25 L of Jeevamrutha per acre, and T7: control (treated with water).

      Recommended cultivation practices

    • Senna (C. Angustifolia) var: Sona seeds were soaked in water for a whole night and treated with Trichoderma to minimize the seeds' correlation with diseases before dibbling in the field at 45 cm × 30 cm spacing. The field was irrigated for the first few weeks; one weeding was performed 30 d after seeding, and N:P:K (kg·ha−1) was applied at the seeding time.

      Quantitative and qualitative traits evaluated

    • Growth and yield contributing attributes were recorded at regular intervals at various phases of plant growth. The sennoside content of leaves and pods was determined using the HPLC method developed by Rama Reddy et al.[15] at the pod formation stage. Finely ground samples of dry leaves and pods (300 mg) were extracted three times with sonication (25 °C) in 30 ml of 70% methanol in water. Before being fed into the chromatographic equipment, the materials were filtered through a 0.45 m membrane. The HPLC study was conducted on a Waters HPLC system outfitted with an SPD-M20 photodiode array detector.

      The dilution plate technique determined each treatment's fungal, bacterial, and actinomycete populations[10,13,16]. For each treatment, a composite of 10 g of soil samples was extracted, and 1 g of each sample was suspended in 1 mL sterile saline (1g NaCl in 100 mL distilled H2O) in a sterile test tube and carefully vortexed. Different treatment tubes were employed to count fungi, bacteria, and actinomycetes as part of the inoculation. Soil samples were taken from the rhizosphere of plants for counting microbial load at harvest for N-fixers and P-solubilizers. Ten grams of soil was serially diluted up to 10−6 by using sterilized distilled water, and cell count per gram of rhizosphere soil was enumerated for P-solubilizers and free-living N-fixer by Pikovaskaya's media (Himedia) and Waksman No.77[13,17,18], respectively, by following the serial dilution plate count technique.

      Soil dehydrogenase activity was determined by reducing 2,3,5-triphenyl tetrazolium chloride[2,10,19]. Protease activity was measured by measuring the amount of tyrosine produced after incubating 1 g of the oven-dry equivalent of a field-moist soil sample in 5 ml of 50 mM Tri's buffer (pH 8.1) and 5 ml of 2% Na-caseinate for 2 h at 50 + 1 °C. The aromatic amino acids were removed, and the residual substrate was precipitated with 0.92 M trichloroacetic acid and calorimetrically quantified at 700 nm using the Folin-Ciocalteu reagent. Protease activity was quantified as mg tyrosine generated g−1·soil·h−1.

      Acid and alkaline phosphatase activities were determined using a standard approach[20]. In a 50 ml flask, 1 g of soil was mixed with 0.2 mL toluene, 4 mL of modified universal buffer (MUB) (pH 6.5 and 11, respectively, for acid and alkaline phosphatase), and 1 mL of p-nitrophenyl phosphate solution. After an hour of incubation, 1 mL of 0.5 M CaCl2 and 4 mL of 0.5 M NaOH were added. After the suspension was filtered, the filtrate's absorbance at 420 nm was measured using a UV-visible spectrophotometer. Controls were prepared by repeating the phosphatase activity assay technique but adding 1 mL of p-nitrophenol solution after adding 0.5 M CaCl2 and 4 mL of 0.5 M NaOH. Determination of β-glucosidase enzyme involves colorimetric estimation of P-nitrophenol released by β-glucosidase activity when soil is incubated in Mcilvaine buffer (pH 4.8) with P-nitrophenyl β-D-glucoside and toluene at 30 °C for 1 h[21] (Fig. 1).

      Figure 1. 

      Field view of the experimental plot of Senna crop.

      Economics

    • The benefit of gross returns was determined by multiplying the total yield by the present cost of each kilogram. The cost of cultivation for each treatment was calculated by summing up the seed cost, land preparation, labour, cultural operations, pesticides, and manure costs. Net returns were computed by subtracting manufacturing costs from gross returns. The benefit-cost ratio was determined by calculating the ratio between cultivation costs and gross returns. It is obtained by dividing the gross returns by the cost of cultivation in USD$·ha−1.

      Statistical analysis

    • The analysis of variance (ANOVA) was performed on the pooled data for the experimental years 2020−2021 and 2021−2022 using CSIR-CIMAP statistical software Ver. 4.0[22].

    Results and discussion

      Influence of biostimulant/Jeevamrutha on growth parameters

    • The obtained results reveal that Jeevamrutha application had a significant influence on all of the characteristics of Senna (C. angustifolia). Amid the various doses of Jeevamrutha, the application of 150 L of Jeevamrutha recorded significantly higher plant height (T1; 43.7 cm) compared to another dose of application and was comparable to the applications of 125 L of Jeevamrutha per acre (T2; 40.2 cm) and 100 L of Jeevamrutha per acre (T3; 39.2 cm). Significantly, lower plant height was noticed in control (T7; 26.9 cm) and was on par with applying Jeevamrutha at 25 L per acre (T6; 29.9 cm). The number of branches and plant leaves per plant, and total dry matter production all followed a similar pattern. Applying 150 L (T1) of biostimulant/Jeevamrutha per acre recorded a substantially higher branch per plant, leaves per plant, and total dry matter production (19.9, 180.3, and 35.9 g·plant−1). It was on par with (T2) 125 L of Jeevamrutha (17.2, 177.2, and 34.2 g·plant−1), and the application of 100 L (T3) of Jeevamrutha (16.8, 176.4 and 33.1 g·plant−1). Senna's plant height and dry matter content may have improved substantially due to the availability of micronutrients and a big beneficial microbial population in Jeevamrutha[1−3,23]; thus, when applied to the crop as a foliar spray and through the soil, they stimulate the necessary plant growth, which encourages vegetative growth and finally increases plant height and metabolic and photosynthetic activity for improving the biological efficiency of the plant, allowing the roots to spread into deeper layers of soil and uptake more nutrients from the soil, resulting in the accumulation of more carbohydrates and higher dry matter. Our results are consistent with those of other researchers[3,16,24−26]. Whereas, chlorophyll content, leaf area, and index also differed significantly with the use of a varied dose of Jeevamrutha, with the application of 150 L (T1) of Jeevamrutha per acre recording significantly higher chlorophyll content (13.2), leaf area (66.2 cm2) and LAI (4.89) comparison with the other treatments and was succeeded with T2 (12.1, 64.2 cm2, 4.76) and T3 (10.2, 63.9 cm2, 4.73) (Fig. 2). The use of Jeevamrutha resulted in faster synthesis, translocation, and accumulation of photosynthates from sources to sinks, ultimately contributing to higher growth and yield metrics (Tables 1 & 2, Fig. 2). These findings are consistent with those of other studies[27,28] in Senna.

      Figure 2. 

      Influence of different doses of biostimulant/Jeevamrutha on leaf yield (kg·ha−1) and pod yield (kg·ha−1) of Senna.

      Table 2.  Microbial population in bio stimulant.

      OrganismsBio stimulant (Jeevamrutha)
      Bacteria (cfu·mL−1)15.42 × 105
      Fungi (cfu·mL−1)12.12 × 103
      Actinomycetes (cfu·mL−1)2.92 × 103
      Free-living nitrogen fixers (cfu·mL−1)5.20 × 102
      Phosphate solubilizing organisms (cfu·mL−1)3.20 × 102

      Effect of biostimulant/Jeevamrutha on the pods per plant

    • The pods/plant produced significantly depended on the dose of Jeevamrutha used. Among the various Jeevamrutha dosages, the application of Jeevamrutha at 150 L per acre recorded significantly higher pods per plant (T1; 726) compared to other treatments and was on par with (T2; 720) and (T3; 689). The significantly lower pods per plant were noticed in control (T7; 700.8) and were followed by T6 (T6; 758.9) (Table 3). The increase in pods per plant might be due to Jeevamrutha, which increases the production of growth hormones, viz., IAA, GA, and dehydrozeatin, resulting in good pod characteristics[1,29,30]. These phytohormones increased cell proliferation, elongation, and nutrient uptake, increasing pods per plant. Ramesh Babu[31] found similar results in Ashwagandha (Table 3).

      Table 3.  Effect of different doses of bio stimulant (Jeevamrutha) on growth and yield parameters of Senna in semi-arid regions of India.

      TreatmentsPlant height (cm)No. of branches per plantNo. of leaves per plantTotal dry matter production (g·plant−1)Chlorophyll contentLeaf areaLAINo of pods
      per plant
      T143.719.9180.335.9113.2566.024.89726
      T240.217.2177.234.2512.1364.214.76720
      T339.216.8176.433.1210.2463.924.73689
      T434.214.2165.229.749.2359.214.39654
      T531.513.8154.725.159.0156.274.17598
      T629.910.2144.323.218.7855.324.10546
      T726.98.5135.221.588.0349.133.64487
      S.Em±1.820.912.81.340.521.40.1118.2
      CD (P = 0.05)5.412.748.44.021.564.20.3454.7
      T1: 150 L of bio stimulant per acre, T2: 125 L of bio stimulant per acre, T3: 100 L of bio stimulant per acre, T4: 75 L of bio stimulant per acre, T5: 50 L of bio stimulant per acre, T6: 25 L of bio stimulant per acre, T7: Control.

      Effect of Jeevamrutha on leaf and pod yield

    • Leaf and pod yield of C. angustifolia differ significantly with a varied dose of Jeevamrutha. Among the varied treatments, the application of 150 L (T1) of Jeevamrutha per acre recorded significantly higher leaf yield (1,085.2 kg·ha−1) and pod yield (318.7kg·ha−1) in comparison to the rest of the treatments. It was on par with T2 i.e., applying 125 L of Jeevamrutha per acre (1,022.5 kg·ha−1, 312.1 kg·ha−1) followed by T3, i.e., application of 100 L of Jeevamrutha per acre (998.5 kg·ha−1, 288.5 kg·ha−1, respectively). Significantly, lower leaf (700.2 kg·ha−1) and pod yield (487 kg·ha−1) were noticed in the control (T7) (Fig. 3). Raised nutrient availability, enhanced soil health, and an appropriate supply of macro and micronutrients might all have contributed to the rise in leaf and pod yield, which raised seed yield. Furthermore, Jeevamrutha may have created a favorable environment in the soil for nitrogen buildup in addition to boosting nutrient availability (Fig. 3). Hemalatha et al.[32] found similar results in kalmegh[13,32], and Kalyanasundaram et al.[33] in the sweet flag, and Anuja & Jayasri[34] in sweet basil[30,34]. The sustained availability of nutrients by applying Jeevamrutha throughout the cropping period increased soil microbial activity, and the photosynthetic rate might have increased the leaf and pod yield[4,8,35−38].

      Figure 3. 

      Influence of biostimulant/Jeevamrutha on gross and net return in Senna.

      Effect of biostimulant/Jeevamrutha on sennoside content

    • Despite the Jeevamrutha dose, the sennoside concentration of Senna (C. angustifolia) pods is always higher than that of the leaves. Sennoside content in both leaf and pod altered drastically following Jeevamrutha treatment, as seen in (Table 2). Among the different treatments, T1, i.e., application of 150 l of Jeevamrutha per acre, recorded significantly higher sennoside content in leaves (2.01%) and pods (3.11%) in comparison to the rest of the treatment and was followed by T2 (1.98%, 3.09%) and T3 (1.89%, 2.97%). This feature could be related to an increase in enzyme activity associated with the sennoside biosynthesis pathway, as well as a shift from primary to secondary metabolite synthesis[39−43]. Lower sennoside content in leaves and pods is recorded in control (T7; 1.52%, 2.42%). A similar trend was noticed in sennoside yield with T1, i.e., application of Jeevamrutha at 150 L per acre recorded significantly higher sennoside yield (31.7 kg−1) compared to other treatments. It was followed by T2 (29.9 kg·ha−1) and T3 (27.4 kg·ha−1). Lower sennoside yield was noticed in control (T7; 15.2 kg·ha−1) (Table 4). This attribute might be owing to increased yield and sennoside content in the leaf and pod, which in turn, increase the sennoside yield in T1 and T2 treatments, i.e., application of Jeevamrutha at 150 and 125 L per acre, respectively (Tables 4 & 5).

      Table 4.  Effect of bio stimulant (Jeevamrutha) on sennoside content in leaves and pod and sennoside yield.

      TreatmentsSennoside content (%)Sennoside yield
      (kg·ha−1)
      LeavesPod
      T12.013.1131.7
      T21.983.0929.9
      T31.892.9727.4
      T41.932.6922.8
      T51.872.6620.5
      T61.692.5917.9
      T71.522.4215.2
      S.Em±0.030.061.2
      CD (P = 0.05)0.090.123.7
      T1: 150 L of bio stimulant per acre, T2: 125 L of bio stimulant per acre, T3: 100 L of bio stimulant per acre, T4: 75 L of bio stimulant per acre, T5: 50 L of bio stimulant per acre, T6: 25 L of bio stimulant per acre, T7: Control.

      Table 5.  Effect of different doses of bio stimulant (Jeevamrutha) on beneficial microorganisms in the soil.

      TreatmentsBacteria
      (cfu·g−1)      		Fungi
      (cfu·g−1)      		Actinomycetes
      (cfu·g−1)      		Nitrogen fixer
      (cfu·g−1)      		P solubilizers
      (cfu·g−1)
      T18.2 × 1057.3 × 1044.1 × 1031.9 × 1033.9 × 103
      T27.6 × 1056.8 × 1044.0 × 1032.1 × 1033.2 × 103
      T37.1 × 1056.2 × 1043.7 × 1031.7 × 1032.7 × 103
      T46.7 × 1055.8 × 1043.6 × 1031.8 × 1032.5 × 103
      T56.0 × 1055.1 × 1043.4 × 1031.2 × 1031.9 × 103
      T66.2 × 1054.9 × 1042.8 × 1031.4 × 1031.7 × 103
      T75.7 × 1054.2 × 1042.2 × 1031.3 × 1031.6 × 103
      S.Em±0.3 × 1050.4 × 1040.23 × 1030.3 × 1030.1 × 103
      CD
      (P = 0.05)      		0.9 × 1051.2 × 1040.55 × 103NS0.3 × 103
      T1: 150 L of bio stimulant per acre, T2: 125 L of bio stimulant per acre, T3: 100 L of bio stimulant per acre, T4: 75 L of bio stimulant per acre, T5: 50 L of bio stimulant per acre, T6: 25 L of bio stimulant per acre, T7: Control.

      Effect of biostimulant/Jeevamrutha on beneficial microorganisms and enzyme activity

    • Beneficial microorganisms in soil differ significantly with the application of different doses of Jeevamrutha in Senna; with an application of 150 L of Jeevamrutha per acre recorded significantly higher bacteria (8.2 × 105 cfu·g−1), fungi (7.3 × 104 cfu·g−1), actinomycetes (4.1 × 103 cfu·g−1) and P solubilizers (3.9 × 103 cfu·g−1) compared to rest of the treatment and was on par with the application of 150 L of Jeevamrutha per acre (7.6 × 105 cfu·g−1, 6.8 × 104 cfu·g−1, 3.7 × 103 cfu·g−1, and 2.7 × 103 cfu·g−1, respectively).

      Nonetheless, the greater dose of Jeevamrutha resulted in a more substantial microbial population, which might be ascribed to Jeevamrutha acting as a source of carbon and energy for microorganisms, boosting the number of microorganisms in the soil. However, a significantly lower microbial population was noticed in control, i.e., bacteria (5.7 × 105 cfu·g−1), fungi (4.2 × 104 cfu·g−1), actinomycetes (2.2 × 103 cfu·g−1), and P solubilizers (1.6 × 103 cfu·g−1). The low microbial population counts in control could be attributed to a lack of substrate to sustain microbial biomass. The acquired results are consistent with the findings of Boraiah et al.[44]. Similarly, enzyme activity in soil differs dramatically when Jeevamrutha is applied to Senna. Among the different doses of Jeevamrutha, the application of 150 L of Jeevamrutha per acre recorded significantly higher dehydrogenase activity (1.33 µg·TPF−1·g−1·h−1), alkaline phosphatase (412 µg·TPF−1·g−1·h−1), acid phosphatase (367 µg·TPF−1·g−1·h−1), β-Glucosidase (120 µg·TPF−1·g−1·h−1) and protease (154 µg·TPF−1·g−1·h−1) compared to rest of the treatment and was followed by application of 125 L of Jeevamrutha per acre (1.17 µg·TPF−1·g−1·h−1, 374 µg·TPF−1·g−1·h−1, 355 µg·TPF−1·g−1·h−1, 99 µg·TPF−1·g−1·h−1 and 123 µg·TPF−1·g−1·h−1). Enzymatic activity was considerably lower in the control group.

      Nonetheless, the increased enzymatic activity in the soil can be attributed to the important function of the microbial population as a result of the addition of Jeevamrutha, which acted as a tonic for enhanced microbial development[1,2,4,29]. Enzymatic activity in the soil may have increased due to favorable bacterial environments (Tables 5 & 6). The higher enzymatic activity in the Jeevamrutha plot could be explained by enhanced microbial activity[44−47].

      Table 6.  Effect of different doses of bio stimulant (Jeevamrutha) on enzyme activity in the soil.

      TreatmentsDehydrogenase activity (µg·TPF−1·g−1·h−1)Alkaline phosphatase (µg·TPF−1·g−1·h−1)Acid phosphatase (µg·TPF−1·g−1·h−1)β-Glucosidase
      (µg·TPF−1·g−1·h−1)      		Protease
      (µg·TPF−1·g−1·h−1)
      T11.33412367120154
      T21.1737435599123
      T30.9038224884120
      T40.7529120175100
      T50.542771556585
      T60.481321125059
      T70.4188552922
      SEm±0.1512.87.13.94.8
      CD (P = 0.05)0.4538.221.411.714.1
      T1: 150 L of bio stimulant per acre, T2: 125 L of bio stimulant per acre, T3: 100 L of bio stimulant per acre, T4: 75 L of bio stimulant per acre, T5: 50 L of bio stimulant per acre, T6: 25 L of bio stimulant per acre, T7: Control.

      Effect of biostimulant/ Jeevamrutha on economics

    • Economics of Senna (C. angustifolia) may differ significantly about the varied application of Jeevamrutha, with the application of 150 L (T1) of Jeevamrutha per acre recorded substantially higher gross return per ha (USD$1,495) and Net return (USD$1,066.4 compared to other treatments and was on par with the application of 125 L (T2) of Jeevamrutha per acre (USD$1,423.8 and 995.2 respectively) and was followed by T3 (USD$1,369.4 and 940.9). Significantly lower gross return (USD$942.9) and net returns (USD$585.8) were noticed in control (T7) (Fig. 4). Similarly, the benefit-cost ratio differed significantly from T1, i.e., the application of 150 L of Jeevamrutha per acre recorded a higher benefit-cost ratio (3.49) than other treatments. T2 applied 125 L of Jeevamrutha per acre (3.32) (Tables 5−7). In contrast, a lower benefit-cost ratio was noticed in control (T7; 2.64) and was followed by T6 (2.72) (Table 7, Fig. 4).

      Figure 4. 

      Application of 150 L (T1) of Jeevamrutha to Senna crop.

      Table 7.  Effect of different doses of bio stimulant (Jeevamrutha) on gross and net return of Senna.

      TreatmentsGross return
      (USD$·ha−1)      		Net return
      (USD$·ha−1)      		Benefit-cost ratio
      T11,495.01,066.43.49
      T21,423.8995.23.32
      T31,369.4940.93.20
      T41,154.9726.32.99
      T51,067.9689.32.82
      T61,009.4637.92.72
      T7942.9585.82.64
      S.Em±21.821.8
      CD (P = 0.05)64.564.5
      T1: 150 L of bio stimulant per acre, T2: 125 L of bio stimulant per acre, T3: 100 L of bio stimulant per acre, T4: 75 L of bio stimulant per acre, T5: 50 L of bio stimulant per acre, T6: 25 L of bio stimulant per acre, T7: Control.

      Finally, Jeevamrutha is a natural fertilizer that can be used in place of chemical fertilizers. It is a type of organic liquid fertilizer used in organic farming and gardening. It is made from natural ingredients and is believed to be a sustainable and eco-friendly alternative to synthetic fertilizers. While it can be a valuable addition to organic farming practices, it's important to note that its nutrient content, including NPK (Nitrogen, Phosphorus, and Potassium), varies depending on how it's prepared. In general, Jeevamrutha is not typically formulated to have specific NPK values like synthetic fertilizers. Instead, its primary focus is on improving soil health and promoting microbial activity in the soil, which can lead to better nutrient availability for plants over time. It is rich in beneficial microorganisms, such as beneficial bacteria, fungi, and other soil organisms, which help break down organic matter and release nutrients in a form that plants can absorb. Jeevamrutha is more of a soil conditioner and biofertilizer that enhances soil fertility and overall plant health rather than directly providing specific nutrient values like NPK ratios. It is used to improve the structure and fertility of the soil and is often considered a holistic approach to sustainable agriculture. If farmers are looking for specific NPK values in fertilizer, they may need to consider synthetic fertilizers or other organic fertilizers that provide more precise nutrient content. However, many organic and sustainable farmers prefer using Jeevamrutha and similar products to support long-term soil health and reduce their reliance on chemical fertilizers. It is high in macronutrients and micronutrients, which are necessary for plant growth and development. Jeevamrutha promotes microbial activity, which enhances soil fertility. When compared to previous Jeevamrutha doses, using Jeevamrutha at 150 (T1) or 125 (T2) L per acre resulted in significantly higher leaf, pod, and sennoside yields. Meanwhile, increased leaf and pod production from a higher Jeevamrutha dose boosts Senna's gross and net returns, as well as the benefit-cost ratio.

    Conclusions

    • Jeevamrutha is a natural fertilizer that can replace chemical fertilizers. It is an excellent source of macro and micro nutrients for plant growth and development. Jeevamrutha improves soil fertility by stimulating microbial activity. The current study found that applying Jeevamrutha at 150 (T1)/125 (T2) L per acre resulted in significantly higher leaf, pod, and sennoside yields when compared to other Jeevamrutha doses. Meanwhile, increased leaf and pod production from a higher dose of biostimulant/Jeevamrutha raises Senna's gross and net returns and the benefit-cost ratio.

    Author contributions

    • The authors confirm contribution to the paper as follows: study planning, actual experimentation: Jnanesha AC; experimentation: Venugopal S, Kumar SR; Kumar A; data collection: Bisht D; Chemical analysis: Chanotiya CS; statistical analyses, and manuscript preparation: Lal RK. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

      Acknowledgments

      • This work was supported by the Council of Scientific and Industrial Research, India, under HCP 010; the last author is related to an emeritus scientist, CIMAP Publication No. CIMAP/PUB/2021/118. The authors are thankful to the director of CSIR-CIMAP Lucknow, India, for providing facilities and encouragement throughout the work. Thanks also to the Scientist-in-Charge at CRC Hyderabad for the necessary facilities during this investigation.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (4)  Table (7) References (47)
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    Cite this article
    Jnanesha AC, Venugopal S, Kumar SR, Kumar A, Bisht D, et al. 2024. Optimization of a new organic approach to natural biostimulant (Jeevamrutha) for yield and quality management in Senna (Cassia angustifolia Vahl.): an agriculturally highly export-oriented crop. Technology in Horticulture 4: e009 doi: 10.48130/tihort-0024-0006
    Jnanesha AC, Venugopal S, Kumar SR, Kumar A, Bisht D, et al. 2024. Optimization of a new organic approach to natural biostimulant (Jeevamrutha) for yield and quality management in Senna (Cassia angustifolia Vahl.): an agriculturally highly export-oriented crop. Technology in Horticulture 4: e009 doi: 10.48130/tihort-0024-0006
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  • Introduction
  • Materials and methods

  • Experimental field and statistical design

  • Preparation of biostimulant/ Jeevamrutha

  • Treatments

  • Recommended cultivation practices

  • Quantitative and qualitative traits evaluated

  • Economics

  • Statistical analysis

  • Results and discussion

  • Influence of biostimulant/Jeevamrutha on growth parameters

  • Effect of biostimulant/Jeevamrutha on the pods per plant

  • Effect of Jeevamrutha on leaf and pod yield

  • Effect of biostimulant/Jeevamrutha on sennoside content

  • Effect of biostimulant/Jeevamrutha on beneficial microorganisms and enzyme activity

  • Effect of biostimulant/ Jeevamrutha on economics

  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
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