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MAP  >  Technology in Agronomy
2023 Volume 3
Article Contents
Abstract
Introduction
Structure and distribution of main bioactive constituents of Lonicera
Biosynthesis and regulation of the main bioactive constituents of Lonicera
Biosynthesis of chlorogenic acids and flavonoids
Biosynthesis of iridoids and triterpene saponins
Metabolic engineering of the main bioactive compounds of the Lonicera genus
Metabolic engineering of chlorogenic acid, luteolin and their glycosides
Metabolic engineering of iridoids and triterpene saponins
Pharmacological activities of the main bioactive compounds of Lonicera
Anti-inflammatory effects
Antimicrobial effects
Anti-oxidative stress effect
Hepatoprotection
Other pharmacological effects
Chloroplast genomic and phylogenetic relationships of Lonicera species
Conclusions and future perspectives
Author contributions
Data availability
Acknowledgments
Conflict of interest
Rights and permissions
References
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ARTICLE   Open Access    

Agronomic response of soybeans (Glycine max (L.) Merrill) to different management practices in the humid tropics

  • 1.

    Crop Research Programme, Institute of Food Security, Environmental Resources and Agricultural Research (IFSERAR), Federal University of Agriculture, Abeokuta (FUNAAB), P.M.B. 2240, Abeokuta, Nigeria

  • 2.

    Department of Plant Physiology and Crop Production, Federal University of Agriculture, Abeokuta (FUNAAB), Federal University of Agriculture, Abeokuta, Nigeria

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  • Organic soybean (Glycine max (L.) Merrill) remains an under exploited global market niche by farmers in the tropics. Therefore, a study was conducted in two consecutive years to determine the agronomic performance of soybean under different management systems in the humid tropics. The study was carried out during the late cropping season (July–Nov.) in 2020 and 2021 on the organic research plots of the Institute of Food Security, Environmental Resources and Agricultural Research, Nigeria. The six management practices (MP) evaluated were MP1 – Control, MP2 – NoduMax, MP3 – Organic fertilizer, MP4 – NoduMax + Organic fertilizer, MP5 – Conventional, MP6 – Conventional + NoduMax in a Randomized Complete Block Design and replicated three times. Data were collected on phenology, nodulation, grain yield and yield attributes of soybeans. Number of days to full bloom (R2) and physiological maturity (R7) were significantly (p < 0.05) affected by MP in both years, except R7 in 2021. Inoculation with NoduMax (MP2) enhanced nodule number at 8 and 9 weeks after sowing (WAS) during the wetter year 2021. Number of branches and seeds per plant, seed weight per plant and grain yield were significantly (p < 0.05) affected by MP in both years. Soybean grain yield recorded under MP3 and MP4 ranged between 1,198.8–1,962.0 kg/ha in both years and compared well with the average yield values in Nigeria (926.9 kg/ha) and Africa (1,348.0 kg/ha), and significantly below the world (2,784.2 kg/ha). Therefore, MP3 and MP4 can be recommended for potential resource-constrained organic soybean farmers in the humid tropics.

  • Introduction

    The Lonicera Linn. genus is a constituent member of the Caprifoliaceae family[1]. It is the largest genus in this family and comprises at least 200 species with a notable presence in North Africa, North America, Asia, and Europe[1]. Members of the Lonicera genus possess a wide range of economic benefits from their use as ornamental plants to food and as plants credited with numerous health benefits. Conspicuous among the numerous members of this genus with known medicinal uses are L. japonica, L. macranthoides, L. hypoglauca, L. confusa, and L. fulvotomentosa[2]. Though these species feature prominently in the Chinese Pharmacopoeia, other species such as L. acuminata, L. buchananii, and L. similis are recognized as medicinal resources in certain parts of China[1]. Among the aforementioned species, L. japonica takes precedence over the rest due to its high medicinal and nutritional value[3,4]. For instance, the microRNA MIR2911, an isolate from L. japonica, has been reported to inhibit the replication of viruses[5−7]. Also, the water extract of L. japonica has been used to produce various beverages and health products[8]. The Lonicera genus therefore possesses huge prospects in the pharmaceutical, food, and cosmetic industries as an invaluable raw material[9].

    The main active constituents of the Lonicera genus include organic acids, flavonoids, iridoids, and triterpene saponins. Chlorogenic acids, iridoids, and flavones are mainly credited with the anti-inflammatory, antiviral, anticancer, and antioxidant effects of the various Lonicera species[10−13]. Their hepatoprotective, immune modulatory, anti-tumor and anti-Alzheimer’s effects are for the most part ascribed to the triterpene saponins[14−16]. As stated in the Chinese Pharmacopoeia and backed by the findings of diverse research groups, the plants of the Lonicera genus are known to possess high amounts of organic acids (specifically chlorogenic acid) and pentacyclic triterpenoid saponins[2,17−19]. The flower and flower bud have traditionally served as the main medicinal parts of the Lonicera genus even though there is ample evidence that the leaves possess the same chemical composition[20]. A perusal of the current scientific literature reveals the fact that little attention has been devoted to exploring the biosynthesis of the chemical constituents of the Lonicera genus with the view to finding alternative means of obtaining higher yields. It is therefore imperative that priority is given to the exploration of the biosynthesis of these bioactive compounds as a possible means of resource protection. There is also the need for further research on ways to fully tap the medicinal benefits of other plant parts in the Lonicera genus.

    Here, we provide a comprehensive review of relevant scientific literature covering the structure, pharmacology, multi-omics analyses, phylogenetic analysis, biosynthesis, and metabolic engineering of the main bioactive constituents of the Lonicera genus. Finally, we proffer suggestions on the prospects of fully exploiting and utilizing plants of the Lonicera genus as useful medicinal plant resources.

    Structure and distribution of main bioactive constituents of Lonicera

    A total of at least 400 secondary metabolites have been reported for the Lonicera genus. These metabolites are categorized into four main groups (Fig. 1a), including not less than 50 organic acids, 80 flavonoids, 80 iridoids, and 80 triterpene saponins[21−23]. Organic acids are mainly derivatives of p-hydroxycinnamic acid and quinic acid. Among the organic acids, chlorogenic acids are reported to be the main bioactive compounds in L. japonica[24−26]. The organic acids are most abundant in the leaves, while the least amounts are found in the stem of L. japonica. The flowers of the plant are known to contain moderately high amounts of organic acids[27]. The basic core structure of the flavonoids is 2-phenylchromogen. Luteolin and its glycoside which are characteristic flavonoids of the Lonicera genus are most abundant in L. japonica[28]. On the whole, the flavonoid contents in L. japonica are also highest in the leaves, available in moderate amounts in the flowers, and in lowest amounts in the stem[21]. The core structures of the iridoids are iridoid alcohols, the chemical properties of which are similar to hemiacetal. The iridoids often exist in the form of iridoid glycosides in plants. Secoiridoids glycosides are predominant in the Lonicera genus[25]. In L. japonica, the contents of the iridoids are most abundant in the flowers, moderate in leaves, and lowest in the stem[21]. The characteristic saponins of the Lonicera genus are mainly pentacyclic triterpenoids, including the hederin-, oleanane-, lupane-, ursulane- and fernane-types, etc[22]. The hederin-type saponins are reported in the highest amounts in L. macranthoides[17] (Fig. 1b).

    Figure 1.  Core structures of main secondary metabolites and their distribution in five species of Lonicera. (a) 1 and 2, the main core structures of organic acids; 3, the main core structures of flavonoids; 4, the main core structures of iridoids; 5, the main core structures of triterpene saponins. (b) Comparison of dry weight of four kinds of substances in five species of Lonicera[17,28].
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    Biosynthesis and regulation of the main bioactive constituents of Lonicera

    Biosynthesis of chlorogenic acids and flavonoids

    The similarities between chlorogenic acid (CGA) and flavonoids can be traced back to their biosynthesis since p-coumaroyl CoA serves as the common precursor for these compounds[29]. p-coumaroyl CoA is obtained through sequential catalysis of phenylalanine and its biosynthetic intermediates by phenylalanine-ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H) and 4-coumarate CoA ligase (4CL)[30−33].

    CGA is a phenolic acid composed of caffeic acid and quinic acid and is the most important bioactive compound among the organic acids. Its biosynthesis has been relatively well-established; three main biosynthetic routes have been propounded (Fig. 2a). One route relates to the catalysis of caffeoyl-CoA and quinic acid by hydroxycinnamoyl-CoA quinate transferase (HQT)/hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyl transferase (HCT) to produce CGA[34−37]. The HQT-mediated pathway has been deemed the major route for CGA synthesis in in different plant species[38,39]. The second biosynthetic route stems from the biosynthesis of p-coumaroyl quinate through the catalytic effect of HCT/HQT and subsequent hydroxylation of p-coumaroyl quinate under the catalysis of p-coumarate 3'-hydroxylase (C3’H)[34,36,37]. For the third route, caffeoyl glucoside serves as the intermediate to form CGA, a process that is catalyzed by hydroxycinnamyl D-glucose: quinic acid hydroxycinnamyl transferase (HCGQT)[40,41].

    Figure 2.  Biosynthetic pathways of main bioactive constituents of Lonicera. (a) Biosynthetic pathways of chlorogenic acid. (b) Biosynthetic pathways of luteoloside. (c) Biosynthetic pathways of secologanin. (d) Biosynthetic pathways of hederin-type triterpene saponins. PAL, phenylalanine ammonia-lyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-hydroxycinnamoyl CoA ligase; HCT, hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyl transferase; C3’H, p-coumaroyl 3-hydroxylase; HQT, hydroxycinnamoyl-CoA quinate transferase; UGCT, UDP glucose: cinnamate glucosyl transferase; CGH, p-coumaroyl-D-glucose hydroxylase; HCGQT, hydroxycinnamoyl D-glucose: quinate hydroxycinnamoyl transferase; CHS, Chalcone synthase; CHI, Chalcone isomerase; FNS, Flavone synthase; F3H, flavonoid 30-monooxygenase/flavonoid 30-hydroxylase; UF7GT, flavone 7-O-β-glucosyltransferase; GPS, Geranyl pyrophosphatase; GES, geraniol synthase; G8O, geraniol 10-hydroxylase/8-oxidase; 8HO, 8-hydroxygeraniol oxidoreductase; IS, iridoid synthase; IO, iridoid oxidase; 7DLGT, 7-deoxyloganetic acid glucosyltransferase; 7DLH, 7-deoxyloganic acid hydroxylase; LAMT, loganic acid O-methyltransferase; SLS, secologanin synthase; FPS, farnesyl pyrophosphate synthase; SS, squalene synthase; SE, squalene epoxidase; β-AS, β-amyrin synthase; OAS, oleanolic acid synthetase.
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    The key enzymes in the biosynthesis of p-coumaroyl CoA, and invariably CGA, thus, PAL, C4H, and 4CL have been established in diverse studies such as enzyme gene overexpression/knockdown[42], enzyme activity analysis[33] and transcriptomics[18]. However, the centrality of HQT in the biosynthesis of CGA remains disputable. While some studies have reported a strong correlation between HQT expression level with CGA content and distribution[18,35,39,43,44], others found no such link[45], bringing into question the role of HQT as a key enzyme in CGA biosynthesis.

    Few studies have been conducted on the regulation of CGA biosynthesis in the Lonicera genus. It was found that overexpression of the transcription factor, LmMYB15 in Nicotiana benthamiana can promote CGA accumulation by directly activating 4CL or indirectly binding to MYB3 and MYB4 promoters[46]. LjbZIP8 can specifically bind to PAL2 and act as a transcriptional repressor to reduce PAL2 expression levels and CGA content[47]. Under NaCl stress, increased PAL expression promoted the accumulation of phenolic substances in leaves without oxidative damage, a condition that was conducive to the accumulation of the bioactive compounds in leaves[48].

    Luteolin and its derivative luteolin 7-O- glucoside (luteoloside) are representative flavonoids of the Lonicera genus. Similar to CGA, luteolin is biosynthesized from p-coumaroyl CoA but via a different route. The transition from p-coumaroyl CoA to luteolin is underpinned by sequential catalysis by chalcone synthetase (CHS), chalcone isomerase (CHI), flavonoid synthetase (FNS), and flavonoid 3'-monooxygenase/flavonoid 3'-hydroxylase (F3'H)[45,49,50] (Fig. 2b). Luteoloside is synthesized from luteolin by UDP glucose-flavonoid 7-O-β-glucosyltransferase (UF7GT)[51]. Similar to CGA biosynthesis, the key enzymes of luteolin synthesis include PAL, C4H, and 4CL in addition to FNS[33,45,52]. The content of luteoloside was found to be highly abundant in senescing leaves relative to other tissues such as stem, flowers, and even young leaves[52]. Through transcriptomic analysis, luteoloside biosynthesis-related differentially expressed unigenes (DEGs) such as PAL2, C4H2, flavone 7-O-β-glucosyltransferase (UFGT), 4CL, C4H, chalcone synthase 2 and flavonoid 3'-monooxygenase (F3'H) genes were found to be upregulated in the senescing leaves. Biosynthesis-related transcription factors such as MYB, bHLH, and WD40 were also differentially expressed during leaf senescence[52], while bHLH, ERF, MYB, bZIP, and NAC were differentially expressed during flower growth[53]. Further analysis of the transcription factors revealed that MYB12, MYB44, MYB75, MYB114, MYC12, bHLH113, and TTG1 are crucial in luteoloside biosynthesis[52,53].

    Biosynthesis of iridoids and triterpene saponins

    The biosynthesis of terpenoids mainly involves three stages; formation of intermediates, formation of basic structural skeleton, and modification of basic skeleton[54]. The intermediates of terpenoids are mainly formed through the mevalonate (MVA) and methylerythritol phosphate (MEP) pathways, and eventually converted to the universal isoprenoid precursors, isopentenyl pyrophosphate (IPP) and its isomer dimethylallyl pyrophosphate (DMAPP) through a series of enzyme-catalyzed reactions. Under the catalysis of geranyl pyrophosphatase (GPS), IPP is then converted to geranyl pyrophosphate (GPP). Different terpenoids are subsequently derived from GPP as the intermediate product. For instance, in the formation of secoiridoid, GPP first removes the phosphoric acid group to obtain geraniol, second through a series of reactions such as oxidation and cyclization, the skeleton of iridoid, namely iridodial, can be obtained. Finally, through a series of reactions, the basic carbon skeleton of the secoiridoid, namely secologanin, is obtained[55−61] (Fig. 2c). In the formation of triterpene saponins, the key step lies in the formation of the precursor, 2,3-oxidosqualene, a reaction that is catalyzed by squalene epoxidase (SE). There are many pentacyclic triterpenes in the Lonicera genus, the most important type being the hederin-type saponins with hederagenin as aglycones. Hederin-type saponins are produced after the synthesis of oleanolic acid from β-amyrin and catalyzed by β-starch synthetase (β-AS) and Oleanolic acid synthase (OAS)[62,63]. The skeletal modification of the triterpenoid saponins is mainly achieved via the activities of the CYP450 enzymes and UDP-glycosyltransferase (UGT). Hence, the corresponding aglycones are first obtained via oxidation by the CYP450 enzymes (e.g., CYP72A), and further subjected to glycosylation by the appropriate UGT enzyme[63−65] (Fig. 2d). Skeletal formations of the iridoids and triterpene saponins in general have been well researched, but the same cannot be said about the enzymes involved in biosynthesis of these groups of compounds in the Lonicera genus. To fully utilize the iridoids and triterpene saponins in the Lonicera genus, it is necessary to further explore their biosyntheses with the view to enhancing and optimizing the process.

    Metabolic engineering of the main bioactive compounds of the Lonicera genus

    Given the importance of the bioactive compounds in the Lonicera genus, continual isolation of these compounds using the traditional methods are not only tedious and time-consuming, but also unsustainable. With the development and application of microbial metabolic engineering, different strategies have been introduced to produce these bioactive compounds by heterologous synthesis (Table 1).

    Table 1.  Biosynthesis of Lonicera-specialized metabolites using metabolic engineering.
    Engineering bacteriaOperational methodsProductsYieldRefs
    S. cerevisiaeEliminate the tyrosine-induced feedback inhibition, delete genes involved in competing pathways and overexpress rate-limiting enzymesCaffeic acid569.0 mg/L[69]
    S. cerevisiaeEmploye a heterologous tyrosine ammonia lyase and a 4HPA3H complex composed of HpaB and HpaC derived from different speciesCaffeic acid289.4 mg/L[73]
    S. cerevisiaeSupply and recycle of three cofactors: FADH2, S-adenosyl-L-methion, NADPHCaffeic acid
    Ferulic acid    		Caffeic acid: 5.5 g/L;
    Ferulic acid: 3.8 g/L    		[117]
    E. coliKnocking out competing pathwaysCaffeic acid7,922 mg/L[118]
    E. coliArtificial microbial community, a polyculture of three recombinant Escherichia coli strainsChlorogenic acid250 μM[68]
    Cell-free biosynthesisExtract and purify spy-cyclized enzymes (CFBS-mixture)Chlorogenic acid711.26 mg/L[70]
    S. cerevisiaeThree metabolic engineering modules were systematically optimized: shikimate pathway and carbon distribution, branch pathways, CGA pathway genesChlorogenic acidFlask fermentation: 234.8 mg/L;
    Fed-batch fermentation:
    806.8 mg/L    		[119]
    E. coliUsing modular coculture engineering: construction of the defective strain improves the production and utilization of precursor substancesChlorogenic acid131.31 mg/L[122]
    E. coliIntroduce heterologous UDP-glucose biosynthetic genesLuteolin34 mg/L[120]
    Y. lipolyticaOverexpression of the key genes involved in the mevalonate pathway, the gene encoding cytochrome P450 (CYP716A12) to that encoding NADPH-P450 reductaseOleanolic acid129.9 mg/L[85]
    S. cerevisiaeImprove the pairing efficiency between Cytochrome P450 monooxygenase and reductase and the expression level of key genesOleanolic acid606.9 mg/L[121]
    S. cerevisiaeHeterologous expression and optimization of CrAS, CrAO, and AtCPR1, and regulation of ERG1 and NADPH regeneration systemOleanolic acid433.9 mg/L[123]
     | Show Table
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    Metabolic engineering of chlorogenic acid, luteolin and their glycosides

    Due to the demand for CGA in the food, pharmaceutical, chemical, and cosmetic industries, the traditional means of obtaining the same requires a relatively longer period for plant maturation to obtain low yields of the desired product. This therefore brings into question the sustainability and efficiency of this approach. The alternative and sustainable approach has been to produce CGA using synthetic biology and metabolic engineering.

    Current research has sought to utilize Escherichia coli (and its mutant strain) and Saccharomyces cerevisiae to synthetically generate CGA and other flavonoids[66−73]. For instance, Cha et al. employed two strains of E. coli to produce a relatively good yield of CGA (78 mg/L). Their approach was based on the ability of one strain to generate caffeic acid from glucose and the other strain to use the caffeic acid produced and quinic acid as starting materials to synthesize CGA[66]. Using a bioengineered mutant of E. coli (aroD mutant), Kim et al. increased the yield of CGA to as high as 450 mg/L[67]. Others have sought to increase the yield of CGA by employing a polyculture of three E. coli strains that act as specific modules for the de novo biosynthesis of caffeic acid, quinic acid and CGA. This strategy eliminates the competition posed by the precursor of CGA (i.e., caffeic acid and quinic acid) and generally results in improved production of CGA[68]. Saccharomyces cerevisiae is a chassis widely used for the production of natural substances from plants with an intimal structure that can be used for the expression of cytochrome P450 enzymes that cannot be expressed in E. coli. Researchers have used yeast to increase the production of organic acids[69]. A de novo biosynthetic pathway for the construction of CGA in yeast has been reported new cell-free biosynthetic system based on a mixture of chassis cell extracts and purified Spy cyclized enzymes were adopted by Niu et al. to a produce the highest yield of CGA reported so far up to 711.26 ± 15.63 mg/L[70].

    There are many studies on the metabolic engineering for the synthesis of flavonoids, but few on luteolin and its glycosides. Strains of E. coli have been engineered with specific uridine diphosphate (UDP)-dependent glycosyltransferase (UGT) to synthesize three novel flavonoid glycosides. These glycosides were quercetin 3-O-(N-acetyl) quinovosamine (158.3 mg/L), luteolin 7-O-(N-acetyl) glucosaminuronic acid (172.5 mg/L) and quercetin 3-O-(N-acetyl)-xylosamine (160.8 mg/L)[71]. Since most of the flavonoid glycosides synthesized in E. coli are glucosylated, Kim et al. in their bid to synthesize luteolin-7-O-glucuronide, deleted the araA gene that encodes UDP-4-deoxy-4-formamido-L-arabinose formyltransferase/UDP-glucuronic acid C-4'' decarboxylase in E.coli and were able to obtain a yield of 300 mg/L of the desired product[72].

    Metabolic engineering of iridoids and triterpene saponins

    Terpenoidal saponins are mostly derived from slow-growing plants and usually possess multiple chiral centers[74]. Traditional isolation and even chemical synthesis of the terpenoidal saponins are both tedious and uneconomical for large-scale production. Therefore, it is necessary to find other ways to synthesize these compounds known to have diverse pharmacological functions.

    Heterologous synthesis has become an important way to improve the target products. With the development of synthetic biology, heterologous synthesis of triterpene saponins involves chassis of both plant and microbial origin. In this regard, Nicotiana benthamiana is a model plant species for the reconstruction of the biosynthetic pathways of different bioactive compounds including monoterpenes, hemiterpenes, and diterpenes[59,75−77]. Aside from Nicotiana benthamiana, other plants have also been used as heterologous hosts[78]. Heterologous synthesis using microbial hosts mainly involves Saccharomyces cerevisiae and Escherichia coli[79−81], and other microorganisms[82,83]. Comparatively, plants as biosynthetic hosts have the advantages of an established photosynthetic system, abundant supply of relevant enzymes, and presence of cell compartments, etc. They are however not as fast growing as the microorganisms, and it is also difficult to extract and separate the desired synthesized compounds from them as hosts.

    Although heterologous synthesis has many advantages, the premise of successful construction of synthetic pathway in host is to elucidate the unique structure of the compound and the key enzyme reaction mechanism in the biosynthetic pathway. There is little research on metabolic engineering of the hederin-type pentacyclic triterpene saponins in Lonicera, but there are studies on the heterologous synthesis of its aglycone precursor, oleanolic acid[84,85]. There is a dearth of scientific literature on key enzymes in the biosynthesis of pentacyclic triterpenoid saponins in the Lonicera genus.

    Pharmacological activities of the main bioactive compounds of Lonicera

    Scientific evidence by diverse research groups has linked members of the Lonicera genus to a wide range of pharmacological effects (Fig. 3). These pharmacological effects are elicited by different chemical constituents, much of the underlying mechanisms of which have been elucidated by the omics techniques. Here, we summarize the pharmacological effects and pharmacodynamics of the Lonicera genus in the last 6 years.

    Figure 3.  Schematic summary of four main pharmacological effects (anti-inflammatory, antimicrobial, anti-oxidative and hepatoprotective effects) of the Lonicera genus and the underlying mechanisms of actions.
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    Anti-inflammatory effects

    Bioactive compounds of plants in the Lonicera genus have demonstrated varying degrees of anti-inflammatory actions. In a recent study, Lv et al. showed that lonicerin inhibits the activation of NOD-like receptor thermal protein domain associated protein 3 (NLRP3) through regulating EZH2/AtG5-mediated autophagy in bone marrow-derived macrophages of C57BL/6 mice[86]. The polysaccharide extract of L. japonica reduces atopic dermatitis in mice by promoting Nrf2 activation and NLRP3 degradation through p62[87]. Several products of Lonicera have been reported to have ameliorative effects on DSS-induced colitis. Among them, flavonoids of L. rupicola can improve the ulcerative colitis of C57BL/6 mice by inhibiting PI3K/AKT, and pomace of L. japonica can improve the ulcerative colitis of C57BL/6 mice by improving the intestinal barrier and intestinal flora[88,89]. The flavonoids can also ameliorate ulcerative colitis induced by local enema of 2,4,6-trinitrobenzene sulfonic acid (TNBS) in Wistar rats by inhibiting NF-κB pathway[90]. Ethanol extract from L. Japonica has demonstrated the potential to inhibit the expressions of inflammatory cytokines in serum and macrophages of LPS-induced ICR mice[91]. The water extract of L. japonica and luteolin were found to exhibit their anti-inflammatory effects via the inhibition of the JAK/STAT1/3-dependent NF-κB pathway and induction of HO-1 expression in RAW263.7 cells induced by pseudorabies virus (PRV)[92].

    Antimicrobial effects

    Existing scientific evidence indicates that the extracts of plants in the Lonicera genus exhibit strong inhibition against different pathogenic microorganisms. Phenolic compounds from L. japonica demonstrated a particularly significant inhibitory effect against Staphylococcus aureus and Escherichia coli, in vitro, making these compounds potential food preservatives[93]. Influenza A virus is a serious threat to human health. Recent research has found the ethanol extract of L. japonica to possess a strong inhibitory effect against H1N1 influenza virus-infected MDCK cells and ICR mice[94]. The incidence of the COVID-19 pandemic called to action various scientists in a bid to find safe and efficacious treatment[95]. Traditional Chinese medicines became an attractive alternative in this search. The water extract of the flower bud of L. japonica which has traditionally served as a good antipyretic and antitussive agent attracted the attention of researchers. Scientific evidences have confirmed that the water extract of L. japonica can induce let-7a expression in human rhabdomyosarcoma cells or neuronal cells and blood of lactating mice, inhibiting the entry and replication of the virus in vitro and in vivo[96]. In addition, the water extract of L. japonica also inhibits the fusion of human lung cancer cells Calu-3 expressing ACE2 receptor and BGK-21 cells transfected with SARS-CoV-2 spike protein, and up-regulates the expression of miR-148b and miR-146a[97].

    Anti-oxidative stress effect

    Oxidative stress has been implicated in the pathophysiology of many diseases, hence, amelioration of the same could be a good therapeutic approach[98,99]. In keeping with this therapeutic strategy, various compounds from the Lonicera genus have demonstrated the ability to relieve oxidative stress due to their pronounced antioxidant effects. For instance, the polyphenolic extract of L. caerulea berry was found to activate the expression of AMPK-PGC1α-NRF1-TFAM proteins in the skeletal muscle mitochondria, improve the activity of SOD, CAT and GSH-Px enzymes in blood and skeletal muscle, relieve exercise fatigue in mice by reducing oxidative stress in skeletal muscle, and enhance mitochondrial biosynthesis and cell proliferation[100]. The diverse health benefits of the anthocyanins from L. japonica have been mainly credited to their antioxidant and anti-inflammatory effects. The anthocyanin and cyanidin-3-o-glucoside have been reported to possess the potential to prolong life and delay senescence of Drosophila through the activation of the KEAP1/NRF2 signaling pathway[101].

    Hepatoprotection

    The liver is an essential organ that contributes to food digestion and detoxification of the body. These functions expose the liver to diverse toxins and metabolites. The Lonicera genus is rich in phytochemicals that confer protection on the liver against various toxins. The phenolic compound, 4, 5-di-O-Caffeoylquinic acid methyl ester was shown to be able to improve H2O2-induced liver oxidative damage in HepG2 cells by targeting the Keap1/Nrf2 pathway[102]. Hepatic fibrosis is a complex dynamic process, with the propensity to progress to liver cancer in severe cases. The L. japonicae flos water extract solution increased the cell viability of FL83B cells treated with thioacetamide (TAA), decreased the levels of serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP), inhibited the transformation growth factor β1 (TGF-β1) and liver collagen deposition[103]. Sweroside, a secoiridoid glucoside isolate of L. japonica is known to protect the C57BL/6 mice liver from hepatic fibrosis by up-regulating miR-29a and inhibiting COL1 and TIMP1[104].

    Other pharmacological effects

    Aside from the aforementioned, other pharmacological effects have been ascribed to the Lonicera genus. The ethanolic extract of L. caerulea has been reported to inhibit the proliferation of SMMC-7721 and H22 hepatoma cells, while its anthocyanins induced the apoptosis of tumor cells via the release of cytochrome C and activation of caspase[105]. AMPK/PPARα axes play an important role in lipid metabolism. A chlorogenic acid-rich extract of L. Japonica was found to significantly decrease the early onset of high-fat diet-induced diabetes in Sprague-Dawley rats via the CTRPs-AdipoRs-AMPK/PPARα axes[106]. In a high-fat diet-induced non-alcoholic fatty liver disease in C57BL/6 mice, treatment with L. caerulea polyphenol extract decreased serum inflammatory factors and endotoxin levels and the Firmicutes/Bacteroidetes ratio, an indication of its modulatory effect on the gut microbiota[107]. The iridoid-anthocyanin extract from L. caerulea berry contributed to alleviating the symptoms of intestinal infection with spirochaeta in mice[108].

    Chloroplast genomic and phylogenetic relationships of Lonicera species

    The traditional classification of the Lonicera genus based on the morphology of member plants is further categorized into two subgenera, Chamaecerasus and Periclymenum. The Chamaecerasus includes four categories, Coeloxylosteum, Isika, Isoxylosteum and Nintooa. The Periclymenum includes two categories, Subsect. Lonicera and Subsect. Phenianthi (Supplemental Table S1).

    High-throughput chloroplast genome sequencing of L. japonica found its length to be 155078 bp, which is similar to the structure of the typical angiosperm chloroplast genome. It contains a pair of inverted repeat regions (IRa and IRb, 23774 bp), a large single copy region (LSC, 88858 bp) and a small single copy area (SSC, 18672 bp)[109,110]. However, compared with chloroplast genomes of other plants, the chloroplast genome of L. japonica has a unique rearrangement between trnI-CAU and trnN-GUU[110]. Based on the phylogenetic analysis of the plastid genomes of seven plants in the Lonicera genus, 16 diverging hot spots were identified as potential molecular markers for the development of the Lonicera plants[111]. The phylogeny of Lonicera is rarely researched at the molecular level and the pattern of repetitive variation and adaptive evolution of the genome sequence is still unknown. Chloroplast genome sequences are highly conserved, but insertions and deletions, inversions, substitutions, genome rearrangements, and translocations also occur and have become powerful tools for studying plant phylogeny[112,113].

    We present here the phylogenetic tree of the Lonicera genus based on the published complete chloroplast genome sequences downloaded from the National Center for Biotechnology Information (NCBI) database using the Maximum likelihood method (Fig. 4). Based on our chloroplast phylogenies, we propose to merge L. harae into Sect. Isika and L. insularis into Chamaecerasus, but whether L. insularis belongs to Sect. Isika or Sect. Coeloxylosteum is uncertain. Based on protein-coding regions (CDS) of the chloroplast genome or complete chloroplast genomes, Liu et al. and Chen et al. supported the classification of the two subgenera in Lonicera[111,114]. Sun et al. and Srivastav et al. demonstrated a classification between the two subgenera with more species by using sequences of nuclear loci generated, chloroplast genome, and restriction site-associated DNA sequencing (RADSeq)[115,116]. However, our phylogenetic analysis and that of Sun et al. show relations within the subgenus Chamaecerasus are tanglesome in some respects[116]. Plant traits are affected by the environment to varying degrees. Since evidence of plant speciation is implicit in its genome sequence, comparative analysis at the molecular level provides a relatively accurate depiction of inherent changes that might have occurred over time. These findings suggest the need for more species of the Lonicera genus to be sequenced to provide a more accurate theoretical basis for the evolution of the Lonicera plants and a more effective revision in the classification of the Lonicera genus.

    Figure 4.  Phylogenetic tree of 42 species of the Lonicera genus based on complete chloroplast genome sequence data. The phylogenetic tree was constructed by the maximum likelihood method. Coeloxylosteum, Isika, Isoxylosteum, and Nintooa belong to Chamaecerasus and Subsect. Lonicera belongs to the Periclymenum. Chamaecerasus and Periclymenum are the two subgenera of Lonicera. 'Not retrieved' indicates that the species failed to retrieve a subordinate taxon in the Lonicera.
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    Conclusions and future perspectives

    The Lonicera genus is rich in diverse bioactive compounds with immeasurable prospects in many fields. Members of this genus have been used for thousands of years in traditional Chinese medicine for heat-clearing and detoxification. These plants generally have a good taste and form part of the ingredients of various fruit juices. In cosmetics, they are known to possess anti-aging and moisturizing functions. Plants of the Lonicera genus are also known for their good ecological adaptability and can be used to improve soil and ecological environment. Based on the value of the Lonicera genus, besides researching their use through molecular biological means, their efficient utilization can also be promoted in the following ways: (1) The stems and leaves of the plants could be developed for consumption and use since the chemical profiles of these parts do not differ significantly from the flowers. This way, the wastage of this scarce resource could be minimized or avoided. (2) Most of the Lonicera plants are vines or shrubs and their natural regeneration speed is slow, so the introduction and domestication of species could be strengthened to avoid overexploitation of wild resources.

    At present, only the research on the biosynthesis and efficacy of chlorogenic acid is quite comprehensive and has been used widely in various fields. There is limited research on various aspects of other bioactive compounds and should therefore be given priority in future research goals. Currently, the multi-omics analytical approach has gradually evolved as a reliable and helpful analytical platform. Hence, multi-omics research on the Lonicera genus could lead to discoveries in drug discovery and human health.

    Author contributions

    The authors confirm contribution to the paper as follows: study conception and design, draft manuscript preparation: Yin X, Chen X, Li W, Tran LSP, Lu X; manuscript revision: Yin X, Chen X, Li W, Tran LSP, Lu X, Chen X, Yin X, Alolga RN; data/literature collection: Chen X, Yin X; figure preparation: Chen X, Yin X; figure revision: Alolga RN, Yin X, Chen X, Li W, Tran LSP, Lu X. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    All data generated or analyzed during this study are included in this published article and its supplementary information file.

    Acknowledgments

    This work was partially supported by the National Natural Science Foundation of China (NSFC, Nos 82173918 and 82373983).

  • The authors declare that they have no conflict of interest. Xiaojian Yin is the Editorial Board member of Medicinal Plant Biology who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and the research groups.

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  • Cite this article

    Olowe VIO, Odueme PU, Fadeyi OJ, Somefun OT. 2023. Agronomic response of soybeans (Glycine max (L.) Merrill) to different management practices in the humid tropics. Technology in Agronomy 3:18 doi: 10.48130/TIA-2023-0018
    Olowe VIO, Odueme PU, Fadeyi OJ, Somefun OT. 2023. Agronomic response of soybeans (Glycine max (L.) Merrill) to different management practices in the humid tropics. Technology in Agronomy 3:18 doi: 10.48130/TIA-2023-0018
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Agronomic response of soybeans (Glycine max (L.) Merrill) to different management practices in the humid tropics

  • 1.

    Crop Research Programme, Institute of Food Security, Environmental Resources and Agricultural Research (IFSERAR), Federal University of Agriculture, Abeokuta (FUNAAB), P.M.B. 2240, Abeokuta, Nigeria

  • 2.

    Department of Plant Physiology and Crop Production, Federal University of Agriculture, Abeokuta (FUNAAB), Federal University of Agriculture, Abeokuta, Nigeria

  • Received: 18 August 2023
  • Accepted: 21 November 2023
  • Published online: 15 December 2023
Technology in Agronomy  3,  Article number: 18  (2023)  |  Cite this article

Abstract: Organic soybean (Glycine max (L.) Merrill) remains an under exploited global market niche by farmers in the tropics. Therefore, a study was conducted in two consecutive years to determine the agronomic performance of soybean under different management systems in the humid tropics. The study was carried out during the late cropping season (July–Nov.) in 2020 and 2021 on the organic research plots of the Institute of Food Security, Environmental Resources and Agricultural Research, Nigeria. The six management practices (MP) evaluated were MP1 – Control, MP2 – NoduMax, MP3 – Organic fertilizer, MP4 – NoduMax + Organic fertilizer, MP5 – Conventional, MP6 – Conventional + NoduMax in a Randomized Complete Block Design and replicated three times. Data were collected on phenology, nodulation, grain yield and yield attributes of soybeans. Number of days to full bloom (R2) and physiological maturity (R7) were significantly (p < 0.05) affected by MP in both years, except R7 in 2021. Inoculation with NoduMax (MP2) enhanced nodule number at 8 and 9 weeks after sowing (WAS) during the wetter year 2021. Number of branches and seeds per plant, seed weight per plant and grain yield were significantly (p < 0.05) affected by MP in both years. Soybean grain yield recorded under MP3 and MP4 ranged between 1,198.8–1,962.0 kg/ha in both years and compared well with the average yield values in Nigeria (926.9 kg/ha) and Africa (1,348.0 kg/ha), and significantly below the world (2,784.2 kg/ha). Therefore, MP3 and MP4 can be recommended for potential resource-constrained organic soybean farmers in the humid tropics.

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    Introduction

    • Soybean (Glycine max (L.) Merrill) is a multipurpose oilseed crop widely grown in tropical, subtropical and temperate climates. The crop is mainly cultivated for its seed that contains 30%–40% quality protein and 18%–25% cholesterol-free oil[1,2]. This legume crop is very strategic in most cropping systems because it fixes atmospheric nitrogen and thereby improves soil fertility[3], important in human and livestock nutrition[4], and capable of generating income for resource-constrained farmers in the tropics[5]. In 2020, about 353,463,735 million tonnes of soybeans were produced in the world and Africa accounted for just 0.9% (3,438,611 million tonnes) as reported in FAOSTAT[6]. Soybean seed yield in Africa has remained almost stagnant in the last decade. The yield stagnancy can be attributed to multifaceted factors such as cultivation of poor performing varieties, low fertility soil status, limited or non-fertilizer application, especially phosphorus, poor agronomic management practices, erratic rainfall and distribution and lack or poor usage of inoculants on soils without the previous history of soybean production and weed interference among others[7−9].

      Organic soybean production remains less than 0.1% of the total world production despite the crop being the most widely cultivated oilseed crop. According to Willer et al.[10] organic soybean accounted for 55% of oilseeds cultivated on organic lands in 2021. Yield penalty of about 15%–40% has been reported in literature for organic soybeans relative to conventional production system[11−13]. Furthermore, seed yield values often recorded on smallholder farms for soybeans are lower than the potential yields of the varieties. The actual yield reported by FAO for Africa and Nigeria in 2020 were 1.7 and 2.4 times lower than the potential yield reported for the most widely adopted soybean variety (TGx 1448-2E) in Nigeria[14]. Consequently, several research studies have been conducted to bridge the gap between actual and potential yield, and reduce yield penalty of organic soybeans in the tropics. Such efforts include use of synthetic foliar fertilisers[15,16], soil[9] and foliar applied organic fertilisers[17], crop rotation[18,19], different cropping systems[20], biofertilizers and organic manures[21] integrated use of organic and inorganic fertilization[22,23], intercropping[24,25], integrated use of organic and inorganic fertilizers in mixtures[26]. Recently, application of phosphorus at the rate of 21–30 kg P ha−1 and sowing at the appropriate time were reported to have increased soybean yield by 48% and 300%, respectively in sub Saharan Africa (SSA) by Omondi et al.[27]. Nevertheless, the yield gap is yet to be substantially bridged in the tropics despite the use of the afore mentioned approaches. Also, the use of decision support tools was recently suggested as an approach to boost the output of smallholder farmers in crop production in Africa[28].

      Crop management has been described as a set of technically and environmentally sound agricultural practices usually carried out to improve the growth, development and get potential yield and high net returns in any crop or crop sequence[29,30]. If properly carried out, these management practices are expected to reduce the problems in agricultural food production and thereby boost production output. A critical analysis of the literature reveals that no single piece of literature has combined the use of management practices such as organic and conventional systems, and inoculation to quantify the productivity of soybeans in the tropics. The hypothesis of the study was that there is no difference in the productivity of soybeans irrespective of the management system adopted in the tropics. Therefore, this study was carried out to evaluate the effects of different management practices on the phenology, grain yield and yield attributes of soybeans in the humid tropics. Additionally, the research findings from this study will establish the most appropriate management practice for optimum soybean production.

    Materials and methods

      Site description

    • This experiment was conducted on the organic research plot of the Institute of Food Security, Environmental Resources and Agricultural Research, (IFSERAR), Federal University of Agriculture Abeokuta (FUNAAB) located on latitude 7°13'53'16' N and longitude 3°23'49.12'' E, and 3023'51.86' E 139 m above sea level, Abeokuta, Ogun State, South West Nigeria during the late cropping season (July–November) of 2020 and 2021. Traditionally, this area experiences bimodal rainfall distribution with two peaks in July and September and a dry spell in August termed 'august break”. However, this trend was not recorded in both years of experimentation. Rainfall only peaked in September of 2020 and August of 2021 as illustrated in Fig. 1. The late cropping season of 2020 growing season was 139 mm dryer than the average 35 year average (1985–2019) while 2021 was 12.8 mm wetter. August was markedly dry in 2020 but September recorded the highest rainfall that year. The two years of experimentation were similar in temperature and just 0.3 °C cooler than the long-term average. Overall, the weather conditions during the late cropping season were suitable for soybean production. On average, the soil of the experimental site was loamy sand in texture, slightly alkaline, low in nitrogen, high in phosphorus and low in potassium (Table 1)

      Figure 1. 

      Monthly rainfall and mean monthly temperature during the late cropping season (July–Nov.) of 2020, 2021 and long-term period (1985–2019). Source: Department of Agro Meteorology and Water Management Station, FUNAAB.

      Table 1.  Pre-planting physical and chemical properties of soil in 2020 and 2021

      ParametersConventional plotOrganic plot
      2020202120202021
      Physical properties
      Sand (%)88.385.487.779.2
      Silt (%)11.011.410.917.7
      Clay (%)0.73.21.43.1
      Textural classLSLSLSLS
      Chemical properties
      pH7.257.557.346.98
      N (g/kg)0.600.610.620.49
      OM (g/kg)12.0712.2712.479.85
      P (mg/kg)44.8139.8229.5948.87
      K (mg/kg)0.250.840.270.83
      Ca (mg/kg)671.44905.18631.62682.73
      Mg (mg/kg)410.84258.14410.11357.28
      Na (mg/kg)99.6739.9690.0148.79
      Mn (mg/kg)4.203.074.704.55
      Fe (mg/kg)13.607.5618.8012.65
      Zn (mg/kg)6.809.759.708.33

      Experimental materials

    • The test variety of soybean planted in the study was TGx 1448-2E, a late maturing variety (115–120 d), days to flowering (46 d), with good nodulation ability, good at causing suicidal germination to Striga hermonthica (parasite), resistant to lodging and pod shattering[14]. The organic fertilizer used was Aleshinloye Grade B, an abattoir based fertilizer. The fertilizer was analysed for nutrient content composition each year before any application and it contained 1.23% and 1.03% nitrogen in 2020 and 2021, respectively (Table 2). The inoculant used was NoduMax, a peat based inoculant that ensured the infection of soybean roots by symbiotic rhizobium bacteria thereby enhancing root nodulation and biological nitrogen fixation (BNF). It contains 50% culture rhizobia and 50% of the carrier material, peat. NoduMax was sourced from the International Institute for Tropical Agriculture (IITA) in Nigeria (https://propas.iita.org).

      Table 2.  Nutrient composition of Aleshinloye organic fertilizer Grade B (Abattoir-based) in 2020 and 2021.

      ParametersYear
      20202021
      N (g/kg)12.3010.30
      P (g/kg)3.402.90
      K(g/kg)2.003.50
      Ca (cmol/kg)1.901.00
      Mg (cmol/kg)1.100.50
      Na (cmol/kg)0.300.10
      Mn(mg/kg)53.7044.80
      Fe (mg/kg)28.1012.80
      Zn (mg/kg)10.3011.00

      Treatments and experimental design

    • The experiment involved the following six management practices (MP):

      1. Control

      2. Nodumax (inoculant)

      3. Organic fertilizer

      4. Nodumax + organic fertilizers

      5. Conventional system (use of inorganic fertilizers and herbicides)

      6. Nodumax + conventional system

      The organic fertilizer (Aleshinloye Fertilizer (Grade B) contained 1.23% and 1.06% N, 33.18 and 29.08 mg/kg P, 0.20 and 0.35 mg/kg K in 2020 and 2021, respectively and was applied in quantities equivalent to 30 kg N ha–1[9]. This is line with the recommendation of starter nitrogen for soybean to boost its growth[31]. The conventional system involved the application of compounded fertilizer (30 kg N ha–1, 56 kg P2O5, and 60 kg K2O ha–1) and the spraying of pre-emergence herbicide (Satellite) immediately after sowing in both years. The six treatments were laid out in a randomized complete block design (RCBD) and replicated three times.

      Crop husbandry

    • The experimental land was ploughed twice and harrowed once. Sowing of the test variety was done on July 28, 2020 and August 9, 2021 at a spacing of 60 cm × 5 cm to give a corresponding plant population of 333,000 plants per ha at the seed rate was 50 kg per hectare. The seeds were sown at a depth of 5 cm. The seeds sown under management practices (MP2, MP4 and MP6) were treated with the inoculant in the morning on the day of sowing. Weeding was done manually twice at 3 and 6 weeks after sowing (WAS). Each experimental gross plot size was 4 m × 3 m (12 m2) and net plot size 3 m × 2 m (6 m2), with walk way of 0.5 m between plots and 1 m between replicates. The total land area was 286 m2. The conventional plots that received synthetic agrochemicals were established at about 50 m away from the organic plots to avoid any commingling. Harvesting of soybeans was done annually at the harvest maturity stage (R8) according to Fehr & Caviness[32].

      Data collection

    • For data collection, five randomly selected plants were tagged within the net plot at 3 WAS. Data collected on soybean phenology (number of phenological days to full bloom (R2), initial seed filling (R5), full seed (R6) and physiological maturity (R7), Grain Filling Period (GFP), nodulation (nodule count and weight). The GFP was calculated as difference between the number of phenological days to R6 and R5. Plant height at R7 was taken on the tagged plants on plot basis before harvest. After harvest, yield components such as the number of branches per plant (NBP), number of pods per plant (NPP), number of seeds per plant (NSP), pod weight (g) per plant (PWT) and seed weight (g) per plant, (SWT), and 100 seed weight, 1000 SWT (g) were measured. Seed yield was determined on plot basis after threshing all the pods and the weight per plot later converted to weight per unit land area in kg/ha. Every weight measurement was done using METTLER PJ260 Delta Range Model manufactured by Metler Instrument Ltd., High Wycombe, Switzerland.

      Data analysis

    • All the data collected on the parameters were subjected to analysis of variance (ANOVA) to test the effects of the management systems for the two field trials separately by using the MSTATC package version 1.42[33]. After the ANOVA test, significant (p < 0.05, F-test) treatment means were separated by the least significant difference method at p = 0.05.

    Results

      Growth conditions

    • Rainfall distribution and mean temperature during the periods of experimentation in 2020 and 2021 are presented in Fig. 1. Year 2021 late cropping season was wetter than that of 2020.

      The late cropping season of 2020 growing season was 139 mm dryer than the 35 year average (1985–2019) while 2021 was 12.8 mm wetter. August was markedly dry in 2020 but September recorded the highest rainfall that year. The two years of experimentation were similar in temperature and just 0.3 °C cooler than the long-term average. Overall, the weather conditions during the late cropping season were suitable for soybean production. On average, the soil of the experimental site was loamy sand in texture, slightly alkaline, and low in nitrogen, high in phosphorus and low in potassium (Table 1).

      Effects of management practices on the phonology of soybeans in 2020 and 2021

    • Management practices significantly (p < 0.05) affected number of phenological days to full bloom (R2) and physiological maturity (R7) of soybeans in both years, except R7 in 2021. On average, days to R2 was delayed following the application inoculant (MP2), inoculant + organic fertilizer (MP4), synthetic fertilizers (MP5) and inoculant + synthetic fertilizers (MP6) during the dry year 2020 (Table 3). However, soybeans under MP6 was the latest to mature. Management practice significantly (p < 0.05) affected the duration of Grain Filling Period only during the wetter year 2021 (Table 4). Application of inoculant (MP2) and organic fertilizer (MP3) alone and in combination (MP4) significantly (p < 0.05) prolonged grain filling period by 5–7 d in 2021. This tend however, did not translate to significantly higher grain yields among the management practices in both years (Tables 5 & 6), especially when compared to the conventional system (MP5).

      Table 3.  Effects of management practices on the phenology of soybeans in 2020.

      Management
      practices      		Number of days toGrain filling
      period
      R2R5R6R7
      MP159.00c86.00a99.00a107.00ab13.00a
      MP261.33b89.00a100.00b106.33ab11.00a
      MP360.33bc89.67a100.67a107.33ab11.00a
      MP461.00b90.00a102.00a106.33b12.00a
      MP561.00b88.00a98.67a109.00ab10.33a
      MP663.33a90.33a101.00a110.00a10.67a
      SE ± (df-10)0.6152.3882.3461.3581.653
      MP1 – Control, MP2 – NoduMax, MP3 – Organic fertilizer, MP4 – NoduMax + Organic fertilizer, MP5 – Conventional, MP6 – Conventional + NoduMax, R2 – Full bloom, R5 – Initial seed filling, R6 – Full Seed, R7 – Physiological maturity. Means with the same letter along the column are not significantly different according to Duncan's Multiple Range Test (DMRT) at 5% probability level.

      Table 4.  Effects of management practices on the phenology of soybeans in 2021.

      Management
      practices      		Number of days toGrain filling
      period
      R2R5R6R7
      MP159.33b80.00c94.33c110.67a13.67b
      MP261.33a80.33c99.67ab112.67a19.67a
      MP359.67b82.00b101.33a111.67a20.00a
      MP461.67a83.33a99.67ab109.33a18.00a
      MP559.33b80.67c97.00b110.67a16.33b
      MP659.67b80.67c97.33b110.00a16.67b
      SE ± (df 10)0.5770.4941.5171.5121.723
      MP1 – Control, MP2 – NoduMax, MP3 – Organic fertilizer, MP4 – NoduMax + Organic fertilizer, MP5 – Conventional, MP6 - Conventional + NoduMax, R2 – Full bloom, R5 – Initial seed filling, R6 – Full Seed, R7 – Physiological maturity. Means with the same letter along the column are not significantly different according to Duncan's Multiple Range Test (DMRT) at 5% probability level.

      Table 5.  Effects of management practices on seed yield and yield attributes of soybeans in 2020.

      Management practicesPHTR7NBRNPODSNSEEDSSWT (g)PWT (g)100SWT (g)GYD (kg/ha)
      MP154.80ab3.33b40.07c68.80d4.70c8.47c6.90a846.00c
      MP252.67ab4.77b79.57bc97.93bcd7.57b12.83bc7.73a1362.00b
      MP356.77a8.43ab138.87a137.20a10.90a24.17a8.00a1962.00a
      MP456.33a8.57ab86.87abc126.90ab9.37a14.97abc7.57a1686.00a
      MP542.70b11.50a102.37ab111.33abc9.47a19.53ab6.57a1704.00a
      MP649.43ab5.90ab112.63ab79.53cd6.47b23.47ab8.20a1164.00b
      SE ± (df-10)5.1522.63221.85214.7820.7724.6011.271138.901
      MP1 – Control, MP2 – NoduMax, MP3 – Organic fertilizer, MP4 – NoduMax + Organic fertilizer, MP5 – Conventional, MP6 – Conventional + NoduMax, PHTR7 – Plant height at physiological maturity, NBR – Number of branches, NPODS – Number of pods, NSEDDS – Number of seeds, SWT – Weight of seeds, PWT – Pod weight, 100SWT (100 seed weight), GYD – Grain yield. Means with the same letter along the column are not significantly different according to Duncan's Multiple Range Test (DMRT) at 5% probability level.

      Table 6.  Effects of management practices on seed yield and yield attributes of soybeans in 2021.

      Management practicesPHTR7 (cm)NBRNPODSNSEEDSSWT (g)PWT (g)100SWT (g)GYD (kg/ha)
      MP170.60a4.47b37.47a166.13b2.27c6.33a8.92a754.80c
      MP272.00a4.87ab39.13a212.30b3.47abc12.00a8.12a1155.40abc
      MP371.80a4.03b33.13a189.53b3.60ab8.10a7.37abc1198.80ab
      MP467.87a5.67ab39.87a241.47b4.20ab10.27a7.59ab1398.60ab
      MP559.27a5.47ab57.93a413.93b4.67a9.00a4.87b1554.00a
      MP659.80a7.13a55.40a819.57a3.03b9.33a5.02b999.03b
      SE ± (df-10)5.6771.01212.089182.1920.5483.5321.121182.836
      MP1 – Control, MP2 – NoduMax, MP3 – Organic fertilizer, MP4 – NoduMax + Organic fertilizer, MP5 – Conventional, MP6 – Conventional + NoduMax, PHTR7 – Plant height at physiological maturity, NBR – Number of branches, NPODS – Number of pods, NSEDDS – Number of seeds, SWT – Weight of seeds, PWT – Pod weight, 100SWT (one hundred seed weight), GYD – Grain yield. Means with the same letter along the column are not significantly different according to Duncan's Multiple Range Test (DMRT) at 5% probability level.

      Effects of management practices on nodulation of soybeans in 2020 and 2021

    • Varying results were obtained on the effects of management practices on nodule count and dry weight per plant in both years (Fig. 2a, b). Higher number of nodules was recorded in 2021 than 2020 probably because of more favourable rainfall distribution. During the dry year 2020, the highest number and dry weight of nodules was recorded when organic fertilizer was applied alone (MP3) at 8WAS. Similarly, a combination of NoduMax and organic fertilizer (MP4) resulted in the highest number and dry weight of nodules at 9WAS in 2020. Comparatively high number of nodules were recorded at 8, 9 and 10WAS in 2021 when soybean seeds were inoculated with NoduMax alone (MP2) or in combination with organic fertilizer (MP4) at 9WAS or in combination with conventional practice (MP6) 8 and 10WAS (Fig. 2a).

      Figure 2. 

      (a) Nodule count and (b) nodule weight of soybean in 2020 and 2021; MP1, Control; MP2 NoduMax; MP3, Organic fertilizer; MP4, Nodumax and Organic fertilizer; MP5, Conventional system; MP6, Nodumax and Conventional system at 7, 8, 9 and 10 weeks after sowing, WAS. Capped vertical lines represent SED (df + 10).

      Effects of management practices on some agronomic traits of soybeans in 2020 and 2021

    • Management practice significantly (p < 0.05) affected plant height at R7 during the drier year 2020 (Table 5) and no significant effect was recorded in 2021(Table 6). In 2020, organic fertilizer application alone (MP3) and in combination with inoculation (MP4) resulted in soybean plants that were significantly (p < 0.05) taller than soybean plants grown under conventional system (MP5). The number of branches per plant of soybeans was significantly (p < 0.05) affected by management practice in both years with conventional practice (MP5) and Conventional practice + inoculation (MP6) recording the highest number of branches in 2020 and 2021, respectively. Number of pods and seeds per plant and weight of seeds per plant were significantly (p < 0.05) affected by management practice in this study in both years, except number of pods per plant in 2021. Pod weight significantly (p < 0.05) increased following the application of organic fertilizer (PM3) in 2020 relative to the pod weight recorded under the control (PM1) and NoduMax (PM2) alone (Table 5). Inoculation with NoduMax alone (MP2) and in combination with organic fertilizer (MP4) enhanced pod weight in 2021 though not significantly (Table 6). Soybean grain yield under all the management practices where the seeds were either inoculated alone (MP2) or in combination with organic fertilizer (MP4) or conventional practice (MP6) and the conventional practice (MP5) was significantly (p < 0.05) greater than the yield from the control (MP1) in both years (Tables 5 & 6). Inoculation alone (MP2) failed to significantly enhance soybean grain yield when compared with other management practices (MP3–MP5) in 2020. A 15.1% yield advantage of soybean yield under organic practice (MP3) over the conventional practice (MP5) in the relatively dry year 2020.

    Discussion

    • Varying response of soybean to the different management practices in this study could partly be attributed to the varying weather conditions during the periods of experimentation. As shown in Fig. 1, rainfall distribution recorded in 2020 (493.8 mm) and 2021 (645.6 mm) compared well with the range of 500–750 mm recommended as adequate for a good seed yield of soybeans[34]. Although, the month of August in 2020 was markedly dry, rainfall was highest (246.3 mm) in September and adequate in October (127.6 mm). Rainfall distribution favoured soybean development during September and October that coincided with the critical periods (flowering – grain filling) of soybean for moisture requirement.

      Attainment of full bloom (R2) was delayed following the application of inoculant (MP2), inoculant + organic fertilizer (MP4), synthetic fertilizers (MP5) and inoculant + synthetic fertilizers (MP6) during the dry year 2020. Application nitrogen has been reported to promote vegetative growth and thereby induce flowering in soybeans grown in Sudan[35]. However, in our study, the slight delay in days to R2 following the addition of nutrients could be attributed to more nutrient availability in the soil medium relative to the control. Lack of significant effect following the application of organic nutrients from different sources on days to R2, R5 and R7 of soybeans was recently reported in the humid tropics[9]. Grain Filling Period is usually determined by the interaction between genotype and environmental factors through their effects on the availability and translocation of assimilates from the leaves to the seeds as described by Patterson & Raper[36]. Application of inoculant alone (MP2) and organic fertilizer (MP3), and in combination with organic fertilizer (MP4) did not result in increased grain yield relative to the conventional system (MP5) despite prolonging GFP by 5–7 d. This finding confirmed earlier report that GFP has no direct effect on final grain yield of soybeans[37].

      Application of inoculant and organic fertilizer (MP4) enhanced nodule count and weight during the dryer late cropping season of 2020 at 9WAS. This could be attributed to the ability of the organic fertilizer to improve water retention through the organic matter in it and also improve dissolution of some nutrients such as phosphorus as earlier reported by Olupot et al.[38]. These factors might have jointly improved the soil rhizosphere to make it more conducive for nodulation and plant growth[39]. Inoculation has also been reported to increase nodulation, plant growth and grain yield of soybeans in South Africa[40].

      Soybean plant height is an important agronomic trait and it depends largely on the genetic makeup of the variety[41] and nitrogen availability[42]. Lack of significant effect of organic fertilizer application on soybean plant height was also recently reported in Indonesia[43]. In our study, combination of inoculant and organic fertilizer (MP4) enhanced plant height, especially during the more favourable year 2021. Similar increase in plant height in earlier studies have been attributed to the slow release of nutrients from the organic fertilizer and improved microbial activities in the soil[44] and enhanced nutrient uptake by plants and increased photosynthetic rate[45]. Application of synthetic fertilizer and inoculant (MP6) resulted in increased number of branches in this study. Increase in number of branches per plant had earlier been reported in soybean following the integrated use of organic and inorganic fertilizers in Nigeria[46]. Number of pods and seeds per plant and weight of seeds per plant have been reported as the major seed yield components of soybeans[47,48]. Increase in the values of these traits, especially seed weight per plant following the application of organic fertilizer and under conventional practice could be attributed to improved supply and availability of nutrients necessary for partitioning of assimilates to the various structural components of the plants under these management practices, especially in 2022. Similar results have been reported following the integrated use of farm yard manure and synthetic fertilizer on soybeans in Iran[49]. It was recently reported in a four year study that soybeans under organic production system produced fewer pods per plant and greater seed weight than soybeans under conventional system[50] . This trend was only consistent with our findings during the more favourable year 2021 though their differences (MP3 vs MP5) were not significant in both years. Similarly, earlier reports had also reported improvement in soybean pod weight following the application of Rhizobium japonicum alone in combination with phosphorus fertilizer[39]. The grain yield performance of the test variety in this study under different management practices (846.0–1,962.0 kg/ha in 2020) and (754.8–1,554.0 kg/ha in 2021) compared well with the average yield values in Nigeria (926.9 kg/ha) and Africa (1,348.0 kg/ha), and significantly below the world (2,784.2 kg/ha) based on FAOSTAT[6]. In our study, inoculation alone (MP2) did not significantly increase soybean grain yield more than the other management practices (MP3–MP5). Similar results were reported for soybeans in Pakistan[21]. This suggests that beyond microbial activities in the soil, other growth factors like nutrients and moisture availability are also crucial in the output any crop. The 15.1% yield advantage under organic system (MP4) over the conventional system (MP5) in 2020 confirmed earlier report that yields from organic plots tend to be greater than the conventional plots under dry conditions because of the inherent advantage of better water holding capacity under the organic system compared to the conventional system[13]. The recorded range of yield penalty is similar to values (15%–40%) reported in literature[11−13]. However, during the dryer year 2020, a very small yield penalty (1.0%) was recorded when the conventional system (MP5) was compared with the combination of organic fertilizer and NoduMax (MP4), while soybean that received only organic fertilizer application (MP3) recorded grain yield that was 15.1% superior to the yield under conventional system (MP5). The recorded trend confirmed the potential of organic soybean production in the tropics even under erratic rainfall distribution.

    Conclusions

    • The results of this study have revealed that prospective soybean growers in the humid tropics can cultivate organic soybean using organic fertilizer with (MP4) or without (MP3) seed inoculation and still record comparable grain yield obtainable from the conventional management system especially when the rainfall amount and distribution are optimum. These two management practices (MP3 and MP4) can be recommended for resource-constrained farmers in the tropics with limited capacity to afford the high cost of synthetic fertilizers that have access to organic fertilizers or other organic soil amendments.

    Author contributions

    • The authors confirm contribution to the paper as follows: conceptualization: Olowe VIO; methodology: Olowe VIO, Odueme PU, Fadeyi OJ; data collection: Olowe VIO, Odueme PU, Somefun OT; data analysis: Olowe VIO; draft manuscript preparation: Olowe VIO; manuscript review and editing: Odueme PU, Fadeyi OJ, Somefun OT; field operations: Fadeyi OJ. 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.

    Acknowledgements

    • This is to sincerely express our profound gratitude to the technical staff of the Institute of Food Security, Environmental Resources and Agricultural Research (IFSERAR) for their support and assistance in preparing the land for the experiment and the management of the Federal University of Agriculture, Abeokuta (FUNAAB) for providing all the farm equipment and implements used for the research.

      Conflict of interest

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

      Rights and permissions
      • Copyright: © 2023 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 (2)  Table (6) References (50)
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    Olowe VIO, Odueme PU, Fadeyi OJ, Somefun OT. 2023. Agronomic response of soybeans (Glycine max (L.) Merrill) to different management practices in the humid tropics. Technology in Agronomy 3:18 doi: 10.48130/TIA-2023-0018
    Olowe VIO, Odueme PU, Fadeyi OJ, Somefun OT. 2023. Agronomic response of soybeans (Glycine max (L.) Merrill) to different management practices in the humid tropics. Technology in Agronomy 3:18 doi: 10.48130/TIA-2023-0018
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  • Introduction
  • Materials and methods

  • Site description

  • Experimental materials

  • Treatments and experimental design

  • Crop husbandry

  • Data collection

  • Data analysis

  • Results

  • Growth conditions

  • Effects of management practices on the phonology of soybeans in 2020 and 2021

  • Effects of management practices on nodulation of soybeans in 2020 and 2021

  • Effects of management practices on some agronomic traits of soybeans in 2020 and 2021

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