Next Article in Journal
Microbial Biosensors for Wastewater Monitoring: Mini-Review
Next Article in Special Issue
The Plant Growth Regulator 14-OH BR Can Minimize the Application Content of CPPU in Kiwifruit (Actinidia chinensis) ‘Donghong’ and Increase Postharvest Time without Sacrificing the Yield
Previous Article in Journal
Effects of Calcium Sulfate and Chitosan on Textural Modification and Microstructure of Tofu Made from Lentils (Lens culinaris)
Previous Article in Special Issue
Exogenous Proline Optimizes Osmotic Adjustment Substances and Active Oxygen Metabolism of Maize Embryo under Low-Temperature Stress and Metabolomic Analysis
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Microbial Prospection for Bioherbicide Production and Evaluation of Methodologies for Maximizing Phytotoxic Activity

by
Dinalva Schein
1,
Maicon S. N. Santos
2,
Silvana Schmaltz
1,
Luiz E. P. Nicola
1,
Cristiane F. Bianchin
1,
Renata G. Ninaus
1,
Bryan B. de Menezes
3,
Ricardo C. dos Santos
4,
Giovani Leone Zabot
2,
Marcus V. Tres
2,* and
Marcio A. Mazutti
1
1
Department of Chemical Engineering, Federal University of Santa Maria, 1000 Roraima Av., Camobi, Santa Maria 97105-900, Brazil
2
Laboratory of Agroindustrial Processes Engineering (LAPE), Federal University of Santa Maria, 1040 Sete de Setembro St., Center DC, Cachoeira do Sul 96508-010, Brazil
3
Department of Chemistry, Federal University of Santa Maria, 1000 Roraima Av., Camobi, Santa Maria 97105-900, Brazil
4
SoluBio Agricultural Technologies, Highway GO 184, Km 09, Rural Area, Jataí 75809-899, Brazil
*
Author to whom correspondence should be addressed.
Processes 2022, 10(10), 2001; https://doi.org/10.3390/pr10102001
Submission received: 30 August 2022 / Revised: 27 September 2022 / Accepted: 30 September 2022 / Published: 4 October 2022
(This article belongs to the Special Issue Crops Chemical Control Principle and Technology)

Abstract

:
The occurrence of several weeds in crops of high economic value directly affects grain yield. This scenario led to the mass investigation of chemical products that circumvent these adversities and provide control potential. Nonetheless, the excessive application of chemical herbicides has generated significant concerns about the environment. Accordingly, the adoption of alternative practices, such as the application of microbial metabolites, emerges as strategic control actions, having a sustainability bias and allowing the reduction of risks of human and animal contamination. Appropriately, this study proposed to conduct a microbial prospection of microorganisms capable of producing secondary metabolites to inhibit growth and generate phytotoxicity in weeds. Furthermore, to increase the herbicidal activity, different strategies were evaluated involving microbial co-cultivation and ultrasound-assisted extraction after fermentation. Accordingly, 63 microorganisms were isolated from weeds with disease symptoms and submitted separately to submerged fermentation. Initially, the bioherbicidal activity was evaluated in Cucumis sativus plants, and the most promising were applied in Amaranthus hybridus and Echinochloa crusgalli. Treatments with fermented broth obtained from co-cultivation and ultrasound-assisted extraction after the fermentation process indicated an inhibition of plant development. The most promising strains were A14.2 and B22.2 (Nigrospora sphaerica), B14 (Bacillus velezensis), and γ (Aspergillus flavus).

1. Introduction

Weeds are one of the main problems in agriculture [1,2]. They have a high capacity to compete for resources such as micronutrients, light, and chemical and biological resources [3]. The weed species of the genera, Amaranthus sp., Conyza sp., Echinochloa sp., and Bidens sp., have been reported to cause damage to different important crops worldwide [4]. Bidens pilosa and Amaranthus sp. affect rice, corn, and soybean crops; Conyza sp. affects mainly soybean, cotton, and corn plantations [5]; and Echinochloa sp. is commonly found in rice plantations [6]. These four weeds mentioned have resistance already registered to chemical herbicides [5], especially Conyza sp., which is known as one of the most resistant to chemical herbicides [7,8,9].
Modern agriculture has relied almost exclusively on chemical herbicides, due to their high short-term efficiency and relatively low cost compared to other weed control management technologies [10]. Nonetheless, chemical herbicides present high levels of toxicity and can easily leach into groundwater or enter the food chain. Soil persistence facilitates the development of resistance of some prevalent weed species, continually increasing the demand for the application of these herbicides [11,12,13]. Accordingly, sustainability-based management strategies, considering an increasing weed resistance scenario, drive the research for other methods to regulate weed infestation [14,15].
Alternatives to the intensive use of chemical herbicides are bioherbicides or bio-based herbicides, which are based on natural living agents, such as bacteria, fungi, plants, and the metabolites of these microorganisms produced during growth [16,17,18,19,20]. Phytopathogens, especially fungi, are among the most studied bioagents, as they have the specific ability to produce significant toxic substances [7,21,22]. The metabolites produced by these microorganisms are capable of penetrating plant leaves, disintegrating their cellular structure, and inducing the production of necrotic lesions or chlorotic halo [13,23].
Submerged fermentation is commonly used in the production of metabolites in studies for bioherbicides [24,25,26]. Microorganisms are increasingly being studied for this purpose, such as Pseudomonas aeruginosa, which, in its cultivation, produces secondary metabolites that inhibit the germination and growth of Amaranthus hybridus [27]. In studies with Fusarium nygamai chlorosis, necrosis, and inhibition of the root growth of Striga hermonthica were obtained [28]. Studies with the application of secondary metabolites of Fusarium fujikuroi in Cucumis sativus and Sorghum bicolor have achieved chlorosis, necrosis, and a decrease in plant height and root [26]. Some strategies are being adopted to maximize the phytotoxic effects on the plants of interest, such as post-fermentation ultrasound-assisted extraction (PF-UAE) [29,30] and co-cultivation techniques [31,32], but they still need to be investigated according to each microorganism.
An ultrasonic field, in the presence of liquids such as water, undergoes the process of acoustic cavitation [33]. This cavitation consists of the formation of low-pressure bubbles that then collapse, generating highly energetic shock waves [34]. Sufficiently energetic shock waves can promote disruption of cell walls and membranes, extracting intracellular material [35]. With the disruption of cell membranes, several metabolites of interest that had not yet been excreted end up being extracted into the medium [36,37,38]. Furthermore, there is the possibility of extracting molecules from intracellular metabolism, which were not originally present in the fermentation broth. One concern about the use of ultrasound for this application is the possibility of the metabolites of interest being thermal-sensible, suffering degradation with the thermal energy dissipated by the ultrasound [30].
The idea of joining microorganisms through co-culture, which aims to optimize the production of compounds that are not produced in monoculture and to find new chemical structures, has increased over the years [31,39,40]. Co-cultivation is an approach that involves two or more microorganisms, through interactions between species in the same confined environment, and can be achieved through solid or liquid media [31,41,42]. Cultures interact with each other through cell-to-cell contact or signaling molecules that can lead to the discovery of new chemical substances [42]. Therefore, the existence of different microorganisms in the same medium can affect the morphology of the microorganism, their development, and the synthesis of secondary metabolites [43].
Nevertheless, there are only thirteen bioherbicides registered in the world, nine based on fungi, three on bacteria, and one derived from plant extracts [6,20]. Though the specificity of bioherbicides is an advantageous aspect, it is necessary to shed light on different bioherbicides for each weed of interest [1,44]. Additionally, the development of bio-based products is a recent area of research, and with few commercial products registered worldwide, there is a clear requirement to prospect new bioproducts to contemplate the largest possible number of weeds that affect the current agricultural production [20].
Appropriately, the purpose of this study was to establish a microbial prospection to investigate the potential of viable bioherbicides for the control of Amaranthus hybridus, Bidens pilosa, Conyza sp., and Echinochloa crusgalli, isolating bioagents from several weeds presenting disease symptoms found in areas without the application of herbicides. Moreover, we performed a series of steps to maximize the phytotoxic potential of microorganisms that presented inhibitory activity on these weeds, including the application of the ultrasound-assisted extraction strategy after the fermentation process, microbial co-cultivation, and higher dosages of the fermented broth.

2. Materials and Methods

2.1. Chemicals

Potato Dextrose Agar (PDA) (Sigma-Aldrich), glucose (Dinâmica LTDA®), yeast extract (Acumedia®), peptone (TM MEDIA), (NH4)SO4 (Synth®), FeSO4·7H2O (Synth®), MnSO4·H2O (Synth®), MgSO4 (Synth®), meat extract (Acumedia®), and NaCl (Sigma-Aldrich) were used as purchased without further purification.

2.2. Microbial Prospecting and Isolation of Microorganisms

Pathogenic microorganisms were isolated from weeds that manifested symptoms of disease and lesions possibly caused by some biological agents, located in different municipalities of Rio Grande do Sul, Brazil, in areas without the application of chemical herbicides. Accordingly, Table 1 presents the plant collection locations, the species collected, as well as the geographic coordinates, and the number of microorganisms isolated from each location.
The plant collection was performed from November 2019 to March 2020, and the samples were kept at 4 °C at the Bioprocess Laboratory of the Federal University of Santa Maria, Santa Maria, Brazil, for the isolation of microorganisms. The plants were identified based on morphological characteristics and the separation of each sample was performed with the identification of the plant species and its location. Each injured tissue was transferred to a Petri dish containing PDA culture medium and incubated in a Biochemical Oxygen Demand (B.O.D.) (ELETROlab®, São Paulo, Brazil)) incubator with a photoperiod of 12 h for 7 days at 25 °C [45]. Successive subcultures were performed until a pure culture was obtained. After the isolation of all prospected microorganisms, different culture media were used for microorganisms of fungal or bacterial morphology. To fungi, the subcultures were made with a 6mm disk extracted from the cultured mycelium, and inoculated in a new PDA Petri dish, previously sterilized and prepared [7]. To bacteria, the isolations were performed by a simple streaking technique in a new Petri dish with 1.0 g L−1 of meat extract, 5.0 g L−1 of peptone, 2.0 g L−1 of yeast extract, 5.0 g L−1 of NaCl, and 15.0 g L−1 of agar, previously sterilized and prepared [46,47].

2.3. Submerged Fermentation

The microorganisms were separated according to their morphology, according to visual characteristics during growth in Petri dishes, and were submitted to different culture media for submerged fermentation. The liquid culture medium for microorganisms with fungal (LCM-F) morphology consisted of 10.0 g L−1 of glucose, 10.0 g L−1 of yeast extract, 2.0 g L−1 of peptone, 0.5 g L−1 of (NH4)SO4, 1.0 g L−1 of FeSO4·7H2O, 1.0 g L−1 of MnSO4·H2O, and 0.5 g L−1 of MgSO4, pH 6.0 [26]. The liquid culture medium for the microorganisms that presented bacterial morphology (LCM-B) was prepared with 1.0 g L−1 of meat extract, 5.0 g L−1 of peptone, 2.0 g L−1 of yeast extract, 5.0 g L−1 of NaCl, and pH 7.4 [46]. Inoculation was performed by transferring two 6 mm discs to a 250 mL Erlenmeyer flask containing 125 mL of sterile culture medium. The Erlenmeyer flasks were incubated in an orbital shaker (New Brunswick, model Innova 44) for 7 days for microorganisms with fungal morphology, and 3 days for microorganisms with bacterial morphology, at 28 °C and 120 rpm. After fermentation, the cells were filtered (Whatman®, Merck KGaA, Darmstadt, Germany) and separated by centrifugation at 4000 rpm for 20 min (Eppendorf, model 5804R), and the supernatant was subjected to membrane filtration (Merck Millipore TM), with a pore size of 0.45 µm and diameter of 47 mm [7]. The filtered supernatant was used in the bioassays. The supernatant was incubated to verify microbial growth, with negative results. The 63 microorganisms were considered distinct treatments for the tests, and the bioherbicidal activity was evaluated by a series of analyses.

2.4. Strategies to Increase the Herbicidal Activity of Fermentation Broth

2.4.1. Co-Cultivation Medium

The selection of microorganisms was performed initially by 10 combinations of pairs of microorganisms cultivated in Petri dishes to evaluate the viability of co-cultivation, and to verify possible unwanted inhibitions between strains. Each combination was composed of two microorganisms, and the number of combinations was determined through combinatorial analysis for simple combinations. The combinations were performed with the inoculation of a 6 mm disk extracted from the Petri dish culture of each microorganism in a new PDA Petri dish, previously sterilized and prepared. After selecting the best combinations, fermentation was carried out, and a specific culture medium was used for each morphology. For combinations between microorganisms that presented fungal morphology, the LCM-F was utilized [26]. Considering the combinations of microorganisms with bacterial morphology, the LCM-B was utilized [46]. Finally, for the combinations with fungi and bacteria, the addition of all the components of both media (LCM-F plus LCM-B) was performed [26,46]. The culture media were incubated and maintained under conditions as described above.

2.4.2. Post-Fermentation Ultrasound-Assisted Extraction (PF-UAE)

After 7 days of fermentation, the broth and biomass were directly subjected to Ultrasound-Assisted Extraction (UAE). An ultrasonic processor, UP400S (Hielscher Ultrasonics GmbH), Sonotrode H22 (Hielscher Ultrasonics GmbH) with a diameter of 22 mm was used. Sonication was performed directly by coupling a sonication cell with a cooling bath at 10 °C, and inside the cell, it was filled with the fermentation broth with the biomass. The process conditions were 90% amplitude and 0.5 s cycle for 40 min [48]. Afterward, the cells were separated from the medium, centrifuged at 4000 rpm for 20 min (Eppendorf, model 5804R), and filtered by membrane (Merck Millipore TM) with a pore size of 0.45 µm and a diameter of 47 mm to obtain a cell-free supernatant [7].

2.4.3. Co-Cultivation with PF-UAE

Assays were performed associating co-cultivation with the use of PF-UAE. The co-cultivation was conducted as described in Section 2.4.1, and after the fermentation period, the broth and biomass were submitted to UAE as described in Section 2.4.2.

2.5. Application in Plant Species

2.5.1. Bioherbicidal Activity in C. sativus

The 63 microorganisms isolated were considered distinct treatments, and each treatment consisted of four plants of C. sativus, and each plant was considered a repetition (experimental unit). Sowing was performed in trays; after emergence, the seedlings were replanted in 1 L pots containing 50% soil and 50% Mecplant® commercial substrate. The seeds were obtained from the local market, without any pre-sowing treatment. In this first stage, each application was performed with a manual sprayer, with the application of 1 mL per plant, directly on the shoot. For the application in the control plants, the broth was replaced with distilled water. The application was performed 9 days after sowing. The visual assessment of phytotoxicity was performed 21 days after application (DAA), based on the scale of Frans and Crowley (1986) [49].

2.5.2. Bioherbicidal Activity on Weeds

The best results obtained in the assays with C. sativus were selected, and the bioherbicidal activity in Amaranthus sp., Conyza sp., B. pilosa, and E. crusgalli were evaluated. The plants were sown and replanted into 1 L pots containing commercial Mecplant® substrate and kept in a greenhouse. Applications were made with a manual sprayer.
To evaluate the bioherbicidal activity of the crude broth, 1 mL of supernatant per plant was applied, in 4 repetitions, where each plant was considered a repetition. For the control plants, the broth was replaced with distilled water [50]. The crude broth assay and control plants were kept under the same conditions, and the evaluations were performed at 21 DAA. The evaluation assays of the processes with PF-UAE, co-cultivation, and a triple application of broth, consisted only of the application in A. hybridus and E. crusgalli. One seedling of A. hybridus and 2 to 3 seedlings of E. crusgalli were replanted in pots; each pot was considered a plant, and each plant a repetition. For one of the methodologies, the triple crude broth was applied; that is, 3 mL per plant, and the others followed as previously described [25]. The applications were conducted in 7 repetitions, and the evaluations were carried out in 14 DAA. In all weed applications, plant height (PH), root length (RL), shoot fresh mass (SFM), root fresh mass (RFM), shoot dry mass (SDM), and root dry mass (RDM) were evaluated [51].

2.6. Identification of Microorganisms

The molecular identification of promising microorganisms was performed by the Biological Institute of São Paulo, São Paulo, Brazil. The DNA of the isolates was extracted according to the method described by [52] from the microorganism grown in a culture medium. The extracted genomic DNA sample was subjected to a polymerase chain reaction (PCR) for amplification of the ITS (internal transcribed spacer) region of the rDNA (isolates A14.2 and B22.2), beta-tubulin gene segment (γ isolate), and gyrB gene segment (B14 isolate). The oligonucleotide primers for the ITS region were SR6R (5′-AAGWAAAAGTCGTAACAAGG-3′) and LR1 (5′-GGTTGGTTTCTTTTCCT-3′) (Vilgalys and Hester, 1990); for the beta-tubulin gene, they were TUB2Fd (5′-GTBCACCTYCARACCGGYCARTG-3′) and TUB4Rd (5′-CCRGAYTGRCCRAARACRAAGTTGTC-3′) [53]; and for the gyrB gene, they were gyrB-F (5′-GTNYAYCGTGAYGGNAAAATYCA-3′) and gyrB-R (5′-GCAGARTCWCCCTCTACRATATA-3′) (developed by R. Harakava, Biological Institute of São Paulo). The PCR mix consisted of 1 µL of DNA, 1 µL of each primer at 10 µM, 10 µL of 5X PCR buffer, 1 µL of 10 mM dNTPs, 0.2 µL of GoTaq DNA polymerase 5U/µL (Promega), and 35.8 µL autoclaved MilliQ H2O, to a final volume of 50 µL. The amplification program consisted of initial denaturation at 94 °C/2 min followed by 40 cycles of denaturation at 94 °C/10 s, annealing at 54 °C/30 s, extension at 72 °C/45 s, and final extension at 72 °C/4 min. The verification of the amplified products was performed by electrophoresis in a 0.8% agarose gel stained with ethidium bromide. The amplified products were purified by precipitation with polyethylene glycol, directed to sequencing reaction by the chain termination method using the Big Dye 3.1 reagent (Applied Biosystems), and analyzed in a 3500 xL automatic capillary sequencer (Applied Biosystems) [54]. For the construction of the phylogenetic trees, the Neighbor-Joining method was used with a bootstrap of 1000 repetitions in the MEGA 6.0 program [55].

2.7. Statistical Performance

The means were compared by Student’s t-test in the Statistica® software, version 8.0 [56], using a probability of error lower than 5% (p ≤ 0.05) as a significant difference criterion.

3. Results

3.1. Initial Bioprospecting

Considering the 63 different strains of microorganisms isolated, 21 microbial strains exhibited visual phytotoxic effects on C. sativus plants (Figure 1), such as necrosis, yellowing, and leaf spots. The others showed no visual effects on the plants. The effects were already observed in the first week of evaluation.
Five microorganisms presented significant results concerning phytotoxicity: strain B22.2, strain γ, strain A14.2, strain B13, and strain B14, as they presented mortality of at least one of the plants in the stand, and phytotoxicity equal to or higher than 80%. The other microorganisms showed less intense effects and plant recovery throughout the trial evaluation period.

3.2. Inhibition Potential on the Studied Weeds

Table 2 presents the quantitative evaluation of six different variables studied, namely: plant height (PH), root length (RL), shoot fresh mass (SFM), root fresh mass (RFM), shoot dry mass (SDM), and root dry mass (RDM).
In plants of Conyza sp., no significant inhibitory effects were observed. Similar results were found for the species, B. pilosa, which indicated a reduction in plant height only in A14.2 (33%) and in B22.2 (16%). The other variables showed no inhibition effects. The E. crusgalli and A. hybridus plants presented higher sensitivity to the treatments applied. In E. crusgalli plants, all treatments indicated inhibitory effects of fresh and dry mass, both in shoots and in roots. The treatment, B14, showed the best inhibition rates in PH (19%), and had variations between 52 and 67% of inhibition in the variables, SFM, RFM, SDM, and RDM. The results observed in A. hybridus plants demonstrated the ability of the B13 treatment to reduce PH, SFM, and SDM, with reduction percentages between 22 and 73%. Additionally, no visible phytotoxicity lesions were observed in any of the treatments and plants.

3.3. Strategies to Increase the Herbicidal Activity of the Fermented Broth

Considering the results obtained in the tests of application of the crude broth, the two species that suffered significant effects (E. crusgalli and A. hybridus) were selected. Tests were performed with different methods after fermentation to potentiate the inhibitory effects observed in the previous stage of the study. Table 3 and Table 4 indicate the results of all the methods proposed and applied to E. crusgalli and A. hybridus plants.

3.4. Triple Dose Application

In E. crusgalli plants (Table 4), the best treatments with the application of triple dosage were B14 and γ, which only did not change the PH. B14 remained with the same amount of inhibition as presented in the crude broth with a single dose. The treatments, B13 and B14, indicated some wilting symptoms in E. crusgalli plants after 3 DAA, indicating possible effects. Nevertheless, the plants recovered until the finalization of the experimental step.
Moreover, the treatments with B13 and γ demonstrated that the application of more doses influences the inhibition, doubling the number of inhibited variables. The opposite was observed in the treatment with B22.2, where better results were not observed with a higher volume of broth applied.

3.5. Post-Fermentation Ultrasound-Assisted Extraction (PF-UAE) of the Raw Broth

The treatments of strains, B13, B14, and B22.2, did not show significant inhibition effects on A. hybridus plants. The results were better with crude broth application than when compared with the PF-UAE.
The best effect on A. hybridus was when the γ treatment was applied, which indicated significant results in RL (33%), SFM (67%), SDM (52%), RFM (73%), and RDM (70%). Compared with the application of the broth without the PF-UAE, an improvement in the bioherbicidal activity was observed, evidencing the potential of this microorganism.
Inhibitions were higher in E. crusgalli plants, where the treatments, B22.2 and A14.2, presented the best inhibitory effects, influencing the development of all variables. Inhibitions ranged from 15% to 60% for B22.2, and 17% to 60% for A14.2. Compared with the application of the raw broth, it was observed that the PF-UAE improved the efficiency of most treatments applied to E. crusgalli.

3.6. Co-Cultivation Medium

The tests were performed with the addition of microorganisms from the row and column from the matrix presented in Table 5. The data are expressed as the first symbol referring to the row and the second referring to the column. The − sign indicates an inhibition effect and a + indicates a growth effect of the microorganism.
The selected combinations were A14.2/B22.2, B13/B14, B14/γ, and B22.2/γ. In this methodology, the broth of the co-cultivation of the microorganisms, B14 and γ, presented better performance than the other treatments in the application in plants of A. hybridus, besides obtaining better performance than those evaluated alone.
The treatment A14.2/B22.2 had no effects on A. hybridus, but the opposite was observed in E. crusgalli plants, where this treatment was the only one that presented the inhibition of variables. The inhibitions observed were in the fresh and dry mass, both in the shoots and roots.

3.7. Post-Fermentation Ultrasound-Assisted Extraction (PF-UAE) of the Co-Cultivation Broth

Evaluating the plants of E. crusgalli with the treatment derived from PF-UAE over co-cultivation, a significant improvement was observed against the treatment of co-cultivation broth without UAE, also compared to the raw broth. In E. crusgalli, A14.2/B22.2 presented a higher percentage of reduction of variables compared to the treatment of co-culture without UAE, also showing a reduction of CR (12%). Metabolites from the co-cultures, B14/γ and B22.2/γ, showed inhibition of five variables in E. crusgalli both with and without the use of PF-UAE over the broth.
In A. hybridus, growth effects are systematically observed in all methodologies explored, with suspicion of troubles in the growth of the control plants, since the masses of the control plants are systematically lower than the average masses of the other tested plants. It is important to note the positive results achieved in the first assays carried with raw broth (Table 2).

3.8. Microbial Identification

The morphology of the microorganisms was visually determined according to the growth in the Petri dish. Strain B14, showed bacterial morphology, whereas the strains, A14.2, B22.2, and γ, showed fungal morphology. The treatments, B14/γ, and strains, A14.2 and B22.2, from UAE inhibited all variables in the methodologies previously tested; therefore, the four strains that compose these treatments were selected for identification (A14.2, B14, B22.2, and γ). Strain B14 was identified as Bacillus velezensis—GenBank DQ903176, strain γ as Aspergillus flavus—GenBank AY017536, and strains A14.2 and B22.2 belong to the same species: Nigrospora sphaerica—GenBank KX985964.

4. Discussion

4.1. Initial Bioprospecting

The results presented in the applications of C. sativus indicated that the broths from these microorganisms are promising, and the bioherbicide potential in weeds can be evaluated. The other microorganisms manifested less intense effects and recovery of the plants throughout the trial evaluation period.
Accordingly, the initial application in C. sativus gives considerable information to the next steps. C. sativus is commonly explored to perform bioassays, since it is a plant with high sensitivity to phytotoxic compounds, and is easy to grow [26,57]. Yellowing and leaf spots were also reported in studies, which led to the selection and identification of the fungus, Diaporthe sp. [57]. The fungus, Fusarium fujikuroi, was also selected by prospecting for microorganisms tested in C. sativus, showing dark spots and leaf chlorosis [26].

4.2. Inhibition Potential on the Studied Weeds

The four weeds initially evaluated have an important commercial interest, since they are known for affecting the production of important crops. Conyza sp. affects the crops of soy, cotton, and corn [5,58]; B. pilosa and A. hybridus affect the crops of rice, corn, and soy [4,5]; and E. crusgalli affects mainly rice crops [5,59]. Moreover, all these four weeds have been reported to exhibit resistance to commercial herbicides [5].
In these weeds, the six evaluated variables (PH, RL, SFM, RFM, SDM, RDM) were chosen for being meaningful in the impairing potential of the weeds over the crops. The reduction in weed height decreases the intensity of available light, due to the shading caused by the crop of interest, affecting its photosynthesis. Therefore, the reduction in root length/mass reduces the consumption of nutrients, such as water absorption, nitrogen compounds, and minerals, reducing weed development, as well as increasing the availability of these nutrients for the crop of interest [60,61,62].
Promissory results were observed in E. crusgalli and A. hybridus. Some reports in the literature already present studies of microorganisms, such as Lasiodiplodia pseudotheobromae, Pseudomonas aeruginosa, and Carum carvi, promissory for these species [27,63]. Studies have reported a reduction in height and shoot and root fresh and dry mass of E. crusgalli and B. pilosa from metabolites of the fungus, Diaphorte schini [51]. Furthermore, few studies present the biological behavior of the plant when submitted to secondary metabolite phytotoxins [20]. The reduction of weed fresh mass was also observed with the application of the secondary metabolites of Phoma dimorpha in Senna obtusifolia plants [64]. The delay and/or inhibition of plant growth may be associated with phytotoxin interference in the biosynthesis of amino acids, such as valine, leucine, and isoleucine, which are essential for plant growth and better performance [51,65].
The unsatisfactory results for Conyza sp. may be related to the fact that the genus, Conyza, has records of resistance to several chemical herbicides, such as Paraquat, glyphosate, and 2,4-dichlorophenoxyacetic acid (2,4-D) [5], besides being known to be one of the most resistant to chemical herbicides [7,8,9]. The species, B. pilosa, also gives insufficient results, which only presented a significant reduction in plant height in A14.2 (33%) and B22.2 (16%). The other variables showed no inhibition effects. The low effects may be associated with the low concentration of metabolites in the supernatant [66].
Due to the lack of significant results from the application tests of the crude broth on the Conyza sp. and B. pilosa, the next steps were carried out with the two species that were susceptible to the treatments (E. crusgalli and A. hybridus).

4.3. Strategies to Increase the Herbicidal Activity of the Fermented Broth

The low concentration of metabolites may be associated with the lack of phytotoxic results, as well as the rapid volatilization of the product, causing the broth not to be absorbed by plant tissues [25,67,68,69]. Moreover, some bioherbicides of microbiological origin require post-fermentation steps, as well as the use of adjuvants, in search of methods that improve product efficiency [50,68]. Aiming to potentialize the inhibitory effects over these weeds, tests were performed with four different methods: dose augmentation, co-cultivation of microorganisms, post-fermentation UAE (PF-UAE) of raw broth, and PF-UAE over co-cultivation broth. Higher dosage aims to directly increase the amounts of metabolites available to interact with the plant tissues, whereas co-cultivation and PF-UAE can give rise to synergetic effects between metabolites, or even the emergence of new ones.

4.4. Triple Dose Application

The increase in the dose of broth applied aims to simulate the equivalent effect of the application of broths with a higher concentration of metabolites, and it is commonly used in agriculture, as it aims to increase the effectiveness of the applied product [70]. Wilt symptoms were also observed with the application of secondary metabolites produced by Phoma sp. [25]. The doubling in the number of inhibited variables for treatments with B13 and γ with the increase of application dose indicates that these microorganisms have the potential for bioherbicidal activity in E. crusgalli. Studies indicated that increasing the applied dose potentiates the expected effects on plants, as it consequently increases the number of metabolites disposed of in the plant [25,71].

4.5. Post-Fermentation Ultrasound-Assisted Extraction (PF-UAE) of the Raw Broth

The worst results obtained with the application of PF-UAE in bacteria (strains B13 and B14) may be associated with the metabolites being sensitive to the effects of the process. Bacteria have a few complex structures, such as protein compartments that have some enzymes necessary for cellular metabolism, and there are not many intracellular products to be extracted [72,73]. Compared to the application of the raw broth, it was observed that the PF-UAE improved the efficiency of most treatments applied to E. crusgalli. Some metabolites can injure and damage some weed species more than others [74]. The γ treatment showed significant results in A. hybridus and not in E. crusgalli. The application of this methodology indicated that the possible metabolites from the intracellular medium have more pronounced effects on grasses. The more pronounced improvements were in microorganisms with fungal morphology, indicating that the higher cellular complexity of the fungi possibly plays a role in the achieved results. Fungal growth can be filamentous, with dispersed hyphae, or in the form of spherical pellets, consisting of compact hyphae [75,76]. These microorganisms grew in the form of pellets, which may interfere with the transport of substrates into the cell, which may have significant effects on cellular metabolism and the synthesis of products and defense mechanisms [77].
As fungi are eukaryotic organisms, the complexity of the intracellular environment is higher when compared to the prokaryotic cells of bacteria [77]. Fungi have a range of organelles, including fungal vacuoles that are responsible for producing and storing digestive enzymes, among other functions [78,79]. Considering these differences between fungal and bacterial cells, the PF-UAE over the intracellular components from fungi tends to be more promising compared to bacteria, given the higher diversity of metabolites, mainly digestive enzymes found in fungal cells [79].

4.6. Co-Cultivation Medium

This technique promotes advantages in improving quality; it has potential in the replacement of substrates, but mainly stands out for the increase in product yield [80]. Different metabolites may have been produced by co-cultivation, which improved the performance of the treatment with the γ microorganism, and reinforced the bioherbicidal potential of the B14 microorganism, which had already shown efficiency in other methodologies. This is due to competition between microorganisms, which, when inoculated together, are exposed to stress-inducing factors that stimulate gene complexes that have not yet been expressed under normal culture conditions and can induce the production of new compounds [32,81]. For the other treatments, co-cultivation was not more efficient than the crude broth in A. hybridus. Additionally, to the production of new compounds, the loss of some metabolites present in the culture may also occur when submitted to co-cultivation [81].

4.7. Post-Fermentation Ultrasound-Assisted Extraction (PF-UAE) of the Co-Cultivation Broth

The combination of PF-UAE and co-cultivation techniques promotes a few significant improvements compared with these two techniques employed isolated, mainly in A14.2/B22.2 cultivation over E. crusgalli. This is possibly due to the synergy in the characteristics of these two techniques, as already discussed in Section 4.5 and Section 4.6.
Studies claimed that Amaranthus is among the most difficult species to control globally, and in field experiments, A. hybridus was indicated to be more resistant to treatment applications than E. crusgalli [82].
Growth effects are systematically observed in all methodologies explored (PF-UAE and co-cultivation medium) against A. hybridus, where it was observed that the averages of all parameters of the control were lower than most treatments. This observation may be directly related to the inadequate development of the control plants, as some factors influence plant evolution, such as water, temperature, and light, which is the most important variable that affects photosynthesis and plant growth and development [65]. The hypothesis of problems with the growth of control is reinforced by the positive results achieved in the first assays carried with raw broth, presented in Section 3.2 and Section 4.2.

4.8. Microbial Identification

The properties and applications of Bacillus velezensis have been studied both in agriculture and in the fermentation industry. This microorganism is easy to grow and has a high ability to resist stress. The literature presents reports of applications as a pesticide to combat classified airborne fungi or soil diseases [83]. Furthermore, some reports indicated Nigrospora sphaerica as an important species against the water hyacinth [84]. Additionally, in Table 4, the results show A. hybridus presents higher resistance compared to E. crusgalli plants. The significant differences mostly refer to growth. There was only inhibition in the plant height with B14 and γ treatment. Growth effects are systematically observed in all methodologies used (UAE and co-cultivation medium) against A. hybridus, where it was verified that the averages of all parameters of the controls were lower than most treatments. The microorganisms presented by the bioprospecting performed indicated a high potential to produce secondary metabolites for weed control.

5. Conclusions

The purpose addressed in this study was promising in obtaining a bioherbicide for weed control through the production of metabolites by submerged fermentation. The highest growth-inhibiting effects on E. crusgalli plants were obtained when the A14.2 and B22.2 (N. sphaerica) broth with biomass was submitted to PF-UAE for cell disruption. The metabolites from the co-cultivation of B14 (B. velezensis) with γ (A. flavus) were indicated to be significantly efficient in inhibiting the development of A. hybridus. The exploration of methodologies such as co-cultivation and ultrasound-assisted extraction proved to be excellent tools for obtaining new compounds, which may be promising in the production of a bioherbicide. The results obtained encourage the development of more studies of concentration and formulation for the potentiation of the bioherbicidal activity of the metabolites coming from the microorganisms explored in the bioprospecting performed in this study.

Author Contributions

Conceptualization, methodology, software, formal analysis, investigation, data curation, writing—original draft preparation, D.S.; formal analysis, writing—review and editing, visualization, M.S.N.S.; formal analysis, investigation, S.S.; formal analysis, investigation, L.E.P.N.; formal analysis, investigation, C.F.B.; formal analysis, investigation, R.G.N.; software, formal analysis, writing—review and editing, visualization, B.B.d.M.; funding, visualization, R.C.d.S.; visualization, supervision, G.L.Z.; writing—review and editing, visualization, supervision, M.V.T.; writing—review and editing, visualization, supervision, project administration, M.A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Coordination for the Improvement of Higher Education Personnel (CAPES - 001), National Council of Technological and Scientific Development (CNPq - 306241/2020-0; 428180/2018-3), the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS - 21/2551-0002253-1), and SoluBio® (03/2021).

Data Availability Statement

Not applicable.

Acknowledgments

This work was supported by the National Council of Technological and Scientific Development (CNPq) and the partner company SoluBio®.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Klaic, R.; Sallet, D.; Foletto, E.L.; Jacques, R.J.S.; Guedes, J.V.C.; Kuhn, R.C.; Mazutti, M.A. Optimization of solid-state fermentation for bioherbicide production by Phoma sp. Braz. J. Chem. Eng. 2017, 34, 377–384. [Google Scholar] [CrossRef] [Green Version]
  2. Vasilakoglou, I.B.; Eleftherohorinos, I.; Dhima, K.V. Propanil-Resistant Barnyardgrass (Echinochloa crus-galli) Biotypes Found in Greece 1. Weed Technol. 2000, 14, 524–529. [Google Scholar] [CrossRef]
  3. Mathur, M.; Gehlot, P. Recruit the Plant Pathogen for Weed Management: Bioherbicide a Sustainable Strategy. In Fungi and their Role in Sustainable Development: Current Perspective; Springer: Singapore, 2018; pp. 159–181. ISBN 9789811303937. [Google Scholar]
  4. Heap, I.; Duke, S.O. Overview of glyphosate-resistant weeds worldwide. Pest Manag. Sci. 2018, 74, 1040–1049. [Google Scholar] [CrossRef] [PubMed]
  5. Heap, I. The International Herbicide-Resistant Weed Database. Available online: www.weedscience.org (accessed on 21 April 2022).
  6. Cordeau, S.; Triolet, M.; Wayman, S.; Steinberg, C.; Guillemin, J.-P. Bioherbicides: Dead in the water? A review of the existing products for integrated weed management. Crop Prot. 2016, 87, 44–49. [Google Scholar] [CrossRef]
  7. Daniel, J.; Brun, T.; Todero, I.; Almeida, T.C.; Confortin, T.C.; Kuhn, K.R. Association of Adjuvants with Culture Filtrate from Fusarium fujikuroi for Increasing the Control of Conyza sp. Biointerface Res. Appl. Chem. 2020, 10, 6481–6487. [Google Scholar] [CrossRef]
  8. Green, J.M. Current state of herbicides in herbicide-resistant crops. Pest Manag. Sci. 2014, 70, 1351–1357. [Google Scholar] [CrossRef]
  9. Duarte, L.L.; Santos, F.M.C.; Barreto, R.W. Mycobiota of the weed Conyza canadensis (Asteraceae) in Brazil. Fungal Biol. 2016, 120, 1118–1134. [Google Scholar] [CrossRef]
  10. Zhu, H.; Ma, Y.; Guo, Q.; Xu, B. Biological weed control using Trichoderma polysporum strain HZ-31. Crop Prot. 2020, 134, 105161. [Google Scholar] [CrossRef]
  11. Bo, A.B.; Kim, J.D.; Kim, Y.S.; Sin, H.T.; Kim, H.J.; Khaitov, B.; Ko, Y.K.; Park, K.W.; Choi, J.S. Isolation, identification and characterization of Streptomyces metabolites as a potential bioherbicide. PLoS ONE 2019, 14, e0222933. [Google Scholar] [CrossRef]
  12. Triolet, M.; Guillemin, J.; Andre, O.; Steinberg, C. Fungal-based bioherbicides for weed control: A myth or a reality? Weed Res. 2019, 60, 60–77. [Google Scholar] [CrossRef]
  13. Hasan, M.; Ahmad-Hamdani, M.; Rosli, A.; Hamdan, H. Bioherbicides: An Eco-Friendly Tool for Sustainable Weed Management. Plants 2021, 10, 1212. [Google Scholar] [CrossRef] [PubMed]
  14. Kołodziejczyk, M. The effect of living mulches and conventional methods of weed control on weed infestation and potato yield. Sci. Hortic. 2015, 191, 127–133. [Google Scholar] [CrossRef]
  15. Abbas, T.; Zahir, Z.A.; Naveed, M.; Kremer, R.J. Limitations of Existing Weed Control Practices Necessitate Development of Alternative Techniques Based on Biological Approaches. In Advances in Agronomy; Sparks, D.L., Ed.; Academic Press: Cambridge, MA, USA, 2018; Volume 147, pp. 239–280. [Google Scholar] [CrossRef]
  16. Harding, D.P.; Raizada, M.N. Controlling weeds with fungi, bacteria and viruses: A review. Front. Plant Sci. 2015, 6, 659. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Aneja, K.R.; Kumar, V.; Jiloha, P.; Kaur, M.; Sharma, C.; Surain, P.; Dhiman, R.; Aneja, A. Potential Bioherbicides: Indian Perspectives. In Biotechnology: Prospects and Applications; Springer: New Delhi, India, 2013; Volume 9788132216834, pp. 197–215. [Google Scholar]
  18. Stefanski, F.S.; Camargo, A.F.; Scapini, T.; Bonatto, C.; Venturin, B.; Weirich, S.N.; Ulkovski, C.; Carezia, C.; Ulrich, A.; Michelon, W.; et al. Potential Use of Biological Herbicides in a Circular Economy Context: A Sustainable Approach. Front. Sustain. Food Syst. 2020, 4, 195. [Google Scholar] [CrossRef]
  19. Stenberg, J.A.; Sundh, I.; Becher, P.G.; Björkman, C.; Dubey, M.; Egan, P.A.; Friberg, H.; Gil, J.F.; Jensen, D.F.; Jonsson, M.; et al. When is it biological control? A framework of definitions, mechanisms, and classifications. J. Pest Sci. 2021, 94, 665–676. [Google Scholar] [CrossRef]
  20. Radhakrishnan, R.; Alqarawi, A.A.; Abd_Allah, E.F. Bioherbicides: Current knowledge on weed control mechanism. Ecotoxicol. Environ. Saf. 2018, 158, 131–138. [Google Scholar] [CrossRef]
  21. Neto, J.R.C.; dos Santos, M.S.N.; Mazutti, M.A.; Zabot, G.L.; Tres, M.V. Phoma dimorpha phytotoxic activity potentialization for bioherbicide production. Biocatal. Agric. Biotechnol. 2021, 33, 101986. [Google Scholar] [CrossRef]
  22. Todero, I.; Confortin, T.C.; Luft, L.; Seibel, J.; Kuhn, R.C.; Tres, M.V.; Zabot, G.L.; Mazutti, M.A. Concentration of exopolysaccharides produced by Fusarium fujikuroi and application of bioproduct as an effective bioherbicide. Environ. Technol. 2019, 41, 2742–2749. [Google Scholar] [CrossRef]
  23. Li, Y.; Sun, Z.; Zhuang, X.; Xu, L.; Chen, S.; Li, M. Research progress on microbial herbicides. Crop Prot. 2003, 22, 247–252. [Google Scholar] [CrossRef]
  24. Xu, D.; Xue, M.; Shen, Z.; Jia, X.; Hou, X.; Lai, D.; Zhou, L. Phytotoxic Secondary Metabolites from Fungi. Toxins 2021, 13, 261. [Google Scholar] [CrossRef]
  25. Todero, I.; Confortin, T.C.; Luft, L.; Brun, T.; Ugalde, G.A.; de Almeida, T.C.; Arnemann, J.A.; Zabot, G.L.; Mazutti, M.A. Formulation of a bioherbicide with metabolites from Phoma sp. Sci. Hortic. 2018, 241, 285–292. [Google Scholar] [CrossRef]
  26. Daniel, J.J.; Zabot, G.L.; Tres, M.V.; Harakava, R.; Kuhn, R.C.; Mazutti, M.A. Fusarium fujikuroi: A novel source of metabolites with herbicidal activity. Biocatal. Agric. Biotechnol. 2018, 14, 314–320. [Google Scholar] [CrossRef]
  27. Adetunji, C.O.; Oloke, J.K.; Bello, O.M.; Pradeep, M.; Jolly, R.S. Isolation, structural elucidation and bioherbicidal activity of an eco-friendly bioactive 2-(hydroxymethyl) phenol, from Pseudomonas aeruginosa (C1501) and its ecotoxicological evaluation on soil. Environ. Technol. Innov. 2019, 13, 304–317. [Google Scholar] [CrossRef]
  28. Capasso, R.; Evidente, A.; Cutignano, A.; Vurro, M.; Zonno, M.C.; Bottalico, A. Fusaric and 9,10-dehydrofusaric acids and their methyl esters from Fusarium nygamai. Phytochemistry 1996, 41, 1035–1039. [Google Scholar] [CrossRef]
  29. Luo, J.; Fang, Z.; Smith, R.L., Jr. Ultrasound-enhanced conversion of biomass to biofuels. Prog. Energy Combust. Sci. 2014, 41, 56–93. [Google Scholar] [CrossRef]
  30. Bundhoo, Z.M.; Mohee, R. Ultrasound-assisted biological conversion of biomass and waste materials to biofuels: A review. Ultrason. Sonochemistry 2018, 40, 298–313. [Google Scholar] [CrossRef]
  31. Boruta, T.; Milczarek, I.; Bizukojc, M. Evaluating the outcomes of submerged co-cultivation: Production of lovastatin and other secondary metabolites by Aspergillus terreus in fungal co-cultures. Appl. Microbiol. Biotechnol. 2019, 103, 5593–5605. [Google Scholar] [CrossRef] [Green Version]
  32. Marmann, A.; Aly, A.H.; Lin, W.; Wang, B.; Proksch, P. Co-Cultivation—A Powerful Emerging Tool for Enhancing the Chemical Diversity of Microorganisms. Mar. Drugs 2014, 12, 1043–1065. [Google Scholar] [CrossRef] [Green Version]
  33. Ashokkumar, M. The characterization of acoustic cavitation bubbles—An overview. Ultrason. Sonochem. 2011, 18, 864–872. [Google Scholar] [CrossRef]
  34. Ohl, S.-W.; Klaseboer, E.; Khoo, B.C. Bubbles with shock waves and ultrasound: A review. Interface Focus 2015, 5, 20150019. [Google Scholar] [CrossRef]
  35. Cochran, S.A.; Prausnitz, M.R. Sonoluminescence as an Indicator of Cell Membrane Disruption by Acoustic Cavitation. In Ultrasound in Medicine & Biology; Elsevier: Amsterdam, The Netherlands, 2001. [Google Scholar]
  36. Capocelli, M.; Prisciandaro, M.; Lancia, A.; Musmarra, D. Comparison between hydrodynamic and acoustic cavitation in microbial cell disruption. Chem. Eng. Trans. 2014, 38, 13–18. [Google Scholar] [CrossRef]
  37. Save, S.; Pandit, A.; Joshi, J. Microbial cell disruption: Role of cavitation. Chem. Eng. J. Biochem. Eng. J. 1994, 55, B67–B72. [Google Scholar] [CrossRef]
  38. Jadhav, A.; Holkar, C.; Goswami, A.D.; Pandit, A.B.; Pinjari, D.V. Acoustic Cavitation as a Novel Approach for Extraction of Oil from Waste Date Seeds. ACS Sustain. Chem. Eng. 2016, 4, 4256–4263. [Google Scholar] [CrossRef]
  39. Gehlot, P.; Singh, J. Fungi and Their Role in Sustainable Development: Current Perspectives; Springer: Singapore, 2018; pp. 383–411. ISBN 9789811303937. [Google Scholar]
  40. Sari, M.; Kusharyoto, W.; Brazia, A.C. Profiling Bioactive Compounds in Secondary Metabolites from Co-Cultivation between Actinomycetes and Pathogenic Bacteria. In IOP Conference Series: Earth and Environmental Science; Institute of Physics Publishing: Bristol, UK, 2020; Volume 439. [Google Scholar] [CrossRef]
  41. Bertrand, S.; Bohni, N.; Schnee, S.; Schumpp, O.; Gindro, K.; Wolfender, J.-L. Metabolite induction via microorganism co-culture: A potential way to enhance chemical diversity for drug discovery. Biotechnol. Adv. 2014, 32, 1180–1204. [Google Scholar] [CrossRef] [PubMed]
  42. Harwani, D.; Begani, J.; Lakhani, J. Co-Cultivation Strategies to Induce De Novo Synthesis of Novel Chemical Scaffolds from Cryptic Secondary Metabolite Gene Clusters. In Fungi and their Role in Sustainable Development: Current Perspectives; Springer: Singapore, 2018; pp. 617–631. [Google Scholar] [CrossRef]
  43. Sandland, G.J.; Rodgers, J.K.; Minchella, D.J. Interspecific antagonism and virulence in hosts exposed to two parasite species. J. Invertebr. Pathol. 2007, 96, 43–47. [Google Scholar] [CrossRef]
  44. Júnior, F.W.R.; Scariot, M.A.; Forte, C.T.; Pandolfi, L.; Dil, J.M.; Weirich, S.; Carezia, C.; Mulinari, J.; Mazutti, M.A.; Fongaro, G.; et al. New perspectives for weeds control using autochthonous fungi with selective bioherbicide potential. Heliyon 2019, 5, e01676. [Google Scholar] [CrossRef] [Green Version]
  45. Carollo, E.M.; Santos Filho, H.P. Manual Básico de Técnicas Fitopatológicas. Embrapa Mandioca Frutic. 2016, 109. Available online: https://ainfo.cnptia.embrapa.br/digital/bitstream/item/148757/1/Cartilha-ManualFito-215-14-Hermes.pdf (accessed on 9 August 2022).
  46. Rabinovitch, L.; de Oliveira, E.J. Coletânea de Procedimentos Técnicos e Metodologias Empregadas Para o Estudo de Bacillus e Gêneros Esporulados Aeróbios Correlatos, 1st ed.; Montenegro Comunicação: Rio de Janeiro, Brazil, 2015; ISBN 9788567506043. [Google Scholar]
  47. Leber, A.L. Processing, Isolation, Detection, and Interpretation of Aerobic Bacteriology Cultures. In Clinical Microbiology Procedures Handbook; ASM Press: Washington, DC, USA, 2016; pp. 3.3.1.1–3.3.2.15. [Google Scholar]
  48. Schmaltz, S.; Aita, B.C.; Alves, E.A.; Fochi, A.; Bolson, V.F.; Navarro-Díaz, H.J.; Kuhn, R.C.; Mazutti, M.A. Ultrasound-assisted fermentation for production of β-1,3-glucanase and chitinase by Beauveria bassiana. J. Chem. Technol. Biotechnol. 2020, 96, 88–98. [Google Scholar] [CrossRef]
  49. Frans, R.; Crowley, H. Experimental Design and Techniques for Measuring and Analyzing Plant Responses to Weed Control Practices. In Southern Weed Science Society, Research Methods in Weed Science, 3rd ed.; Camper, N.D., Ed.; WSSA: Champaign, IL, USA, 1986; pp. 29–46. [Google Scholar]
  50. de Oliveira, C.T.; Alves, E.A.; Todero, I.; Kuhn, R.C.; de Oliveira, D.; Mazutti, M.A. Production of cutinase by solid-state fermentation and its use as adjuvant in bioherbicide formulation. Bioprocess Biosyst. Eng. 2019, 42, 829–838. [Google Scholar] [CrossRef]
  51. Brun, T.; Rabuske, J.E.; Confortin, T.C.; Luft, L.; Todero, I.; Fischer, M.; Zabot, G.L.; Mazutti, M.A. Weed control by metabolites produced from Diaporthe schini. Environ. Technol. 2020, 43, 139–148. [Google Scholar] [CrossRef]
  52. Doyle, J.J.; Doyle, J.L. A Rapid DNA Isolation Procedure for Small Quantities of Fresh Leaf Tissue. Phytochem. Bull. 1987, 19, 11–15. Available online: https://webpages.charlotte.edu/~jweller2/pages/BINF8350f2011/BINF8350_Readings/Doyle_plantDNAextractCTAB_1987.pdf (accessed on 8 September 2022).
  53. Aveskamp, M.M.; Verkley, G.J.; de Gruyter, J.; Murace, M.A.; Perelló, A.; Woudenberg, J.H.; Groenewald, J.Z.; Crous, P.W. DNA phylogeny reveals polyphyly of Phoma section Peyronellaea and multiple taxonomic novelties. Mycologia 2009, 101, 363–382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Schmitz, A.; Riesner, D. Purification of nucleic acids by selective precipitation with polyethylene glycol 6000. Anal. Biochem. 2006, 354, 311–313. [Google Scholar] [CrossRef] [PubMed]
  55. Tamura, K.; Stecher, G.; Peterson, D.; Filipski, A.; Kumar, S. MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Mol. Biol. Evol. 2013, 30, 2725–2729. [Google Scholar] [CrossRef] [Green Version]
  56. StatSoft Inc. Statistica (Data Analysis Software System); Version 8; StatSoft Inc.: Tulsa, OK, USA, 2008. [Google Scholar]
  57. De Souza, A.R.C.; Baldoni, D.B.; Lima, J.; Porto, V.; Marcuz, C.; Machado, C.; Ferraz, R.C.; Kuhn, R.C.; Jacques, R.J.S.; Guedes, J.V.; et al. Selection, isolation, and identification of fungi for bioherbicide production. Braz. J. Microbiol. 2016, 48, 101–108. [Google Scholar] [CrossRef] [Green Version]
  58. Manalil, S.; Coast, O.; Werth, J.; Chauhan, B.S. Weed management in cotton (Gossypium hirsutum L.) through weed-crop competition: A review. Crop Prot. 2017, 95, 53–59. [Google Scholar] [CrossRef] [Green Version]
  59. Kong, C.-H.; Hu, F.; Wang, P.; Wu, J.-L. Effect of allelopathic rice varieties combined with cultural management options on paddy field weeds. Pest Manag. Sci. 2008, 64, 276–282. [Google Scholar] [CrossRef]
  60. Cochavi, A.; Cohen, I.H.; Rachmilevitch, S. The role of different root orders in nutrient uptake. Environ. Exp. Bot. 2020, 179, 104212. [Google Scholar] [CrossRef]
  61. Anten, N.P.; Hirose, T. Limitations on photosynthesis of competing individuals in stands and the consequences for canopy structure. Oecologia 2001, 129, 186–196. [Google Scholar] [CrossRef]
  62. Wang, L.-W.; Showalter, A.M.; Ungar, I.A. Effects of intraspecific competition on growth and photosynthesis of Atriplex prostrata. Aquat. Bot. 2005, 83, 187–192. [Google Scholar] [CrossRef]
  63. Adetunji, C.O.; Oloke, J.K.; Osemwegie, O.O. Environmental fate and effects of granular pesta formulation from strains of Pseudomonas aeruginosa C1501 and Lasiodiplodia pseudotheobromae C1136 on soil activity and weeds. Chemosphere 2018, 195, 98–107. [Google Scholar] [CrossRef] [PubMed]
  64. Neto, J.R.C.; Mazutti, M.A.; Zabot, G.L.; Tres, M.V. Bioherbicidal action of Phoma dimorpha fermented broth on seeds and plants of Senna obtusifolia. Pesqui. Agropecu. Trop. 2020, 50, e56894. [Google Scholar] [CrossRef]
  65. Maiquel, P.P.; Marcio, A.M.; Thiago, C.A.; Luis, E.C.; Adriano, A.M.; Jerson, V.C.G.; Raquel, C.K. Bioherbicide based on Diaporthe sp. secondary metabolites in the control of three tough weeds. Afr. J. Agric. Res. 2016, 11, 4242–4249. [Google Scholar] [CrossRef] [Green Version]
  66. Todero, I.; Confortin, T.C.; Soares, J.F.; Brun, T.; Luft, L.; Rabuske, J.E.; Kuhn, R.C.; Tres, M.V.; Zabot, G.L.; Mazutti, M.A. Concentration of metabolites from Phoma sp. using microfiltration membrane for increasing bioherbicidal activity. Environ. Technol. 2018, 40, 2364–2372. [Google Scholar] [CrossRef] [PubMed]
  67. Vermeer, R.; Baur, P. O-TEQ®, a Formulation Concept That Overcomes the Incompatibility between Water and Oil. Pflanzenschutz-Nachr. Bayer 2007, 60, 7–26. [Google Scholar]
  68. Gasic, S.; Tanovic, B. Biopesticide formulations, possibility of application and future trends. Pestic. Fitomedicina 2013, 28, 97–102. [Google Scholar] [CrossRef]
  69. Taban, A.; Saharkhiz, M.J.; Khorram, M. Formulation and assessment of nano-encapsulated bioherbicides based on biopolymers and essential oil. Ind. Crops Prod. 2020, 149, 112348. [Google Scholar] [CrossRef]
  70. Haarmann, J.A.; Young, B.G.; Johnson, W.G. Control of Palmer amaranth (Amaranthus palmeri) regrowth following failed applications of glufosinate and fomesafen. Weed Technol. 2021, 35, 464–470. [Google Scholar] [CrossRef]
  71. Syahri, R.; Widaryanto, E.; Wicaksono, K.P. Bioactive compound from mangoes leaves extract as potential soil bioherbicide to control amaranth weed (Amaranthus spinosus Linn.). J. Degraded Min. Lands Manag. 2017, 4, 829–836. [Google Scholar] [CrossRef] [Green Version]
  72. Tanaka, S.; Sawaya, M.R.; Yeates, T.O. Structure and Mechanisms of a Protein-Based Organelle in Escherichia coli. Science 2010, 327, 81–84. [Google Scholar] [CrossRef]
  73. Yeates, T.O.; Kerfeld, C.A.; Heinhorst, S.; Cannon, G.C.; Shively, J.M. Protein-based organelles in bacteria: Carboxysomes and related microcompartments. Nat. Rev. Genet. 2008, 6, 681–691. [Google Scholar] [CrossRef] [PubMed]
  74. Bailey, K.; Pitt, W.; Falk, S.; Derby, J. The effects of Phoma macrostoma on nontarget plant and target weed species. Biol. Control 2011, 58, 379–386. [Google Scholar] [CrossRef]
  75. Ehgartner, D.; Herwig, C.; Fricke, J. Morphological analysis of the filamentous fungus Penicillium chrysogenum using flow cytometry—The fast alternative to microscopic image analysis. Appl. Microbiol. Biotechnol. 2017, 101, 7675–7688. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Veiter, L.; Rajamanickam, V.; Herwig, C. The filamentous fungal pellet—Relationship between morphology and productivity. Appl. Microbiol. Biotechnol. 2018, 102, 2997–3006. [Google Scholar] [CrossRef] [Green Version]
  77. Papagianni, M. Fungal morphology and metabolite production in submerged mycelial processes. Biotechnol. Adv. 2003, 22, 189–259. [Google Scholar] [CrossRef] [PubMed]
  78. Li, S.C.; Kane, P.M. The yeast lysosome-like vacuole: Endpoint and crossroads. Biochim. Biophys. Acta 2009, 1793, 650–663. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. El-Gendi, H.; Saleh, A.K.; Badierah, R.; Redwan, E.M.; El-Maradny, Y.A.; El-Fakharany, E.M. A Comprehensive Insight into Fungal Enzymes: Structure, Classification, and Their Role in Mankind’s Challenges. J. Fungi 2021, 8, 23. [Google Scholar] [CrossRef]
  80. Ogawa, M.; García-Martínez, T.; Bisson, L.; Mauricio, J.C.; Moreno, J.; Moreno-García, J. Mapping the intracellular metabolome of yeast biocapsules—Spherical structures of yeast attached to fungal pellets. New Biotechnol. 2020, 58, 55–60. [Google Scholar] [CrossRef]
  81. Caudal, F.; Tapissier-Bontemps, N.; Edrada-Ebel, R.A. Impact of Co-Culture on the Metabolism of Marine Microorganisms. Mar. Drugs 2022, 20, 153. [Google Scholar] [CrossRef]
  82. Tranel, P.J. Herbicide resistance in Amaranthus tuberculatus. Pest Manag. Sci. 2020, 77, 43–54. [Google Scholar] [CrossRef]
  83. Ye, M.; Tang, X.; Yang, R.; Zhang, H.; Li, F.; Tao, F.; Li, F.; Wang, Z. Characteristics and Application of a Novel Species of Bacillus: Bacillus velezensis. ACS Chem. Biol. 2018, 13, 500–505. [Google Scholar] [CrossRef] [PubMed]
  84. Sharma, S.; Pandey, L.M. Prospective of fungal pathogen-based bioherbicides for the control of water hyacinth: A review. J. Basic Microbiol. 2022, 62, 415–427. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Phytotoxicity (%) of supernatants at 21 days after application (DAA) in C. sativus.
Figure 1. Phytotoxicity (%) of supernatants at 21 days after application (DAA) in C. sativus.
Processes 10 02001 g001
Table 1. Geographic coordinates, plants collected, and the number of microorganisms isolated for this study.
Table 1. Geographic coordinates, plants collected, and the number of microorganisms isolated for this study.
RegionGeographical CoordinatesCollected SpeciesNumber of
Isolated Strains
Santa Maria, Brazil−29.6914 S, −53.8008 WConyza sp. And
Contaminations *
31
Cerro Largo, Brazil−28.1506 S, −54.7386 WConyza sp. And B. pilosa6
Marcelino Ramos, Brazil−27.4673 S, −519085 WConyza sp.,
Spermacoce latifolia,
and E. crusgalli
26
* Fungi isolated from contamination of culture plates.
Table 2. Phytotoxic effects of microorganism metabolites on Conyza sp., B. pilosa, A. hybridus, and E. crusgalli plants after 21 days after application (DAA).
Table 2. Phytotoxic effects of microorganism metabolites on Conyza sp., B. pilosa, A. hybridus, and E. crusgalli plants after 21 days after application (DAA).
PH (cm)RL (cm)SFM (g)SDM (g)RFM (g)RDM (g)
E. crusgalli
B1337.0 ± 5.432.5 ± 2.5 G12.482 ± 3.787 I2.572 ± 0.118 I7.275 ± 2.4433.316 ± 0.972
B1431.0 ± 1.4 I29.0 ± 7.08.462 ± 3.684 I1.931 ± 0.885 I5.060 ± 2.101 I2.261 ± 0.854 I
A14.231.0 ± 4.3 I28.9 ± 7.812.151 ± 3.546 I3.524 ± 1.0307.862 ± 3.7593.207 ± 1.170
B22.237.5 ± 3.525.4 ± 4.39.370 ± 2.088 I2.589 ± 0.353 I6.733 ± 2.2933.119 ± 0.669
γ34.1 ± 1.4 I24.0 ± 1.816.871 ± 4.150 I3.880 ± 0.87715.662 ± 9.6244.801 ± 0.847
Control38.3 ± 2.324.0 ± 1.825.733 ± 1.8724.700 ± 0.52910.633 ± 2.6694.200 ± 0.557
A. hybridus
B1318.0 ± 5.6 I17.5 ± 2.12.465 ± 1.085 I0.902 ± 0.386I1.174 ± 0.7340.607 ± 0.390
B1429.5 ± 1.722.0 ± 2.45.233 ± 1.062 I2.542 ± 0.5392.490 ± 0.6621.680 ± 0.355
A14.224.8 ± 0.5 I18.3 ± 2.67.254 ± 0.5282.221 ± 0.164 I1.497 ± 0.3970.545 ± 0.169
B22.221.9 ± 11.518.1 ± 2.74.720 ± 3.6841.460 ± 1.194 I1.928 ± 1.2110.854 ± 0.525
γ24.9 ± 8.117.3 ± 6.83.987 ± 2.894 I1.318 ± 0.953 I1.470 ± 1.1710.697 ± 0.580
Control31.0 ± 1.522.5 ± 5.88.355 ± 1.0623.337 ± 0.0432.450 ± 1.3431.247 ± 0.769
B. pilosa
B1322.9 ± 2.427.3 ± 4.16.478 ± 2.1151.855 ± 0.4251.431 ± 0.4600.875 ± 0.037 G
B1422.0 ± 2.928.8 ± 3.04.859 ± 1.7451.632 ± 0.7961.112 ± 0.4050.793 ± 0.144
A14.223.4 ± 2.029.0 ± 0.04.137 ± 0.8671.781 ± 0.3940.964 ± 0.2730.763 ± 0.198
B22.216.5 ± 5.1I26.3 ± 5.62.572 ± 1.4410.854 ± 0.5620.660 ± 0.4740.389 ± 0.268
γ20.5 ± 1.3I14.0 ± 1.04.347 ± 2.0811.213 ± 0.6951.004 ± 0.6280.438 ± 0.262
Control24.6 ± 1.925.5 ± 10.74.460 ± 1.8861.553 ± 0.3741.089 ± 0.5810.520 ± 0.184
Conyza sp.
B1321.0 ± 2.218.3 ± 2.59.695 ± 3.6872.368 ± 0.5458.285 ± 2.2801.900 ± 0.466 G
B1422.5 ± 3.023.5 ± 7.712.175 ± 4.4132.515 ± 0.82112.797 ± 0.476 G2.315 ± 1.007 G
A14.218.0 ± 5.018.5 ± 5.26.290 ± 3.0381.515 ± 0.7696.538 ± 2.3541.510 ± 0.653
B22.225.5 ± 5.224.0 ± 5.813.493 ± 6.6202.710 ± 1.36912.138 ± 6.0802.265 ± 1.589
γ19.3 ± 5.114.8 ± 4.510.775 ± 6.3662.360 ± 1.3298.238 ± 4.4651.735 ± 1.052
Control24.5 ± 4.719.0 ± 0.810.585 ± 4.8262.010 ± 1.0666.263 ± 2.3330.850 ± 0.417
I Significant inhibition according to Student’s t-test, 95% confidence. G Significant growth according to Student’s t-test, 95% confidence.
Table 3. Effects of metabolites from the crude broth, triple dose and UAE, co-cultivation, and co-cultivation and PF-UAE on E. crusgalli parameters at 14 days after application (DAA).
Table 3. Effects of metabolites from the crude broth, triple dose and UAE, co-cultivation, and co-cultivation and PF-UAE on E. crusgalli parameters at 14 days after application (DAA).
PH (cm)RL (cm)SFM (g)SDM (g)RFM (g)RDM (g)
Raw broth–Triple dose
B1353.9 ± 3.129.3 ± 3.6 I17.097 ± 4.014 I1.698 ± 0.313 I3.010 ± 0.9010.305 ± 0.043 I
B1455.6 ± 4.227.7 ± 1.1 I16.218 ± 4.909 I1.734 ± 0.380 I2.012 ± 0.536 I0.251 ± 0.079 I
B22.256.4 ± 3.231.3 ± 3.816.440 ± 3.366 I2.013 ± 0.278 I2.301 ± 0.3180.435 ± 0.081
γ55.0 ± 4.822.7 ± 2.4 I9.743 ± 2.005 I1.210 ± 0.378 I0.539 ± 0.176 I0.127 ± 0.052 I
A14.255.4 ± 2.732.9 ± 2.622.639 ± 2.6832.286 ± 0.3083.801 ± 0.859 G0.457 ± 0.092
Raw broth–UAE
B1361.4 ± 4.830.7 ± 4.422.394 ± 2.6101.807 ± 0.235 I2.416 ± 0.3440.247 ± 0.034 I
B1455.9 ± 2.328.0 ± 2.5 I16.360 ± 5.431 I1.718 ± 0.441 I1.744 ± 0.665 I0.312 ± 0.126 I
B22.249.0 ± 3.9 I28.4 ± 2.2 I9.002 ± 2.591 I1.295 ± 0.189 I1.478 ± 0.513 I0.231 ± 0.055 I
γ61.6 ± 4.231.1 ± 4.322.933 ± 5.7262.135 ± 0.5492.683 ± 0.6210.296 ± 0.088 I
A14.247.7 ± 1.9 I26.9 ± 2.7 I12.095 ± 1.870 I0.985 ± 0.245 I1.789 ± 0.266 I0.198 ± 0.079 I
Co-cultivation
A14.2/B22.253.4 ± 2.930.4 ± 4.415.171 ± 2.802 I1.702 ± 0.293 I1.414 ± 0.475 I0.272 ± 0.091 I
B13/B1455.1 ± 3.034.4 ± 3.420.064 ± 4.0612.027 ± 0.5052.296 ± 0.5660.371 ± 0.129
B14/γ63.3 ± 3.9 G30.3 ± 4.025.727 ± 3.9222.227 ± 0.2902.941 ± 0.5020.349 ± 0.073
B22.2/γ57.7 ± 3.631.1 ± 3.423.787 ± 3.9342.375 ± 0.3713.223 ± 0.6410.413 ± 0.111
Co-cultivation and UAE
A14.2/B22.257.1 ± 4.429.6 ± 3.8 I12.992 ± 2.178 I1.438 ± 0.200 I1.426 ± 0.388 I0.194 ± 0.055 I
B13/B1459.4 ± 3.432.9 ± 3.219.930 ± 5.0652.038 ± 0.356 I2.430 ± 0.5750.283 ± 0.068 I
B14/γ56.9 ± 3.627.1 ± 1.9 I15.890 ± 3.856 I1.697 ± 0.329 I0.917 ± 0.167 I0.203 ± 0.067 I
B22.2/γ59.9 ± 1.830.3 ± 1.8 I18.420 ± 4.003 I1.828 ± 0.407 I1.818 ± 0.640 I0.260 ± 0.112 I
Control57.4 ± 4.533.7 ± 2.922.298 ± 1.9822.434 ± 0.2282.769 ± 0.5290.437 ± 0.081
I Significant inhibition according to Student’s t-test, 95% confidence. G Significant growth according to Student’s t-test, 95% confidence.
Table 4. Effects of metabolites from the crude broth, triple dose, and PF-UAE, as well as co-cultivation, and co-cultivation with PF-UAE on A. hybridus parameters at 14 days after application (DAA).
Table 4. Effects of metabolites from the crude broth, triple dose, and PF-UAE, as well as co-cultivation, and co-cultivation with PF-UAE on A. hybridus parameters at 14 days after application (DAA).
PH (cm)RL (cm)SFM (g)SDM (g)RFM (g)RDM (g)
Raw broth—Triple dose
B1319.0 ± 3.032.1 ± 5.63.832 ± 1.284 G0.573 ± 0.136 G0.664 ± 0.237 G0.100 ± 0.039 G
B1416.2 ± 2.0 I36.0 ± 6.9 G3.023 ± 0.6980.476 ± 0.0880.502 ± 0.149 G0.081 ± 0.027
B22.219.0 ± 2.239.4 ± 5.7 G3.772 ± 0.888 G0.603 ± 0.129 G0.511 ± 0.177 G0.108 ± 0.036 G
γ16.0 ± 2.4 I 25.9 ± 7.52.466 ± 1.1000.464 ± 0.1160.312 ± 0.1370.060 ± 0.027
A14.223.0 ± 3.845.0 ± 8.2 G5.660 ± 1.396 G0.800 ± 0.104 G0.946 ± 0.302 G0.161 ± 0.036 G
Raw broth—UAE
B1321.0 ± 2.830.4 ± 2.43.567 ± 0.833 G0.446 ± 0.2010.516 ± 0.133 G0.091 ± 0.024 G
B1418.4 ± 2.335.1 ± 9.42.734 ± 0.6310.457 ± 0.0820.292 ± 0.0550.061 ± 0.021
B22.218.6 ± 4.030.1 ± 6.03.367 ± 1.1210.538 ± 0.124 G0.548 ± 0.228 G0.079 ± 0.040
γ21.1 ± 2.318.1 ± 5.1 I0.843 ± 0.273 I0.198 ± 0.035 I0.083 ± 0.045 I0.018 ± 0.009 I
A14.211.6 ± 2.4 I36.4 ± 6.9 G4.571 ± 1.439 G0.694 ± 0.173 G0.788 ± 0.286 G0.132 ± 0.048 G
Co-cultivation
A14.2/B22.223.4 ± 3.424.9 ± 7.93.384 ± 1.0240.468 ± 0.1140.440 ± 0.2630.065 ± 0.015
B13/B1419.0 ± 1.725.3 ± 4.32.662 ± 0.5060.413 ± 0.0610.312 ± 0.1110.055 ± 0.021
B14/γ16.3 ± 2.3 I20.1 ± 3.3 I1.420 ± 0.394 I0.271 ± 0.040 I0.129 ± 0.068 I0.031 ± 0.015 I
B22.2/γ17.4 ± 2.625.0 ± 2.92.139 ± 0.4410.398 ± 0.0420.226 ± 0.054 I0.038 ± 0.008 I
Co-cultivation and UAE
A14.2/B22.222.7 ± 5.637.0 ± 6.4 G4.972 ± 1.655 G0.679 ± 0.202 G0.896 ± 0.255 G0.138 ± 0.045 G
B13/B1416.6 ± 4.127.4 ± 11.52.134 ± 1.0000.425 ± 0.1450.273 ± 0.0590.058 ± 0.027
B14/γ19.8 ± 2.631.1 ± 3.03.367 ± 0.651 G0.614 ± 0.152 G0.479 ± 0.141 G0.091 ± 0.025 G
B22.2/γ18.5 ± 3.927.3 ± 1.92.703 ± 0.7090.457 ± 0.0720.312 ± 0.0860.050 ± 0.013
Control20.2 ± 2.327.3 ± 5.22.567 ± 0.5040.414 ± 0.0530.311 ± 0.0760.058 ± 0.011
I Significant inhibition according to Student’s t-test, 95% confidence. G Significant growth according to Student’s t-test, 95% confidence.
Table 5. Matrix of inhibition and growth results of the 10 combinations of microorganisms in a Petri dish.
Table 5. Matrix of inhibition and growth results of the 10 combinations of microorganisms in a Petri dish.
CodeγB22.2B14B13A14.2
A14.2− ++ +− +− +
B13− ++ −+ +
B14+ ++ −
B22.2+ +
γ
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Schein, D.; Santos, M.S.N.; Schmaltz, S.; Nicola, L.E.P.; Bianchin, C.F.; Ninaus, R.G.; Menezes, B.B.d.; Santos, R.C.d.; Zabot, G.L.; Tres, M.V.; et al. Microbial Prospection for Bioherbicide Production and Evaluation of Methodologies for Maximizing Phytotoxic Activity. Processes 2022, 10, 2001. https://doi.org/10.3390/pr10102001

AMA Style

Schein D, Santos MSN, Schmaltz S, Nicola LEP, Bianchin CF, Ninaus RG, Menezes BBd, Santos RCd, Zabot GL, Tres MV, et al. Microbial Prospection for Bioherbicide Production and Evaluation of Methodologies for Maximizing Phytotoxic Activity. Processes. 2022; 10(10):2001. https://doi.org/10.3390/pr10102001

Chicago/Turabian Style

Schein, Dinalva, Maicon S. N. Santos, Silvana Schmaltz, Luiz E. P. Nicola, Cristiane F. Bianchin, Renata G. Ninaus, Bryan B. de Menezes, Ricardo C. dos Santos, Giovani Leone Zabot, Marcus V. Tres, and et al. 2022. "Microbial Prospection for Bioherbicide Production and Evaluation of Methodologies for Maximizing Phytotoxic Activity" Processes 10, no. 10: 2001. https://doi.org/10.3390/pr10102001

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop