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Screening and identification of potential Striga [Striga hermonthica (Del.)] suppressing rhizobacteria associated with Sorghum [Sorghum bicolor (L.) Moench] in Northern Ethiopia

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  • Sorghum (Sorghum bicolor (L.) Moench) is one of the globally important cereal crops well adapted to Sub-Saharan Africa (SSA) agro-ecology. However, the productivity of sorghum is hindered by both abiotic and biotic factors including drought, Striga, insect pests, poor soil fertility, and diseases. Among the constraints, Striga (genus), also called witch weed, is the most important production problem in the area. Although there have been various control methods practiced for years, none of these have been practically effective in eradicating Striga, neither are they easily accessible for small holder farmers, while some are also not environmentally friendly. Therefore, this study was designed with the objective of identifying potential Striga suppressing rhizobacteria associated with sorghum. Treatment of S. hermonthica seeds with isolates E19G12, E29G2b and E19G10 resulted in the lowest S. hermonthica seed germination of 0%, 1%, and 2.7% respectively, which were significantly lower than any of the treatments. Mean germination percentage ranged from 9 to 59.7 and 0 to 27 in the absence and presence of host plants, respectively. The results showed a statistically significant germination inhibition (p < 0.001). Finally, the most effective isolates were shortlisted, E19G6a, E19G9, E19G6b, E19G10, E19B, E19G12, E29G2a, and E29G7 were morphologically and biochemically identified to belong to the genera of Pseudomonas, Klebssiella, Bacillus and Entrobacter. The results of the study demonstrated the existence of promising soil-borne bacteria that could be exploited as bioherbicides to control Striga infestation on sorghum provided that broader samples from various parts of the country are explored.
  • In a recent report on Latin America's next petroleum boom, The Economist refers to the current and future situation in oil producing countries in the region. In the case of Argentina, the increase in oil and gas output 'have led to an increase in production in Vaca Muerta, a mammoth field in Argentina's far west. It holds the world's second-largest shale gas deposits and its fourth-largest shale oil reserves… Rystad Energy expects shell-oil production in Argentina will more than double by the end of the decade, to over a million barrels per day'[1].

    Oil production in Argentina is currently dominated by three Patagonian areas: Neuquén, San Jorge, and Austral. Based on 2021 information, 49% of oil reserves of Argentina are located in Neuquén, whereas San Jorge has 46%. Neuquén is also the largest source of oil (57%) and gas (37%) in the country. According to 2018 data, conventional oil produced in Argentina amounts to 87%, whereas non-conventional, shale production represents 13%; however, non-conventional oil is increasing due to Vaca Muerta shale oil exploitation.

    This increase of production in the Patagonian fields requires the use of a large fluid storage capacity by means of vertical oil storage tanks having different sizes and configurations. Tanks are required to store not just oil but also water. The exploitation of nonconventional reservoirs, such as Vaca Muerta, involves massive water storage to carry out hydraulic stimulation in low-permeability fields, and for managing the return fluid and production water at different stages of the process (storage, treatment, and final disposal).

    Storage tanks in the oil industry are large steel structures; they may have different sizes, and also different shell configurations, such as vertical cylinders with a fixed roof or with a floating roof and opened at the top[2]. It is now clear that such oil infrastructure is vulnerable to accidents caused by extreme weather events[35].

    Data from emergencies occurring in oil fields shows that accidents due to regional winds, with wind speed between 150 and 240 km/h, may cause severe tank damage. Seismic activity in the region, on the other hand, is of less concern to tank designers in Patagonia.

    Damage and failure mechanisms of these tanks largely depend on tank size and configuration, and their structural response should be considered from the perspective of shell mechanics and their consequences. In a report on damage observed in tanks following hurricanes Katrina and Rita in 2005[6,7], several types of damage were identified. The most common damage initiation process is due to shell buckling[811], which may progress into plasticity at higher wind speeds. In open-top tanks, a floating roof does not properly slide on a buckled cylindrical shell, and this situation may lead to different failure mechanisms. Further, damage and loss of integrity have the potential to induce oil spills, with direct consequences of soil contamination and also of fire initiation.

    Concern about an emergency caused by such wind-induced hazards involves several stakeholders, because the consequences may affect the operation of oil plants, the local and regional economies, the safety of the population living in the area of a refinery or storage farm, and the environment[6]. In view of the importance of preserving the shell integrity and avoiding tank damage, there is a need to evaluate risk of existing tanks at a regional level, such as in the Neuquén and San Jorge areas. This information may help decision makers in adopting strategies (such as structural reinforcement of tanks to withstand expected wind loads) or post-event actions (like damaged infrastructure repair or replacement).

    The studies leading to the evaluation of risk in the oil infrastructure are known as vulnerability studies, and the most common techniques currently used are fragility curves[12]. These curves evaluate the probability of reaching or exceeding a given damage level as a function of a load parameter (such as wind speed in this case).

    Early studies in the field of fragility of tanks were published[13] from post-event earthquake damage observations. Studies based on computational simulation of tank behavior under seismic loads were reported[14]. The Federal Emergency Management Administration in the US developed fragility curves for tanks under seismic loads for regions in the United States, and more recently, this has been extended to hurricane and flood events in coastal areas[15]. Seismic fragility in Europe has been reviewed by Pitilakis et al.[16], in which general concepts of fragility are discussed. Bernier & Padgett[17] evaluated the failure of tanks due to hurricane Harvey using data from aerial images and government databases. Fragility curves were developed based on finite element analyses and damage of the tank population was identified in the Houston Ship Channel. Flood and wind due to hurricane Harvey were also considered[18] to develop fragility curves.

    Because fragility curves for tanks under wind depend on the wind source (either hurricane or regional winds), and the type and size of tanks identified in a region, fragility curves developed for one area are not possible to be directly used in other areas under very different inventory and wind conditions.

    This paper addresses problems of shell buckling and loss of integrity of open top tanks, with wind-girders and floating roof and it focuses on the development of fragility curves as a way to estimate damage states under a given wind pressure. The region of interest in this work covers the oil producing areas in Patagonia, Argentina. Damage of tanks under several wind pressures are evaluated by finite element analyses together with methodologies to evaluate the structural stability.

    The construction of fragility curves requires information from the following areas: First, an inventory of tanks to be included in the analysis; second, data about the loads in the region considered; third, data about structural damage, either observed or computed via modeling; and fourth, a statistical model that links damage and load/structure data. This section describes the main features of the tank population considered in the study.

    The construction of an inventory at a regional level is a very complex task, which is largely due to a lack of cooperation from oil companies to share information about their infrastructure. Thus, to understand the type of tanks in an oil producing region, one is left collecting a limited number of structural drawings and aerial photography. A detailed inventory of the Houston Ship Channel was carried out by Bernier et al.[19], who identified 390 floating roof tanks. An inventory for Puerto Rico[20] identified 82 floating roof tanks. Although both inventories used different methodologies and addressed very different tank populations, some common features were found in both cases.

    An alternative strategy to carry out fragility studies is to develop a database using a small number of tanks, for which a detailed structural behavior is investigated using finite element analysis. This is a time-consuming task, but it allows identification of buckling pressures, buckling modes, and shell plasticity. This information serves to build approximate fragility curves, and it can also be used to develop what are known as meta-models, which predict structural damage based on tank/load characteristics. Such meta-models take the form of equations that include the tank geometry and wind speed to estimate damage. Meta-models were used, for example, in the work of Kameshwar & Padgett[18].

    This work employs a simplified strategy, and addresses the first part of the procedure described above. The use of a limited number of tanks in a database, for which a finite element structural analysis is carried out. This leads to fragility curves based on a simplified tank population (reported in this work) and the development of a meta-model together with enhanced fragility results will be reported and compared in a future work.

    Partial information of tanks in the Patagonian region was obtained from government sources, and this was supplemented by aerial photography showing details of tank farms in the region. As a result of that, it was possible to establish ranges of tank dimensions from which an artificial database was constructed.

    The present study is restricted to open-top tanks with a wind girder at the top. They are assumed to have floating roofs, which are designed and fabricated to allow the normal operation of the roof without the need of human intervention. The main characteristics of tanks investigated in this paper, are illustrated in Fig. 1.

    Figure 1.  Geometric characteristics of open-topped oil storage considered in this paper.

    The range of interest in terms of tank diameter D was established between 35 m < D < 60 m. Based on observation of tanks in the region, the ratios D/H were found to be in the range 0.20 < D/H < 0.60, leading to cylinder height H in the range 12 m < H < 20 m. These tanks were next designed using API 650[21] regulations to compute their shell thickness and wind girder dimensions. A variable thickness was adopted in elevation, assuming 3 m height shell courses. The geometries considered are listed in Table 1, with a total of 30 tanks having combinations of five values of H and six values of D. The volume of these tanks range between 55,640 and 272,520 m3.

    Table 1.  Geometry and course thickness of 30 tanks considered in this work.
    H
    (m)
    CoursesThickness t (m)
    D = 35 mD = 40 mD = 45 mD = 50 mD = 55 mD = 60 m
    12V10.0140.0160.0180.0180.0200.022
    V20.0120.0120.0140.0160.0160.018
    V30.0080.0100.0100.0100.0120.012
    V40.0060.0080.0080.0080.0080.008
    14V10.0160.0180.0200.0220.0250.025
    V20.0140.0140.0160.0180.0200.020
    V30.0100.0120.0120.0140.0140.016
    V40.0080.0080.0080.0100.0100.010
    V50.0060.0080.0080.0080.0080.008
    16V10.0180.0200.0220.0250.0280.028
    V20.0160.0180.0180.0200.0220.025
    V30.0120.0140.0160.0160.0180.020
    V40.0100.0100.0120.0120.0140.014
    V50.0060.0080.0080.0080.0080.010
    V60.0060.0080.0080.0080.0080.010
    18V10.0200.0220.0250.0280.0300.032
    V20.0180.0200.0220.0250.0250.028
    V30.0140.0160.0180.0200.0200.022
    V40.0120.0120.0140.0160.0160.018
    V50.0080.0100.0100.0100.0120.012
    V60.0080.0100.0100.0100.0120.012
    20V10.0220.0250.0280.0300.0320.035
    V20.0200.0220.0250.0280.0280.032
    V30.0160.0180.0200.0220.0250.028
    V40.0140.0140.0160.0180.0200.020
    V50.0100.0120.0120.0140.0140.016
    V60.0100.0120.0120.0140.0140.016
    V70.0100.0120.0120.0140.0140.016
     | Show Table
    DownLoad: CSV

    The material assumed in the computations was A36 steel, with modulus of elasticity E = 201 GPa and Poisson's ratio ν = 0.3.

    For each tank, a ring stiffener was designed as established by API 650[21], in order to prevent buckling modes at the top of the tank. The minimum modulus Z to avoid ovalization at the top of the tank is given by

    Z=D2H17(V190)2 (1)

    where V is the wind speed, in this case taken as V = 172.8 km/h for the Patagonian region. Intermediate ring stiffeners were not observed in oil tanks in Patagonia, so they were not included in the present inventory.

    Because a large number of tanks need to be investigated in fragility studies, it is customary to accept some simplifications in modeling the structure to reduce the computational effort. The geometry of a typical ring stiffener at the top is shown in Fig. 2a, as designed by API 650. A simplified version was included in this research in the finite element model, in which the ring stiffener is replaced by an equivalent thickness at the top, as suggested in API Standard 650[21]. This approach has been followed by most researchers in the field. The equivalent model is shown in Fig. 2b.

    Figure 2.  Ring stiffener, (a) design according to API 650, (b) equivalent section[22].

    The pressure distribution due to wind around a short cylindrical shell has been investigated in the past using wind tunnels and computational fluid dynamics, and a summary of results has been included in design regulations.

    There is a vast number of investigations on the pressures in storage tanks due to wind, even if one is limited to isolated tanks, as in the present paper. For a summary of results, see, for example, Godoy[11], and only a couple of studies are mentioned here to illustrate the type of research carried out in various countries. Wind tunnel tests were performed in Australia[23], which have been the basis of most subsequent studies. Recent tests in Japan on small scale open top tanks were reported[24,25]. In China, Lin & Zhao[26] reported tests on fixed roof tanks. CFD models, on the other hand, were computed[27] for open top tanks with an internal floating roof under wind flow. Although there are differences between pressures obtained in different wind tunnels, the results show an overall agreement.

    The largest positive pressures occur in the windward meridian covering an angle between 30° and 45° from windward. Negative pressures (suction), on the other hand, reach a maximum at meridians located between 80° and 90° from windward. An evaluation of US and European design recommendations has been reported[28,29], who also considered the influence of fuel stored in the tank.

    The circumferential variation of pressures is usually written in terms of a cosine Fourier series. The present authors adopted the series coefficients proposed by ASCE regulations[30], following the analytical expression:

    q=λinCicos(iφ) (2)

    in which λ is the amplification factor; the angle φ is measured from the windward meridian; and coefficients Ci represent the contribution of each term in the series. The following coefficients were adopted in this work (ASCE): C0 = −0.2765, C1 = 0.3419, C2 = 0.5418, C3 = 0.3872, C4 = 0.0525, C5 = 0.0771, C6 = −0.0039 and C7 = 0.0341. For short tanks, such as those considered in this paper, previous research reported[31] that for D/H = 0.5 the variation of the pressure coefficients in elevation is small and may be neglected to simplify computations. Thus, the present work assumes a uniform pressure distribution in elevation at each shell meridian.

    In fragility studies, wind speed, rather than wind pressures, are considered, so that the following relation from ASCE is adopted in this work:

    qz=0.613KztKdV2IV=qz0.613KztKdI (3)

    in which I is the importance factor; Kd is the directionality factor; and Kzt is the topographic factor. Values of I = 1.15, Kd = 0.95 and Kzt = 1, were adopted for the computations reported in this paper.

    Because shell buckling was primarily investigated in this work using a bifurcation analysis, the scalar λ was increased in the analysis until the finite element analysis detected a singularity.

    Fragility curves are functions that describe the probability of failure of a structural system (oil tanks in the present case) for a range of loads (wind pressures) to which the system could be exposed. In cases with low uncertainty in the structural capacity and acting loads, fragility curves take the form of a step-function showing a sudden jump (see Fig. 3a). Zero probability occurs before the jump and probability equals to one is assumed after the jump. But in most cases, in which there is uncertainty about the structural capacity to withstand the load, fragility curves form an 'S' shape, as shown in Fig. 3a and b probabilistic study is required to evaluate fragility.

    Figure 3.  Examples of fragility curves, (a) step-function, (b) 'S' shape function.

    The construction of fragility curves is often achieved by use of a log-normal distribution. In this case, the probability of reaching a certain damage level is obtained by use of an exponential function applied to a variable having a normal distribution with mean value μ and standard deviation σ. If a variable x follows a log-normal distribution, then the variable log(x) has a normal distribution, with the following properties:

    • For x < 0, a probability equal to 0 is assigned. Thus, the probability of failure for this range is zero.

    • It can be used for variables that are computed by means of a number of random variables.

    • The expected value in a log-normal distribution is higher than its mean value, thus assigning more importance to large values of failure rates than would be obtained in a normal distribution.

    The probability density function for a log-normal distribution may be written in the form[32]:

    f(xi)=12πσ21xexp[(lnxµ)2/(2σ2)] (4)

    in which f(xi) depends on the load level considered, and is evaluated for a range of interest of variable x; and μ* is the mean value of the logarithm of variable x associated with each damage level. Damage levels xi are given by Eqn (5).

    µ(xi)=1NNn=1ln(xin) (5)

    where the mean value is computed for a damage level xi, corresponding to I = DSi; summation in n extends to the number of tanks considered in the computation of the mean value. Damage levels in this work are evaluated using computational modeling and are defined in the next section. Variance is the discrete variable xi 2), computed from:

    σ2(xi,µ)=1NNn=1(ln(xin)µ)2=1NNn=1ln(xin)2µ2 (6)

    The probability of reaching or exceeding a damage level DSi is computed by the integral of the density function using Eqn (7), for a load level considered (the wind speed in this case):

    P[DS/x]=x=V0x=0f(x)dx (7)

    where V0 is the wind speed at which computations are carried out, and x is represented by wind speed V.

    Various forms of structural damage may occur as a consequence of wind loads, including elastic or plastic deflections, causing deviations from the initial perfect geometry; crack initiation or crack extension; localized or extended plastic material behavior; and structural collapse under extreme conditions. For the tanks considered in this work, there are also operational consequences of structural damage, such as blocking of a floating roof due to buckling under wind loads that are much lower than the collapse load. For this reason, a damage study is interested in several structural consequences but also in questions of normal operation of the infrastructure. Several authors pointed out that there is no direct relation between structural damage and economic losses caused by an interruption of normal operation of the infrastructure.

    Types of damage are usually identified through reconnaissance post-event missions, for example following Hurricanes Katrina and Rita[6,7]. Damage states reported in Godoy[7] include shell buckling, roof buckling, loss of thermal insulation, tank displacement as a rigid body, and failure of tank/pipe connections. These are qualitative studies, in which damage states previously reported in other events are identified and new damage mechanisms are of great interest in order to understand damage and failure modes not taken into account by current design codes.

    In this work, in which interest is restricted to open top tanks having a wind girder at the top, four damage states were explored, as shown in Table 2. Regarding the loss of functionality of a tank, several conditions may occur: (1) No consequences for the normal operation of a tank; (2) Partial loss of operation capacity; (3) Complete loss of operation.

    Table 2.  Damage states under wind for open-top tanks with a wind girder.
    Damage states (DS)Description
    DS0No damage
    DS1Large deflections on the cylindrical shell
    DS2Buckling of the cylindrical shell
    DS3Large deflections on the stiffening ring
     | Show Table
    DownLoad: CSV

    DS1 involves displacements in some area of the cylindrical body of the tank, and this may block the free vertical displacement of the floating roof. Notice that this part of the tank operation is vital to prevent the accumulation of inflammable gases on top of the fluid stored. Blocking of the floating roof may cause a separation between the fuel and the floating roof, which in turn may be the initial cause of fire or explosion.

    DS2 is associated with large shell deflections, which may cause failure of pipe/tank connections. High local stresses may also arise in the support of helicoidal ladders or inspection doors, with the possibility of having oil spills.

    DS3 is identified for a loss of circularity of the wind girder. The consequences include new deflections being transferred to the cylindrical shell in the form of geometrical imperfections.

    In summary, DS1 and DS3 may affect the normal operation of a floating roof due to large shell or wind-girder deflections caused by buckling.

    Tank modeling was carried out in this work using a finite element discretization within the ABAQUS environment[33] using rectangular elements with quadratic interpolation functions and reduced integration (S8R5 in the ABAQUS nomenclature). Two types of shell analysis were performed: Linear Bifurcation Analysis (LBA), and Geometrically Nonlinear Analysis with Imperfections (GNIA). The tank perimeter was divided into equal 0.35 m segments, leading to between 315 and 550 elements around the circumference, depending on tank size. Convergence studies were performed and errors in LBA eigenvalues were found to be lower than 0.1%.

    The aim of an LBA study is to identify a first critical buckling state and buckling mode by means of an eigenvalue problem. The following expression is employed:

    (K0+λCKG)ΦC=0 (8)

    where K0 is the linear stiffness matrix of the system; KG is the load-geometry matrix, which includes the non-linear terms of the kinematic relations; λC is the eigenvalue (buckling load); and ΦC is the critical mode (eigenvector). For a reference wind state, λ is a scalar parameter. One of the consequences of shell buckling is that geometric deviations from a perfect geometry are introduced in the shell, so that, due to imperfection sensitivity, there is a reduced shell capacity for any future events.

    The aim of the GNIA study is to follow a static (non-linear) equilibrium path for increasing load levels. The GNIA study is implemented in this work using the Riks method[34,35], which can follow paths in which the load or the displacement decrease. The geometric imperfection was assumed with the shape of the first eigenvector at the critical state in the LBA study, and the amplitude of the imperfection was defined by means of a scalar ξ [10]. To illustrate this amplitude, for a tank with D = 45 m and H = 12 m, the amplitude of imperfection is equal to half the minimum shell thickness (ξ = 4 mm in this case).

    It was assumed that a damage level DS1 is reached when the displacement amplitudes do not allow the free vertical displacement of the floating roof. Based on information from tanks in the Patagonian region, the limit displacement was taken as 10 mm. This state was detected by GNIA, and the associated load level is identified as λ = λDS1.

    The load at which damage state DS2 occurs was obtained by LBA, leading to a critical load factor λC and a buckling mode. An example of damage levels is shown in Fig. 4.

    Figure 4.  Damage computed for a tank with D = 45 m and H = 12 m. (a) Deflected shape for damage DS1; (b) Equilibrium path for node A (DS1); (c) Deflected shape for damage DS2 (critical mode).

    An LBA study does not account for geometric imperfections. It is well known that the elastic buckling of shells is sensitive to imperfections, so that a reduction in the order of 20% should be expected for cylindrical shells under lateral pressure. This consideration allows to estimate DS0 (a state without damage) as a lower bound of the LBA study. An approach to establish lower bounds for steel storage tanks is the Reduced Stiffness Method (RSM)[3640]. Results for tanks using the RSM to estimate safe loads show that λDS0 = 0.5λDS2 provides a conservative estimate for present purposes.

    DS3 was computed using a linear elastic analysis to evaluate the wind pressure at which a 10 mm displacement of the wind girder is obtained.

    In a similar study for tanks with a fixed conical roof, Muñoz et al.[41] estimated a collapse load based on plastic behavior. However, in the present case the top ring has a significant stiffness, and this leads to extremely high wind speeds before reaching collapse (higher than 500 km/h). For this reason, the most severe damage level considered here was that of excessive out-of-plane displacements of the wind girder, and not shell collapse.

    The methodology to construct fragility curves has been presented by several authors[42,43]. The following procedure was adapted here[44]: (1) Establish qualitative damage categories (Table 2). (2) Compute a data base for different tanks, using LBA and GNIA. In each case, the damage category based on step (1) was identified (Table 3). (3) Approximate data obtained from step (2) using a log-normal distribution. (4) Plot the probabilities computed in step (3) with respect to wind speed x.

    Table 3.  Wind speed for each tank considered reaching a damage level.
    HDIDDS0DS1DS2DS3
    H12D351137.76162.06194.82336.02
    D402160.62181.31227.16360.73
    D453153.32174.19216.82374.04
    D504145.23165.27205.39373.76
    D555152.76180.83216.03374.75
    D606145.11170.75205.22370.98
    H14D357145.57162.05205.87295.03
    D408148.55166.20210.08311.24
    D459136.42153.72192.92334.54
    D5010155.36177.51219.71339.86
    D5511145.24165.34205.39343,17
    D6012141.89167.26200.67338.77
    H16D3513131.32161.94185.71262.20
    D4014146.95163.99207.82277.08
    D4515150.58170.90212.95293.37
    D5016138.97161.05196.54303.62
    D5517138.51174.17195.88313.97
    D6018156.34182.78221.10326.83
    H18D3519146.80160.79207.60223.18
    D4020159.01177.71224.87243.63
    D4521157.10179.51222.17265.32
    D5022152.54172.17215.72293.32
    D5523164.93188.10233.25305.94
    D6024163.69180.32231.49315.63
    H20D3525163.64199.59231.42195.03
    D4026171.24195.14242.18216.47
    D4527171.58203.68242.64293.32
    D5028182.46209.43258.03259.41
    D5529178.95208.23253.07272.48
    D6030174.47196.11246.74290.86
     | Show Table
    DownLoad: CSV

    Wind speeds for each tank, obtained via Eqn (3), are shown in Table 3 for the pressure level associated with each damage level DSi. A scalar ID was included in the table to identify each tank of the population in the random selection process. Wind speed was also taken as a random variable, so that wind speed in the range between 130 and 350 km/h have been considered at 5 km/h increase, with intervals of −2.5 and +2.5 km/h.

    Out of the 30-tank population considered, a sample of 15 tanks were chosen at random and were subjected to random wind forces. The random algorithm allowed for the same tank geometry to be chosen more than once as part of the sample.

    The type of damage obtained in each case for wind speed lower or equal to the upper bound of the interval were identified. Table 4 shows a random selection of tanks, together with the wind speed required to reach each damage level. For example, for a wind speed of 165 km/h, the wind interval is [162.5 km/h, 167.5 km/h]. This allows computation of a damage matrix (shown in Table 5). A value 1 indicates that a damage level was reached, whereas a value 0 shows that a damage level was not reached. In this example, 13 tanks reached DS0; six tanks reached DS1; and there were no tanks reaching DS2 or DS3. The ratio between the number of tanks with a given damage DSi and the total number of tanks selected is h, the relative accumulated frequency. The process was repeated for each wind speed and tank selection considered.

    Table 4.  Random tank selection for V = 165 km/h, assuming wind interval [162.5 km/h, 167.5 km/h].
    IDDS0DS1DS2DS3
    11145.2165.3205.4343.2
    6145.1170.7205.2371.0
    3153.3174.2216.8374.0
    9136.4153.7192.9334.5
    28182.5209.4258.0259.4
    22152.5172.2215.7293.3
    13131.3161.9185.7262.2
    19146.8160.8207.6223.2
    3153.3174.2216.8374.0
    12141.9167.3200.7338.8
    30174.5196.1246.7290.9
    23164.9188.1233.2305.9
    2160.6181.3227.2360.7
    17138.5174.2195.9314.0
    11145.2165.3205.4343.2
     | Show Table
    DownLoad: CSV
    Table 5.  Damage matrix for random tank selection (V = 165 km/h), assuming wind interval [162.5 km/h, 167.5 km/h].
    DS0DS1DS2DS3
    1100
    1000
    1000
    1100
    0000
    1000
    1100
    1100
    1000
    1100
    0000
    1000
    1000
    1000
    1100
    Total13600
    hi0.870.400
     | Show Table
    DownLoad: CSV

    Table 6 shows the evaluation of the fragility curve for damage level DS0. This requires obtaining the number of tanks for each wind speed (fi), the cumulative number as wind speed is increased (Fi), and the frequency with respect to the total number of the sample of 15 tanks is written on the right-hand side of Table 6, for relative frequency (hi) and accumulated frequency (Hi).

    Table 6.  Damage DS0: Wind speed intervals [km/h] shown on the left; logarithm of wind speed; and relative and absolute frequencies (shown on the right).
    V inf
    (km/h)
    V m
    (km/h)
    V sup
    (km/h)
    Ln
    (Vm)
    fiFihiHi
    127.5130132.54.87000.0000
    132.5135137.54.91220.1330.133
    137.5140142.54.94130.0670.200
    142.5145147.54.98360.2000.400
    147.5150152.55.01170.0670.467
    152.5155157.55.044110.2670.733
    157.5160162.55.080110.0000.733
    162.5165167.55.112130.1330.867
    167.5170172.55.140130.0000.867
    172.5175177.55.160130.0000.867
    177.5180182.55.192150.1331.000
     | Show Table
    DownLoad: CSV

    With the values of mean and deviation computed with Eqns (5) & (6), it is possible to establish the log normal distribution of variable V for damage level DS0, usually denoted as P[DS0/V]. Values obtained in discrete form and the log-normal distribution are shown in Fig. 5a for DS0. For the selection shown in Table 6, the media is μ* = 5.03 and the deviation is σ = 0.09.

    Figure 5.  Probability of reaching a damage level P[DSi/V], (a) DS0, (b) DS0, DS1, DS2 and DS3.

    The process is repeated for each damage level to obtain fragility curves for DS1, DS2, and DS3 (Fig. 5b). Notice that the wind speeds required to reach DS3 are much higher than those obtained for the other damage levels. Such values should be compared with the regional wind speeds in Patagonia, and this is done in the next section.

    The oil producing regions in Argentina having the largest oil reserves are the Neuquén and the San Jorge regions, both located in Patagonia. This needs to be placed side by side with wind loads to understand the risk associated with such oil production.

    Figure 6 shows the geographical location of these regions. The Neuquén region includes large areas of four provinces in Argentina (Neuquén, south of Mendoza, west of La Pampa, and Río Negro). The San Jorge region is in the central Patagonia area, including two provinces (south of Chubut, north of Santa Cruz). Another area is the Austral region covering part of a Patagonian province (Santa Cruz).

    Figure 6.  Oil producing regions in Argentina. (Adapted from IAPG[47]).

    A map of basic wind speed for Argentina is available in the Argentinian code CIRSOC 102[45], which is shown in Fig. 7. Notice that the highest wind speeds are found in Patagonia, and affect the oil-producing regions mentioned in this work. For the Neuquén region, wind speeds range from 42 to 48 m/s (151.2 to 172.8 km/h), whereas for San Jorge Gulf region they range between 52 and 66 m/s (187.2 and 237.6 km/h).

    Figure 7.  Wind speed map of Argentina. (Adapted from CIRSOC 102[45]).

    The wind values provided by CIRSOC 102[45] were next used to estimate potential shell damage due to wind. Considering the fragility curves presented in Fig. 4, for damage levels DS0, DS1, DS2 and DS3 based on a log-normal distribution, it may be seen that it would be possible to have some form of damage in tanks located in almost any region of Argentina because CIRSOC specifies wind speeds higher than 36 m/s (129.6 km/h). The fragility curve DS0 represents the onset of damage for wind speeds higher than 130 km/h, so that only winds lower than that would not cause tank damage.

    Based on the fragility curves shown in Fig. 8, it is possible to estimate probable damage levels for the wind speed defined by CIRSOC. Because design winds in Patagonia are higher than 165.6 km/h (46 m/s), it is possible to conclude that there is 81% probability to reach DS0 and 25% to reach DS1.

    Figure 8.  Probability P[DSi/V] to reach damage levels DS1, DS2 and DS3 in tanks located in the Patagonia region of Argentina.

    For the geographical area of the Neuquén region in Fig. 6, together with the wind map of Fig. 7, the expected winds range from 150 to 172.8 km/h (42 to 48 m/s). Such wind range is associated with a DS0 probability between 41% and 92%, whereas the DS1 probability is in the order of 48%.

    A similar analysis was carried out for the San Jorge region, in which winds between 187.2 and 237 km/h (52 and 66 m/s). The probability of reaching DS1 is 87%, and the probability of DS2 is 88%. Wind girder damage DS3 could only occur in this region, with a lower probability of 18%.

    This work focuses on open top tanks having a floating roof, and explores the probability of reaching damage levels for wind loads, using the methodology of fragility curves. A population of 30 tanks was defined with H/D ratios between 0.2 and 0.6; such aspect ratios were found to be the most common in the oil producing regions of Patagonia. The data employed assumed diameters D between 35 and 60 m, together with height between 12 and 20 m. The tanks were designed using current API 650 regulations which are used in the region, in order to define the shell thickness and wind girder. All tanks were assumed to be empty, which is the worst condition for shell stability because a fluid stored in a tank has a stabilizing effect and causes the buckling load to be higher.

    Both structural damage (shell buckling) and operational damage (blocking of the floating roof due to deflections of the cylindrical shell) were considered in the analysis. The qualitative definition of damage levels in this work was as follows: The condition of no damage was obtained from a lower bound of buckling loads. This accounts for geometric imperfections and mode coupling of the shell. Shell buckling was evaluated using linear bifurcation analysis to identify damage level DS2. A geometrically non-linear analysis with imperfections was used to identify deflection levels that would block a floating roof, a damage level identified as DS1. Finally, deflections in the wind girder were investigated using a linear elastic analysis to define damage DS3.

    The present results were compared with the wind conditions of Patagonia, to show that several damage levels may occur as a consequence of wind speeds higher than 130 km/h, which is the expected base value identified for the region. The most frequent expected damage is due to the loss of vertical displacements of the floating roof due to large displacements in the cylindrical shell of the tank, and this may occur for wind speed up to 200 km/h. Damage caused by shell buckling may occur for wind speeds higher than 190 km/h, and for that wind speed, further damage due to displacements in the wind girder may also occur, but with a lower probability. This latter damage form requires much higher wind speed to reach a probability of 20%, and would be more representative of regions subjected to hurricanes.

    The number of tanks considered in the present analysis was relatively low, mainly because the aim of this work was to collect data to build a meta-model, i.e. a simple model that may estimate damage based on shell and load characteristics[46]. In future work, the authors expect to develop and apply such meta-models to a larger number of tank/wind configurations, in order to obtain more reliable fragility curves.

    Fragility studies for an oil producing region, like those reported in this work, may be important to several stakeholders in this problem. The fragility information links wind speed levels to expected infrastructure damage, and may be of great use to government agencies, engineering companies, and society at large, regarding the risk associated with regional oil facilities. At a government level, this helps decision makers in allocating funding to address potential oil-related emergencies cause by wind. This can also serve as a guide to develop further modifications of design codes relevant to the oil infrastructure. The engineering consequences may emphasize the need to strengthen the present regional infrastructure to reduce risk of structural damage and its consequences. The impact of damage in the oil infrastructure on society was illustrated in the case of Hurricane Katrina in 2005, in which a large number of residents had to be relocated due to the conditions created by the consequences of infrastructure failure.

    The authors confirm contribution to the paper as follows: study conception and design: Jaca RC, Godoy LA; data collection: Grill J, Pareti N; analysis and interpretation of results: Jaca RC, Bramardi S, Godoy LA; draft manuscript preparation: Jaca RC, Godoy LA. All authors reviewed the results and approved the final version of the manuscript.

    All data generated or analyzed during this study are included in this published article.

    The authors are thankful for the support of a grant received from the National Agency for the Promotion of Research, Technological Development and Innovation of Argentina and the YPF Foundation. Luis A. Godoy thanks Prof. Ali Saffar (University of Puerto Rico at Mayaguez) for introducing him to the field of fragility studies.

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

  • Supplementary Table S1 List of soil samples used in the study based on their collection Regional States and zones.
    Supplementary Table S2 Bacteria isolates obtained from soils collected from different sorghum growing areas in Ethiopia during cropping season.
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  • Cite this article

    Tulu UT, Haileselassie T, Abera S, Tessema T. 2024. Screening and identification of potential Striga [Striga hermonthica (Del.)] suppressing rhizobacteria associated with Sorghum [Sorghum bicolor (L.) Moench] in Northern Ethiopia. Technology in Agronomy 4: e013 doi: 10.48130/tia-0024-0008
    Tulu UT, Haileselassie T, Abera S, Tessema T. 2024. Screening and identification of potential Striga [Striga hermonthica (Del.)] suppressing rhizobacteria associated with Sorghum [Sorghum bicolor (L.) Moench] in Northern Ethiopia. Technology in Agronomy 4: e013 doi: 10.48130/tia-0024-0008

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Screening and identification of potential Striga [Striga hermonthica (Del.)] suppressing rhizobacteria associated with Sorghum [Sorghum bicolor (L.) Moench] in Northern Ethiopia

Technology in Agronomy  4 Article number: e013  (2024)  |  Cite this article

Abstract: Sorghum (Sorghum bicolor (L.) Moench) is one of the globally important cereal crops well adapted to Sub-Saharan Africa (SSA) agro-ecology. However, the productivity of sorghum is hindered by both abiotic and biotic factors including drought, Striga, insect pests, poor soil fertility, and diseases. Among the constraints, Striga (genus), also called witch weed, is the most important production problem in the area. Although there have been various control methods practiced for years, none of these have been practically effective in eradicating Striga, neither are they easily accessible for small holder farmers, while some are also not environmentally friendly. Therefore, this study was designed with the objective of identifying potential Striga suppressing rhizobacteria associated with sorghum. Treatment of S. hermonthica seeds with isolates E19G12, E29G2b and E19G10 resulted in the lowest S. hermonthica seed germination of 0%, 1%, and 2.7% respectively, which were significantly lower than any of the treatments. Mean germination percentage ranged from 9 to 59.7 and 0 to 27 in the absence and presence of host plants, respectively. The results showed a statistically significant germination inhibition (p < 0.001). Finally, the most effective isolates were shortlisted, E19G6a, E19G9, E19G6b, E19G10, E19B, E19G12, E29G2a, and E29G7 were morphologically and biochemically identified to belong to the genera of Pseudomonas, Klebssiella, Bacillus and Entrobacter. The results of the study demonstrated the existence of promising soil-borne bacteria that could be exploited as bioherbicides to control Striga infestation on sorghum provided that broader samples from various parts of the country are explored.

    • Sorghum (Sorghum bicolor (L.) Moench) belongs to the Poaceace family and it is one of the most globally important cereal crops. It is widely grown in the semi-arid tropics, where frequent drought is experienced. It is the fifth in line of production after maize, rice, wheat, and barley and feeds over 500 million people in the developing world[13]. Ethiopia is the fourth top sorghum producing country in the world following the USA, Nigeria, and Mexico[4]. Of the total cereal production of the country, sorghum accounts for 18.5% with a productivity of about 2.8 tons per hectare[5].

      However, due to abiotic and biotic constraints, sorghum productivity potential is being compromised. Among the abiotic factors are low soil fertility, drought, and salinity. Agriculturally important biotic constraints include the hemi-parasitic weed Striga, panicle diseases, stem borers, and insects[6]. In Ethiopia, sorghum production challenges associated with both biotic and abiotic constraints vary from region to region. However, drought and Striga (Striga hermonthica) are the most important problems across the country[7].

      Several of the Striga control approaches widely investigated and developed include cultural, chemical, biological, genetic or breeding for resistance and a combinations of more than one of these[810]. Many of these methods are either not practically successful or not economically feasible for low-income farmers in SSA[11].

      Small-scale farmers, particularly in the Northern region of Ethiopia need easy, accessible, and effective S. hermonthica management strategies that are compatible with their production practices. Soil borne bacteria have potential to perturb the early stages of Striga and Orobanche growth by reducing their incidence by 90% to 100 %[12]. It has been also shown that soil- borne fluorescent Pseudomonad strains suppressed the germination of S. hermonthica and Orobanche seeds[13]. Moreover, a few pathogenic bacteria were found to be effective to control S. hermonthica and replace commercial chemical herbicides[14]. Mounde[15] showed the significant suppression of the key stages of Striga development by Bacillus strains. The study by Neondo[16] identified microbes that were potent against S. hermonthica and proposed their use in the reduction of S. hermonthica seed bank in infested soils. The communication between microbes and S. hermonthica depends on signal transduction, the expression of pathogenicity, and virulence factors of the microbe[16]. Thus, inoculation of microbes such as rhizobacteria could minimize the competition of cereal crops with weeds and may reduce the use of chemical herbicides and could benefit agriculture contributing to increased crop yields. However, isolation, characterization, and utilization of specific microbial agents capable of causing S. hermonthica seed decay has not been thoroughly exploited in Ethiopia. Therefore, the aim of this study was to identify specific rhizobacteria that have the potential of suppressing Striga infestation on sorghum.

    • The experiment was carried out in the Microbial Biotechnology and Striga Bioassay Laboratories at National Agricultural Biotechnology Research Center (NABRC), Holeta. NABRC is located at 9°3'N latitude and 38°30'E longitude, 34 km away from Addis Ababa, in the central part of Ethiopia, West Shoa Zone of Oromia Regional State.

    • Soil samples were collected from three sorghum growing fields (sites) in the northern region of Ethiopia during the 2018 main crop season. These were Artuma Fursi district in Oromia zone of Amhara region (site 1), Kewet district in Semien Shoa Zone of Amhara region (site 2), and Qaftay Humera in the district West Tigray zone of Tigray region (site 3) with medium, low and high Striga infestation, respectively (Fig. 1). At each site, the soil samples were collected from four random spots in four quadrants after locating the fields using Global Positioning System (GPS) coordinates and recording the altitude for each site (Supplemental Table S1). Soil samples were collected using a sterile shovel at a depth of 20−30 cm and put into a labeled clean plastic bag and transported to the greenhouse facility at National Agricultural Biotechnology Research Center, Holeta.

      Figure 1. 

      Map of soil sample collection areas.

    • Seeds of different sorghum germplasm that are known to be Striga susceptible, Striga resistant, drought tolerant, widely used, released, and local land races were used in this study (Table 1). The seeds were stored at the National Agricultural Biotechnology Research Center cold room by the national Integrated Striga Control (ISC) project under Ethiopian Institute of Agricultural Research.

      Table 1.  Sorghum genotype selection for greenhouse planting and isolation of bacteria.

      CodeNameSourceCharacterSelection criteria
      G1ETSL101847TigrayLocal land raceLand race and widely used
      G2ETWS 90754AmharaWild typeWild type
      G3ETWS 91242BeneshangulWild typeWild type
      G4FramidaPurdue UniversityStriga resistanceStriga resistant and widely used
      G5ETSL100046Land raceLGSLand race and LGS
      G6ETSL101853Land raceHGSLand race, widely used and HGS
      G7MisikirMI_Drought_ScoreDrought tolerantDrought tolerant
      G8S35ICRISATStay greenStay green or Drought tolerant
      G9Shanqui redChinaStriga susceptible varietyHGS and Striga susceptible variety
      G10SR5-RibkaIBCStriga resistant and fusarium compatibilityStriga resistant and fusarium compatibility
      G11SRN39Purdue UniversityStriga resistanceStriga resistant and widely used
      G12TeshaleICRISATBest released susceptible varietiesWidely used
      LGS = low germination stimulant; HGS = high germination stimulant, G = genotype.
    • S. hermonthica seeds used for this experiment were collected from S. hermonthica-infested sorghum in farmers' fields in Ethiopia, Tigray Region, Central Zone Abergele District Titay Hagum Kebele during 2018 main cropping season (altitude: 1,466 m; latitude: 13.25'51.8'' East; longitude: 38.59'50.3'' North).

    • Seeds of sorghum that were well matured and with good morphological characteristics were selected and surface sterilized in 1.5% bleach for 30 min. The seeds were then allowed to germinate for about 30 h in an incubator set to 30 °C. Seedlings were transferred to a pot and grown on the soils collected from various sorghum growing sites in greenhouses. The management including watering and weeding was made accordingly until it was set three leaves ready for harvesting rhizosphere soil samples.

    • Rhizosphere soil sample collection was made following the method described previously[17]. After setting the third leaf, the sorghum was uprooted and vigorously shaken by hand for 5 min until non-adhering soil was completely removed. Rhizosphere soil was collected by removing the sorghum's soil parts with a sterile blade and shaking the roots for 10 min in 50 mL falcon tubes containing 35 mL sterile distilled water to remove the adhering soil. The soil suspensions were then incubated to homogenize the soil content on a shaker (300 rpm, 90 min, and 25 °C) before being centrifuged at room temperature for 10 min to concentrate soil particles in the pellet.

    • Rhizosphere bacteria were isolated by serial dilution technique. One gram of each soil pellet was suspended, each in 90 mL sterile distilled water in a 50 mL falcon tubes and mixed thoroughly overnight using a mechanical shaker at 110 rpm, until completely dispersed. Then a 100 µl aliquot was transferred with sterile pipettes to 9 mL sterile 0.85% saline solution in a test tube. A serial dilution (up to 10–8) was prepared. From each 10–4, 10–5, and 10–6 serial dilutions, 0.1 mL of an aliquot was spread on nutrient agar on Petri-dishes (90 mm), for each dilution in triplicate. Plates were incubated at 28 °C for 24 hours. Representative types of bacterial colonies were further purified by sub-culturing on fresh medium and used for downstream work or stored in 35% glycerol at −80 °C[12,17,18].

    • Rhizobacteria isolates were first screened for production of HCN followed by screening HCN positive isolates for IAA production. Common producers of HCN and IAA isolates were selected for further evaluation of their effects on S. hermonthica germination inhibition in vitro.

    • HCN production by the bacterial isolates was tested qualitatively using the method previously reported[19] with slight modification in incubation period. The bacterial isolate cultures were streaked on Trypto Soya Agar amended with 4.4 g/L glycine. Whatman filter papers were sterilized and soaked in 2% sodium carbonate in 0.5% picric acid solution was placed in the top of each plate. Plates were sealed with parafilm and incubated for 4 days at 28 °C. The change in the color of Whatman filter paper from yellow to light brown, brown or reddish brown was observed as an indication of weak, moderate or strongly hydrogen cyanide producers, respectively.

    • The ability to produce IAA of the isolate was detected from the culture of the bacterial isolates following the procedure described previously[20]. Briefly, pure colonies from a 24-h culture were inoculated into nutrient broth supplemented with 2 % tryptophan and in the absence of tryptophan (control), and incubated at 28 °C for 48 h. Five milliliter culture was removed from each tube and centrifuged at 12,000x g for 15 min. Two milliliter aliquot of the supernatant was transferred to a fresh tube. This was then treated with 2 mL salkowsky reagent (1 mL 0.5 M FeCl in 50 mL HClO4) and incubated at room temperature for 25 min. Development of pink color indicates positive result for IAA production.

    • Each common HCN and IAA producing isolates was evaluated for their Striga germination and seed decay activity in Striga bioassay laboratory using Agar Gel Assay (AGA) and Whatman filter paper. To do this, S. hermonthica seed germination test was conducted to determine its viability prior to use in vitro evaluation of the effects of rhizobacterial isolates on the seed in the absence and presence of susceptible hosts. However, the seed had to be exposed to the right environmental conditions for the optimum period of time to break dormancy and ready for germination. Hence, the Striga seed was conditioned by incubating at 29 °C for 10−14 d. In each case, S. hermonthica seed was treated with each isolate and germination percentage computed to see the germination inhibitory activity of the isolates[21, 22].

    • S. hermonthica seed surface sterilization and preconditioning was made according to the protocol previously reported[22]. First, seeds were surface sterilized in 75% ethanol under a hood in a 50 mL flask for 2 min and rinsed three times with sterile double distilled water. This was followed by washing the seed with activated metricide (fungicide) for 2 min and rinsed three times with sterile double distilled water. Finally, 14.5 mL ddH2O and 1.5 mL of Benomyl solution (conditioning solution) was added to the flask. The flask was wrapped with aluminum foil and incubated at 30 °C for 10 d for preconditioning.

      After 10 d of preconditioning, about hundred sterilized S. hermonthica seeds were transferred into a sterile glass fiber disc on a Petri plate lined with moist Whatman filter paper. Three glass fiber discs on each plate containing preconditioned S. hermonthica seeds were germinated by adding 20 µl of 0.1 ppm GR24 and incubated for 2 d at 28 °C[23]. Negative controls containing preconditioned S. hermonthica seeds were added to sterile distilled water. The numbers of germinated and non-germinated S. hermonthica seeds were counted using a binocular microscope fitted with a digital camera (Power Shot A640, Canon Inc., China). Germination percentage of Striga was determined by counting the total number of seeds on each disc and germinated Striga[22,16].

    • The isolates were evaluated for their ability to reduce Striga germination by using GR-24, a synthetic germination stimulant. Striga seed surface sterilization and preconditioning was done as described in a previous section. About 100 preconditioned Striga seeds were added to glass fiber disc placed in Petri plate lined with double sterile filter papers and moistened with 3 mL of sterile ddH2O. The experiment was replicated 3 times each (three glass fiber discs per Petri plate). The seeds on the disc were treated with 100 µl of three days old bacterial suspensions in broth. In the control treatment, blank broth was added to discs containing preconditioned S. hermonthica seeds. The Petri plates were sealed with parafilm and incubated at 30 °C in the dark for 48 h.

      After 48 h, 20 µl of 0.1 ppm GR24 was added to keep the germination uniform except for the effect of isolates and further incubated overnight at 29 °C. The number of total S. hermonthica seeds and the number of germinated/ inhibited per replicate was recorded under a stereomicroscope fitted with a camera[16]. Germination percentage for each replicate was calculated using the formula described previously[22].

      Germinationpercentage(GP)=No.ofgerminatedStrigaseedsTotalno.ofStrigaseeds×100
    • Striga conditioning was made as explained in the above section, but in this case, it was embedded in agar (bacto agar) solution after 5 d. By using a glass Pasteur pipette, a drop of preconditioned Striga seeds were added to the center bottom of a sterile plate in the conditioning flask. The seeds were treated with 0.5 mL of three days old of bacterial suspension in broth and kept for 30 min. In the control treatment, seeds were treated with blank nutrient broth media. Each treatment was replicated three times and arranged in RCD in an incubator at 30 °C. 0.7% (g/l) agar solution was prepared and autoclaved for 15 min and then allowed to cool in containment room water bath to 50 °C. The liquid agar was directly poured over the striga under hood until the agar reaches the sides of the plate and the striga seeds were distributed evenly across the plate. Plates were allowed to cool for 10 min before covering and placed in a dark at 30 °C in incubator for 10 d from the conditioning start date of the Striga seed.

      Simultaneously with Striga conditioning, surface sterilization was made on susceptible sorghum seeds called Teshale using 1.5% bleach (containing a drop of Tween-20) and agitated three times for 30 min. The bleach solution was then poured off and rinsed two times with sterile ddH2O. The seeds were then soaked overnight to imbibe in 5 mL of a 5% (w/v) Captain solution. Next day, the Captain slurry was poured off under a laminar flow hood and rinsed with 5 mL sddH2O. Then, the seeds and water were poured into labeled sterile Petri dishes, each containing two Whatman filter paper (90 mm) circles and incubated until radicles emerged[22].

      Next day, the germinated sorghum was gently picked up with sterile forceps and planted 1 cm from the edge of the plate pointing to the center of the plate in agar in which the Striga seeds were already embedded. The plates were incubated at 30 °C in an incubator where they remained for 3 d.

      After 3 d, a 2 cm × 2.5 cm area measured was made along the main sorghum root 2 cm from the kernel at the back side of the agar plate using a thick water-resistant marker pen. This area is with high probability of Striga seeds coming into contact with sorghum root exudates. Total and germinated Striga seeds in each area were counted under a stereomicroscope and germination percentage computed using the methods described in the previous section[24,15].

    • The most efficient bacterial isolates with production of HCN and IAA and corresponding inhibitor of S. hermonthica indicated by low mean germination percentage were selected. These isolates were morphologically and biochemically characterized using the method described previously[25,26] as described below.

    • The efficient bacterial isolates were characterized by growing on nutrient plate for 24 h at 28 °C. Best candidate of bacterial isolates were observed under stereomicroscope for colony size, shape, color, arrangement and gram reaction. For Gram staining, slide was cleaned with detergent and marked by codes of isolates. With the help of sterile wire loop, single colony of bacterial culture was made on clean glass slide and air dried and heat fixed. Then smear was covered with crystal violet for 1 min and slide was washed with drop of distilled water. Smear was covered with 2 drops of iodine solution for 30 s and slide was washed with alcohol and then distilled water. The smear was covered with 1 drop of safranin for 1min and then washed by distilled water, air dried and observed under microscope.

    • Each efficient bacterial isolate was tested for sugar utilization, production of methyl red, indole and catalase. This would help to identify the isolates at genus level.

    • The ability of the isolates to utilize carbohydrate sugars as a sole carbon source was determined in broth media containing specific sugar (glucose, fructose and sucrose) and Bromocrsol purple (0.4 g/l). A 96 deep well ELISA plate filled with 1 mL broth was inoculated by 0.1 mL of fresh culture in triplicate including control. The culture was incubated at 28 °C for 24 h and observed for the formation of yellow color as positive results.

    • Broth containing (5 g of each Peptone, Glucose, Potassium phosphate and 1,000 ml distilled water; pH = 7) was prepared and steam sterilized using autoclave. In test tubes, 1.5 ml of the broth was poured and each was inoculated with test organism, and then incubated at 28 °C for 48 h. Four drops of methyl red indicator was added to each tube and gently shaken for 30 s. The tubes were kept for 15 min and observed for color change (where, positive test = bright red and negative test = yellow to orange)

    • The nutrient agar slants were inoculated with test isolates. An inoculated nutrient agar slant was kept as control. The cultures were incubated at 28 °C for 24 h. A loop full of bacterial culture was kept on a clean slide with the isolate label. A drop of 3% hydrogen peroxide was added on a slide. The culture was then observed for the gas bubble formation.

    • All the experimental units were arranged in CRD. Data on effects of selected isolates in S. hermonthica seeds germination was recorded. R software version 3.5.3 was used to perform analysis of variance (ANOVA) for all measured data. Tukey's test was used to compare and separate the means for significance level at 5%.

    • A total of 117 bacteria were isolated from rhizosphere of 12 sorghum varieties grown on soil collected from three different sorghum growing regions in Ethiopia (Supplemental Table S2). The isolates were first tested for their qualitative hydrogen cyanide production on a nutrient agar plate. From these, only 47 (40.2%) of the isolates were found to produce HCN with different levels (low producers, medium producers and strong producers).

      Forty seven isolates capable of producing HCN were again tested for IAA production, another weed suppressive trait of rhizobacteria. Accordingly, six (12.8%) isolates were strong producers, 9 (19%) were moderate producers and 7 (14.9%) were low or weak producers and 25 (53.2%) were not producers of IAA at all.

      From both test, 21 isolates were common producers of HCN and IAA. These include E19G1, E19G3, E19G6a, E19G9, E19G11a, E19G6b, E19G10, E19B, E19G7, E19G11b, E19G12, E29G2a, E29G11, E29G2b, E29G9, E29G7, E40G1a, E40G5, E40G1b, E40G10, and E40G12. These isolates were selected for further evaluation of their effects on S. hermonthica germination inhibition in vitro. Majority of the selected isolates did vary in their HCN and IAA production abilities. Some strong producers of HCN were comparably moderate and weak producers of IAA and vice versa. A few isolates, however, showed similarity in their HCN and IAA production. Two isolates, namely E19G12 and E29G7 were strong producers of both HCN and IAA in common.

    • In this study, the germination test for S. hermonthica resulted in 63% germination upon conditioning the seeds for 10 d and treating with GR-24, a synthetic germination stimulant.

    • In this study, bacterial isolates were evaluated for their effects on Striga germination/inhibition in vitro. The results of the assay showed that significant differences (p < 0.001) were observed between some isolates on the effects of rhizobacteria isolates on GR-24 induced S. hermonthica germination in the filter paper (Figs 2 & 3). Regardless of considerable variation in their inhibition effects, all isolates showed a significant reduction in germination percentage compared to the control (broth treatment). But, the extent of germination inhibition varies from 9 to 59.7 mean germination percentage.

      Figure 2. 

      Effect of rhizobacteria isolates on GR-24 induced S. hermonthica germination in filter paper assay. Values are means of combined data of three replicates each. Means followed by the same letter are not statistically different at p ≤ 0.05 according to the Tukey-test.

      Figure 3. 

      Glass fiber filter paper based S. hermonthica germination assay.

    • This was an activity done as an alternative to the greenhouse evaluation to see whether there are similar or different trends compared to the evaluation using GR-24 as a stimulant. The study indicated that the germination of Striga in the presence of host plant was lower than that of GR-24 induced germination in all treatments. There was significant difference (p < 0.001) in mean germination percentage among isolates (Figs 4 & 5).

      Figure 4. 

      Effects of rhizobacterial isolates on S. hermonthica seed germination in the presence of susceptible host plant.Values are means of combined data of three replicates each. Means followed by the same letter are not statistically different at p ≤ 0.05 according to the Tukey-test.

      Figure 5. 

      Striga seed germination using agar gel assay.

      A few isolates showed increased germination of S. hermonthica seeds, while many of them showed a significant suppression of S. hermonthica seed germination (p < 0.001) compared to control treatment (broth). The highest germination percentage (27%) were recorded in the control (blank broth treated seeds), followed by isolates E19G9 (24%) and E40G5 (20%). Treatment of S. hermonthica seeds with isolates E19G12, E29G2b, and E19G10 resulted in the lowest S. hermonthica seed germination of 0% 1%, and 2.7% respectively, which were significantly lower than any of the treatment (Fig. 4). Germination inhibition followed an almost similar pattern to the treatments without the presence of host plant except that treatment with the synthetic stimulant GR-24 caused an elevated germination percentage compared to treatment in the presence of host plant sorghum.

      Regarding mean germination percentage in the absence of host plant, eight isolates, namely, E19G6a, E19G9, E19G6b, E19G10, E19B, E19G12, E29G2b, and E29G7 showed significant inhibition of S. hermonthica germination as indicated by low mean germination percentage, 16, 10, 26, 9, 29.7, 14, 12 and 18, respectively. Similarly, some isolates that have indicated high Striga germination inhibition in the absence of host plants also showed reduced germination in the presence of host plants although not correspondently or in the same pattern. There was also no consistent pattern in all isolates and parameters evaluated in association with various sorghum genotypes from where they have been isolated and the three soil types. But, majority of the bacterial isolated from the soil of low Striga infested site (E29) potentially inhibited Striga germination in the absence of host plant.

    • Finally, upon in vitro evaluation, eight efficient rhizosphere bacteria isolates with different Striga suppressive effects were further morphologically and biochemically characterized for gram reaction, colony color, size, shape, margin, elevation, sugar utilization ability, catalase and methyl red test. Accordingly, 6 (75 % ) of the rhizosphere bacteria inhibiting Striga germination were found to be gram negative, 2 (25%) gram positive, 4 (50%) glucose positive, 7 (87.5% ) fructose positive, 2 (25%) sucrose positive, 6 (75%) methyl red positive, 6 (75%) catalase positive (Tables 2 & 3).

      Table 2.  Morphological characterization and identification of the most effective rhizobacteria isolates.

      Isolates Morphological characterization
      PigmentShapeSizeElevationMarginGram staining
      E19G6aWhiteCircularMediumRaisedEntire
      E19G9WhiteCircularMediumRaisedEntire
      E19G6bBrownCircularMediumRaisedEntire+
      E19G10WhiteCircularMediumRaisedEntire
      E19BBrownIrregularLargeRaisedFlat
      E19G12BrownCircularMediumRaisedEntire+
      E29G2aWhiteIrregularLargeraisedMucoid
      E29G7WhiteCircularMediumRaisedEntire
      + = Positive for a given test, – = Negative for a given test under consideration.

      Table 3.  Biochemical characterization of the most effective rhizobacteria isolates.

      IsolatesBiochemical tests
      GlucoseFructoseSucroseCatalaseMethyl redTentative identification
      E19G6a++++Pseudomonas sp.
      E19G9++Pseudomonas sp.
      E19G6b+++Bacillus sp.
      E19G10+++Klebsiella sp.
      E19B++++Pseudomonas sp.
      E19G12+++Bacillus sp.
      E29G2a++Entrobacter sp.
      E29G7++++Pseudomonas sp.
      – = no sugar utilization, catalase and methyl red negative, + = sugar utilization, catalase and methyl red positive.

      Based on the comparative analysis of various morphological and biochemical characteristics, the bacterial isolates are identified to fall under four genera: Bacillus, Pseudomonas, Enterobacter and Klebsiella. Among the bacterial genera, four were Pseudomonas, two Bacillus, one Enterobacter and one Klebsiella (Table 3).

    • The potential of growth suppressive effects of rhizobacteria and their possible use as a biological control options in the management of S. hermonthica have been investigated to be agriculturally important to boost crop productivity[16,27]. A group of microorganisms with potential as biological control agents of weeds are the deleterious rhizosphere inhabiting bacteria (DRB) characterized as nonparasitic rhizobacteria colonizing plant root surfaces and being able to suppress plant growth[2830]. Rhizosphere is the zone at the interface of soil-plant roots that harbors the most complex microbial communities[29,31]. The deleterious activity toward weed seed viability and seedling growth by most microorganisms under study for biological control is due to the production of phytotoxins. The common metabolites produced in the rhizosphere of plants that can be phytotoxic at higher than physiologic concentrations include the auxins and hydrogen cyanide[32].

      HCN producing rhizobacteria have been known to act as biocontrol agents against weeds[33]. In this study, 117 rhizobacteria isolates were tested for HCN production and 47 (40.2%) were capable of producing HCN with different levels. Out of which, 11 (23.4%) isolates were strong producers, 15 (31.9%) were moderate producers and 21 (47%) were low or weak producers of hydrogen cyanide. Heydari et al.[34] conducted a similar study on weed germination inhibition potential of rhizosphere Pseudomonas and obtained 37% capability of HCN production of isolates and this capacity was different among the strains. According to the study by Kremer & Souissi[35], rhizobacteria such as Pseudomonads are known for their ability to produce HCN, but the quantity produced varies widely among species and strains of the bacterium.

      It has been reported by Knowles[36] and Schippers et al.[33] that glycine is a direct precursor of HCN found in root exudates though several factors significantly influence its production across bacteria. For example, the level of HCN produced in root-free soil by P. putida and A. delafieldii generally increased with higher amounts of supplemental glycine, with P. putida typically generating more HCN at a given glycine level[37,38]. Studies have shown that HCN is a potential inhibitor of enzymes involved in metabolic processes like respiration, CO2 and nitrate assimilation, and carbohydrate metabolism. Hence, this gas is known to negatively affect root metabolism and root growth[33,39]. Furthermore, cyanide interacts with the protein plastocyanin, which inhibits the photosynthetic electron transport[35].

      Many authors reported on the potential of cyanogenic rhizobacteria for weed suppression by producing HCN and the role they play in biological control of weed[40]. Cyanide producing rhizobacteria are specific in their actions and they do not generally negatively affect the host plants. A major group of rhizobacteria producing secondary metabolite hydrogen cyanide and with potential for biological control is the Pseudomonas[28,41]. Rhizosphere bacteria particularly, Pseudomonas spp. have the ability to reduce weed growth and they were proved to produce HCN. Pseudomonads isolated from rhizosphere of velvet leaf were able to reduce velvetleaf viability and emergence significantly[42,43].

      The production of the IAA phytohormone is another common trait of rhizobacteria[44,45]. Indole-3-acetic acid (IAA) is the major naturally occurring auxins which influences the root and shoot growth of the plant, stimulating ethylene production, cell division and differentiation. The rate of production of ethylene is directly proportional to the concentration of IAA[46,47]. It has been noted that 80% of rhizospheric bacteria produce IAA by metabolizing L-tryptophan[48]. Rhizosphere-inhabiting soil microbes synthesize and release auxins as secondary metabolites because of rich supplies of substrates exuded from plant roots. Some microbes produce auxins in the presence of enough precursor molecules such as tryptophan[44,49].

      The current study has shown that 46.8% of the tested isolates were capable of producing IAA. Similar study has been conducted by Idris et al.[50] on growth promotion of rhizobacterial isolates from the rhizosphere of sorghum and grasses in Ethiopia and South Africa and found 73% production of IAA in tested isolates in the presence of tryptophan. The authors further noted the tendency of decreasing IAA concentration in the absence of tryptophan. The lower number of IAA producers in this study than the previous report could be due the difference in sorghum varieties from where the rhizobacteria isolated and other factors in the soil.

      Similarly, the report on the study of the effects of rhizobacteria on plants indicated their use as bioherbicides to control weeds[51]. Rhizosphere microorganisms mediated suppression of plant growth during interaction is linked to the secretion of secondary metabolites from microorganisms[52]. Detection of Enterobacter sp. found significant amount of IAA secretion due to the presence of an increased activity of tryptophan deaminase, an enzyme which produces IAA from its precursor molecule tryptophan[53]. The negative effect of IAA is associated with the elevated levels of IAA production[54] For example, Patten & Glick[55] demonstrated the role of accumulated production of IAA by P. putida and its effects in inhibition on plant growth. The increased IAA production stimulates biosynthesis of ethylene by the enzyme aminocyclopropane-1-carboxylate (ACC)[56]. IAA producing Enterobacter sp also showed to inhibit lettuce plant growth and enhanced ethylene synthesis[57].

      Prior to conducting any germination assay, seed viability test and knowing its percentage of germination are fundamentally important to use it in the subsequent experimentation. In the current study, S. hermonthica seed viability test resulted in 63% germination percentage on conditioning the seed in benomyl solution for 10 d and treating the seed with a synthetic germination stimulant GR-24. This agrees with a previous report[22] which suggested that the germination percentage of Striga seed has to be at least 30% for downstream application of the seed and[18] indicated 55%−57% germination induction by GR-24 in seed condition in water in testing Striga seed germination.

      Isolation of weeds inhibiting rhizobacteria was made from sorghum rhizosphere and the mechanism involved in weed inhibition of Striga seed germination inhibition was undertaken to identify potential Striga suppressive rhizobacteria associated with the host plant sorghum. In vitro evaluation of the effects of inoculation of bacterial isolates on the inhibition of S. hermonthica was studied under laboratory bioassay. This technique was developed for the selection of bacteria inhibitory to the germination of S. hermonthica seeds in such a way that adequate contact between the bacteria and S. hermonthica seeds was ensured without the bacterial culture medium itself inhibiting S. hermonthica seed germination. The study focused on germination inhibition at the early stage of S. hermonthica and generated information to develop reliable and accessible Striga control strategies for small holder farmers.

      There was considerable variation in the inhibition of Striga germination by bacterial isolates obtained from sorghum rhizosphere grown on soil collected from different sites. The lowest (9%) and highest (59.7%) germination percentage was observed in E19G10 and broth (control treatment), respectively. A similar study on rhizobacterial strains for suppression of germination of S. hermonthica[13] found a wide range of results (13%−50% germination of S. hermonthica seeds). Our study indicated the potential of rhizosphere bacteria in inhibiting the early stages of S. hermonthica development. This helps to reduce much of the damage caused by Strigas before emerging above the ground.

      The study showed variations in the inhibition of Striga germination by isolates obtained from sorghum rhizosphere grown on soil collected from different sites. For example, many of the bacteria isolated from soil E19 (Amhara Region, Oromo Special Zone) significantly reduced the germination percentage of S. hermonthica. Soil E19 was obtained from site 1, where there was low Striga infestation. The low infestation of Striga in the field from where soil E19 was obtained may be associated with Striga germination inhibition by the bacterial population and other factors in the vicinity. Some soils are known to be suppressive to Striga, and their suppression was linked to the microbial populations[58].

      On the other hand, isolating E19G11a from the same soil E19 but isolated from the rhizosphere of Striga resistant SRN-39 did not significantly reduce Striga germination. The enhanced germination of Striga by this isolate may be again explained by the nature of the microbiome of the soil. This is consistent with previous findings[13] in which few isolates increased germination of S. hermonthica seeds, others had no effect on seed germination, while some showed a significant suppression of S. hermonthica seed germination compared with the check (no bacterium). Furthermore, it has been previously suggested[59] that both inhibition and promotion of Striga germination can be attributed to microbial action and this can be achieved by manipulation of ethylene biosynthesis, ethylene action, or by promotion of ethylene metabolism or that of its immediate precursor ACC (1-aminocyclopropane-1-carboxylic acid). Generally, there was no consistent pattern in Striga germination inhibition of the soils collected from the three sites. However, many of the isolates collected from site 1 (E19) resulted in low mean germination percentage regardless of the sorghum variety from where the bacteria were isolated.

      The use of weed management strategies involving chemical herbicides generally alters soil structure going alongside with changes in the microbial community[40]. Using soil microorganisms to control weeds is an alternative method to herbicides that may reduce dependence on chemical herbicides and increase the use of environmentally sound practices that are easily available to small holder farmers. The soil microbiome plays an important role in the establishment of weeds and invasive plants with which they are associated and build up close relationships. For example, sorghum seedlings [Sorghum bicolor (L) Moench] of different genotypes differ in association with soil microorganisms[60].

      Evaluation of the effects of isolates from various sorghum varieties grown on soil collected from different sorghum growing regions in Ethiopia was also conducted in the presence of a host plants called Teshale variety (sorghum) using Agar Gel Assay (AGA). This method helps to overcome the limitations experienced during field evaluation in establishing a uniform environment to study host-parasite interaction, as it happens in a controlled environment laboratory. The method also allows observation of host-parasite interaction at various stages of Striga life cycle[22].

      In this study, the lowest (0%) and highest (27%) mean germination of S. hermonthica seeds was obtained in the presence of the host plant. This is much higher than the finding of Ahonsi et al.[13] who obtained the lowest (13%) reduction in germination percentage of S. hermonthica inoculated with bacterial isolates in the presence of host plant sorghum. Similarly, Babalola et al.[18] studied the use of rhizobacteria to control S. hermonthica and observed the inhibition of Striga germination by bacterial isolates. The germination inhibition of bacterial isolates could be associated with a direct effect of the isolates on the seed or indirectly via the production of chemicals that are toxic to seeds, inhibitors/ promoters of ethylene biosynthesis or its action[12,61,62].

      Furthermore, the study demonstrated that the control (broth) treatment in the presence of host plant resulted in the highest mean germination percentage, but it was still lower than GR- 24 induced germination percentage of seeds in the absence of host. This finding agreed with the study by Barillot et al.[17] on the effectiveness of GR-24 in S. hermonthica seed germination stimulatory activity.

      Morphological and biochemical characterization of the most effective shortlisted isolates in vitro evaluation finally resulted in four different genera of bacteria: Pseudomonas, Bacillus, Klebssiella and Eentrobacter (Table 3). A variety of rhizobacteria, including Bacillus[63], Pseudomonas[64], Azospirillum[65] species are commonly found in the rhizosphere of crops. The study indicated that majority of the isolates were strong producer of HCN and IAA and deduced to belong to the Pseudomonas genera. This agrees with a previous report[41] that HCN production is found to be a common trait of Pseudomonas (88.89%) and Bacillus (50%) in the rhizospheric soil and plant root nodules.

      Pseudomonas is among the common plant root inhabiting soil bacteria[66]. Rhizobacetria genera including Pseudomonas sp., Klebsiella oxytoca and Enterobacter sakazakii were also shown to inhibit S. hermonthica seed germination[67].

    • The results of this study have revealed that there are novel rhizobacteria with a great potential of inhibiting Striga seed germination resulting in reduction of parasitic infestation on sorghum. This potential can be exploited by isolating and characterizing rhizospheric bacteria associated with sorghum and evaluating their Striga suppressive effects. The suppression effects of rhizobacteria on Striga seed could be associated with microbial production of phytotoxic secondary metabolites and inhibitory chemicals such as HCN and IAA that could induce a biocontrol effect. Many of the isolates with most effective Striga suppression were obtained from low Striga infested field (E19) indicating that rhizospheric bacteria could contribute to the reduction in parasitic infestation. It has been also shown that, regardless of the level of inhibition, all rhizobacterial isolates suppressed Striga germination up on in vitro evaluation in the presence and absence of host plant sorghum. The most effective Striga suppressive isolates were identified under four bacterial genera and the majority of them belong to the Pseudomonas genus. The isolates are good candidates for addressing Striga associated constraints in sorghum production where there is a low input for small holder farmers in Ethiopia.

    • The authors confirm contribution to the paper as follows: study conception and design: Tulu US, Abera S, Haileselassie T: experiments and statistical analysis: Tulu UT; draft manuscript preparation: Tulu UT, Haileselassie T, Tessema T. All authors read and approved the final manuscript.

    • The datasets used and analyzed during the current study are available from the corresponding author on reasonable request

    • We thank the National Agricultural Biotechnology Research Center and its staff for helping with the provision of facilities during the study. We are also grateful to the Ethiopian Institute of Agricultural Research for facilitating this research from sample collection to laboratory analysis of the evaluation of the isolates.

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

      • Supplementary Table S1 List of soil samples used in the study based on their collection Regional States and zones.
      • Supplementary Table S2 Bacteria isolates obtained from soils collected from different sorghum growing areas in Ethiopia during cropping season.
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (5)  Table (3) References (67)
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    Tulu UT, Haileselassie T, Abera S, Tessema T. 2024. Screening and identification of potential Striga [Striga hermonthica (Del.)] suppressing rhizobacteria associated with Sorghum [Sorghum bicolor (L.) Moench] in Northern Ethiopia. Technology in Agronomy 4: e013 doi: 10.48130/tia-0024-0008
    Tulu UT, Haileselassie T, Abera S, Tessema T. 2024. Screening and identification of potential Striga [Striga hermonthica (Del.)] suppressing rhizobacteria associated with Sorghum [Sorghum bicolor (L.) Moench] in Northern Ethiopia. Technology in Agronomy 4: e013 doi: 10.48130/tia-0024-0008

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