Soil composition is generally conditioned by many factors such as vegetation, climate (agro-climate) and environmental factors as well as by microorganism including bacteria, fungi and variety of insects and worms alongside protozoa activities on the earth. Continued interactions between these features enable guaranteeing and maintaining long term soil productiveness. Plant development is strongly correlated with soil nutriment availability. So, for constant growth, plants need several macro-elements such as nitrogen (N), phosphorous (P), potassium (K), calcium (Ca), magnesium (Mg) and sulfur (S). Also, deficiency of these inorganic chemical elements in soil intended for agricultural practices compel farmers to use chemical fertilizers that have been recovered over time to be potential environmental and water pollutants. Thus, cultivable soil composition dynamism strongly depend on farmers practices, due to the use of pesticides as well as fertilizers. For optimal plant growth, the soil must be capable of storing the above-mentioned nutrients and transporting them to the root surface for endorsement by plant. However, findings suggest that, one of the most important factors in the generation of high yields from cereal cultivars is nitrogen (N) fertilizer; hence, farmers keep applying high amounts of the fertilizers which is very expensive and make the environment perilous especially when used discriminately. In addition, more than 50% of the applied nitrogen (N) fertilizers are somehow lost through different processes which is not limited to cash loss by the farmers. N is lost through different mechanisms including ammonia volatilization, denitrification and leaching losses, causing environmental pollution problems (Choudhury and Kennedy, 2005). Crop scientists all over the world are facing this alarming situation and are trying to overcome this condition by exploring alternative sources which is cost effective and save the environment. Bio-fertilizer, an alternative source of N-fertilizer, especially plant growth promoting Rhizobacteria (PGPR) affects plant growth by producing and releasing secondary metabolites, facilitating the availability and uptake of certain nutrients from the root environment and inhibiting plant pathogenic organisms in the rhizosphere (Kumar et al., 2008). It is noteworthy to emphasize that PGPR are free living bacteria, isolated from the rhizosphere, which when applied to seeds or crops, enhance the growth of the plant or reduce the damage from soil borne plant pathogens. Plant growth promoting Rhizobacteria can on the one hand form a symbiotic relationship with the plants and on the other hand, freely live in the soil though are found near or even the roots of plants. Generally,  the  impact  of  rhizobacteria  on  plant growth and health may be classified as neutral, deleterious or beneficial. Beneficial free living bacteria referred to as plant growth promoting rhizobacteria are found in the rhizosphere of the roots of many different plants. Among the means available to achieve sustainability in agriculture production, bio-fertilizers plays an important and key role because it processes many desirable soil properties and exerts beneficial effect on the physical, chemical and biological characteristics of the soil. Based on this, bio-fertilizers because of its vital role in maintaining long term soil fertility could be requested for many agricultural soils (Khosro and Yousef, 2012; Biari et al., 2008). It has been fully sustained that intensive use of mineral fertilizers can cause problems as the flow of these fertilizers into rivers, lakes and streams constitutes a source of pollution (van Keulen and Breman, 1990, Umesha et al., 2014). It is therefore appropriate to find alternative solutions at the overuse of chemical fertilizers. Bio-fertilizer are found to positively contribute to soil fertility, resulting in an increase in crop yield without causing any environmental, water or soil pollution hazards (Umesha et al, 2014). Research work has already been undertaken on the potential effect of bio-fertilizers on crop yields. Hernandez et al. (2010) claimed that inoculation of maize seed with rhizobacteria in combination with a dose of 120 kg N ha^-1 results in an increase of around 25% of returns compared to those obtained with the same dose of nitrogen but without inoculation with microorganisms. Also, the combination of rhizobacteria raised with a dose of 120 kg N ha^-1 can increase over 60% the maize yields in comparison with those obtained in plots without nitrogen application or inoculation of bacteria (Hernandez et al. 2010). From the previous studies and others, a positive interaction between Pseudomonas fluorescence and foliar bio-fertilizer promoting rice (Oryza sativa L.), maize (Zea mays L.) and soybean (Glycine max L.) crops growth and yield was suspected, as well as enhancing the resistance against pathogens (Ahemad and Khan, 2011; Adolphe et al, 2012; Munees et al. 2013; Pérez-Montaño et al, 2014; Diarrassouba et al, 2015). Also, recent findings suggested that the combined use of Pseudomonas fluorescence bio-fertilizer as plant growth-promoting rhizobacteria (PGPR) and inorganic nitrogen fertilizer has been found promising not only in maintaining and sustaining high productivity but in providing stability to rice crop (Oryza sativa L.) production and minimizing the cost of inputs and environmental pollution (Nehal, 2015). Consequently, integrated use of PGPR Pseudomonas along with inorganic and organic nutrient sources provides a sustainable agricultural ecosystem. From the results and observations, it was necessary to evaluate via a computational   bio-statistical    survey,    the    interaction between rhizobacteria microorganisms (P. fluorescence) and foliar bio-fertilizer in promoting maize (Zea mays. L.) and soybean (G. max) growth and yield (development) as well as dry biomass features interaction in an arid agro-climate characterized by arid and low fertile soil.

Dry biomass

 

For estimation  of  dry  biomass,  the  fresh  weight of the vegetative material for both maize and soybean separately were first determined and next the material was dried using an oven-dried for 48 h (at a temperature of 60°C: time and temperature required for a constant dry weight). The weights were measured using a precision balance 10^-4 g (Toledo). Dry biomass was calculated using the following formula:

 

Dry biomass = [(Fresh weight of leaves) × (Dry weight of the leaf sample) / (Fresh weight of the leaf sample)] + [(Fresh weight rods) x (Dry weight of the sample rod ) / (Wet weight of the sample rods)].

 

Statistical and computational statistical survey

 

Data normalization process

 

Data for the present experiment were statistically analyzed based on several scripts from R statistical package (R Core Team, 2014). Maize and soybean growth and yield parameters, because of their heterogeneity, were submitted to normalization by scaling between 0 and 1, since in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series (Dodge, 2003).

 

Computational Pipeline content for statistical analysis

 

The present study developed computational statistical pipeline (computational  pipeline)   analyzing   the  relationship between   (Z. mays L.) and soybean (G. max) plants growth and yield as well as dry biomass parameters based on several R version 3.4.3. Details as regards that computational statistical pipeline is reported in supplementary material chapter (Appendix).

Impact of rhizobacteria microorganism and foliar bio-fertilizer on maize (Z. mays. L.) and Soybean (G. max) plants development

 

Maize (Z. mays. L.) and soybean (G. max) seeds inoculated by P. fluorescence, P. fluorescence + Foliar bio-fertilizer mixture and foliar bio-fertilizer, exhibited different behavior in terms of cereals growth and yield features data distribution, when compared to T₀ treatment (seeds without P. fluorescence and foliar bio-fertilizer treatment and/or in soil standard condition) (Figure 1). Ratio value between growth and yield parameters under T₀ treatment resulted to around 0.97. The same ratio value concerning T₁, T₂ and T₃ treatments were 0.76, 0.97 and 0.91 respectively (Table 1). So, a substantial difference has been observed between analyzed cereals growth and yield parameters for all processed treatments (Figure 1, Table 1). Moreover, T₀ (soil standard condition) and T₂ treatments resulting in P. fluorescence + foliar bio-fertilizer  mixture,  exhibited   the   same   ratio   value  by comparing processed plants growth and yield data. This finding evoked normalization as well as a synergic impact of P. fluorescence + foliar bio-fertilizer mixture with regards both maize and soybean plants growth and yield features (Table 1 and Figure 1). Additionally, maize (Z. mays L.) and soybean (Glycine max) seeds under rhizobacteria microorganisms (p=2.57 4e-09) and rhizobacteria microorganism + Foliar bio-fertilizer mixture (p= 0.07) treatments, exhibited a positive impact by processing cereal yield parameters as opposed to the same seeds under foliar bio-fertilizer treatment (p = 0.6), when treatment T₀ and/or experimental site soil standard condition was considered as reference. Also, cereal seeds under T₁, T₂ and T₃ treatments induced a high dynamism for both maize and soybean crops yield parameters as opposed to their growth features (Figure 1). Taking everything into consideration, the present results confirmed the normalization effect of rhizobacteria microorganisms + Foliar bio-fertilizer mixture on processed plants yield and growth features. 

 

 

 

Effect of rhizobacteria microorganism on maize (Z. mays L.) and Soybean (G. max) plants growth and yield parameters relationship

 

Cluster dendrogram graph based on Euclidian distance of Pearson correlation survey showed two distinct groups and/or tendencies (Figure 2). Indeed, processed parameters clustered disregarding rhizobacteria microorganisms (P. fluorescence), foliar bio-fertilizer as well as P. fluorescence + Foliar bio-fertilizer mixture treatments. However, findings suggested high variability of maize and soybean plants yield parameters with respect to growth features. In the same tendency, rhizobacteria microorganisms (P. fluorescens) treatment and rhizobacteria microorganisms (P. fluorescens) + Foliar bio-fertilizer  mixture   referred  as  T₂  treatment  were recorded as inducing high variability in processed plants yield parameters (p<0.05) as opposed to T₀ and T₃ treatments (Figure 2). Also, the present result suggested a relative positive effect of both (i) P. fluorescence + Foliar bio-fertilizer mixture and (ii) rhizobacteria microorganism (P. fluorescence) treatment on maize (Zea mays L.) and soybean (Glycine max) plants growth parameters (Figure 2) as opposed to both T₀ and T₃ treatments. Further, findings exhibited no substantial difference between T₀ and T₃ treatments with regard to maize (Zea mays L.) and soybean (Glycine max) growth and yield parameters (Figure 2). Taking together, the present survey suggested a selective difference between (i) processed plants from seeds inoculated with P. fluorescens as well as with P. fluorescens + Foliar bio-fertilizer mixture referred as T₁ and T₂ treatments respectively and (ii) those from seeds in experimental site soil standard condition and with foliar bio-fertilizer inoculation denoted as T₀ and T₃ treatments respectively.

 

 

Performance assessment of rhizobacteria microorganisms’ and foliar bio-fertilizer treatments impacting maize (Z. mays L) and Soybean (G. max) yield and growth features data dispersion

 

Findings suggested that one component (PC1) do not sufficiently explain the present process data dispersion (Figure 3). Data dispersion evidence has been highlighted by introducing and/or combining two principal component factors referred to as PC1 and PC2 (Figure 3). Furthermore, test of the hypothesis that 2 factors are sufficient (degrees of freedom for the model is 13) based on 22 observations, displayed a Chi Square value = 30.42 with an associated p value = 0.004. Also, the root mean square of the residuals of this model is 0.1 (p<0.05). Moreover, data distribution source became strongly evident by introducing a third component (PC3). Indeed, test of the hypothesis evaluating 3 factors and explaining processed data dispersion (degrees of freedom for the model is 7) based on 22 observations, exhibited a Chi Square value = 29.16 with an associated p value =0.0001. The root mean square of the residual associate to this model is 0.06 (p<0.05). These results suggested 3 component factors as strongly efficient in discriminating  maize   and   soybean   growth   and  yield features data dispersion source with respect to the other considered systems (Figure 3). Thus, Figure 3 clearly showed T₁ referred to yield and growth parameters of maize and soybean plants from seeds inoculated by P. fluorescence and T₂ referred to yield and growth features of maize and soybean plants from seeds under P. fluorescence and foliar bio-fertilizer mixture treatments as potential sources of presently analyzed data variability. Also, merging the present result (Figure 3) with the previous one (Figure 2); it seem obvious to deduce both maize (Zea mays. L.) and soybean (Glycine max) cereal plants yield attributes as significantly influenced by P. fluorescence as well as by P. Fluorescence microorganism activities on foliar bio-fertilizer (P. Fluorescence + Foliar bio-fertilizer mixture). 

 

 

R fitting curve weighing rhizobacteria microorganism activities on foliar bio-fertilizer assessing the relationship between both maize and soybean plants yield features

 

Here, the performance of maize (Z. mays L) and soybean (G. max) yield parameters for seeds under rhizobactera microorganism’s  (P.   Fluorescence)    and   rhizobactera microorganism + Foliar bio-fertilizer treatments was assessed assuming plants from T₀ treatment as reference. Processed maize (Z. mays L.) and soybean (G. max) plants yield parameters from maize and soybean seeds inoculated by rhizobacteria micro-organisms (P. fluorescence), exhibited a heterogeneous behavior as opposed to those from seeds inoculated by rhizobacteria microorganisms (P. fluorescence) + Foliar bio-fertilizer mixture and foliar bio-fertilizer when experimental site soil standard condition was assumed as reference and/or benchmark. Interestingly, this analysis showed seeds inoculated by rhizobacteria micro-organisms + Foliar bio-fertilizer mixture, as improving and normalizing maize (Z. mays L.) and soybean (G. max) cereal plants yield parameters as opposed to plants from seeds inoculated by rhizobacteria microorganisms only (Figure 4). Overall, these findings suggested the implication of rhizobateria microorganisms (P. fluorescence) activity on foliar bio-fertilizer in the normalization procedure of both maize (Z. mays L.) and soybean (G. max) crops yield parameters improving plants development performances in an arid agro-climate low fertile soil. 

 

ANOVAs and multiple linear regression survey linking T₀, T₁, and T₂ and T₃ treatments by handling maize and soybean plants yield features data

 

Results concerning the present analysis of variance, displayed 0.11 as residual standard error, on 18 degrees of freedom with a multiple R-squared value =0.52 (R²= 0.52) and adjusted R-squared = 0.4452. Fisher test, associated to this variance analysis exhibited F-statistic = 6.618 on 3 and 18 Degree of Freedom (DF) with p=0.00, indicating that the null hypothesis that the yield parameter variables concerning maize and soybean plants from seeds inoculated by rhizobacteria microorganisms referred as T₁ treatment (YP.T₁), and rhizobacteria microorganisms + Foliar bio-fertilizer mixture yield denoted T₂ treatment (YP.T₂) and foliar bio-fertilizer (YP.T₃), collectively were not influenced by previous maize (Z. mays. L.) and soybean (G. max) seeds treatment should be clearly rejected. Results (Table 2) also showed cereal plants yield parameter from seeds under T₁ (p-value=0.91) and T₂ (p-value= 0.34) treatments, as not significantly controlling for yield parameter variable of the cereal plants from cereal seeds under T₃ treatment (p = 0.00). However, the same analysis exhibited processed maize and soybean plants yield parameter from seeds inoculated by foliar bio-fertilizer as  controlling  both   maize  and soybean cereal plants yield parameter under T₁ and T₂ treatments (p=0.00). Also, results of the partial analysis of variance have been reported as follows:  F = 0.55   with   p = 0.47.  Thus, the null hypothesis (β₁= 0) at the 5% level of significance cannot be rejected. It seems that the variables YP.T₁ (p=0.46) and YP.T₂ (p=0.12) do not contribute significant   information   to   maize  (Z. mays. L.) and soybean (G. max) treated seeds, once yield parameter variable from seeds under T₃ treatment (YP.T₃) has been taken into consideration. In other words, both maize (Z. mays L.) and soybean (G. max) seeds treated by rhizobacteria microorganism + Foliar bio-fertilizer mixture and rhizobacteria microorganism exhibited a contrast behavior among themselves and also with respect to seeds without inoculation plus seeds under foliar bio-fertilizer treatment. Altogether, these surveys are in agreement with the previous results (Figures 2 and 3).

 

 

Relationship between maize and soybean dry bio-mass and both yield and growth features evaluating P. fluorescence activity on foliar bio-fertilizer

 

Here, several multiple linear regression surveys aimed at understanding the relationship between maize (Z. mays L.) and soybean (G. max) plants dry bio-mass together with both yield and growth parameters were performed. Developed linear regression model based on maize and soybean plants from seeds without rhizobacteria microorganisms and foliar bio-fertilizer inoculation, was as follow; (E₁): Dry Biomass=6.82*Yield parameter + 28.74*Growth parameter -398.95. This equation evokes high contribution of cereals growth parameters in impacting cereals dry biomass attribute with respect to yield component. In other words, dry biomass of both maize and soybean plants from seeds without previous bio-fertilizer inoculation resulted to being highly influenced by plants growth features. However, intercept value (-398.95) concerning this model (E₁) seems to contrast with that estimated dry biomass performance. Unlike the previous considered model (E1), the second developed multiple linear regression model; (E₂): Dry Biomass=10.61*Yield parameter + 1.343*Growth parameter -34.65 from both maize and soybean cereal plants from seeds inoculated with rhizobacteria microorganisms, exhibited a high concordance between plants dry biomass and yield attribute. Moreover, multiple linear regression models based on (i) foliar bio-fertilizer, (E3): Dry Biomass=0.43*Yield parameter + 0.76*Growth parameter + 6.26 and (ii) P. fluorescence + Foliar bio-fertilizer mixture, (E₄): Dry Biomass=0.43*Yield parameter +1.92*Growth parameter -39.98, suggested a reasonable synergy between both growth and yield features by improving processed maize and soybean plants dry biomass component. Furthermore, equation (E3), proposed foliar bio-fertilizer as having a relatively high influence on growth parameters with respect to yield attributes. Also, considering equation (E₄) as the resultant of both equations (E₂) and (E₃), we can suppose (i) rhizobacteria microorganisms and (ii) foliar bio-fertilizer treatment as influencing processed plants yield and growth parameters respectively. Next, the relationship between maize and  soybean  plants  dry  biomass,  yield and growth parameters was analyzed by merging all processed treatments. Results of the developed multiple linear regression model was as follow; (E₅): Dry Biomass=2.19*Yield parameter – 4.04*Growth parameter + 63.28. This survey hypothesized a positive effect of yield parameters on analyzed plants dry biomass attribute as opposed to growth parameter. Also, equation E₅ suspected a potential drop of maize and soybean plants dry biomass by a strong upturn of cereal growth parameters (p=0.17). Collectively, these analysis suggested high fluctuation as regards analyzed plants dry biomass parameters for each considered treatments (Figure 5) and exhibited the combination between rhizobacteria microorganisms and foliar bio-fertilizer as a suitable practice normalizing the relationship among considered plants yield and growth features data by improving their dry biomass component, since high dry biomass value were recorded for processed plants from seeds inoculated by P. fluorescence + Foliar bio-fertilizer mixture (Table 3 and Figure 5).  

 

Relationship between maize and soybean dry biomass features for seeds under P. fluorescence and foliar bio-fertilizer treatment by processing variable number of principal components

 

We performed a relationship analysis between considered plants dry bio-mass components was performed aimed at assessing rhizobacteria microorganism’s activity on foliar bio-fertilizer through a principal component analysis by processing a variable number of principal component (Figure 6). As expected, the present analysis suggested 2 factors and/or components (Component 1 and 2) as enough for explaining the variability as well as the difference ratio among presently processed cereal plants dry biomass parameters (Figures 5 and 6). So, this analysis revealed Components 3 and 4 as unable to explain dry biomass data dispersion (Figure 6). Considering both Components 1 and 2 of Figure 6, our results suspected a high difference and/or an opposed behavior between both maize (Z. mays L) and soybean (G. max) plants dry biomass parameters for seeds under T₀ (plants from seeds without treatment) and T₃ (plants from seed inoculated by foliar bio-fertilizer) treatments (Figure 6). This result is in agreement with previous results reported in Table 3. Also, principal components 1 and 2 exhibited a relative difference in terms of data distribution with regards to dry biomass parameter referred to in both T₁ (maize and soybean seeds inoculated by P. fluorescence microorganisms) and T₂ (maize and soybean seeds under rhizobacteria microorganism + Foliar bio-fertilizer mixture inoculation) treatments (Figure 6). However, considering soil standard condition as benchmark, the present findings supported the high normalization aptitude of rhizobacteria microorganism  + Foliar  bio- fertilizer  mixture  treatment, on maize (Z. mays L) and soybean (Glycine max) growth and yield features data as opposed to T₁ and T₃ treatments (Figure 6). Combining this result with those of Tables 3, we can suggest rhizobacterium microorganism’s activities on leaf bio-fertilizer, as being responsible for the adjustment of the synergy between growth and yield parameters, while improving the dry bio-mass features.

 

 

 

 

 

 

 

 

The inoculation of plant growth promotor rhizobacteria P. fluorescence in cereal plant may increase number of panicles, number of grains panicle, percentage of filled grains because of enhancement of plant biomass, root elongation, uptake of NPK and ultimately increase all characters without deteriorating soil health. Subsequently, they act as potential bio-fertilizers and antagonistic agents to save synthetic chemical fertilizers and pesticides, respectively and become an ecofriendly alternative in upcoming days (Khorshidi et al., 2011; Kumar and Dangar, 2013). This study examined the interaction between Rhizobacteria microorganism (P. fluorescence) and foliar bio-fertilizer with the purpose to highlight their influences and/or interference on maize and soybean crops performance in  an  arid  agro-climate low fertile soil. The study revealed a consistent normalization effect of P. fluorescence + Foliar bio-fertilizer mixture on both processed maize and soybean growth and yield attributes. Also our findings showed that rhizobacteria microorganism improved cereal production (Khorshidi et al., 2011) unlike foliar bio-fertilizer, which would appear to have an impact on the considered plants growth process. Indeed, several studies showed that the interaction effect of rhizobacterium and different soil macronutrients (P, N) levels significantly influenced cereals plants yield feature data (Hossein et al., 2015).

 

Then, the high performance of rizhobacteria microorganism (P. fluorescence) + Foliar bio-fertilizer mixture in presently considered agricultural practice can be attributed to the interaction effect between the latter’s increasing soil macronutrient. This finding is in agreement with Alam et al. (2008) who reported that cereal plants yield attributes increased as the levels of macronutrients (P, N) increased. Further, findings showed significant variation between both maize and soybean plants yield and growth attributes for seeds inoculated with rhizobacteria microorganism alone as well as foliar bio-fertilizer. However, results suggested a high instability in processed plants yield features data as opposed to growth attributes (p-value<0.05) (Diarrassouba et al., 2015;  Noel et al., 2016). In addition, our findings showed a high susceptibility of maize and soybean cereal plants yield parameters by replying to rhizobacteria microorganisms (P. fluorescence) + Foliar bio-fertilizer mixture as well as rhizobacteria microorganisms (P. fluorescence) stimulus. These results suspected rhizobacteria microorganism activities in arid soil as improving maize and soybean crops yield attributes. In the other words, our findings linked the performance of processed plants yield attribute to the rhizobacteria microorganism’s activities on foliar bio-fertilizer. Indeed, Hernandez et al. (2010) showed that inoculation of maize cereal seed with rhizobacteria in combination with nitrogen (N), enabled increase in maize yield attribute in comparison with those obtained with the same dose of nitrogen but without inoculation with microorganisms. Also, our finding suspected plants yield parameters from seeds inoculated with rhizobacteria microorganism (P. fluorescence) + Foliar bio-fertilizer mixture and rhizobacteria microorganisms alone as a source of data variability, suggesting the consistent impact of the former’s and the latter’s on that plants yield attributes. Therefore, we showed both maize and soybean plants yield attributes as significantly influenced by P. fluorescence microorganism as well as by P. fluorescence microorganisms activities on foliar bio-fertilizer. Several studies suggested a significant variation of the effects of different levels of soil macronutrient on cereal yield parameters (Hossein et al. 2015). Based on these evidences and others (Hernandez et al, 2010), the interaction between rhizobacteria microorganism and foliar bio-fertilizer as increasing soil macronutrients can be suggested, allowing the correct development of cereal plants. Shaharoona et al. (2006) showed the efficiency of Pseudomonas increasing significantly maize plant growth and production when adequate quantity of nitrate has been provided. Previous studies showed that rhizobacteria improve maize productivity increasing the absorption of nitrate, phosphate, potassium, zinc, manganese, copper and iron (Biari et al., 2008). Our findings via an R fitting curve survey highlighted the strong normalization effect of rhizobacteria microorganism and foliar bio-fertilizer mixture on yield attributes of both analyzed maize and soybean plants. The same analysis suggested a high instability of rhizobacteria microorganism’s vis-à-vis of analyzed plants yield parameter as opposed to both (i) rhizobacteria microorganisms and foliar bio-fertilizer mixture and (ii) foliar bio-fertilizer treatments when soil standard condition referred as T₀ treatment was assumed as benchmark. Also, performed ANOVA analysis showed that, maize and soybean plants from seeds inoculated with P. fluorescens + Foliar bio-fertilizer mixture and rhizobacteria microorganism exhibited a contrast behavior among themselves as well as with respect to plants from seeds without inoculation and with seeds inoculated with foliar bio-fertilizer in term of processed plants yield attributes. Interestingly,  our   developed  multiple  linear  regression analysis suggested a selective effect of rhizobacteria microorganism and foliar bio-fertilizer on maize and soybean plants yield and growth attributes respectively. The same survey evoked a high fluctuation as regards analyzed plants dry biomass parameters for each considered seeds treatments and exhibited the combination between rhizobacteria microorganisms and foliar bio-fertilizer as a suitable practice in normalizing the relationship among considered plants yield and growth feature data (Dago et al., 2015) by improving dry biomass component, since high dry biomass value were recorded for plants from seeds inoculated by P. fluorescence + Foliar bio-fertilizer mixture. Also, findings referring to above mentioned developed multiple regression survey, linked presently analyzed cereals plants dry biomass attribute with yield component. Principal component analysis based on multiple component factors enhanced normalization and synergic aptitude of rhizobacteria activity on foliar bio-fertilizer on both maize and soybean plants yield and growth attributes. Lack of knowledge with regards to chemical composition of considered experimental earth site represented a limit to the present work, since chemical characterization of the former could help to clearly understand the interaction between rhizobacteria microorganisms (P. fluorescence) and foliar bio-fertilizer in improving maize (Z. mays. L.) and soybean (G. max) growth and yield features.

This study assessed the interaction relationship between rhizobacteria microorganism (P. fluorescence) and foliar bio-fertilizer improving maize and soybean crops dry biomass by processing a computational statistical survey. Findings exhibited rhizobacteria microorganisms (P. fluorescence) and foliar bio-fertilizer as influencing both processed plants yield and growth attributes respectively. Rhizobacteria microorganism’s activities on foliar bio-fertilizer was suspected to trigger the high proportion of dry bio-mass by normalizing the synergic relationship among analyzed maize (Z. mays. L.) and soybean (G. max) plants growth and yield parameters.