Applications of New Rhizobacteria Pseudomonas Isolates in Agroecology via Fundamental Processes Complementing Plant Growth

Pseudomonas isolates have frequently been isolated from the rhizosphere of plants, and several of them have been reported as plant growth-promoting rhizobacteria. In the present work, tomato (Solanum lycopersicum) seeds were germinated in greenhouse conditions, and the seedling height, length of plants, collar diameter and number of leaves were measured from plants grown in soil inoculated by bacterial isolates. Pseudomonas isolates were isolated from the rhizosphere. We used the Newman-Keuls test to ascertain pairwise differences. Isolates were identified as a new Pseudomonas species by rpoD gene sequencing. The results showed that isolates of Pseudomonas sp. (Q6B) increased seed germination (P = 0.01); Pseudomonas sp. (Q6B, Q14B, Q7B, Q1B and Q13B) also promoted seedling height (P = 0.01). All five isolates promoted plant length and enlarged the collar diameter (P = 0.01). Pseudomonas sp. (Q1B) also increased leaf number (P = 0.01). The investigation found that Pseudomonas isolates were able to solubilize phosphate, produce siderophores, ammonia, and indole-3-acetic acid and colonize the roots of tomato plants. This study shows that these five novel Pseudomonas sp. isolates can be effective new plant growth-promoting rhizobacteria.


Supplementary datasets
Biostatistics is a branch of applied statistics (Mathematics) to a wide range of topics in biology. It must be taught with the focus being on its various applications in scientist research. The utility of agricultural statistics is even more important. The quantitative agricultural researches, in fact, are largely based on statistical data.
In this study we have measured plant growth parameters like seeds germination, seedling height, the length of plants, collar diameter and the number of leaves in addition to mechanisms parameters such as the ability to solubilize phosphate, produce siderophore, ammonia, indole-3-acetic acid and colonize the roots of tomato plants.
To scientifically value these measurements, data analysis were subjected to one-way analyses of variance (ANOVA) using the following equations = Eq1 Where MSF is the mean square of treatments and MSE is the mean square of error.
The one-way ANOVA tests the null hypothesis (H 0 ) and compares the means between the groups and determines the difference if it exists, : Where q is the Studentized Range X A and X B are the group means being compared, and n is the number of observations per group. The calculated q statistic is compared to the critical values listed in the studentized range. Then, we will deduce the degree of difference between the tested groups. Any difference mentioned is significant at p=0.01.

Example of calculation;
To clarify the steps and equations were used, the plants height parameter was chosen as an example.
The data below resulted from measuring the plants height treated by six different treatments (including control). The treatments were replicated 10 times.  Using the F distribution table at α =0. 01, we have F 0. 01. 5,54 = 3. 377. F calculated is much larger than the F critical , so we reject the null hypothesis (H 0 ) and conclude that there is a significant difference between the treatment means. But we don't know exactly where the difference exists. In this case, Newman-keuls test has to be used.
For this test, the group means are ordered from the smallest to the largest. The test starts by evaluating the largest difference which corresponds to the difference between Q13B and the control: X ̅ 6 : X ̅ 1 N is the total number of measures and K is the number of treatments, and a parameter R, which is the number of means being tested. (N=60, K=6, df = 54, MSE = 8,62, n=10, and α=0. 01) The q observed is greater than q critical and H 0 is rejected for the largest pair. That means there is a significant difference between Q13B and the control (α=0. 01) 2 nd -Now we proceed to test the means with a range of 5, namely the differences between X ̅ 5 : X ̅ 1 and between X ̅ 6 :X ̅ 2 with α=0. 01, R = 5 and, q critical (r, df) = 4. 818 The q observed is greater than q critical and H 0 is rejected for this pair. That means there is a significant difference between Q14B and the control (α=0. 01) The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q13B and Q6B (α=0. 01) 3 rd-Now we proceed to test the means with a range of 4, namely the differences between X ̅ 4 : X ̅ 1 , X ̅ 5 :X ̅ 2 and between X ̅ 6 : X ̅ 3 with α=0. 01, R = 4 and, q critical (r, df) = 4. 594 • X ̅ 4 : X ̅ 1 K = X 4 − X 1 $GJ N = 37. 5 − 30. 8 8. 62 10 = 7,21 The q observed is greater than q critical and H 0 is rejected for this pair. That means there is a significant difference between Q1B and the control (α=0. 01) The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q14B and Q6B (α=0. 01) • X ̅ 6 : X ̅ 3 K = X 6 − X 3 $GJ N = 40. 5 − 37. 05 8. 62 10 = 3,71 The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q13B and the Q7B (α=0. 01) 4 th-Now we proceed to test the means with a range of 3, namely the differences between X ̅ 3 : X ̅ 1 , X ̅ 4 :X ̅ 2 , X ̅ 5 : X ̅ 3 and between X ̅ 6 : X ̅ 4 with α=0. 01, R = 3 and, q critical (R, df) = 4. 282 • X ̅ 3 : X ̅ 1 K = X 3 − X 1 $GJ N = 37. 05 − 30. 8 8. 62 10 = 6.73 The q observed is greater than q critical and H 0 is rejected for this pair. That means there is a significant difference between Q7B and the control (α=0. 01) • X ̅ 4 : X ̅ 2 K = X 4 − X 2 $GJ N = 37. 5 − 36. 2 8. 62 10 = 1,4 The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q1B and the Q6B (α=0. 01) • X ̅ 5 : X ̅ 3 K = X 5 − X 3 $GJ N = 38. 9 − 37. 05 8. 62 10 = 1.99 The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q14B and the Q7B (α=0. 01) • X ̅ 6 : X ̅ 4 K = X 6 − X 4 $GJ N = 40. 5 − 37. 5 8. 62 10 = 3.23 The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q13B and the Q1B (α=0. 01) 5 th-Now we proceed to test the means with a range of 3, namely the differences between X ̅ 2 : X ̅ 1 , X ̅ 3 :X ̅ 2 , X ̅ 4 : X ̅ 3 and betweenX ̅ 5 : X ̅ 4 with α=0. 01, R = 2 and, q critical (R, df) = 3. 762 The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q14B and Q1B (α=0. 01) • X ̅ 6 : X ̅ 5 K = X 6 − X 5 $GJ N = 40. 5 − 38. 9 8. 62 10 = 1. 72 The q observed is smaller than q critical and H 0 is accepted for this. That means there is not a significant difference between Q13B and Q14B (α=0. 01) The table below represents the total sum-up of the Newman-keuls test analysis. The summary of data analysis is also showed in following diagram:  very wide roles, which make them required at different levels, agricultural policy to take decision and to apply a proot.