Soil samples were analyzed with the following values: pH 7.3±0.17, EC 0.02±0.001 (dS/cm), calcium carbonate 1,7±0.15%, OM 1,65±0.14% (Table 1).
Table 1. Some Physical and Chemical Properties of the Trial Soil
The variance analysis and application averages showing the interaction difference of fertilizer applications are given in Tables 2 and 3. According to the variance analysis, the effect on parameters such as plant weight, height, diameter, root length, chlorophyll content, and leaf count was found to be significant (p < 0.01), with the C.V% values generally ranging between 20 and 30 when considered. This indicates that application errors or deviations are within acceptable limits Upon examining the application averages (Table 3), it was observed that fertilizers exhibit a significant positive difference compared to the control group. Especially, applications where the positive control is combined with PGPB and PPMs have been found to yield better results compared to other groups. This indicates the active role of bacteria in the PGPB and PPMs groups in making the nutrients in the fertilizer beneficial to the plant. Similar results were also obtained with combined applications involving microalgae-containing fertilizers.
In conclusion, applying these treatments along with chemical fertilizers in lettuce plants has improved plant photosynthetic efficiency, resulting in better plant yield and quality.
Table 2. Analysis of Variance (ANOVA)
Table 3. Application Averages on Plant Growth Parameters
Path analysis, presented in Table 4, examines the effects of factors considered as dependent variables on plant weight. Path analysis is concerned with estimating the magnitude of connections between variables and providing information about the underlying causal processes (Simons, Conger, & Whitbeck, 1988). Upon examining Table 4, significant correlations at the 5% - 1% level were found among all the examined factors, except for the correlations between chlorophyll/plant weight and root length/plant weight.
When discussing the correlations between plant weight and other factors, it is observed that the highest correlation with plant weight is leaf count. According to path analysis, while the direct effect of plant height on plant weight is 11.3%, the highest indirect effect is between leaf count (35.946%) and root length (42.95%) with a total indirect effect of 78.896%. For plant diameter, the direct effect on plant weight is 5.63%, while the highest indirect effects are through leaf count (43.78%) and root length (38.035%) with a total indirect effect of 87.415%. The direct effect of leaf count on plant weight is 57.61%, while the highest indirect effect is through root length (0.85%) with a total indirect effect of 39.85%. The effect of chlorophyll content on plant weight is 39.93%, with the highest indirect effect through leaf count (40.639%) and root length (39.235%). The direct effect of root length on plant weight is 44.552%, with the highest indirect effect through chlorophyll content (39.235%).
Path analysis shows that the marketable value of the plant weight is mainly shaped under the influence of leaf count and root length. The vegetative weight, which expresses plant yield, is the result of a complex chain of activities involving all these factors. However, some factors are more significant than others, as evidenced by their importance relative to other factors (Saleh & environment, 2012). Accordingly, in our study, leaf count and root length have been shown to be the most influential factors affecting plant weight. Leaves are crucial organs containing metabolic activities involving both catabolic and anabolic processes, and they play a key role in photosynthetic activities and dry matter production. Similarly, roots are the most important part of the plant as they transport water and nutrients to the plant. As root length increases, the plant's attachment to the soil and uptake of water and nutrients also increase (Lai, Wang, Peng, & Chen, 2011). The application of PGPB, PPMs, and microalgae in conjunction with chemical fertilizers in our study positively affected leaf count and root length, the most influential factors affecting plant weight, thus, this combination can be safely used to increase plant yield.
Table 4. Path Analysis
Principal component analysis can be summarized as a graphical method that compresses data obtained through classification, making it more understandable by reducing dimensions(ROTARU, POP, VATCĂ, CIOBAN, & Horticulture, 2012).The aim of this technique is to examine multidimensional data and express them with fewer variables based on fundamental characteristics (Eriksson, Byrne, Johansson, Trygg, & Vikström, 2013). Principal component analysis is widely used in agriculture to reliably demonstrate the relationship and performance of applied factors and examined elements (Granato et al., 2018). The factors examined in our study and their relationship with each other are shown in Figure 1. As seen in Figure 1, the examined elements and applied factors have formed three different groups.
- Group: Microalgae, Microalgae + positive control, PGPB + Positive control, leaf count, plant height, plant diameter, chlorophyll, and root weight.
- Group: Plant weight, PPMs, and PPMs + Positive Control.
- Group: Control, Positive Control, and PGPB.
Although the applications PPMs and PPMs + Positive Control in Group 2 were identified as the most influential applications on plant weight, they only manifest themselves when the plant is healthy and when the maximum benefit from fertilizer is desired. In other words, the effectiveness of plant probiotics can be more possible when the plant is well-developed and healthy. In the first group mentioned (Microalgae, Microalgae + Positive Control, PGPB + Positive Control), the factors affecting plant weight, such as root length, chlorophyll count, plant diameter, plant height, and leaf count, are included in the same group, and this group has been identified as the most effective group on plant growth and yield. In other words, the most effective applications on plant weight are Microalgae, Microalgae + Positive Control, and PGPB + Positive Control. The individual applications of Positive Control and PGPB did not show the expected effect on plant growth. In conclusion, Microalgae, Microalgae + Positive Control, PGPB + Positive Control, PPMs + Positive Control, and PPMs can be recommended as the most effective groups for plant performance. The effectiveness of chemical fertilizers in use is known to be around 5-10% in the soil.
Figure 1. PCA Analysis
Decision tree analysis is a method commonly used in social sciences but can also be applied in agricultural and natural sciences. The primary purpose of this analysis is to determine which main factors have the most influence on the dependent variable and how other factors are shaped by the scarcity or abundance of this main factor, ultimately revealing the numeric value of the dependent variable (Rajeswari, Suthendran, & Agriculture, 2019). The tree diagram illustrating the analysis of data obtained from the research is presented in Figure 2.
In the analysis, it was determined that the main factor affecting the dependent variable, which is plant weight, is the leaf count. As the leaf count increases, the plant weight also increases. When the leaf count is less than 45, the plant weight is determined to be 166.743 grams, while it can reach an estimated plant weight of 320.4 grams when the leaf count exceeds 45. When the leaf count is less than 45, root weight comes into play as the main factor. As root length increases, so does plant weight. When the root length is between 3-4 cm, the estimated plant weight is 116.00 grams. Similarly, when the root length is between 3-4 cm, the main factor affecting plant weight is plant diameter. When the plant diameter is less than 16 cm, the estimated plant weight is 105.250 grams, whereas it is predicted to be 137.5 grams when the plant diameter exceeds 16 cm.
In cases where the root length is 5,6,8 and 10-13 cm, chlorophyll amount emerges as the main factor. When the chlorophyll amount decreases below 163 SPAD, the estimated plant weight is 143.843 grams, while it is 209 grams when the chlorophyll amount is between 163-177, and 147.22 grams when the chlorophyll amount exceeds 177 SPAD. It is determined that the SPAD range of 163-177 provides the highest plant weight.
When the root length is between 7 and 9 cm, the leaf count emerges as the main factor, and as the leaf count increases up to a certain point, the estimated plant weight also increases. Based on the variation in leaf count, the estimated plant weight ranges from 151 grams when the leaf count is less than 35 to 265 grams when it exceeds 43.
As a result, the main factor affecting plant weight is the leaf count, followed by root length. Additionally, plant diameter and chlorophyll are identified as significant factors depending on the increase or decrease of the two main factors. The conditions for achieving the highest plant yield are determined to be a leaf count between 40-43, root length between 7-9 cm, a relatively large plant diameter, and chlorophyll levels between 163-177 SPAD values.
Figure 2. Decision Tree Analysis
There are many reviews offering an overview of the vast information about microorganisms, and specially bacteria, that are plant growth promoters and biocontrol agents, and also reviewing the mechanisms associated with these particular functions, as well as more publications showing and/or reviewing the benefits to plants, such as the enhancement of bioactive compounds in the edible parts, among others (Gouda & Saranga, 2018) (Etesami, Maheshwari, & safety, 2018; Menendez & Garcia-Fraile, 2017) (Chiboub, Jebara, Abid, & Jebara, 2020; Santoyo, Moreno-Hagelsieb, del Carmen Orozco-Mosqueda, & Glick, 2016).
Briefly, PPBs have features involved in the
(i) facilitation of nutrient acquisition,
(ii) production of phytohormones and modulation of their levels,
(iii) tolerance to either abiotic or biotic stresses,
(iv) production of siderophores and other metabolites, and
(v) induction of disease resistance, among
other properties not listed here.
The use of plant probiotic bacteria as inoculants has the potential to increase crop yield without the overapplication of chemical fertilizers, pesticides, and fungicides, and consequently to reduce the environmental impact in agriculture and maximize the production of heathier and safer foods. PPB establish synergies and act complementarily when forming a consortium, providing beneficial effects to crops. Although a high number of these associations are well-documented and most of them show positive results, there is still a lack of knowledge on how these consortia behave and interact with plants, environments, and other the microbiome. The microbes that live in and on the plant microbiome are critical for plant health and exert their influence by facilitating the nutrient acquisition, regulating plant hormone levels, and helping to withstand pathogen attack. Plants are meta-organisms that are associated with complex microbiomes. The majority of the microorgansims including epiphytes and endophytes generally play a significant role in providing essential nutrients to the plants where they live.