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Evaluation of Organic Substrates and Microorganisms as Bio-Fertilisation Tool in Container Crop Production

Jose Luis Ruiz
1 and
Maria del Carmen Salas
Doctoral Program in Protected Agriculture, Almeria University, 04120 Almeria, Spain
Department of Agronomy, Almeria University, Campus de Excelencia Internacional Agroalimentario, ceiA3, La Cañada, 04120 Almeria, Spain
Author to whom correspondence should be addressed.
Agronomy 2019, 9(11), 705;
Submission received: 11 October 2019 / Revised: 28 October 2019 / Accepted: 28 October 2019 / Published: 1 November 2019


Microorganisms are only effective when adequate conditions for their survival and development are provided. Among the factors that influence its effectiveness includes the type of soil or culture substrate, which works as an energy source reserve. Therefore, a tomato and a melon crop were established in different cycles to assess the effect of the physicochemical properties of organic substrates based on coconut fibre and vermicompost in three proportions, 0:100, 40:60 and 60:40 (% v:v), on the microbial activity in the rhizosphere when the bacteria Azotobacter vinelandii, Bacillus megaterium and Frateuria aurantia were applied. Concentrations of NO3, H2PO4, K+ and Ca2+ in the petiole cellular extract (PCE) were quantified at 60, 90 and 120 days after transplantation (DAT) for tomato and 45 and 65 DAT for melon. We analysed dehydrogenase activity (DHA), acid phosphatase activity (FTA) and β-glucosidase activity (β-GLU). In order to maintain optimal volumetric moisture for the survival of microorganisms, automatic control was used to manage the irrigation frequency between 22%–28%. The results showed that physicochemical substrate properties, by incorporating 40% vermicompost into the coconut fibre mixture, increased enzymatic activity. Plants that were inoculated with Azotobacter vinelandii and Frateuria aurantia showed an improvement in NO3 and K+ assimilation achieving highest yields.

1. Introduction

Bio-fertilisation practices with plant growth-promoting bacteria (PGPB) have increased considerably in the last decade due to the implementation of changes in the processes of current agriculture. The promoter effect of PGPB occurs because they are an integral part of the biological cycle of nutrients, facilitating their availability for plants in ionic forms. This effect depends on the origin of the organic material, as well as the process to which it has previously been subjected. As described, it is necessary to characterise organic materials to determine their ability to influence microbial activity and processes of mineralisation and/or solubilisation of nutrients, which is a fundamental factor in maintaining sustainability and productivity [1]. One of the most used organic substrates in intensive horticulture is coconut fibre (CF), which is obtained from the extraction of the mesocarp fibres from the fruit, and its physicochemical properties make it a very stable material that can be reused for several consecutive growing cycles. An alternative substrate to CF that can be used in organic horticulture is vermicompost (VC), which has been studied as a substrate for container cultivation and as a source of nutrients, resulting in increases in the production of various plant species and reduced use of mineral fertilisers [2,3]. Research has confirmed that VC modifies the soil structure by improving aeration, drainage, water retention capacity, nutrient supply, organic matter and microorganisms [4,5]. Therefore, it is crucial to determine the physicochemical and biological properties of substrates that have a direct effect on the survival and activity of microbial populations and that allow their plant growth-promoting abilities to be triggered. In turn, the implementation of microorganisms in agriculture forces the issue of having greater control to ensure the survival and bio-fertilising capacity of PGPB once they are inoculated into the rhizosphere of crops.
Analysis of physicochemical properties of soils/substrates has commonly been used to measure their production potential. However, when working with microorganisms, such as bio-fertilisers, it is necessary to analyse the activity of soil-substrate enzymes since they are direct indicators of the quality of organic material [6]. Enzymatic activity is also considered indicative of changes in soil quality that occur as a result of management practices and the biotic and abiotic factors to which it is subjected and is used to monitor microbial activity [7]. Enzymes are also indispensable in soil function because they play a vital role in organic matter decomposition and nutrient cycle transformation [8,9].
There are different enzymes in the soil, and each catalyses a specific reaction so that the analysis of a single enzyme can be inaccurate [10]. Enzymes involved in the transformation of organic materials are divided into hydrolases and oxidases and are the most studied when assessing soil quality [6]. Among the most common enzymes used to characterise microbial activity are dehydrogenase, amidase, protease, cellulase, glucosidase, urease and phosphatase [6].
The dehydrogenase activity allows, in a global way, knowledge of the microbial processes that occur in the soil as it is only present in living systems, and is used as an indicator of the oxidation rate of organic matter when transferring hydrogen from organic substrates to inorganic acceptors [11]. Phosphatases are a group of enzymes that are produced by certain microorganisms and plant roots, which participate in the biological cycle of phosphorus, mineralising it from organic soil phosphates. The enzyme β-glucosidase is an integral part of the carbon cycle and acts in the last phase of cellulose degradation. It is the most analysed enzyme in the assessment of soil quality [12]. The final product of this reaction is glucose, which is an important source of carbon for microorganisms [13].
The application of VC to soil together with microorganisms has proven to be an efficient practice in crop nutrition and production [14], although the variability in the results is high due to the conditions of each type of soil or substrate. Considering the previous context, the objective of this research was to assess the effect of the physicochemical properties of three substrates according to the VC and CF ratio, on the microbial activity of three PGPB species inoculated into two horticultural crops in containers and assess if there is an improvement in the bio-fertilising capacity of the organic solution as part of the substrate-PGPB set.

2. Materials and Methods

Two independent experiments were conducted to assess the effect of PGPB inoculation on three types of organic substrates according to the ratio of vegetable vermicompost to horticultural waste (VC) incorporated into the CF mixture. The PGPBs’ used in this study were: A. vinelandii (AV), B. megaterium (BM) and F. aurantia (FA), at a dose of 108 colony forming units (CFU) per plant [15]. Three applications were performed per culture, the first at the time of transplant and the next two at intervals of 30 days. A control treatment (C) was established, without inoculation of bacteria. Twenty-four plants were established per treatment, distributed in random blocks with three replications. The experiments were conducted at the experimental farm of the UAL-Anecoop Foundation, located in south-eastern Spain (36.861905–2.282529), in an area of 900 m−2 using a greenhouse with a plastic cover and automated ventilation by zenithal windows. In the first experiment, a tomato crop (Solanum lycopersicum cv Ramylee) was established at a density of 1.25 plants m−2 in polystyrene containers (27 L), with a transplant date in September 2016 and end of cultivation in March 2017. As substrates, proportional mixtures of VC (Organic matter (OM) 17.20% on dry matter (dm), organic nitrogen (Norg) 1.11%, C:N 7.70] and CF (OM 73.1% dm, Norg 0.89%, C:N 41) were made.
Treatments were established according to the type of PGPB and the proportion of VC and CF (% v:v) 0:100, 40:60 and 60:40, respectively (Table 1). The substrates were reused for the second crop. For the second experiment, a melon crop (Cucumis melo. var. Brisa) was established in March 2017 on the same substrate and containers of the first experiment with tomatoes, at a density of 1 plants m−2. In both crops, pest control was carried out through the release of natural predators in accordance with the principles and practices of organic farming. As a nutrient solution (NS), we used an aqueous extract of vermicompost tea of vegetable waste aerated between 12 and 16 days (NO3, 6.89; NH4+, 0.18; H2PO4, 0.39; K+, 5.11; Ca2+, 3.81; Mg2+, 0.92).
In order to have a constant supply of NS during the crop cycle, five containers of 1000 L each, with automated air injection, were available for the elaboration of aqueous extract to be applied by fertigation. To determine the irrigation demand based on the type of substrate, automatic control by dielectric probes was used to manage the irrigation frequency needed to maintain volumetric moisture of 22%–28%, according to the stage of the crop, and maintain optimal moisture for the survival of microorganisms in the substrate [16]. For irrigation, we used 4 L h−1 drippers. The electric conductivity, pH, irrigation and drainage volume were also quantified as measures of fertigation control in soilless cultivation.

2.1. Physicochemical Properties of Substrates

Tests were performed before transplantation of the first crop (tomato) and at the end of the second growing cycle. For each substrate mixture, the OM and Norg contents were determined according to UNE-EN 13039:2012 and UNE-EN13654:2002, respectively. For physical indicators, total porosity (TP), bulk density (BD), air volume percentage (AVP) and readily available water (RAW) were analysed using the methodology established in UNE-EN 13041:2012.

2.2. Production

We randomly selected nine tomato plants and nine melon plants, considering three repetitions per treatment and culture. Production (kg m−2) refers only to fruits considered to be commercial in quality.

Microbial Activity in the Substrate

We analysed dehydrogenase activity (DHA), acid phosphatase activity (FTA) and β-glucosidase activity (β-GLU), which are crucial enzymes in biological and biochemical processes, such as those that occur during the degradation of OM and the mineralisation of nutrients [17,18]. Each sample was isolated from the root zone of the crops, 10 cm deep. Enzyme tests were performed at 60, 90 and 120 days after transplantation (DAT) for tomato and 40, 60 and 80 DAT for melon crops.
DHA allows quantification of the metabolic activity of viable microorganisms in the substrate and their oxidative capacity [19]. This was calculated based on the amount of triphenylformazan (TPF) formed when the microorganisms in the substrate reduced triphenyl tetrazole chloride [20]. The activity of FTA is an indicator of phosphorus mineralisation in soils, while the β-GLU indicates the degradation of OM in the form of cellulose [21], a very important soil-substrate quality index being the most common polysaccharide in nature [22]. The levels of these enzymes were quantified by the concentration of P-nitrophenol (PNP) G−1 soil−1 H−1 [23].

2.3. Nutritional Crop Status

Nutritional diagnosis, by analysing the petiole cellular extract (PCE), is a method that allows knowledge of the ranges of nutrient concentrations assimilated by a crop according to the physiological stage of the plant species, and determination of possible deficiencies or excesses of essential nutrients, which allows modification of nutrition management or correlation of nutrient concentrations to the specific factors involved in a crop’s production [24]. Fully developed leaf petiole samples were taken, extracted by mechanical pressing and analysed in the laboratory by potentiometry using a modular probe of specific ions IMA CIMUS (Nt Sensors) and a colourimetry method for phosphates [25]. In this way, concentrations of NO3, NH4+, H2PO4, K+, Ca2+, Mg2+, Cl and Na+ were quantified at 60, 90 and 120 DAT for tomato and 45 and 65 DAT for melon crops.

2.4. Statistical Analysis

We used a multifactorial analysis to assess the type of PGPB applied, the substrate, as well as the time of sampling during experiments. Variance analysis (ANOVA) was chosen for statistical analysis of the evaluated parameters. A 95% confidence interval was applied in multi-range tests to determine the significance level, using Fisher’s comparison test of means with the statistically least significant difference (LSD). To explore the links between the physicochemical properties of substrates with enzymatic activity and crop production, Pearson’s correlations were made. Principal component analysis (PCA) was carried out to determine the variability, correlations and synergisms among the different variables. The entire statistical analysis was performed with STATGRAPHICS Centurion XVIII software.

3. Results and Discussion

3.1. Physicochemical Properties of Substrates

The physical and chemical characteristics of the three substrate mixtures show variations when comparing their values before and after the crops. The process of mineralisation of N begins from the Norg content that is transformed by ammonification and nitrification processes to mineral N [5]. Table 2 shows a decrease in the Norg content in the final properties of the three substrate mixtures, possibly due to microorganism mineralisation. The OM percentage was directly related to the CF in the substrates because it contains three to four times more OM than the VC used in this analysis. The OM values were increased, as an effect constant fertigation with vermicompost tea throughout the experiment that represented a frequent addition of OM at a higher rate than degradation. The effect on the substrate 0:100 being more remarkable, which showed an increase of 12% making it a stable substrate [26], which is observed with its C/N ratio much higher than other substrates. Another factor related to the increase in OM at the end was the reuse of the substrates since the second melon culture was transplanted into the same substrate used for tomato cultivation while preserving the previous root system.
The C/N ratio is an indicator in OM degradation processes as it is used as an indicator of quality and stability of a material derived from composting processes. The VC is a very stable material, so its C/N ratio is not so modified over time because its material has previously undergone degradation. Studies mention that compost can be of quality when its initial C/N is <16, while over 20 generates N immobilization [27].
In substrates with 40% and 60% VC, the C/N ratio remained below 10, being suitable for mineralising N [28]. Other authors suggest that the optimal C/N ratio for microbial activity in organic materials should be <20 [29], in addition to the high influence of biological N fixation with a decrease in the C/N ratio by up to 16% [30]. RAW is related to soil aeration and its ability to use pore space to be occupied by water and benefit microbial populations in soils [31].
The air volume percentage (AVP) was calculated based on the volume of air retained at 10 cm suction. In this sense, a high correlation between AVP and TP was observed (Table 3). Total porous space is defined as the total volume of the substrate not occupied by organic particles or minerals. The highest values for TP and AVP were obtained in the substrate with coconut fibre, demonstrating the influence of OM on the properties of TP and AVP by increasing the size of the porosities in addition to adding OM, which reduces the compaction effect.
Similarly, the three substrates of the experiment were above 80%, optimal for horticultural crops [32]. Optimal AVP ranges are from 20–30 of volume [33], a range obtained only by substrates 0:100 and 40:60, while the 60:40 mixture was below the recommended lower limit.
BD refers to the relationship between mass and volume under certain conditions. No changes were observed over time on any of the substrates, with very similar values at the beginning and end of the experiment (Table 2). In general, the results demonstrated that the BD for these organic substrates was related to the amount of VC, with a negative correlation with the rest of the properties analysed with the exception of Norg, associated with the VC (Table 3).

3.2. Effect of Substrate and PGPB on Yield

Soil microbial populations are directly related to soil biochemical processes and are crucial in soil functioning, as they play a vital role in the decomposition and transformation of OM in the biological nutrient cycle [9,34]. The type of substrate, according to the mixture and the type of microorganism, had a significant effect on yield. From the results obtained (Table 4), the substrates 40:60 and 60:40 (VC:FC) recorded the highest yield (kg m−2) in the tomato growing cycle with significant differences (p < 0.05) compared to 0:100, while in the melon crop, only 40:60 [35] mentioned a significant increase in NO3 and NH4+ in soil when vermicompost was applied, in addition to increasing the populations of microorganisms in the soil and improving N fixation capacity by microorganisms in the rhizosphere. In the tomato crop, the highest yield was obtained by Azotobacter vinelandii inoculation in the 40:60 substrate, achieving increases of 0.84 kg m−2 in relation to the control treatment. In this way, when fertigated in a soilless culture with organic solutions of aqueous extracts of VC from plant waste, an improvement in the bio-fertilising effect by AV and organic materials as a substrate was shown.
In the tomato crop, the 0:100 substrate mixture recorded the lowest yield, besides that there were no statistical differences between treatments, coinciding with the lower levels of enzymatic activity, highlighting the limited ability of this substrate for use in combination with PGPB as analysed under the conditions of this experiment.
In the melon crop, treatments with PGPB application also showed considerably higher production. Higher yields are the result of an improvement in enzymatic activity in the culture medium with successive inoculations over time. The 40:60 substrate, in combination with FA, obtained the largest increases in yield at 6.23 kg m−2. These increases in yield showed the bio-fertiliser effect of applying FA, which can be seen as an improvement of the nutritional status of the crop in the PCE analysis.

3.3. Microbial Activity

An enzymatic activity represents an indicator of the biological transformation of the nutrient cycle and soil quality [7]. Table 5 shows the results of DHA, FTA and β-GLU based on the PGPB and substrate mixture for each plant species. Regarding dehydrogenase activity (DHA) (Table 5), it was observed that a greater amount of VC in the substrate increases the concentrations of triphenylformazan (TPF), due in large part to the high bacterial load and chemical composition of VC that improve crop production [36,37].
In tomato cultivation, the average values of DHA activity for substrates 60:40 and 40:60 were 152% and 146% higher than 0:100. This showed that the activity of DHA was largely related to the physicochemical and biological properties of substrates or soils. AV and FA treatments recorded the highest levels of this enzyme in 60:40 and 40:60 substrate mixtures, respectively, being statistically significant. These results coincided with the higher production yields and concentration of NO3 in the PCE.
In tomato crop, the effect of increase DHA was seen when using AV in 60:40 and FA in 40:60, while in melon were recorded with AV in 0:100 and FA with 0:100 and 60:40 mixture, which shows that applying PGPB promotes an increase of the metabolic activity of the substrate [38]. DHA is proportional to the biomass of microorganisms in the soil, so it is only present in living systems [39]. This experiment found a positive correlation between the proportion of VC in the substrate mixture and enzymatic activity. The correlation rate for DHA with % VC was 0.88 (p < 0.05) and 0.68 (p < 0.05) for the tomato and melon experiments, respectively.
Similar behaviour was found in melon cultivation among substrates, highlighting 40:60 with an average value of 431 g TPF g−1 dm 24 h−1, followed by 60:40 with 321 g TPF g−1 dm 24h−1. Among PGPB, the maximum values were obtained with FA and AV at 40:60, although without statistical differences between the rest of the treatments for that substrate. DHA showed a constant increase over time, achieving the highest levels in the second crop cycle (Figure 1).
In a study presented by [1], they analysed the effect of time on enzymatic activities for 13 crops over five years, finding constant increases during the first nine crops. In another trial by [3], the highest levels of TPF derived from DHA were obtained in the second year of cultivation after four crops were established on the same substrate. In this research, the maximum values were reached during the final stage of melon cultivation with a steady increase in all treatments possibly due to the increase in ambient temperature for the second growing cycle, which was performed during the onset of spring season with an increase from the previous crop, as DHA is influenced by environmental temperature [40].
Regarding the activity of the phosphatase enzyme (FTA), significant differences were found in the treatments with PGPB with respect to the control only in the 40:60 substrate in both crops.
In 0:100 and 60:40, no effect was observed when applying PGPB (Table 5). The FTA is usually influenced by a large number of factors, so its interpretation depends on the analysis of other variables, such as the properties of the substrate, in addition to the FTA also produced by the plant roots [41].
The values of our experiment coincide with the ranges presented by [42] (55.6 to 4017 µg PNP g−1 h−1) and [12] (324 to 2191 µg PNP g−1 h−1), although well below those presented by [43] (10.4–307 µmol g−1 h−1). No relationship was observed between the activity of this enzyme and treatment with BM bacteria to which phosphate solubilisation effects are attributed. Ref [44] mentioned that interactions between microorganisms and plant species affect FTA, which is usually higher in the rhizosphere through the activity of root exudates that stimulate bacterial activity, and that it depends on the type of exudate. The factor that most influenced FTA activity was the type of substrate when incorporating VC, where we obtained a correlation rate of 0.86 (p < 0.05) for tomato and 0.95 (p < 0.05) for melon crops.
Glucosidase is a very common enzyme found in soils, which is used as a biochemical indicator to measure processes of degradation and stabilisation of OM [45] as it is involved in the carbon cycle [39]. In this context, it is necessary to know the activity of this enzyme to determine the stability of the substrate since it is proven that β-GLU hydrolyses cellulose into glucose [46]. Table 5 presents the mean values of β-GLU activity where statistically significant differences between the type of PGPB and substrate mixture are shown. Greater activity was observed on substrates when incorporating VC, being higher in the substrate 60:40 in all cases.
As in the rest of the enzyme activities analysed, a correlation was found between the VC ratio and β-GLU, with 0.87 for tomato and 0.88 for melon. In any case, the higher values of β-GLU were associated with VC substrates, and in the two crops, these increments, in turn, had a negative correlation with the AVP and TP. These properties decreased with an increase of microbial activity, behaviour that should be considered as they are important physical properties for materials that are used as substrates in soilless cultures.

3.4. Pearson Correlations between Microbial Activity and Yield

The Pearson correlation rate between the yield of each crop and the three enzymatic activity parameters showed a positive correlation with statistically significant differences (p < 0.05) in all cases. The increase in production was highly correlated with all enzymatic activities, coinciding with the results presented by [1], where they mentioned a positive correlation between soil enzyme activity and yield in a tomato crop in a greenhouse. As described above, the chemical composition of VC improves microbial activity, which promotes enzyme synthesis and provides nutrients in assimilable forms, while CF is typically used for its suitability and the stability of its properties for the production of vegetables in containers and not as a source of nutrients.
DHA reveals the overall metabolic activity of all three substrate mixtures. The detected concentrations increased over time, which according to [47] might be due to a higher rate of mineralisation of OM coinciding with a decrease in the C/N ratio and the Norg content in the substrates at the end of the experiment. The total correlations between enzymatic activities and production were statistically significant (p < 0.05). In the tomato crop, correlations between yield and enzymatic activity were 0.76, 0.83 and 0.76 for DHA, FTA and β-GLU, respectively; while in the melon crop, the correlation rates for the same enzymes were 0.94, 0.77 and 0.75, respectively (Table 6).
The yield increase in DHA and β-GLU is largely due to the increased amount of nutrients in mineral forms derived from biological processes of fixation, solubilisation and mineralisation of the organic forms present in the substrates, coinciding with [3], who mentioned that substrates with higher vermicompost ratios contain more anions and cations forms compared than coconut fibre as substrate. On the other hand, [8] mentioned that the higher enzymatic activity could be explained in terms of a larger microbial population, which in addition to fixing atmospheric N and mineralisation and solubilisation of essential nutrients, metabolises amino acids, sugars, acids phytohormones and other compounds from the roots of plants and soil OM.

3.5. Pearson Correlations between Microbial Activity and Substrate Properties

Enzyme activity correlates strongly with soil physicochemical parameters. Studies mentioned that soil properties influenced the level of response of enzymatic activities being, in many cases, strongly correlated [48,49] with the pH, OM and total N [46]. In the correlation analysis (Table 7), we found an inverse relationship between OM content and enzymatic activity, largely due to the high amount of OM contained in the CF, and a positive correlation with the initial and final Norg content.
It is found that very low relationships between the effect of nutrients available in the soil on the enzyme rate [47]. They analysed the effect of applying mineral nitrogen and phosphorus to the soil and found no clear effect on enzymatic activity when applying fertilisers and so concluded that the greatest influence is given by the physical-chemical properties of the soil. In other experiments, it was shown that applying mineral sources of fertilisation decreased the activity of FTA and β-GLU while incorporating organic materials, such as compost and VC, significantly increased FTA activity [44], β-GLU [50] and DHA [38], coinciding with the results of this experiment.
Regarding the C/N ratio, an inverse correlation with enzymatic activity was found, as the substrate 100% CF had the highest C/N ratio and was the one that recorded the lowest enzyme values in this experiment. This relationship indicates the degree of stability of the OM and decreases with its degradation. The results for all substrates showed a decrease in C/N, indicating mineralisation processes, being an important factor to consider when working with organic materials to avoid N immobilisation problems in immature materials [33].
In the case of RAW, a direct correlation between enzymatic activity and initial RAW, and inverse with final RAW, i.e., enzymatic activity was constantly increasing during the two cultures while RAW decreased during the second crop, was found.
TP and AVP had an inverse relationship to enzymatic activity in general, derived from the fact that these properties are attributed to a greater 0:100, which had no significant changes in these properties during the two crop cycles, confirming the good stability of CF in terms of degradation over time.
To analyse the contribution of each factor to the total variability of treatments, the analysis of the main components (PCA) was performed. A small number of linear combinations of the total variables was obtained that explained the greatest variability in the data. The criterion for determining the number of major components was to consider those groups with an eigenvalue >1. Figure 2 shows the influence of the factors on the total variability of the analysed parameters, which were grouped by weights of each component in the tomato crop. In this case, two components were extracted that together explain 96.71% of the variability in the original data.
Within component 1, DHA, FTA, β-GLU, RAW I, BD I, BD F and Norg F were grouped together, which shows a correlation or synergism between them and explains more than 90% of the variance, while in component 2, the rest of the substrate properties were integrated and represent 6.43% of the variance. These data showed that the activity of β-GLU and DHA were related to soil moisture [38,51]. In addition, it was found that β-GLU increased considerably by providing OM with a low C/N ratio [6]. The results matched those of [46], confirming that physicochemical properties influence the response of enzymatic activity by finding a high correlation between soil N content and enzymatic activities. In the PCA results for the melon crop, two main components were identified with an eigenvalue >1, which together explain more than 98% of the variability in the original data, where component 1 explains almost 91% of the total variance, while the second component influences 7.51% of the total (Figure 3). Enzymatic activities with the highest percentage weight were grouped into the first component.
A strong correlation between Norg and DHA was observed (r = 0.96, p < 0.5). The results concur with those of [52] who found a high correlation between moisture content with β-GLU and FTA, which was observed in the component graphic (Figure 2). It is clear that there is a relationship between the type of OM and DHA, but it is necessary to highlight that not only is the amount of OM in the soil important but especially its quality, since the OM affects the energy supply for microbial growth and enzyme production [53]. AVP and TP were not determining factors for DHA as the aeration of substrates was not limited; on the contrary, it has been found that DHA activity is increased under conditions of limited O2 and even in flood situation anaerobiosis [40].

3.6. Crop Nutritional Status

The elemental macronutrient content in the PCE was analysed according to the PGPB bacteria, substrate mixture and growth phase, finding significant statistical differences (p < 0.05) between treatments. The average concentration for NO3 (mg L−1) in decreasing order was 830, 772 and 653 for substrate mixtures 60:40, 40:60 and 0:100, respectively, with maximum values at 90 DAT. PGPB showed an improvement in the assimilation of NO3 in plants that were inoculated with N-fixative bacteria, Azotobacter vinelandii (AV) and with the K+ solubiliser, Frateuria Aurantia (FA). The effect of these bacteria was most clearly observed in VC substrates, where NO3 concentrations were found to be around 20% more than in control. These values were within the reference levels proposed by [54]; however, they were lower than those reported by [24] who mentioned a range from 700 to 1200 ppm NO3 for a tomato crop with mineral nutrition in a greenhouse. In melon, a similar behaviour to tomato was observed with substrates 60:40 and 40:60, which had a higher Norg content to be mineralised by PGPB for plant disposal. The bacteria FA (1006 mg L−1 NO3) and AV (929 mg L−1 NO3) were highlighted in ranges suitable for melon [24]. A high correlation was observed between the concentration of NO3 in PCE and the yield of the tomato crop (r = 0.77, p = 0.0031) and melon (r = 0.59, p = 0.0416), as this is an essential element in horticultural nutrition.
Regarding H2PO4, higher concentrations in PCE were detected in the plants established in the 60:40 (VC:CF) substrate in all samplings. Among bacteria, BM stood out by increasing the phosphate concentration in the PCE by 19% compared to the control in the global average.
Table 5, of the enzyme results, shows that substrate 0:100 (VC:FC) had the lowest FTA, linked to phosphate mineralisation processes, as well as the lower concentration in PCE, and that by its very nature, this substrate did not have a positive effect on PGPBs as there were no statistical differences from the control (Table 8). The maximum average value obtained by PGPB was 292 mg L−1 for BM, followed by FA (278 mg L−1) and AV (267 mg L−1), which are also associated with inorganic phosphate solubilisation capabilities [55], while the control recorded the lowest phosphate value of 245 mg L−1 in PCE. The solubilising capacity of phosphates by BM in substrates with VC was observed, by improving the nutritional status of the plants in the analyses at 90 and 120 DAT.
A significant increase in H2PO4 concentrations was also observed in the melon culture in PCE when applying BM and FA in substrate 40:60 (VC:FC) at 50 and 80 DAT and AV in 40:60 and 60:40 at 80 DAT (Table 9). This stands out as, in the tomato crop, the BM treatment was superior to the rest of treatments with a statistical significance (p = 0.05). In Figure 4, the correlation rate between the phosphate content in PCE and yield is presented, with a positive correlation observed only in the second crop.
Regarding the concentration of K+ in PCE (Table 9), only in 0:100, we saw statistical differences in the effect of applying PGPB to substrates. The greatest variation was positively correlated with the percentage of VC in the substrate, as it is a substrate with a high K+ content [16]. In the tomato crop, the average concentrations for the substrates were 2633, 3214 and 3055 mg L−1 for (VC:FC) 0:100, 40:60 and 60:40, respectively. Substrates with %VC of 40 and 60 achieved acceptable K+ levels in PCE. In the melon crop, values of 3180, 3779 and 3912 mg L−1 (0, 40, 60% VC) were recorded; values very close to the lower limit of K+ in PCE [24]. In both cases, there was no improvement in the concentration of K+ with FA, associated with K+ solubilisation processes in soils [56].
In the case of Ca2+ differences in PCE, the content was mostly associated with the substrate mixture than the bacteria. No clear effect was observed when applying PGPB, as it did not show significant differences from the control. However, the recorded levels were suitable for the recommendations for tomatoes. In addition, there was no Ca2+ deficiency due to the fertigation with VC tea, so it was not a limitation. On the contrary, the values of Ca2+ and K+ showed a high correlation with production (Figure 4).

4. Conclusions

According to the physical and chemical properties of the substrate mixtures used in the experiment, the initial Norg content presents the highest positive correlation with microbial activity, which is also related to the nutritional status of the crop yield.
The highest yield was obtained in tomato and melon plants when VC was used in the substrate mixture (40:60 and 60:40% VC:FC). The increases in production were positively correlated with the enzymatic activity of the substrate and were reflected in an improvement in the nutritional status of the crops, with higher concentrations of NO3 and K+ in the cellular extract of the petioles.
There was a positive correlation between DHA, FTA and β-GLU activity with the RAW, BD and Norg of the substrate, which was given by the ratio of VC.
The results of this research showed that the potential of the bio-fertilisation effect by PGPB to improve crop nutrition and productivity is in function with the physical-chemical properties of the substrate used as a growing medium.
The highest production, with significant statistical differences, was obtained in tomato and melon plants grown on substrates 40:60 and 60:40 (VC:FC), inoculated with AV and FA bacteria. BM bacteria did not affect production, however, presents the highest concentrations of H2PO4 in PCE.
Higher levels of NO3 in PCE improves the nutritional status of plants when inoculated with the bacterium Frateuria aurantia in melon and tomato crops, so it is necessary to investigate this bacterium in more detail in processes related to the nitrogen cycle since the historical research has only identified it as a potassium solubiliser.
The results of this experiment showed that it is possible to provide the nutrient needs of long-cycle horticultural crops in a soilless culture with good yields, by using vermicomposted horticultural waste as a substrate and as an organic nutrient solution in fertigation combined with the application of PGPB.
The enzyme activity of the substrate should be considered as an additional control parameter (Electric conductivity, pH, ionic concentration) for the nutritional management of the crop when organic solutions and PGPB are used in culture without soil in the container.
When working with organic nutritious solutions and PGPB in soilless cultivation with coconut fibre as a substrate in 100% proportion, the production of tomato and melon crops decreases due to lower microbial activity, and lower nutrient assimilation according to the petiole cellular extract (PCE) analysis regarding mixing with vermicompost.
Based on this research, the importance of monitoring enzymatic activity, being highly correlated with crop production, should be a factor to consider.

Author Contributions

Conceptualization, J.L.R. and M.d.C.S.; methodology, J.L.R. and M.d.C.S.; software, J.L.R. and M.d.C.S.; validation, J.L.R. and M.d.C.S.; formal analysis, J.L.R. and M.d.C.S.; investigation, J.L.R. and M.d.C.S.; resources, J.L.R. and M.d.C.S.; data curation, J.L.R.; writing—original draft preparation, J.L.R.; writing-review and editing, J.L.R. and M.d.C.S.; visualization,, J.L.R. and M.d.C.S.; supervision, M.d.C.S.; project administration, M.d.C.S.; funding acquisition, M.d.C.S.


This research was funded by Consejo Nacional de Ciencia y Tecnología (Mexico), grant number 010050.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Evolution of dehydrogenase activity (DHA) activity during two growing cycles. AV, Azotobacter vinelandii; BM, Bacillus megaterium; FA, Frateuria aurantia; C, Control; DAT, days after transplant.
Figure 1. Evolution of dehydrogenase activity (DHA) activity during two growing cycles. AV, Azotobacter vinelandii; BM, Bacillus megaterium; FA, Frateuria aurantia; C, Control; DAT, days after transplant.
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Figure 2. Principal component analysis in the tomato experiment. Two components were extracted with an eigenvalue >1, which together explain 96.71% of the variability in the original data. DHA, dehydrogenase; FTA, phosphatase; β-GLU, β-glucosidase; N, Azotobacter vinelandii; P, Bacillus megaterium; K, Frateuria aurantia; C, control treatment; Norg, organic nitrogen; OM, organic matter; C/N, carbon/nitrogen ratio; TP, total porosity; AVP, air volume percentage; BD, bulk density.
Figure 2. Principal component analysis in the tomato experiment. Two components were extracted with an eigenvalue >1, which together explain 96.71% of the variability in the original data. DHA, dehydrogenase; FTA, phosphatase; β-GLU, β-glucosidase; N, Azotobacter vinelandii; P, Bacillus megaterium; K, Frateuria aurantia; C, control treatment; Norg, organic nitrogen; OM, organic matter; C/N, carbon/nitrogen ratio; TP, total porosity; AVP, air volume percentage; BD, bulk density.
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Figure 3. Principal component analysis in the melon crop. Two components were extracted with an eigenvalue >1, which together explain 98.23% of the variability in the original data. DHA, dehydrogenase; FTA, phosphatase; β-GLU, β-glucosidase; N, Azotobacter vinelandii; P, Bacillus megaterium; K, Frateuria aurantia; C, control treatment; Norg, organic nitrogen; OM, organic matter; C/N, carbon/nitrogen ratio; TP, total porosity; AVP, air volume percentage; BD, bulk density.
Figure 3. Principal component analysis in the melon crop. Two components were extracted with an eigenvalue >1, which together explain 98.23% of the variability in the original data. DHA, dehydrogenase; FTA, phosphatase; β-GLU, β-glucosidase; N, Azotobacter vinelandii; P, Bacillus megaterium; K, Frateuria aurantia; C, control treatment; Norg, organic nitrogen; OM, organic matter; C/N, carbon/nitrogen ratio; TP, total porosity; AVP, air volume percentage; BD, bulk density.
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Figure 4. Pearson correlation analysis between yield and petiole cellular extract (PCE) concentrations.
Figure 4. Pearson correlation analysis between yield and petiole cellular extract (PCE) concentrations.
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Table 1. Treatment description according to substrate mixture and microorganisms (PGPB) applied.
Table 1. Treatment description according to substrate mixture and microorganisms (PGPB) applied.
Substrate VC:CF
TreatmentMicroorganisms (PGPB)Concentration
(CFU g−1)
0:100AZAzotobacter vinelandii1 × 108
BMBacillus megaterium1 × 108
FAFrateuria aurantia1 × 108
40:60AZAzotobacter vinelandii1 × 108
BMBacillus megaterium1 × 108
FAFrateuria aurantia1 × 108
60:40AZAzotobacter vinelandii1 × 108
BMBacillus megaterium1 × 108
FAFrateuria aurantia1 × 108
VC, vermicompost from horticultural waste; CF, coconut fibre; PGPB, Plant growth-promoting bacteria; CFU, colony-forming units.
Table 2. Initial physicochemical properties (left column) and finals (right column) for each substrate mixture (% v:v, VC = vermicompost from horticultural crop waste, CF = coconut fibre).
Table 2. Initial physicochemical properties (left column) and finals (right column) for each substrate mixture (% v:v, VC = vermicompost from horticultural crop waste, CF = coconut fibre).
Norg %OM %C/NRAW %TP %AVP %BD g dm/L
Norg, organic nitrogen; OM, organic matter, C/N, carbon/nitrogen ratio; RAW, readily available water; TP, total porosity; AVP air volume percentage, BD, bulk density. Different letters indicate significant differences between groups (p < 0.05).
Table 3. Correlations between the initial (I) and final (F) properties of substrates.
Table 3. Correlations between the initial (I) and final (F) properties of substrates.
Norg F0.84−0.99−0.990.99−0.92−0.961.00
OM F−0.901.001.00−0.970.870.92−0.98
C/N F−0.901.001.00−0.970.870.92−0.98
RAW F−0.540.860.86−0.961.000.99−0.94
TP F−0.810.990.99−1.000.940.97−1.00
AVP F−0.07 *0.39 *0.38 *−0.590.780.70−0.55
BD F0.82−0.99−0.991.00−0.94−0.971.00
Norg, Organic nitrogen; OM, organic matter; C/N, carbon/nitrogen ratio; TP, total porosity; AVP, air volume percentage; BD, bulk density. * Non-significant value (p > 0.05).
Table 4. Yield (kg m−2) for both crops (tomato and melon) in containers according to the bacteria and substrate mixture (VC:CF % v:v).
Table 4. Yield (kg m−2) for both crops (tomato and melon) in containers according to the bacteria and substrate mixture (VC:CF % v:v).
CropTreatmentSubstrate Mixture
0:100 40:60 60:40 2 PGPB
1 substrate6.46B 7.62A 7.50A
1 substrate4.24C 5.93A 5.59B
VC, vermicompost; CF, coconut fibre; AV, Azotobacter vinelandii; BM, Bacillus megaterium; FA, Frateuria aurantia; and control treatment. Lowercase letters express significant differences (p < 0.05) between treatments on the same substrate. Horizontal capital letters indicate differences by treatment and substrate. 1 Horizontal italic letters indicate differences between the mean value of substrates and 2 vertically considered mean value for each treatment (PGPB Plant growth promoting bacteria).
Table 5. Mean values of enzymatic activity analysed by treatment and substrate.
Table 5. Mean values of enzymatic activity analysed by treatment and substrate.
EnzymeTreatTomato Melon
0:100 40:60 60:40 0:100 40:60 60:40
(µ PNP g−1 24 h−1)
AV187 ± 20a241 ± 21ab264 ± 16a206 ± 14a455 ± 76a310 ± 95ab
BM183 ± 17a233 ± 32b247 ± 20ab166 ± 28b421 ± 87a323 ± 102ab
FA191 ± 40a270 ± 26a252 ± 24ab207 ± 32a470 ± 104a358 ± 23a
C168 ± 34a211 ± 30b233 ± 18b155 ± 22b380 ± 108a293 ± 83b
Mean183 ± 29B239 ± 33A249 ± 20A184 ± 33C431 ± 95A321 ± 80B
(μg PNP g−1 h−1)
AV390 ± 77a479 ± 30a474 ± 48a471 ± 117a660 ± 143a769 ± 40a
BM398 ± 52a450 ± 56ab450 ± 32a446 ± 82a655 ± 120a777 ± 89a
FA359 ± 48a480 ± 24a471 ± 28a454 ± 89a681 ± 144a761 ± 102a
C361 ± 55a424 ± 51b400 ± 20a413 ± 57a518 ± 136b734 ± 52a
Mean377 ± 58B449 ± 46A459 ± 44A446 ± 86C629 ± 121B760 ± 72A
(μg PNP g−1 h−1)
AV276 ± 20a365 ± 23a365 ± 29a539 ± 98a707 ± 98a717 ± 80a
BM277 ± 17a328 ± 76ab352 ± 19ab588 ± 48a648 ± 71a742 ± 63a
FA262 ± 27a303 ± 26b361 ± 23ab550 ± 76a708 ± 82a686 ± 48a
C273 ± 32a283 ± 36b338 ± 38b540 ± 83a691 ± 89a749 ± 61a
Mean272 ± 24B321 ± 52A335 ± 28A554 ± 90B689 ± 64A724 ± 54A
AV, Azotobacter vinelandii; BM, Bacillus megaterium; FA, Frateuria aurantia; C, control treatment; DHA, dehydrogenase activity; FTA, acid phosphatase activity; β-GLU, β-glucosidase activity; PNP, P-nitrophenol. Vertical lowercase letters express differences between PGPB for each substrate type. Horizontal capital letters express statistical differences between substrates (p < 0.05).
Table 6. Pearson correlation between yield and enzymatic activity.
Table 6. Pearson correlation between yield and enzymatic activity.
Yield 0.760.830.76 0.940.770.75
p-Value 0.00400.00080.0044 0.00000.00330.0045
DHA0.76 0.940.810.94 0.640.72
p-Value0.0040 0.00000.00160.0000 0.02500.0081
FTA0.830.94 0.880.770.64 0.79
p-Value0.00080.0000 0.00020.00330.0250 0.0024
B-GLU0.760.810.88 0.750.720.79
p-Value0.00440.00160.0002 0.00450.00810.0024
DHA, dehydrogenase; FTA, phosphatase, β-GLU, β-glucosidase.
Table 7. Correlations between initial physicochemical (I) and final (F) properties of the substrates and enzymatic activity.
Table 7. Correlations between initial physicochemical (I) and final (F) properties of the substrates and enzymatic activity.
β-GLU0.561 *0.833−0.802−0.796−0.797−0.7950.859−0.863−0.504 *−0.844−0.703−0.8070.8490.842
DHA0.9640.799−0.851−0.858−0.857−0.8590.722−0.505 *−0.962−0.772−0.931−0.8440.7570.775
FTA0.6140.912−0.878−0.871−0.872−0.8710.941−0.946−0.551 *−0.924−0.770−0.8030.9300.923
Crop 1 – Tomato, Crop 2 – Melon. Norg, organic nitrogen; OM, organic matter; C/N, carbon/nitrogen ratio; TP, total porosity; AVP, air volume percentage; BD, bulk density. * Does not represent significant value (p <0.05).
Table 8. Petiole cellular extract analysis for the tomato crop (mg L−1). DAT, days after transplant.
Table 8. Petiole cellular extract analysis for the tomato crop (mg L−1). DAT, days after transplant.
IonTreatment60 DAT90 DAT120 DAT
AV, Azotobacter vinelandii; FA, Frateuria aurantia; BM, Bacillus megaterium; average value. Different lowercase letters represent statistically significant differences between PGPB, while capital letters refer to differences between substrate mixture (p < 0.05).
Table 9. Petiole cellular extract analysis for the melon crop (mg L−1.). DAT, days after transplant.
Table 9. Petiole cellular extract analysis for the melon crop (mg L−1.). DAT, days after transplant.
IonTreatment50 DAT80 DAT
0:10040:60 60:40 0:100 40:60 60:40
1 substrate866B920A928A859B935A875B
1 substrate322A386A350A367A398A407A
1 substrate2586A2430B2666A3776B5128A5157A
1 substrate251B447A433A423C531B596A
AV, Azotobacter vinelandii; FA, Frateuria aurantia; BM, Bacillus megaterium. Lowercase letters express significant differences (p < 0.05) between treatments on the same substrate. 1 Horizontal capital letters indicate differences by treatment and substrate.

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Ruiz, J.L.; Salas, M.d.C. Evaluation of Organic Substrates and Microorganisms as Bio-Fertilisation Tool in Container Crop Production. Agronomy 2019, 9, 705.

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Ruiz JL, Salas MdC. Evaluation of Organic Substrates and Microorganisms as Bio-Fertilisation Tool in Container Crop Production. Agronomy. 2019; 9(11):705.

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Ruiz, Jose Luis, and Maria del Carmen Salas. 2019. "Evaluation of Organic Substrates and Microorganisms as Bio-Fertilisation Tool in Container Crop Production" Agronomy 9, no. 11: 705.

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