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Article

Different Nutritional Regimes in a Tomato Soilless System Affect the Bacterial Communities with Consequences on the Crop Quality

by
Luciano Beneduce
1,*,
Federica Piergiacomo
1,2 and
Kalina Sikorska-Zimny
3
1
Department of Agriculture, Food, Natural Resources and Engineering (DAFNE), University of Foggia, Via Napoli 25, 71122 Foggia, Italy
2
Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano-Bozen, Piazza Università 1, 39100 Bolzano-Bozen, Italy
3
Fruit and Vegetables Storage and Processing Department, Division of Fruit and Vegetable Storage and Postharvest Physiology, The Institute of Horticulture—National Research Institute, Konstytucji 3 Maja 1/3 Str., 96-100 Skierniewice, Poland
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(12), 2254; https://doi.org/10.3390/agriculture14122254
Submission received: 7 October 2024 / Revised: 5 December 2024 / Accepted: 6 December 2024 / Published: 10 December 2024
(This article belongs to the Section Crop Production)
Browse Figures
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Abstract

:
This study investigates the impact of different nutritional regimes on the bacterial communities within the root-growing substrate of a soilless tomato production system and the effects on crop quality. The experiment was conducted with two tomato varieties, Conchita and Sweetelle, under three nutritional treatments: standard, nutrient solution with 20% increased salts and nitrogen and supplementation with the biostimulant Bio-algeen S-90. Bacterial communities in the root substrate were influenced by both the tomato variety and the nutritional regime. Sweetelle exhibited more pronounced shifts in bacterial communities compared to Conchita. An overall increase in bacterial populations with time was observed (+0.38 Log). Specifically, the 20% enhanced nutrient solution had varying effects on bacterial counts in the two tomato varieties, while the biostimulant promoted an increase in ammonia-oxidizing bacteria (+0.4 Log). Microbial community analysis highlighted the distinct impact of each nutritional regime on nitrogen-cycling bacteria, which correlated with differences in quality parameters such as the L-ascorbic acid and lycopene contents. In the first case, a decrease (25–30%) was observed, while the lycopene content decreased after harvest (−51% in Conchita variety) but was more stable in the postharvest phase (66–70% lycopene retained, only 44% in the control). This study highlights how increased sources of nutrients and the differential responses of microbial communities to nutritional regimes do not necessarily increase the crop quality and that tailored approaches are required for different tomato varieties.

1. Introduction

This study aims to assess the impact of different nutritional regimes of soilless tomato crop on the root bacterial community, specifically examining the nitrogen (N) cycle functional groups (namely, ammonia-oxidizing, denitrifiers and N-fixing bacteria), with the aim of evaluating possible microbial population shifts and correlating these with the final crop yield and quality. Our study aimed to enhance our understanding of the interplay between nutrient regimes and microbial communities, with consequences on tomato quality outcomes.
Soilless cultivation systems are characterized by nutrient delivery through irrigation with nutritive solution delivered to plant roots within an inert substrate [1]. Tomato (Solanum lycopersicum L.) is one of the most extensively cultivated crops in soilless systems [2,3]. The benefits of soilless tomato cultivation include optimized irrigation management, higher yields, a decreased incidence of plant pathogens and reduced pesticide application [1,4]. However, suboptimal culture conditions such as temperature imbalances and the accumulation of nutrient ions in the recirculating water may have negative effects on crop production and the environment [5,6].
The control of nutrient solutions and use of organic fertilizer are the most studied strategies to minimize drawbacks and increase the yield and quality of soilless tomato production [7,8,9]. Conversely, the response of root–substrate microbial communities to varying soilless system conditions is still a poorly covered area of research. Microbial populations colonizing the roots, substrate and nutrient solutions are crucial for crop health and productivity through mechanisms such as plant pathogen suppression and increased nutrient bioavailability (i.e., phosphates). Indeed, a microbial community imbalance may hinder nutrient absorption by plants, potentially compromising plant health and crop yield [10,11]. Therefore, the effective management of nutrient solutions in soilless systems requires a better understanding of microbial interactions with plant roots and their impact on the health and productivity.
Recent findings have reported how microbial communities are differently shaped in soil-based and soilless tomato systems [12] and their variable responses to different fertilization regimes [3]. The specific response of nitrogen-cycling microorganisms in soilless cultivation of tomato was recently investigated, but the study was limited to ammonia-oxidizing bacteria (AOB) [13]. Indeed, in a recent study on tomato treated with different nitrogen fertilizers (inorganic and organic), a clear impact was found on AOB, as well as a strong influence of tomato roots in reducing ammonia-oxidizing activity, independent of the nitrogen source [14]. No study so far aimed at evaluating other key N-cycling groups such as nitrogen-fixing and denitrifying bacterial populations that could also influence the nitrogen forms that plants can assimilate and the consequent effects on product yield and quality. Regarding the influence of soilless cultivation methods on tomato nutritional and quality attributes (such as antioxidants, lycopene, L-ascorbic acid, sugar content), there is some evidence that the nutrient solution composition and other strategies [9,15] can have a positive influence, but no relation with root microbial community dynamics was considered so far.
Our hypothesis is that different nutritional regimes can affect the bacterial community of the root–substrate system and, consequently, affect the final quality of the production. In particular, the nitrogen-cycling key bacterial groups could be influenced by an increase in the inorganic or organic nitrogen source, which can affect the nitrogen cycle and change the uptake of this essential nutrient by plants. In other studies, biostimulant strains were tested (as single strains or in combination) to assess their positive effects on tomato production [16]. This approach is very promising but requires extreme care in the inoculation procedures of allochthonous microbial groups, leading to very variable results. We hypothesized that naturally occurring microbial communities can contribute to product quality and that the possible addition of an increased nitrogen source might not directly correlate with improved quality, but rather, indirectly influences the nitrogen cycle bacterial population and thus exerts beneficial effects on the final quality of the crop.

2. Materials and Methods

2.1. Greenhouse Setup

The experiment was conducted in a greenhouse located at the Institute of Horticulture in Skierniewice (Poland), where a crop cycle of cherry tomato plants cv. Conchita (CON) and cv. Sweetelle (SL) was settled, by cultivating tomato plants on three rows hosting 14 plants each. Each row consisted of 12 plants for each treatment, plus external plants of different tomato varieties to protect the rows but were not included in the analyses. The average daily solar radiation sum was 1476.2 J cm−1, the average temperature D/N (day/night) was 25/21 °C, the humidity was kept about 75%, and the CO2 concentration averaged 800 ppm.
The tomato seedlings were placed on slabs with rockwool (Grodan, Roermond, The Netherlands) at 23.5 cm distance from each other and were fitted with a dripping watering system. The first and last were tomato plants of a different variety, not considered for experimental purposes, and were used as environmental protection for the seedlings grown within the row. The tomato seedlings were planted on 27 May 2021, and the crop cycle ended on 5 September, for a total of 19 weeks. Each row was provided with different concentrations of the nutrient solutions supplied to plants as follows: (1) standard nutrient solution as a control (NC); (2) nutrient solution + 20% enhanced supplement with salts and nitrogen (N20); and (3) nutrient solution supplied with Bio-algeen S-90 (NBA).
The nutrient solution was supplied at 100–150 mL per plant, per 8–18 h, depending on the environmental conditions (humidity, temperature) and growth stage. The addition of chemicals to nutrient solutions was performed after the chemical analysis of the water. Twice a day, the electrical conductivity (EC-2.7–3.2 mS cm−1) and pH (5.7–5.8) were adjusted. The formulations of the control nutrient solution (CTRL) and that with increased nutrients (N20) are shown in Table S1. The NBA nutrient solution was equal to the control with the addition of the bio-stimulator Bio-algeen S-90 (Schulze et Hermsen GmbH, Dahlenburg, Germany) solution at 0.4% and was distributed as 100 mL/plant.

2.2. Sampling for Microbial Analyses

The tomato seedlings were sampled on arrival in the greenhouse and placed at −80 °C prior to DNA extraction. Seedlings constitute the “zero time” (T0) of the experiment. After transplantation to the greenhouse, the root and rockwool samples were taken in two stages: immediately after flowering (T1 = 11 July 2021) and at the end of the harvesting period (T2 = 5 September 2021). Triplicate samples were aseptically taken from each experimental treatment by using a cylindrical cork borer with a 1.5 cm diameter as reported in the sampling scheme (Figure 1). The rockwool surface was drilled at four points to a depth of 5 cm (to reach the roots, at the beginning, in the middle and at the end of the greenhouse row). The four cylinders taken, which formed a single sample unit, were stored at −80 °C prior to DNA extraction.

2.3. DNA Extraction and Quantitation

DNA was extracted with the DNeasy Powersoil-pro DNA Isolation kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions. About 0.5 g of sample consisting of both roots and rockwool was collected with sterile forceps and a scalpel from different positions. The total sample was obtained by taking about 0.12 g of sample from each of the 4 cylinders, along the entire length of each slab.
The concentration and quality of extracted DNA was determined by fluorometric analysis and agarose gel electrophoresis. For the fluorometric analysis, the Qubit 2.0 Fluorometer and dsDNA HS kit (Thermo Fisher Scientific, Waltham, MA, USA) were used. Electrophoresis was performed using 2.0 µL of extracted DNA on a 1% (wt/v) agarose gel (diluted in 50 mL of TAE 1X buffer) stained with 0.75 µL (1% solution in H2O) of Sybr Safe DNA gel stain (Invitrogen) and visualized through a Gel Doc EZ system (Bio-Rad, Hercules, CA, USA).

2.4. Quantitative Real-Time PCR

Quantitative PCR was used to quantify the following microbial groups: total Bacteria, total Archaea, ammonia-oxidizing bacteria (AOB, AmoA gene), nitrogen-fixing bacteria (NFB, NifH gene) and denitrifying bacteria (DEN–NosZ, NirS, NirK genes), in tomato soilless samples. Each method was standardized and validated using standard DNA containing the target sequence for each pair of specific primers and for the different microbial targets (Table S2). Calibration curves were obtained for total Archaea, total Bacteria, AOB, NFB and denitrifying bacteria according to Behrens et al. [17] and evaluated according to the MIQE guidelines for qPCR absolute quantification of target genes [18]. All PCRs were carried out in the Applied BioSystem 7300 Real-Time PCR cycler. The 25 µL reaction mixtures contained PowerUp™ SYBR™ Green Master Mix (Thermo-Fisher). Primers (forward and reverse for each target gene), DNA and DNase-free deionized water were added in defined volumes based on the gene detected. In this study, 3 µL of DNA was added to the reaction mixture, and linearized plasmids harboring each of the target genes (GeneArt gene synthesis–Thermo Fisher) were used as internal quantitative standards. All qPCR data are expressed as gene copy number (GCN) g−1 of sample (dry weight).

2.5. PCR-ARISA

The PCR-ARISA (Automated Ribosomal Internal Spacer Analysis) reaction was performed using 0.75 μL of 0.25 mM (each) ITSF (5′-GTCGTAACAAGGTAGCCGTA-3′)/ITSReub (5′-GCCAAGGCATCCACC-3′) primers targeting the bacterial internal transcriber region (ITS) in a reaction mixture containing 5 μL of 5X PCR buffer, 0.25 μL of 1.5 U Taq DNA polymerase (Phusion HF DNA Polymerase Thermo Fisher Scientific), 0.5 μL of 0.2 mM (each) deoxynucleoside triphosphate and sterile deionized water in a final volume of 25 μL [19]. Primer ITSReub was 5′-labeled with HEX fluorochrome (5′-Hexachlorofluorescein phosphoramidite) to detect ITS fragments. The results obtained from the reaction were visualized by running the 1.5% agarose gel electrophoresis gel through a transilluminator (Bio-Rad). After agarose-gel electrophoresis, the PCR-ARISA products were quantified and sent to the fragment analysis service provided by STAB-VIDA (Caparica, Portugal) to be subjected to a capillary electrophoresis. The data were analyzed using the Peak scanner v1.0 software program (Applied Biosystem, Waltham, MA, USA). The program output is an electropherogram in which every peak represents ITS fragments of different lengths belonging to a different operational taxonomic unit (OTU). The sizes of peaks are estimated by comparison with fragments of the LIZ 1200 internal size standard (Thermo Fisher Scientific). Software analysis allowed for the measurement of the height and the area of each peak produced in the electropherogram of each sample. Before the downstream analyses, all the samples with a size of less than 100 bp and more than 1000 bp (possible PCR artifacts) were erased. Moreover, all the peaks below 50 fluorescence units were discarded. The quantitative matrix was square root transformed and normalized to 100% compared to the whole fluorescence signal produced for each sample. The ARISA experiment was carried out on three replicates for each DNA sample.

2.6. Chemical Analyses

The tomatoes harvested at the end of the production cycle were analyzed for qualitative parameters as follows: micro- and macro-elements of the fruits and leaves were determined after mineralization in a closed system (Milestone, Ethos 1, Brøndby Kommune, Denmark) with 5 mL of concentrated HNO3 per 0.5 g sample by inductively coupled plasma mass spectrometry (ICP-MS) using a Perkin Elmer Optima DV 2000 at appropriate wavelengths (Table S3). The lycopene content was determined according to the Fish method [20] by spectrophotometric measurements (SPECORD S600 spectrophotometer, Analytik Jena AG, Jena, Germany) using hexane, acetone and ethanol as solvents (Chempur, HPLC grade). Vitamin C (the sum of L-dehydroascorbic acid and L-ascorbic acid) was determined using the Tillmans method (where 2,6-dichlorophenolindophenol [DCPIP] is reduced by L-ascorbic acid during the reaction and turns the solution pink upon the oxidation of the acid, which visually indicates the end of the reaction). The sugar content (total and reducing) was evaluated using the Bertrand method [21].

2.7. Statistical Analyses

Statistical analyses were conducted by using Past 4.07b Software [22]. Quantitative data obtained from the qPCR trials were evaluated by one-way and two-way analysis of variance (ANOVA), showing the significant differences by means of the “post hoc” test (p < 0.05). The PCR-ARISA cluster analysis was carried out using the number and position of the ARISA peaks of the samples as an index of the presence/absence of a given taxon and the height of the corresponding peaks as an index of the abundance of each taxon. The dendrogram was generated from the matrix obtained by measuring the dissimilarity using the Bray–Curtis algorithm and then applying the Jaccard index. Principal component analysis (PCA) was conducted by including the bacterial communities’ similarity (PCR-ARISA) and the qualitative parameters of tomato production (Table S3).

3. Results

3.1. Bacterial Communities of the Root-Substrate System

The bacterial community dynamics throughout the experimental period, assessed via the PCR-ARISA similarity profiles, are summarized in Figure 2. Bacterial communities within the rockwool–root system exhibited temporal clustering patterns, primary aligning with sampling stages. Four main clusters emerged: T0 (pre-transplant seedlings), T1 (flowering stage), T2 (harvest stage) and an additional cluster exclusively associated with the NBA treatment at time 2. At time zero, the seedling samples clustered separately and were distinct for each tomato variety, reflecting the specific bacterial communities arising from the respective nursery environments. By the flowering stage (T1), the variety effect persisted, yet the control samples formed a unified cluster across both varieties. The nutrient solutions differentially influenced the varieties: in the Sweetelle variety, both the N20 and NBA treatments clustered apart from the control, while for Conchita, only the N20 treatment induced a distinct clustering pattern. At harvest (T2), the influence of the nutritional treatments became more pronounced, with control treatments forming a distinct subcluster. In this case, the irrigation solutions had a marked effect on the bacterial communities of the two varieties: while for the Conchita variety, the NBA treatment exhibited similarity with CTRL, the Sweetelle bacterial community is highly dissimilar compared to all other samples, displaying greater divergence with the NBA treatment and forming a separate cluster from the control and N20 treatments.
These findings indicate that the bacterial communities associated with each tomato variety were distinct at the seedling stage and were subsequently modified by nutritional regimes. Notably, the bacterial beta-diversity revealed that the NBA and N20 treatments exerted differential impacts, with Conchita exhibiting lower sensitivity to nutrient variation, whereas Sweetelle bacterial communities displayed pronounced shifts, particularly under NBA supplementation.

3.2. Quantification of Bacteria, Archaea and Nitrogen Cycle-Associated Microbial Groups

Quantitative PCR was used to target total bacteria, total archaea and the key N-cycling bacterial groups. Across all samples, total bacteria populations significantly increased over time (+0.38 ± 0.08 Log GCN g−1, Table 1) independent of other variables. Two-way ANOVA revealed significant inter-variety differences, with a significant bacterial decrease in Conchita and corresponding increase in Sweetelle. The decrease in Conchita with the N20 treatment evidences a specific interaction with nutrient treatment (Figure 3).
In contrast, the archaeal population demonstrated a general reduction from the beginning of the crop cycle to harvest (−0.2 ± 0.06 Log GCN g−1). Neither variety nor nutritional treatment produced substantial impacts on archaeal levels, although Conchita showed a higher, albeit nonsignificant, reduction (p > 0.05, Figure 3).
For N-cycling microbial groups, the nitrogen-fixing bacteria (NFB) and ammonia-oxidizing bacteria (AOB) populations declined with time independent of cultivar and the nutrient solution (Table 1).
In the Conchita cultivar, the reductions in NFB and AOB populations were particularly marked. The comparison of cultivars and nutritional impacts on AOB (two-way ANOVA; Table 1, Figure 3) indicated that Bio-algeen increased the AOB counts across cultivars (+0.4 ± 0.12 Log GCN g−1), whereas the salt+N20 solution suppressed AOB relative to control levels. Generally, the Conchita variety had a higher number of AOB (7.9 ± 0.18 versus 7.5 ± 0.32) and was more responsive to different solution treatments (p < 0.05), though no significant cultivar–nutrient interaction was observed.
The denitrifier populations (assessed via the NosZ, NirK and Nir S genes) showed stability throughout the experiment independent of time and other variables and are detailed in the Supplementary Materials (Table S4). According to the qPCR data, only the AOB were shown to be dynamic throughout the experiment. Indeed, AOB were influenced by the different nutrient solutions, with specific effects for the two cultivars used.

3.3. Tomato Production Quality Attributes

The two varieties of tomato demonstrated intrinsic differences in L-ascorbic acid levels. Across both varieties, the Bio-algeen treatment reduced L-ascorbic acid levels by 38% in Conchita and 24% for Sweetelle, while the N20 supplementation increased levels in Conchita, though without statistical significance (Figure 4A).
Regarding reducing sugars, Sweetelle naturally exhibited higher average levels (5.20 ± 0.2%) than Conchita (3.6 ± 0.07%) and was more affected by the nutritional treatments. As shown in Figure 4B, Bio-algeen (NBA treatment) had a negative effect on reducing sugars in the Sweetelle variety (decrease of 19%), while no significant impact was found for the N20 treatment. The total sugar content only showed an intrinsic difference in the two tested varieties (Figure 4C), and no significant effect was observed among the tested treatments.
The Conchita variety exhibited a higher lycopene content than Sweetelle (136.18 ± 13.61 versus 118.5 ± 5.57 mg kg−1, respectively), yet the lycopene levels declined with the N20 treatment and significantly declined with the NBA treatment (−51% p < 0.05) (Figure 4D). Although both nutritional supplements exhibited lycopene reductions at harvest, they both showed enhanced lycopene stability during storage, irrespective of the temperature (12 °C or 20 °C). Particularly after 15 days at 12 °C, lycopene in N20-treated Conchita was 21% higher than CTRL, suggesting a stabilization effect (Figure 4E), while at 20 °C incubation, the effect was not statistically significant (Figure 4F).
To better evaluate the effect of the nutritive solutions on the two varieties, principal component analysis (PCA) was conducted (Figure 5), including the main quality (L-ascorbic acid, lycopene, reducing sugars, total sugar and acidity) and compositional parameters (see Section 2, Table S3). Conchita was found to be more susceptible to nutrient treatments, forming distinct groups along the two components, while Sweetelle generated a single group. The variations in lycopene levels at harvest and post-storage along with L-ascorbic acid were the significant quality determinants. Compositional traits, such as the dry matter and the Fe and Mn contents, accounted for the secondary sources of variance across treatments.

4. Discussion

4.1. Tomato Varieties

The experiment focused on the application of different nutritional regimes to different tomato cultivars, with the aim of evaluating the impact on the microbial communities of the root–substrate system and the final quality of the crop production. We used two different cultivars, namely, Sweetelle and Conchita, that represent different types of cherry tomato: Sweetelle is an F1 grape tomato hybrid variety with small elliptic fruits (av. weight 12 g) that is richer in sugars and adapted for a long shelf-life. Conchita is a round tomato high-yield hybrid variety that is adapted for loose or truss harvest (average weight 20.0 g) and has a long shelf-life and firm fruits [23]. These varieties were selected for their different characteristics and diffusion in greenhouses in Poland.

4.2. Qualitative Parameters of Tomato Production

The tomato qualitative parameters (L-ascorbic acid, lycopene, total and reducing sugars) did not consistently improve with enhanced nutrient treatments. The N20 supplementation showed a slight but nonsignificant increase in L-ascorbic acid for Conchita, while other parameters were comparable with the control. Bio-algeen did not show significant positive effects. Our findings are in agreement with the variability observed for Bio-algeen supplementation on the quality parameters of crop production. Indeed, that variability was found for several crops, including potato [24,25], chamomile [26] and pepper fruit [27]. In the latter study, an increase in L-ascorbic acid and lycopene was consistent with the Bio-algeen treatment.
Although the lycopene content in Conchita decreased and was largely unaffected in Sweetelle, postharvest observations at 12 and 20 °C revealed slower lycopene degradation in treated samples compared to controls. While the complexity of pre- and postharvest factors affecting lycopene and other antioxidant precludes definitive conclusions [28], these findings provide a basis for further investigation of the lycopene stability mechanisms under different nutritional regimes.

4.3. Bacterial Diversity

The varied nutritional regimes, including inorganic (N20) and organic (NBA) supplements, influenced the bacterial communities within the root environment of the soilless-grown tomatoes. The initial beta-diversity (T0) evaluated using PCR-ARISA revealed cultivar-specific differences. At the flowering stage (T1), CTRL samples clustered independently of cultivar, while the effects of the nutritional supplements diverged across varieties: Conchita NBA clustered with CTRL, with N20 forming a distinct subcluster, while Sweetelle exhibited diversity shifts in both the NBA and N20 treatments relative to CTRL. At harvest (T2), the nutritional effects were more pronounced, with Sweetelle displaying substantial shifts in clustering for both the N20 and NBA treatments. These findings are in accordance with prior studies [14] demonstrating the impacts of root influence and temporal factors on bacterial diversity in soilless tomato systems under various growing media. The effect of the organic fertilizer supplied was evident only at harvest time. Our results are consistent with those observations, since in our case, the effects of both nutritional regimes on the bacterial community dynamics were evident at harvest, while the cultivar and time effects were predominant at T0 and T1 (Figure 2).

4.4. Bacterial, Archaeal and N-Cycling Bacterial Populations

In our study, the observed general increase in total bacteria with time (9.6 ± 0.17 versus 9.2 ± 0.25 Log GCN g−1 as determined by qPCR, Table 1) likely reflects the rhizosphere effect of tomato roots, although the two cultivars had contrasting impacts under control and nutritional supplements (increasing in Sweetelle and decreasing in Conchita, see Figure 3). This finding, along with the bacterial community ARISA profile in T2 (Figure 2), suggests cultivar-specific contributions to shaping the root-associated bacterial communities, which respond variably to nutrient regimes. Indeed, the bacterial communities of the two tested tomato cultivars reacted distinctively when N20 and NBA were used as nutritional supplements. Specifically, NBA (Bio-algeen) induced divergent bacterial responses across cultivars, consistent with previous studies on potatoes [24] and sweet peppers [27].
It must be taken into account that when studies are conducted on soil cropping systems, the bacterial abundance can be one order of magnitude higher than in soilless systems, as reported in previous research [29]. The prevalence of rhizosphere effects and distinct cultivars in shaping the bacterial abundance and diversity, compared to nutritional supplements, seems to be independent of the soil/soilless factor, albeit further studies are still needed to confirm this statement.
Regarding the archaea abundance, we found that a general decrease occurred during the crop cycle, independent from the cultivar. In recent research conducted to evaluate archaeal communities in tomato seeds and root, a general increase and a marked influence of the rhizosphere on the archaeal diversity was found, and cultivar-specific enrichment effects were also reported [30]. The difference between these observations can be due to the crucial contribution of soil to enriching the archaeal abundance and diversity, while in our case, the root–substrate-circulating solution system lacks this pivotal support of the archaeal population associated with tomato roots, according to a comparative study by Anzalone et al. [12]. Another explanation for the decline in the archaeal population is related to the lower diversity and abundance generally found in plant roots due to the lower interaction that archaea can establish with eukaryotic hosts, particularly plant roots, as observed in tomato in a previous study [30].
It is well known that nitrogen-cycling microorganisms can contribute to the increase in quantity (N-fixation) or bioavailability (nitrification of ammonia to nitrate, the preferred form of nitrogen for uptake by root systems) in soilless cultivation methods [31]. On the other hand, negative roles can be attributed to nitrogen loss by denitrifiers (that may completely reduce nitrate to N2), as well as N2O and other NOx emissions in atmosphere, by the same group (through incomplete nitrate reduction) or through NO2- intermediates produced by nitrifiers [32,33]. Our results showed that ammonia-oxidizing bacteria (AOB) were the most dynamic throughout the crop cycle, with NBA treatments stimulating their increase regardless of cultivar (Figure 3). Given that the mineral solution supplied only nitrate as a nitrogen source, while Bio-algeen included organic forms of nitrogen (amino acids, peptides, polyuronic acids) [34], it is plausible that the additional source of ammonia from the mineralization of organic nitrogen stimulated the increase in AOB. The bio-alginate component in Bio-algeen may have further supported the AOB fitness in the root–substrate environment, as suggested by previous studies [35].
In summary, several variables influenced the bacterial communities in soilless tomato cultivation: rhizosphere effects, cultivar and time were dominant in the early stages, with the impact of nutritional regimes on the root–substrate microbiome becoming more pronounced later (harvest stage). In particular, ammonia-oxidizing bacteria were responsive to Bio-algeen treatment, an observation worthy of interest, albeit further studies will be necessary to explore the potential competition for ammonia among plants and AOB, as well as nitrification inhibition within the root–substrate system, as suggested in other studies [36].

4.5. Correlations of Bacterial Communities with Tomato Quality

By correlating the bacterial community profiles with the quality attributes of crop production (Figure 5), we observed a general cultivar-specific response. At harvest, Conchita communities were more influenced by nutritional treatments, with associated variations in the lycopene content (higher in the control and N20 treatments compared to the NBA), while in Sweetelle, no significant variations were observed. Interestingly, the reduction in lycopene occurred only at harvest, while in the postharvest storage phase, this important antioxidant was more stable. In previous research, direct correlations between the lycopene content and single bacteria inoculants [37] or specific groups of microbial taxa among the whole community [38] were observed. Particularly in the latter study, specifically in hydroponic tomatoes, a reduction in lycopene was found, with enhanced oxidative cleavage of carotenoids and a lower sugar content, associated with Burkholderia, Acinetobacter, Mesorhyzobium and other rhizosphere bacterial taxa. Our study confirms these findings, indicating that nutritional regimes affect quality attributes via both direct nutrient uptake and indirect microbial community dynamics in the root–substrate system, influencing compounds such as lycopene. This pre-harvest influence could explain the relatively high stability of lycopene during the storage phase at different temperatures that we observed (Figure 4). Despite being a preliminary observation, this finding poses the basis for further investigations. The Vitamin C (L-ascorbic acid) content was also influenced by the treatments and specific cultivar, along with other quality parameters such as the dry matter, total sugars and Fe and Mn contents (Table S3). While the total sugars and dry matter were intrinsic features of the two varieties, the different Fe and Mn levels found could be associated with the different profiles of the bacterial communities in the two varieties that may have led to competitive absorption of the levels of these two microelements, according to recent studies [39]. However, the higher variability in the quality parameters of the Conchita cultivar compared to Sweetelle makes it difficult to evaluate possible indirect effects due to the differential abundances and structure of the bacterial communities.

5. Conclusions

Our study demonstrates that distinct nutrient regimes in soilless tomato cultivation significantly affect root-associated bacterial communities, especially in the later crop stages. Nitrogen-cycle microorganisms were marginally impacted by the different sources of nitrogen (inorganic, organic), except for ammonia-oxidizing bacteria, which responded sensitively to changes in the nutritional solution. While the direct effect of nutritional regimes partially explains the variability in tomato nutritional properties, microbial community interactions with the root–substrate system contribute substantially to the crop’s final nutritional quality. These findings underscore the importance of further research to optimize nutritional regimes alongside a deeper understanding of the root microbiome contribution to plant health and produce quality.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture14122254/s1, Table S1: Composition of nutrient solution used in the soilless tomato cultivation experiment; Table S2: Target genes and primers used for quantitative PCR for each of the microbial groups analyzed in the study; Table S3: Chemical parameters of macro- and microelements of tomato yield at harvest; Table S4: qPCR quantification of target genes of denitrifiers in the sampled root–substrate system.

Author Contributions

Conceptualization, L.B. and K.S.-Z.; methodology, L.B. and F.P.; validation, L.B. and K.S.-Z.; formal analysis, L.B. and F.P.; investigation, K.S.-Z.; resources, K.S.-Z.; data curation, F.P.; writing—original draft preparation, L.B.; writing—review and editing, L.B. and K.S.-Z.; visualization, K.S.-Z.; supervision, L.B. and K.S.-Z. All authors have read and agreed to the published version of the manuscript.

Funding

The experiment was carried out within the Statutory Program of The National Institute of Horticultural Research in Skierniewice, Task no 6.1.2 “The influence of nutrient solutions of different chemical composition on storage potential and the content of health promoting components in cocktail tomato cultivated in rockwool”. The microbiological research was funded by the University of Foggia PRA2018 research call, in the framework of the project “Microbial community dynamics and plant response associated with the use of biochar in soilless growing systems: integrated approach for potential innovation in hydroponic agriculture and its economic impact”.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available at https://zenodo.org/records/13906873 (accessed on 5 October 2024), reference number 13906873.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agriculture 14 02254 g001
Figure 1. Sampling scheme of the rockwool–root system for soilless tomato cultivation. (A): sampling points for DNA extraction on the top and bottom of the plant from 4 pooled subsamples. (B): section of the Grodan rockwool support for root growth. Blue dots represent the sampling points where the root and rockwool was taken (red dot for the opposite side). Each replicate was a pooled sample from the 4 spots.
Figure 1. Sampling scheme of the rockwool–root system for soilless tomato cultivation. (A): sampling points for DNA extraction on the top and bottom of the plant from 4 pooled subsamples. (B): section of the Grodan rockwool support for root growth. Blue dots represent the sampling points where the root and rockwool was taken (red dot for the opposite side). Each replicate was a pooled sample from the 4 spots.
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Figure 2. UPGMA dendrogram based on the ARISA profiles of bacterial communities in the rockwool–root system of the cultivated tomatoes. The dendrogram is based on the Bray–Curtis similarity matrix. t0 = seedling before greenhouse transplant; t1 = flowering stage; t2 = harvest stage; SL = Sweetelle variety; CO = Conchita variety; CTRL = standard nutrient solution: N20 = nutrient solution +20% salt and N; NBA = nutrient solution + Bio-algeen S-90.
Figure 2. UPGMA dendrogram based on the ARISA profiles of bacterial communities in the rockwool–root system of the cultivated tomatoes. The dendrogram is based on the Bray–Curtis similarity matrix. t0 = seedling before greenhouse transplant; t1 = flowering stage; t2 = harvest stage; SL = Sweetelle variety; CO = Conchita variety; CTRL = standard nutrient solution: N20 = nutrient solution +20% salt and N; NBA = nutrient solution + Bio-algeen S-90.
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Figure 3. Graphical summary of the observed statistically significant effects (two-way ANOVA, Tukey’s post hoc test) of the treatments and the tomato varieties on the total Bacteria, Archaea and AOB population based on qPCR results. Orange = Sweetelle variety; blue = Conchita variety. Bars represent the standard deviation. CTRL = standard nutrient solution; N20 = nutrient solution + 20% salt and N; NBA = nutrient solution + Bio-algeen S-90.3.3.
Figure 3. Graphical summary of the observed statistically significant effects (two-way ANOVA, Tukey’s post hoc test) of the treatments and the tomato varieties on the total Bacteria, Archaea and AOB population based on qPCR results. Orange = Sweetelle variety; blue = Conchita variety. Bars represent the standard deviation. CTRL = standard nutrient solution; N20 = nutrient solution + 20% salt and N; NBA = nutrient solution + Bio-algeen S-90.3.3.
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Figure 4. Quality parameters of harvested tomatoes of the two tested varieties under different nutritional solutions. (A) = L-ascorbic acid, (B) = reducing sugars, (C) = total sugars, (D) = lycopene, (E) = lycopene after 15 days of storage at 12 °C, (F) = lycopene after 15 days of storage at 20 °C. The letters above histogram bars show statistical significance of pairwise comparisons and Dunn’s post hoc test, p < 0.05. Different letters indicate significant differences (p < 0.05). CTRL = standard nutrient solution; N20 = nutrient solution +20% salt and N; NBA = nutrient solution + Bio-algeen S-90.
Figure 4. Quality parameters of harvested tomatoes of the two tested varieties under different nutritional solutions. (A) = L-ascorbic acid, (B) = reducing sugars, (C) = total sugars, (D) = lycopene, (E) = lycopene after 15 days of storage at 12 °C, (F) = lycopene after 15 days of storage at 20 °C. The letters above histogram bars show statistical significance of pairwise comparisons and Dunn’s post hoc test, p < 0.05. Different letters indicate significant differences (p < 0.05). CTRL = standard nutrient solution; N20 = nutrient solution +20% salt and N; NBA = nutrient solution + Bio-algeen S-90.
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Figure 5. Principal component analysis conducted on tomato samples, including compositional and quality parameters. Blue = var. Conchita; Orange = var. Sweetelle; dots = CTRL; squares = N20; diamonds = NBA.
Figure 5. Principal component analysis conducted on tomato samples, including compositional and quality parameters. Blue = var. Conchita; Orange = var. Sweetelle; dots = CTRL; squares = N20; diamonds = NBA.
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Table 1. Quantification of total bacteria, archaea and most dynamical N-cycling key bacterial groups, based on qPCR. The quantities are expressed as Log gene copy number g−1 soil ± s.d. AOB = ammonia-oxidizing bacteria, NFB = nitrogen-fixing bacteria. Two-way ANOVA: effect of time, tomato variety and nutrient solutions were evaluated. Significant effects are presented in bold and with asterisks: * p <0.05; ** p< 0.01.
Table 1. Quantification of total bacteria, archaea and most dynamical N-cycling key bacterial groups, based on qPCR. The quantities are expressed as Log gene copy number g−1 soil ± s.d. AOB = ammonia-oxidizing bacteria, NFB = nitrogen-fixing bacteria. Two-way ANOVA: effect of time, tomato variety and nutrient solutions were evaluated. Significant effects are presented in bold and with asterisks: * p <0.05; ** p< 0.01.
VarietyTimeTreatmentBacteriaArchaeaAOBNFB
SweetelleT1CTRL9.20 ± 0.236.47 ± 0.237.74 ± 0.286.96 ± 0.20
N209.35 ± 0.226.49 ± 0.097.37 ± 0.237.06 ± 0.36
NBA9.39 ± 0.146.59 ± 0.128.35 ± 0.176.87 ± 0.20
T2CTRL9.60 ± 0.116.52 ± 0.127.49 ± 0.067.02 ± 0.27
N209.84 ± 0.036.29 ± 0.197.49 ± 0.306.60 ± 0.15
NBA9.57 ± 0.066.40 ± 0.167.30 ± 0.296.57 ± 0.24
ConchitaT1CTRL9.54 ± 0.176.77 ± 0.248.31 ± 0.497.34 ± 0.15
N208.91 ± 0.076.70 ± 0.167.85 ± 0.207.06 ± 0.14
NBA9.17 ± 0.176.54 ± 0.148.32 ± 0.317.19 ± 0.29
T2CTRL9.53 ± 0.096.61 ± 0.317.43 ± 0.076.83 ± 0.13
N209.35 ± 0.106.24 ± 0.277.01 ± 0.196.41 ± 0.40
NBA9.52 ± 0.166.42 ± 0.238.37 ± 0.107.14 ± 0.29
p (same)
Time0.00011 **0.01104 *0.03957 *0.00307 *
Variety0.02026 *0.254050.07525 *0.1739
Nutrition0.690210.19980.00112 **0.1565
Interaction Var./Nut.0.00489 **0.51660.440570.1215
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Beneduce, L.; Piergiacomo, F.; Sikorska-Zimny, K. Different Nutritional Regimes in a Tomato Soilless System Affect the Bacterial Communities with Consequences on the Crop Quality. Agriculture 2024, 14, 2254. https://doi.org/10.3390/agriculture14122254

AMA Style

Beneduce L, Piergiacomo F, Sikorska-Zimny K. Different Nutritional Regimes in a Tomato Soilless System Affect the Bacterial Communities with Consequences on the Crop Quality. Agriculture. 2024; 14(12):2254. https://doi.org/10.3390/agriculture14122254

Chicago/Turabian Style

Beneduce, Luciano, Federica Piergiacomo, and Kalina Sikorska-Zimny. 2024. "Different Nutritional Regimes in a Tomato Soilless System Affect the Bacterial Communities with Consequences on the Crop Quality" Agriculture 14, no. 12: 2254. https://doi.org/10.3390/agriculture14122254

APA Style

Beneduce, L., Piergiacomo, F., & Sikorska-Zimny, K. (2024). Different Nutritional Regimes in a Tomato Soilless System Affect the Bacterial Communities with Consequences on the Crop Quality. Agriculture, 14(12), 2254. https://doi.org/10.3390/agriculture14122254

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