*Article* **Coir-Based Growing Media with Municipal Compost and Biochar and Their Impacts on Growth and Some Quality Parameters in Lettuce Seedlings**

**Tiago Carreira Martins 1, Rui M. A. Machado 1,\*, Isabel Alves-Pereira 2, Rui Ferreira 2,\* and Nazim S. Gruda <sup>3</sup>**


**Abstract:** The purpose of this study was to develop substrates with little or no peat by combining coir-based growing media with municipal compost and/or acacia biochar, two locally produced renewable resources, and to assess their effects on lettuce seedling emergence and growth, as well as their content in photosynthetic pigments and total phenols. Two experiments were carried out, the first with six mixes using compost and biochar blended with perlite, pine bark, and blonde peat to adjust some physicochemical characteristics. The mixes of coir: compost: pine bark: blonde peat (73:12:5:10, *v*/*v*) and coir: compost: biochar: blonde peat (73:12:10:5, *v*/*v*) had physicochemical characteristics closer to or within the normal range of the substrates. The presence of 12% compost and 10% biochar in the mixtures had no adverse effect on lettuce seed germination and cumulative seed emergence, which ranged from 90 to 99%. The seedling growth in those mixes was vigorous and higher than in other mixtures. Coir-based growing media with municipal solid waste compost and compost plus biochar can reduce the use of peat to a percentage of 5–10% *v*/*v* and the use of 17–22% *v*/*v* of locally produced renewable resources. In addition, mixtures affected the total phenol content in the lettuce leaves. Future research is needed to assess the behavior of seedlings after their transplantation.

**Keywords:** *Lactuca sativa* L.; sustainable substrate; peat alternatives; pH; electrical conductivity; seedling emergence; total phenols

#### **1. Introduction**

Transplanting seedlings is the most-common method for vegetable crop establishment in Portugal and it is increasing worldwide. It promotes plant growth, early maturation, yield, harvest-time plant uniformity, and efficient land use. Furthermore, it may require less irrigation water and herbicides than crops established by sowing [1].

Peat or peat-rich substrates are the most-common growing medium for vegetable transplants. However, peat is a non-renewable resource and its extraction has detrimental effects on the environment and ecosystems. In addition, peatlands are natural carbon sinks [2,3]. Hence, when peat is used as a substrate, stored carbon is released, negatively affecting CO2 balance [3]. Therefore, peat use in horticulture is restricted or regulated, mainly in some European countries [4,5]. As a result, using less peat is the primary goal of soilless-grown plants today [2,5,6].

An alternative strategy could be to use selectively collected municipal solid organic waste (MSW) and biochar, two organic resources, in substrates [7–9]. The use of MSW contributes to reducing organic waste accumulation in landfills and carbon footprint while

**Citation:** Martins, T.C.; Machado, R.M.A.; Alves-Pereira, I.; Ferreira, R.; Gruda, N.S. Coir-Based Growing Media with Municipal Compost and Biochar and Their Impacts on Growth and Some Quality Parameters in Lettuce Seedlings. *Horticulturae* **2023**, *9*, 105. https://doi.org/10.3390/ horticulturae9010105

Academic Editor: Angelo Signore

Received: 2 December 2022 Revised: 6 January 2023 Accepted: 9 January 2023 Published: 12 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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also boosting nutrient recycling [10–12]. Perhaps the most beneficial effect of compost inclusion in a growth medium is its nutritional contribution. Matured compost may act as a slow-release fertilizer [13]. Additionally, it might have a biostimulant effect and suppress plant diseases caused by various pathogens and pests [1,14]. Organic compost tends to have peat-like porosity and aeration properties [15].

Biochar can be a beneficial component in substrates as it can increase plant growth, reduce dependence on non-renewable substrate components, and contribute to carbon sequestration [7]. In addition, biochar can replace peat [16,17], perlite, and vermiculite without compromising seedling quality [17].

Raw materials used as growing media constituents should be free from phytotoxic compounds [18] and should demonstrate good chemical properties, such as a suitable pH [19,20] and the content of certain elements and/or salt content [5,21–23]. However, municipal organic compost and biochar have some chemical characteristics, such as pH and electrical conductivity (EC), that may negatively affect plant growth, limiting their use as stand-alone substrates. For example, most biochar often has a high pH [20,24]. The pH of biochar varies depending on the feedstock type, temperature during its production [25,26], and, as recently investigated, particle size. The fractions from the same biochar can have different pH levels [5,20]. In addition, MSW compost usually has high electrical conductivity and pH values [27].

Finally, it should be noted that plants are more sensitive to salt stress at earlier plantgrowth stages (germination, seedling, establishment) than plants at later stages [28]. Therefore, organic composts may also have human pathogens. To reduce them in the future, the compost must be certified as to the raw materials used and the maximum temperature and time of exposure to these during the thermophilic phase. The use of green compost can reduce the risk of human pathogens' presence [4]. A mixture of MSW and coir could overcome its limitations. Coir has a low pH and density, good physical stability, aeration, and water-holding capacity [9,29–31]. The effects of biochar incorporation on plant growth in container substrates depend on biochar properties, plant type, percentage of biochar applied, and other container substrate components mixed with biochar [32]. For example, tomato plant heights and bell pepper (*Capsicum annuum* L.) dry weights increased with the addition of 1, 3, and 5% (*w*/*w*) to a soilless mixture of coconut fiber and tuff (volcanic ash) [33]. On the other hand, the mix of coir with MSW and biochar in a ratio of 3:1 (75% coir by volume in the mixture) decreased the EC and pH, but not to adequate levels [34].

Municipal compost and biochar are two locally produced renewable resources whose use lessens Portugal's dependency on peat and coir imports, keeps organic waste out of landfills, and reduces the carbon footprint [35] and greenhouse gas (GHG) emissions [12].

This research aimed to reduce the use of peat in substrates by investigating the suitability of coir-based substrates in combination with selectively harvested municipal solid organic waste and/or acacia biochar for successful horticultural plant cultivation. Further, we investigated the effect of mixes on lettuce seedling emergence, growth, and the content of photosynthetic pigments and total phenols.

#### **2. Materials and Methods**

Two experiments were conducted at the Center of Studies and Experimentation of Mitra in Évora, Portugal (38◦57 N. 8◦32 W. elevation 200 m).

We designed the first experiment to create mixes with the characteristics of an ideal substrate. First, we mixed a coir-based medium with municipal compost or biochar to achieve adequate substrate characteristics. Next, we added perlite, blonde peat, and pine bark to blend the mixes.

Further, we evaluated how adding fertilizer to mixes affected their physicochemical properties. Finally, we investigated the emergence and growth of lettuce seedlings and the levels of photosynthetic pigments and total phenols in the leaves.

The second experiment was realized in sequence with the first. The goal was to enhance the mixes' physicochemical properties, particularly pH and EC. Moreover, we aimed to evaluate these effects on seedling growth, photosynthetic pigment content, and total phenol content in a lettuce.

#### *2.1. Components of Mixes*

The following components were used to make the mixtures: coir, municipal solid organic compost, acacia wood biochar, perlite, pine bark, and blonde peat.

A 100% coir pith was used. The coir had a pH of 5.5 to 6.0, an electrical conductivity (EC) greater than 1.5 dS m−1, granulometry 0–10 mm, total porosity = 95% *v*/*v*, air = 25% *v*/*v*, and CEC = 60–120 meq/100 g. The physiochemical characteristics of compost (Nutrimais, Lipor Company, Baguim do Monte, Portugal) and acacia wood biochar (Ibero Massa, Oliveira de Azeméis, Portugal) are presented in an earlier study [35]. Biochar was pyrolyzed at a temperature of 400 to 500 ◦C. The raw materials used in the "Nutrimais" manufacturing process include horticultural products, food scraps carefully selected from restaurants, canteens, and similar establishments, forest exploitation residues (e.g., branches and foliage), and green residues (e.g., flowers, grasses, and pruning). According to the manufacturer, the compost used in this study is free of pathogens. The compost had wood fragments larger than 1 cm, which were ground down to a particle size of between 3 and 4 mm to allow for the introduction of the mixtures into the wells of the plastic trays.

Perlite (Knauf, Dortmund, Germany) has particles from 2 to 6 mm (coarse perlite), is pH-neutral, and is chemically inert. The pH and the electrical conductivity (1:5 H2O) of the blonde peat (Greenterra Ltd., Riga, Latvia) were, respectively, 5.5 to 6.5 and 1 dS m<sup>−</sup>1. The pH and the EC of the components in the mixes were measured in the aqueous extract (1:5 substrate: distilled water, *w*/*v*) according to the methodology presented by Machado et al. (2021). The nitrate (NO3-N) levels in aqueous extracts (1:5 substrate:water, *v*/*v*) were also determined using an ion-specific electrode and meter (Crison Instruments, Barcelona, Spain).

#### *2.2. Seedling-Growth Experiments*

#### 2.2.1. Growth Conditions and Mixes

The experiments were conducted between 27 March and 11 May 2022. The average daily temperature inside the greenhouse at the shoot seedling level ranged from 17 to 26 ◦C. These values were within the range of temperatures suitable for the germination of lettuce seeds (15 to 25 ◦C) [36].

Solar radiation ranged from 127.3 to 348.8 W·m−2·d<sup>−</sup>1. Seeds of lettuce (*Lactuca sativa* L. cv. Grand Rapids) with a mean germination rate of 95%, evaluated through a germination test, were used in both experiments.

Experiment 1 was carried out with twelve treatments: six mixes unfertilized and fertilized (Table 1).


**Table 1.** Constitution and proportion of the different components in the mixes (experiment one).

1—C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat.

The mixes were fertilized with a 1 g controlled-release fertilizer (6N-5.3P-10K + 1.2% Mg + 0.02% B + 0.05% Cu, 0.2% Fe, 0.06% Mn 0.02%, Mn and 0.015% Zn) l−<sup>1</sup> of growing media. Each replicate's treatments (mixes) occupied two rows of plastic trays (26 wells). Each well had a volume of 25 cm3.

The second experiment was carried out with five mixes, whose constitution is presented in Table 2. At each mix, we added 1 g of controlled-release fertilizer (6N-5.3P-10K + 1.2% Mg + 0.02% B + 0.05% Cu, 0.2% Fe, 0.06% Mn, 0.02% Mn, and 0.015% Zn) per L of growing medium.

**Table 2.** Constitution and proportion of the different components in the mixes (experiment two).


1—C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat.

Both experiments were arranged in a complete randomized block design with five replicates. Each replicate's treatments (mixes) occupied two rows of plastic trays (26 wells). Each well had a volume of 25 cm3. The seeds were manually sown in plastic trays; one seed was placed in each compartment at 1.5 cm depth and covered. Nursery trays were watered by micro sprinklers three to six times per day in order to keep the substrate well-moistened.

Fresh tap water was used for irrigation; it had an electrical conductivity (EC) of 0.3 d*S* m−1, a pH of 7, and 0.10 to 0.30 mmol L−<sup>1</sup> NO3, 0.12 to 0.20 mmol L−<sup>1</sup> Ca, 0.15 to 0.22 mmol L−<sup>1</sup> Mg, and 2.1 mmol L−<sup>1</sup> Cl and 0.7 mmol L−<sup>1</sup> Na.

#### 2.2.2. Measurements

In both experiments, the same methodology was used to evaluate the initial physicochemical properties of the mixes and their effects on the emergence and growth of seedlings. Thus, the measurements made will be presented together. The initial physicochemical characteristics of the mixtures measured were pH, EC, mass wetness, moisture content, total porosity, and bulk density. The pH and the EC were measured in the aqueous extract (1:5 substrate: water, *w*/*v)* according to the methodology presented by [9]. Moisture content, total porosity, and bulk density were determined following the methodology described in [37]. The number of seedlings that emerged in each mix of all replicates was recorded throughout the experimental period. In each treatment, six seedlings were randomly collected from each replication. In these, the weight of the root system and the shoot, the number of leaves, and the leaf area were measured. The root system of the seedlings was separated from the substrate by washing it in running water with a net underneath to avoid root loss. We measured the leaf area using a leaf area meter (LI-COR Model LI–3000A).

Leaf samples of 0.5000 g from three lettuce plants were collected from all repetitions in each treatment. The seedlings were macerated in a mortar and homogenized in 4 mL of methanol/water (90:10, *v*/*v*; MW90 extract) or methanol/water (80:20, *v*/*v*; MW80 extract) for 1 min. Aliquots of the methanolic extracts MW90 or MW80 were obtained after centrifugation at 4 ◦C and 6440× *g* for 5 min was preserved at −20 ◦C for later use [37].

The following equations were used to determine the concentration (mg/100 g FW) of chlorophyll a (Chl a), chlorophyll b (Chl b), and carotenoids (Cc) of MW90 extract, where A denotes absorbance, following [38]:


The total phenolic compound (TPC) content in the MW80 extract was determined according to that described by [39] by reacting an appropriate volume of sample or standard with 1/10 diluted Folin–Ciocalteau reagent and 7.5% sodium carbonate. After stirring the reaction mixture in the vortex, we waited for 90 min at room temperature in the dark. The absorbance

of the chromophore then formed and was read at 760 nm. Finally, the TPC concentration, expressed as milligrams of gallic acid equivalent (GAE) per 100 g of fresh weight (FW), was calculated using a calibration curve (GAE, *n* = 6 concentrations from 0 to 200 mg/L).

#### 2.2.3. Data Analysis

Data were analyzed using analysis of variance using SPSS 25 software (Chicago, IL, USA) licensed to the University of Évora. The means were separated at the 5% level using Duncan's new multiple-range test.

#### **3. Results and Discussion**

#### *3.1. Physicochemical Characteristics of the Components*

Table 3 shows some of the characteristics of the components used in the mixes. The pH and EC were relatively high in the municipal solid organic compost, as was the nitrate content. Further, also noteworthy was the high pH of biochar (8.76), while the EC was low (0.22 dS m<sup>−</sup>1) (Table 3).


**Table 3.** Physicochemical characteristics of the components.

1—MSW—Municipal solid organic waste. 2—The granulometry of the biochar was also determined through the use of sieves, as described by [40]. The biochar granulometry, expressed as a percentage by weight, was: ≥ 2 mm (28.11%); ≥1 mm < 2 mm (30.05%); ≥ 0.5 mm < 1 mm (15.60%); < 0.5 mm (26.24%).

#### *3.2. Experiment 1*

3.2.1. Initial Physicochemical Characteristics of the Mixes

The physicochemical properties of the mixes were unaffected by the interactions of treatments with the fertilizer supply (Table 4). Despite the initial pH of biochar being higher than that of MSW (Table 1), the pH of mixtures containing biochar ranged from 7.14 to 7.77, while that of mixtures including compost ranged from 7.81 to 8.09. This was probably due to the high cation-exchange capacity of composts, which increased the buffering capacity of the growing medium [41]. On the other hand, fresh biochar typically has a low CEC, as the high temperatures during pyrolysis reduce the concentration of functional groups (e.g., –OH, –COOH, –CH, and –C=O) [42].

**Table 4.** Physicochemical characteristics of the mixes of experiment 1.


1—C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat. 2—Means followed by different letters within a column are significantly different. \*\*\* significant at *p* < 0.001 level. NS—not significant. Mean separation was performed using Duncan's multiple-range test. Means are based on four replicates. 3—Mass wetness—the water content of a sample on a dry mass basis; this is calculated as (wet weight—dry weight)/dry weight.

Regardless of the differences, the pH values of the different blends were higher than the maximum value of the adequate range for plant growth in substrates (6.5) [43–45]. As a result, the ratios of the components in the mixes of experiment 2 (Table 2) were altered to decrease the pH.

The EC of mixes with biochar (ranging from 0.98 to 1.60 dS m−1) was much lower than that of mixtures with MSW (Table 4). Regarding the mixes with biochar, compost led to increases in EC of 1.2 to 2.3 dS m−1. EC values in mixes with compost ranged from 2.80 to 3.42 dS m−<sup>1</sup> (Table 3), which may influence seed germination and seedling growth. Lettuce is moderately sensitive to salinity, having a salinity threshold of 2 dS m−<sup>1</sup> in soil. Nevertheless, plants in their early stages (germination, seedling) are more susceptible to salt stress than plants in their later stages [28]. Although the EC value of substrates varies depending on the method used to determine it [46,47], the highest EC level in the range appropriate for growing plants in substrates is generally higher than in soil. According to Martinez and Roca [44], the appropriate range of the EC for substrates ranges from 0.75 to 3.5 dS m<sup>−</sup>1, but they did not discriminate the method used to determine EC. Salinity levels ranging from 2 to 3.49 dS m−<sup>1</sup> in saturated media extract are satisfactory for most plants, but the growth of some sensitive plants may be reduced [46]. The EC in mixes with MSW was lower in the mixture (coir + MSW + perlite) (2.8 d*S* m<sup>−</sup>1) than in the other mixes. As a result, the change in the proportions of the components in this mixture in experiment 2was reduced (Table 2).

Bulk density ranged from 0.18 to 0.21 g cm−3. These values were adequate for substrates [45,48,49]. However, the bulk density of an ideal substrate for vegetable seedlings should not exceed 0.4 g cm−<sup>3</sup> [43].

Although perlite is generally added to the substrate to increase the proportion of large pores and reduce the water-holding capacity, the mass wetness was higher in mixes with perlite. This may be due to the low proportion of perlite added (2%) (Table 1). All mixtures had a total porosity above 85% (from 97.9 to 99.3%), which is regarded as suitable for substrates [46] (Table 4). Moisture content ranged from 78.2 to 82.8, with the coir + biochar + perlite + biochar mix having a lower moisture content.

#### 3.2.2. Seed Emergence

The addition of fertilizer and the interaction between treatments did not affect the seedling emergence. At 5 DAS (days after sowing), the percentage of seed emergence was affected by the mixture (Figure 1). The seed emergence was higher in mixes with biochar at 5 DAS, ranging from 97 to 100%. Low rates of biochar can have a stimulatory effect on germination [42]. The seed emergence precocity was lower in the coir + MSW + blonde peat + pine bark (65:20:5:10. *v*/*v*) and coir + MSW + pine bark (70:20:10, *v*/*v*) mixes as compared to other mixtures. This may be due to the high percentage of MSW in the mixture (20%, *v*/*v*) (Table 1). Reference [50] reported that the percentage of MSW in mixes with peat affected seed emergence. However, at 16 DAS, the cumulative seedling emergence ranged from 98 to 100% and was not significantly affected by the mixes (*p* < 0.05). This indicates that the presence of MSW and biochar in percentages ranging from 14 to 20% *v*/*v* did not affect the germination since the average cumulative seedling emergence and seedling survival were higher than the average germination rate of the seeds determined (95%).

#### 3.2.3. Photosynthetic Pigments and Total Phenols

The interaction between treatments significantly affected leaf photosynthetic pigments and total phenol content (Table 5). However, adding fertilizer appears to increase the content of chl a, chl b, total chl, and carotenoids (Cc) in all substrates. Nutrient availability is essential for photosynthetic pigment biosynthesis [51]

**Figure 1.** Influence of mixes on cumulative seedling emergence (C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat).

**Table 5.** Effect of fertilization and mixes on leaf photosynthetic pigments and in total phenol content.


1—C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat. FW—Fresh weight. 2—Means followed by different letters within a column are significantly different. \*\*\* significant at *p* < 0.001 level. NS—not significant. Mean separation was performed using Duncan's multiple-range test.

Chl a, Chl b, and total Chl contents were higher in the fertilized coir + biochar + perlite mix (Table 5). Chl a, Chl b, and total Chl contents ranged from 5.8 to 14.7, 7.98 to 19.2 and 13.8 to 33.9 mg/g of leaf fresh weight, respectively. These values were in the same range or slightly higher than those reported by [51] for lettuce seedlings. Chl b, as reported by [52], also had higher contents than Chl a.

Regarding the total content of phenols, it appears that adding fertilizer contributed to its decrease, except in the coir + biochar + perlite mix. This indicates that the seedlings may have been subjected to significant abiotic stress in the unfertilized mixtures, probably due to nutrient deficiency. Nutrient deficiency in lettuces increases total phenol content [53].

The fertilized mixes with four components, with blonde peat (10%, *v*/*v*), had the highest levels of total phenols (Table 5). The TPC in the different treatments ranged from 54.43 to 149.37 (mg GAE 100−<sup>1</sup> FW). These values are lower than those mentioned in lettuce seedlings by [23], which range between 400 and 600 mg GAE 100−<sup>1</sup> FW. However, as is known, TFC is affected by many factors, including genotype, growing conditions, and others [54].

3.2.4. Seedling Growth

Growth parameters, except for dry matter %, were not significantly affected by the interaction of treatments (Table 6).


**Table 6.** Effect of the mix on seedling growth, experiment 1.

1—C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat. 2—Means followed by different letters within a column are significantly different. \* and \*\*\* significant at *p* < 0.05 and 0.001 levels, respectively. NS—not significant. Mean separation was performed using Duncan's multiplerange test.

Lettuce seedling growth was significantly affected by fertilizer addition and growing media mixtures (Table 6). In unfertilized mixes, seedlings grown in biochar grew less than those grown in mixes with MSW. This indicates that MSW contributed to seedling nutrition and that mixes with biochar have a lower ability to feed them. The EC of mixes with biochar had low EC values (0.98 to 1.60 dS m−1). Biochar had a lower EC and nitrate level than MSW (Table 3). Biochar has a low content of extractable macronutrients, except for K [55]. Chrysargyris et al. [24] also reported that applying fertilizer to mixtures containing biochar can increase the growth of lettuce seedlings, but it depends on the percentage of biochar in the mix.

On the other hand, the biochar particle size of the fractions from the same biochar could also influence pH and the nutrient availability of Ca and Mg. This could lead to nutrient imbalances during the cultivation of plants [5,20]. Thus, future research is required to determine whether the lower growth of seedlings on substrates containing biochar is due to a nutritional deficit.

Additional fertilizer to the mixes with biochar increased seedling growth (Table 6). Fertilizer addition to mixes with MSW also increases the shoot and total dry weight. The total dry weight of the seedlings in mixtures containing the same proportion of MSW and biochar (Table 1), without or with fertilizer, was significantly higher in the mixtures containing MSW.

Seedling growth in coir + MSW + pine bark + blonde peat (65:20:5:10, *v*/*v*) and coir + MSW + pine bark (70:20:10, *v*/*v*) mixes was higher than that in the other mixes (Table 6). Compared to the Coir + MSW + pine bark mix, the mix with 10% blond peat (Coir + MSW + pine bark + blonde peat) boosted shoot dry and total dry weight by nearly 30%. The seedlings grown in these mixes, despite having a pH > 7.9 and an EC > 3.2 (Table 3), presented a higher growth than those grown in the other mixes. They did not present any visual symptoms of nutrient deficiencies or excess salts and the roots were healthy (Figure 2). This may be due to the presence of humic acids, which account for 13% [34] of the dry weight of the compost, which was higher in these mixes (20%). Humic acids may contribute to the availability of nutrients, especially micronutrients, by chelating and co-transporting micronutrients to plants [56] and increasing H+ exudation [57].

**Figure 2.** Seedlings grown in coir + MSW + pine bark + blonde peat (65:20:5:10, *v*/*v*) (**A**—fertilized, **B**—unfertilized) and in coir + biochar + pine bark + blonde peat (65:20:5:10, *v*/*v*) (**C**—fertilized, **D**—unfertilized).

Although plants are more sensitive in the initial phase, lettuce seedlings from the mixes with high EC (> 3.2) did not present any visual symptoms of excess salts. According to [58], the initial EC of the mixes with compost assessed in the saturated extract should not exceed 2.5 d*S* m−<sup>1</sup> for tomato seedlings, which are more tolerant to salt stress than lettuce. However, the response to salinity depends on environmental conditions and the moisture content of the substrate. In the present study, the effects of salt stress may have been reduced due to mild temperatures and frequent irrigation that decrease the substrate's osmotic potential. On the other hand, humic acids may also reduce the salt stress effects since they may increase osmoprotection and ion homeostasis [59,60].

#### *3.3. Experiment 2*

#### 3.3.1. Initial Physicochemical Characteristics of the Mixes

The average pH values were affected by the mix. The mixes with blonde peat had a lower pH than other mixes (Table 7). In the mixes' coir + organic compost + blonde peat and coir + organic compost + pine bark + blonde peat, the pH (6.56) was within the range considered suitable for substrates. In the mixture with biochar (10%. *v*/*v*), the average pH value (7.16) was slightly higher than the maximum value of the adequate range. Except for the coir + MSW + perlite mix, the goal of lowering the initial pH in the mixes with MSW was met. The mixes also affected EC, ranging from 2.44 to 2.79 dS/m (Table 7). Despite the differences in EC of the mixes, they are within an adequate range for substrates, as previously mentioned. The addition of blonde peat to the mixtures contributed to the decrease in EC (Table 7).

Bulk density ranged from 0.12 to 0.14 g/cm3 (Table 7). As previously mentioned, these values were within an adequate range for substrates. The total porosity was not significantly affected by the mixtures and was above 85%, as required for substrates. Mass wetness was consistently higher than 6.32 g of water per g substrate in all growing media, and their values increased relative to the previous experiment.


**Table 7.** Physicochemical characteristics of the mixes of experiment 2.

1—C—Coir, B—Biochar, MSW- Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP- Blonde peat. 2—Means followed by different letters within a column are significantly different. \* and \*\*\* significant at *p* < 0.05 and 0.001 levels, respectively. NS—not significant. Mean separation was performed using Duncan's multiple-range test. Means are based on four replicates. 3—Mass wetness—the water content of a sample on a dry mass basis; this is calculated as (wet weight—dry weight)/dry weight.

#### 3.3.2. Seed Emergence

Seedling emergence in coir + compost + blond peat was lower than in the other mixes but still very high (91%) (Figure 3). At 9 DAS, the emergence was rapid in the remaining substrates, ranging from 91 to 100%. In these mixes at 23 DAS, cumulative seedling emergence ranged from 97 to 100%. The presence of MSW (12–13%) and MSW (12%) + biochar (10%) in the mixture, as in the previous experiment, had no significant effect on seed germination and cumulative seed emergence.

**Figure 3.** Influence of mixes on cumulative seedling emergence (C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat).

#### 3.3.3. Photosynthetic Pigments and Total Phenols

The mixes affected the average content of photosynthetic pigments in the leaves. For example, seedlings grown in the coir + MSW + blonde peat (80:12:8. *v*/*v*) mix had higher levels of chl a, chl b, total chl, and carotenoids in their leaves than plants grown in the other mixes (Table 8).

Chl a, chl b, and total Chl content ranged from 10.1 to 13.2, 13.5 to 15.4, and 24.8 to 29.7 mg/g of leaf fresh-weight, respectively. These values were slightly higher than those reported by [52] for lettuce seedlings.

The average TPC in the different mixes ranged from 45.79 to 70.04 mg GAE 100−<sup>1</sup> FW (Table 8). These values were lower than those reported by [24] for lettuce seedlings.

The TPC of seedlings grown in the mixes coir + MSW + biochar + blonde peat (45.79 mg GAE 100 g−<sup>1</sup> FW) and Coir + MSW + pine bark (49.05 mg GAE 100 g−<sup>1</sup> FW) was lower than that grown in the other mixes. In lettuce seedlings grown on substrates with blonde peat and biochar, the total phenol content decreased with the addition of biochar, regardless of fertilization [24].

The TPC in the seedlings of the other mixes was higher than in previous mixes. Seedlings with high total phenol content may have a more remarkable ability to resist abiotic stress after transplantation. Thus, future research is required to assess how the seedlings from different mixes behave following transplantation.


**Table 8.** Effect of mix on leaf photosynthetic pigments and in total phenol content.

1—C—Coir, B—Biochar, MSW—Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat, FW—fresh weight. 2—Means followed by different letters within a column are significantly different. \* and \*\* significant at *p* < 0.05 and 0.01 levels, respectively. NS—not significant. Mean separation was performed using Duncan's multiple-range test.

#### 3.3.4. Seedling Growth

The mixes significantly affected seedling growth, which was higher in the mixtures with four components than in the mixtures with three (Table 9). Seedling shoot (0.20 g/plant) and total dry weight (0.27 g/plant) were higher in seedlings grown in the coir + MSW + pine bark + blonde peat (73:12:5:10, *v*/*v*) mix than the other mixes. It should be noted, nonetheless, that seedling shoot fresh weight, leaf area, number of leaves, and total dry weight, grown in the mix with biochar coir + MSW + biochar + blond peat (73:12:10:5; *v*/*v*), did not differ significantly from those grown in the coir + MSW + pine bark + blonde peat mix (Table 9). However, the shoot dry weight of the seedlings grown in coir + MSW + biochar + blond peat was lower than that grown in the mix coir + MSW + pine bark + blonde peat due to a higher allocation of biomass in the root system. This may indicate that seedlings in a mixture with biochar were subject to higher growth-constraining resources than those grown in the coir + MSW + pine bark + blonde peat. When nutrients are scarce, roots may allocate more biomass [61]. The two previous mixes had the highest seedling dry-matter accumulation, and their physicochemical characteristics were within an adequate range for substrate characteristics or slightly higher in the case of pH.

**Table 9.** Effect of the mix on seedling growth, experiment 2.


1—C—Coir, B—Biochar, MSW- Municipal solid organic waste, P—Perlite, Pi—Pine bark, BP—Blonde peat. 2—Means followed by different letters within a column are significantly different \* significant at *p* < 0.05 level. NS—not significant. Mean separation was performed using Duncan's multiple-range test.

The growth parameters do not correlate with the content of leaf photosynthetic pigments or total phenol. However, seedling total dry weight increased linearly with total shoot Chl (seedling total dry weight (g) = 0.466 × (total shoot Chl) + 0.0832, r2 = 0.824, *p* < 0.01) that was higher in mixes with four components. Seedling survival after transplanting is related to dry-weight accumulation. These results indicate that the mixes may use between 17 and 22% *v*/*v* of locally produced renewable resources and are suitable for lettuce seedling growth.

The percentage of compost could be further increased when green raw materials are used. For instance, ref. [62] suggests the use of up to 50% compost as a component in a growing medium. Green waste compost is made from greenhouse vegetables, nursery shrubs, branches, plant trimmings, leaves, and grass from gardens, public green spaces, and other landscapes. The woody material is chopped, mixed with the remaining green residues, and gathered in clamps [4,63].

In the remaining mixes, the shoot fresh weight, shoot total dry weight, leaf area, and number of leaves did not differ significantly (*p* < 0.05) from those mixes grown in coir + MSW + biochar + blond peat (73:12:10:5; *v*/*v*). Despite the differences in seedling growth in different mixes, all seedlings from different treatments had well-developed shoot and root systems. The seedlings presented vigorous growth without any visual symptoms of deficiencies or toxicities. These results agree with those from [64]. Lettuce seedling growth in a fine-wood fiber substrate showed a good development in root mass and a lower leaf/root dry weight ratio, even by reducing the pot size. The pot size decreased, to some degree, the quality of lettuce seedling parameters. However, no differences in lettuce yield were found after transplanting to the field [64]. According to the authors, culture methods, such as, for instance, irrigation and good root development of seedlings in wood fiber substrates, have been responsible for these results. Thus, an adapted irrigation strategy to the substrate used plays a crucial role [65,66].

As previously mentioned, in addition to affecting biomass accumulation, in our study, the mixes also affected leaf TPC, which may influence seedling tolerance to abiotic stress after transplanting. Therefore, future research will be needed to assess the behavior of the seedlings after transplantation and compare their growth to that of seedlings grown on commercial substrates.

#### **4. Conclusions**

The findings of this study show that coir-based growing media with municipal solid waste compost and compost plus biochar can reduce the use of peat to a percentage of 5–10% *v*/*v* and the use of 17–22% *v*/*v* of locally produced renewable resources. The initial EC and pH of the mixes coir + MSW + pine bark + blonde peat (73:12:5:10 *v*/*v*) and coir + MSW + biochar + blonde peat (73:12:10:5, *v*/*v*) were within or were slightly higher than the maximal values of the range considered adequate for substrates. The presence of MSW (12%) and MSW (12%) + biochar (10%) in the mixtures had no adverse effects on seed germination and cumulative seed emergence. The seedling growth in those mixes was vigorous and higher than that of those grown in other mixtures. However, further research must compare lettuce seedling growth in these and commercial mixes and their use to grow other vegetable transplants. In addition, coir-based mixes affect total phenol content. As total phenol content increases tolerance to abiotic stress, future studies are needed to evaluate the behavior of the seedlings of the mixes after transplantation.

**Author Contributions:** R.M.A.M. conceived and designed the experiments; performed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools, or data; and wrote the paper. T.C.M. performed the experiments and analyzed the data. I.A.-P. and R.F. performed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools, or data; and wrote the paper. N.S.G. reviewed, corrected, and edited the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded by National Funds through FCT—Foundation for Science and Technology under the Project UIDB/05183/2020.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **Effects of Mixes of Peat with Different Rates of Spruce, Pine Fibers, or Perlite on the Growth of Blueberry Saplings**

**Laima Cesonien ˇ e˙ 1,\*, Riˇcardas Krikštolaitis 2, Remigijus Daubaras <sup>1</sup> and Romas Mažeika <sup>3</sup>**


**Abstract:** Investigations of substrates for growing plant saplings is the basis for the search for new components. Currently, large numbers of saplings are grown for blueberry plantations. Studies on the use of various organic and inorganic components in substrates is relevant in order to reduce the amount of excavated peat. The goal of this study was to analyze the effects of mixes of peat with different rates of spruce, pine fibers and perlite on the growth of blueberry saplings. To define the suitability of substrates, plant vigor assessments of the cultivar 'Duke', including plant height and leaf weight, as well as the chlorophyll fluorescence, content of extractable macronutrients and organic carbon in leaves, were investigated. The best effect on the growth of blueberry saplings, the optimal content of macronutrients in the leaves, was shown for substrates in which a part of the peat was replaced by 15–45% *v*/*v* of pine wood fiber and by 15–30% *v*/*v* of spruce wood fiber. Pine bark fiber in the mix should not exceed 30% *v*/*v*. The addition of spruce bark fibers in the different rates had a negative effect on the vegetative growth of the saplings. The quantity of peat in the substrates can also be significantly reduced by adding 15–45% *v*/*v* of perlite. These results confirm that pine and spruce fibers or perlite in substrates for blueberry sapling growing could reduce the demand for peat and should significantly contribute to the preservation of unique wetland ecosystems.

**Keywords:** blueberry; fiber; peat; substrate; sapling

#### **1. Introduction**

Substrates (media) for the cultivation of berry plants are an important component of a sustainable food production chain. The use of suitable growing substrates in modern industrial horticulture meets the needs of plants and ensures their productivity. Peat currently represents 77–80% of the growing substrates used annually in the horticultural industry in Europe [1]. Peat is an extremely important component in substrates; however, its extraction threatens sensitive ecosystems, causes carbon sinks, and increases greenhousegas emissions [2–4]. Different studies on bogs have confirmed that these ecosystems can substantially contribute to reducing atmospheric greenhouse gases [5,6].

Therefore, substrates in which peat can be replaced by alternative components of organic or mineral origin are relevant to preserving unique wetland ecosystems. The suitability of various growing substrates in horticulture has been studied, i.e., certain quality parameters have been evaluated, including the degree of decomposition, the content of extractable nutrients, pH, bulk density, electrical conductivity and porosity. Various scientific sources indicated the possibility of using tree or coconut fibers, compost, tree bark, perlite, and other components that can be mixed to create appropriate growing substrates [7,8]. When studying substrate compositions, the vegetative growth of plants should be assessed because the substrate should provide plants with an appropriate amount of water and nutrients [9].

**Citation:** Cesonien ˇ e, L.; Krikštolaitis, ˙ R.; Daubaras, R.; Mažeika, R. Effects of Mixes of Peat with Different Rates of Spruce, Pine Fibers, or Perlite on the Growth of Blueberry Saplings. *Horticulturae* **2023**, *9*, 151. https:// doi.org/10.3390/horticulturae9020151

Academic Editors: Nazim Gruda, Rui Manuel Almeida Machado and Erik van Os

Received: 5 December 2022 Revised: 18 January 2023 Accepted: 19 January 2023 Published: 24 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

47

Recently, there has been a rapid increase in interest in blueberries and cranberries in Europe and around the world. Consequently, plantations of species from the Ericaceae Juss. family have increased the demand for large quantities of planting material. Wild species of the genus *Vaccinium* L. grow in areas such as high moors and bogs, where the soil may be peaty and the pH ranges from 2.6 to 6.0 [10]. As Hoover et al. [11] reported, blueberries tolerate a wide range of soils. Notwithstanding, it was found that standard substrates containing higher amounts of fertilizers, especially nitrogen fertilizers, are not suitable for these plants [12]. When searching for substrates for blueberry, it is important to consider the characteristics of its roots. Blueberry roots do not have root hairs, and the thin roots that are responsible for water and nutrient absorption are inhabited by mycorrhiza [13].

In the production of substrates, renewable resources, such as wood chips and tree bark, can be used. Such substrates contribute to the utilization of logging waste [14,15]. Lignin, which is found in plant cell walls, degrades more slowly compared to cellulose or hemicellulose and the degradation process of the substrate also slows down [16]. Meanwhile, the bark of trees is rich in organic compounds (lignin, terpenes, fats, resins, sterols, glycosides, tannins, saccharides, acids, and others), which can change the quality of the substrates and affect the germination of plant seeds or the growth of saplings. It was determined that the quantitative and qualitative chemical composition of the bark of different tree species varies, and it is important to determine these variations. The bark's compounds can affect the chemical characteristics of the substrates differently, for example, approximately 8% mannose has been found in spruce bark and approximately 9% arabinose in pine bark [7,17]. Other studies showed that the phloem and the outer bark are richer in chemical compounds than the wood and also differ significantly among wood species [18]. Kemppainen et al. [19] investigated Norway spruce bark and detected a significant amount of tannins, 10.0%. Other researchers reported that fibrous materials are strong contenders in the replacement of peat in growing media, with a focus on the physical properties [16]. As Vandecasteele et al. [20] indicated, plant fibers have the potential for peat replacement and can provide protection against plant diseases.

Perlite is a non-renewable resource and is used throughout the world in horticultural applications. The physical and chemical properties of perlite as an component of substrates and the effect of this material on human health have been analyzed, and different studies confirmed that perlite can improve porosity and oxygenation to plant roots [21].

Based on these previous studies, we hypothesized that the mix of spruce and pine fibers or perlite additions with peat could ensure the growth and quality of blueberry saplings. In this experiment, the effects of mixes of peat with different rates of spruce or pine fibers and perlite on vegetative growth, the content of extractable macronutrients and chlorophyll fluorescence in the leaves of blueberry saplings were studied.

#### **2. Materials and Methods**

#### *2.1. Plant Material and Substrate Composition*

Five hundred saplings of the highbush blueberry cultivar 'Duke' were purchased from a commercial nursery PLANTIN (Poland). Plants were propagated in in-vitro cultures in the laboratory of this nursery and were replanted to multi-pots after acclimatization. For this experiment, saplings with 1–2 lateral branches reaching a height of 8–12 cm were used. The saplings were transplanted into 2.0 L plastic containers filled with the appropriate substrates. In each substrate variant, thirty saplings were planted.

The substrates were composed of Scots pine *Pinus sylvestris* L. and Norway spruce *Picea abies* (L.) H.Karst. wood or bark fibers and high moor peat, which were used in various proportions. The experiment included 15 treatments: five mixes (peat + fiber of pine wood, peat + fiber of spruce wood, peat + fiber of pine bark, peat + fiber of spruce bark, and peat + perlite), each with three rates (Figure 1, Table 1).

**Figure 1.** Components of the studied substrates: (**a**)—high moor peat, (**b**)—fiber of pine bark, (**c**)—fiber of pine wood, (**d**)—fiber of spruce bark, (**e**)—fiber of spruce wood, and (**f**)—perlite.

**Table 1.** Composition of the substrates.


The blueberry saplings were grown under natural light conditions in the greenhouse. The greenhouse temperature and relative humidity were maintained at 25 ◦C (day) and 15 ◦C (night) and 60%, respectively.

#### *2.2. Content of Extractable Macronutrients and Organic Carbon and Peat Decomposition in Substrates*

Before the planting of the rooted saplings, the content of extractable macronutrients was evaluated in the substrates. Additionally, the content of organic carbon and the degree of peat decomposition were assessed.

The degree of peat decomposition was determined according to LST 1957:2022 [22] and the pH was determined according to LST EN 13037 with the potentiometric method [23]. The pH values of the prepared substrate mixes ranged from 4.5 to 5.4. As Trehane [10] reported, blueberries require a soil pH between 4.0 and 5.2. The content of organic carbon ranged from 27.54% to 41.66%, and the decomposition of peat was 29.1–38.8% (Table 2).


**Table 2.** Chemical composition of the substrates.

\* Using potentiometric method with an error ±0.2; \*\* using spectrometric analysis method with an error ±10%; \*\*\* using flame photometry method with an error ±20%.

The detection of elements available for plants (K, P, Ca, and Mg) was accomplished according to LST EN 13652 [24]. In the water extracts, the phosphorus (P) concentration was detected using the spectrometric method with ammonium molybdenum complexes in Shimadzu UV 1800; the concentration of potassium (K) was measured using the flame photometric method with a Flame Photometer Sherwood M410; and the calcium (Ca) and magnesium (Mg) concentrations were determined using the atomic absorption spectrometric method using Atomic Absorption Spectrometer (Perkin Elmer AAnalyst 200, Waltham, MA, USA) (Table 2).

Mineral nitrogen (N) was extracted in a 1:5 (wt./vol) substrate suspension of 1 M KCl solution. The suspension was shaken for 60 min at 20 ± 2 ◦C. After shaking, the suspension was filtrated and analyzed using a flow injection analysis (FIA) system with FIASTAR 5000 analyzer. After the addition of an acidic sulfanilamide solution, the nitrates in the substrate extract were converted to nitrites in the cadmium column. They, then, reacted with N-(1-naphthyl) ethylenediamine dihydrochloride to form a purple azo dye whose absorbance can be measured at 540 nm and 720 nm. The substrate extract was injected into a flowing carrier solution, where ammonium was mixed with sodium hydroxide to form gaseous ammonia, which passed through a gas-permeable membrane into the indicator stream. Acidic indicators changed color in this stream when they reacted with ammonium gas. Photometric measurements were performed at 540 nm and 720 nm. The calculation of mineral nitrogen involved adding the combined amounts of nitrate and nitrite nitrogen to the ammonia nitrogen.

The organic carbon content was determined using dry combustion, where the sample was heated to 900 ◦C in a stream of air, and the carbon dioxide formed was measured using infrared spectroscopy. The amount of organic carbon in the substrate was determined according to the standard ISO 10694:1995 with an organic carbon analyzer multi-EA 4000 Analytik Jena [25].

#### *2.3. Growth of Blueberry Saplings and Content of Extractable Macronutrients in the Leaves*

Saplings' height and fresh leaf weight per plant were determined for all variants of substrates by evaluating all thirty saplings. Leaves were collected from each plant separately and the average weight was determined at the end of the first growth flush of vegetative shoots, i.e., at 90–95 days after transplanting, in the last decade of July. Plant height was measured with a measuring tape. To determine the nutrient concentration, samples of fully expanded leaves from the current season shoots were prepared. From five to ten leaves were collected per plant and mixed before being sent to the laboratory. A total of 200 g of raw material per substrate was prepared. The leaves of the blueberry saplings were air dried, then ground and analyzed using the appropriate methods: nitrogen (N) with the Kjeldahl method, potassium (K) with the flame photometric method, phosphorus (P) with the photometric method with molybdovanadate, calcium (Ca) with the oxalic acid method, and magnesium (Mg) with atomic absorption spectrophotometry at 285·2 nm [26–29]. The amounts of organic carbon in the blueberry leaves were determined according to the standard ISO 10694:1995 with the organic carbon analyzer multi-EA 4000 Analytik Jena [25]. All analyses were performed in triplicate.

#### *2.4. Determination of Chlorophyll Fluorescence*

For the investigation of the maximum photochemical efficiency of Photosystem II (Fv/Fm), the leaves were fully dark, adapted, prior to the measurement. Dark-adapted leaf areas were achieved using lightweight leaf clips for 20 min. The chlorophyll fluorescence was measured with a chlorophyll fluorimeter (Pocket PEA Chlorophyll Fluorimeters, Hansatech Instruments Ltd., Norfolk, UK) with a Fv/Fm test duration of 1 s. A total of 5 measurements per plant were taken from leaves located in different directions, at an average height of 0.3–0.4 m, using leaf clips. Ten replications for each substrate variant were performed. The maximum photochemical efficiency of photosystem II was quantified (Fv/Fm) using the following relationship proposed by Maxwell and Johnson [30].

According to Murchie and Lawson [31], the Fv/Fm of non-stressed plant material should be in the range of 0.81–0.83. A much smaller relative interval (0.79 ≤ Fv/Fm ≤ 0.84) was indicated by Maxwell and Johnson [30].

#### *2.5. Statistical Analysis*

In the evaluation of the average height and leaf weight of saplings, and chlorophyll fluorescence, a non-parametric Kruskal–Wallis test comparing the ranks of the samples was used. For all hypotheses, statistically significant differences were evaluated at a significance level of *p* = 0.05. The same level of significance was used in testing for differences between means by employing a one-way ANOVA with a multiple (pairwise) comparison procedure using Duncan's test. The statistical calculations were carried out using the IBM SPSS Statistics 27 software application.

#### **3. Results and Discussion**

#### *3.1. Effect of Different Substrate Mixess on the Growth of Blueberry Saplings*

As presented in Table 3 and Figure S1, the height of blueberry saplings grown in mixes of peat with different amounts of pine wood fiber were not significantly different.



\* Substrate variants: 1—15% *v*/*v*; 2—30% *v*/*v*; 3—45% *v*/*v*. Values followed by different lowercase letters, within the line, indicate statistically significant differences by Duncan's test, *p* = 0.05.

The same trend was found for the average weight of leaves per plant (Table 4, Figure S2). Among the substrates with the spruce wood fiber, significantly higher blueberry saplings (52.59 ± 8.28 cm) were detected for the substrate with the lowest amount of fibers (15% *v*/*v*), while the larger fiber content (30% *v*/*v*) led to the significantly lower height in plants (44.02 ± 8.29 cm). Accordingly, the minimum leaf weight was determined for plants growing in the substrate with 45% *v*/*v* of spruce wood fiber (9.31 ± 2.89 g). In substrates with pine bark fiber, blueberry saplings reached the maximum height when fibers made up 15% *v*/*v* of the total capacity. It was determined that the height of the blueberry saplings decreased significantly as the amount of spruce bark fiber increased. When assessing the influence of perlite on plant growth, it was established that the plants reached a height ranging from 44.95 ± 9.54 cm (15% *v*/*v* of perlite) to 50.41 ± 8.41 cm (45% *v*/*v* of perlite). No differences in leaf weight were found among substrates with this mineral addition.

**Table 4.** Effect of mixes of peat with different amounts of wood and bark fibers and perlite on the leaf weight of blueberry saplings.


\* Substrate variants: 1—15% *v*/*v*; 2—30% *v*/*v*; 3–45% *v*/*v*. Values followed by different lowercase letters, within the line, indicate statistically significant differences by Duncan's test, *p* = 0.05.

It can be summarized that blueberry saplings grown in substrates with pine and spruce wood fiber or perlite additions reached a height ranging from 40.57 ± 5.52 cm to 52.59 ± 8.28 cm during the first year of vegetation, which is important in order to produce high-quality planting material for blueberry plantations [10,11].

Corresponding differences were determined in terms of leaf weight when the average leaf weight per plant was only 1.07 ± 0.35 g at 45% *v*/*v* of spruce bark fiber in the substrate (Figure S2, Table 4). The blueberry plants were more vigorous in the substrate with even 45% *v*/*v* of perlite compared to the substrate with 15% *v*/*v* of perlite. Consequently, leaf weight per plant did not differ significantly among the substrates with different perlite additions. Perlite is widely preferred because it reduces the risk of damping off, provides a balance between air and water in root zone and stimulates faster root growth [31]. Comparison of equal amounts of different additions confirmed that plant growth was very poor in substrates with 15% *v*/*v*, 30% *v*/*v*, and 45% *v*/*v* of spruce bark fiber (Tables S1 and S2). Accordingly, the saplings achieved the minimum leaf weight per plant in these substrates. Statistically significant differences found when comparing the same amount of different additions confirmed the necessity of choosing the most suitable variants and quantities of the additions.

#### *3.2. Effect of Different Substrate Mixes on the Chlorophyll Fluorescence in the Leaves of Blueberry Saplings*

The determined values of chlorophyll florescence showed no significant differences among 1–3 variants of each substrate. Therefore, the various amounts of pine and spruce wood or bark fiber and perlite did not significantly affect the Fv/Fm ratio (Table 5, Figure S3). On the other hand, the significant differences among substrates with the same amount of particular additions were determined (Table S3). The lowest values of Fv/Fm were detected for mixes of peat with 15% *v*/*v*, 30% *v*/*v*, and 45% *v*/*v* of spruce bark fiber. The growth of saplings was also lower in these substrates (Tables S1 and S2). In this study, the highest Fv/Fm ratio was determined for the pine and spruce substrates with 15% *v*/*v* and 30% *v*/*v* of wood fiber. Moreover, the Fv/Fm values were close to or lower than 0.80. As other authors have reported, the time of measurement during the day may have influenced the

chlorophyll fluorescence [32]. In the study of Björkman and Deming [33], it was stated that Fv/Fm is virtually constant when measured under no-stress conditions, being in the range of 0.75 ≤ Fv/Fm ≤ 0.86.

**Table 5.** Effect of mixes of peat with different amounts of wood and bark fibers and perlite on the chlorophyll fluorescence (Fv/Fm) of the blueberry saplings leaves.


\* Substrate variants: 1—15% *v*/*v*; 2—30% *v*/*v*; 3—45% *v*/*v*. Values followed by different lowercase letters, within the line, indicate statistically significant differences by Duncan's test, *p* = 0.05.

A non-invasive measurement of the chlorophyll-fluorescence parameter photochemical efficiency of PSII (Fv/Fm) is a commonly used technique in plant physiology. It has been confirmed that the determination of the Fv/Fm ratio can be used to identify nitrogen deficiency [33,34]. Previous studies also showed a significant correlation between nitrogen concentration and the leaves' Fv/Fm ratio. It was determined that the Fv/Fm ratio correlates not only with low nitrogen amounts but also with low chlorophyll levels and low biomass growth [35]. Different soil pH treatments had various effects on the photosynthetic characteristics [34]. The chlorophyll fluorescence Fv/Fm ratio is correlated with the efficiency of leaf photosynthesis, and a decline in this ratio is a good indicator of photoinhibition damage when plants suffer from a wide range of environmental stresses [36]. In this study, the Fv/Fm values of plants in all substrates with the spruce bark fiber showed a decrease in the Fv/Fm ratio, which confirms that plants may have suffered from stress in some of the substrates studied [33,37].

#### *3.3. Effect of Different Substrate Mixes on the Content of Extractable Macronutrients*

The nutritional status of blueberry plants is mainly assessed on the basis of studies on the chemical composition of leaves [38]. The obtained results on the content of extractable macronutrients in blueberry leaves showed significant variation among saplings grown in different substrates (Table 6). Attention was paid to whether our data corresponded to the proper foliar concentrations of nutrients for blueberry indicated in the studies of other researchers [39–42]. In our study, the amount of nitrogen (N) in blueberry leaves ranged from 0.78% (substrate with 45% *v*/*v* of spruce bark fiber) to 1.98% *v*/*v* (substrates with 15% *v*/*v* of pine wood fiber and with 30% *v*/*v* of perlite). The data presented in Table 6 shows that the leaves of the saplings grown in substrates with 45% *v*/*v* of spruce bark fiber were distinguished by lower nitrogen content than the limits indicated in the above-mentioned references. Glonek and Komosa [43] reported that the optimum ranges of N in leaves collected in the middle of the summer should be 1.52–2.17%. Studies with other plants have shown that N-immobilization can cause nutritional imbalance on young seedlings grown in organic substrates with wood fiber. In such cases, the use of N-impregnated media and an additional supply of nutrients is necessary [44].

Compared with the sufficient or normal concentration of phosphorus in blueberry leaves determined by Hart et al. [41] and Fugua et al. [42], the results of our research showed the proper content of phosphorus (P) in all variants of substrates, while the amounts of phosphorus ranged from 0.11% (45% *v*/*v* of pine bark fiber) to 0.22% (45% *v*/*v* of spruce bark fiber). The obtained leaf N:P ratio ranged from 3.55 to 13.67 (Table 6). Dibar et al. [45] reported that plants with a higher nitrogen concentration and a low N:P ratio, especially in the photosynthetic active organs, are the best-adapted to the environment. In our study, blueberry saplings grown in substrates with 30% *v*/*v* and 45% *v*/*v* of spruce bark fibers showed a particularly weak growth, and, in addition, not only low amounts of nitrogen

but also the lowest N:P ratio were determined in the leaves. Xia et al. [46] presented the possibility of using N:P ratio as an effective indicator for the health condition and growth status of plants.

**Table 6.** Content of extractable macronutrients and organic carbon in the leaves of blueberry saplings according to the different additions of fiber and perlite.


Values followed by different lowercase letters, within the column, indicate statistically significant differences by Duncan's test, *p* = 0.05.

A high amount of potassium (K) was found in the leaves of blueberry saplings grown in the substrates with the addition of spruce bark fiber. Even the plants grown in the substrate with 45% *v*/*v* of spruce bark fiber had exceptionally high amounts of potassium, 1.75%, compared to the proper amounts of potassium in blueberry leaves determined by other authors [39–42].

A high content of calcium (Ca) in the leaves was found in both saplings grown in the mixes of peat with pine and spruce wood fibers, while adequate calcium amounts were determined in the leaves of plants in all variants with spruce bark fiber and in substrates with perlite additions. The content of magnesium (Mg) varied significantly in the leaves of all studied plants, and high amounts for substrates with 30% *v*/*v* of pine bark fiber and 45% *v*/*v* of spruce bark fiber were determined.

Leaf organic carbon (C) content was significantly higher in saplings grown in substrates with additions of spruce bark fiber and in substrates with 15% *v*/*v* and 45% *v*/*v* of spruce wood fiber (Table 3).

The bark of various tree species has been evaluated, highlighting not only physicalchemical properties but also the different methods of medical, energetic, and industrial utilization [18,47]. In this research, substrates of peat with mixes of pine and spruce wood and bark fibers were studied. The use of tree bark in the production of substrates could be a novelty; however, the use of tree bark for the production of peat substrate mixes needs to be investigated in more detail. This study confirmed that mixes of peat with 15–45% *v*/*v* of spruce fibers had a negative effect on the growth of blueberry saplings. As other authors have reported, it is necessary to study what toxic substances are released from the new components, which could inhibit plant growth [48,49]. In the mixes of peat with fiber additions, microorganisms that need mineral nitrogen must be also evaluated [20].

The challenges presented by climate change require a new approach to the conservation of natural resources, including peat. The search for innovative substrates, evaluating the possibilities of using renewable materials, has great potential [50,51]. This study confirmed the possibility of reducing the amount of peat in substrates using tree fibers and perlite. In the continuation of this research, it would be promising to investigate substrate compositions with mixtures of organic and mineral additions.

#### **4. Conclusions**

In this study, mixes of peat with various rates of wood and bark fibers or perlite were compared to evaluate the possibility of reducing the amount of peat. The research carried out on the growth of blueberry saplings showed that the best characteristics of plants were achieved for substrates with 15–45% *v*/*v* of pine wood fiber and with 15–30% *v*/*v* of spruce wood fiber. Different amounts of spruce bark fiber had the strongest negative effect on vegetative growth and the lowest values of chlorophyll fluorescence Fv/Fm (0.689–0.738) in the leaves of the saplings were determined. Investigations of extractable macronutrients in the leaves confirmed the qualitative and quantitative composition of peat mixes suitable for the cultivation of blueberry saplings. Nitrogen and potassium levels did not meet the accepted standards and a low N:P ratio was found in the leaves of plants grown in substrates with 30–45% *v*/*v* of spruce bark fibers. The results of the investigations corroborated that 15–45% *v*/*v* of perlite in peat substrates is suitable for the purpose of growing blueberry saplings.

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/horticulturae9020151/s1, Figure S1: Effect of mixes of peat with different additions of wood and bark fibers and perlite on the height of blueberry saplings; Figure S2: Effect of mixes of peat with different additions of wood and bark fibers and perlite on the height of blueberry saplings; Figure S3: Effect of mixes of peat with different additions of wood and bark fibers and perlite on the chlorophyll fluorescence (Fv/Fm) of blueberry-saplings leaves; Table S1: Effect of mixes of peat with the same percentage of components on the height of blueberry saplings; Table S2: Effect of mixes of peat with the same percentage of components on the leaf weight of blueberry saplings; Table S3: Effect of mixes of peat with the same percentage of components on the chlorophyll fluorescence (Fv/Fm) of blueberry-saplings leaves.

**Author Contributions:** Conceptualization, L.C. and R.K.; methodology, R.M.; validation, R.K.; formal ˇ analysis, R.D.; investigation, L.C. and R.M.; resources, R.D.; data curation, R.K.; writing—original ˇ draft preparation, L.C.; writing—review and editing, R.D.; visualization, R.K.; supervision, L. ˇ C. and ˇ R.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by the project from EU Funds Investment Action Program (project No. 01.2.1-LVPA-K-856-01-0086).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available in the article.

**Acknowledgments:** The authors thank Klasmann-Deilmann GmbH Šilute for the professional prepa- ˙ ration of peat, wood, bark, and perlite blends, and their submission for research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **Estimating Nitrogen Release from Organic Fertilizers for Soilless Production by Analysis of C and N Pools**

**Dieter Lohr 1,\*, Nazim S. Gruda <sup>2</sup> and Elke Meinken <sup>1</sup>**


**Abstract:** The use of organic fertilizers in soilless pot plant production has sharply increased in recent years. However, there is still a lack of methods for characterizing the N release from organic fertilizers. This bears the risk of an inadequate nutrient supply and, thus, a waste of resources. Therefore, the current research analyzed fourteen commercial organic fertilizers for various C and N pools by extraction in cold and hot water, acid hydrolysis, and thermal fractionation. Furthermore, we conducted an incubation test using a growing medium (80 vol% peat plus 20 vol% green waste compost) and fitted the nitrogen release to different kinetic models. Finally, we calculated the correlations among the best-suited kinetic model parameters and the C and N pools. The C and N pools soluble in water and weak hydrochloride acid varied significantly among the fourteen fertilizers but were closely correlated with each other. The N release from most organic fertilizers could be described very well using the Gompertz function (R<sup>2</sup> > 0.9), and the parameters of the Gompertz function showed significant correlations with the C and N pools. Hydrolyzable C and N pools provided valuable information about the N release characteristics of organic fertilizers.

**Keywords:** incubation experiment; growing medium; hydrolyzable C and N; kinetic models; Gompertz function

#### **1. Introduction**

In the last two decades, organic greenhouse production has rapidly grown, mainly focusing on producing fruits and vegetables in soil. However, consumers' demand for soilless products, such as vegetables, herbs, and ornamentals, is also increasing [1–4]. A survey by Burnett and Stack [5] revealed fertilization as a significant issue in the organic cultivation of bedding plants. This is particularly true for nitrogen (N) supply, as N applied with organic fertilizers must be mineralized first, and thus, it becomes plant-available only with a delay. To ensure an adequate N supply, growers need reliable information about the time course of the N release from the applied fertilizer [6].

The decomposition pattern of organic residue has been extensively characterized in the literature using various mathematical models. These models include simple first and second-order rate equations [7–9], consecutive reaction models that combine multiple firstorder rate equations [10–12], and flexible sigmoid-shaped functions, such as the Richards, Gompertz, and Weibull functions [13–18]. However, simple first-order rate models, which assume N mineralization as a simple function of the substrate N concentration, and consecutive models, which assume two or more pools with different rate coefficients (e.g., labile and refractory fractions), regularly overestimate N release at the beginning [10,11,15,17]. This initial lag phase, which might be due to inhibitory compounds, such as polyphenols [11,19], or by the initial acclimation and regrouping of microbial biomass [14], is quite well-modeled by the unitless and scale-independent shape factor in flexible functions [18].

**Citation:** Lohr, D.; Gruda, N.S.; Meinken, E. Estimating Nitrogen Release from Organic Fertilizers for Soilless Production by Analysis of C and N Pools. *Horticulturae* **2023**, *9*, 767. https://doi.org/10.3390/ horticulturae9070767

Academic Editor: Elena Baldi

Received: 25 May 2023 Revised: 22 June 2023 Accepted: 29 June 2023 Published: 4 July 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

59

Subsequently, kinetic parameters can be correlated to the biochemical characteristics of the material [19].

The importance of biochemical characteristics for predicting mineralization rates has been shown in various studies for crop residues and other organic input materials, such as manure [9,20–26] and, to a lesser extent, for commercial organic fertilizers [27–31]. In addition, countless studies have examined the correlations between the biochemical properties of soil organic matter (SOM) and nitrogen turnover in soils [32]. In all cases—for crop residues and organic fertilizers, as well as for SOM—the most common parameters were the total nitrogen (TN), as well as the total organic carbon (TOC) or total carbon (TC) and the resulting C/N ratios. However, these parameters only show a rough correlation to the nitrogen release of organic fertilizers [27]. A more suitable approach seems to be the characterization of individual nitrogen and carbon pools either by extraction procedures with both hot and cold water, saline, acidic, or alkaline solutions or by thermal analytical techniques, such as thermogravimetry, analysis of evolved gas, or Rock-Eval pyrolysis. Von Lützow et al. [33] gave a detailed overview of the mentioned techniques. De Neve and Hofman [9] used the sequential procedure proposed by Stevenson [34] to characterize the N and C pools of vegetable crop residues whose N mineralization was fitted to a first-order rate equation. The potentially mineralizable nitrogen (NP) was closely correlated to the C/N ratio of the lignin, lignin content, and water-soluble N. Furthermore, Jensen et al. [24] reported close correlations between water-soluble N and early N mineralization. In addition, the NP was well described by amounts of various N fractions, whereas the correlations of the NP to the C/N ratio of the lignin and lignin content were relatively poor. However, they did not describe the N mineralization by kinetic models but correlated the N mineralization at defined dates to analyze the C and N pools. Moreover, De Neve and Hofman [9] and Jensen et al. [24] have reported close correlations between N mineralization and the total N content, and the rate constant was significantly correlated to the percentage of organic N. This is likely attributed to the common practice of applying residues on a dry matter basis, resulting in an increased application of nitrogen (N) as the percentage of organic N increases.

As outlined by Jensen et al. [24], the physiochemical and biochemical properties of the soil used for incubation can significantly affect mineralization patterns. Thus, the results of the listed studies with different mineral soils are hardly comparable and cannot be transferred to growing media for soilless cultivation as their physiochemical and biochemical properties are quite different from those of mineral soils. Therefore, the current research aimed to characterize the N release of various commercial organic fertilizers in a peat-based growing medium by a single kinetic model, and subsequently, to describe the kinetic parameters of C and N pools analyzed by several extraction procedures and evolved gas analysis under pyrolytic conditions.

#### **2. Materials and Methods**

#### *2.1. Commercial Organic Fertilizers*

Fourteen solid organic fertilizers were purchased from various companies. Six were unprocessed raw materials, three were of animal origin, and three were coarsely ground legumes. Four fertilizers were made of processed plant material, e.g., from the production of starch adhesives. No detailed information about the origin was available for the remaining four fertilizers. In the following section, the fertilizers and their analyzed N, P, K, Mg, and S contents (in brackets), suppliers, raw materials, and pre-processing methods—as far as they are available—are summarized.

Coarse horn meal (CHM; 13.8 + 0.6 + 0.0 + 0.1 + 2.0): CHM is made of the horns and hooves of slaughtered ungulates, mainly cattle, with minor adhesions of fur, meat, bones, blood, and manure. According to the regulations of the European Union, hygienization by heating or treatment with propionic acid is mandatory [35]. The main component of horns and hooves is keratin, a group of fibrous proteins [36]. The CHM was purchased from Beckmann & Brehm GmbH (Beckeln/Germany).

Sheep wool pellets (SWPs; 9.7 + 0.1 + 3.5 + 0.1 + 2.1): SWPs were obtained from Düngepellet Produkt und Vertriebs GmbH (Lauchammer/Germany). Raw and uncleaned wool is a waste product from sheep husbandry and it is pelletized by a thermal–mechanical process. The primary N source of SWPs is keratin. However, the usually unwashed wool contains many waxes, dirt, and feces [37].

Pig bristle pellets (PBPs; 13.9 + 0.1 + 0.1+0+ 2.1): PBPs, also named hair meal pellets, are slaughterhouse waste. The bristles are removed from slaughtered carcasses and typically pasteurized, dried, ground, and pelletized [35]. As with CHM and SWPs, keratin is the primary N source of PBPs. However, pig bristles are less soiled than CHM and SWPs, respectively. The pig bristles were purchased from Beckmann & Brehm GmbH (Beckeln/Germany).

Fodder pea grist (FPG; 4.3 + 0.3 + 1 + 0.1 + 0.1): The seeds of fodder peas were crushed by us in a malt mill, as this is the current practice for legumes used as fertilizers in organic agriculture [35].

Faba bean grist (FBG; 3.2 + 0.2 + 0.8 + 0.1 + 0.1): Similar to the seeds of fodder peas, faba bean seeds were also crushed by us.

Lupine grist (LG; 5.2 + 0.2 + 0.7 + 0.2 + 0.3): Similar to the fodder pea and faba bean seeds, lupine seeds were crushed by us.

Phytomalz (PHZ; 5.8 + 0.7 + 1.1 + 0.2 + 0.6): Provita® Phytomalz (Beckmann & Brehm, Beckeln/Germany) is made from protein-rich residues from the food-processing industry. The principal components are malt culms and corn, which are pressed into small pellets, with the addition of vinasse.

Phytogran (PHN; 5.3 + 0.5 + 1.7 + 0.5 + 0.5): Provita® Phytogran (Beckmann & Brehm, Beckeln/Germany) is a granulate with a particle size of 2–5 mm. The raw material (residues from the food-processing industry) is dissolved in water, steam-sterilized, and fermented by yeasts. Afterwards, the fermented biomass is dried at about 100 ◦C, mixed with molasses, and granulated.

Phytogrieß (PHS; 6.2 + 0.8 + 0.8 + 0.2 + 1.1): Provita® Phytogrieß (Beckmann & Brehm, Beckeln/Germany) is derived from the fermented residues and glucose of corn gluten production. The granules have a particle size between 0.2 and 2 mm.

Maltaflor (MAF; 4.9 + 0.6 + 1.2 + 0.4 + 0.7): Maltaflor® (Maltaflor EUROPA GmbH, Boppard/Germany) is made of malt culms from breweries, vinasse, and vinasse–potassium, as well as grain hulls. After the malting process, the culms are dried, pelletized, and mixed with the vinasse [35].

OPF granular (OPF; 7.0 + 1.2 + 8.8 + 0.2 + 7): According to the supplier (Plant Health Cure B.V., Oisterwijk/Netherlands), OPF is made of various herbal substances, e.g., fermented sugar beets, and is adjusted to promote the growth of beneficial soil bacteria and mycorrhizal fungi. Most of the nitrogen is amino-derived. The British Soil Association tests all raw materials.

UP—fruit and vegetables (UP; 8.3 + 1 + 4.6 + 0.2 + 3.8): UP was purchased from Umweltpionier GmbH (Perg/Austria). Similar to OPF, no specific information about the raw materials and processing was available. However, according to the supplier, the fertilizer is a mixture of plant material, clay minerals, and microorganisms. A unique feature of UP is its classification as a foodstuff.

Cuxin Xtra-1 (CX1; 10.7 + 0.1 + 3.3 + 0.7 + 4.8): DCM ECO-XTRA® 1 (Deutsche CUXIN Marketing GmbH, Telgte/Germany) is a mixture of animal residues (slaughterhouse waste according to EU ordinance No. 1069/2009), residues of the food, beverage, and feed industry, and tannins from forestry. CX1 is formulated as fine granules, with a particle size between 0.8 and 2.5 mm.

Cuxin Eco-Mix 4 (CE4; 6.3 + 0.1 + 0.3 + 0.2 + 0.6): DCM Öko-Mix® 4 (Deutsche CUXIN Marketing GmbH, Telgte/Germany), similar to CX1, is a mixture of animal residues (slaughterhouse waste according to EU ordinance No. 1069/2009), residues of the food, beverage, and feed industry, and cocoa shells or vinasse. The particle size of CE4 is similar to that of CX1.

#### *2.2. Characterization of Carbon and Nitrogen Pools*

The total nitrogen (TN) was measured by the Dumas method (TrueSpec N, LECO Cooperation, Moenchengladbach/Germany; according to the VDLUFA Methods Book II.1, No. 3.5.2.7; [38]). The total organic and total carbon (TOC and TC) were measured by combustion under oxygen at 550 and 1000 ◦C, respectively, and by evolved gas analysis (VDLUFA Methods Book I; No. A 4.1.3.2 and No. A 4.1.3.1; [39]) using an RC 612 elemental analyzer (LECO Cooperation, Moenchengladbach/Germany). Furthermore, the nitrogen was extracted in cold and hot water (CW\_N and HW\_N) according to the analysis of slowrelease urea fertilizers (VDLUFA Methods Book II.1, No. 3.10; [38]). Additionally, nitrogen and carbon hydrolyzable in 0.005 M and 1 M hydrochloric acid (0.005 HA\_N/HA\_C and 1 HA\_N/HA\_C) were analyzed [40].

For all extracts, the total soluble nitrogen was measured spectrophotometrically as nitrate after UV-assisted digestion [41] on an AA3 continuous flow analyzer (Bran+Lübbe, Norderstedt/Germany). Furthermore, we analyzed the mineral nitrogen (MN) as a sum of ammonium- and nitrate-N in the cold water extract using the same AA3 continuous flow analyzer. Finally, the soluble organic N (ON) was calculated in all extracts as the difference between the total soluble N in the respective extract and the cold-water-soluble MN.

The determination of the organic carbon hydrolyzable in hydrochloric acid was carried out by ICP-OES (iCAP 6300 Duo, Thermo Scientific, Dreieich/Germany) at 193 nm after strong acidification with nitric acid and purging with high-purity argon [42]. Additionally, the organic carbon pools were characterized by stepwise combustion under pyrolytic conditions at 250, 300, 350, 400, 450, 500, 600, and 1000 ◦C and subsequent analysis of the evolved CO2 (Py-TA-EGA; [43]) using a modified RC 612 elemental analyzer (LECO Cooperation, Moenchengladbach/Germany).

The TN, TOC, and TC analyses, Py-TA-EGA, and cold and hot water extractions were conducted in duplicate. Hydrolysis in hydrochloric acid, on the other hand, was performed in triplicate. Control samples (e.g., standard materials) were included in each analytical run to ensure the quality of the analysis and to maintain analytical precision.

#### *2.3. Incubation Experiment*

The incubation experiment was conducted according to the procedure proposed by the association of German agricultural analysis and research institutes (VDLUFA) for testing the N dynamics of organic growing media constituents (VDLUFA Methods Book I; No. A 13.5.1; [39]). The organic fertilizers were added on the basis of 1000 mg total N per liter of growing medium consisting of 80% peat (H3-H5 [44]) and 20% green waste compost by volume. The compost successfully fulfilled the growing media compost type II requirements of the German Federal Compost Quality Association [45]. The growing medium was limed to a pH of 5.5 (determined in a CaCl2 suspension according to the VDLUFA Methods Book I, No. A 5.1.1; [39]). Due to the compost amendment, no other fertilizers except commercial organic fertilizers were added. All fertilizers were incubated in two ways to assess the influence of the particle size. Firstly, the fertilizers were used as received, and only large particles (especially the sheep wool pellets) were carefully crushed by hand to ensure their homogeneous addition to the incubation vessels. Secondly, the fertilizers were chopped in a cutting mill using a 0.5 mm bottom sieve (ZM 1, Retsch, Haan/Germany). This allowed for the evaluation of the effects of the particle size on the nitrogen release.

The Incubation ran for 58 days at 25 ◦C and 90% relative humidity in the dark. First, the growing medium was moistened with deionized water to 60% of the maximum water capacity (determined according to the VDLUFA Methods Book I, No. A 13.6; [39]), which is assumed to be well suited for microbial activity. Then, the water loss of the growing medium was compensated three times a week during the entire incubation period. On eight dates (days 0, 3, 7, 10, 16, 23, 37, and 58), three incubation vessels per treatment were taken from the incubator and analyzed for CaCl2/DTPA-soluble ammonium- and nitrate-N photometrically (VDLUFA Methods Book I, No. A 6.1.4.1; [39]). As a basis for the N release

calculation, the controls without fertilizer and those with 500 mg N per liter as ammonium nitrate were treated similarly.

On each analysis day, the net mineralization of the fertilizers was calculated as the difference between the ammonium- and nitrate-N contents in the treatments with and without the respective fertilizers. Furthermore, the data from the control fertilized with ammonium nitrate were used to estimate the N turnover (mobilization and immobilization) in the growing media, as the results of the incubation experiment might be less reliable at high nitrogen turnover rates.

#### *2.4. Calculations and Statistical Analyses*

First, various kinetic models used in the literature to describe the decomposition of organic residues in mineral soils (Table 1) were fitted to the N release pattern of the fourteen commercial organic fertilizers. Fitting was performed by an iterative non-linear approach using the generalized reduced gradient algorithm of Microsoft's SOLVER [46,47]. The starting conditions were set by hand identically for each fertilizer, and the first fitting was calculated. The results obtained were taken as the starting conditions for a new run in the next step. This procedure was repeated four times. As the fitness-of-purpose criterion of the models, the total sum of squares (TSSQ) of the fourteen fertilizers was used.

Furthermore, fitting the fertilizer-specific equations to the N mineralization data was visually rated, and the coefficient of determination (R2) was calculated for each fertilizer. Additionally, the parameters of the most suitable model and some selected points characterizing the time course of the N mineralization were computed by calculating the first and second derivations: (i) the turning point (T) of the original function, which defines the maximum mineralization rate, and (ii) the turning points of the first derivation, indicating the start (E1) and the end (E2) of the phase with the maximum mineralization rate. Furthermore, the time until the release of 90% of the potentially mineralizable nitrogen (N90) was calculated. Finally, correlations of the analyzed N and C pools and the C/N ratios to the parameters of the model that best describe the course of N mineralization were computed to identify the pools that were well suited for characterizing the N release of the organic fertilizers. Thereby, correlations were calculated for the entire dataset and after removing the outliers determined by Cook's distance, with 4/n as the cut-off point [48]. The fitting and visualization of the kinetic models were carried out with MS Excel 2016 (version 16.0, Microsoft Corporation, Redmond, WA, USA). The software package Minitab21 (Minitab, LLC, State College, PA, USA) was used to visualize the C and N pools and to calculate the correlations.

**Table 1.** Kinetic models for characterization of net N release (NR in mg L<sup>−</sup>1) from organic fertilizers in relation to incubation period (t) in days; parameters NP and NE represent the potentially and mineralizable N (mg L<sup>−</sup>1), respectively, parameters h, k, and k(1,2) represent the specific rate constants (mg L−<sup>1</sup> d−1), k0 is the zero-order rate constant for recalcitrant N pools (mg L−<sup>1</sup> d−1), F is the proportion of NP with fast turnover in a simultaneous reaction model, and d and c are unitless shape factors in Richards and Weibull functions (for details, refer to the given references).



#### **Table 1.** *Cont*.

#### **3. Results and Discussion**

#### *3.1. Characterization of Nitrogen and Carbon Pools*

As fertilizers were added to the incubation experiment on the basis of the TN, the nitrogen and carbon pools were also related to the TN. As shown in Figure 1, most of the fertilizers contained nearly no mineral nitrogen (<50 mg N per g TN), whereas very high amounts of ammonium-N (419 mg N per g TN) were found in the OPF and, to a lesser extent (138 mg N per g TN), in the MAF. Except for 1 HA\_ON, the soluble organic N pools differed considerably among the fertilizers. Their proportions of TN ranged from 3.8% to 38% for CW\_ON and from 5.3% to 48% for HW\_ON, comparable to the values reported by Rubins and Bear [55] for various raw and processed plant (7.0% to 47.5%) and animal (0.2% to 39.3%) products. Using 0.005 M hydrochloric acid gave slightly higher (7.9% to 66%) values and 1 M hydrochloric acid gave remarkably higher (42% to 94%) values, whereas the lowest values were found for the OPF (42%) and MAF (77%) due to their high contents of mineral N.

**Figure 1.** Variation in cold-soluble mineral nitrogen (CW\_MN), cold- and hot-water-soluble organic nitrogen (CW\_ON and HW\_ON), and organic nitrogen hydrolyzable in 0.005 and 1 M hydrochloric acid (0.005 HA\_N and 1 HA\_N) among the fourteen organic fertilizers in relation to the total nitrogen (asterisks indicate outliers).

Due to the low amount of mineral nitrogen in most fertilizers, the percentages of the total soluble N of the TN were equivalent to those of the soluble organic N of the TN. In addition to their similar ranges, the cold water-, hot water-, and 0.005 hydrochloric acid-soluble N pools were highly correlated with each other but not with the 1 M\_HA pool (Table 2).

**Table 2.** Correlations between total soluble and organic N pools for the fourteen organic fertilizers (Pearson's correlation coefficient, numbers in brackets indicate significance levels).


The variation in the organic C pools hydrolyzable in HA, and the TOC and organic carbon analyzed by Py-TA-EGA (POC), are summarized in Figure 2. The absolute values of TOC ranged between 2523 mg per g TN for the OPF and 11,514 mg per g TN for the FPG. Also, the amounts among the other organic C pools varied considerably from 19% to 88%, 57% to 96%, and 51% to 88% of TOC for 0.005 HA\_C, 1 HA\_C, and POC, respectively (Figure 2). Most organic carbon pools, whether analyzed by acid hydrolysis or by Py-TA-EGA, were positively correlated with each other (r ≥ 0.87, *p* < 0.01). This indicates that Py-TA-EGA provided no more information about the organic carbon quality than acid hydrolysis.

With the exception of the TOC/TN ratio, for the calculation of the C/N ratios, only the organic N pools (CW\_ON, HW\_ON, 0.005 HA\_ON, 1 HA\_ON, TON)—calculated as the difference between the respective total N and the cold-water-soluble ammonium-N and nitrate-N—were used. The ratios of TOC to TN, TON, and 1 HA\_ON, respectively, ranged from 3 to 12 and were quite similar to the values reported by Stadler et al. [30]. With the increasing strength of the extractant (CW < HW < 0.005 M HCl < 1 M HCl), the range of the TOC/ON ratios decreased. This was also true for the ratios of the organic C and organic N pools, both hydrolyzable in 0.005 (0.005 HA\_C/ON) and 1 M hydrochloric acid (1 HA\_C/ON), respectively (Figure 3).

**Figure 2.** Variation in organic carbon hydrolyzable in 0.005 and 1 M hydrochloric acid (0.005 HA\_C and 1 HA\_C) and in total organic carbon (TOC) and organic carbon measured by pyrolytic combustion (POC) among the fourteen organic fertilizers in relation to the total nitrogen (asterisks indicate outliers).

**Figure 3.** Variation in ratios of different carbon and nitrogen pools among the fourteen organic fertilizers (TOC = total organic carbon, TN = total nitrogen, TON = total organic nitrogen, CW\_ON = coldwater-soluble organic nitrogen, HW\_ON = hot-water-soluble organic nitrogen, 0.005 HA\_ON = organic nitrogen hydrolyzable in 0.005 M hydrochloric acid, 1 HA\_ON = organic nitrogen hydrolyzable in 1 M hydrochloric acid, 0.005 HA\_C/ON = ratio of organic carbon and organic nitrogen hydrolyzable in 0.005 M hydrochloric acid, HA\_C/ON = ratio of organic carbon and organic nitrogen hydrolyzable in 1 M hydrochloric acid; asterisks indicate outliers).

Due to the described correlations among the different pools of N and C and among several N and C pools whose data are not shown, the ratios of TOC to organic N pools soluble in weak extractants (CW, HW, and 0.005 M HA) were highly correlated with each other (Table 3). Furthermore, the TOC to organic N hydrolyzable ratio in 1 M hydrochloric acid (1 HA\_ON) was correlated to the TOC/TN and TOC/TON ratios, respectively, which were also closely correlated. A comparably high correlation was found for the ratios of 1 HA\_C/ON to TOC/TN, TOC/TON, and TOC/1 HA\_ON, respectively. Additionally, a significant correlation existed between the ratios of C/ON analyzed in 0.005 and 1 M HA. This indicates that all tested weak extractants (cold water, hot water, and 0.005 M hydrochloric acid) provided more or less the same information about the organic N and C pools of the organic fertilizers and that in 1 M hydrochloric acid, the hydrolyzable organic C and N are more related to the total content than to the specific pools. This is quite different from other findings on the characterization of soil organic matter [56] but might be explainable by the higher proportion of readily degradable nitrogen-containing compounds in the organic fertilizers compared to soil organic matter. In contrast to soils, where approximately one-third of the organic nitrogen (N) remained non-hydrolyzable even after treatment with 6 M hydrochloric acid [57], the organic fertilizers contained a significantly higher proportion of hydrolyzable N (88 ± 1.5% using 1 M hydrochloric acid). **Table 3.** Correlations among C/N ratios calculated from different C and N pools (TOC = total organic carbon, TN = total nitrogen, TON = total organic nitrogen, CW\_ON = cold-water-soluble organic nitrogen, HW\_ON = hot-water-soluble organic nitrogen, 0.005/1 HA C/ON = organic carbon/organic nitrogen hydrolyzable in 0.005/1 M hydrochloric acid) of the fourteen organic fertilizers (Pearson's correlation coefficient, numbers in brackets indicate significance levels).


#### *3.2. Nitrogen Release*

Overall, the nitrogen turnover in the control treatments without fertilizer and with 500 mg N per liter as ammonium nitrate was negligible, respectively. In the unfertilized control, the mineral N increased slightly from 14 ± 0.5 at the beginning up to <sup>35</sup> ± 12.0 mg L−<sup>1</sup> after 58 days. Nevertheless, the N release from organic fertilizers was corrected for the mineral N in the unfertilized control at each date. No clear trend was observed in the control fertilized with ammonium nitrate. The mineral N oscillated at each date closely (484 ± 17.4 mg L<sup>−</sup>1) around the target value of 500 mg N per liter. Furthermore, differences in the N release patterns between the chopped and unchopped fertilizers were rather small, and no systematic effect of particle size was found. This result was confirmed in a more detailed subsequent examination using horn shavings milled to defined grain sizes (<1 mm, 1–2 mm, and 2–4 mm). Only in the first two weeks was a slightly faster N release from finer materials observed. In the following four weeks, no differences were apparent [58]. Due to the slightly better reproducibility, only the N release from the chopped fertilizers was considered in the following.

The N release from organic fertilizers is described best by flexible sigmoid-shaped functions, in particular by the Richards and Gompertz functions. As found by Rahn and Lillywhite [59] and Nendel and Reuter [11], for brassica leaves and grape stalks, some fertilizers have shown a lag of mineralization within the first several days of incubation experiments. This was most pronounced for the SWPs, where no mineral N was found within the first seven days. Following the results of Simard and N'dayegamiye [16] for meadows, and of Hara [54] for coated urea fertilizers, this lag phase could not be described by first- or second-order rate equations or by consecutive reaction models but by flexible sigmoid Gompertz and Richards functions. As the parameter d in the Richards function was zero for most fertilizers—except for the OPF and SWPs—the Richards function is almost identical to the Gompertz function [54].

Furthermore, the TSSQ, more than twofold higher for the Gompertz compared to the Richards function (Figure 4), is nearly exclusively due to the relatively poor fitting of the Gompertz function to the N release from OPF. In this case, the SSQ for the OPF (226·10<sup>−</sup>3) contributed nearly half of the TSSQ (500·10<sup>−</sup>3). However, due to the significant proportion of cold-water-soluble ammonium-N discussed in the previous section, it appears that OPF functioned more as a mineral rather than as an organic nitrogen fertilizer. This observation aligns with the fact that 500 out of the 1000 mg TN L−<sup>1</sup> added was already present as CAT-soluble mineral N at the start of the 58-day incubation experiment. Despite this, the N mineralization only amounted to approximately 150 mg L−<sup>1</sup> by the end of the experiment. Consequently, the subsequent paragraphs only minimally address the OPF.

**Figure 4.** Sum of squares (SSQ multiplied by 1000) for fitting of different kinetic models to the N release of the fourteen organic fertilizers (for details of kinetic models, refer to Table 1; numbers above box plots indicate the total sum of squares (TSSQ) for all fertilizers except OPF for each kinetic model; asterisks indicate outliers).

As shown in Figure 4, adding a term for the easily mineralizable N pool (NE) did not remarkably reduce the TSSQ for the remaining 13 fertilizers for the Gompertz function (274·10−<sup>3</sup> to 273·10<sup>−</sup>3) but did for the Richards function (274·10−<sup>3</sup> to 162·10<sup>−</sup>3). However, as described before, parameter d of the Richards function remained near zero for most fertilizers. This was also true when a zero-order rate constant (k0) for less degradable N pools was added, which reduced the TSSQ remarkably for both the Gompertz and Richards functions. Finally, as the Richards function is nearly identically to the Gompertz function and the fitting was already very good for the simpler Gompertz function using only the three parameters NP, h, and k (R2 > 0.91, with an exception for OPF: R2 = 0.67), this function was selected as the most suitable one. In addition, Gompertz's function also has a biological justification: The application of easily degradable organic carbon to soils triggers a rapid increase in microbial biomass [60–62]. As a result, especially the growth rate of

bacteria is significantly enhanced [62]. Concurrently, the Gompertz function has been demonstrated as a reliable model for bacterial growth. The parameters of this function have physiological significance as they can be traced back to the three phases of bacterial growth: the initial lag phase (parameter h), the phase of the maximum growth rate (parameter k), and the stationary phase in which the maximum population density (parameter NP) is reached [63,64].

Figure 5 shows the fitting of the Gompertz function to the N release from the 14 organic fertilizers. The turning point (T) indicates the day when the maximum mineralization rate was reached, as well as the beginning (E1) and end (E2) of the nearly linear N release phase. Additionally, the goodness of fit (SSQ and R2) and the period until a 90% release of NP (N90) are listed. The percentage of mineralizable organic nitrogen was around 55% of the total added N; it was the lowest for CX1, with 45%, and the highest for UP, with 63%. The same range has been reported for various commercial organic fertilizers by Prasad et al. [65] and Dion et al. [66], using peat-based growing media for incubation experiments, as well as by Müller and von Fragstein und Niemsdorff [28] and Stadler et al. [30], who conducted incubation experiments in mineral soils. Furthermore, Koch et al. [67] and Heuberger et al. [27] calculated only a slightly higher N efficiency (between 40% and 60% of the total added N) for organic fertilizer in pot experiments with pelargonium and basil, respectively. Thus, to ensure a sufficient nitrogen supply, growers have to fertilize on a total N basis, which is about twice the demand of the plants. Except for the SWPs and, to a lesser extent, the FPG and FBG, the fertilizers did not have a lag phase (indicated by parameter k), so linear mineralization started directly after adding the fertilizers (E1 < 1). The maximum daily N mineralization rate (indicated by parameter h) was lowest for the SWPs, with 16, and highest for UP, with 65 mg L−<sup>1</sup> d−1. For the SWPs, due to the already mentioned lag phase of nearly two weeks and the low daily mineralization rate, it took nearly five weeks until two-thirds of the total mineralized nitrogen was released. For the FPG and FBG, which had a lag phase of several days and also a relatively low daily N mineralization rate (<30 mg L−<sup>1</sup> d<sup>−</sup>1), and for CX1, which had no remarkable lag phase but a similar low daily N mineralization rate (18 mg L−<sup>1</sup> d<sup>−</sup>1) to the SWPs, this point was reached within 14 to 20 days. All other fertilizers passed the 66% level within ten days. The CHM, PBPs, MAF, and UP exceeded 90% within 14 days. In addition to the relatively poor nitrogen efficiency, most organic fertilizers' high velocity of N release might be problematic for growers. For instance, in the cultivation of potted basil, if the entire N demand of about 1000 mg total N per liter [27] is applied at the date of sowing, two weeks later, the mineral N in the growing medium will be between 250 and 500 mg L<sup>−</sup>1. However, by this date, the seedlings will have just emerged and might be harmed, in particular due to osmotic stress. Thus, complete stockpiling, as recommended by Heuberger et al. [27], is quite risky. Furthermore, in the case of missing nitrification, ammonium-N will accumulate, damaging plants [68]. However, in the current research—except for the OPF—nitrification already started within the first week of incubation. This was probably mainly due to the addition of compost. A similar enhancement of nitrification by compost amendment has been found, e.g., by Delics et al. [58] and Frerichs et al. [68]. However, in contrast to nitrification, neither reported a clear effect of compost amendment on the height or time course of the N mineralization. This might have been due to the fact that none of the composts used in these experiments contained remarkable amounts of mineral N [69].

**Figure 5.** Fitting of Gompertz growth function to the N release of the 14 organic fertilizers (rhombs mark measures values of mineral N and error bars indicate 2·SE at each date (n = 3); vertical dashed lines mark points E1 and E2 = days until start and end of nearly linear mineralization, and T = days until maximum mineralization rate) and parameters of the Gompertz function (NP = potentially mineralizable N in mg L<sup>−</sup>1, k, and h = rate constants in mg L−<sup>1</sup> d<sup>−</sup>1), goodness of fit (SSQ = sum of the square of each fertilizer, R2 = coefficient of determination), and days until the release of 90% of NP (N90).

#### *3.3. Relative Importance of N and C Pools*

As mentioned before, OPF is a mineral rather than an organic fertilizer. Thus, we excluded it from the following evaluation of the importance of N and C pools for N release from organic fertilizers. The level of N release to the total applied N (indicated by parameter NP of the Gompertz function) was closely correlated to the easily soluble N pools (cold water, hot water, and 0.005 M hydrochloric acid) for most fertilizers. The only exception was UP, which had a very high N release in relation to the soluble N pool compared to all other fertilizers. As Cook's D for UP exceeded the cut-off point more than twice, correlations between the N release and the soluble N pools were calculated with and without the consideration of UP. When all fertilizers were included, the amount of mineralizable nitrogen (NP) was not significantly correlated to the soluble N pools (r ≤ 0.48; *p* ≥ 0.08). However, omitting the UP resulted in highly significant correlations for the three named N pools (r ≥ 0.69; *p* < 0.01), of which the relationship was closest for the total cold-water-soluble N (Figure 6a). A positive relationship between the easily soluble N pools and the mineralization potential was also shown by Iratani and Arnold [20], who found water-soluble N to be twice as influential as water-insoluble N in affecting the N release from various crop residues. Rubins and Bear [55] also emphasized a positive correlation between water-soluble N and N release. Furthermore, they reported decreasing net mineralization with an increasing C/N ratio of the non-lignin fraction. Even immobilization was observed if the C/N ratio was above 20. Coincidentally, a positive correlation between the ratios of the C and N pools hydrolyzable in weak hydrochloric acid and the lag phase of mineralization (described by parameter k) was found (Figure 6b). The hypothesis that the ratio of hydrolyzable C and N pools might be a reliable indicator for short-term net mineralization is supported by the results of Jensen et al. [24], Bushong et al. [70], and Ahn et al. [71]. All authors have reported positive relationships of soluble N and C pools with the short-term mineralization of organic matter. Thus, in products such as FBG, UP, and FPG, with higher ratios of C and N pools in the readily decomposable pools, even the net immobilization might occur within the first days, whereas for products with a lower ratio, the net N mineralization was found right from the beginning. However, the exceptionally high value of k for the SWPs (2.44 mg L−<sup>1</sup> d−1) and, thus, the lag phase of nearly 14 days, could not be solely attributed to the ratio of carbon and nitrogen in the easily decomposable pools. The discrepancy between the SWPs and all other fertilizers was confirmed by a Cook's D > 4/n for the SWPs. One possible reason might be the high amount of wool wax—mainly lanolin—in sheep wool. As the percentages of lanolin and total N are similar in raw sheep wool [72], the amount of added lanolin in the incubation experiment was about 1 g L<sup>−</sup>1. According to Arunkumar et al. [73], only certain groups of bacteria effectively degrade such wax-rich agricultural residues. Studies on wood fibers have shown that waxes can additionally act as physical and bio-chemical protection against microbial breakdown [72,74,75]. The extended lag phase of the SWPs might be related to the time needed to develop such a wax-degrading bacterial population. Furthermore, keratins—the fibrous proteins forming sheep wool—are highly resistant to hydrolysis by common proteolytic enzymes [76]. This seems, at first sight, contradictory to the fact that hooves (CHM) and pig bristles (PBPs), which also mainly consist of keratins, had no lag phase, and their daily N mineralization rates (45 and 47 mg L−<sup>1</sup> d−1) were among the highest of all fertilizers. However, the degradability of keratin-rich residues is closely related to the percentage of different keratin groups (α-, β-, and γ-keratins), the amino acid composition, and the amount of sulfur [76–78], which all differ among hooves, hairs, and feathers [37,77,78]. As a coincidence, the amount of easily (0.005 M hydrochloric acid) hydrolyzable organic nitrogen was about five times higher in the CHM and PBPs than in the SWPs. In contrast, the amount of organic nitrogen soluble in 1 M hydrochloric acid was the same for all three fertilizers. Moreover, as for the NP mentioned before, the maximum daily N mineralization rate (indicated by parameter h of the Gompertz function) of easily hydrolyzable organic N pools was significantly higher for the UP than for the remaining twelve fertilizers. However, for the remaining fertilizers, quickly hydrolyzable

organic N pools were only a rough indicator of the maximum daily N mineralization rate (Figure 6c). Thus, the results present here are useful as indicators of the N release from organic fertilizers but cannot be used for a precise prediction thereof.

**Figure 6.** Correlations among parameters (**a**) NP, (**b**) k, and (**c**) h of Gompertz function and particular C and N pools (Pearson's coefficient of correlation once calculated for all organic fertilizers with the exception of OPF (all) and a second time additionally without outliers according to Cook's D (rout), which are plotted as void symbols and labeled with acronyms).

Before transferring the results to the greenhouse, the influence of plants should be considered as an additional factor. Plant exudate and the microbiology within the rootzone can contribute to this effect [79]. For instance, the study by Gruda and Schnitzler [80] indicated that immobilized nitrogen levels were higher in experiments with plants than those without plants. In addition, root exudates affect the root and shoot growth of plants by attracting beneficial microbiota, chelating nutrients in the rootzone, modulating rootzone pH, and enhancing the availability of specific nutrient elements [79,81]. Furthermore, Helal and Sauerbeck [82] demonstrated that the activation of microorganisms, especially in proximity to the roots, might enhance carbon mobilization from the organic matter. Consequently, the demand for nitrogen increases to support the proliferation of microorganisms. Furthermore, the ongoing substitution of peat in growing media to other organic materials will make the topic more complex, as not only the N release from organic fertilizers but also N immobilization by growing media constituents, such as wood fiber, and the interaction between both processes are of increasing importance [69]. This should be addressed in detail in future research, in which the role of microbial activity and the structure of the microbial community have to be considered.

#### **4. Conclusions**

Water-soluble and easily hydrolyzable N and C pools of organic fertilizers are valuable indicators for the potentially mineralizable N and the time course of N release, which can be estimated very well by flexible-shaped sigmoid growth functions—mainly the Gompertz function. This makes organic fertilizers more comparable and can help match the N supply and demand better, thus increasing the efficiency and sustainability of organic pot plant production. In contrast to chemical composition, the particle size was less important for the time course of N release under the test conditions. Furthermore, the presented data highlight the two main challenges growers face when applying organic N fertilizers. First, growers must consider that the N release is only about half of the total applied N; thus, they must double the N supply compared to the N demand. Secondly, N release is relatively fast, which might cause salt damage and make the complete application of nitrogen before planting impossible.

**Author Contributions:** Conceptualization and methodology, D.L. and E.M.; formal analysis, investigation, data curation, writing—original draft preparation, D.L.; writing—review and editing, E.M. and N.S.G.; visualization, D.L.; supervision, E.M.; project administration, E.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The data supporting this study's findings are available from the corresponding author upon reasonable request.

**Acknowledgments:** The authors like to thank Jakob Burmann for his assistance in the lab work and data acquisition.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


39. *VDLUFA Method Book I: Analysis of Soils*, 4th ed.; with 1–7 Supplements; VDLUFA-Verlag: Darmstadt, Germany, 2016.


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## *Article* **Using Respirometry to Investigate Biological Stability of Growing Media in Aerobic Conditions**

**Sonia Newman 1, Paul Alexander 2, Neil Bragg <sup>2</sup> and Graham Howell 1,\***


**\*** Correspondence: graham.howell@open.ac.uk

**Abstract:** Materials used to replace peat in growing media include wood fibre (WF), often used in combination with composted bark (BC), coir (CR), green compost (GC), and anaerobic digestate fibre (AD). The physical and chemical properties of these materials are relatively well characterised; however, biological properties are less well understood. Biological stability of growing media is an important factor in plant performance. The aim of this research was to identify whether dynamic respirometry methods are suitable for measuring growing media stability and to assess the effect of blending two raw materials in a mix. Raw materials were run for 42 days in aerated conditions at 35 ◦C. Except for AD, individually run samples were considered stable, with CO2 production over 7 days ranked BC < CR < WF < GC << AD in the early stages of the test. The AD was run at two moisture levels, with greater biological activity at lower moisture content. In the most active mixture, AD and WF, there was an increase of activity when nutrients were added at 28 days, indicating major elements were limiting microbial activity. There were interaction effects in sample mixtures, with the CO2 production of WF + GC, WF + CR greater than the sum of the CO2 production from the separate components.

**Keywords:** growing media; soilless culture; stability; microbial activity; wood fibre

#### **1. Introduction**

Over the past decade there has been a shift in the use of peat within the horticulture industry from a prominent component of growing media blends to being phased out in some European countries [1–3]. As a result, a variety of alternative raw materials have been used to replace peat within the industry. The main components of peat-free blends within UK horticulture include coir, wood fibre, bark, anaerobic digestate fibre, and green composts [4]. Wood fibre is increasingly being used as a component of growing media mixes due to its useful physical properties, including water-holding capacity and ability to reduce the hydrophobicity of other mix components [5,6]. Within the literature, a number of materials, often residual materials of other industries, have also been evaluated for their potential use, such as miscanthus and bracken [7,8]. The physical and chemical properties of the main alternative components are generally well understood by the industry and amendments can be made to ensure they are suitable for use within horticulture [3,9,10]. The biological properties of peat-free raw materials are less well understood, though increasing numbers of studies have characterised the microbial populations present within some growing media raw materials [11–13].

Biological stability of growing media can be considered the lack of microbial activity. Microbial activity in growing media has the potential to alter the carefully designed physical and chemical properties during storage or within the pot during cultivation, potentially resulting in sub-optimal plant growth performance. This can include changing the physical structure, key to moisture retention and aeration, and chemical properties such as nutrient

77

**Citation:** Newman, S.; Alexander, P.; Bragg, N.; Howell, G. Using Respirometry to Investigate Biological Stability of Growing Media in Aerobic Conditions. *Horticulturae* **2023**, *9*, 1258. https://doi.org/ 10.3390/horticulturae9121258

Academic Editors: Nazim Gruda, Rui Manuel Almeida Machado and Erik van Os

Received: 30 September 2023 Revised: 16 November 2023 Accepted: 17 November 2023 Published: 23 November 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

status, mineralisation, pH, and maintaining ion exchange capacity [14]. Furthermore, this can change conditions for microbial growth over time, resulting in complex interactions.

In use, growing media are expected to be well aerated, with aerobic microbial respiration dominating. Anaerobic activity may be present in micro-sites such as in wet aggregates, but this is not considered here. Aerobic microbial activity may be limited by poor aeration (low oxygen levels), lack of available moisture, lack of key nutrients, or chemical factors including low pH. These could be considered artificial limitations that can cause "false stability" [15], rather than intrinsic to the substrate. Providing optimum, non-limiting conditions should allow determination of substrate quality, especially availability of carbon as the substrate breaks down, in defined conditions. In respirometry, either CO2 production or O2 consumption is used to track respiration.

Common methods for determining the stability or microbial activity of growing media include oxygen uptake rate (OUR) tests, either in vessels with tops that record the pressure changes [16] or as pre- and post-incubation comparison measurements [17]. These tests are useful to give an indication of microbial activity within a strict set of conditions. There are a few limitations with this style of test; for example, the sample size is small (typically 2 g for pressure vessels), which relies on the homogeneity of the test material. Aspray et al. [15] demonstrated that OUR tests can go out of range when the test material is highly active, which could be the case with anaerobic digestate or green compost. The test conditions are not close to the environmental conditions within which a growing medium would be stored or used to grow plants, though various authors have found reasonable correlation between OUR and respirometric tests for compost materials [18,19].

Tests measuring evolved carbon dioxide (CO2) are less commonly used to assess the stability of growing media. Montagne et al. [11] used a modified carbon (C) mineralisation method to calculate the amount of C released as CO2 from coir, peat, and wood fibre samples over 3 months. Vandecasteele [20] used a CO2 respirometry test with daily CO2 measurements, though this was a more similar to a static test, which could lead to O2 becoming limiting.

Established dynamic respiration tests used in the UK composting industry are the four-day dynamic respiration test DR4, designed to monitor composting of waste [21], and ORG0020 [22], used to specify compost quality under the PAS100 scheme [23]. This type of test could be useful for assessing the stability of growing media in conditions similar to those used in glasshouse plant production. These tests are both solid-phase dynamic respiration tests, though the DR4 was designed for more active materials, including compost feedstocks, whereas the ORG is targeted at distinguishing between more stable composted products. Key differences between these tests are shown in Table 1.


**Table 1.** Comparison of DR4 and ORG0020 respirometric tests.

These tests are designed to provide robust comparisons between samples, though they use different conditions and are not directly comparable. Neither is specifically optimised for very low-activity (high-stability) growing media components. The recommended aeration rate for DR4 is higher, expected to provide sufficient aeration for relatively highactivity/low-stability samples. The DR4 is considered a "truly dynamic" test [15] as it forces

air through the sample mass, while ORG0020 passes air through the chamber headspace only, relying on diffusion to supply oxygen throughout the sample. Aspray et al. [15] found good correlation between these tests for ten composted materials, while static chamber tests, OUR, and self-heating were considered less reliable for these materials.

Moisture is expected to be a key variable, with the optimum value unknown and possibly different between sample types. A fixed gravimetric moisture content as used in the DR4 test takes no account of water-holding properties and is therefore not appropriate to the diverse materials tested here. A "hand squeeze test" approach as used in ORG0020 provides a moisture level closer to conditions used for plant growth. Other moisture conditions have been used, e.g., 75% of water-holding capacity [24], though this has been reported to be less reliable than the hand-squeeze method [22]. Temperature is also expected to be an important variable. Nutrients are added in the DR4 test with the intention that major nutrients are not limiting. This is omitted in ORG0020, apparently assuming composted materials will contain sufficient nutrients. Growing media include components with very high C:N ratio, which may require higher levels of nutrient addition, and may lack trace elements.

A further key difference is the use of an inoculum to supply a microbial population in the DR4 test. In all but recently sterilised media, there will be a microbial population present, adapted to the prevailing conditions and substrate [25]. An inoculum can provide a diverse microbial community, as well as a stable physical structure and chemical buffering, providing a more reliable test [15]. ORG0020 relies on the existing microbial population within the sample, though the initial 3-day equilibration period provides time for the existing microbial population to adapt to test conditions.

ORG0020 does not measure CO2 production during an initial 3-day equilibration period, so that measurements cover the period from start of day 4 to end of day 7. This is explained as an initial flush of activity following disturbance that may not be related to longer-term stability [22]. It seems possible that this initial peak activity may be important, relating for instance to conditions in freshly planted growing media. Using automated data collection over the full period allows a fuller dataset and comparison of different time periods.

Previous studies have either focused on the stability of individual raw materials or of three- or four-component blends [11,20,26], without identifying the interaction effects when just two raw materials are mixed. There is the potential for one raw material with a high or diverse microbial population to act as an inoculum for another raw material with a lower microbial population. Wood fibre, for example, has been described as a stable material with a lower microbial diversity than coir [12], but has the potential to act as a readily available source of carbon when mixed with another raw material with a more diverse microbial population. A DR4-style test could be a useful tool to draw out these interactions between raw materials in a blend of growing media.

The aim of this research was to not only identify whether the dynamic respirometry tests described above (i.e., DR4 and ORG0020) are suitable methods for measuring growing media stability, but also assess the effect of blending two raw materials in a mix. As many of the samples of interest were expected to be very stable, tests were extended to observe longer-term effects that may be relevant to growing media in use.

#### **2. Materials and Methods**

#### *2.1. Physico-Chemical Characteristics*

Five types of growing media raw materials commonly used in peat-free growing media blends in the UK were tested. These were anaerobic digestate fibre (AD), bark (BC), coir (CR), wood fibre (WF), and green compost (GC). Two samples of WF were obtained from different production batches at the same source; WF2 was the primary sample reported and used in test mixtures. The WF used in the study was produced by steaming, pressure treating, and expanding wood chips. Two samples of AD were obtained, with AD1 reported in detail and used in tests on moisture and nutrients.

Initial characterisation of dry matter (DM), moisture content, and laboratory-compacted bulk density were determined according to BS EN 13040:2007 [27]. Loss on ignition was determined according to BS EN 13039:2012 [28], as a measure of organic matter. The total carbon and nitrogen content of the raw materials was analysed via an Elementar Vario EL Cube elemental analyser (Elementar Analysensysteme GmbH, Langenselbold, Germany). The main physico-chemical characteristics are summarised in Table 2.

**Table 2.** Characteristics of test materials used in the study. Means and (standard deviations) of three replicates except where indicated.


n/a = not available. \* Means of only two replicates.

#### *2.2. Stability Testing*

Test conditions followed the DR4 test [21], extended to 42 days. Each material was run individually, using 100 g dry weight per chamber, without inoculum, as in ORG0020 [22]. In addition, 100 g dry weight of each material was mixed with 100 g dry weight WF per chamber. A full DR4 was conducted on the WF only. In short, 100 g dry matter (DM) of WF was mixed with 100 g DM of GC inoculum. This was tested alongside a blank (100 g DM of GC) and a reference material (100 g DM of GC + 10 g α-cellulose from Sigma-Aldrich, UK).

All samples were incubated at 35 ◦C throughout the test. All treatments were run in triplicate. Moisture content for each sample was standardised as per the "hand squeeze test" at the start of the experimental run as per ORG0020 [22]. The "hand squeeze test" was performed by the same operative throughout to reduce the potential variability that has been noted with this measure [18]. Nutrients were included in the added water following the DR4 method [21], as NH4Cl and KH2PO4, to provide 0.28 g N, 0.15 g P, and 0.19 g K in each chamber.

To evaluate the effect of moisture on the test, the AD fibre was tested in the ORG style outlined above on an "as received" basis with no additional moisture, other than the initial 10 mL of nutrient solution. The effect of nutrients on this test were determined by running samples of AD and WF with no nutrients added to the test mixture at the start of experiment. These treatments then had the addition of the nutrient solution at 28 days along with all other treatments.

The respirometer setup is shown in Figure 1. The test samples were placed into 4 L respirometer chambers and were incubated at 35 ◦C, with forced air flows maintained in the range of 250–300 mL/min for 42 days. Inlet air was passed through a condenser at 4 ◦C to standardise moisture content. Outlet air was also passed through condensers at 4 ◦C, and further dried before measurement of composition of the dry gas. Inlet flow rates and the CO2 and O2 content of inlet and exhaust gases were recorded at 2 h intervals using on-line analysers (FB8 mass flow meters, MUX3 multiplexers, CAXL CO2 analyser, FCX oxygen analyser, data collection via UI2 interface and ExpeData v.1.8.5 software; Sable Systems International, Las Vegas, NV, USA). Analysers were calibrated using 1% and 2% gas standards in nitrogen for CO2 (Calgaz Ltd., Stoke, UK), ambient outside air for O2, and pure nitrogen as a zero point.

**Figure 1.** Schematic diagram of respirometer.

The chambers were weighed and shaken every 7 days to redistribute moisture and nutrients. Further nutrient stock solution, as specified above, was added at 28 days to all of the test chambers during the shaking procedure.

#### *2.3. Data Analysis*

Initial data processing was carried out in ExpeData® v.1.8.5, using inbuilt macros. Baseline corrections were applied using ambient outside air at three points in each 2 h run to compensate for drift in the analyser readings. Lags between flow data and CO2 and O2 concentrations were corrected, and the most stable flow, CO2, and O2 signals selected for each channel. Data were exported and further data processing was conducted using an R statistical environment (v.4.2.2). The outlet flow was not measured, and gas composition will have changed in the chamber, changing the mass flow. CO2 production was corrected using O2 composition of the outlet gases using gas-exchange equations [29]. The flow rate was more than adequate to maintain aerobic conditions, with outlet O2 not falling below 18 % at peak O2 demand.

The graphs presented below have been simplified for clarity, with error bars only marked for one point in each two days. Lines have been used rather than individual points, unless otherwise stated.

#### **3. Results**

#### *3.1. Microbial Activity of Single Materials*

When run as single materials, four out of five substrate types could be considered biologically stable (Table 3; Figure 2). The UK compost quality specification PAS100 [23] defines a threshold for stable compost using ORG0020 as 16 g/kg volatile solids(VS)/day, or 64 g/kgVS over the 4 days of ORG0020 [23]. Llewelyn [22] suggested a single threshold equivalent to 40 g/kgVS/4 days, or in more detail, respiration rate was considered very low below 32 g/kgVS/4 days, low up to 48 g/kgVS/4 days, medium up to 64 g/kgVS/4 days, high up to 80 g/kgVS/4 days, and very high above 80 g/kgVS/4 days. These are based on stability of mature green compost and are broadly related to compost age. The GC sample used here passes the PAS100 stability threshold, though is slightly above Llewelyn's [22] suggested single threshold (Table 3).

Most materials showed a distinct initial peak in the first few days of the test, followed by steadily declining CO2 production. This forms a cumulative curve tending to an asymptotic value or linear increase (Figure 2c,d). In contrast, the composted bark did not show any initial peak and had low, though measurable, activity throughout the 42 days. The coir showed an increase in CO2 production after a 7-day lag period, peaking at about 15 days.


**Table 3.** Cumulative values of CO2 production at 4 days (equivalent to DR4), days 3–7 (equivalent to ORG0020), 7 and 28 days, 28-day O2 consumption, and percentage C loss, means and (standard deviations) for three replicates. Samples are in rank order for DR4 results.

Assessment according to Llewelyn [22]: \*\*\* extremely high; \*\* medium; \* very low.

**Figure 2.** CO2 production of the single raw materials' (**a**) gas production rate; note the area below the horizontal dotted line is expanded in (**b**) for visibility of the samples with lower CO2 production; (**c**) cumulative gas production; note the area below the horizontal dotted line is expanded in (**d**) for visibility of the samples with lower CO2 production. Graphs show means, error bars show ±1 standard deviation and every 24th point is plotted. The vertical dashed line indicates nutrient addition at 28 days.

The AD was the least stable substrate, with a large initial peak over the first two days, then declining. A comparison of the two AD batches showed a difference in the stability over the first four days (Figure 2a,c) and differing ORG0020 values (99 and 135 g/kgVS/4 days); however, by seven days the cumulative CO2 production was the same. There was an initial lag in the activity of AD2, with peak CO2 production at 3 days, compared with AD1 at 1 day. A similar comparison of the WF batches showed that the biological stability of the two samples over time was almost identical, with all results being within one standard deviation of each other (Table 3).

The WF and GC samples showed a similar initial peak; however, the amount of CO2 produced was much lower in the WF over the first four days (19 and 32 g/kgVS CO2 respectively). The peak in the WF was very sharp and CO2 production then rapidly decreased, whist the GC had more sustained activity over the duration of the whole test.

The BC was the most stable of all the substrate types, with very low CO2 production for the whole time series. There was variability in this data, however, which was attributed to one replicate losing moisture during the test, resulting in higher CO2 production. This suggests the "hand squeeze" test moisture was not optimal for this sample. As a result, CO2 production may have been underestimated by the samples remaining at "hand squeeze" moisture, and stability overestimated in this current test.

The nutrient addition at day 28 appears to have had no obvious effect on the stability of any of the single raw materials, with no pulse in CO2 production seen.

The percentage of carbon loss can be found in Table 3. This shows 30 to 32% carbon loss in the most active samples (AD), with next largest loss from GC at under 8%. Other single materials lost under 5% of carbon in the 42-day test, with WF and BC samples losing least, reflecting the higher stability of these samples over the full period of the test.

#### *3.2. Moisture Effects on Test*

The "as received" sample had a dry matter content of 57% compared with 28% when the sample moisture was adjusted to the "hand squeeze test" (Table 1). The moisture content of the AD substrate influenced the CO2 production in the test (Figure 3). The chambers with added moisture had a larger initial peak than those without, with the CO2 production becoming comparable only at the 28-day point. The cumulative CO2 production at 28 days was less than half the "hand squeeze" moisture sample when no moisture was added to the material (216 g/kgVS CO2 "as received"; 495 g/kgVS CO2 adjusted moisture).

**Figure 3.** CO2 production from one AD sample with added moisture and as received: (**a**) gas production rate, (**b**) cumulative data. Graphs show means, error bars show ±1 standard deviation and every 12th point is plotted. The vertical dashed line indicates nutrient addition at 28 days.

Pulses in CO2 production were noted in the "as received" sample following each disturbance during the weekly shaking events to redistribute moisture within the chambers (Figure 3a). These pulses can also be seen in the AD sample with adjusted moisture; however, the effect is less pronounced.

#### *3.3. Nutrient Effect on Test*

The effects of nutrients on the test were investigated in the AD and WF samples. For both materials, there was a reduction in the CO2 production from the samples when no nutrients were added at the start of the test (Figure 4). The same trend in CO2 production was seen both with and without nutrients, with an initial peak and then decline, but for both substrate types the production was lower in the samples without nutrient addition. The CO2 production at 4 days was 109 g/kgVS CO2 for AD without nutrients compared with 156 g/kgVS CO2 with nutrient addition. For the WF, this was 7.4 g/kgVS CO2 and 18.7 g/kgVS CO2 respectively.

**Figure 4.** CO2 production rate from an AD sample (**a**) and WF sample (**b**) with and without nutrients added at the start of the test. Graphs show means, error bars show ±1 standard deviation. The vertical dashed line indicates nutrient addition at 28 days.

When nutrients were added at 28 days, there was an initial increase in CO2 production in both substrates, which was sustained in the AD sample. This increase was not seen in the AD fibre that had nutrients from the start (Figure 4a). There was also no nutrient effect seen in any of the single materials when nutrients were added at 28 days (Figure 2). The increase of CO2 production in the WF was short-lived and quickly decreased back to the level seen in the WF with nutrients.

A similar nutrient effect can be seen in the AD + WF mix (Figure 5a), with an increase in CO2 production rate once nutrients were added on day 28. The CO2 production increased up to the end of the test at 42 days, resulting in the curve no longer tending to an asymptotic value.

#### *3.4. Interaction Effects*

Figure 5 shows the raw CO2 production in litres from mixtures of 100 g dry matter AD, GC, BC, or CR with 100 g dry matter WF, with the results for individual components (100 g dry matter) shown for comparison. In absence of interaction effects, the mixtures may be expected to produce the sum of CO2 of the two individual components. The sum of CO2 production from the individual components provides a predicted value for the mixture as a range from the sum of minimum replicate values to the sum of maximum replicate values for each component (represented by the shaded area on each graph).

The CO2 production of the AD + WF mix was very similar to the prediction based on the single materials, up until the nutrients were added at day 28 (Figure 5a). From this point, there was an increase in the CO2 production in the mixture, with CO2 production of 6.2 L between days 28 and end of test, 3.5 L more than the median predicted value based on CO2 production from the single materials. This was the only mix where the addition of nutrients had an identifiable effect.

The mix containing GC + WF had the most noticeable interaction effect of all of the mixes (Figure 5b). The CO2 production was much higher in the physical mix and was underpredicted by the simple addition of the individual components.

Initially, there was close agreement between the predicted and observed CO2 production curves for the BC + WF mix; however, from 14 days there appears to have been a slight suppression of CO2 production. There is some uncertainty with this as there was variability

between the BC replicates in the test. As noted above, the CO2 production of the BC sample may have been underestimated for the wetter replicates, or the stability overestimated. The observed CO2 production of the mixture was at the lower boundary of the predicted range.

**Figure 5.** Raw cumulative CO2 production in litres from 100 g dry matter of the single raw materials and mixtures: (**a**) AD + WF; (**b**) GC + WF; (**c**) BC + WF; (**d**) CR + WF. The shaded area indicates the range of CO2 production of individual materials summed together. Note Y-axis scale differs between graphs. Graphs show every sixth mean value, and error bars show ±1 standard deviation and every 24th point is plotted. The vertical dashed vertical line indicates nutrient addition at 28 days.

The CR + WF mix had higher CO2 production than the sum of the individual materials initially, but by day 21 of the test the predicted gas production matched the actual production.

#### **4. Discussion**

#### *4.1. Respirometry*

The biological stability of a material is a function of the material and the environmental conditions to which it is exposed. The aim of this study was to create conditions close to realworld use of growing media and assess the biological stability of commonly used materials, using aspects of existing respirometry techniques to standardise those conditions as far as possible. By removing limiting factors, such as oxygen supply, moisture, and nutrients, the CO2 production measured in the study gives a test comparable to the intended use of materials in horticulture, but in standardised, idealised, and replicable conditions. The study was based on conditions of the DR4 test [21], adapted to incorporate single materials without inoculum as used in ORG0020 [22]. Parameters were chosen to be as robust as possible. The test is considered a good proxy for a measure of microbial activity within growing media, limited only by microbial population and substrate quality.

Aeration was not limiting in the experimental set up, with a continuous flow of external ambient air being forced through the test materials and oxygen in the outlet never falling below 17.5% at peak oxygen demand. The flow rate and flow configuration have been found to be important factors within respirometry testing [30–32]. Guillen Ferrari et al. [33] found that optimising the aeration within the ORG0020 test improved precision within the setup. An airflow configuration that was purely within the headspace of a chamber, as used in ORG0020, may work reliably especially with more stable materials, and may be considered more realistically representative of the exchange of air over a tray or pot within a glasshouse.

The nutrient supply at the beginning of the test was sufficient not to limit microbial activity across all of the individual test materials and their mixes, except for the most active mix of anaerobic digestate fibre (AD) and wood fibre (WF). Nutrient addition at 28 days did not create a pulse in CO2 production in the individual AD and WF samples that were given nutrients at the beginning; however, pulses in CO2 were seen in the AD and WF samples that were not supplied with nutrients at the start. It is therefore likely that the AD + WF mix had become limited for nutrients at some point during the first 28 days of the test, resulting in an overestimation of the stability of that test mix. This may mean reduced availability of nutrients for plant growth, in competition with microbial activity (N immobilisation) [34].

Moisture was another limiting factor standardised during the test using the "hand squeeze" method. This was chosen as an acceptable level of moisture at which a plant might grow well, simulating the wetting up of a pot media when a plant is first planted. The adjusted moisture for the green compost sample (GC) was within the 40–60% moisture content range within the ORG0020 protocol [22]. The other growing media raw materials required more water to be added to reach the same physical point of water release due to squeezing, resulting in over 70% moisture content for the other four materials. Gurusamy et al. [35] found that moisture had a significant impact on stability in some compost materials during an ORG0020 test. This was seen in the moisture experiment, where the drier "as received" AD sample had higher stability compared with that of the same sample with moisture adjusted to the "hand squeeze" level. It should be noted that the "as received" AD had a moisture content of 43%, which is within the acceptable range for ORG0020 [22]. This was not optimal for this particular material and suggests that the moisture range in the ORG0020 test might not be optimal for materials with different physical properties to green compost. The "hand squeeze" test was not necessarily optimal for all of the materials tested either. The variability within the individual bark sample was identified as being related to one sample that dried out during the test and had elevated CO2 production as a result. This suggests that the moisture within this sample may have been limiting, causing a falsely stable result.

Further investigation is required to determine the optimum water content for this kind of test. Other approaches have been used. The DR4 test specifies 50% gravimetric water content [21], which is likely to be reasonable for compost but is arbitrary. The "hand squeeze" test used by Llewelyn [22] may be considered subjective, and an alternative of 75% of water-holding capacity was tested by Adani [24]. It is likely that matric potential is a key factor [36], making any arbitrary gravimetric or volumetric moisture content questionable. This could also shed light on the likely distribution of moisture between components of a mixture, and availability of water to both microbes and plant roots. This complication can be avoided by using a water-based test such as the OUR, but at the expense of conditions for microbial growth closer to the intended real-world application. OUR tests may also be restricted to short-duration tests by supply of oxygen. This limitation has been addressed in the SOUR test [37] by periodic aeration in aqueous medium. It remains a valid question what microbial communities are supported in each of these environments.

#### *4.2. Stability of Individual Raw Materials*

The raw materials tested were a range of the most common peat-free growing media components in UK [4]. All the materials tested, except the AD, were very stable when tests were run individually. Under PAS100, compost is considered sufficiently stable if ORG0020 results are under 16 mg CO2/g VS/d (PAS 100:2011). The wood fibre, coir, and composted bark were all well below this value. Only the testing of the wood fibre was run as a full DR4 [21] including the standard green compost inoculum. This method has no published threshold, though from data in [15], materials under 25 mg CO2/g VS/d may be considered stable. The reference cellulose result demonstrated the test was valid, and the result with GC contribution subtracted can be considered very stable.

Two batches of AD fibre were received and tests were run separately. These were both above the PAS100 threshold, though they differed in activity during the early stages of the test, indicating some initial variability as well as instability. After 7 days in the test conditions, there was no difference between the two samples, suggesting some initial inhibitory effect in one sample, resulting in a short lag in CO2 production. The two batches of WF tested were from the same source and only slightly different in terms of stability. Various authors have noted that the microbial population in composts or wood fibre, for example, is dependent on the production method and source of material [11,38]. For example, Montagne et al. [11] suggests that the physical structure of wood fibre is more important than geographic origin or wood type for determining the microbial population. In this study, the wood fibre was produced by steaming, pressure treating, and expanding wood chips, which could create substrate suitable for a specific microbial community and therefore different levels of stability compared with other methods of production. Testing of batches of raw materials produced by different methods would be necessary to determine the overall variability of substrate types.

An increase in CO2 production was seen in the coir after about 15 days, a similar effect is noted in Montagne et al. [11]. There, coir pith had a lower initial CO2 production rate compared with other materials, such as coir fibre and wood fibre, then after 20 days the rate increased to the same as the coir fibre. The test temperature in the experiment by Montagne et al. [11] was lower than in this study (28 and 35 ◦C respectively), which may explain the difference in lag time. Lag periods have also been identified as correlating with biochemical composition in coir pith, as opposed to fibre [11]; this could help explain the lag identified here.

The rank order of stability of the single materials tested altered over the testing time period, with the results at 7 days different to those at 28 and 42 days. The biggest changes were that the coir became more active after day 15. At the end of the 42-day test, the order of stability was bark > wood fibre > coir > green compost >> anaerobic digestate fibre. Although not all of the same raw materials were tested, Vandecasteele [20] found a similar pattern in the ranking using respirometric CO2 production in various growing media materials, with wood fibre and composted bark more stable and green compost ranking as one of the least stable materials. The O2 consumption in the OUR test in that study produced a different ranking of stability, with changes in rankings of some of the more active substrate categories.

#### *4.3. Interaction Effects of Mixing Raw Materials on Stability*

The wood fibre was chosen as the common material in mixes because it potentially has a low existing microbial population [12] and high carbon content, which could be utilised by the inoculating microbial population from the other materials. As an increasingly large proportion of growing media mixes in the UK [4] include WF, any interactions in terms of biological stability with other raw materials is important to note.

The interaction experiments showed a range of responses to the mixing of individual components. Simply summing the activity of individual materials does not accurately predict the observed responses. Initially, there appears to be an effect of mixing the coir and wood fibre together. The lag that is seen in the coir on its own is no longer present when used as part of a mix. The wood fibre potentially has different forms of carbon, which may be more available than the more recalcitrant forms in the coir [11]. After the initial lag period passed at about 21 days, the predicted and observed CO2 production matched for the rest of the test. This suggests that the overall stability of the mix was the same when compared to the component parts, but that the initial microbial activity was greater.

A large increase in the microbial activity was noted in the GC + WF mix compared with the predicted values, i.e., an overall decrease in the biological stability of the mix. A deviation from predicted microbial activity within any mix could be as a result of physical, chemical, or biological parameters, or potentially a combination of all three. For example, green compost is known to have a diverse microbial population and as such is used as an inoculum in a number of biodegradability tests, such as the DR4 test [21]. Wood fibre has been noted as having a potentially available carbon source [20] and a hydrophilic nature that enhances the moisture distribution in a mix [6], so when added to a diverse microbial population like in the GC, there is the potential for increased microbial activity compared with the raw materials alone.

The interaction effect seen in the AD and WF mix only became apparent when additional nutrients were added part way through the test. As noted above, this suggests that nutrients (most likely N) were limiting during the test. Nitrogen immobilisation (or lock up) is a common effect seen particularly in wood-based materials and can affect plant growth and quality [34].

There is a suggestion of potential suppression of microbial activity in the BC and WF mix, though this is somewhat uncertain due to the variability in the bark test samples. As noted above, this may be due to an effect of sub-optimal moisture conditions within the sample during the test. The observed microbial activity was at the bottom of the range of predicted values for these materials. It is likely that a lower moisture content would be optimal for the bark, and as a result, the microbial activity seen in this test is an overestimation of stability. If this were the case, then the predicted value would be shifted up and a real suppression effect would have been seen. Where the microbial activity is enhanced due to mixing of materials has potential implications for the use of materials as growing media. Blends are carefully constructed by growing media manufacturers to have specific chemical and physical properties when they are produced. The data presented here indicate that there is the potential for large losses of carbon over a 6-week period in optimised conditions, particularly if there is a component with low biological stability in the mix. This carbon loss has the potential to affect the structure of the growing media, particularly if fine fibres are degraded [14]. A low-stability material may not only degrade more rapidly, changing the proportions of the mixture, but also provide a means of degrading more recalcitrant materials through "priming effect" [39].

Microbial activity within growing media raw materials should not necessarily be seen as a negative issue, as there is a wealth of literature showing that microbial genera and species that are known to suppress plant pathogens are present in composted bark, wood fibre, and green composts [12,13,38].

#### **5. Conclusions**

Dynamic respirometry is a suitable tool for evaluation of existing and new growing media raw materials and their interactions in mixes. Using a respirometry technique adapted from standard methods, differences in microbial stability between different growing media were successfully identified. Furthermore, by mimicking real-world yet replicable conditions, this technique can produce realistic cultures with potential for additional microbiological or other characterisation. The specific methods DR4 and ORG0020 are not well optimised to this application. Further work is recommended to refine operational parameters in the adapted method, such as moisture status, for a standardised test.

The separate components tested ranked from most to least stable (lowest to highest CO2 production) were BC < CR < WF < GC << AD after 7 days. This order changed through the test as CO2 production from CR peaked in the third week.

Interactions between components were identified in simple two-component mixes. This may be due physical or chemical factors, or cross-inoculation of microbial populations native to each component. Green compost is expected to contain a wide microbial diversity.

Further work is needed to assess variability within and between sample types, and interactions present in horticulture-relevant mixes.

**Author Contributions:** Conceptualization, S.N., P.A., N.B. and G.H.; Formal analysis, G.H.; Investigation, S.N. and G.H.; Methodology, S.N. and G.H.; Project administration, G.H.; Supervision, S.N.; Validation, G.H.; Visualization, S.N. and G.H.; Writing—original draft, S.N. and G.H.; Writing review and editing, S.N., P.A., N.B. and G.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by Bulrush Horticulture Ltd.

**Data Availability Statement:** Data are contained within the article.

**Acknowledgments:** The authors wish to thank the OU technical team, especially Caroline Gurd and Angus McEwen, for technical assistance. SN and GH would like thank NB and PA for their contributions of horticultural expertise.

**Conflicts of Interest:** This study was designed and carried out by Open University staff. It was exploratory with no prescribed outcome. The funders had no role in the analysis or interpretation of data. The manuscript was written by Open University staff, with expertise on horticultural context provided by NB and PA. SN and GH are grateful for the funders' permission to publish.

#### **References**


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### *Article* **Performance Evaluation of a Cascade Cropping System**

**Eleni Karatsivou, Angeliki Elvanidi, Sofia Faliagka, Ioannis Naounoulis and Nikolaos Katsoulas \***

Laboratory of Agricultural Constructions and Environmental Control, Department of Agriculture Crop Production and Rural Environment, University of Thessaly, Fytokou Str., 38446 Volos, Greece; ekaratsivou@hotmail.com (E.K.); sofia.faliagka@gmail.com (S.F.); giannisnaou@hotmail.com (I.N.)

**\*** Correspondence: nkatsoul@uth.gr; Tel.: +30-24210-93249

**Abstract:** Minimum environmental impact and improved resource efficiency is attainable for soilless cascade systems where the nutrient solution drained from a primary (donor) crop is reused to fertigate a secondary (receiver) crop. However, it is not clear whether the nutrient solution drained from the primary crop can completely satisfy the needs of a secondary crop and if the productivity of the secondary crop is compromised. To test this hypothesis, a prototype soilless cascade system was developed and evaluated. To assess the performance of the system in terms of yield, water and nutrient productivity, a tomato crop was used as the primary crop, while lettuce, spinach and parsley were tested as secondary crops under different drainage management strategies. Measurements of plant growth, crop fresh and dry matter production, leaf chlorophyll and nutrient content, and photosynthesis rate were performed in the secondary crops. In addition, the water productivity and nutrient use efficiency for the fertigation of the primary and secondary crops were recorded. The results showed that the yield of the cascade spinach crop increased by up to 14% compared to the control treatment (monoculture of secondary crop fertigated by standard nutrient solution). The yield of the lettuce and parsley crop was not affected by the reuse of the tomato crop drainage solution. The water productivities of the lettuce, spinach and parsley plants fertigated with pure drainage solution were 50%, 30% and 14% higher than in the control treatment, respectively. The nitrogen and phosphorus use efficiency was improved by more than 50% compared to the control treatments.

**Keywords:** multi-cropping; drainage management; water use efficiency; nutrient use efficiency

#### **1. Introduction**

In greenhouses, closed soilless cultivation systems provide the opportunity to increase the water and nutrient use efficiency and reduce the environmental impact of the cultivation system through the reuse of the drained water and nutrients [1,2]. However, due to the low quality of the water used in the Mediterranean countries (high concentrations of Na<sup>+</sup> and Cl−, but also high Ca2+, Mg2+, and SO4 <sup>2</sup>−, [3]), completely closed soilless systems are not feasible. Appropriate management of the drainage solution (DS) using suitable practices and models may reduce the need to discharge the drainage solution to the environment. To this end, Katsoulas et al. [1] developed a model for automatic drainage solution management in tomato crops grown in semi-closed systems. Nevertheless, partial discharge of the drainage nutrient solution when the levels of electrical conductivity (EC) or of the toxic ions in the system are reached, is still a necessity in these systems.

Many growers in the Mediterranean region operate their soilless systems as open systems, mainly because they do not have the knowledge or the capacity to manage nutrient solution drainage. One of the serious problems of open systems is the effluence of overdosed nutrient solutions from the system into the environment, resulting in eutrophication of soil and groundwater. Abd-Elmoniem et al. [4] showed that the average water consumption of plants grown in open systems was 15% to 17% higher compared to those grown in closed systems. The absolute values of water consumption in lettuce were 68.5 L and 80.5 L per plant for closed and open systems, respectively. Rufi-Salis et al. [5]

**Citation:** Karatsivou, E.; Elvanidi, A.; Faliagka, S.; Naounoulis, I.; Katsoulas, N. Performance Evaluation of a Cascade Cropping System. *Horticulturae* **2023**, *9*, 802. https://doi.org/10.3390/ horticulturae9070802

Academic Editors: Nazim Gruda, Rui Manuel Almeida Machado and Erik van Os

Received: 10 June 2023 Revised: 5 July 2023 Accepted: 11 July 2023 Published: 13 July 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

reported that closed soilless systems reduce daily water and nutrient consumption by 40% and 35–54%, respectively, when compared to open systems in green pea production. Méndez-Cifuentes et al. [6], in a similar study, concluded that 53 L and 22 L of nutrient solution were required in closed and open systems, respectively, to produce 1 kg of tomatoes. Therefore, open systems consumed 86% more water. Fayezizadeh et al. [7] found that closed systems allowed water and nutrient savings of up to 97% compared to open systems. Closed systems are therefore preferable in terms of reducing environmental pollution.

Sustainable management strategies for the reuse and discharge of DS in closed systems are needed. One of the potential strategies for enhancing the circular economy concept in the context of soilless systems is the development and implementation of soilless cascade systems. In these systems, the drainage of a primary donor crop is utilised for the fertigation of one or more secondary receiving crops that possess a higher tolerance to salinity. The reuse may also continue, with the drainage from the secondary crop being utilised for the fertigation of a tertiary, highly salinity-tolerant crop.

Some pioneer studies on cascade fertigation systems were performed more than 10 years ago [8,9]. Some interesting systems have already been developed, mostly in openfield crops, such as the system developed in San Joaquin Valley in California. However, it is not so easy to manage the drainage solution in these systems, since the solution drains directly into the underlying soil. García-Caparrós [10] studied a pilot cascade system in the facilities of the University of Almeria in Spain, in which the drainage solution from melon cultivation was used to cover the needs of rosemary, with encouraging results.

However, the recent advancements in drainage management and the increased need for sustainable production systems with low environmental impact have increased the interest in further research on soilless cascade systems [5,11–17]. In addition to their excellent utilisation of drained water and nutrients, cascade systems may lead to improvements in the quality characteristics of secondary and tertiary crops due to the increased salinity levels in the system. Incrocci et al. [8] showed that fruit dry matter increased, while Avdouli et al. [11] showed that the content of several compounds associated with the organoleptic, nutritional and nutraceutical qualities of many fruits and leafy vegetables increased when they were cultivated as secondary crops in a cascade system. Nevertheless, the efficient management of a cascade system requires knowledge of the salt tolerance levels [17] and of the fertigation needs of the crops in the loop. Furthermore, the drainage solution of a crop may include phytotoxic root exudates [10] or other metabolites, and may impose recirculation plant protection products [16] in the system. However, advanced cascade research needs to focus on the yield quality of secondary and tertiary crops, and to date, there are scant scientific articles referring to this concept.

Additionally, to obtain a sufficient cascade system, optimal management may require correction with respect to pH, EC and macronutrients, since the drained solution may have abnormal mutual nutrient ratios. However, it is not clear whether the drainage solution collected from the main soilless greenhouse crops can be directly utilised for the fertigation of secondary and then tertiary crops. Moreover, it is not clear which management practices are needed in order to fulfil the fertigation needs of secondary and tertiary crops while increasing the water and nutrient use efficiency of the cascade system.

The aim of this work is to test whether some common leafy vegetables (lettuce, spinach and parsley) can be used as secondary receiving crops in soilless cascade systems with tomato as the primary crop in greenhouses in the Mediterranean region. The specific secondary crops selected for testing were chosen due to their high consumer demand, short cultivation cycle, high nutritional value, flexible growth adaptation to soilless facilities [18–20] and high salt tolerance and ability to accumulate sodium as an osmoregulating resistance mechanism [21]. In this sense, lettuce, spinach, and parsley plants were cultivated under different cascade systems.

In this study, we aim to provide knowledge about the progress of the nutrient concentrations in the different parts of cascade systems and on the utilisation/absorption of different nutrients in soilless cascade systems. The amount of water irrigated to, uptaken

by and drained from the plants is also presented. Additionally, yield characteristics (plant height, number of leaves, chlorophyll content index, and photosynthesis rate) of the secondary crops are reported. The evaluation of the different cascade systems was performed on the basis of water productivity (WP) and fertiliser use efficiency (FUE) calculations. With this research, the gap in scientific knowledge with respect to the secondary crop response in cascade systems is decreased.

#### **2. Materials and Methods**

#### *2.1. Greenhouse Facilities and Cascade System Set Up*

The cascade system was installed in a gothic multitunnel greenhouse belonging to the University of Thessaly, located in Velestino, Central Greece (Latitude 39◦22 , longitude 22◦44 and altitude 85 m). The greenhouse was oriented north–south, and the total ground area was 1500 m2, separated into six compartments. The first compartment was used to host the fertigation and control equipment. Four out of the other five compartments were used for cultivation purposes in this work. In each of the culture compartments, six channels, 20 m in length, and carrying 19 rockwool slabs each (Grodan Delta, NL 100 × 15 × 7.5 cm, 0.18 g cm<sup>−</sup>3, 90% water retention capacity, Roermond, The Netherlands), were installed.

All the compartments were covered by a polyethylene film in the roof, while the side walls were covered by polycarbonate sheets. Each compartment was further equipped with a roof vent, a pad and fan system, and a thermal/shading screen, and all the systems/compartments were controlled by a climate control computer (SERCOM, Automation SL, Lisse, The Netherlands). The roof vent was opened when the air temperature within the greenhouse was higher than 20 ◦C or the relative humidity exceeded 87%. The pad-and-fan system was in operation whenever the air temperature inside the greenhouse was higher than 26 ◦C. The shading screen was used when the outdoor solar radiation was higher than 750 W m<sup>−</sup>2. The transmittance coefficient of the cover material was 0.75, while the screen's transparency was 0.50.

Within the hydroponic head (500 L), the nutrient solution (NS) was made by mixing different amounts of nutrients, stored within five stock solution tanks (capacity of 120 L each), with tap water or drainage solution (DS). The amounts of nutrients and water or drainage solution used for the preparation of the NS were based on the desired targeted concentration. The system can prepare five different recipes, stored in different nutrient storage tanks (capacity of 500 L) each time. In the current research, two nutrient solution tanks were used for irrigating the primary crop and two for the secondary crops. Therefore, each of the solution tanks was linked with its own injection pump, thus making it possible to automatically prepare fresh NS separately for each crop level (primary, secondary and tertiary). This operation was accomplished by the fertigation system, which generally works using volumetric or electronic injectors. The quality of raw water is a key factor, and must be known from the outset in order to check whether the water can be utilised as is, or if it needs specific treatment in order to calculate the amount of fertilisers required for the preparation of the nutrient stock. When the nutrient solution had reached the optimum concentration values set by the operator, it was transferred from the mixing tank to one of the eight irrigation tanks. The reused NS was first disinfected using a UV-light disinfection system before being supplied to the plants. Figure 1 presents a schematic diagram of the system.

The nutrient solution drained from the crops was collected in the DS tanks. In each culture compartment, two drainage tanks with a capacity of 300 L were used. In the compartments of the secondary crops, the DS was collected in four tanks with a capacity of 100 L, individually for each treatment, before ending in the final tanks (Figure 1). The pH of each NS preparation was set at 5.8. Correction was performed through the addition of NO3 solution. Through this setup, each compartment of the secondary crop could host two different crop species (two channels 10 m in length), fertigated by two different treatments (two sets of three channels/repetitions). The final DS collected was further reused in the process of preparing a fresh nutrient solution.

**Figure 1.** Scheme of the cascade system setup and the flow of the nutrient solution from the irrigation to the drainage tanks. The system is designed to operate in three consecutive circulation levels, but only the first two were used in the present work.

#### *2.2. Crop Management and Experimental Setup*

The experiments were carried out from March 2019 to October 2019. Tomato plants (*Solanum lycopersicum* cv. Elpida, hybrid F1) were cultivated to serve as the primary crop in two compartments of the greenhouse. The tomato plants were transplanted in a density of 3 plants m−<sup>2</sup> at the stage of five extended leaves and 25 cm height. The plants were transplanted on the 29 March 2019, while the cultivation period lasted for six months.

The NS drained from the tomato crop was used for the fertigation of the secondary crops, which were cultivated in two experimental periods. The first period occurred during the vegetative stage of tomato crop, from April to May. During that period, spinach (*Spinacia oleraceα* cv. Matador) and lettuce (*Lactuca sativa* cv. Batavia, type iceberg) were cultivated at a density of 4 plants m−2. The plants were transplanted at the stage of three true leaves, 19 days after transplanting of the primary crop (DATp). Their cultivation cycle lasted 42 days. The second period occurred during the fruit stage of the primary crop, from August to October. During that period, parsley (*Petroselinum crispum* cv. Mill.) was cultivated at a density of 4 plants m−2. The plants were transplanted at the stage of two true leaves, 137 DATp. The cultivation cycle of the parsley crop lasted 54 days. The cultivation cycle of each secondary crop corresponds, according to local practice, to that of a commercial production. The time interval between the two experiments (June–July) was used to clean the irrigation system and change the cultivation of the secondary crop, so no data were recorded.

To achieve a randomised block design for the secondary cultivation, in each of the six lines of each compartment, 36 plants of each species were cultivated (4 plants per slab; 72 plants per line; 432 plants per species in total). In each secondary crop, in both periods, a total of four irrigation treatments were applied in three repetitions (36 plants per treatment and repetition), in which the plants were supplied with: (i) fresh nutrient solution (FS) comprising the control treatment (T1 treatment: 0%DS + 100%FS); (ii) drainage solution of the primary crop diluted with water (W) at a ratio of 50–50 (T2 treatment: 50%DS + 50%W); (iii) drainage solution of the primary crop diluted with water at a ratio of 75–25 (T3 treatment: 75%DS + 25%W); and (iv) drainage solution of the primary crop without any dilution (T4 treatment: 100%DS + 0%W). In all systems of secondary crops, the drainage was collected, but was not recycled, in order to simulate the conditions of an open-loop system.

The tap water used for the NS preparation had a pH of 7.1, an EC of 0.8 dS m−<sup>1</sup> and an Na+ concentration of 1.3 mM L−1. The composition of the nutrient solution used for both the primary and secondary crops was similar to that applied in Mediterranean climatic conditions, and was modified according to the plant stage. The pH, EC set points and nutrient composition supplied to the crops according to their growth stage are shown in Table 1. The irrigation dose for the primary crop was set to cover at least the 30% of the leaching fraction. The daily dose for the secondary crop was around 0.345 L per plant in lettuce and spinach and 0.199 L per plant in parsley. The amount of water was added in the NS supplied to the secondary crop, and the pH and EC value variations according to the treatment are reported analytically as results.


**Table 1.** The targeted nutrient composition, pH and electrical conductivity (EC) of the nutrient solution supplied to the plants of primary and secondary crop according to growth stage.

#### *2.3. Measurements*

Air temperature (Ta, in ◦C) and relative humidity (RH, in %) were measured using a temperature–humidity sensor (model HD9008TR, Delta Ohm, Italy), which was calibrated before the experimental period and placed 1.8 m above ground level. The irradiance (Rg, i, in W m−2) inside the greenhouse was recorded using a solar pyranometer (model SKS 1110, Skye instruments, Powys, UK) located 1.8 m above ground.

Plant height, number of leaves, and chlorophyll content index were obtained in the plants of the secondary crop twice a week. Plant height (H) was measured by placing a calibrated ruler on the top edge of the slab and measuring to the tip of the last open leaf of the plant (number of samples (*n*) = 30 per treatment). The number of leaves was measured for 30 plants per treatment. Chlorophyll content index (CCI) was recorded using non-destructive sensing by means of an Opti-Science sensor, performing measurements in contact with the leaf (CCM 200, Opti-Science, Hudson, NH, USA). CCI index is the ratio of the chlorophyll's reflectance in the NIR band over the reflectance in the red band. The measurements were performed in young and fully developed leaves during the morning to avoid the effect of direct sunlight on the chlorophyll meter (*n* = 10 per treatment).

Photosynthesis rate (P*N*) (μmol CO2 m−<sup>2</sup> s−1) was measured weekly in young and fully developed leaves (*n* = 5 per treatment) using a portable photosynthesis system (LCpro+ 1.0 ADC, Bioscientific Ltd., Hoddesdon, Hertfordshire, UK).

Two destructive samplings were performed during both periods in order to estimate the fresh matter (FM), dry matter (DM) and nutrient leaf content for each secondary crop. During the first period, destructive sampling was performed 15 and 42 days after each secondary crop had been transplanted (DATs), and in the second period, 36 and 54 DATs (*n* = 9 per treatment). The samples were dried in a forced-air oven for 72 h at 70 ◦C. The data were initially calculated in kg plant−<sup>1</sup> and subsequently adjusted in kg m−2. Total yield of the primary crop was also measured at the end of the seven-month cultivation period to estimate the total biomass expressed in kg m<sup>−</sup>2.

Dried samples of parsley plants were subsequently ground to powder in order to perform mineral analyses of the main macro-micronutrients (N, P, K, Ca, Mg, Fe, Zn, Mn and Cu). In total, 36 plants per destructive process were used to determine the uptake nutrient concentrations of each secondary crop. The extraction was performed using the Kjeldahl Nitrogen method (TKN) based on the Kjeldahl protocol [22]. Nutrient elements were determined by ICP (ICP-OES, SPECTRO Analytical Instruments GmbH, 180 Kleve, Germany).

EC (dS m<sup>−</sup>1) and pH values of the irrigation and drainage solution (IS) were measured automatically with sensors (type GPHU 014 MP-BNC, Greisinger, Regenstauf, Germany) placed within the hydroponic head tank. The volume (V, L) of the drained nutrient solution was automatically recorded using water pressure gauges (Klinkerbeg, Graben-Neudorf, Germany) placed in each drainage tank. Furthermore, during the second period, samples of the irrigation (IR) and drainage (DR) solution were collected manually for quantitative assessment of NO3, P, K, Ca, Na, Mg, Fe, Zn, Mn and Cu content on two sampling dates (DAT 36 and DAT 54). Extraction was performed using the Kjeldahl Nitrogen method (TKN) based on the protocol described by Kjeldahl [22], while the nutrient elements were determined by ICP (ICP-OES, SPECTRO Analytical Instruments GmbH, Kleve, Germany).

#### *2.4. Calculations*

The daily and nightly average Ta and RH were calculated for the periods from 6:00 to 18:00 and from 18:00 to 6:00 (local time), respectively, during the respective cultivation period of primary and secondary crops.

In the primary crop, the total volume of NS applied (VIR), expressed in L m<sup>−</sup>2, was the sum of the volume of water added in each irrigation event (L) for a six-month cultivation period divided by the total cultivated area (m2). The total volume of NS drained (VDR) from the plants (L m<sup>−</sup>2) was the sum of the volume drained after each irrigation event for a six-month cultivation period divided by the total cultivated area (m2).

In the secondary crops, the VIR and VDR data were adjusted in the primary crop cultivation period. To achieve this, the total volume of each secondary crop was divided by the number of the days in each cultivation period, and then multiplied by the number of days for which the primary crop was cultivated. Here, the data for water and primary crop DS added during NS are presented separately. The total volume uptaken (Vup) by the plants consists of the amount applied minus the amount drained from the plants. To evaluate the final impact of each system to the environment, the above data for both primary and secondary crop are summarised.

The crop uptake concentration (Cup), defined as the amount of nutrient absorbed by the plants, was estimated based on the following equation:

$$\mathbf{C\_{IR}} \times \mathbf{V\_{IR}} + \mathbf{C\_{up}} \times \mathbf{V\_{up}} = \mathbf{C\_{DR}} \times \mathbf{V\_{DR}} \tag{1}$$

where (CIR) and (CDR) correspond to the concentration of the nutrient element in the irrigation and drainage solutions, respectively, expressed in mg L<sup>−</sup>1.

The cumulative volume irrigated to and drained from the crop throughout the whole cultivation period for each crop and treatment was used in order to determine the water productivity. The water productivity (WP, in kg m<sup>−</sup>3) and the fertiliser use efficiency (FUE, in kg kg<sup>−</sup>1) of the primary and secondary crops were estimated by dividing the total yield FM (which is the sum of the primary and the secondary crop) with the total volume of the fresh water or fertiliser applied over the six-month cultivation period. Similarly, the nitrogen (NUE) and phosphorus (PUE) use efficiency were calculated by dividing the sum FM by the total amount of nitrogen or phosphorous added, adjusted to the six-month cultivation period. The total amounts of nitrogen and phosphorus were calculated by multiplying the N and P content showed in Table 1 by the VIR.

#### *2.5. Statistical Analysis*

Comparison of means was performed by applying one-way ANOVA at a confidence level of 95% (*p* ≤ 0.05) using SPSS (Statistical Package for Social Sciences, IBM, Armonk, NY, USA). Additionally, least significant difference (LSD) at the 5% level of significance was used to determine whether the drainage solution management affected the quality (yield) and quantity (marketable products) of the production. The mean values and standard deviations (±SD) of the measured parameters are reported.

#### **3. Results**

#### *3.1. Climatic Conditions*

The average daily air temperature and relative humidity in the cultivation area of the tomato crop were 21 ◦C (with standard deviation SD ± 4.0) and 63% (SD ± 18), respectively. In the compartments of the secondary crop, the respective average values were 22 ◦C (SD ± 4.1) and 53% (SD ± 19), respectively. The respective maximum and minimum daily average values measured were 26.5 ◦C and 18 ◦C and 75% and 50%, respectively. During the night, the values remained stable at around 15 ◦C (SD ± 3.9) and 75% (SD ± 19) for both primary and secondary crops. The maximum outdoor global radiation was around 1000 W m−<sup>2</sup> (SD ± 280), and the daily mean radiation varied around 420 W m−2. The daily mean radiation inside the greenhouse was around 315 W m<sup>−</sup>2, indicating an average transmission of the greenhouse to solar radiation of about 75%. The consistent daily climatic conditions occurred across all the treatments had no significant impact on the assessment of the results.

#### *3.2. Nutrient Solution Quality*

To assess the NS quality, it is necessary to examine the status of EC and pH at each stage of the system. As already mentioned, the EC values of the NS supplied to the tomato crop varied from 1.3 to 3.5 dS m−<sup>1</sup> over the total cultivation period (Table 1). The values were changed according to the growth stage of the crop, where at the vegetative stage the values were higher than at the fruit stage. The EC values of the DS were about 30% higher than those of the NS supplied. Similar to the EC, the pH values increased from 5.8, recorded in the supplied NS (Table 1), to more than 6.2, and up to 7.4 (SD ± 0.7). Comparable progress was observed in the EC and pH values of the secondary crop control treatment (T1 treatment) during both periods.

For the other cascade treatments, the EC and the pH values were changed according to the quality of the DS of the primary crop and the amount of water added in the NS preparation process. At the primary crop vegetative stage (first period), where the EC values of the NS supplied were high, the respective values of the cascade treatments were also high, and were higher than those of the control treatment (*p* < 0.05). Only the values of the T2 treatment were lower than those of the control treatment (*p* < 0.05). At the primary crop fruit stage (second period), where the EC values of the NS supplied were low, the three cascade treatments demonstrated values lower than the control (*p* < 0.05). Similar to the primary crop, the EC values of the DS increased by about 30% and the pH values increased from 5.8 to more than 6.2.

The above data of EC variation in the NS supplied to and drained from the plants are presented in Figures 2 and 3, according to the treatment received by each secondary crop. The pH variations of the DS according to the respective treatment are presented in Figure 4. The pH values in the supplied NS are not presented, since the pH was set at 5.8 for each treatment.

#### *3.3. Water Consumption*

The total volumes of NS supplied to and drained from the primary crop during the six-month cultivation period were 882 L m−<sup>2</sup> and 472 L m<sup>−</sup>2, respectively. In the secondary crop, an amount of water and DS was added to the total amount of the supplied NS, changed according to the treatment and for each cultivation period. However, in order to be able to compare the treatments and their efficiency, the secondary crop data of the NS supplied to, uptaken by and drained from the plants were adjusted to a six-month cultivation period, equal to that of the primary crop. The resulting data are presented in Table 2.

According to the adjusted calculations, as was expected, the maximum amount of primary crop DS reused was observed in the T4 treatment. In this case, and for the same cultivation period with tomato, 290 L m−<sup>2</sup> was reused to irrigate the lettuce plants. The same amount was reused in spinach plants, while in parsley plants, the amount was

167 L m<sup>−</sup>2. For the other cascade treatments, the amounts of primary crop DS reused were 50% and 75% lower than the T4 treatment under T2 and T3, respectively.

**Figure 2.** Evolution of EC values of the NS supplied to the secondary crops during (**a**) the first experimental period (19–60 DATp; 1–42 DATs) and (**b**) the second experimental period (137–190 DATp; 1–54 DATs). T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W).

**Figure 3.** Evolution of EC values of the DS of the secondary crops. T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W).

The amount of water added in each treatment affected the amount of NS was uptaken by the plants in different ways. The lettuce and parsley plants in the monoculture system absorbed at least 18% more than the plants in the cascade system receiving any of the treatments (*p* < 0.05). In spinach, the plants receiving the T3 and T4 treatments absorbed similar amounts to with the control treatment, with no significance differences among them (*p* > 0.05). Here, only the plants fertigated with 50% DS and 50% water were not able to absorb the necessary amount provided in the control treatment.

**Figure 4.** Evolution of pH values of the DS of the secondary crops. T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W).

**Table 2.** Total amount of nutrient solution supplied to, drained from and uptaken by primary and secondary crops (L m−2) for the same cultivation period, according to the treatment. The nutrient solution supplied is presented separately from the amount of water and drainage solution added.


T1 (0%D + 100%FS); T2 (50%D + 50%W); T3 (75%D + 25%W); T4 (100%D + 0%W).

Because the plants absorbed different amounts of NS, they also drained different amounts of DS each time. The tomato and lettuce crops provided a total of 605 L m−<sup>2</sup> DS. In the tomato–spinach system layout, the collected DS was 545 L m−<sup>2</sup> and in the tomato– parsley system, 549 L m−2. According to these data, all three cascade systems collected an amount of DS at least 48% lower than that observed in the corresponding monoculture systems. The lowest impact on the environment was considered to be exhibited by the T4 treatment, with amounts equal to 382 L m<sup>−</sup>2, 313 L m−2, 409 L m−<sup>2</sup> for lettuce, spinach and parsley, respectively.

The above values correspond to a primary crop cultivation area equal to that of the secondary crop (1:1 cultivation ratio). Increasing the cultivation area of the secondary crop, these rates could be lower. For instance, at a higher cultivation ratio like 1:2, where the cultivation area of secondary crop is doubled, the unused DS of the primary crop was calculated to be less than 40%. In spinach, a 1:3 cultivation ratio could further reduce the amount of unused DS of the primary crop by 10%.

#### *3.4. Nutrient Concentration*

In Tables 3 and 4, the nutrient concentrations, measured in the laboratory, of the NS supplied to, uptaken by and drained from the plants in the primary and secondary crops on 36 DATs and 54 DATs, respectively, are presetned. For technical reasons, nutrient analysis was performed only for the parsley plants. As expected, the nutrient concentration of the NS supplied to the tomato and control parsley plants was similar to the system settings (Table 1). It is likely that any differences occurred due to the concentrations of nutrients remaining in the tube network.

**Table 3.** The average nutrient concentration in the supplied to, uptaken by and drained from the tomato and parsley plants according to treatment, on 36 DATs of the second experimental period. The concentrations of NO3, P, K, Ca, Na, Mg are expressed in mmol L−<sup>1</sup> and those of Fe, Zn, Mn, Cu in μmol L<sup>−</sup>1.


T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W).


**Table 4.** The average nutrient concentration in the supplied to, uptaken by and drained from the tomato and parsley plants, according to the treatment, on 54 DATs of the second experimental period. The concentrations of NO3, P, K, Ca, Na, and Mg are expressed in mmol L−<sup>1</sup> and those of Fe, Zn, Mn, and Cu in μmol L<sup>−</sup>1.

T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W).

The nutrient concentrations were similar between the DS for the primary crop and the NS supplied to the plants receiving the T4 treatment, at both the 36 and 54 DATs sampling dates. On the other hand, the nutrients supplied to the cascade treatments were different from the target concentration, and varied according to the sampling date. On 36 DATs, most of the macronutrient concentrations of T4 were similar to the targeted concentration applied to the plant of the control treatment (Table 3). Significant differences were observed with respect to micronutrient concentration, with higher values in the DS applied to the primary crop (*p* < 0.05). The macronutrient concentrations of the other treatments—T2 and T3—were between 13% and 59% lower than in the control treatment (*p* < 0.05), and the micronutrient concentrations were between 21% and 69% higher than in the control treatment (*p* < 0.05).

On 54 DATs, the nutrients in the DS primary crop were lower than those collected on 36 DATs, affecting the synthesis of NS supplied to the cascade treatments (Table 4). Therefore, none of the cascade treatments were able to be irrigated with nutrient concentrations close to those targeted. In the T4 treatment, most of the element contents were 25–49% lower than the target.

The synthesis of the NS supplied to the plants affected the amount of NS absorbed by the secondary crops and the resulting synthesis of the DS in different ways. On 36 DATs, the plants receiving T1, T3 and T4 absorbed between 11% and 38% of the nutrients of the supplied NS. In contrast, for the T2 treatment, where the NS supplied was poor, the plants absorbed the majority of the nutrients. On 54 DATs, no significant differences were observed among the treatments (*p* < 0.05).

All the cascade treatments presented lower nutrient concentrations in the DS compared to the control treatment. The DS with the lowest concentrations of NO3, P, Zn and Mn was that used in the T2 treatment. The contents of most of the elements in that treatment were 24–96% lower (depending on the element) than in the control plants. The T3 treatment

presented the lowest concentrations of K, Ca, Mg, Fe, and Cu. The differences between the cascade treatments and the control treatment were similar on both sampling dates. However, on the second sampling date (54 DATs), the contents of most of the macronutrients, except Ca, in the final DS were 25–65% lower than on the first sampling date.

Table 5 presents the nutrient content (N, P, K, Ca and Mg expressed in g per 100 g DM, and microelements Fe, Zn, Mn, Cu expressed in mg kg−<sup>1</sup> DM) in the leaf tissues of lettuce, spinach and parsley plants subjected to the different treatments on the different sampling dates. In lettuce leaves, the lowest concentrations of most of the macronutrients were found for the T4 treatment. In the other treatments, no significant differences were observed. In spinach leaves, the highest concentrations of N, P and K were observed in T1, while Ca and Mg were higher with the T4 treatment. In parsley plants, the concentration of most of the nutrients was lower in the cascade than in the control treatment.

**Table 5.** Nutrient content (N, P, K, Ca and Mg expressed in g per 100 g DM, and microelements Fe, Zn, Mn, Cu expressed in mg kg−<sup>1</sup> DM) in leaf tissues of lettuce, spinach and parsley plants of the different treatments and sampling dates (*n* = 9).


<sup>1</sup> Different uppercase letters (A, B) indicate statistically significant differences between sampling dates and different lowercase letters (a, b) indicate statistically significant differences between the different (T1–T4) treatments (*p* < 0.05). T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W). The optimal level was defined by El-Shinawy and Gawish [23] for lettuce, Öztekin et al. [24] for spinach, and Currey et al. [25] for parsley.

#### *3.5. Yield Performance of Secondary Crops*

To assess the sustainability of each cascade system, it is necessary to study the yield performance of the secondary crops. In this sense, a series of yield characteristics including plants height, number of leaves, chlorophyll content index, photosynthesis rate, FM and DM were analysed. The data presented here correspond to the last day of each secondary crop cultivation period (42 DATs for lettuce and spinach; 54 DATs for parsley). The measurements collected in the earlier pre-harvest cultivation period are not considered, since no significant differences among the treatments were noticed.

Figure 5a presents the average plant height of each secondary crop, according to the treatment, measured during the last day of each cultivation period. According to the results, the nutrient solution applied to the lettuce and parsley did not affect the height (*p* > 0.05) of the plants. Spinach plants showed a final plant height 14% higher for the T4 treatment (*p* < 0.05) compared to the rest of the treatments.

**Figure 5.** Mean values and standard deviations of (**a**) plant height (cm), and (**b**) number of leaves measured on the last day of each secondary cultivation period grown under the different treatments (*n* = 9 samples/treatment). T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W). Different lowercase letters (a, b) indicate statistically significant differences (*p* < 0.05) of the different treatments within each crop, n.s. indicates no significant difference (*p* > 0.05).

The number of leaves per plant for the different treatments during the last day of each secondary crop cultivation period is presented in Figure 5b. Lettuce plants fertigated with the targeted NS (T1 treatment) had significantly lower numbers of leaves compared to the other treatments. Spinach plants with the T2 treatment presented significantly lower numbers of leaves compared to the other treatments. No treatment effect was in the number of leaves of parsley plants (*p* > 0.05).

Figure 6 presents the variation in CCI for each secondary crop according to the treatment measured during the last day of the cultivation period of each secondary crop. The average values of chlorophyll content observed during the measurement period were 32 mg cm<sup>−</sup>2, 55 mg cm−2, and 45 mg cm−<sup>2</sup> for lettuce, spinach, and parsley, respectively. No treatment effects were observed on the CCI (*p* > 0.05).

Figure 7 presents the P*<sup>N</sup>* variation of each secondary crop according to the treatment during the last day of the cultivation period of each secondary crop. The average P*<sup>N</sup>* values observed in the different secondary crops were 12 μmol CO2 m−<sup>2</sup> s−<sup>1</sup> for lettuce and spinach and 10 μmol CO2 m−<sup>2</sup> s−<sup>1</sup> for the parsley crop. Similar to CCI, no treatment effects were observed on P*<sup>N</sup>* (*p* > 0.05).

The values of FM and DM production observed in the different treatments are shown in Table 6. In the lettuce crop, no significant difference was observed among the treatments (*p* > 0.05). The moisture content of the samples ranged from 93% to 95%. In the spinach crop, only the FM with the T2 treatment was less than the FM of the control treatment, by about 22% (*p* < 0.05). The moisture content of the samples ranged from 88 to 90%. Parsley presented the highest yield in the control treatment and the reuse of the tomato drainage solution imposed a decrease in yield by about 30%. The moisture content of the parsley samples was 83%.

**Figure 6.** Mean values and standard deviations of (**a**) CCI and (**b**) P*<sup>N</sup>* (μmol CO2 m−<sup>2</sup> s−1), measured on the last day of each secondary cultivation period under the different treatments (*n* = 9 samples/treatment). T1 (0%DS + 100%FS); T2 (50%DS + 50%W); T3 (75%DS + 25%W); T4 (100%DS + 0%W). n.s. indicates no significant difference (*p* > 0.05).

**Figure 7.** Water productivity in kg of fresh mass per kg of water applied based on the systems layout in combination with the primary crop for a six-month cultivation period. Different lowercase letters (a, b, c) within each secondary crop indicate statistically significant differences (*p* < 0.05).

**Table 6.** Mean value (and standard deviation) of fresh mass (FM) and dry mass (DM) in g m−<sup>2</sup> of the secondary crops on the two sampling dates.



**Table 6.** *Cont.*

<sup>1</sup> Different lowercase letters (a, b) within a row for FM or for DM indicate statistically significant differences (*p* < 0.05).

#### *3.6. Water Productivity and Fertiliser Use Efficiency*

Figure 7 presents the WP (kg FM m−<sup>3</sup> water applied) of each cultivation system. To ensure comparability of data among the systems, the calculations were performed for a growing period equal to that of the primary crop. The WP value estimated for the tomato crop was 26.5 kg m−3. The WP values of the secondary crops with the control treatment were equal to 24 kg m−3, 30 kg m−<sup>3</sup> and 23 kg m−<sup>3</sup> for lettuce, spinach and parsley, respectively. In the cascade system with T4, the WP values were significantly higher than in the monoculture system, by 50%, 30% and 14% for lettuce, spinach and parsley, respectively. In the cascade systems with other treatments, the WP values of parsley and spinach were equal to that of the monoculture system (*p* > 0.05), while in the lettuce crop, WP was slightly higher (*p* < 0.05).

The FUE (kg FM kg−<sup>1</sup> fertiliser applied) of tomato during the six-month cultivation period was 20.9 kg FM kg−<sup>1</sup> fertiliser applied. For the same cultivation period, the FUE was calculated to be equal to 18 kg FM kg−<sup>1</sup> for lettuce, 20 kg FM kg−<sup>1</sup> for spinach and 17 kg FM kg−<sup>1</sup> for parsley. In the cascade system where only DS was used, the FUE was significantly higher than in the monoculture system (T1), by 62% for lettuce and spinach and 22% for parsley. The FUE values of the other cascade treatments were also higher than in the monoculture system. The NUE and PUE showed similar trends (Figure 8). The NUE varied from 42 kg FM kg−<sup>1</sup> to 74 kg FM kg−1, with the maximum values being found for the T4 treatment. The PUE values were significantly higher than those of NUE, since the amount of P added to the irrigated NS was quite low, almost 90% less than the N concentration. Therefore, PUE varied from 463 kg FM kg−<sup>1</sup> to 1270 kg FM kg−1, with the maximum values being observed for the T4 treatment. The other cascade treatments resulted in NUE and PUE values close to those of the monoculture system.

**Figure 8.** Nitrogen and phosphorus use efficiency in kg of fresh mass per kg of nutrient applied based on the system layout in combination with the primary crop for six-month cultivation period. Different lowercase letters (a, b, c) indicate statistically significant differences (*p* < 0.05) in NUE and uppercase letters (A, B, C) indicate statistically significant differences (*p* < 0.05) in PUE.

#### **4. Discussion**

The correct choice of crop combinations and system layouts is the key to the efficient functioning of cascade systems. In the current research, three different cascade systems with three different crop species were compared with the respective monoculture system.

Among the cascade systems, the NS that used pure Ds of the primary crop had a macronutrient concentration closer to the targeted concentration. The micronutrients, on the other hand, were closer to the targeted concentration in the cascade systems where the DS was diluted with water. In the case of Fe concentrations, although the values were much higher at both sampling dates for recycling treatments than when operating as an open system, they were still clearly above critical levels, as suggested by [26,27]. These results were confirmed by the nutrient concentration estimated in leaf tissues.

Spinach and parsley seem to be more adjustable than the lettuce crop in secondary crop cultivation systems, since they are more salt tolerant. Usually, lettuce is considered to be more moderately sensitive to salinity compared to spinach and parley crops, with a threshold electrical conductivity (EC) of 1.3 dS m<sup>−</sup>1, and a negative relative yield decrease slope of 13%. The respective values for spinach and parsley were 2.0 dS m−<sup>1</sup> with a slope of 7.6% per dS m−<sup>1</sup> and 1.8 dS m−<sup>1</sup> with a slope of 6.2% per dS m−<sup>1</sup> [28].

However, in this study, all crops were able to grow sufficiently under the cascade system, although the amounts of nutrients absorbed were different. The CCI and P*<sup>N</sup>* values of all crops were similar in all cultivation systems. The lettuce plants in the cascade systems were sufficiently tall and heavy, with a greater number of leaves than in the monoculture system. The spinach plants subjected to the T4 cascade treatment were taller and heavier than those in the other systems, while the parsley plants demonstrated similar growth progress to that of the monoculture system, but were less heavy. It seems that all of the studied crops could be used as secondary crops in cascade cultivation systems, given that they exhibit sufficient yield performance. The final evaluation of cascade systems, however, should be undertaken in consideration of water productivity and nutrient use efficiency.

#### *4.1. Evaluation of Cascade System Based on Water Productivity*

In the primary crop, the WP values observed (26.5 kg FM m−3) were similar to those reported in [6]. In Katsoulas et al. [1], the WP of tomato crop varied from 20 kg FM m−<sup>3</sup> to 35 kg FM m−3. Nikolaou et al. [29] mentioned that the ratio of product yield to water use increased from 3 kg m−<sup>3</sup> to 17 kg m−<sup>3</sup> in an unheated greenhouse and reached 45 kg m−<sup>3</sup> in a soilless growing system.

In secondary crops, the WP values for the monoculture system were 25 kg m−3, 31 kg m−<sup>3</sup> and 24 kg m−<sup>3</sup> for lettuce, spinach and parsley, respectively. Bozkurt et al. [30] found an FM of 23 kg m−<sup>3</sup> for lettuce plants cultivated in soil under greenhouse conditions. Here, the lettuce plants with T4 exhibited a WP performance 50% than that of the monoculture system. Moreover, the plants in the cascade system subjected to T2 and T3 also had a WP performance 26% higher than in the monoculture system.

Kuslu et al. [31] found a WP of 9.7 kg m−<sup>3</sup> in spinach crops after a cultivation period of about 45 days. Here, for the same cultivation period, the WP of the monoculture system exhibited similar values. During the primary crop cultivation period of six months, the WP was almost tripled. In the cascade system with T4, the WP was even higher, with an increase of about 30% with respect to the monoculture system.

In parsley plants, Martins et al. [32] found WP values ranging from 3.7 kg m−<sup>3</sup> to 4.73 kg m−<sup>3</sup> under greenhouse conditions with a coconut substrate. Here, for the same cultivation period, the WP was 34% higher. During the primary crop cultivation period of six months, WP was about 24 kg m−<sup>3</sup> in the monoculture system. In the cascade system with T4, the WP was 14% higher than that of the monoculture system.

Accordingly, the two-level cascade cultivation system was demonstrated to be most efficient from an agronomical point of view, since the net water input was restricted, and the WP was significantly higher than in the open system. Understanding and further improving WP under cascade system side yield is the primary focus of developing water productive plants in greenhouses. The improvement in WP could impart tolerance to drought and salinity stress, while still accumulating sufficient biomass to make their production commercially viable. According to Damerum et al. [33], it is imperative to develop systems for improving crop WP, particularly in the case of crops such as lettuce, where over 75% of the total production in the US is dominated by the state of California. The combination of DS with fresh nutrient solution may allow this goal to be achieved, and should be further investigated.

#### *4.2. Evaluation of Cascade System Based on Nutrient Use Efficiency*

Compared to open (free drainage) soilless systems, cascade systems can be considered that has higher fertiliser use efficiency, since they make complete use of the drainage produced from the primary (and potentially the secondary) crop.

In the current research, the FUE values of the cascade parsley and lettuce systems (18 kg FM kg−1) were 18% and 62% higher than in the monoculture (control) treatment. The FUE values of the lettuce monoculture system were similar to the values found by Santamaria et al. [34] in lettuce plants cultivated in soilless growth chambers.

In the spinach crop, the FUE value was 0.20 kg FM kg−<sup>1</sup> for the 42-day cultivation period. Chan-Navarrete et al. [35] reported FUE values ranging from 0.14 kg DM kg−<sup>1</sup> to 0.18 kg DM kg−<sup>1</sup> for a 28-day cultivation period. For a six-month cultivation period, the FUE, expressed in FM, was calculated to be equal to 15 kg FM kg−1. The FUE of the T4 treatment was 66% higher than in the monoculture system. For spinach to maintain a satisfactory yield under low nitrogen conditions, high NUE is necessary. This is because spinach is not very efficient at either nitrogen uptake or utilisation, and requires considerable amounts of nitrogen for growth and the establishment of its dark green colour [36,37]. The combination of high nitrate input and low nitrate reduction by spinach leads to high levels of nitrate in the marketable product [35]. In cascade systems, spinach plants can accumulate substantial amounts of nitrate in the leaves, because the extra mineralisation of DS gives a surplus of nitrate to the plant.

Here, the maximum NUE value occurred in cascade with the 100% DS treatment. The NUE in that system was higher than those reported in a currently available commercial hydroponic system. Zhang et al. [38] found that NUE values varied from 44 kg FM kg−<sup>1</sup> to 74 kg FM kg−<sup>1</sup> for a 5-month cultivation period in a microalgae and crop cocultivation system. Similar to the present study, lower NUE values were found in the simple hydroponic system, while the maximum values were found in the co-cultivated system. The PUE values were also higher than those found for a commercial soilless system. The PUE values were more than double those reported by Zhang et al. [38], however, due to low values of P concentration added to the system. Usually, PUE is a complex trait for plant breeding, with many potential interactions and trade-offs with other factors affecting crop yield, such as water use efficiency and energy balance [39]. The effectiveness of cascade approaches in cascade systems based on traits that affect P absorption rates is due to the deeper layer of water productivity occurring.

Our results are in agreement with previous reports by Elvanidi et al. [12] and Muñoz et al. [40], which mentioned that the nitrogen balance in cascade systems shows an important decrease in nutrient leachate. According to Muñoz et al. [40], the adoption of a cascade crop system reduced the environmental impact by 21%. Additionally, García-Caparrós et al. [10] concluded that the establishment of sequential irrigation systems can result in water savings and the removal of nitrates, which are of great advantage in arid and semi-arid regions.

Cascade farming systems represent a promising sustainable alternative cultivation system compared to monoculture systems. Likewise, due to climate change and the increasing population, it is becoming a challenge to balance demand and supply, leading to negative economic externalities. However, cultivation in cascade systems, especially in hydroponic ones, can provide valuable ecosystem services, such as savings in terms of fertilisers, the consumption of less water, the minimisation of energy needs and the maximisation of yield productivity. A well-developed soilless cascade system represents a substantial competitive advantage in overcoming the challenges outlined.

However, to further assess the sustainability of cascade systems, the use of a life cycle assessment (LCA) systems analysis methodology is required for the assessment of their environmental impacts.

#### **5. Conclusions**

Three secondary crops were tested under different treatments in a soilless cascade system using a tomato crop as the primary donor crop. It was found that among the secondary crops, spinach was the most appropriate secondary receiver crop among those considered in this study. The use of the tomato drainage solution for the fertigation of the spinach crop positively affected crop yield. In the case of the other secondary crop species tested, lettuce and parsley, yield was not negatively affected when fertigated by the tomato crop drainage solution. The reuse of the drainage solution significantly increased the water productivity and nutrient use efficiency of the cascade crops. The water productivity in the plants irrigated with pure drainage solution was 50%, 30% and 14% higher for lettuce, spinach and parsley, respectively, than in the monoculture system. The nitrogen and phosphorus use efficiency were improved more than 50% with respect to their values in the monoculture system. The current research gives small holder farmers the ability to convert their cultivations to more sustainable systems, minimising construction costs and environmental impact while maximising yield.

**Author Contributions:** Conceptualisation, N.K.; methodology, A.E. and N.K.; formal analysis, E.K., S.F., I.N. and A.E.; investigation, E.K., A.E., S.F., I.N. and N.K.; resources, N.K.; data curation, E.K., S.F., I.N. and A.E.; writing—original draft preparation, E.K., A.E. and N.K.; writing—review and editing, A.E. and N.K.; supervision, N.K.; project administration, N.K.; funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was carried out in the frame of the CasH project, which is co-financed by the European Union and Greek national funds through the bilateral Greece–GermanyS&T Cooperation Program, Competitiveness, Entrepreneurship & Innovation (EPANEK) (project code: T2DGE-0893).

**Data Availability Statement:** Data is contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **Reusing Coir-Based Substrates for Lettuce Growth: Nutrient Content and Phytonutrients Accumulation**

**Rui M. A. Machado 1,\*, Isabel Alves-Pereira 2,\*, Inês Alves 3, Rui M. A. Ferreira <sup>2</sup> and Nazim S. Gruda <sup>4</sup>**


**Abstract:** This research aimed to assess the influence of reusing coir-based substrates on growth, nutrient content, and phytonutrients accumulation in lettuce. The experiment included a new coir pith and four coir-based mixes (1) coir, biochar, and perlite; (2) coir, compost, and perlite; (3) coir, biochar, and pine bark; and (4) coir, compost, and pine bark. All mixes had been previously utilized to grow transplanted spinach and possessed identical ratios of 78:12:10% (*v*/*v*) among their components. Lettuce (*Lactuca sativa* L. cv. 'Godzilla') seedlings were transplanted into Styrofoam plant boxes. Each day, the planting boxes received a nutrient solution via drip irrigation. Plants grown in reused mixes had similar macronutrient concentrations as those grown in coir for the first time, except for N and K in the third mix. Plants grown in reused mixtures had similar yields as those in new coir. Lettuce heads yielded 4.6–4.9 kg/m2, while plants grown in reused mixtures had equal or higher total phenols than those in new coir. Ascorbic acid content was higher in plants cultivated in reused mixes. Coir-based growing media can be reused for another short-cycle crop, like lettuce, without yield loss or phytonutrients decrease.

**Keywords:** *Lactuca sativa*; soilless system; short-cycle crops; municipal compost; biochar; total phenols; flavonoids; ascorbic acid; circular economy

#### **1. Introduction**

In soilless culture, the reuse of substrates for cultivation is becoming a crucial issue due to the scarcity of resources, the need to reduce environmental impacts, continuous restrictions on the use of peat, rising demand for growing media components, and the increase in costs [1]. Maximizing the effective utilization of available resources offers a means to tackle their scarcity and diminish agriculture's carbon footprint [2]. One of the main objectives for agriculture in the coming ten years is to boost production system efficiency, promote sustainability, and optimize resource use efficiency [3]. Substrate culture, a widely adopted technique in vegetable crop production, is expected to increase in the future due to its numerous advantages over open-field-grown methods. These advantages include increased water and nutrient use efficiency, higher yields, and more precise pest and disease control [2]. Additionally, substrate culture offers a solution to address challenges such as reducing arable land and more frequent climate extremes [4,5]. One disadvantage of this technique is the disposal of the growing medium at the end of cultivation [6,7]. Therefore, reusing growing media is the most environmentally friendly

**Citation:** Machado, R.M.A.; Alves-Pereira, I.; Alves, I.; Ferreira, R.M.A.; Gruda, N.S. Reusing Coir-Based Substrates for Lettuce Growth: Nutrient Content and Phytonutrients Accumulation. *Horticulturae* **2023**, *9*, 1080. https://doi.org/10.3390/ horticulturae9101080

Academic Editor: Moreno Toselli

Received: 23 August 2023 Revised: 11 September 2023 Accepted: 26 September 2023 Published: 27 September 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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approach [4,6,8]. Furthermore, considering the ongoing restrictions on peat usage, reusing substrates becomes even more crucial.

The number of growing cycles during which a substrate can be reused depends on the substrate's nature and the crop [7]. The spent growing media's stability is an essential indicator for its reuse [8]. Organic substrates are subject to decomposition, interfering with their physical and chemical properties [9]. The rate of the degradation reactions largely determines the useful life of an organic substrate. It depends on the substrate type, irrigation management, time-dependent action of roots, microbial activity [10], etc. In previous studies, [11,12] reported that coir could be an alternative to peat to produce spinach. Ref. [13] reported that spinach production and quality in coir-base substrates (78%, *v*/*v*) with municipal solid organic compost, or biochar, in the percentage of 12 by volume and either with perlite or pine bark, at a volume ratio of 10%, were similar to those obtained in coir. The spinach growth period in these mixes was 32 days. Spinach is a short-cycle crop of 25 to 50 days. Thus, the time for the substrate to lose its physical stability is short. Physical stability is defined by [9] as the ability of a product to maintain its physical dimensions and properties. On the other hand, these substrates were predominantly composed of coir, whose stability varies but is generally good [14–16]. It still has the advantage of having relatively low shrinkage [17] and being slightly hydrophobic.

In mixes with biochar, due to its recalcitrant nature [18–20], decay may occur at a slower rate. On the other hand, perlite and pine bark also had good stability [14–16]. According to Lemaire et al. [21], the pine bark remained stable for eight months. Ref. [22] reported that the reutilization of substrates did not pose a problem when fertigation parameters were adjusted to the reused substrate properties.

It is of the utmost importance to meticulously evaluate and contemplate the possible hazards associated with substrate reuse, specifically in relation to the dissemination of soil-borne ailments [23]. It is imperative to adopt suitable procedures to guarantee that any reused substrate is thoroughly sterilized and devoid of any detrimental pathogens that may threaten plant growth or the ecosystem. Adding compost to mixes can help prevent soil-borne diseases. Compost has disease-suppressing properties that can be an effective strategy for reducing risk [24–26]. Biochar can also suppress plant diseases [27–29], reported that biochar mildly improved the survival of beneficial microorganisms in a mix with peat.

Strategies to reduce potential issues related to substrate stability, improve sustainability, and reduce production costs are key to reusing substrates for planting other short-term crops. Lettuce is a short-season vegetable intensively produced in Mediterranean countries and is known to be a good source of health-promoting compounds like phenols [30,31].

We hypothesize that coir-based substrate, used for spinach cultivation, can be reused to grow other short-cycle crops. We predicted that these substrates would maintain their physical and chemical properties adequate for growing lettuce, another short-cycle crop. Here, we investigated the impact of reused coir-based substrate on lettuce growth, nutrient content, and the accumulation of phytonutrients.

#### **2. Materials and Methods**

#### *2.1. Growth Conditions and Substrates*

The experiment was conducted in a greenhouse located at the "Herdade Experimental da Mitra" (38◦57 N, 8◦32 W), University of Évora, Portugal. The greenhouse was covered with polycarbonate and had no supplemental lighting or heating. Diurnal changes in air temperature inside the greenhouse at the plant canopy level ranged from 8 to 27 ◦C. Solar radiation ranged from 34 to 248 W·m−2·d<sup>−</sup>1.

The experiment included five substrates, coir (the control, used for the first time), and four mixes already used to grow transplanted spinach, whose cycle lasted 32 days.

The coir (100% coir pith) (Projar S.A., Spain) had a pH of 5.5–6.0, electrical conductivity (EC) > 1.5 d*S* m−1, granulometry = 0–10 mm, total porosity = 95%, air (%, *v*/*v*) = 25, and CEC (meq/100 g) = 60–120.

The mixes described in detail in Machado et al. [11] were coir-based (78 percent coir by volume, 12 percent biochar or municipal organic compost collected selectively, and 10 percent perlite or pine bark). The initial physicochemical characteristics of these mixes (pH, EC, mass wetness, moisture content, total porosity, and bulk density) were also presented in [11]. Before lettuce planting, the EC in mixes with compost averaged 2.3 mS/m ± 0.3, and in other mixes and in coir, it averaged around 1.9 d*S*/m. The pH in the mix of coir, compost, and perlite was 6.9. In the other mixes and in coir, it averaged 6.3 ± 0.5. It was observed that there was no noticeable shrinkage in the mixes.

Lettuce (*Lactuca sativa* L. cv. 'Godzilla') type Batavia seedlings with green leaves were transplanted into Styrofoam plant boxes (100 × 25 × 10 cm) on 8 April 2021, 35 days after emergence. After the spinach *(Spinacia oleracea* L. cv. Tragopan) harvest, the reused mixes remained undisturbed in the boxes. The reused substrates were not subject to sanitation. The seedlings were spaced at 20 cm in a row in the center of the box, with a plant density of 16 plants m<sup>−</sup>2.

Treatments were arranged in a complete randomized block design with five replicates. Two 8 L/h pressure-compensating and anti-drain emitters were placed in each planting box. The emitters were attached to four fine tubes with a 70 cm length and 5 mm diameter. Thus, two water emission points were inserted into the substrate near the plant base, one on each side of the row crop.

The irrigation schedule was optimized for coir. It was based on substrate volumetric water content at Styrofoam box control (coir), measured using a soil moisture probe (SM105T Delta devices, Burwell, UK), and the volume of water drained. The nutrient solution was applied three to eight times daily and averaged 10 to 20% drainage (leaching fraction) for each application.

A nutrient solution was injected continuously into the irrigation system throughout the growing cycle. The nutrient solution used contained 14 mmol L−<sup>1</sup> NO3-N, 6.3 mmol L−<sup>1</sup> NH4-N, 1.32 mmol L−<sup>1</sup> P, 11 mmol L−<sup>1</sup> K, 3.5 mmol L−<sup>1</sup> Ca, 3.5 mmol L−<sup>1</sup> Mg, 1.31 mmol L−<sup>1</sup> S, 46 μmol L−<sup>1</sup> B, 7.86 μmol L−<sup>1</sup> Cu chelated by EDTA, 8.95 μmol L−<sup>1</sup> Fe chelated by EDTA, 18.3 μmol L−<sup>1</sup> Mn chelated by EDTA, 1 μmol L−<sup>1</sup> Mo, 2 μmol L−<sup>1</sup> Zn chelated by EDTA, 2.1 mmol L−<sup>1</sup> Cl, and 0.7 mmol L−<sup>1</sup> Na. The EC-value of the nutrient solution ranged over time: 2 ± 0.2 dS m−<sup>1</sup> ((from transplanting to 12 days after transplanting (DAP)) and 2.5 ± 0.3 d*<sup>S</sup>* <sup>m</sup>−<sup>1</sup> ((from 13 DAP until harvest (29 DAP)).

#### *2.2. Measurements*

The pH and ECW of the drainage water from each box were measured weekly using a potentiometer (pH Micro 2000 Crison) and a conductivity meter (LF 330 WTW, Weilheim, Germany).

Lettuce plants (heads) were harvested at 29 DAP. Two lettuce plants (heads) from each box were washed, oven-dried at 70 ◦C for 2–3 days, weighed, and ground so that they would pass through a 40-mesh sieve. The ground samples were analyzed for N, P, K, Ca, Mg, Na, Fe, B, Mn, and Zn. The total N was analyzed using a combustion analyzer (Leco Corp., St. Josef, MI, USA). The K and Na were diagnosed by flame photometry (Jenway, Dunmow, UK). The P and B were analyzed using a UV/Vis spectrometer (Perkin Elmer Lamba 25). The remaining nutrients were analyzed using an atomic absorption spectrometer (Perkin Elmer, Inc., Shelton, CT, USA).

The leaf area of two plants was measured using a leaf area meter (LI-COR Model LI-3000A).

The head samples, including inner, middle, and outer leaves, were collected in a 2 cm thick disc obtained by cross-cutting at a height of 6 cm from the base and cutting with a knife. Samples of lettuce leaf discs weighing 1.000 g were macerated in a mortar and, then homogenized for 1 min in 8 mL of a methanol/water solution (80:20 (*v*/*v*), MW80 extract) [32] to determine the total content of phenolic compounds (TPC) [33], flavonoids [34], anthocyanins [35,36], ascorbate (AsA) [37], proline [38], and FRAP antioxidant activity [33]. After that, samples were centrifuged at 4 ◦C at 6440× *g* for 5 min in a centrífuge Hermle Z323 K. Aliquots of the methanol extracts were kept at −20 ◦C for further use.

Samples of lettuce leaf discs weighing 1.000 g from each treatment were macerated in a mortar and then homogenized in 8 mL of methanol:water solution (90:10 (*v*/*v*), MW90 extract) for 1 min. They were then centrifuged at 4 ◦C at 6440× *g* for 5 min. to determine the amount of photosynthetic pigment present. Chlorophyll a and b and carotenoids were quantified in aliquots of MW90-extract by UV-vis spectrophotometry [38].

Total phenolic compounds (TPCs) were determined following Bouayed et al. [33], using the Folin–Ciocalteau phenol reagent by reading the absorbance at 760 nm. TPC content was estimated using a calibration curve (GAE, *n* = 6 concentrations from 0 to 50 mg/L) and expressed as milligrams of gallic acid equivalent (GAE) per 100 g of fresh weight (FW).

A reaction mixture of 100 μL of MW80 extract, 20 μL of 10% AlCl3, 500 mL of 1 M potassium acetate, and 380 μL of distilled water was prepared to determine the flavonoid content. After that, this combination was incubated for 30 min at 25 ◦C. Total flavonoid content was determined by measuring the absorbance at 420 nm, using an extinction coefficient of 0.004 μM−<sup>1</sup> cm<sup>−</sup>1, and expressed in mg of quercetin equivalent (QE) per 100 g of fresh weight [34].

A reaction mixture composed of 500 μL of MW80 extract, 500 μL of 50% ethanol (*v*/*v*), and 84 μL of 37% HCl was used to determine the total anthocyanin content [35]. After 30 min of incubation at 60◦C, the absorbance of the mixture was measured at 530, 620, and 650 nm, and the absorbance of cyanidin-3-glycoside was estimated. The total anthocyanin content, expressed as mg of cyanidin-3-glycoside equivalent (C3GE) per 100 g of fresh weight, was calculated using the molar extinction coefficient of 34,300 M−<sup>1</sup> cm−<sup>1</sup> and the molar mass of 449.2 gmol−<sup>1</sup> [36].

For the determination of AsA content, each sample (extracts or standards suitably di-luted) was incubated in a mixture containing 5% TCA in ethanol, 0.4% H3PO4, 0.5% βphenanthroline in ethanol, and 0.03% FeCl3 in ethanol and warmed at 30 ◦C, for 90 min [37]. The absorbance of the Fe (II)–β-phenanthroline complex formed was read at 534 nm. AsA concentration was calculated from a calibration curve (ascorbic acid, *n* = 6 concentrations from 0 to 30 mg/L) freshly prepared.

The Free Pro content of MW80-extracts was determined using the acid ninhydrin reaction with the amino acid and reading the absorbance of the formed formazan at 546 nm. The concentration of proline was calculated using a calibration curve prepared from standard solutions of pure proline (L-proline, *n* = 6 concentrations between 0 and 20 mg/L) [38].

To determine the ferric-reducing antioxidant power (FRAP) of the lettuce extracts, a reaction mixture of 0.050 mL of the sample (plant extracts) or standards was mixed with 0.950 mL of the FRAP reagent. The absorbance change was read at 593 nm at 37 ◦C for 180 s. FRAP reagent was freshly prepared by mixing 300 mM acetate buffer pH 3.6, 10 mM TPTZ solution in 40 mM HCl, and 20 mM iron (III) chloride solution (10:1:1, *v/v/v*) at 37 ◦C. The antioxidant activity reported as milligrams of Trolox equivalents per 100 g FW was calculated using a calibration curve (Trolox solution, *n* = 8 concentrations from 0 to 1120 mg L<sup>−</sup>1) [33].

Samples of 0.2500 g of spinach leaves were macerated in liquid N2 and homogenized in 50 mM phosphate buffer pH 7.0 to determine the proline dehydrogenase (PDH) enzyme activity. This extract was centrifuged for 15 min at 15,000× *g* at 4 ◦C to obtain the supernatant, which was then collected and kept in aliquots at 20 ◦C (PB-extract) for later use [39,40]. PDH enzyme activity was measured following the reduction of NAD+ at 340 nm at 30 ◦C for 180 s [40]. A reaction mixture containing 100 mM Na2CO3-NaHCO3 buffer pH 10.3, 10 mM NAD+, and PB-extract was used during the assay. The addition of 2 mM L-proline was used to initiate the reaction. Enzyme activity was estimated from the reaction curve slope (A340 vs. t) using the extinction coefficient of 6.22 mM−<sup>1</sup> cm−1. PDH activity was expressed in nmol min−1/mg protein. According to the Lowry method [41], the amount of water-soluble protein in the PB extract was evaluated using a calibration curve (bovine serum albumin, BSA; *n* = 6 concentrations from 0 to 200 mg/mL).

A Genesys 10S UV-Vis spectrophotometer was used for all spectrometric measurements.

#### *2.3. Data Analysis*

Data were analyzed using the analysis of variance (ANOVA I) using SPSS Statistics 25 software (Chicago, IL, USA), licensed to the University of Évora. Means were separated at the 5% level using Duncan's new multiple-range test.

#### **3. Results and Discussion**

#### *3.1. Leachate pH and EC*

The nutrient solution, irrigation scheduling, and substrate may affect the leachate fraction's pH and electrical conductivity (ECW). In the present study, the differences in hydronium ions concentration and ECW of drained water were related to substrate (Figure 1). It can affect the volume of water drained, cation exchange capacity, and pH buffering capacity. The average leachate pH was higher in mixes with perlite than in other substrates during the first three sample dates. The highest pH occurs in the coir, compost, and perlite mix.

**Figure 1.** Effect of reused substrates on pH (**A**) and ECW (**B**) in the drainage water. Each symbol represents the mean of five replicates, and the error bars represent ± 1 SE. DAP- days after transplanting.

The pH of this mixture ranged from 7.0 to 7.1, which is slightly higher than the nutritional solution's pH of 6.4 ± 0.3 and may negatively affect plant nutrition. In coir leachate, the pH was slightly higher than in the incoming nutrient solution. On the last sampling date, the pH of the drained water was not significantly affected by the mixes and ranged from 6.4 to 6.7.

The ECW of leachate in the first sampling (8 DAP) was slightly higher in mixes with compost than in other mixes and coir (Figure 1). In the mix of coir, compost, and pine bark, the ECW was 2.5 d*S* m−1, 0.3 d*S* m−<sup>1</sup> higher than the incoming solution (2 ± 0.2 d*<sup>S</sup>* <sup>m</sup>−1). This slight increase could be attributed to this mix containing additional residual nutrients from the previous crop and/or a lower volume of water drainage. On the last three sampling dates in mix coir, compost, and pine bark, the ECW was always higher than the EC of the nutrient solution (2.5 ± 0.3 d*<sup>S</sup>* <sup>m</sup><sup>−</sup>1). The ECW in this mix reached

high values between 3.33 and 4.05 d*S* m<sup>−</sup>1, i.e., 0.7 to 1.24 d*S* m<sup>−</sup>1, which is higher than the EC of the nutrient solution (2.5 ± 0.3 d*<sup>S</sup>* <sup>m</sup><sup>−</sup>1).

This can indicate a tendency for salt buildup in the substrate. The ECW was slightly lower on mixes with biochar than in coir or the incoming solution. In coir, the ECW was 0.34 to 0.55 units higher than the nutrient solution, which may be considered adequate. As the differences may be related to the volume of water drained, the irrigation schedule must be adjusted for each mix.

#### *3.2. Photosynthetic Pigments*

Total chlorophyll, chlorophyll a and b contents of lettuce were different for different mixtures (Table 1). Plants grown in mixtures with pine bark had lower total chlorophyll and chlorophyll a content than those grown in mixtures with perlite (Table 1). Leaf chlorophyll b of the plants grown in reused substrates was not significantly different from those grown in coir. Leaf average chlorophyll b concentrations in the various plants ranged from 7.5 to 8.6 mg per 100 g of fresh weight (FW). Chlorophyll b levels were higher than chlorophyll a, which is not typical. However, this has also been observed in green and red lettuce cultivars by [42]. The Chla/Chlb ratio was high in coir, which may suggest that the plant is optimizing its photosynthetic capacity. The total chlorophyll content within this spectrum surpassed the findings of [43], who observed 1.0 to 1.5 mg.100 g−<sup>1</sup> FW in lettuce cultivated via a floating culture system under varying nitrogen levels. However, the chlorophyll content remained notably inferior to the results of [44], who recorded a range of 26.8 to 52.3 mg 100 g−<sup>1</sup> FW for lettuce exposed to diverse light intensities and nutrient solution concentrations. This could be due to various factors such as growing conditions, season, and genotype. For instance, the nutrient solution composition affected the chlorophyll content. [45].


**Table 1.** Effect of substrates on leaf photosynthetic pigments content and Chl a/Chl b ratio.

† Means followed by different letters within a column are significantly different at *<sup>p</sup>* ≤ 0.05. NS—nonsignificant, \*\* significant at *p* < 0.01 level, 1—municipal solid organic compost collected selectively. FW—fresh weight. Total Chl—total chlorophyl; Chl a—chlorophyl a; Chl b—chlorophyl b; Cc—carotenoids; Chl a/Chl b—chlorophyl a/chlorophyl b ratio.

Leaf carotenoid content was not significantly affected by treatments. The average carotenoid content in the leaves ranged from 3.51 to 5.54 mg/100 g FW. Thus, the carotenoid content was lower than those reported by [46] (6.1–7.3 mg/100 g FW). As chlorophyll, the carotenoid content may be affected by several factors, including growing conditions, light intensity, temperature, genotype, leaf age, and position. The outer leaves generally have higher carotenoid levels than the inner leaves, which are exposed to higher light intensity, promoting carotenoid biosynthesis [47].

The lower carotenoid content observed in this study may be due to the dilution effect caused by the sample, which included inner, middle, and outer leaves.

#### *3.3. Shoot Nutrient Concentration*

Shoot macronutrient concentrations of plants from the reused mixes, except for N and K in the coir, biochar, and pine bark mix (4.34%), were not significantly different from those of plants grown in the new coir (Table 2). The low content of N and K may contribute to lower levels of Chl a and b in mix coir, biochar, and perlite than the other substrates (Table 1). Shoot B, Zn, and Na content in plants grown in reused growing media were not significantly different from those grown in new coir (Table 2).


**Table 2.** Effect of reused substrates on shoot lettuce nutrient concentrations.

† Means followed by different letters within a column are significantly different at *<sup>p</sup>* ≤ 0.05. NS—nonsignificant. \* and \*\*\* significant at *p* < 0.05 and 0.001 levels, respectively. 1—Although sodium is not a micronutrient, it is included here for convenience. 2—municipal solid organic compost collected selectively.

Plants grown in mixes that contained biochar had higher levels of Mn. Spinach, grown for the first time in these mixes, also increases shoot Mn content, as reported by [13]. Biochar may increase Mn availability in substrate solutions. Extractable Mn in biochar depends on the feedstock and the particle size [48]. Extractable Mn is high in particles smaller than 1 mm [48], and in the biochar used in this experiment, 42% of the particle, expressed as a percentage by weight, was <1 mm [49]. It could contribute to the Mn increased availability in the root medium. This emphasizes the significance of evaluating nutrient availability in the root medium of the blends for customizing nutrient solutions.

Pine bark in mixes led to a significant decrease in shoot iron content (Table 2). This could be due to iron immobilization caused by an increase in microbial activity resulting from the decomposition of pine bark, as highlighted by [50]. As previously mentioned, in future studies measuring nutrient availability in root medium is necessary. The shoot iron content was lower in plants cultivated in the coir, compost, and pine bark mix (58.8 <sup>μ</sup>g·g<sup>−</sup>1). Despite the differences in the nitrogen, potassium, iron, and manganese concentrations, the plants grown in the different media did not show any visible signs of nutrient deficiency or toxicity.

Thus, the study suggests that reusing growing mixes can maintain shoot nutrient concentrations similar to those in coir used for the first time, with minor exceptions.

#### *3.4. Plant Growth and Yield*

Despite the low chlorophyll a in reused mixes with pine bark and low shoot K and Mn content in mix coir, biochar, and pine bark, the shoot dry weight, leaf number and area, and fresh yield were similar (Table 3). Thus, in terms of yield, the reuse of the mixes allowed yields similar to those obtained when coir was used for the first time. This is advantageous because pine bark can be locally sourced in Portugal. On the other hand, it may reduce the need for importing perlite, whose manufacturing process is resource-intensive and requires significant energy consumption [51], as well as lessen transportation-related greenhouse gas emissions. Refs. [22,52,53] also reported that the yields of some horticultural crops grown on reused organic substrates were comparable to or greater than those grown on new substrates. Lettuce plants grown on the different substrates exhibited no signs of disease during the growing cycle. Fresh yield average values ranged from 4.6 to 4.9 kg/m2. These yields were similar to or higher than those obtained when lettuce was grown in a floating system [44], and greater than those obtained in soil in an open field and a greenhouse [54]. This finding indicates that, in terms of yield, the reuse of the mixes allowed yields similar to those obtained in coir used for the first time. On the other hand, carefully adjusting

the nutrient solution and the irrigation schedule to each mix to control the pH, ECW, and volume of the leaching fraction could potentially lead to an increase in yield.

**Table 3.** Effect of reused substrates on shoot dry weight, number of leaves, leaf area, and head fresh weight yield.


NS—nonsignificant, 1—municipal solid organic compost collected selectively.

#### *3.5. Phytonutrients Accumulation*

The leaf total phenols of the plants grown in reused mixes were higher or equal to those grown in coir, used for the first time (Table 4). This may be due to different water availability, salinity, and pH in the root medium, as indicated by the ECW and pH of the drained water. Water availability and salinity generally affect the total phenolic content in plants [55–57]. In lettuce, the electrical conductivity of the nutrient solution is associated with the biosynthesis of secondary metabolites, such as phenolic compounds [58,59].

**Table 4.** Effect of reused substrates on total phenols, anthocyanins, flavonoids, and ascorbic acid.


† Means followed by different letters within a column are significantly different at *<sup>p</sup>* ≤ 0.05. NS—nonsignificant. \*\* and \*\*\* significant at *p* < 0.01 and 0.001 levels, respectively. <sup>1</sup> TPC—total phenolic compounds GAE—galic acid equivalent. <sup>2</sup> G3GE—cianidine-3-glicoside equivalent. <sup>3</sup> QE—quercetine equivalente.

The highest total phenols occurred in plants cultivated in mixes of coir, biochar, and perlite (138.96 mg GAE 100 g−<sup>1</sup> FW) and coir, compost, and pine bark (92.3 mg GAE 100 g−<sup>1</sup> FW).

The average leaf total phenol of plants ranged from 54.52 to 138.96 mg GAE/100 g−<sup>1</sup> FW. Leaf total phenol content in lettuce varies with several factors such as genotype, growing conditions, harvest time, leaf position, etc. [31,60,61]. The outer leaves have the highest phytonutrient content and antioxidant properties [42,47,60]. Despite all leaves being mixed in the study samples, the leaf total phenols values were within the range reported by Kim et al. [31] (50–270 mg GAE g−<sup>1</sup> FW), Llorach et al. [30] (18.2–571.2 mg GAE g−<sup>1</sup> FW) for different lettuce varieties, and Petropoulos et al. [62] for green lettuce (18 to 203 mg GAE/100 g−<sup>1</sup> FW).

Leaf averages of flavonoids and anthocyanin contents of the plants grown in reused substrates did not differ significantly from those grown in the coir used for the first time (Table 4).

The leaf ascorbic acid (AsA) content of the plants grown in the reused mixes was higher or similar to those grown in coir used for the first time. Leaf AsA in the different treatments ranged from 1.17 to 2.85 mg/100 g FW). These were lower or similar to the lower end of the range reported for lettuces with green leaves by Cozzolino et al. [61] (3.0–19.3 mg/100 g FW), Jibril et al. [63] (2.27–6.91 mg/100 g FW), and Llorach et al. [30] for lettuces of different leaf colors (2.8–9.5 mg/100 g FW). The lower values may be related to the genotype, growth conditions, and sampling method. Leaf AsA ranged with leaf position [64,65], and in the present study, leaf AsA represents the average of different leaves. The low AsA content may also be related to the environmental conditions in the greenhouse. Light intensity was low in the greenhouse, not only due to the time of year (early spring) but also because of the opacity of the plastic cover film used in our greenhouse. The condition in substrates affected leaf proline, which was lower in reused mixes than in coir used for the first time (Figure 2A).

**Figure 2.** Effect of reused substrates on proline content (**A**) and proline dehydrogenase activity (**B**), (C), municipal compost (M), biochar (B), perlite (P), or pine bark (Pin). Each bar represents the mean of five replicates, and the error bars represent ± SE. Means with different letters are significantly different at *p* < 0.05.

Proline accumulation in plants [66], is an essential component of plant defense mechanisms [67]. One of the most effective osmoregulatory mechanisms at the molecular level involves the buildup of intracellular proline to lower water activity within the cytoplasm [68]. Overall, the water supply and the water condition in the substrate are significantly related to the proline content in plants [68,69]. In the present study, lettuce leaf proline content of the different treatments ranged from 0.38 to 1.00 mg/100 g FW (Figure 2A). These values were lower than those reported by Machado et al. [70] in leaf blades of spinach grown in the substrate (1.9 to 2.5 mg/100 g) and by Machado et al. [71] in coriander grown in soil (14.5–49.7 mg/100 g). The lower proline content in lettuce may be due to species since proline content is species-dependent [68,69]. The lower values of proline may also be linked to favorable growing conditions [72], indicating that the plants in the mixes were grown under favorable conditions.

Leaf proline dehydrogenase activity was higher in mixes with municipal compost, regardless of whether they had perlite or pine bark (Figure 2B). Proline dehydrogenase is an enzyme involved in the breakdown of proline into pyrroline-5-carboxylate (P5C). This process is part of the proline degradation pathway and is often associated with plant stress responses. Elevated proline dehydrogenase activity can also be triggered by variations in water availability and salinity [73]. Leaf PDH activity ranged from 18.00 to 47.00 nmol min<sup>−</sup>1/mg protein.

#### *3.6. Antioxidant Activity*

The antioxidant activity measured by the ability of lettuce leaf extracts to reduce iron (Fe3+) FRAP was higher in coir and in the mix of coir, compost, and perlite (Figure 3). Leaf FRAP values in the substrates range from 9.06 to 13.9 mg TEAC g−<sup>1</sup> FW). These were much lower than those observed by Llorach et al. [30] (98.2 to 323.4 mg TEAC g−<sup>1</sup> FW) in three green varieties of lettuce.

**Figure 3.** Effect of reused substrates on antioxidant activity estimated by FRAP, (C), municipal compost (M), biochar (B), perlite (P), or pine bark (Pin). Each bar represents the mean of five replicates and the errors bars represent ± SE. Means with different letters are significantly different at (*p* < 0.05).

Despite the observed effects on proline content, proline dehydrogenase activity, and antioxidant activity (FRAP), their magnitude was insufficient to affect the lettuce yield.

The differences observed in proline content, proline dehydrogenase activity, and antioxidant activity (FRAP) could potentially be reduced by optimizing irrigation scheduling and fertilization for each mix.

On the other hand, as the levels of total phenols, flavonoids, anthocyanins, and ascorbic acid in the lettuce leaves were either higher or comparable to those grown in coir used for the first time, this indicates that the reuse of coir-based substrates did not result in a decrease in yield and the product quality of lettuce.

#### **4. Conclusions**

The results show that coir-based growing media mixed with 12% compost or biochar and 10% perlite or pine bark, after its use in growing spinach, can still be successfully utilized for cultivating another short-cycle crop lettuce. Lettuce yield in reused substrates ranged from 4.6 to 4.8 kg/m2, equal to the yield obtained in coir (4.9 kg/m2) used for the first time.

The shoot nitrogen, phosphorus, potassium, calcium, and magnesium macronutrient concentrations, except in the coir, biochar, and pine bark mix, did not significantly differ between the reused and coir. Furthermore, the accumulation of total phenols, flavonoids, anthocyanins, and AsA in the leaves of plants grown on reused substrates was similar or even higher compared to those grown on coir used for the first time. Globally, the coir, biochar, and perlite mix allowed for better crop performance.

As the substrates reused did not undergo a sanitization procedure, it is strongly recommended that agricultural practitioners adopt a vigilant approach towards overseeing the growth and development of the prior crop and undertake thorough bioassays in case of uncertainties before considering substrate reuse. This proactive measure aims to preclude any unforeseen repercussions that might arise from the reuse, thereby ensuring the optimal outcome of subsequent cultivation.

**Author Contributions:** R.M.A.M. conceived and designed the experiments; performed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools, or data; and wrote the paper. I.A. performed the experiments and analyzed the data. I.A.-P. and R.M.A.F. designed and performed the enzymatic and other chemical assays; analyzed and interpreted the data; contributed reagents, materials, analysis tools, or data; and wrote the paper. N.S.G. reviewed, corrected, and edited the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded by National Funds through FCT—Foundation for Science and Technology under Project UIDB/05183/2020.

**Data Availability Statement:** Not applicable

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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