**Horticultural Crop Response to Different Environmental and Nutritional Stress**

Editor

**Stefano Marino**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editor* Stefano Marino University of Molise Italy

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This is a reprint of articles from the Special Issue published online in the open access journal *Horticulturae* (ISSN 2311-7524) (available at: https://www.mdpi.com/journal/horticulturae/special issues/Horticultural Crop Response).

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## **Contents**



Water Use and Yield Responses of Chile Pepper Cultivars Irrigated with Brackish Groundwater and Reverse Osmosis Concentrate


### **Christine Schlering, Jana Zinkernagel, Helmut Dietrich, Matthias Frisch and Ralf Schweiggert**


### **Anthony L. Witcher, Jeremy M. Pickens and Eugene K. Blythe**


### **Atinderpal Singh, Sanjit K. Deb, Sukhbir Singh, Parmodh Sharma and Jasjit S. Kang**


### **Israel Joukhadar and Stephanie Walker**


## **About the Editor**

**Stefano Marino** is Professor in Precision farming at the Department of Agricultural, Environmental and Food Sciences since 2021. He obtained his master's degree at University of Molise, Italy, in 2002, and Ph.D. degrees in Protection and quality of agro-food production at University of Molise, Itraly, in 2006. His research areas cover the fields of precision farming, irrigation, plant nutrition, horticulture, Proximal and remote sensing, Crop management; Abiotic stresses

### *Editorial* **Horticultural Crop Response to Different Environmental and Nutritional Stress**

**Stefano Marino**

Department of Agricultural, Environmental and Food Sciences (DAEFS), University of Molise, Via De Sanctis, 86100 Campobasso, Italy; stefanomarino@unimol.it

**Abstract:** Environmental conditions and nutritional stress may greatly affect crop performance. Abiotic stresses such as temperature (cold, heat), water (drought, flooding), irradiance, salinity, nutrients, and heavy metals can strongly affect plant growth dynamics and the yield and quality of horticultural products. Such effects have become of greater importance during the course of global climate change. Different strategies and techniques can be used to detect, investigate, and mitigate the effects of environmental and nutritional stress. Horticultural crop management is moving towards digitized, precision management through wireless remote-control solutions, but data analysis, although a traditional approach, remains the basis of stress detection and crop management. This Special Issue summarizes the recent progress in agronomic management strategies to detect and reduce environmental and nutritional stress effects on the yield and quality of horticultural crops.

### **1. Introduction**

Food and agriculture systems may follow alternative pathways, depending on the evolution of a variety of factors, such as population growth, dietary choices, technological progress, income distribution, the state and use of natural resources, climatic changes and efforts to prevent and resolve conflicts. These pathways can and will be impacted by strategic choices and policy decisions. Swift and purposeful actions are needed to ensure the sustainability of food and agriculture systems in the long term [1].

Climate change is considered as one of the future challenges that either directly or indirectly affect all sectors negatively [2]. Environmental interactions also affect sectors that have a direct reliance on natural resources for production, highlighting their significance for national socio-economic development. The agriculture sector, in turn, has about 2.5 billion livelihoods that are dependent on it. The quality and yield of horticultural crops need to be improved for their production, cultivation management, and biotic/abiotic resistances. Biotic and abiotic factors are the main factors limiting production in agricultural systems [3,4]. Abiotic stresses such as temperature (cold, heat), water (drought, flooding), irradiance, salinity, nutrients, and heavy metals can strongly affect plant growth dynamics, increase crop yield losses and the yield and quality of horticultural products.

Such effects become more and more important in the course of global climate change. Water scarcity, climate change, and drought are the main hurdles in our efforts to make our agri-food systems resilient and sustainable [4]. Different strategies and techniques can be used to detect, investigate, and mitigate the effects of environmental and nutritional stress. Horticultural crop management is moving towards digitized, precision management through wireless remote-control solutions, but data analysis, although a traditional approach, remains the basis of stress detection and crop management.

### **2. Special Issue Overview**

This Special Issue collects current research findings that deal with a wide range of topics to detect environmental and nutritional stress effects on the yield and quality of horticultural crops.

**Citation:** Marino, S. Horticultural Crop Response to Different Environmental and Nutritional Stress. *Horticulturae* **2021**, *7*, 240. https:// doi.org/10.3390/horticulturae7080240

Received: 3 August 2021 Accepted: 10 August 2021 Published: 11 August 2021


**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the author. 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/).

The effect of environment conditions (temperature, rainfall, altitude, soil types, hail) [5–9], nutritional strategies [5,6,10–14], water (stress, salinity) [5,8,12,15], container substrate of cultivation [16] on yield, yield traits and quality were analyzed in different crops in an open field [8,9,13,14,17] and greenhouses [5–7,10–12,15,16].

Different innovative strategies were presented that included protected structures, container type, cultivation techniques [5,10,16], symbiotic relationships [6,7], fertigation strategies [10] new agricultural management technologies (remote sensing, smartphone) [11] novel hybrids for breeding [12] spraying of Gibberellic acid [17], water supplies [8,15] and substrate [16] cover crops management [13] and stand reduction [9].

### *2.1. Nutrient Concentration of African Horned Cucumber (Cucumis metalliferous* L.*) Fruit under Different Soil Types, Environments, and Varying Irrigation Water Levels*

This study was conducted during the 2017/18 and 2018/19 growing seasons, under the greenhouse, shade net, and open-space environment at the Florida science campus of the University of South Africa [5]. The aim was to determine the effect of different water stress levels, soil types, and growing environments (greenhouse, shade net, and open field) on the nutrient concentration of the African horned cucumber fruit. Total soluble sugars, crude proteins, β-carotene, vitamin C, vitamin E, total flavonoids, total phenols and micro-nutrients were analyzed. The results showed that African horned cucumber fruits are nutrient-dense when grown under moderate water stress treatment on a loamy or sandy loam substrate in shade-net and open-field environments. Quality parameters (total flavonoids, total phenols, micro-nutrients and vitamins metabolites) seem to be treatment-imposed. The data show that this crop can grow well under protected structures, which eliminates the potential damage caused by higher rainfall, hail, and extreme heat in summer.

### *2.2. Growth and Competitive Infection Behaviors of Bradyrhizobium japonicum and Bradyrhizobium elkanii at Different Temperatures*

Growth and competitive infection behaviors of two sets of *Bradyrhizobium* spp. strains were examined at different temperatures to explain strain-specific soybean nodulation under local climate conditions [6]. Each set consist of three strains—*B. japonicum* Hh 16-9 (Bj11-1), *B. japonicum* Hh 16-25 (Bj11-2), and *B. elkanii* Hk 16-7 (BeL7); and *B. japonicum* Kh 16-43 (Bj10J-2), *B. japonicum* Kh 16-64 (Bj10J-4), and *B. elkanii* Kh 16-7 (BeL7)—which were isolated from the soybean nodules cultivated in Fukagawa and Miyazaki soils, respectively. The authors compared growth and infection behaviors at different temperatures in Japan (Fukagawa and Miyazaki soils) and elucidated the reason why the species-specific nodule compositions are present in the Fukagawa and Miyazaki soils and locations. The experiments performed in liquid cultures revealed better growth of *B. japonicum* at lower temperatures and *B. elkanii* at higher temperatures, and therefore it can be assumed that the temperature of soil affects rhizobia growth in the rhizosphere and could be a reason for the different competitive properties of *B. japonicum* and *B. elkanii* strains at different temperatures. In addition, competitive infection was suggested between the *B. japonicum* strains.

### *2.3. Effects of Fertigation Management on the Quality of Organic Legumes Grown in Protected Cultivation*

The experimental trial was carried out in a protected cultivation certified organic farm in the province of Almería, South-East Spain on four legume cultivars of *Phaseolus* and *Pisum* [10]. The objective of the study was to determine the effects of two fertigation treatments, normal (T100) and 50% sustained deficit (T50), on the physico-chemical quality of legumes. The fertigation treatments had significant effects on the morphometric traits (width for mangetout and French bean; fresh weight for French bean; seed height for Pea cv. Lincoln). Furthermore, only French bean plants had significantly lowered productivity under 50% fertigation conditions. Furthermore, mangetout became the highest source of total soluble solids, reaching higher content at 50% fertigation treatment. Fertigation treatments did not significantly affect the antioxidant compounds (total polyphenols and ascorbic acid), minerals and protein fraction contents of the legumes studied.

### *2.4. Latitudinal Characteristic Nodule Composition of Soybean-Nodulating Bradyrhizobia: Temperature-Dependent Proliferation in Soil or Infection?*

To examine the possible reasons for the temperature-dependent distribution of soybeannodulating rhizobia, competitive inoculation experiments at different temperatures were conducted [7]. The study chose three locations Fukagawa, Matsue and Miyazaki which are considered to have different climatic conditions in Japan. The aim was to elucidate the possible reasons for the latitudinal characteristic distribution of soybean-nodulating rhizobia in local climate conditions. Rainfall might affect the soil conditions during the winter season, though the difference in the soil storage did not seem to be serious on the composition of rhizobia and the difference in rainfall would not significantly affect the nodule composition. The most interesting result was that the soil temperature mainly affected the dominant nodule composition in the different environmental conditions.

### *2.5. A Novel Method for Estimating Nitrogen Stress in Plants Using Smartphones*

The objectives of the research were to (i) test the hypothesis that the ratio of blue light reflectance to that of combined reflectance in the visible band can be used as an index for N stress, (ii) study the association between the N stress index and the physiological pathways in plants, and (iii) develop a smartphone application to measure the N stress index in species with differences in plant architecture [11]. The experiment was conducted in a greenhouse at Purdue University, West Lafayette, IN, USA. Nitrogen stress was provided by supplying a fertilizer solution with an EC of 0.75 dS·m−<sup>1</sup> and maintaining a <sup>θ</sup> level of 0.48 m3·m−<sup>3</sup> . The study developed an index, calculated as the ratio of reflectance of blue relative to the reflectance of combined wavelengths in the visible wavelengths band. The index value decreased when plants were exposed to nitrogen stress relative to optimal conditions. Furthermore, the index value decreased gradually with increasing N stress in plants. Therefore, the continuous measurement of the index can aid in the timely detection of N stress in plants.

### *2.6. Cucurbita Rootstocks Improve Salt Tolerance of Melon Scions by Inducing Physiological, Biochemical and Nutritional Responses*

An experiment was conducted to evaluate whether grafting with hybrid *Cucurbita maxima* × *Cucurbita moschata* rootstocks could improve the salt tolerance of melon and to determine the physiological, biochemical, and nutritional responses induced by Cucurbita rootstocks under hydroponic salt stress [12]. Results indicated that the shoot and root growths of grafted and nongrafted melon plants were detrimentally affected by salt stress. Significant reductions were recorded in some agronomic and physiological plant traits. Susceptible plants responded to salt stress by increasing leaf proline and malondialdehyde (MDA), ion leakage, and leaf Na<sup>+</sup> and Cl<sup>−</sup> contents. The highest plant growth performance was exhibited by Citirex/Nun9075 and Citirex/Kardosa graft combinations. These Cucurbita cultivars had a high rootstock potential for melon, and their significant contributions to salt tolerance were closely associated with inducing the physiological and the biochemical responses of the scions. These traits could be useful for the selection and breeding of salt-tolerant rootstocks for sustainable agriculture in the future.

### *2.7. The Effects of Gibberellic Acid and Emasculation Treatments on Seed and Fruit Production in the Prickly Pear (Opuntia ficus-indica (*L.*) Mill.) cv. "Gialla"*

The author tested the application of two methods (injection and spraying) of gibberellic acid (GA3) on the prickly pear cactus both at pre- and post-blooming in order to obtain well-formed seedless fruits in emasculated flowers [17]. The experiments were conducted in the Apulia region, Italy. Different application methods (injection and spraying) and concentrations of GA3 (0, 100, 200, 250, and 500 ppm) combined with floral-bud emasculation were applied to a commercial plantation to evaluate their effects on the weight, length,

and diameter of the fruits, total seed number, hard-coated viable seed number, and seed weight per fruit. The application of 500 ppm GA3 sprayed on emasculated floral buds was the most effective method for reducing the seed numbers of prickly pear fruits (−46.0%). The injection method resulted in a very low number of seeds (−50.7%) but produced unmarketable fruit. The spraying of the GA3 (both at low and high levels) enhanced the growth performance of all analyzed variables of the treated fruits, while the application of these treatments in an industrial-scale requires support to evaluate the processes.

### *2.8. Water Use and Yield Responses of Chile Pepper Cultivars Irrigated with Brackish Groundwater and Reverse Osmosis Concentrate*

Freshwater availability is declining in most of the semi-arid and the arid regions across the world [15]. The study evaluated the effects of natural brackish groundwater and RO concentrate irrigation on the water use, leaching fraction, and yield responses of Chile pepper cultivars (*Capsicum annuum* L.). The study was conducted in a greenhouse located at the New Mexico State University (NMSU). Saline irrigation caused a reduction in the water uptake of the Chile peppers and increased LFs. The four saline water treatments used for irrigation were tap water with an electrical conductivity (EC) of 0.6 dS m−<sup>1</sup> (control), groundwater with EC 3 and 5 dS m−1, and an RO concentrate with EC 8 dS m<sup>−</sup>1. The WUE was not substantially different but decreased significantly in the other two higher salinity treatments. Therefore, irrigating Chile peppers with up to 3 dS m−<sup>1</sup> brackish water could be possible by maintaining appropriate leaching fractions to sustain Chile pepper production in freshwater-scare areas where brackish groundwater is the only available source of irrigation. The yield response curves showed that the yield reductions in the Chile peppers irrigated with natural brackish water were less, compared to those of NaCldominant solution studies. Low yield reductions could be related to significant Ca2+ concentrations in the brackish groundwater and RO concentrate.

### *2.9. Alterations in the Chemical Composition of Spinach (Spinacia oleracea* L.*) as Provoked by Season and Moderately Limited Water Supply in Open Field Cultivation*

The study shows the relationship of the irrigation water supply with that of the chemical composition of the Spinach (*Spinacia oleracea* L.) [8]. Trials of the study recorded a slight effect on the chemical composition of the plant from providing a moderate water supply which ultimately influenced the product quality of field-grown spinach plants. In the reduced water supply treatment, the total amount of supplied water, including both irrigation and natural precipitation, amounted to 90%, 94% and 96% in 2015, 2016 and 2017, respectively, of the full optimal water supply treatment. The study was carried out on Spinach cv. 'Silverwhale' grown under open field conditions at Geiseheim University, Germany. The chemical composition of both the dry and the fresh biomass of spinach was shown to be strongly influenced by the climatic conditions and/or the water supply. Fresh biomass-related levels of ascorbic acid, potassium, nitrogen, phosphorous as well as total flavonoids and carotenoids increased upon limiting the water supply. Considering the composition of the dry biomass itself, authors demonstrated that even mild water supply reductions led to significant increases of inositol, zinc and manganese levels, while malic acid, phosphate and chloride levels decreased. The nutritional composition of spinach was sensitive to even moderately reduced water supply, but the overall quality of fresh spinach did not suffer regarding the levels of health-promoting constituents such as minerals, trace elements, flavonoids and carotenoids.

### *2.10. Container Type and Substrate Affects Root Zone Temperature and Growth of 'Green Giant' Arborvitae*

The objective of this research was to evaluate the combined effects of the container type and the substrate on RZT and growth of *Thuja standishii* × *plicata* 'Green Giant' [16]. Two separate studies were conducted concurrently at the Tennessee State University and the Auburn University Ornamental Horticulture Research Center, USA. Trade gallon arborvitae were transplanted into black, white, or air pruning containers filled with pine bark (PB)

or 4 PB: 1 peatmoss (*v:v*) (PB:PM). Plants grown in PB:PM were larger and had greater shoot and root biomass than plants grown in PB, likely due to the increased volumetric water content. Plant growth response to container type varied by location, but white containers with PB:PM produced larger plants and greater biomass compared with the other container types. Root zone temperature was greatest in black containers and remained above 38 ◦C and 46 ◦C for 15% and 17% longer than white and air pruning containers, respectively. Utilizing light color containers in combination with substrates containing peatmoss can reduce RZT and increase substrate moisture content thus improving crop growth and quality.

### *2.11. Effects of Non-Leguminous Cover Crops on Yield and Quality of Baby Corn (Zea mays* L.*) Grown under Subtropical Conditions*

The objective of this study was to evaluate the effects of non-leguminous cover crops and increments in chopping time versus Days After Planting (DAP) on the yield and quality of no-till baby corn (*Zea mays* L.) [13]. The experiment was carried out during kharif seasons under the subtropical climatic conditions. The experiment was conducted at the Punjab Agricultural University, Ludhiana, India. Three cover crops (pearl millet (*Pennisetum glaucum* L.), fodder maize (*Zea mays* L.), and sorghum (*Sorghum bicolor* L.)) and the control (no cover crop) were in the main plots and chopping time treatments (25, 35, 45 days after planting (DAP)) in the subplots. The yield (cob and green fodder yield) and dry matter accumulation of baby corn following cover crop treatments were significantly higher than the control (no cover crop) and improved with increment in chopping time. Increment in chopping time (from 25 DAP to 45 DAP) had a significant effect on the protein and sugar content of the baby corn cob. Chopping of cover crops at 45 DAP showed the highest yield and dry matter. Non-leguminous cover crops and their times of chopping evaluated in this study could be used for a sustainable maize crop production system to improve baby corn growth and yield, baby corn quality, and topsoil quality.

### *2.12. Effect of Stand Reduction at Different Growth Stages on Yield of Paprika-Type Chile Pepper*

The goal of this study was to understand how a simulation of population losses by four levels of stand reduction at three different growth stages affected the yield and yield components of the paprika-type red Chile [9]. Two trials, one per year, were conducted in southern New Mexico. 'LB-25', a standard commercial cultivar, was direct seeded on 29 March 2016 and 4 April 2017. Field experiments were conducted at the New Mexico State University, USA. Plants were thinned at three different growth stages; early seedling, first bloom, and peak bloom at four different levels at each phenological stage: 0% stand reduction (control; ~200,000 plants ha<sup>−</sup>1), 60% stand reduction (~82,000 plants ha−1), 70% stand reduction (~60,000 plants ha−1), and 80% stand reduction (~41,000 plant ha−1). The timing of stand reductions (growth stage) for paprika-type Chile did not impact the marketable red yields. Paprika-type Chile has some capacity to recover and compensate for stand reduction losses. Data show that a farmer could lose up to 70% of their paprika-type Chile stand due to hail damage and experience minimal to no impact on their yields. Furthermore, the paprika-type Chile crop losses can be estimated based on percentage of stand losses instead of growth stage.

### *2.13. Fertilization and Soil Nutrients Impact Differentially Cranberry Yield and Quality in Eastern Canada*

The objective of the research activities was to support site-specific nutrient management decisions in cranberry agroecosystems [14]. A 3-year trial was conducted on permanent plots at four production sites in Quebec, Canada. This paper quantified the trade-off between berry yield and quality as driven primarily by N fertilization. Berry yield was closely related to the number of fruiting uprights (r = 0.92), berry counts per fruiting upright (r = 0.91), number of reproductive uprights (r = 0.83), and fruit set (r = 0.77). Nitrogen increased berry yield nonlinearly but decreased berry firmness, total anthocyanin content (TAcy), and total soluble solids content (◦Brix) linearly, indicating a trade-off between berry yield and quality. Fertilizer dosage at a high-yield level ranged between 30 and 45 kg N ha−<sup>1</sup> in both conventional and organic farming systems. Berry yield could be predicted most accurately from berry counts per fruiting upright. Nitrogen fertilization increased berry yield nonlinearly and decreased fruit quality-based indices in a linear trend. As shown by redundancy analysis (RDA), cranberry performance was related to soil pH and soil test nutrients. The K and Ca were negatively correlated between them, indicating an upper limit for K additions. The RDA indicated close relationships between cranberry performance indices and soil properties, and thus supported the need for further soil test calibration.

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

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

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** We gratefully acknowledge all the authors that participated in this Special Issues.

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

### **References**


**Reza Jamaly 1, Serge-Étienne Parent 1,2 and Léon E. Parent 1,3,\***


**Abstract:** High berry yield and quality of conventionally and organically grown cranberry stands require proper nutrient sources and dosage. Our objective was to model the response of cultivar "Stevens" to N, P, K, Mg, Cu, and B fertilization under conventional and organic farming systems. A 3-year trial was conducted on permanent plots at four production sites in Quebec, Canada. We analyzed yield predictors, marketable yield, and fruit quality in response to fertilization and soil properties. Cranberry responded primarily to nitrogen fertilization and, to a lesser extent, to potassium. Berry yield was closely related to the number of fruiting uprights (*r* = 0.92), berry counts per fruiting upright (*r* = 0.91), number of reproductive uprights (*r* = 0.83), and fruit set (*r* = 0.77). Nitrogen increased berry yield nonlinearly but decreased berry firmness, total anthocyanin content (TAcy), and total soluble solids content (◦Brix) linearly, indicating a trade-off between berry yield and quality. Fertilizer dosage at a high-yield level ranged between 30 and 45 kg N ha−<sup>1</sup> in both conventional and organic farming systems. Slow-release fertilizers delayed crop maturity and should thus be managed differently than ammonium sulfate. Berry weight increased with added K. Redundancy analysis showed a close correlation between marketable yield, berry quality indices, and soil tests, especially K and Ca, indicating the need for soil test calibration.

**Keywords:** Brix; TAcy; nitrogen; potassium; compositional data; cranberry yield parameters; firmness; local diagnosis; redundancy analysis

### **1. Introduction**

Cranberry (*Vaccinium macrocarpon* Ait.) is grown in low-fertility acidic (pH 4.0 to 5.0) sandy or peaty soils located in low-lying landscape positions to facilitate water transfer [1]. Wisconsin, Quebec, and Massachusetts are the world's largest cranberry producers [2]. Quebec is the world leader in organically grown cranberries. Berry yield and quality depend on site, fertilization, cultivar, maturity, harvest date, and temperature [3,4]. While the fertilization of conventional systems has been documented to some degree, information on the fertilization of organic systems is scanty.

Fruit set, number of uprights, relative abundance of reproductive uprights (% of total uprights), number of flowers per reproductive upright, and berry weight are useful yield predictors [5,6]. A cranberry plant typically produces one to three fruits per reproductive upright from two to seven flowers. Depending on the cultivar and weather, berries take 60 to 120 d or even more to reach maturity and deep coloring [7]. Berry firmness, size, soluble solids, and ascorbic acid and anthocyanin content are the most important traits for the industry [8–10]. Berry moisture before harvest is also crucial to reduce the economic loss in terms of yield, quality, and drying cost [11,12].

**Citation:** Jamaly, R.; Parent, S.-É.; Parent, L.E. Fertilization and Soil Nutrients Impact Differentially Cranberry Yield and Quality in Eastern Canada. *Horticulturae* **2021**, *7*, 191. https://doi.org/10.3390/ horticulturae7070191


Academic Editor: Stefano Marino

Received: 8 June 2021 Accepted: 9 July 2021 Published: 13 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

Fertilization timing, source, and dosage must be managed to balance berry yield and quality [13]. Nitrogen fertilization is the most effective means to stimulate cranberry growth but it may affect floral induction negatively [14]. Excessive N dosage can produce intraspecific competition among neighboring plants [15]. The N source, timing, and rate should thus be managed carefully [16–18]. If applied in excess, N decreases berry yield and quality [16,19] and increases the risk of overgrowth of vegetative parts [20]. Ammonium sulfate is the most common N fertilizer in cranberry production. Slow-release nitrogen fertilizers are other sources of N fertilizer that may delay berry maturity depending on the N release rate [21]. Decision on proper N dosage and sources involves a trade-off between berry yield and quality that must be addressed locally but has not yet been modeled under the climatic conditions of eastern Canada.

Soil tests have been little documented in cranberry production. Potassium and phosphorus showed variable effects on berry yield and quality [22]. Soil test P and P fertilization were found to be poorly correlated with crop yield [23,24]. The K dosage was found to depend on soil test K, cultivar, and site, hence requiring local calibration [4]. Tissue testing is the most frequent method to diagnose other elements [4,7,25,26].

On the other hand, redundancy analysis provides a means to explore the relationship between cranberry performance and soil tests. To run a multivariate analysis such as redundancy analysis (RDA), soil compositions should be log-ratio transformed to remove false correlations between components that resonate on each other within the constrained sample space of nutrient compositions [27,28].

We hypothesized that (1) berry yield parameters and berry quality are impacted by N, P, K, Mg, B, and Cu dosage and the N source in conventional and organic cranberry agroecosystems, and (2) cranberry performance indices are correlated with soil test. The first hypothesis was tested using a mixed model. The second hypothesis was addressed by RDA. Our objective was to support site-specific nutrient management decisions in cranberry agroecosystems.

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

### *2.1. Experimental Site and Design*

One hundred and forty-four permanent plots of cultivar 'Stevens' were delineated in four commercial fields in southern Quebec, Canada (Figure 1). 'Stevens' is characterized by high yield and moderate color development [29]. The stands established in 1995, 1999, 2004, and 2007 at sites 9, 45, A9, and 10, respectively, were monitored from the spring of 2014 to the fall of 2016. Site A9 was under organic farming. Others were under conventional farming. Meteorological data during the 2014 to 2016 period (Appendix A, Figure A1) were obtained from the closest weather stations of Environment Canada [30]. Cranberry stands were irrigated to maintain soil matric potential between −3 and −7 kPa [31].

### *2.2. Soil Analysis*

Soil samples (0 to 15 cm) were collected in each plot before fertilization in June 2014 and every year thereafter. Samples were air-dried and sieved to less than 2 mm. Grain-size distribution was determined by sedimentation [32] and sand composition by hand-sieving. Grain-size distribution is presented in Table 1. Minerals were extracted using the Mehlich III method [33] and quantified by ICP-OES (inductively coupled plasma optical emission spectrometry). The C and N contents were quantified by combustion (Leco CNS-2000 analyzer, St. Joseph, MI, USA). Soil pH was reported as pHcacl2. Results of soil analyses at the onset of the experiment are presented in Table 1.

**Figure 1.** Map of the four study sites located on the south shore of the St. Lawrence River in Quebec, Canada.

**Table 1.** Soil particle size distribution and mean and standard deviation (SD) of soil data at each experimental site across 144 plots in June 2014.



**Table 1.** *Cont*.

### *2.3. Fertilization*

The experiment comprised 18 treatments (Appendix A, Table A1) arranged as randomized block designs and replicated twice at each site, for a total of eight observations per treatment per year. Plot size was 12 m2 (4 by 3 m). Fertilizer treatments are presented in Table 2. The transition to organic farming at site A9 started in 2015. The N treatments (0, 15, 30, 45, or 60 kg N ha−1) comprised ammonium sulfate (21% N) or sulfur-coated urea (SCU = 24% N, 5% P2O5, 11% K2O) on conventional sites and certified fish emulsions (6% N, 1% P2O5, 1% K2O) or amino acids of plant origin (8% N) on the organic site (Table 2). The K doses (0, 40, 80, or 120 kg K ha−1) were applied as potassium sulfate (50% K) or sulfate of potassium and magnesium (18% K and 9% Mg). Phosphorus was supplied as triple superphosphate (46% P2O5) on the conventional sites or as bone meal (13% P2O5) on the organic site. Two Mg doses (0 or 12 kg Mg ha−1), two Cu doses (0 or 2 kg Cu ha−1), and two B doses (0 or 1 kg B ha−1) were applied as Epsom salt (9% Mg), copper sulfate (25% Cu), and sodium borate (20% B), respectively.

Where one element was varied, other elements were applied at rates of 45 kg N ha<sup>−</sup>1, 15 kg P ha−1, 80 kg K ha−1, 12 kg Mg ha−1, 2 kg Cu ha−1, and 1 kg B ha−1. Fertilizers were applied uniformly by hand during the growing season. The N fertilizer was applied within a 3- to 4-week window that coincides with fruit set and initial bud formation [34]. From early June to mid-June, B, Cu, and Mg fertilizer were applied during bud break and bud elongation. Thereafter, NPK were applied at four occasions as follows: 15% at early flowering (29 June to 2 July), 35% at 50% flowering (8 to 11 July), 35% at 50% fruit set (16 to 19 July), and 15% 1 to 2 weeks after the third application.


**Table 2.** Fertilization treatments during the 2014–2016 period at conventional and organic sites.

### *2.4. Plant Measurements*

Yield parameters used as yield predictors were flower counts, number of reproductive uprights, number of flowers per reproductive upright, berry counts, number of fruiting uprights, berry counts per fruiting upright, and fruit set (ratio of berry counts to flower counts). The counts of flowers and reproductive uprights were measured in 2014 and 2015 at the end of June on four representative areas totaling 0.37 m2 per plot while fruits and fruiting uprights were counted in mid-August. Berries were hand-harvested in four areas totaling 0.37 m2 in each plot, one to two weeks before starting the planned flooding operation at the beginning of October, to avoid too early flooding. Berries were counted and weighed to derive average berry weight (g) and marketable yield (Mg ha<sup>−</sup>1).

### *2.5. Berry Quality*

Fruit quality followed commercial criteria of the USDA shipping point and market inspection instructions for fresh cranberries (USDA, 2007). One kilogram of randomly selected berries was weighed to determine berry quality after discarding unmarketable fruits. Berry quality was determined as moisture content, total soluble solid concentration (Brix), total anthocyanin concentration, acidity, and firmness [10].

Berries were frozen at −10 ◦C and analyzed for moisture content, TAcy [35], and ◦Brix (refractometry) for soluble solids at the Ocean Spray quality department in Warren, Wisconsin. As berries were harvested before commercial harvesting, average TAcy indices [24] could be lower than market requirements of 350 to 450 mg kg−<sup>1</sup> for bonus payments reachable at harvest. Berry firmness was quantified using the TA.TX2 Texture Analyzer (Texture Technologies Inc., Scarsdale, NY, USA) [36]. Fifty berries per treatment were refrigerated overnight then maintained at room temperature for 1 to 2 h before performing the test. Pre-test speed was 1 mm s−1, test speed was 2 mm s−1, post-test speed was 10 mm s−1, and trigger force was 0.1 N. Firmness was reported in N mm <sup>−</sup><sup>1</sup> as the mean and standard deviation of 50 samples.

### *2.6. Statistical Analysis*

Statistical analyses were conducted in the R statistical environment version 4.0.5 [37]. We used the R meta-package tidyverse version 1.3.0 [38] for generic data analysis, weathercan [39] for historical weather data, and ggmap [40] for spatial visualization. There were 13 dependent variables including seven yield predictors, as follows: flower counts, number of reproductive uprights, number of flowers per reproductive upright, berry counts, number of fruiting uprights, berry counts per fruiting upright, and fruit set. Other dependent variables were quality indices (TAcy, Brix, firmness, and berry moisture), marketable yield, and berry weight. The experimental setup was analyzed as a mixed model with treatments as fixed factors and years, sites, and replications as random factors [41]. Ammonium sulfate (21-0-0) was set as the reference fertilizer treatment to run the nlme model. Outliers were removed by Z-score test [42] if they exceeded 5 times the standard deviation (5.85% of total observations).

Tests of significance (*p* = 0.05) were used to reject the null hypothesis, but not to accept it as true [43]. Non-significant results did not mean that there was no difference between groups or there were no treatment effects [44]. For each primary outcome, we computed 95% compatibility intervals [44].

There were twelve soil properties and six cranberry performance indicators. The dataset of matrix Y (cranberry yield and quality indices) and explanatory matrix X (pH, N, P, K, Mg, Cu, Ca, Zn, Mn, Fe, Al, and C) was explored by redundancy analysis (RDA) to analyze the impact of "explanatory variables" on "response variables". The R packages to run RDA were the vegan version 2.5-7 [45] for RDA, R meta-package for ordination, and compositions for *clr* transformations to avoid spurious correlations. A permutation procedure was performed (anova.cca function in vegan package) [45] to test the significance of RDA models. Soil test nutrient concentrations were transformed into centered logratios [46] before conducting RDA due to Euclidean geometry. The centered log-ratios (clr) were computed as follows [27,28]:

$$clr(\mathbf{x}\_i) = \ln\left(\frac{\mathbf{x}\_i}{\mathbf{g}(\mathbf{x}\_i)}\right)$$

where *xi* is the *i th* nutrient soil concentration and g(xi) is the geometric mean. The *clr* transformation allows for computing Euclidean distances between any two compositions [29]. Redundancy analysis (RDA) related matrix Y (cranberry performance) to explanatory matrix X (soil test) based on Euclidean distance between observations [47,48]. Indeed, due to closure to the bounded measurement unit, compositional data should be log-ratio transformed before running linear univariate or multivariate statistical analyses [49].

### **3. Results**

### *3.1. Effect of Nitrogen Source on Berry Yield and Quality*

Berry quality was impacted significantly (*p* < 0.05) by N sources (Figure 2). The effects of SCU and fish emulsions differed compared to ammonium sulfate depending on the variable tested. Berry counts, flower counts, number of fruiting and reproductive uprights, and percentage of fruit set tended to decrease adding amino acids (8-0-0). Organic fertilizers (6-1-1 and 8-0-0) had similar effects on berry yield (*p* > 0.05). Fish emulsions and SCU decreased TAcy by four to six units while fish emulsions increased firmness by 11%, indicating delayed maturity. Compared to ammonium sulfate, SCU increased berry yield by 13% (*p* < 0.05) but reduced TAcy (*p* < 0.05). TAcy responded differently than firmness and ◦Brix to fish emulsions. Overall, average anthocyanin content decreased (7.23 mg TAcy 100 g<sup>−</sup>1) significantly with the organic fertilizer (6-1-1). The ◦Brix and berry moisture showed no significant differences between nitrogen sources.

**Figure 2.** Coefficients of the linear mixed model on the x axes showing the effects on berry yield and quality of amino acids (8-0-0), sulfur-coated urea (24-5-11), and fish emulsions (6-1-1) compared to ammonium sulfate (21-0-0) (y axes). The in-box black line shows 95% confidence intervals. The on-line values are the mean and lower and upper limits for 95% confidence intervals.

### *3.2. Effect of N, P, and K Regimes on Yield Parameters and Fruit Quality*

The N and K fertilization impacted significantly berry yield and quality while the effect of P fertilization was not significant (Figures 3 and 4). The effects of Mg, B, and Cu regimes were also not significant (Appendix A, Figures A2 and A3). Berry yield responded non-linearly to N fertilization (*p* < 0.05) and tended to plateau between 30 and 60 kg N ha−1. The highest yield of 33 Mg ha−<sup>1</sup> was reached at 45 kg N ha−<sup>1</sup> under organic farming. The highest yield average of 48 Mg ha−<sup>1</sup> was reached at 30 to 45 N ha−<sup>1</sup> under conventional farming.

**Figure 3.** Response of cranberry yield components to added N, P, and K. The solid line represents the model fit with the slope and intercept of the line; the shaded area represents the 95% confidence interval.

**Figure 4.** Response of berry yield, weight, and quality indices to added N, P, and K. The solid line represents the model fit with the slope and intercept of the line; the shaded area represents the 95% confidence interval.

Fruit set increased linearly between 0 and 60 kg N ha−<sup>1</sup> and between 0 and 120 kg K ha−<sup>1</sup> (Figure 3). Likewise, counts of fruiting uprights increased linearly from 95 to 123 by adding N and K. Berry count per fruiting upright increased from 205 to 257 with K additions between 0 and 120 kg K ha<sup>−</sup>1. Berry weight was highest with 30 to 45 kg N ha−<sup>1</sup> and 40 to 120 kg K ha−1. There was no significant yield response (*p* > 0.05) to added K in 2015, where the maximum yield was 36 Mg ha−1, but there was a significant response (*p* < 0.05) in 2014 and 2016 where yields reached 54 and 44 Mg ha−1, respectively. Hence, yield response to K fertilization was apparently related to yield level.

There were 3.9 flowers per reproductive upright, 2.1 berries per fruiting upright, and 48% of fruit set at N application rate of 45 kg N ha<sup>−</sup>1. Each kg of N per ha increased berry moisture by 0.023% unit in the range of 0 to 60 kg N ha−<sup>1</sup> (Figure 4). Cranberry responded negatively to added N for ◦Brix, TAcy, firmness, and positively for the percentage of berry moisture (Figure 4). There was a non-linear response to added N for berry weight and berry yield. The K positively impacted ◦Brix, TAcy, and firmness, increasing gently between 0 and 120 kg K ha−1. Response to added P was not significant. Response to Mg, B, and Cu regimes was also not significant (Appendix A, Figures A2 and A3).

### *3.3. Correlations among Berry Yield and Quality Parameters*

Relationships among yield parameters are presented in Figure 5. There were close correlations between the number of reproductive uprights, flower counts, number of fruiting uprights, and berry counts (Figure 5). Flower counts and the number of reproductive uprights (*r* = 0.95), as well as berry counts and number of fruiting uprights (*r* = 0.93) were closely related with marketable yield. Fruit set showed moderate correlation (*r* = 0.77) with berry counts and flower counts. Fruit set fluctuated between 33% to 59% in 2014 and 36% to 41% in 2015. The relationship between fruit set and berry yield was thus inconsistent across years.

**Figure 5.** Matrix of correlations among yield components in the cranberry data set. The diagonal shows graphs of the original data after adjusting for all other variables. Upper: Pearson correlation. \*\* Correlation significant at the 0.05 level; \*\*\* Correlation significant at the 0.01 level.

### *3.4. Redundancy Analysis*

Relationships between soil test and cranberry performance are illustrated in Figure 6. The first RDA axis was significant (RDA1: F = 19.44, *p* = 0.001), and explained 85.9% of the total variation. The anova.cca ranking was Ca (F = 11.98, *p* < 0.001), K (F = 3.92, *p* < 0.05), Fe (F = 2.57, *p* < 0.05), and pHCaCl2 (0.01 M CaCl2) (F = 4.53, *p* < 0.05). Soil pHCaCl2 was the most important soil factor affecting cranberry performance. The optimum pHCaCl2 was 4.14 ± 1.66. Among soil nutrient tests, Ca ranked first, explaining 56.3% of the cumulative variance. The second key factor was K with 18.4% of the cumulative variance, followed by Fe, and Mg. The K and Ca were negatively related.

Berry yield and quality indices were related to soil tests. Arrows in RDA illustrated the complex relationships between them (Figure 6). Berry weight was related negatively to TAcy. The ◦Brix was negatively related to berry moisture. Berry moisture was positively related to pH. There was a positive relationship between soil test Ca and TAcy. Soil test K, Fe, and Mg were related positively to ◦Brix. Berry yield was negatively related to soil test K, Fe, and Mg. Berry weight was related negatively to soil test Ca and Zn. Yield and berry moisture were located in the lower right quadrant, and were positively related to pH. Berry firmness and weight were closely related to each other. The Ca and TAcy located in the upper right quadrants were positively related. In contrast with K fertilization applied as potassium sulfate (Appendix A, Table A1), soil test K was positively related to ◦Brix but negatively related to berry yield.

**Figure 6.** Redundancy analysis relating soil properties on the left to cranberry performance indices on the right. Distances were computed using the Euclidean distance.

### **4. Discussion**

### *4.1. Impact of Fertilization on Berry Quality*

Fruit quality characteristics are of prime importance in cranberry breeding programs [29]. Fruit quality depends on genetics, management, and the environment [50,51]. Berry quality traits comprise anthocyanin content (color), fruit texture characteristics (crispness, hardness, juiciness, and mealiness), fruit anatomy (skin, flesh, or air pocket), and fruit external appearance (size and shape) [52]. Cultivar 'Stevens' showed the smallest average berry size (1.51 cm) among commercial cultivars [8]. Stevens [53] first suggested that factors such as temperature and rainfall could impact fruit keeping quality through fungal infection and disturbed fruit physiology.

The N and K fertilization regimes can also influence cranberry production. Increased N dosage resulted in a linear decrease in ◦Brix, firmness, and TAcy, and increased moisture content. In general, a high N dosage was found to reduce TAcy [4,54], firmness, [55] and ◦Brix [29]. In contrast, Davenport [56] found no significant effect on TAcy by applying up to 44 kg N ha−1. As anthocyanins are located primarily in the fruit epidermal layers [57], TAcy decreases as fruit size increases [58]. While TAcy increased [59], fruit firmness was found to decrease as the fruit ripened [10]. Bourne [60] found that similar to cranberry, apple firmness decreased with N additions. In contrast with previous research showing a linear response of hybrid cultivars [61], we found a quadratic relationship between added N and berry weight of 'Stevens'. Cranberry quality indices showed a small but significant response trend adding 80 to 120 kg K ha−1. Crop response to added K was found to be related to yield level in eastern Canada, which was not necessarily the case across cultivars and sites in North America [19,56].

### *4.2. Impact of Fertilization on Yield Parameters*

The numbers of flowers per reproductive upright and number of berries per fruiting upright were within the range of published values [7,62]. As the number of reproductive uprights and flower counts m−<sup>2</sup> are related to fruit yield, they provided performance indices a few months before harvest [63]. Berry count m−<sup>2</sup> was found to be the yield parameter most closely related to berry yield. Fruit set is also an important indicator of yield variation [5,64] as related to sunlight [65], pollination [64], and yield [66], but the relationship between carbohydrate concentration, fruit set, and yield can be inconsistent [64]. Carbon allocation between reproductive and vegetative parts [67] depends on temperature [63,68] and is affected by excessive rainfall or drought [69].

Marketable yields were higher by 10% to 25% under the conventional vs. organic systems as reported elsewhere [70,71]. In both conventional and organic farming systems, the effect of N dosage on yield plateaued between 30 and 60 kg N ha−1, within the 20 to 65 kg N ha−<sup>1</sup> range reported in Davenport [56] and the 39 to 56 kg N ha−<sup>1</sup> range reported in DeMoranville and Ghantous [21]. While 'Stevens' yield was found to plateau at 20 Mg ha−<sup>1</sup> after adding 22 to 44 kg N ha−<sup>1</sup> [56], 'Stevens' yields up to 40 Mg ha−<sup>1</sup> decreased and stolon weight increased where N dosage exceeded 34 kg N ha−<sup>1</sup> [20]. The N dosage is site-specific. A too high N dosage (60 kg N ha−1) lead to plant crowding [15,50] and overgrowth of the vegetative parts [72].

Slow-release N fertilizers such as SCU in conventional cranberry production and certified fish emulsions in organic production should be managed differently than ammonium sulfate. Berry firmness increased by applying an organic source likely due to delay in berry maturity. Anthocyanin content tended to decrease where ammonium sulfate was replaced by organic nitrogen or SCU as also reported in [18]. Compared to ammonium sulfate, SCU and fish emulsions should be applied earlier in the season to sustain N release during the whole season and avoid delaying berry maturity and the reddening finish close to harvest time.

### *4.3. Ranking of Soil Test Variables*

Soil test calibration has been little addressed in cranberry production except for P [23,24]. As first shown by Bray [73,74], soil tests for nutrients showing low mobility in the soil as well as nutrient source and placement must show different coefficients of efficiency. Crop response to fertilization dosage and soil test value are generally addressed separately then assembled into a modified Mitscherlich equation. In this paper, we addressed crop response to added nutrients separately using a mixed model and soil test using RDA.

As shown by RDA, soil test K appeared to be the most discriminant nutrient for cranberry performance, followed by Ca, Fe, and Mg. Soil test K is often low in cranberry soils because K is easily leached at soil pH values less than 5.5 [75], and cation-exchange capacity is low in sandy soils [76]. Soil K can also be supplied as non-exchangeable K by primary and secondary soil minerals such as feldspar, mica, and illite, which are common in soils of eastern Canada [77–79]. Mica K is released much faster compared to phlogopite, biotite, and muscovite [80]. The reactivity of soil minerals also depends on the grain-size distribution, pH [81], and rhizosphere exploration of the soil [82]. Owing to the low clay and high sand contents in cranberry soils, soil minerals are assumed to contribute little to cranberry K requirements. It is thus difficult to maintain high soil test K values in acidic cranberry sandy soils. Low soil test K results in low berry yield [19,22], requiring fertilization.

The K fertilization should meet annual K requirements, at a rate that avoids affecting the uptake of other cations. Added K may trigger Ca leaching and reduce Ca uptake [17,22, 26]. Since tissue K increases with K dosage or soil test K level while tissue Mg might decline [4,17], the tissue K–Mg interaction should also be monitored [83]. The RDA supported calibrating soil tests to provide minimum critical soil test values to sustain the cranberry production on sandy soils. Nevertheless, our results supported the present K recommendation of 54 to 92 kg K ha−<sup>1</sup> at a low soil test K [84]. The minimum soil test K value to be maintained in cranberry soils should be addressed in future research.

The RDA indicated that soil pH played a key role in cranberry nutrient management [18,85]. High levels of Ca in cranberry soils can reduce the absorption of Mn, Fe, and Zn, potentially reducing berry yield and size [83]. To address nutritional balance in cranberry crops and predict cranberry yield and nutrient requirements, tissue testing is complementary to soil testing [86].

### **5. Conclusions**

This paper quantified the trade-off between berry yield and quality as driven primarily by N fertilization. Berry count per fruiting upright, fruit set, and berry weight responded consistently to N treatments. Berry yield could be predicted most accurately from berry counts per fruiting upright. Nitrogen fertilization increased berry yield nonlinearly and decreased berry quality indices linearly. The SCU and fish emulsions delayed berry maturity and should thus be managed differently than ammonium sulfate through earlier applications to account for the slow-release patterns.

Cranberry responded moderately to K where yield potential was high. As shown by RDA, cranberry performance was related to soil pH and soil test nutrients. The K and Ca were negatively correlated between them, indicating an upper limit for K additions. The RDA indicated close relationships between cranberry performance indices and soil properties, and thus supported the need for further soil test calibration.

**Author Contributions:** Conceptualization, R.J., S.-É.P. and L.E.P.; data curation, R.J., S.-É.P. and L.E.P.; formal analysis, R.J., S.-É.P. and L.E.P.; funding acquisition, S.-É.P. and L.E.P.; methodology, R.J., S.-É.P. and L.E.P.; project administration, S.-É.P.; Software: R.J. and S.-É.P.; supervision, S.-É.P. and L.E.P.; validation, R.J., S.-É.P. and L.E.P.; writing—original draft preparation, R.J., S.-É.P. and L.E.P.; writing—review and editing, R.J., S.-É.P. and L.E.P. All authors have read and agreed with the published version of the manuscript.

**Funding:** This collaborative research project was supported by Les Atocas de l'Érable Inc., Les Atocas Blandford Inc., La Cannebergière Inc., the Natural Sciences and Engineering Research Council of Canada (RDCPJ-469358-14).

**Informed Consent Statement:** Not applicable.

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

**Appendix A**

**Figure A1.** Monthly mean air temperature and total precipitation at the four experimental sites in Quebec, Canada.



**Table A1.** *Cont*.


**Figure A2.** Response of cranberry yield components to added B, Cu, and Mg. The solid line represents the model fit with the slope and intercept of the line; the shaded area represents the 95% confidence interval.

**Figure A3.** Response of berry yield, weight, and quality indices to added B, Cu, and Mg. The solid line represents the model fit with the slope and intercept of the line; the shaded area represents the 95% confidence interval.

### **References**


## *Article* **Nutrient Concentration of African Horned Cucumber (***Cucumis metuliferus* **L) Fruit under Different Soil Types, Environments, and Varying Irrigation Water Levels**

**Mdungazi K Maluleke 1,\*, Shadung J Moja 2, Melvin Nyathi <sup>3</sup> and David M Modise <sup>4</sup>**


**Abstract:** The nutrient concentration of most crops depends on factors such as amount of water, growing environment, sunlight, and soil types. However, the factors influencing nutrient concentration of African horned cucumber fruit are not yet known. The objective of the study was to determine the effect of different water stress levels, soil types, and growing environments on the nutrient concentration of African horned cucumber fruit. Freeze-dried fruit samples were used in the quantification of *β-carotene* and total soluble sugars. The results demonstrated that plants grown under the shade net, combined with severe water stress level and loamy soil, had increased total soluble sugars (from 8 to 16 ◦Brix). Under the shade-net environment, the combination of moderate water stress level and loamy soil resulted in increased crude protein content (from 6.22 to 6.34% ◦Brix). In addition, the severe water stress treatment combined with loamy soil, under greenhouse conditions, resulted in increased *β-carotene* content (from 1.5 to 1.7 mg 100 g−<sup>1</sup> DW). The results showed that African horned cucumber fruits are nutrient-dense when grown under moderate water stress treatment on the loamy or sandy loam substrate in the shade-net and open-field environments.

**Keywords:** biochemical constituents; *β-carotene*; vitamins; micro-nutrients; growing environments

### **1. Introduction**

In Sub-Saharan Africa, indigenous crops have been a source of food for rural resourcepoor households who experience nutritional food insecurity [1]. However, deficiencies in micronutrients, such as zinc, iron, and *β-carotene*, have been described as a major nutritional challenge faced by many rural households [2]. Several researchers claimed that the benefits of indigenous crops are that (i) they grow naturally in the wild [3]; (ii) are resistant to most pests and diseases; (iii) have better environmental stress tolerance; (iv) require low agricultural inputs, such as irrigation and fertilizers; and (v) have a shorter period to mature and are readily available for consumption [2]. However, most indigenous fruits and vegetables have not yet been commercialized, particularly in Southern Africa, because they are not produced under well-defined agronomic practices, and there is a lack of market value chain, since they do not have a high demand [2,4]. The nutritional composition of these crops has not been widely investigated, despite their usefulness to the communities. There appears to be scanty knowledge about their nutritional content, particularly when grown under different growing conditions. This knowledge could aid in influencing policymakers in the commercialization and products innovation in many countries, since the crop is adaptable in various growing environments. Ref. [2] reports that most of these crops have the potential to supplement several nutrients needed by

**Citation:** Maluleke, M.K; Moja, S.J; Nyathi, M.; Modise, D.M Nutrient Concentration of African Horned Cucumber (*Cucumis metuliferus* L) Fruit under Different Soil Types, Environments, and Varying Irrigation Water Levels. *Horticulturae* **2021**, *7*, 76. https://doi.org/10.3390/ horticulturae7040076


Academic Editor: Stefano Marino

Received: 18 February 2021 Accepted: 18 March 2021 Published: 10 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

the human body, in both smaller and larger quantities. Ref. [1] iterated that there is a need to promote the consumption of indigenous crops, and that can be achieved by the investigation of their agronomical viability and qualities, such as nutritional content. The African horned cucumber fruit is palatable, with a similar taste of a mixture of banana and pineapple [5]. The internal part of the fruit contains a high moisture content, which can aid body hydration [6]. Ref. [7] the other benefits of consuming this fruit: (i) It is a source of vitamin C, and (ii) it contains biochemical compounds such as phenols, which help the body to eliminate toxins; thus, growing this crop is of the outmost important in terms of promoting biodiversity and stewardship of the natural heritage and ecosystem of the Sub-Sahara region. The objective of the study was to determine the effect of different water stress levels, varying soil types, and growing environments on the nutrient concentration of the African horned cucumber fruit.

### **2. Material and Methods**

This study was conducted during the 2017/18 and 2018/19 growing seasons, under the greenhouse, shade net, and open-space environment at the Florida science campus of the University of South Africa (26◦10 30 S, 27◦55 22.8 E). Before plant cultivation, gravimetric water content (GWC) was carried out, determining the field water capacity of the soils. Briefly, dry soil was filled in a 30 cm depth planting pot, weighed, and then watered to filled capacity (3000 mL). The pots were then weighed after 72 h, when drainage was completed. The process was repeated until the soil reached permanent wilting point. Water stress levels was then determined by using the formula (e.g., 3000 mL–filled capacity ×75 ÷ 100 = 2250 mL moderate stress, while 3000 mL × 35 ÷ 100 = 1050 mL severe water stress). Soil samples (loamy soil and sandy loam) were analyzed for mineral and/or chemical content (Table 1), using the method followed by [8]. The above analysis was conducted at the Agricultural Research Council, Institute for Soil, Climate and Water (ARC-ISWC) in Pretoria (25◦ 44 19.4 S28◦ 12 26.4 E). Sterilized growth media (loamy soil and sandy loam) were used. In addition, certified seeds of African horned cucumber were purchased from Seeds for Africa, Cape Town. A factorial experiment with two factors, i.e., soil (loamy soil and sandy loam soil) and irrigation water levels (no water stress, moderate water stress, and severe water stress), was conducted. The pot experiment was a completely randomized design with nine (9) replicates per treatment. The pots were spaced 1 m apart, and an up-rope vertical trellising was used to support the plants. On each site, pots were either filled with loamy soil or sandy loam. Each block comprised 18 plants in pots, resulting in 54 plants per site. A total of 162 plants were used for the experiment. Each site had plants used as guard plants, in order to separate the plants from the external effects outside the experimental plot. Well-established, uniform, and healthy African horned cucumber seedlings, germinated from peat substrate, that were 30 days old, were transplanted into 30 cm depth × 30 cm width. Briefly Area (depth × width) 30 cm × 30 cm = 900 cm2,A= *<sup>π</sup> d* 2 × 2 d = 286.5 cm2 planting pots, and the treatments were imposed four (4) weeks later, after establishment. Plants were well irrigated prior to imposition of the treatments. Granules fertilizers (potassium phosphate), 10 g per plant pot, were applied once every 7th day of the week during the experimental period.

The impact of soil, water, and growing environment on the nutrient composition of African horned cucumber fruit was evaluated at 12 weeks after planting during 2017/18 and 2018/2019. Prior to fruit analysis, optimization analysis of crude protein and total soluble sugars of fruit was carried out before the actual fruit analysis, whereby fruit were harvested from each irrigation water level (no-water-stress control, moderate water stress, and severe water stress), soil type, and growing environment. The goal for the fruit optimization analysis was to find the optimum value for one or more target variables among African horned cucumber fruit harvested under different treatments.


**Table 1.** Soil analysis for the experiment (mineral/chemical analysis).

### *2.1. Determination of β-Carotene*

The analysis of *β-carotene* was carried out with a Prominence-i High-Performance Liquid Chromatography–PDA model system equipped with a sample cooler LC-2030C (Shimadzu, Japan), with slight modifications (triplicate), as described by [4], since most of the compounds measured were expected to be similar to those of the current study. A mixture of approximately 0.1 g/mL of extracted sample with ice-cold hexane:acetone (1:1, *v/v*) was vortexed for two (2) minutes, before being centrifuged at 2000 rpm for two (2) minutes. The organic phase was decanted into a tube containing saturated sodium chloride solution and placed on ice. The remaining residue was similarly re-extracted until the extract was colorless. Each time, the extract separated organic phase was filtered through 0.45 μm syringe filtered before injection into the HPLC. Chromatographic separation was achieved, using a C18 Luna® column (150 × 4.6 mm, 5μ) maintained at 35 ◦C.

An isocratic mobile phase which consisted of acetonitrile:dichloromethane:methanol (7:2:1) was used, with a flow rate of 1 mL/min, an injection volume of 20 μL, and the detection was at 450 nm. Peak identification and quantification of the compound (*βcarotene*) were both achieved based on authentic *β-carotene* standard, which was used for plotting the calibration curves [9].

### *2.2. Determination of Total Soluble Sugars*

The African horned cucumber fruit harvested from the greenhouse, shade net, and open field, irrigated with different water levels and soil types, were analyzed for total soluble sugars concentration (◦Brix) following the method by [10]. The fruit was cut into two portions, then juice was squeezed from a fruit portion by hand to release about 0.03 mL juice onto the aperture of the hand refractometer (HI 96801 Refractometer, USA) and readings were taken immediately. About 18 fruits were measured per treatment. The aperture was washed between different juices samples, with distilled water, and dried with a soft paper towel.

### *2.3. Determination of Vitamin C and E*

The fruit samples were freeze-dried for 72 h, using a freeze drier (HARVEST-RIGHT, Barcelona). The freeze-dried fruit slices were rigorously homogenized, using a sterilized food blender, and mixed with dried powder before nutritional analysis. The method described by [4] was followed with slight modifications (triplicate). Individual samples were weighed (1 g) into tube, followed by the addition of 5% metaphosphoric acid (10 mL). It was sonicated 15 min before centrifuged and then filtrated in the ice-cold water bath. The analysis was carried out on the model system described above, Prominence-i HLCP– PDA. A C18 Luna ® column (150/4.6 mm, 5 μL) held at 25 μC was used to achieve chromatographic separation. A water-based isocratic mobile phase: acetonitrile: formic acid (99:0.9:0.1) was used at a flow rate of 1 mL/min. The volume of injection was 20 μL and 245 nm of detection was set. Depending on the calibration curve plotted by using L-ascorbic acid, sample quantification was achieved.

### *2.4. Determination of Total Flavonoids*

The African horned cucumber fruit samples were quantified, using the aluminum chloride colorimetric method described by [4]. Catechin was used as a standard for calibration curve, and total flavonoids content was expressed in mg catechin equivalents (CEs) per dry weight.

### *2.5. Determination of Total Phenolic Content*

Total phenolic content of the fruit samples was carried out, using [4], with a slight modification (triplicate). Garlic used as standard for plotting curve. Total phenolic content was expressed in mg garlic acid equivalents (GAEs) per g dry weight (DW).

### *2.6. Determination of Micro-Nutrients*

Freeze-dried fruit samples were digested in a diffused microwave system (MLS 1200 Mega; Milestone S.r. L, Sorisole, Italy), and the samples were further congelated–dried, following the procedure described by [4] with minor modifications. The modifications were that samples were measured in three (3) replicates per treatment (around 15–25 mg) weighed into polytetrafluoroethylene vessels and 2 mL HNO3 (67%, analphur) and 1 mL H202 (30%, analytical grade) added in the vessels [4f]. Every solution was diluted to 15 mL, in a deionized-water test tube, after digestion, and analyzed by Inductively Coupled Plasma–Mass Spectrometry (ICP–MS). An ICP–MS (Agilent 7700; Agilent Technologies, Tokyo, Japan) based on quadrupole mass analyzer and octapole reaction system (ORS 3) was used to conduct the analysis. Nutrient elements, such as zinc (Zn), iron (Fe), molybdenum (Mo), copper (Cu), and manganese (Mn), were analyzed.

The calibration solution was prepared by appropriate dilution of the single element certified reference material with 1.000 g/L for each element (Analytika Ltd., Czech Republic) with deionized water (18.2 MΩ·cm, Direct-Q; Millipore, France). Measurement of accuracy was verified by using certified reference material of water TM-15.2 (National Water Research Institution, Ontario, Canada).

### *2.7. Statistical Analysis*

Analysis of variance (ANOVA) was performed with a three-way ANOVA), to determine the main and interaction effects of all studied variables (crude protein, total soluble sugars, *Beta carotene*, vitamin C, vitamin E, total phenols, total flavonoids, and macroand micro-nutrients). Homogeneity and uniformity tests were carried to determine the difference and similarities between variance. Mean separation was done by using the Fischer's unprotected least significance difference test at 5% significance level. Treatment means for each measured parameter were compared, and differences were noted. All statistical analyses were done, using GenStat (version 14, VSN, Rothamstead, UK).

### **3. Results**

### *3.1. Total Soluble Sugars*

Figure 1 presents the treatment interaction effect on total soluble sugars content of African horned cucumber fruit grown at different environments (greenhouse, shade net, and open field), soil types (loamy soil and sandy loam), and water stress levels (no water stress, moderate water stress, and severe water stress). The results indicated that there was no significant (*p* > 0.05) interaction between location, different water stress levels, and soil types on total soluble sugars content of African horned cucumber fruit during both growing seasons. However, fruit total soluble sugars ranged from 8.0 to 16 ◦Brix. In addition, the study revealed that there was a significant (*p* ≤ 0.05) difference in total soluble sugars under varying water levels. Total soluble sugars among different water levels ranged from 11.4 to 14.4 ◦Brix. Furthermore, the results illustrated that the severe-water-stress level obtained the highest total soluble sugar content (14.4 ◦Brix), while the lowest content was observed from the no-water-stress (control) water level, with 11.4 ◦Brix.

**Figure 1.** Treatment effect on the total soluble sugars content of African horned cucumber fruit grown in different environments; (**a**) effect of different water stress levels and loamy soil, in different environments, during different seasons (2017/18, season one; and 2018/19, season two); (**b**) effect of different water stress levels and sandy loam, in different environments, during different seasons (2017/18, season one; and 2018/2019, season two); 35 means severe water stress, 75 means moderate water stress, and 100 means no water stress (control). LSD0.05 is the least significant difference of means.

### *3.2. Crude Proteins*

For crude protein content, the results of the study showed that there was no significant (*p* > 0.05) difference in crude protein content between interaction of growing environment, water stress levels, and soil types (Figure 2). However, the results delineated that fruit crude protein ranged from 6.22 to 6.29%. Moreover, the results of the study demonstrated two extremes: The treatment of no water stress and severe water stress combined with both soil types (loamy soil and sandy loam) at growing conditions (greenhouse and shade net) during both seasons decreased crude protein content from 6.29 to 6.22% (Figure 2a,b), whereas the treatment of severe water stress combined with loamy soil at shade-net conditions increased crude protein content from 6.22 to 6.29% (Figure 2a). In addition, results showed evinced that there was a significant (*p* ≤ 0.05) difference for crude protein content under different growing environment. Crude protein under varying growing environments

ranged from 6.24 to 6.28%. Moreover, results showed that shade-net growing environment obtained the highest crude protein, at 6.28%, while the greenhouse environment expressed the lowest content, at 6.24%.

**Figure 2.** Treatment interaction effect on crude protein content of African horned cucumber fruit; (**a**) interaction effect of different water stress levels and loamy soil, in different environments, during season one (2017/2018); (**b**) interaction effect of different water stress levels and sandy loam, in different environments, during season two (2018/19); 35 means severe water stress, 75 means moderate water stress, and 100 means no water stress (control). LSD0.05 is the least significant difference.

### *3.3. β-Carotene*

Table 2 presents the treatment effect on the *β-carotene,* vitamin C, vitamin E, total flavonoids, and total phenols of African horned cucumber fruit under different growing environments. For the greenhouse, shade, and open-field environment, the results illustrated that there was a significant (*p* ≤ 0.05) difference between the interaction of water stress levels and soil types. *β-carotene* ranged from 1.5 to 17 mg 100 g−<sup>1</sup> DW. In addition, the results demonstrated that the severe water stress combined with sandy loam slightly

decreased *β-carotene* from 1.7 to 1.5 mg 100 g−<sup>1</sup> DW, whereas the treatment of severe water stress combined with loamy soil increased it from 1.5 to 1.7 mg 100 g−<sup>1</sup> DW. For the shade-net environment, *β-carotene* ranged from 1.5 to 1.6 mg 100 g−<sup>1</sup> DW. In addition, the results showed that water stress levels (moderate and severe water stress) combined with both soils slightly decreased *β-carotene* from 1.6 to 1.5 mg 100 g−<sup>1</sup> DW, whereas moderate water stress treatment combined with loamy soil increased it from 1.5 to 1.6 mg 100 g−<sup>1</sup> DW. Under the open-field environment, *β-carotene* increased from 1.5 to 1.6 mg 100 g−<sup>1</sup> DW. The treatment of water levels (moderate and severe water stress) indicated a decrease from 1.6 to 1.5 mg 100 g−<sup>1</sup> DW, whereas no-water-stress treatment (control) combined with both soils expressed an increase from 1.5 to 1.6 mg 100 g−<sup>1</sup> DW.

**Table 2.** Treatment effect on biochemical constituents of African horned cucumber fruit harvested from different growing environments.


W1 means no water stress (control); W2 means moderate water stress; W3 means severe water stress. S1 is loamy soil, and S2 is sandy loam soil. Numbers in brackets represent the standard deviations of the mean. LSD0.05 is the least significant difference of means. The *p*-values in bold are lower than 0.05. Note that only season two results are presented, due to logistical costs, as analysis could not be done for both seasons one treatments.

### *3.4. Vitamin C*

For vitamin C, the results showed that there was a significant (*p* ≤ 0.05) difference between the interaction of different water levels and soil types under the greenhouse and open-field environment. However, there was no significant (*p* > 0.05) difference between different water levels and soil types in the shade-net environment (Table 2). Under the greenhouse environment, vitamin C content ranged from 23.2 to 30.3 mg 100 g−<sup>1</sup> DW. The results illustrated that treatment of severe water stress combined with sandy loam decreased vitamin C from 30.3 to 23.2 100 g−<sup>1</sup> DW, whereas moderate water stress treatment combined with sandy loam increased it from 23.2 to 30.3 100 g−<sup>1</sup> DW. For the shade-net environment, vitamin C content ranged from 22.6 to 33.1 100 g−<sup>1</sup> DW. Our results revealed that severe water stress treatment combined with sandy loam decreased vitamin C from 33.1 to 22.6 100 g−<sup>1</sup> DW, whereas no-water-stress (control) treatment combined with loamy soil increased it from 22.6 to 33.1 100 g−<sup>1</sup> DW.

Regarding the open-field environment, vitamin C content ranged from 15.5 to 27.5 100 g−<sup>1</sup> DW. The results of the study indicated that the treatment of severe water stress combined with sandy loam decreased vitamin C content from 27.5 to 15.5 100 g−<sup>1</sup> DW, whereas moderate water stress treatment and sandy loam increased it from 15.5 to 27 100 g−<sup>1</sup> DW. It is worth to note that the treatment of severe water stress combined sandy loam soil under the open-field environment indicated the lowest vitamin C content (15.5 100 g−<sup>1</sup> DW), whereas the no-water-stress (control) treatment combined with loamy soil under the shade-net environment obtained the highest vitamin C content (33.1 100 g−<sup>1</sup> DW).

### *3.5. Vitamin E*

The results of the study revealed that there was no significant (*p* > 0.05) difference for vitamin E content from the interaction between different water levels and soil types under all growing environments (greenhouse, shade net, and open field). For the greenhouse environment, vitamin E content ranged from 9.3 to 35.1 100 g−<sup>1</sup> DW. In addition, the results demonstrated that no-water-stress (control) treatment combined with sandy loam decreased vitamin E content from 35.1 to 9.3 100 g−<sup>1</sup> DW, whereas the treatment of severe water stress and sandy loam increased it from 9.3 to 35.1 100 g−<sup>1</sup> DW (Table 2). Under the shade-net environment, the no-water-stress treatment (control) combined with sandy loam decreased vitamin E content from 18.1 to 10.0 100 g−<sup>1</sup> DW, whereas no water stress (control) and loamy soil increased it from 10.0 to 18.1 100 g−<sup>1</sup> DW. On the other hand, open-field vitamin E content ranged from 8.3 to 13.5 100 g−<sup>1</sup> DW. Results delineated that treatment of severe water stress combined with loamy soil decreased vitamin E content from 13.5 to 8.3 100 g−<sup>1</sup> DW, whereas the severe water stress and sandy loam increased it from 8.3 to 13.5 100 g−<sup>1</sup> DW (Table 2).

### *3.6. Total Flavonoids*

Table 2 illustrates that there was a significant (*p* ≤ 0.05) difference in total flavonoids, depending on the interaction of different water levels and soil types under varying growing environment (greenhouse, shade net, and open field). For the greenhouse environment, total flavonoids ranged from ranged from 0.21 to 0.66 CE g−<sup>1</sup> DW. In addition, the results illustrated that the treatment of severe water stress combined with sandy loam reduced total flavonoids from 0.66 to 0.21 CE g−<sup>1</sup> DW, whereas treatment of no water stress (control) combined with loam soil increased it from 0.21 to 0.66 CE g−<sup>1</sup> DW. Under the shade-net environment, our results showed that total flavonoids ranged from 0.49 to 0.84 CE g−<sup>1</sup> DW. The results indicated that severe water stress treatment combined with sandy loam reduced total flavonoids from 0.84 to 0.49 CE g−<sup>1</sup> DW, whereas no-water-stress (control) treatment increased it from 0.49 to 0.84 CE g−<sup>1</sup> DW. For total flavonoids content in the open-field environment, the results showed that it ranged from 0.41 to 0.85 CE g−<sup>1</sup> DW. In addition, the results illustrate that water stress and sandy loam decreased total flavonoids from 0.85 to 0.41 CE g−<sup>1</sup> DW, whereas severe water stress and loamy soil increased it from 0.41 to 0.85 CE g−<sup>1</sup> DW (Table 2). The observed trend shows that the combination of severe water stress and loamy soil under the open-field environment obtained the highest total flavonoids content, at 0.85 CE g−<sup>1</sup> DW, whereas the lowest content was observed on treatment combined.

### *3.7. Total Phenols*

The results indicate that there was a significant (*p* ≤ 0.05) difference on the total phenolic content of African horned cucumber between interaction of different water levels and soil types under varying growing environment (greenhouse, shade net, and open field). The greenhouse environment total phenols ranged from 3.1 to 5.8 GAE g−<sup>1</sup> DW. Our results illustrated that the treatment of no water stress (control) combined with loamy soil decreased total phenols content from 5.8 to 3.1 GAE g−<sup>1</sup> DW, whereas the severe water stress treatment combined with sandy loam increased it from 3.1 to 5.8 GAE g−<sup>1</sup> DW.

For the shade-net environment, total phenols content ranged from 3.5 to 5.3 GAE g−<sup>1</sup> DW. The study results showed that the combination of severe water stress treatment and sandy loam reduced total phenols content from 5.3 to 3.5 GAE g−<sup>1</sup> DW. Under the openfield environment, total phenols content ranged from 3.1 to 6.4. In addition, the results of the study indicated that severe water stress treatment combined with sandy loam decreased total phenols content from 6.4 to 31 GAE g−<sup>1</sup> DW, whereas treatment combination of no water stress (control) and loamy soil increased it from 3.1 to 6.4 GAE g−<sup>1</sup> DW. For the openfield environment, our results showed that total phenols ranged from 3.1 to 6.1 GAE g−<sup>1</sup> DW. In addition, the results showed that severe water stress treatment combined with sandy loam decreased total phenols from 6.1 to 3.1 GAE g−<sup>1</sup> DW, whereas no-water-stress level (control) combined with loamy soil increased it from 3.1 to 6.4 GAE g−<sup>1</sup> DW.

### *3.8. Micro-Nutrients*

Table 3 presents the micronutrient concentration of African horned cucumber. Significant (*p* ≤ 0.05) interactions were observed for manganese and zinc, under the open environment, whereas for the shade, significant interactions were observed for iron and zinc. For the open-field environment, significant interactions were found under zinc only. The greenhouse zinc content ranged from 7.7 to 12.7 μg g DW. In addition, results illustrated that treatment of no water stress (control) combined with loam soil presented a decreased zinc content from 12.7 to 7.7 μg g DW, whereas there was a double increase in zinc content from treatment combination of no water stress (control) and sandy loam, from 7.7 to 12.7 μg g DW (Table 3). For the shade-net environment, zinc content ranged from 6.4 to 8.8 μg/g DW. The results demonstrated that treatment of severe water stress combined with sandy loam decreased zinc content from 8.8 to 6.4 μg g DW, whereas no-water-stress (control) treatment combined with sandy loam increased it from 6.4 to 8.8 μg g DW. Under an open-field environment, zinc content ranged 5.1 to 7.9 μg g DW. The lowest zinc content was observed from combination of no water stress (control) and loamy soil at 5.1 μg g DW, while treatment of moderate water stress and loamy soil presented an increase at 7.9 μg g DW. Under the shade-net environment, iron ranged from 1.4 to 1.8 μg g DW. The lowest iron content was observed from treatment of no water stress and sandy loam at 1.4 μg g DW, whereas treatment combination of moderate water stress and sandy loam illustrated higher content, at 1.8 μg g DW.

**Table 3.** Treatment interaction effect of irrigation water regimes, soil types, and environment on micro-nutrients (μg g DW) of African horned cucumber fruit.



**Table 3.** *Cont.*

W1 means no water stress (control); W2 means moderate stress; W3 means severe water stress. S1 means loamy soil, and S2 means sandy loam. Values are average over treatments mentioned. Numbers in brackets represent the standard deviations of the mean. LSD0.05 is the least significant difference of means. The *p*-values in bold are lower than 0.05. Note that only season two results are presented, due to logistical costs, as analysis could not be done for season-one treatments.

### **4. Discussion**

This study investigated the effect of different water stress levels and varying substrates on the nutrient concentration of African horned cucumber fruit grown in three different environments (greenhouse, shade net, and open field). Previous studies conducted by [11,12] have evaluated the nutrient concentration of leafy vegetables grown under different water stress levels. In addition, studies conducted by [1,2] focused on iron and zinc. However, these studies did not evaluate biochemical constituents, such as crude protein, total soluble sugars, total flavonoids, total phenols, and vitamins. Therefore, the findings of this research study serve as a benchmark for the biochemical constituents of African horned cucumber fruit.

### *4.1. Bio-Chemical Constituents*

Ref. [4] determined the mineral constituents and phytochemicals of crops harvested from different locations. Ref. [13] recommended that it is crucial to note the effect of water, irrigation and rainfall received by crops on the mineral constituents, such as *βcarotene*, total phenols, vitamins, total flavonoids, and micro- and micro-nutrients, so that growers can make an informed decision, to ensure that quality produce is supplied to their target market.

### *4.2. Total Soluble Sugars*

Fruit sugar content is affected by a number of factors, including climate, water supply, and soil type. Total soluble sugars in fruits have a variety of health benefits, including provision of glucose, preventing colorectal cancer, and variety of diseases [14]. Fruit intake

is currently recommended by most dietary practitioners for improvement of health and disease prevention. The findings of this study demonstrated that the treatment affects the total soluble sugars of African horned cucumber fruit grown under varying environment. When plants were subjected to severe water stress under shade-net conditions, total soluble sugars increased, but they decreased under no-water-stress treatment. This implies that, when plants are exposed to different water levels, there is variation in fruit nutrient content.

Refs. [10,15] reported a significant difference in total soluble sugars of kiwi fruit harvested from different sites, due to variation in temperatures and rainfall patterns.

High total soluble sugar content was expected from open-field fruit under moderate water stress, as reported by [14], on pomegranate trees. These authors concluded that active osmoregulation caused by water stress was responsible for sugar variation in fruits, since there is imbalanced fluid movement within plant cells. Similarly, this study's findings unveiled that fruits harvested from water stress treatment had a higher total soluble sugar content, when compared to the other treatments. Therefore, a relatively high total soluble sugar level in fruit is crucial for human nutrition, especially when the ◦Brix level is above 5. However, the values obtained from this study are slightly higher, making it an important fruit for the fresh and juice market. This suggests that the fruit is valuable and should be considered for commercialization, as the fruit shows potential benefits for human nutrition.

### *4.3. Crude Proteins*

Crude proteins are important in human nutrition because they aid in cell formation, nutrient storage, pH balance, and immune system improvement, and they serve as a messenger [9]. Previous studies have often reached conflicting findings regarding crude protein content of crops harvested from different treatments and growing conditions. For example, [16] presented their findings on crude protein of potatoes harvested under different regions that experience varying weather conditions and treated with varying level of fertilizers. They concluded that potatoes harvested from regions with moderate temperatures subjected to moderate nitrogen fertilizers resulted in higher significant crude protein content, when compared to other treatments, due to high enzyme activities within cells, caused by different nitrogen content. For this study, shade-net conditions expressed high crude protein content, compared to the other growing environment. Perhaps the growing environment of the shade net favored higher crude proteins in moderate and no-water-stress treatments, as compared to the water stressed treatment.

When the surrounding conditions (adequate sunlight and water) are favorable, cells can carry out chemical reaction at an optimum rate, but at a lower rate under stress environment, such as excessive radiation and water stress. These results agree with the fact that excessive temperatures negatively affect protein activities (denature) and have other general destructive effects on plant cells, as reported by [17], who found higher crude protein content in fruits harvested from protected structures, but low in those harvested from open-field conditions. This advocates that African horned cucumber, if grown under optimum environmental conditions may have several health benefits in human nutrition and may also be a potential solution for a hunger and health issues globally.

### *4.4. β-Carotene*

*β-carotene*, famously known as a major source for Vitamin A, has been reported by [18] as an important compound for human health. It is (i) responsible for the formation and maintenance of teeth, (ii) formation of muscle tissues, and (iii) improvement of eyesight. The grand mean showed that *β-carotene* content was higher in severe water stress treatment under greenhouse environment, but significantly decreased by the same water stress treatment under open-field environment. In addition, loamy soil seemed to increase *β-carotene*, whereas sandy loam reduced it.

The fact that carotene is responsible for radiation interception in plants could have been the cause for variation, since there is control of light intensity in the greenhouse, due to cladding material used for protection, as compared to an open-space area. [19] found

that there was variation in *β-carotene* among some plant varieties subjected to reduced water supply. Their findings are in harmony with those of the current study, whereby varying water levels under different growing conditions significantly altered the *β-carotene* content of African horned cucumber fruit. *β-carotene* promotes cell and tissue development, strengthens the immune system, and slows the aging process. Furthermore, it effectively enhances eye vision, skin, nail, and hair function. African horned cucumber fruit contains reasonable amount of *β-carotene*, which can be converted to vitamin A in the body, to complement it. Therefore, optimum growing environment could serve as strong evidence for mass production and commercialization globally.

### *4.5. Vitamin C*

In the present study, the grand mean showed that vitamin C was higher on fruit grown under greenhouse environment (25.3 mg 100 g−1 DW), as compared to the other growing environments. In addition, vitamin C increased in plants subjected to no water stress (control) under the shade-net environment, but it decreased under severe water stress under the open-field environment. Higher fluctuation in the vitamin C content could be the result of unbalanced turgor pressure in plants, caused by varying irrigation water levels, water holding capacity by a specific substrate, and different growing environments, as reported by [20,21], who mentioned that water and fertilizers stimulate the vitamin C content of cucumber and citrus fruit grown in an open field and semi-protected structure. This was authenticated by [22], when they reported that plants respond to harsh environmental conditions, such as excessive sunlight, heat, and water stress, by producing vitamin C as a defensive mechanism to protect themselves [23].

The mean results also showed that the vitamin C reduction was more on plants subjected to adverse conditions, such as water stress level and open space, as compared to plants that were grown under a protected environment (greenhouse and shade net). The current study findings agree with findings by [24], who reported that plants can tolerate moderate water stress. However, such alteration has a negative impact on the fruit vitamin C content of various fruit crops. Even though vitamin C deficiency is uncommon in today's world, dieticians prescribe vitamin C because it plays a critical role in the production of collagen, iron absorption, wound healing, and bone and tooth health. Determination of optimal conditions that increase African horned cucumber vitamin C content could fill the void in human nutrition, globally, and increase its consumption.

### *4.6. Vitamin E*

For vitamin E, the means illustrated that vitamin E content was greater in the greenhouse environment (23.7 mg 100 g−<sup>1</sup> DW), as compared to other growing environments. In addition, the current study findings exhibited that the treatment imposed (water levels and soils types) did not caused significant variation in vitamin E content. However, there was a slight increase on severe water stress treatment under greenhouse conditions, but there was a significant decrease under severe water stress in the open-field environment. Perhaps the evapotranspiration rate, which regulates the osmoregulation, played an important role in the vitamin E variation, since there was a change in stomatal opening and closure, due to alteration in turgor pressure within the guard cells. Carbon dioxide interception is higher when there is balance of solutes movement within the open guard cells, but they close when there is imbalance concentration due to high evapotranspiration rate caused by excessive conditions, such as high wind and radiation, subsequently limiting the ability to synthesis vitamin E due to limited activities in the chloroplast caused by stomatal closure. Closing of stoma not only prevents water loss, but also prevents the plant's ability to synthesize vitamins and other biochemical compounds. The study findings affirm that water stress levels under varying growing environment were the critical contributors of vitamin E content of African horned cucumber fruit, as compared to other factors. These findings agree with [18,21,25], who found significant differences in vitamin E content of fruit such as chilies and peppers subjected to varying water stress, due to the balanced

osmotic flow within plant organs. Vitamin E has a variety of functions in the human body, including preventing free radical damage and acting as an antioxidant. In addition, the vitamin deficiency is associated with stunted development. The values in this study serve as benchmarks required by policymakers for commercialization of this crop, since it has nutritional benefit to humans.

### *4.7. Total Flavonoids*

The shade-net grand mean showed higher total flavonoids (0.67 CE g−<sup>1</sup> DW), relative to greenhouse at (0.4 CE g−<sup>1</sup> DW). The study findings also remarked that the reduction was more on the severe water stress treatment, relative to moderate and no-water-stress treatment (control). The alteration in total flavonoids could have been caused by turgor pressure within the plant's cells, which subsequently allows the plant to access surrounding atmospheric elements through the epidermal cells, thus allowing the plant to absorb atmospheric elements needed by plants for cellular activities. When the stomata close, plant cellular activities get negatively affected, but they function normally when there is good movement of water within plant organs. However, contradictory findings were noticed by [13,26] on opuntia and red grapes. They determined that total flavonoids significantly increased in fruit harvested from regions with a low rainfall pattern, but decreased in fruit harvested from regions experiencing higher rainfall patterns, due to varying active osmoregulation within plant organs, since plants were trying to cope with stress caused by the environmental conditions. Their findings are consistent with observations made in this current study, whereby severe-water-stress fruit demonstrated a significant increase in total flavonoids when compared to stressed-free fruit. Total flavonoids are well-known in human health for their function in controlling cellular activity, as well as fighting free radicals that cause oxidative stress. The total flavonoids values of African horned cucumber fruit serve as benchmark information required by policymakers; therefore, the crop can be recommended for commercialization, if grown under optimal conditions.

### *4.8. Total Phenols*

In terms of total phenolic content, the study findings outline that open space grand mean exhibited higher total phenols (4.8 GAE g−<sup>1</sup> DW), relative to the greenhouse (4.5 GAE g−<sup>1</sup> DW) and shade-net environment (4.2 GAE g−<sup>1</sup> DW). The study findings showed increased total phenolic content under normal watering on loamy soil from the open-field environment but decreased when subjected to water stress under a similar growing environment. Perhaps variation in water stress and soil types under different growing conditions could have been the major cause in variation of total phenolic content of African horned cucumber fruit since xylem and phloem functions effectively under active-osmoregulation, but solutes uptake decrease when the is lower water movement within the cells cause by higher temperature. For example, [27] found significant differences in several edible fruits such as blackberry and cherry harvested from different locations experiencing varying rainfall patterns. [10] also found a significant difference in total phenols of walnuts' green husks harvested during different periods. They found that fruits harvested earlier have a higher total phenolic content than those which were harvested late, after ripening, due to different metabolites released by plants at different stages of growth.

The current study affirmed that different water stress levels are major triggers of metabolites responsible for this compound, since the plant has to adapt to variation in water levels, as reported by [28], who found a significant total phenolic content in strawberries exposed to different environmental conditions such as water stress and growing conditions. Total phenols are known in human health for their antioxidant properties, which stop free radicals from reacting with other molecules in the body and prevent DNA damage, which is usually caused by a variety of health effects. Therefore, values in this study serve as a concreate evidence needed by policymakers in order to consider this crop for commercialization.

### *4.9. Micro-Nutrients*

Micronutrients deficiency, including of iron, copper, and zinc, may lead to decreased intellectual ability, development, bone mineralization, and immune response, whereas deficiency in zinc may lead to poor digestion, metabolism, reproduction, and wound healing. According to WHO, the recommended daily nutrients intake of zinc for children between four and six years should range from a minimum of 9.6 μg g DW and above. The study findings showed that zinc grand mean of greenhouse grown fruit was higher, at 9.8 μg g DW, relative to shade net at 8.2 μg g DW and open field (7.0 μg g DW). This micronutrient is vital for metabolism and reproduction. Its deficiency may lead to poor digestion and bone diseases. The zinc content of the African horned cucumber fruit serves as a benchmark for commercialization of this fruit, since it has the potential to meet human nutritional needs. In addition, the study findings have shown that moderate water stress and sandy loam increase zinc content under greenhouse environment, and this has added to the information needed by potential growers, since they will be able to create suitable growing environment in order to increase vital micronutrients content for the African horned cucumber fruit.

Iron is another micronutrient that is vital for blood health, bone development, and immune system. Shortage of iron may reduce intellectual capacity, slow growth and poor bone development. According to WHO, recommended daily nutrient intake (RNI) of iron by children between the age of one and three should be 5.8. A range of (0.5 to 3.5 μg g DW) was observed on the African horned cucumber fruit, which is slightly lesser that the recommended daily intake (RNI) by WHO [29]. However, the study findings showed that the fruit has a high potential of meeting the recommended nutrient intake (RNI) if grown from treatment of moderate water stress level combined with sandy loam soil under greenhouse environment. The study's findings serve as a benchmark on the potential nutritional benefit of African horned cucumber fruit. Other researchers, such as [21], reported that growing environment and temperature as growth factors are able to cause variation in nutrient content of crops.

They have shown that a higher evapotranspiration rate, caused by extreme temperatures, could cause a significant variation in fruit nutrient content, as osmotic balance is directly affected, subsequently causing an abnormal flow rate of water and other soluble nutrients within xylem and phloem. Similar findings were observed in the current study, whereby interaction between irrigation water levels and soil types under different growing environments affected the micro-nutrient content (Zn and Fe) African horned cucumber fruit. Several authors remarked that water levels and soil types affect micro-nutrient content of fruits. For example, [30] found that variation in nutrient content may occur when plants are subjected to water stress. They have also demonstrated that, when the plant is subjected to water stress, stomata close, but they open under normal watering.

Their findings unveiled that when the stomatal opening reduces, there is limited carbon dioxide entry in leaves, subsequently affecting the plant's ability to synthesize its own nutrients. [31] report that less frequencies in irrigation significantly increased nutrient content in tomatoes, when compared to treatment that received more irrigation frequencies [31]. They have shown that there is a direct relationship between stomatal conductance and active osmoregulation under less frequencies, but complications occur when there is over-/under-supply of water in plants, as it negatively affects the xylem functions. It worth noting that Cu and Mn were not significantly affected by treatment imposed. [23] observe that a nutrient element such as Mn depends on environmental factors such as adequate water supply, temperature, and plant genotype. However, in this study, irrigation water levels and soil type under different growing environments did not significantly cause variation in some of African horned cucumber fruit, but they significantly affected the Zn and Fe content.

### **5. Conclusions and Future Research**

Quantification of quality parameters such total soluble sugars and macro- and micronutrients contribute to the factors required by policymakers before commercializing a specific crop. Therefore, the outcome of this study has shown that African horned cucumber fruit contain vital biochemical constituents required by humans in both larger and smaller quantities. In addition, this research has provided evidence that the African horned cucumber fruit quality content is significantly affected by treatments. This is useful information to farmers, as quality has become more significant to most consumers worldwide. When grown in the open field, total soluble sugars increased; this is important for the juice-manufacturing industry and for fresh markets, where many fruits are required to meet the demand. Quality parameters such as total flavonoids, total phenols, micro-nutrients and vitamins metabolites seem to be treatment-imposed. This is an important finding, as these factors influence the flavor of fruits. Where the market is geared towards organoleptic quality—in expensive markets, for example—it may be best to grow this crop under a specific growing environment, depending on your target market. The other advantage is that the crop can grow well under protected structures, which eliminate potential damage caused by higher rainfall, hail, and extreme heat in summer.

**Author Contributions:** M.K.M. was responsible in study designing, drafting, data collection, analysis and interpretation. S.J.M., M.N. and D.M.M. were involved in interpretation of data and critical revision of the draft. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partially funded by the University of South Africa.

**Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data generated for this study is available from the corresponding author on formal request.

**Conflicts of Interest:** Authors state that there have no conflict of interest.

### **References**


## *Article* **Growth and Competitive Infection Behaviors of** *Bradyrhizobium japonicum* **and** *Bradyrhizobium elkanii* **at Different Temperatures**

**Md Hafizur Rahman Hafiz 1,2, Ahsanul Salehin <sup>3</sup> and Kazuhito Itoh 1,3,\***


**Abstract:** Growth and competitive infection behaviors of two sets of *Bradyrhizobium* spp. strains were examined at different temperatures to explain strain-specific soybean nodulation under local climate conditions. Each set consisted of three strains—*B. japonicum* Hh 16-9 (Bj11-1), *B. japonicum* Hh 16-25 (Bj11-2), and *B. elkanii* Hk 16-7 (BeL7); and *B. japonicum* Kh 16-43 (Bj10J-2), *B. japonicum* Kh 16-64 (Bj10J-4), and *B. elkanii* Kh 16-7 (BeL7)—which were isolated from the soybean nodules cultivated in Fukagawa and Miyazaki soils, respectively. The growth of each strain was evaluated in Yeast Mannitol (YM) liquid medium at 15, 20, 25, 30, and 35 ◦C with shaking at 125 rpm for one week while measuring their OD660 daily. In the competitive infection experiment, each set of the strains was inoculated in sterilized vermiculite followed by sowing surface-sterilized soybean seeds, and they were cultivated at 20/18 ◦C and 30/28 ◦C in a 16/8 h (day/night) cycle in a phytotron for three weeks, then nodule compositions were determined based on the partial 16S-23R rRNA internal transcribes spacer (ITS) gene sequence of DNA extracted from the nodules. The optimum growth temperatures were at 15–20 ◦C for all *B. japonicum* strains, while they were at 25–35 ◦C for all *B. elkanii* strains. In the competitive experiment with the Fukagawa strains, Bj11-1 and BeL7 dominated in the nodules at the low and high temperatures, respectively. In the Miyazaki strains, BjS10J-2 and BeL7 dominated at the low and high temperatures, respectively. It can be assumed that temperature of soil affects rhizobia growth in rhizospheres and could be a reason for the different competitive properties of *B. japonicum* and *B. elkanii* strains at different temperatures. In addition, competitive infection was suggested between the *B. japonicum* strains.

**Keywords:** *Bradyrhizobium japonicum*; *Bradyrhizobium elkanii*; temperature effects; growth; competitive infection; nodule composition

### **1. Introduction**

Soybean-nodulating bacteria have distributed worldwide [1,2] and established important symbiotic relationships with host plants to fix atmospheric nitrogen [3]. *Bradyrhizobium japonicum* and *B. elkanii* are reported as the major soybean nodulating rhizobia [4,5] and their nodulation behaviors in the field need to be clarified in relation to environmental conditions because their nodulation and nitrogen fixation are known to be highly dependent on environmental conditions [6]. In previous studies, latitudinal-characteristic nodulation of *B. japonicum* and *B. elkanii* has been reported in Japan [7,8], the United States [9], and Nepal [10], in which *B. japonicum* and *B. elkanii* dominate in soybean nodules in northern and southern regions, respectively. These results suggest that the temperature of the soybean-growing location contributes to the nodule composition of *B. japonicum* and *B. elkanii*.

Itoh, K. Growth and Competitive Infection Behaviors of *Bradyrhizobium japonicum* and *Bradyrhizobium elkanii* at Different Temperatures. *Horticulturae* **2021**, *7*, 41. https:// doi.org/10.3390/horticulturae7030041

**Citation:** Hafiz, M.H.R.; Salehin, A.;


Academic Editor: Stefano Marino

Received: 12 February 2021 Accepted: 25 February 2021 Published: 28 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

To elucidate the possible reason, laboratory competitive inoculation experiments have been conducted at different temperatures. Kluson et al. [11] reported that *B. japonicum* strains dominated in nodules at lower temperatures, while *B. elkanii* strains dominated at higher temperature. Suzuki et al. [12] examined the relative population of *B. japonicum* and *B. elkanii* strains in the rhizospheres of soybeans and their nodule compositions at different temperatures and revealed that the *B. japonicum* strain dominated in nodules at lower temperature even though the relative populations of both strains were similar in the rhizosphere, while at higher temperature, the *B. elkanii* strains dominated in nodules due to their larger relative population in the rhizosphere. Shiro et al. [13] reported that the nodule occupancy of *B. elkanii* increased at higher temperatures, whereas that of *B. japonicum* increased at lower temperatures, corresponding to their temperature-dependent *nodC* gene expressions. These results suggest that the temperature-dependent infections and proliferations in soils are possible reasons for the temperature-dependent nodule compositions of rhizobia in the field. However, it has been uncertain which factor, namely, temperature-dependent infection or proliferation in soil, contributes to the temperaturedependent distribution of rhizobia in nodules.

For elucidating which factor is more involved in the soybean nodule composition under local climatic conditions, Hafiz et al. [7] examined the changes in the nodule composition when soil samples were used for soybean cultivation under the different climatic conditions from the original locations, and found that the *B. japonicum* strains nodulated dominantly in the Fukagawa location (temperate continental climate) and the dominancy of *B. japonicum* did not change when soybean was cultivated in the Matsue and Miyazaki locations (humid sub-tropical climate) using the Fukagawa soil. The results suggest that the *B. japonicum* strains proliferated dominantly in the Fukagawa soil leading to their nodule dominancy because *B. elkanii* did not appear in the Matsue and Miyazaki locations. On the other hand, the *B. elkanii* strains dominated in the Miyazaki soil and location while the *B. japonicum* strains dominated when soybean was cultivated in the Fukagawa location using the Miyazaki soil, suggesting that temperature-dependent infection would lead to nodule dominancy of the *B. elkanii* and *B. japonicum* strains in the Miyazaki and Fukagawa locations, respectively.

In addition, in the Fukagawa soil and location, phylogenetic sub-group *B. japonicum* Bj11-1, which was characterized as a slow grower, dominated the nodules compared to another sub-group *B. japonicum* Bj11-2, which was characterized as a fast grower [7], suggesting that infection preference might determine the nodule composition among the *B. japonicum* strains rather than their growth properties. In the Miyazaki soil and location, it was suggested that both *B. japonicum* and *B. elkanii* strains proliferated, and that the species-specific nodule compositions under the different local climatic conditions might be due to the temperature-dependent growth and infection properties of the *Bradyrhizobium* strains [7].

These hypotheses presented in the previous study [7] should be confirmed by in vitro growth and inoculation experiments under the controlled temperatures using the *B. japonicum* and *B. elkanii* strains isolated from the corresponding soils and locations. In this study, we compared growth and infection behaviors at different temperatures of the *B. japonicum* and *B. elkanii* strains isolated from the soybean nodules cultivated in the Fukagawa and Miyazaki soils, and elucidated the reason why the species-specific nodule compositions are present in the Fukagawa and Miyazaki soils and locations.

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

### *2.1. Effect of Temperature on Growth of Bradyrhizobium spp. in Liquid Culture*

The strains used are listed in Table 1. They were isolated from nodules of soybean cultivated in the Fukagawa and Miyazaki soils and study locations in 2016, and selected based on their phylogenetic characteristics based on the 16S rRNA and 16S-23S rRNA internal transcribes spacer (ITS) gene sequences [7].


**Table 1.** *Bradyrhizobium* strains used in this study.

<sup>a</sup> The strains were isolated from nodules of soybean cultivated using Fukagawa (H) and Miyazaki (K) soils at Fukagawa (h) and Miyazaki (k) study locations in 2016. The isolates were designated by soil, location, year, and strain number. <sup>b</sup> Group based on gene sequence of 16S-23S rRNA internal transcribes spacer (ITS) region. <sup>c</sup> Gene accession number of 16S rRNA and ITS sequences.

Considering the temperature ranges during the soybean cultivation period in the study locations (Table 2), the temperatures were set at 15 ◦C (around average daily minimum temperature in the Fukagawa location), 20 ◦C (around average daily temperature in the Fukagawa location), 25 ◦C (around average daily maximum and minimum temperatures in the Fukagawa and Miyazaki locations, respectively), 30 ◦C (around average daily temperature in the Miyazaki location), and 35 ◦C (around average daily maximum temperature in the Miyazaki location).

**Table 2.** Geographical and climatic characteristics of the study locations in Japan [7].


<sup>a</sup> Average daily minimum and maximum temperatures and total rainfall during the cultivation period in 2016/2017. <sup>b</sup> Figures in parenthesis indicate those during one month after sowing. (https://www.jma.go.jp, accessed on 28 February 2021).

Each strain was pre-incubated on Yeast Mannitol (YM) [14] agar medium at 26 ◦C for 5–10 days, and a part of the colony was taken into 3 mL of YM liquid medium to adjust OD660 at 0.03, then incubated with shaking at 125 rpm for seven days while measuring their OD660 at 24-h intervals. All the experiments were done in triplicate.

### *2.2. Effect of Temperature on Competitive Infection of Bradyrhizobium spp. in Soybean*

For the competition experiment, each set consisting of three strains from each soil was used as follows: *B. japonicum* Hh 16-9 (Bj11-1), *B. japonicum* Hh 16-25 (Bj11-2), and *B. elkanii* Hk 16-7 (BeL7) from the Fukagawa soil; *B. japonicum* Kh 16-43 (Bj10J-2), *B. japonicum* Kh 16-64 (Bj10J-4), and *B. elkanii* Kh 16-7 (BeL7) from the Miyazaki soil.

The strains were cultured in YM liquid medium with shaking at 25 ◦C for seven days, then each cell density was adjusted to 10<sup>9</sup> colony forming unit (CFU)/mL with sterilized distilled water based on OD-CFU/mL correlated linear equations prepared for each strain. Each one milliliter aliquot of the culture was added onto sterilized vermiculite in a 400 mL Leonard jar [15], which was supplemented with sterilized N-free nutrient solution [16]. Three jars were prepared for each treatment. After mixing the inoculated vermiculite thoroughly, three soybean seeds, cv. Orihime (non-Rj) were sown in each Leonard jar and cultivated in a phytotron (LH-220S, NK system, Osaka, Japan) at 20/18 ◦C and 30/28 ◦C in 16/8 h (day/night) cycle with an occasional supply of the N-free nutrient solution. The soybean seeds were surface-sterilized prior to sowing with 70% ethanol for 30 s and then with 2.5% NaOCl solution for 3 min [17]. Seedlings were thinned to one plant per jar one week after germination. At three weeks after sowing, the length and weight of the shoot and root were measured, and the number of nodules was counted. Then, nodule composition of the inoculated strains was examined using ten randomly-selected nodules

per plant. Control plants without inoculation were prepared to check contamination, and the experiment was conducted in triplicate. Each nodule was surface sterilized with 70% ethanol for 30 s followed by washing six times with sterilized distilled water, then each nodule was crushed with 200 μL of sterilized MilliQ water for extraction of DNA [18]. The inoculated strain in each nodule was specified by PCR and nucleotide sequence of the 16S-23S rRNA internal transcribed spacer (ITS) region, according to the procedures described previously [7].

### *2.3. Statistical Analysis*

Statistical analysis of the soybean growth and nodule compositions of *Bradyrhizobium* spp. were performed using the MSTAT-C 6.1.4 software package [19]. The data were subjected to Duncan's multiple range test after one-way ANOVA.

### **3. Results**

### *3.1. Effect of Temperature on Growth of Bradyrhizobium spp. Strains in Liquid Culture*

The effects of temperature on the proliferation of the *Bradyrhizobium* spp. strains are presented in Figure 1. The responses to different temperatures varied among the strains. At 15–20 ◦C, the growth rates of *B. japonicum* Bj11-1 and Bj11-2 were similar and higher than those of *B. elkanii* BeL7 in the Fukagawa strains, and similar growth patterns were observed in *B. japonicum* BjS10J-2 and BjS10J-4, and *B. elkanii* BeL7 in the Miyazaki strains. At 25–35 ◦C, *B. elkanii* BeL7 proliferated better than the *B. japonicum* strains in the Fukagawa strains, and *B. japonicum* Bj11-1 did not proliferate at 35 ◦C. Similarly, in the Miyazaki strains, the growth rate of *B. elkanii* BeL7 increased at high temperatures, while those of the *B. japonicum* strains decreased at 30–35 ◦C, and *B. japonicum* BJS10J-2 did not proliferate at 35 ◦C.

For each strain, OD660 at 5 days of incubation is shown in Figure 2 and normalized as a relative % of OD660 to the maximum value in the range of temperatures examined. In the Fukagawa strains, the relative % of Bj11-1 and Bj11-2 were 93–100% at 15–20 ◦C, while those of BeL7 were 11–13%. The relative % of all strains were more than 80% at 25 ◦C. At higher temperatures, those of Bj11-1 and Bj11-2 decreased significantly at above 25 and 30 ◦C, respectively, while those of Bel7 were similar at 25–35 ◦C. In the Miyawaki strains, BjS10J-2 showed a larger relative % than BjS10j-4 at lower temperatures, and those of BeL7 were less than 20%. At higher temperature, those of BjS10J-2, BjS10j-4, and Bel7 decreased significantly at above 20, 25, and 30 ◦C, respectively.

### *3.2. Effect of Temperature on Growth and Nodule Number of Soybean Inoculated with a Set of Bradyrhizobium spp. Strains*

Effect of temperature on the growth and nodule number of soybean is presented in Figure 3. The shoot and root lengths, and the shoot and root weights were significantly higher at 30/28 ◦C than 20/18 ◦C in all treatments except for the root lengths of the soybeans inoculated with Miyazaki strains. While the nodule numbers were not significantly different between the different temperature conditions. The inoculation of the *Bradyrhizobium* spp. strains significantly affected the shoot length and the root weight of soybean at 30/28 ◦C while the effects were not observed at 20/18 ◦C. Significant difference in these effects was not present between the Fukagawa and Miyazaki strains. No nodule was recorded in the control plants, indicating that there was no contamination in the experimental procedure.

**Figure 1.** Effects of temperature on growth of *Bradyrhizobium* spp. strains in liquid culture. Fukagawa strains: *B. japonicum* Hh 16-9 (Bj11-1) (•), *B. japonicum* Hh 16-25 (Bj11-2) (-), and B. *elkanii* Hk 16-7 (BeL7) (-); Miyazaki strains: *B. japonicum* Kh 16-43 (Bj10J-2) (), *B. japonicum* Kh 16-64 (Bj10J-4) (), and *B. elkanii* Kh 16-7 (BeL7) (). The bars represent the standard deviation (*n* = 3).

**Figure 2.** Effects of temperature on growth of *Bradyrhizobium* spp. strains in liquid culture. Upper: OD660 at 5 days, lower: relative percentage of OD660 to maximum for each strain. Fukagawa strains: *B. japonicum* Hh 16-9 (Bj11-1) (), *B. japonicum* Hh 16-25 (Bj11-2) (), and B. *elkanii* Hk 16-7 (BeL7) ( ); Miyazaki strains: *B. japonicum* Kh 16-43 (Bj10J-2) (), *B. japonicum* Kh 16-64 (Bj10J-4) (), and *B. elkanii* Kh 16-7 (BeL7) ( ).

### *3.3. Effect of Temperature on Soybean Nodule Composition of Inoculated Bradyrhizobium spp. Stains*

The relative nodule composition of the inoculated *Bradyrhizobium* spp. strains is presented in Figure 4. Under the competitive conditions for the Fukagawa strains, only Bj11-1 formed the nodules at 20/18 ◦C, while only BeL7 did at 30/28 ◦C. For the Miyazaki strains, BjS10J-2 was dominant in the nodules at 20/18 ◦C with the minor presence of BjS10J-4. At high temperature (30/28 ◦C) BeL7 was dominant and BjS10J-4 was minor in the nodules. Mixed colonization of nodules with two or three strains in the same nodule would be possible, but minor signals were not visibly observed in the nucleotide chromatogram.

**Figure 3.** Effects of temperature on growth and nodule number of soybean inoculated with a mixture of the *Bradyrhizobium* spp. strains. Soybean was cultivated in a phytotron at 20/18 ◦C (day/night) and 30/28 ◦C at 16/8 h cycle. Fukagawa strains: *B. japonicum* Hh 16-9 (Bj11-1), *B. japonicum* Hh 16-25 (Bj11-2), and *B*. *elkanii* Hk 16-7 (BeL7); Miyazaki strains: *B. japonicum* Kh 16-43 (Bj10J-2), *B. japonicum* Kh 16-64 (Bj10J-4), and *B. elkanii* Kh 16-7 (BeL7); control: no inoculation. The bars represent the standard deviation (*n* = 3) and different letters indicate significant differences at *p* < 0.05 by Duncan's test.

**Figure 4.** Effects of temperature on relative abundancy of inoculated *Bradyrhizobium* spp. strains in soybean. Soybean was cultivated in a phytotron at 20/18 ◦C (day/night) and 30/28 ◦C at 16/8 h cycle. Fukagawa strains: *B. japonicum* Hh 16-9 (Bj11-1), *B. japonicum* Hh 16-25 (Bj11-2), and *B*. *elkanii* Hk 16-7 (BeL7); Miyazaki strains: *B. japonicum* Kh 16-43 (Bj10J-2), *B. japonicum* Kh 16-64 (Bj10J-4), and *B. elkanii* Kh 16-7 (BeL7). The bars represent the standard deviation (*n* = 3) and different letters indicate significant differences at *p* < 0.05 by Duncan's test.

### **4. Discussion**

Although the number is limited, a similar temperature-dependent growth tendency in liquid media of the two *Bradyrhizobium* species has been reported previously. Three *B. japonicum* strains grew better at 15 ◦C than 25 ◦C, and could not grow at 35 ◦C, while one *B. elkanii* strain grew better at 25–35 ◦C than at 15 ◦C [20]. Kluson et al. [11] also reported that optimum growth of two *B. elkanii* strains was around 25 ◦C while two *B. japonicum* strains grew best at 20 ◦C in the range of 20–35 ◦C. These results suggest that *B. japonicum* and *B. elkanii* have species-specific temperature preference in their proliferations. The tendencies are consistent with the previous results on the latitudinal characteristic nodulation of *B. japonicum* and *B. elkanii* in Japan [7,8], the United States [9], and Nepal [10].

In the infection experiment, we used sterilized vermiculite to simplify the experimental conditions—the same population of the inoculants and elimination of the effects of indigenous soil microorganisms on the competition. Sterilization of soil samples by autoclaving could change its physicochemical conditions. Actually, the population of the inoculated rhizobia decreased in the sterilized Fukagawa soil due to unknown reasons in a preliminary experiment (data not shown). Therefore, we could not use the soil samples in this study.

The better growth of soybean at higher temperature has been reported previously in the similar range of temperatures [11,21,22]. The number of nodules was temperatureindependent in this study (Figure 3), while temperature-dependent nodule formation, that is, in this study, the higher temperature, the larger nodule number in the similar temperature range, has been reported when *B. japonicum* strains were inoculated in laboratory experiments [21,23]. In this study, the *B. japonicum* and *B. elkanii* strains were co-inoculated and a different strain was dominant among the inoculated strains in the nodules depending on the temperature (Figure 4), therefore, the nodule number would be dependent on the nodulating properties of the dominant strains in the nodules rather than the temperature.

The high nodule dominancy of *B. elkanii* BeL7 (Hk 16-7 and Kh 16-7) at high temperature (30/28 ◦C) is presumed to be due to the difference in temperature sensitivity between the *B. japonicum* and *B. elkanii* strains (Figure 2), in addition to the up-regulated expression of *nodC* in *B. elkanii* at high temperature, compared with *B. japonicum* [13]. The temperature-dependent growth properties of the *Bradyrhizobium* spp. strains suggests high nodule dominancy of the *B. japonicum* strains at low temperature (20/18 ◦C). However, one of the two *B. japonicum* strains for each soil was dominant in the nodules even though their growth properties were similar (Figure 1). Differences in expression levels of nodulation genes and in responses to isoflavones secreted from soybean roots might determine the nodule composition between them. The same temperature-dependent nodule composition; dominancy of B. *japonicum* and *B. elkanii* at low and high temperatures, respectively, has been reported in the other laboratory competitive studies [11–13].

Generally, the composition of soybean rhizobia in field soil has been estimated by nodule composition. Regarding the latitudinal characteristic nodule composition of soybean rhizobia [7–10], competitive inoculation experiments have revealed that the nodule composition is affected by species-specific, temperature-dependent infection and proliferation in soils [11–13]. However, it is uncertain which factor contributes to the temperaturedependent nodule composition.

In our previous study [7], we selected three study locations of different local climatic conditions in Japan, and each soil sample of the study locations was used for soybean cultivation at all the study locations to examine the changes in the nodule compositions under the different local climatic conditions. As a result, we assumed that *B. japonicum* dominantly proliferate in the Fukagawa soil, leading to their dominant nodule composition, because the nodule composition was not affected under warmer climatic conditions in Miyazaki location. To confirm our assumption, the competitive inoculation experiment was conducted using the rhizobial strains isolated from soybean nodules cultivated in Fukagawa soil, and the results showed that *B. japonicum* dominated nodules at lower temperature while *B. elkanii* dominated at higher temperature (Figure 4), supporting our

assumption that *B. japonicum* dominantly proliferate in the Fukagawa soil because the dominancy of *B. elkanii* did not increase at higher temperature in the Miyazaki location.

We also assumed that both *B. japonicum* and *B. elkanii* exist in the Miyazaki soil and the dominant nodule composition of *B. elkanii* is due to their preferred infection because the nodule composition was affected under cooler climatic conditions in Fukagawa location. In the competitive inoculation experiment using the Miyazaki rhizobial strains, *B. japonicum* and *B. elkanii* dominated nodules at lower and higher temperatures, respectively (Figure 4), also supporting our assumption that both *B. japonicum* and *B. elkanii* exist in the Miyazaki soil and their preferred infection determined the nodule composition.

### **5. Conclusions**

The experiments performed in the liquid cultures revealed better growth of *B. japonicum* at lower temperatures and *B. elkanii* at higher temperatures, and therefore it can be assumed that the temperature of soil affects rhizobia growth in the rhizosphere and could be a reason for the different competitive properties of *B. japonicum* and *B. elkanii* strains at different temperatures. In addition, competitive infection was suggested between the *B. japonicum* strains.

**Author Contributions:** M.H.R.H. and K.I. conceptualized the study and designed the experiments; M.H.R.H. performed the experiments; A.S. helped to conduct the experiment and in the data analysis; and M.H.R.H. wrote the article, with a substantial contribution from K.I. All authors have read and agreed to the published version of the manuscript.

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

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

### **References**


### *Article* **Effects of Fertigation Management on the Quality of Organic Legumes Grown in Protected Cultivation**

**María del Carmen García-García 1,\*, Rafael Font 2, Pedro Gómez 1, Juan Luis Valenzuela 3, Juan A. Fernández <sup>4</sup> and Mercedes Del Río-Celestino <sup>2</sup>**


**Abstract:** Appropriate fertigation management plays an important role in increasing crop quality and economizing water. The objective of the study was to determine the effects of two fertigation treatments, normal (T100) and 50% sustained deficit (T50), on the physico-chemical quality of legumes. The determinations were performed on the edible parts of peas, French beans and *mangetout*. The trials were conducted in a protected cultivation certified organic farm. The response of legumes to the treatments varied between the cultivars tested. The fertigation treatments had a significant effect on the morphometric traits (width for *mangetout* and French bean; fresh weight for French bean; seed height for Pea cv. Lincoln). The total soluble solids and citric acid content have been shown to be increased by low soil water availability (T50) for *mangetout*. Fertigation treatments did not significantly affect the antioxidant compounds (total phenolic and ascorbic acid), minerals and protein fraction contents of legumes studied. Regarding legume health benefits, the most prominent cultivars were BC-033620 pea and French bean because of their high total phenolic (65 mg gallic acid equivalent 100 g−<sup>1</sup> fresh weight) and ascorbic acid content (55 mg ascorbic acid 100 g−<sup>1</sup> fresh weight), respectively. The results expand our knowledge concerning the nutraceutical quality and appropriate cultivation methods of legumes in order to make the system more sustainable and to encourage their consumption.

**Keywords:** French bean; *mangetout*; peas; antioxidant; ascorbic acid; total phenolic content; mineral composition

### **1. Introduction**

The production of greenhouse crops in Almería (Southeast Spain) accounts for 3.3 million tons, with a surface of 30,000 ha and a value of €1782.4 million. In terms of organic production, Almería ranks first among all Spanish provinces, with more than 3000 ha of greenhouses [1]. Since legumes for consumption of pods and fresh grain are not major crops in the greenhouses of Spain, they can be considered an important alternative, providing diversification in the organic cultivation of legumes under greenhouse conditions. The most economically important legumes consumed as vegetables are green pods of cowpea, snow pea (*mangetout*), common bean, faba bean, and green pea seeds.

Legumes constitute one of the most important botanical families (*Papilionaceae* or *Fabaceae*) from a socioeconomic point of view, with significant implications for agriculture, the environment and food. They are a valuable source of proteins for both animal and human food [2], with known health benefits [3,4], being one of the basic pillars of the

**Citation:** García-García, M.d.C.; Font, R.; Gómez, P.; Valenzuela, J.L.; Fernández, J.A.; Del Río-Celestino, M. Effects of Fertigation Management on the Quality of Organic Legumes Grown in Protected Cultivation. *Horticulturae* **2021**, *7*, 28. https://doi.org/10.3390/ horticulturae7020028


Academic Editor: Stefano Marino Received: 24 December 2020 Accepted: 4 February 2021 Published: 7 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

Mediterranean diet. Numerous studies show the beneficial role of the consumption of legumes for health, including blood pressure and cholesterol levels [5]; high soluble fibre and oligosaccharide content has been associated with an improvement in gastrointestinal health [6,7], and these can also play an important role in diabetes prevention and treatment [8].

Among the different legumes, the most widely eaten in the Mediterranean diet are French beans (*Phaseolus vulgaris*) and peas (*Pisum* ssp.) (Figure 1). Peas can be cultivated with the aim of obtaining dry and fresh peas (*P. sativum* L.) but also as *P. sativum* L. ssp. *arvense*, which are absent parchment pods, sweet, crisp and colloquially known as *tirabeque* or *mangetout*.

**Figure 1.** Plant material tested (from top to bottom): French bean "Helda" (*Phaseolus vulgaris* L.), *mangetout* "Tirabí" (*Pisum sativum* L. ssp. *arvense*), pea cv. Lincoln (*Pisum sativum* L.) and pea cv. BGE\*-033620 (*Pisum sativum* L.). \* BGE: Spanish germplasm bank.

In recent decades, many studies have been conducted to optimize the nutrient and water supply for maximizing crop yield and quality as well as minimizing leaching below the rooting volume according to crop requirements e.g., [9,10]. However, the scarcity of water in some intensive horticultural areas like Almería has resulted in the implementation of new sustainable technological adaptations based on improvements in water use efficiency through automated fertigation, localized irrigation systems and the use of tensiometers [11]. Nowadays, most greenhouses in the region have automated fertigation systems, allowing farmers to have greater control of irrigation parameters [12].

Nevertheless, published research results regarding the impact of fertigation on quality characteristics of green pods are scarce. Thus, previous studies have found significantly different effects on quality parameters such as length, width, number of seeds per pod, fresh fruit weight and pod colour (L\* and a\* parameters) in French bean (*Phaseolus vulgaris* L.) pods under both fertigation levels and frequencies [13]. Indeed, as reported by the abovementioned authors, large fertigation intervals reduced the colour brightness (L\* parameter) and increased the pod greenness (a\* parameter) in French bean pods. Moreover, irrigation management could influence green pod quality. Thus, the application volume of irrigation water based on replacing 80% of evapotranspiration improved the pod parameters and nutritional composition of green beans [14]. Furthermore, inadequate supply of irrigation water to French beans may also increase the fibre content in pods, as indicated by the results of Singer et al. [15] obtained after a reduction in the water supply from 100% to 75% or 50% of the field capacity.

Since the fundamental principles of organic farming are the preservation of natural resources and the increase in biodiversity, the objective of this work was to study the effect of reducing the dose of fertigation and, consequently, the dose of irrigation on the physico-chemical quality of different legume cultivars (French beans, *mangetout* and peas) under protected organic conditions.

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

*2.1. Experimental Framework, Plant Species and Applied Treatments*

Four legume cultivars were used: French bean "Helda" (*Phaseolus vulgaris* L.), *mangetout* "Tirabí" (*Pisum sativum* L. ssp. *arvense*), pea cv. BGE-033620 (*Pisum sativum* L.) from: Spanish germplasm bank of the Centro de Recursos Fitogenéticos-INIA (Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria and a commercial pea cv. Lincoln (*Pisum sativum* L.).

The experimental trial was carried out in the Ifapa Center La Mojonera, in the province of Almería, South-East Spain. The crops were grown in a greenhouse of 1600 m2. The type of greenhouse was a symmetrical multi-tunnel, without active climate control, although equipped with temperature and humidity meters, fertigation system and officially certified as organic by an accredited company within the previous 15 years. The minimum, average and maximum relative humidity were 35.8, 74.3, and 98.1%, respectively. The minimum, average and maximum air temperature were 12.2, 17.2, and 26.2 ◦C, respectively. Pests and diseases were monitored weekly and biological control was applied as the main control method.

Sheep manure was applied to the soil at a dose of 0.7 kg m−<sup>2</sup> and with a percentage composition of dry matter corresponding to 45.6%; 17.7 g kg−<sup>1</sup> total nitrogen; 520.0 mg kg−<sup>1</sup> nitrate; 2.2 g kg−<sup>1</sup> phosphorus; 16.5 g kg−<sup>1</sup> potassium; 889.0 mg kg−<sup>1</sup> ammonium; 100.9 g kg−<sup>1</sup> calcium. All fertilizers are listed in Annex I of the EU Regulation (Commission Regulation (EC) No. 889/2008). The legumes were transplanted in October 2016, with a density of 2 plants m−<sup>2</sup> .

Fertigation was applied by a controller, via drip fertigation and a nutrient solution prepared with groundwater pH 7.3, regulated to 6.5 by acetic acid. The fertigation controller was composed of a programmer with venturis injectors and four fertilizer tanks with the following solutions: tank A: chelated calcium (MgO 0.5% + CaO 15%) and microelements; tank B: humic and fulvic acids (total humic extract 26% *w*/*w*; humic acids 10% *w*/*w* + fulvic acids 16% *w*/*w*); tank C: potassium sulphate (K2O 52% + SO3 45%); tank D (injectors): amino acids (free amino acids 24% *w*/*w* + total nitrogen 3.3% *w*/*w*, organic nitrogen 3% *w*/*w* and ammonia nitrogen 0.3% *w*/*w*). A final electric conductivity (EC) of 2.4 dS m−<sup>1</sup> was reached.

Two treatments, T100 and T50, were arranged in a randomized complete design. T100 consisted of water and fertilizer provided according to fertigation management. This programming was carried out through the use of 3 tensiometers installed at a depth of 15 centimetres, randomly allocated in the 100% treatment plots. The command used was to fertigate when the average of the tensiometers located only in the plot of T100 was 22 cb, matric potential usually applied in horticultural greenhouses in Almeria [16]. T50 consisted of the water supply corresponding to half the fertigation time compared to T100. The concentration of the nutrient solution provided in each treatment was the same, therefore, T100 consists of double the amount of water and fertilizer as T50. Fertigation times of the two treatments were varied throughout the cultivation. During the period of maximum crop growth, it was fertigated for 30 min for T100 and half of that time (15 min) for T50. At the end of the trial, the total water volume applied in T100 was 60 L m−<sup>2</sup> and 30 L m−<sup>2</sup> for T50.

The soil texture was sandy clay loam, determined by Bouyoucos-hydrometer analysis [17]; pH and electrical conductivity (EC) were determined in the saturated extract by pHmeter (model MicropH 2002 Crison) and conductivity meter (model GLP31 Crison); Chemical elements were determined in the water extract from saturated soil paste. Organic matter was determined using the Walkley–Black method [18]. A soil analysis was carried out before planting and after harvesting of T100 and T50 treatments.

A total of 35 legume plants were grown per cultivar (BGE-033620, Lincoln, Helda, Tirabí), per replicate and for each treatment (T100 and T50). Legume-pod samples were taken from 10 randomly distributed plants in each replicate during the maximum production period. The samples for chemical analysis consisted of pods of French-bean and

*mangetout* and pea grains, consistent with the edible format for each legume. Then, vegetal material was packaged in polypropylene plastic containers and sent to the laboratory for analysis.

### *2.2. Physical Traits*

The morphometry was determined by measuring the length, width and height of the pods (French bean and *mangetout*) and maximum and minimum diameter in peas with digital calibre Laser 4263.

The determination of fresh weight was measured with a precision digital balance Mettler Toledo XPE1203S. For the moisture determination, dry weight of the samples was calculated drying in a Memmert UF110 stove at 45 ◦C for 72 h. All determinations were carried out on pods. To determine fruit firmness, a texturometer was used on pods (Texture Analyzer TA.XT Plus, Stable Micro Systems Texture Analyzer, Surrey, UK) equipped with a fine-cut probe at a speed of 1 mm s−1, for 5 s. The colour was determined by a CM-700d portable colorimeter (Konica Minolta Sensing Americas, Inc. Ramsey, NJ, USA). The determinations of the Hue and Chroma colour parameters were made in two different external points of the pod's equatorial plane with a colorimeter (model CR-200, Minolta, Ahrensburg, Germany).

### *2.3. Chemical Traits*

All parameters were measured for the edible part of each legume: seeds for peas and pods for *mangetout* and French bean.

The juice was extracted from the fruits for the determination of the soluble solid content by means of a digital refractometer (Smart-1, Atago, Japan). Titratable acidity was measured by titrating 10 mL of juice with NaOH 0.1 N up to pH 8.2 using an automatic titrator (Metrohm 862 Compact. Titrosampler, Herisau, Suiza). Legume fruit acidity was reported as the percentage of citric acid. The pH value of the sample was determined using a digital pH meter (WTW pH 330; WTW; Weilheim, Germany) equipped with an electrode (Sen Tix 41; WTW, Weilheim, Germany).

Legumes (10 g) were mixed in a blender a stirred with 10 mL methanol. The mixture was homogenised (Polytron PT3100; Kinematica AG, Littau, Switzerland) and centrifuged at 4 ◦C (Beckman J2-21M/E; Beckman Instruments Inc., Fullerton, CA, USA) for 10 min. The supernatant was decanted into a 25 mL measuring flask. The pellet was resuspended in 10 mL 70% methanol in water (*v*/*v*), followed by centrifugation. The combined supernatants were diluted to 25 mL with 70% methanol. The extracts were frozen in small tubes at −80 ◦C until further analysis. The solution was diluted to volume (25 mL) with distilled deionised water. The solution was incubated at room temperature in the dark for 90 min, and the absorbance was read at 750 nm against a blank solution. Finally, results were reported in gallic acid equivalents (mg g−<sup>1</sup> DW).

Total polyphenol content was determined according to the Folin–Ciocalteu procedure [19]. To the diluted methanol extract (200 μL), in a cuvette, 1 mL of Folin–Ciocalteu solution (diluted 1:10 in water) was added. After 2 min, 800 μL Na2CO3 (7.5%) was added, mixed for 5 s on a whirl mixer and incubated in the dark at room temperature for 60 min. The absorbance was measured at 765 nm with a ThermoSpectronic UV–visible Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Gallic acid was used as standard and total phenolics were expressed as mg gallic acid equivalent (GAE) 100 g−<sup>1</sup> fresh weight.

The reference values for Ascorbic Acid (AA) were obtained using an automatic titration (Metrohm, 862 Compact Titrosampler, Metrohm, Riverview, FL, USA) by the iodine titration method with minor modification [20]. Thus, 5 g of sample juice was mixed with distilled deioniser water until final weight of 50 g and treated with 2 mL glyoxal solution (40%), stirred briefly and allowed to stand for 5 min. After the addition of 5 mL sulphuric acid (25%), it was titrated with iodine (0.01 mol L<sup>−</sup>1) up to the endpoint (EP1). The linearity of the method was determined using AA as an external standard. Finally, the ascorbic acid content was expressed as mg g−<sup>1</sup> fresh weight (FW).

For mineral composition determination of the legume cultivars, the dry mineralization method was used [21]. Dried samples in a furnace at 100 ◦C to constant weight were homogenized and then weighed into porcelain crucibles. Later, they were incinerated in a muffle furnace at 460 ◦C for 15 h. The ash was bleached after cooling by adding 2 mL of 2 mol L−<sup>1</sup> nitric acid, then drying it on thermostatic hotplates and finally maintaining it in a muffle furnace at 460 ◦C for 1 h. Ash recovery was performed with 5 mL of 2 mol L−<sup>1</sup> Suprapur nitric acid, making up to 15 mL with 0.1 mol L−<sup>1</sup> Suprapur nitric acid. The determinations were carried out by flame atomic absorption spectrophotometry, except for Na and K, which were analysed by flame atomic emission. Elemental analyses were performed with a PerkinElmer (Waltham, MA, USA) model 2100 atomic absorption spectrophotometer equipped with a PerkinElmer AS-50 autosampler, standard air–acetylene flame and single-element hollow cathode lamps and background correction with deuterium lamp for Mn. The nitrogen (N) content was determined according to the Kjeldahl method [22] and the protein content was calculated (N × 6.25).

The quantification of the protein fraction content was determined in fresh pea grain (Lincoln and BGE-033620 cultivars). To obtain soluble protein 10 mg of pea flour was weighed and 1 M NaOH was added. The protein fractions were obtained based on the methods postulated by Hu and Esen [23] and Knabe et al. [24]. The protein fractionation of the legumes was performed with 4 different solvents: H2O, NaCl 0.5 M, 2-propanol (IPA) 70% and glacial acetic acid 50% for determining the albumin, globulin, prolamin and glutelin content, respectively. The supernatant absorbances were measured at 595 nm with a ThermoSpectronic UV–visible Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). A bovine serum albumin (BSA) dilution curve was used as standard [25]. The results were expressed in mg protein fraction per 100 g dry weight (DW).

### *2.4. Statistical Analyses*

Physico-chemical data were subject to one-way analysis of variance (ANOVA), and Tukey's multiple range test was used in cases where significance at *p* < 0.05 variance was found among treatments (T50 and T100) per each cultivar. Statistical analyses were performed using SPSS 13.0 (SPSS Inc., Chicago, IL, USA).

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

### *3.1. Soil Analysis*

The saturated extract soil indicates what plants can take from soil when irrigation water and solubilized and minerals are applied. The EC of the samples collected from the T50 (2.79 dS·m<sup>−</sup>1) after harvesting were higher compared to the T100 treatment (4.45 dS·m<sup>−</sup>1) (Table 1). Soil EC is affected by cropping, irrigation, land use, and application of fertilizer. The higher EC in T100 could be due to a higher salt accumulation in the soil since irrigation by means of tensiometers at the above-mentioned matric potential considerably reduces the quantities of leaching. The activation of fertigation with a threshold value of soil matric tension −22 kPa allows no drainage, as shown by the research carried out in our cultivation area [26,27]. EC of saturated extract of soil obtained in T50 (2.79 dS·m<sup>−</sup>1), being similar to the electric conductivity (EC) of the nutritive solution used (2.40 dS·m<sup>−</sup>1), which indicates an optimal use of the nutrients provided in T50.


**Table 1.** Saturated extract soil analysis.

The increase in NO3 − and K in the analysis after harvest is due to the continuous supply of N (via aminoacids and humic and fulvic acids) and K (potassium sulphate) through the nutrient solution. Higher supply in the case of T100 treatment implied higher NO3 − and K content in the soil. Regarding nitrate, the used N fertilizers are normally retained in the soil and slowly mineralized. Thus, the nitrate is slowly released, and, asfar as nitrate leaching is reduced, its content in the soil is increased. Furthermore, the soil sampling was carried out after a few days of irrigation events, which would explain the high amount of nitrate released by mineralization at the end of the cycle. In addition, plant root uptake of NO3 − could be negatively influenced by the external supply of amino acids [28,29]; in particular, the supplying of amino acids to roots could reduce NO3 − uptake [30], resulting in hardly any differences in produced biomass and tissues analysed between treatments. In relation to K, its concentration tended to increase in soil saturation extract with increasing salt concentration because of the release of absorbed K [31]. Additionally, at the pH of 9 in soil (Table 1), potassium phosphates would not be produced, since phosphorus would precipitate as calcium and ferric phosphates, among others. In any case, if potassium phosphates were formed, this compound would be soluble, and, in the saturation extract, it would be redissolved (which would not occur in calcium and ferric phosphates, which are insoluble). Furthermore, the availability of this element in the soil is affected by moisture levels; more moisture makes more K available, this fact being more frequent in T100 than in T50, and in Almería greenhouse cultivation conditions [32,33].

### *3.2. Physical Parameters*

The most important physical traits used to assess the external quality of green pods consumed as legumes are, among others: length, width and height of the pod, the individual pod weight, the firmness of the pod, and the colour of the pod when harvested. With respect to green seeds, the criteria used to assess their external quality are mainly the texture, the shape (round, oval, etc.) and the individual seed weight. The texture of pea green seeds is considered one of the most important quality attributes for consumers [34].

Table 2 shows the physical parameters for the different cultivars tested grown under two fertigation regimes. In relation to length, width and height of the pod, significant differences were found between treatments for fresh pod width in *mangetout* and French beans and also for grain height in peas cv. Lincoln (17 mm and 15 mm in T50 and T100, respectively).


**Table 2.** Physical parameters of pea, French beans and mangetout grown under normal (T100) and sustained deficit (T50) fertigation tested under protected cultivation conditions. Data are displayed as mean ± standard deviation.

<sup>a</sup> Length, width and height measured in mangetout and French bean pods, and in pea seeds. <sup>b</sup> Fresh weight, moisture, C\* and Hue parameters measured in mangetout, French bean and pea pods. <sup>c</sup> \* *p* < 0.05 denote a statistically significant difference between treatments (T50 and T100) for each cultivar (ANOVA followed by Tukey's multiple range test).

> When analyzing the impact of fertigation on the fresh weight in the legume plants, significant differences between treatments were found for French bean pods with 15.0 ± 0.8 g and 20.1 ± 1.8 g fruit−<sup>1</sup> in T50 and T100, respectively.

> The comparison of the yield of the diverse species and varieties, in response to fertigation deficit, evidenced that only French bean plants significantly decreased in their productivity under 50% fertigation conditions. In this regard, the commercial production of French beans under control conditions (full fertigation), 1.60 kg m−2, decreased by 32.5%, until reaching 1.08 kg m−2, when exposed to 50% fertigation. The sensitivity of French beans to water shortage, especially during flowering initiation and development, is well demonstrated; this directly affects the final yield [35]. Our results are in agreement with those of Martelo-Nuñez et al. [36] who demonstrated that the fertigation regime was especially relevant for the production of French beans, which were more sensitive than other legume crops to water stress.

> In addition, the fertilizer requirements in legumes are low, particularly nitrogen fertilization, which is not generally required or is required in small amounts, although the application of "starter" nitrogen fertilization at a low dose rate seems to enhance the nodulation process and onset of nitrogen fixation in most of the legume crops [37], including peas [38] and French beans [39]. Thus, normally, peas grown in fertile soils are not very dependent on fertilization, particularly on N doses, except during the initial stage of development [40], while in French bean an increase in fertilization, up to a threshold, significantly augmented green pod yield [41]. Therefore, the differences in yield responses between species to different fertilizer amounts applied could be due to higher fertilizer requirements of French beans with respect to pea varieties.

> In relation to moisture (%), the analysis of variance indicated no significant differences between treatments for any legume cultivars (Table 2). The percentages of dry matter were lower in grain (75–79.7%) than in pods (85–91.1%).

> Regarding the firmness parameters, significant differences were not found between treatments (T100 and T50) except for the BGE cultivar with 21 N and 16 N, respectively (Table 2). Currently, published research results relevant to the impact of fertigation on quality traits like texture legumes are scarce.

> Color is one of the main external characteristics that determine the acceptance of the product by the consumer. The mean values for the C\* chromatic parameter varied from 27 to 59 (French bean and pea cv. BGE-033620, respectively) and the h\* parameter varying from 105 to 111 (BGE-033620 and French bean, respectively), which exhibited green surface. Significant differences were not found between the fertigation treatments tested for any chromatic parameter. Previous work reported by Sezen et al. (2008) [42] showed that large fertigation intervals reduced the color brightness (L\* parameter) and increased the pod greenness (a\* parameter) in snap bean pods.

> The total soluble solids (TSS) content ranged from 5.82 (French bean) to 11.29 ◦Brix (*mangetout*) (Figure 2). Previous results are in agreement with those obtained in this study for ◦Brix of French bean (5.0–5.7 ◦Brix) grown under protected crop conditions [43].

*Mangetout* pods are rich in TSS content in comparison with other legume pods, thus cowpea accessions from Spain, Greece and Portugal have shown low TSS contents (5.07–7.57) in previous studies [44]. Pea cultivars also displayed a high TSS content (8.54–10.83) in consonance with values obtained by Mera et al. [45], for open air spring pea crops (11 ◦Brix). Green peas of high quality should be tender enough, but with a high sugar content [34]. Significant differences were found between fertigation treatments for *mangetout*, reaching higher TSS contents at T50 (11.3 ◦Brix) compared to T100 (9.1 ◦Brix). No significant differences were found between treatments for French beans or for the two pea cultivars.

**Figure 2.** Total soluble solids (expressed as ◦Brix) in edible parts of *mangetout*, French beans and peas grown under normal (T100) and sustained deficit (T50) fertigation tested under protected cultivation conditions. \* and "ns" indicate significant differences at *p* < 0.05, and non-significant differences between treatments (T50 and T100) for each cultivar, respectively (ANOVA followed by Tukey's multiple range test).

Figure 3 shows the pH of the different legume cultivars tested under protected cultivation conditions. The pH ranged from 6.1 to 6.9, which is in agreement with previous studies on legumes [43,46–48]. Significant differences were not found between fertigation treatments.

The titratable acidity of the different legume cultivars tested, expressed as percentage of citric acid (major organic acid in legumes) is shown in Figure 3. The citric acid content varied from 0.11 (French bean) to 0.29% (pea cv. Lincoln). Previous studies have found citric acid values similar for French beans (0.10–0.18% citric acid) [49]. The data on citric acid content of the pods/seeds indicated a significant increment under drought by *mangetout* (0.23) and Lincoln pea (0.32) compared to T100 treatment (0.19 and 0.14, respectively).

Although numerous studies are available on the effects of either salinity or drought/deficit fertigation on plant growth and yield of grain and vegetable legumes, only a few of them also address pod and/or immature seed quality parameters.

Soils from T100 treatment had greater salt content (4.45 dS m−1) compared to T50 treatment, as indicated the highest EC value observed (Table 1). Further studies are required to confirm these results by molecular evidence. The tolerant genotypes could be utilized for further breeding programmes to evolve new legume genotypes for better salt stress tolerance with higher quality.

**Figure 3.** pH (**A**) and titratable acidity (**B**) (expressed as g of citric acid 100 g−<sup>1</sup> fresh weight) in edible parts of *mangetout*, French beans and peas grown under normal (T100) and sustained deficit (T50) fertigation tested under protected cultivation conditions. \*, \*\* and "ns" indicate significant differences at *p* < 0.05, *p* < 0.01 and non-significant differences between treatments (T50 and T100) for each cultivar, respectively (ANOVA followed by Tukey's multiple range test).

Previous studies on cucumber, melon, tomato and pepper fruits displayed similar results; thus, the salinity of the nutrient solution did not increase the TSS levels [50]. On the other hand, reduced watering application in faba beans increases the carbohydrate concentrations in seeds [51].

### *3.3. Nutritional Parameters*

Vegetable legumes contain less proteins and more water than those consumed as dry pulses. In addition, they are richer sources of antioxidants, such as phenolics and vitamin C among other compounds [52]. Therefore, their consumption is intended to provide a balanced nutritional source full of healthy promoting compounds rather than to serve as a primary protein source.

Figure 4 shows the mean total phenol content of the different cultivars of legumes tested under organic farming, expressed as mg GAE 100 g−<sup>1</sup> of fresh weight. Significant differences were not observed between the normal and restricted fertigation treatments; therefore, a deficit at 50% fertigation did not affect the total phenol content. The lower

values were found in French bean and pea cv. Lincoln (20–30 mg GAE 100 g−<sup>1</sup> FW). The BGE-033620 pea cultivar showed the highest total phenolic content, with 60–65 mg GAE 100 g−<sup>1</sup> FW. These results are in consonance with those reported for the seeds of cultivars of *Lupinus albus*, *L. luteus*, and *L. angustifolius* with total phenol contents varying from 212 to 317 mg 100 g−<sup>1</sup> DM (approximately 43–64 mg 100 g−<sup>1</sup> FW) [53].

**Figure 4.** Antioxidant compound content (Total polyphenol (**A**) and ascorbic acid content (**B**)) in edible parts of *mangetout*, French beans and peas grown under normal (T100) and sustained deficit (T50) fertigation tested under protected cultivation conditions. "ns" indicates non-significant differences between treatments for each cultivar (ANOVA followed by Tukey's multiple range test).

According to the European Food Information Council [54], there is no official dietary recommendation for the consumption of phenolic compounds. However, some studies are being carried out to determine consumption recommendations for different adult population groups, such as that of Ovaskainen et al. [55], which proposed Recommended Dietary Allowances (RDA) for the intake of total phenolic content (461–1377 mg day−<sup>1</sup> for men and 449–1185 mg day−<sup>1</sup> for women). According to our data, the intake of a 200 g ration of Fresh bean, *mangetout* and pea cv. Lincoln would provide 5–13% of the RDA for adults. Pea cv. BGE-033620 would provide up to 9–26% of the RDA for adults.

Figure 4 shows the mean ascorbic acid content of the different legumes tested under greenhouse cultivation. No significant differences were observed between the normal and restricted fertigation treatments. The ascorbic acid content ranged from 10–15 mg 100 g−<sup>1</sup> FW (pea cultivars) to 26.32 mg 100 g−<sup>1</sup> FW for French beans (Figure 4). Previous studies reported lower concentrations for French beans with 16.7 mg 100 g−<sup>1</sup> FW [56].

Vitamin C is found naturally as L-ascorbic acid, being widely distributed in fresh plant foods, among which, citrus fruits, kiwi, strawberry and melon are the largest source of vitamin C [57]. Other vegetables like tomato also contain high vitamin C content, varying from 2 to 21 mg 100 g−<sup>1</sup> FW [58]. According our data, Fresh beans and *mangetout* could be considered rich sources of vitamin C. According to the RDA (Recommended Dietary Allowances) for the Spanish population [59], the daily intake of vitamin C for adult men and women is 60 mg day−1. According to our data, a portion size of approximately 200 g FW of French beans and *mangetout* would provide 83 and 70%, respectively, of the recommended daily intake.

Table 3 shows the mean protein and mineral contents (dry weight) for each legume cultivar. The analyses statistically showed non-significant differences for the protein and mineral contents except for Mn in the BGE-033620 cultivar.

**Table 3.** Protein and mineralcontents (dry weight) in legume cultivars grown under normal (T100) and sustained deficit (T50) fertigation tested under protected cultivation conditions. Data are displayed as mean ± standard deviation.


<sup>a</sup> \* *p* < 0.05 denote a statistically significant difference between treatments within the same cultivar (ANOVA followed by Tukey's multiple range test).

> The minimum and maximum mean mineral content (dry weight) in each cultivar were: 2.5 (French bean) and 4.5 g 100 g−<sup>1</sup> (pea cv. Lincoln) of N; 1.4 (peas) and 2.3 g 100 g−<sup>1</sup> of (French bean) K; 0.1 (pea cv. Lincoln) and 0.5 g 100 g−<sup>1</sup> (French bean) of Ca; 0.1 (pea cv. Lincoln) and 0.3 g 100 g−<sup>1</sup> (French bean) of Mg; 0.01 (French bean and pea cv. Lincoln) and 0.05 g 100 g−<sup>1</sup> (*mangetout*) of Na; 44 (French bean) and 54 (pea cv. Lincoln) mg kg−<sup>1</sup> of Fe; 26.8 (French bean) and 49 (pea cv. Lincoln) mg kg−<sup>1</sup> of Zn; 2 (pea cv. BGE-033620) and 5.5 (pea cv. Lincoln) mg kg−<sup>1</sup> of Cu; 7.5 (pea cv. Lincoln) and 17.8 mg kg−<sup>1</sup> (French bean) of Mn. Minerals in diets are required for metabolic reactions, transmission of nerve impulses, rigid bone formation and regulation of water and salt balance [60]. The daily requirements of an adult person are as follows (mg d<sup>−</sup>1): 9–18 Fe, 1.1 Cu, 3100 K, 7–9.5 Zn, 1300–1500 Na, 300–350 Mg, 1.8–2.3 Mn, 900–1000 Ca and 41–56 g protein [61]. According to our data, supposing that a person consumes a course of legumes of approximately 200 g d−<sup>1</sup> (and taking into account a mean moisture content of 80%), the calculated content for all the minerals is below the recommended values (2.16 Fe, 0.22 Cu, 920 K, 1.96 Zn, 16 Na, 120 Mg, 0.72 Mn, 200 Ca, expressed in mg and 11.2 g protein). The highest contents were observed for K, Mg and protein. Therefore, consumption of 200 g of legumes can provide 24% Fe, 20% Cu, 1.22% Na, 22.22% Ca, 40.66% Mn, 28% Zn, 29.66% K, 40% Mg and 27.30% protein of the recommended intake. The low Na content (<2%) and the high K concentration recommend the use of these legumes in an antihypertensive diet. Thus, K from vegetables and fruits can reduce blood pressure [62].

> Figure 5 shows the mean protein fraction content in edible parts of peas, French beans and *mangetout* grown under two different fertigation treatments.

**Figure 5.** Protein fractions in edible parts of Lincoln and BGE-033620 peas grown under normal (T100) and sustained deficit (T50) fertigation tested under protected cultivation conditions. "ns" indicates non-significant differences between treatments (T50 and T100) for each cultivar (ANOVA followed by Tukey's multiple range test).

The protein fractions analysed were: glutelins which are only produced in plant material, and are mostly found in cereals and legume grains; globulins are present in numerous seeds and in legumes; albumins are proteins that are found in blood plasma and are necessary for the correct distribution of body fluids; and prolamins, contained in legumes and that neutralize the anticoagulant effect of heparin [63].

Significant differences were not found between T50 and T100 for protein fractions (glutelins, globulins, albumins and prolamines). The highest protein fraction content was globulin (Lincoln: 8.9 and 8.8 mg 100 g−<sup>1</sup> dry weight for T50 and T100, respectively, BGE: 6.8 and 7.0 mg·100 g−<sup>1</sup> dry weight for T50 and T100, respectively). Previous studies [64,65], indicated that most of the protein fractions of legume seeds contained globulins and albumins and, in some cases, prolamins and glutelins. Therefore, it should be noted that legume seeds do not show a specific soluble fraction profile, in comparison to cereals [23].

Paredes et al. [66] agree with our findings; in fact, they indicated that the globulin fraction in legumes was the highest (60–90%), followed by prolamins and glutelins (Figure 5). The peas studied can be considered as a rich source of proteins, an alternative to the protein from cereals and animals.

In summary, the results, under the controlled system of the presented study using two fertigation treatments, supported the hypothesis that physical and chemical traits varied between varieties and species in different ways. Our results are in accordance with the findings of Saleh et al., 2018 [14], El-Noemani et al., 2010 [67], and Shalaby et al., 2016 [68], having showed a wide variation among legume cultivars in terms of their performance and response to water stress.

### **4. Conclusions**

This work describes, firstly, the different responses to abiotic stress (fertigation deficit) in terms of the physico-chemical quality of peas, *mangetout* and French beans; this information contributes to rational decisions regarding the agronomical management of such crops.

The response of legumes to the treatments varied between the cultivars tested. The fertigation treatments had significant effects on the morphometric traits (width for *mangetout* and French bean; fresh weight for French bean; seed height for Pea cv. Lincoln). Furthermore, only French bean plants significantly lowered productivity under 50% fertigation conditions

Interestingly, from the present study, *mangetout* came out as the highest source of total soluble solids, reaching higher content at 50% fertigation treatment. It was found that fertigation treatments did not significantly affect the antioxidant compounds (total polyphenols and ascorbic acid), minerals and protein fraction contents of the legumes studied. According to our data, French beans and *mangetout* are a rich source of vitamin C.

This study also reflects the importance of legumes in the contribution of mineral content, especially in the contribution of K, Mg and protein to the human diet.

On the other hand, the landraces seemed to be an interesting genetic material. Thus, the BGE-033620 landrace showed the highest total polyphenol content.

This study has shown that these legume species have relevant interest and benefits, at both the agronomic and nutritional levels, and open good perspectives for the improvement of cropping systems and the creation of innovative food products. The available biodiversity and the identified large variation in quality between cultivars and species of legumes is an underutilized resource, still requiring further studies to expand our knowledge about the quality and uses of landraces for consumption, either as fresh vegetables, or after canning or freezing.

**Author Contributions:** M.d.C.G.-G. wrote this manuscript and performed the crop assay; M.d.C.G.-G., R.F. and P.G., performed nutritional analysis; M.d.C.G.-G. and J.L.V. performed physical and chemical analysis; M.d.C.G.-G. and J.A.F. reviewed the manuscript; M.D.R.-C. designed this study and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors wish to express their thanks to the Projects (PP.AVA.AVA201601.7 and PP.TRA.TRA201600.9) and FEDER for the funding of this research.

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

**Informed Consent Statement:** Not applicable.

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

### **References**


## *Article* **Latitudinal Characteristic Nodule Composition of Soybean-Nodulating Bradyrhizobia: Temperature-Dependent Proliferation in Soil or Infection?**

**Md Hafizur Rahman Hafiz 1,2, Ahsanul Salehin 3, Fumihiko Adachi 1, Masayuki Omichi 4, Yuichi Saeki 5, Akihiro Yamamoto 5, Shohei Hayashi <sup>1</sup> and Kazuhito Itoh 1,3,\***


**Abstract:** A species-specific latitudinal distribution of soybean rhizobia has been reported; *Bradyrhizobium japonicum* and *B. elkanii* dominate in nodules in northern and southern areas, respectively. The aim of this study was to elucidate whether temperature-dependent proliferation in soil or infection is more reliable for determining the latitudinal characteristic distribution of soybean-nodulating rhizobia under local climate conditions. Three study locations, Fukagawa (temperate continental climate), Matsue and Miyazaki (humid sub-tropical climate), were selected in Japan. Each soil sample was transported to the other study locations, and soybean cv. Orihime (non-Rj) was pot-cultivated using three soils at three study locations for two successive years. Species composition of *Bradyrhizobium* in the nodules was analyzed based on the partial 16S rRNA and 16S–23S rRNA ITS gene sequences. Two *Bradyrhizobium japonicum* (Bj11 and BjS10J) clusters and one *B. elkanii* (BeL7) cluster were phylogenetically sub-grouped into two (Bj11-1-2) and four clusters (BjS10J-1-4) based on the ITS sequence. In the Fukagawa soil, Bj11-1 dominated (80–87%) in all study locations. In the Matsue soil, the composition was similar in the Matsue and Miyazaki locations, in which BeL7 dominated (70–73%), while in the Fukagawa location, BeL7 decreased to 53% and Bj11-1 and BjS10J-3 increased. In the Miyazaki soil, BeL7 dominated at 77%, and BeL7 decreased to 13% and 33% in the Fukagawa and Matsue locations, respectively, while BjS10J-2 and BjS10J-4 increased. It was supposed that the *B. japonicum* strain preferably proliferated in the Fukagawa location, leading to its nodule dominancy, while in the Miyazaki location, temperature-dependent infection would lead to the nodule dominancy of *B. elkanii*, and both factors would be involved in the Matsue location.

**Keywords:** *Bradyrhizobium*; temperature-dependent distribution; nodule composition; proliferation in soil; infection

### **1. Introduction**

Soybean (*Glycine max* [L] Merr.) originated in north-eastern China and is presently cultivated around the globe under various soils and climatic conditions [1–3]. The high concentrations of protein and oil in soybean seeds indicate its significance in daily life. Soybean is an easy-to-cultivate crop belonging to the Leguminosae family that can grow in nitrogen-poor soils. Soybean-nodulating rhizobia can establish symbiosis with soybeans through effective nitrogen fixation.

**Citation:** Hafiz, M.H.R.; Salehin, A.; Adachi, F.; Omichi, M.; Saeki, Y.; Yamamoto, A.; Hayashi, S.; Itoh, K. Latitudinal Characteristic Nodule Composition of Soybean-Nodulating Bradyrhizobia: Temperature-Dependent Proliferation in Soil or Infection? *Horticulturae* **2021**, *7*, 22. https://doi.org/10.3390/ horticulturae7020022


Academic Editor: Stefano Marino Received: 1 December 2020 Accepted: 26 January 2021 Published: 29 January 2021

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**Copyright:** © 2021 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/).

Diverse soybean-nodulating rhizobia belong to the genera *Bradyrhizobium, Sinorhizobium* (*Ensifer*) and *Mesorhizobium* [4,5], among which *Bradyrhizobium* is recognized as a slow grower, while *Sinorhizobium* (*Ensifer*) is recognized as a fast grower and *Mesorhizobium* as a variable one [6]. *B. japonicum* and *B. elakanii* are the major soybean-nodulating rhizobia having a high nitrogen-fixing ability and have been used as inoculants for improving crop production. However, the inoculants could not dominate in nodules due to competition with indigenous rhizobia in the field [7]. Therefore, it is essential to evaluate the ecological behavior of the indigenous soybean-nodulating rhizobia in relation to the environmental conditions.

Saeki et al. [8] studied the geographical distribution of soybean-nodulating rhizobia in Japan using soil samples around the country as inoculants and showed the species-specific latitudinal distribution of *B. japonicum* and *B. elkanii*, in which the former dominated in nodules when the northern soils were used, while the latter did in southern soils. Shiro et al. [9] examined the genetic diversity of indigenous soybean-nodulating rhizobia in the USA using a similar method and found the same latitudinal distribution of *B. japonicum* and *B. elkanii* from north to south. Adhikari et al. [10] examined nodules from different locations in Nepal and found that *B. japonicum* dominated in temperate regions, while in subtropical locations, *B. elkanii*, *B. yuanmingense* and *B. liaoningense* dominated in acidic, moderately acidic and slightly alkaline soils, respectively. Li et al. [11] also reported the pH-dependent distribution of rhizobia in Chinese soils, in which *B. japonicum* and *B. elkanii* dominated in neutral soils, while *B. yuanmingense*, *B. liaoningense* and *Sinorhizobium* dominated in alkaline soils. These results suggest that temperature and soil pH determine the species-specific distribution of soybean-nodulating rhizobia in soils.

To examine the possible reasons for the temperature-dependent distribution of soybeannodulating rhizobia, competitive inoculation experiments at different temperatures have been conducted. Kluson et al. [12] reported that *B. japonicum* USDA 6 and *B. diazoefficiens* USDA 110 dominated in nodules at lower temperatures, while *B. elkanii* USDA 76 and *B. elkanii* USDA 94 did at higher temperature. Suzuki et al. [13] examined the nodule occupancy as well as relative population of *B. japonicum* and *B. elkanii* strains in the rhizosphere of soybean cultivated using sterilized vermiculite. Under competitive conditions, *B. japonicum* strains dominated in nodules at lower temperature even though the relative populations of both strains were similar in the rhizosphere, while at higher temperature, *B. elkanii* strains dominated in nodules due to their larger relative population in the rhizosphere. Shiro et al. [14] examined the gene expression of *nodC* and the nodule occupancy of *Bradyrhizobia* at different temperatures. In the inoculation experiment with mixes of three strains, the nodule occupancy of *B. elkanii* USDA 31 increased at higher temperatures, whereas that of *B. japonicum* USDA 123 increased at lower temperatures, corresponding to their temperature-dependent *nodC* gene expressions. These results support the temperature-dependent distribution of soybean-nodulating rhizobia in the field and suggest that the temperature influenced their preference for infection and/or proliferation in soils. Since the species-specific distribution of rhizobia in field soils is evaluated by their distribution in nodules, it is uncertain which factor, namely, temperature-dependent infection or proliferation in soil, contributes to the temperature-dependent distribution of rhizobia in nodules.

Considering the two above-mentioned factors, we selected three study locations with different climatic conditions in Japan, and each soil sample of the sites was used for soybean cultivation at all the locations for two successive years to examine the changes in the distribution of rhizobia in the nodules after the transfer of the soil samples to the different climatic conditions and to follow the changes in the second year in the new environments. If the predominance of some rhizobia in the soil determines the nodule occupancy, changing climatic conditions would not affect the nodule occupancy; on the other hand, if temperature-dependent infection determines the nodule occupancy, it would be changed in different climatic conditions. The aim of this study is to elucidate the possible reasons for the latitudinal characteristic distribution of soybean-nodulating rhizobia in local climate conditions.

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

### *2.1. Study Locations*

To examine the temperature-dependent nodule occupancy of soybean rhizobia, three study locations, Fukagawa (fu), Matsue (ma) and Miyazaki (mi), were selected in Japan. According to Koppen's climatic classification, Fukagawa belongs to the Dfb (temperate continental climate) region, and Matsue and Miyazaki belong to the Cfa (humid sub-tropical climate) region. Soil samples were collected from the experimental fields of Takushoku University of Hokkaido College, Shimane University and Miyazaki University and used for soybean cultivation at all study locations. There had been no history of legumes cultivation in all soils. Basic information on the site and climatic parameters are presented in Table 1. The soil properties were reported previously (Table S1, [15]).

**Table 1.** Geographical and climatic characteristics of the study locations in Japan.


<sup>a</sup> Average daily minimum and maximum temperatures and total rainfall during the cultivation period in 2016/2017. <sup>b</sup> Figures in parenthesis indicate those during one month after sowing. (https://www.jma.go.jp).

### *2.2. Soybean Cultivation*

The soil samples with a total weight of about 25 Kg were collected from several sites of the experimental field and mixed together for each study location. Each soil sample was divided into three parts (ca. 7.5 Kg) and used for soybean cultivation at each study location. Each soil sample was put in three plastic pots (20 cm in diameter and 25 cm in height), which were placed on a plastic sheet or a wooden duck board in the open field. Seeds of soybean cv. Orihime (non-Rj) from the same lot were used at all study locations. Three healthy seedlings per pot remained at seven days after germination, then they were cultivated for ca. 2–3 months depending on the conditions of the study locations in each year until harvest without fertilization and then the fresh weight of the whole plant and number of nodules were measured. After harvesting of the soybean plants in 2016, in the case of the Matsue location, the pots with the soil were kept in the open filed until the next cultivation season. For the Miyazaki and Fukagawa locations, the triplicate soil samples were mixed and kept in a paper bag in a warehouse under the same temperature conditions as outdoors until the next cultivation in 2017. The different procedures were due to space issues at the study locations.

### *2.3. Nodule Sampling and Isolation of Rhizobia*

After harvesting from flowering to the early fruiting period, the roots were washed carefully with tap water and the whole plant fresh weight was measured after removal of surface water with tissue towel, and the number of nodules in each plant was counted, then the nodules were preserved at low temperature in a vial containing desiccating silica gel until isolation of rhizobia.

For isolation of rhizobia, ten nodules were randomly selected from one plant for each replication and kept in sterilized distilled water overnight. When the number of nodules was less than 10, two plants were used. After surface sterilization with 95% ethanol for 30 s followed by 3% sodium hypochlorite solution for 30 s, and rinsing in sterilized distilled water at least seven times, each nodule was crushed in an Eppendorf tube with 1 mL of sterilized distilled water, then a drop of suspension was streaked onto yeast mannitol agar (YMA) medium [16] and incubated at 25 ◦C for 5–12 days. Two randomly selected colonies per nodule were purified, and a total of 540 isolates (3 replications, 3 soils from 3 study locations, 10 nodules per plant and 2 isolates per nodule) were further analyzed molecularly.

### *2.4. Phylogenetic Analysis of the Rhizobia Based on Genes of 16S rRNA and 16S–23S rRNA Internal Transcribed Spacer (ITS) Region*

A small amount of the colony was directly subjected as the template for the PCR amplification of the partial 16S rRNA gene using the universal primers fD1 and rP2 [17]. The components of the PCR mixtures and the PCR running conditions are summarized in Tables S2 and S3, respectively. PCR products were purified and subjected to PCR cycle sequencing, according to the procedures described previously [10]. Taxonomic position of the isolates was determined based on the database (https://www.ncbi.nlm.nih.gov/) using a BLAST [18] search. Multiple sequence alignments were constructed using ClustalW 2.1 [19]. Alignments were manually edited and phylogenetic trees with the related reference strains were constructed using ClustalW 2.1 with the neighbor-joining method and the tree was visualized by MEGA 7 [20].

Among the isolates with the same phylogeny in the 16S rRNA gene in each Soil– Location–Year combination, two representatives were randomly selected for analysis of the ITS region. PCR amplification of the ITS region was conducted using the universal ITS primers 1512F and 23R [21]. The procedures were the same as described above.

### *2.5. Nucleotide Sequence Accession Numbers*

The sequence data generated in this study were deposited in the DDBJ Nucleotide Submission System under the accession numbers LC582850 to LC582907 for the 16S rRNA gene, and LC579845 to LC579902 for the 16S–23S rRNA ITS region.

### *2.6. Statistical Analysis*

Statistical analysis of the soybean cultivation data was carried out using the MSTAT-C 6.1.4 [22] software package. The data were subjected to Duncan's multiple range test after one-way ANOVA.

### **3. Results**

### *3.1. Fresh Plant Weight and Number of Nodules of Soybean*

At each study location, the fresh plant weight and the nodule numbers were not significantly different among soils in both years with a few exceptions (Figure 1). When all data in each study location were analyzed, the fresh plant weight showed the tendency of increasing from northern (FU) to southern (MI) sites, whereas the nodule number showed the opposite tendency of significantly decreasing from northern to southern sites (Figure 2).

### *3.2. Phylogenetical Characterizations of the Rhizobia*

Based on the 16S rRNA gene analysis, the isolated rhizobia were most closely related to one of the three groups, *Bradyrhizobium japonicum* Bj11 (KY000638), *Bradyrhizobium japonicum* S10J (MF664374) and *Bradyrhizobium elkanii* L7 (KY412842). Similarities (%) of the sequences between the isolates and the corresponding type strains were 98–100%, 96–100% and 97–100% for *B. japonicum* Bj11, *B. japonicum* S10J and *B. elkanii* L7, respectively. The phylogenetic tree of the ITS region of the selected isolates indicated that the rhizobial strains were further grouped into sub-groups (Figure 3). The most similar sequences in the database are listed in Table 2.

*B. japonicum* Bj11 was grouped into Bj11-1 and Bj11-2 based on the ITS sequence, and the two groups were characterized by their physiological properties, that is, it took more than one week for Bj11-1 to form visible colonies on the YMA agar plate, compared to 5–6 days for Bj11-2. *B. japonicum* S10J was grouped from BjS10J-1 to BjS10J-4 based on the

ITS sequence. The ITS sequences of BjS10J-1 had more similarity to those of *B. japonicum* Bj11 than the other groups of *B. japonicum* S10J. BjS10J-2 and BjS10J-3 were characterized by their origin, that is, BjS10J-2 and BjS10J-3 were isolated from soybeans cultivated in Miyazaki and Matsue soils, respectively. *B. elkanii* L7 was isolated from soybeans cultivated in all soils and study locations, and its ITS sequences were not distinguished among them.

**Figure 1.** Fresh weight and number of nodules of soybean cultivated at Fukagawa (**A**), Matsue (**B**) and Miyazaki (**C**) locations using the soil samples (FU, MA and MI) collected from the corresponding study locations. The bars represent standard deviation (n = 3) and different letters indicate significant differences at *p* < 0.05 by Duncan's test.

**Figure 2.** Fresh weight and number of nodules of soybean cultivated at Fukagawa (fu), Matsue (ma) and Miyazaki (mi) locations. The bars represent standard deviation (n = 18) and different letters indicate significant differences at *p* < 0.05 by Duncan's test.

**Figure 3.** Phylogenetic tree of the 16S–23S rRNA ITS gene regions of the soybean rhizobial strains isolated in this study with reference strains. The isolates were designated by the soil [Fukagawa (H), Matsue (M) and Miyazaki (K)], the study location (h, m and k), year of the cultivation and the strain number. The scale bar indicates the number of substitutions per site.


**Table 2.** Group of soybean rhizobial strains isolated in this study based on phylogeny of 16S–23S rRNA genes' ITS region.

<sup>a</sup> Bj; *Bradyrhizobium japonicum*, Be; *Bradyrhizobium elkanii*. <sup>b</sup> Gene accession number in database.

### *3.3. Relative Composition of the Strains in Relation to Soil and Climate in 2016 and 2017*

In the Fukagawa soil, the soybean rhizobia consisted of Bj11-1, Bj11-2 and BeL7 in all study locations (Table 3 and Figure 4). Bj11-1 dominated (80–87%) in all study locations in 2016. In 2017, Bj11-1 was maintained in the Fukagawa soil at 80%; however, the compositions decreased in the Matsue and Miyazaki locations at 53% and 60%, respectively, along with the increase in BeL7 to 40% and 30%, respectively. Bj11-2 was present minorly at 7–17% in all study locations and in both years.

**Table 3.** Relative composition (%) of rhizobial strains isolated in this study based on phylogeny of the 16S–23S rRNA ITS gene regions.


In the Matsue soil, Bj11, BjS10J and BeL7 were isolated in all study locations (Table 3 and Figure 4). The composition and behavior were similar between the Matsue and Miyazaki locations, with Bj11 decreasing from 20% and 24% in 2016 to 10% and 0% in 2017, respectively, while BeL7 increased from 70% and 73% in 2016 to 87% and 97% in 2017, respectively. BjS10J was present minorly at 3–10% in both years. In the Fukagawa location, Bj11 was present at 24% in 2016 and was maintained at 23% in 2017, although the major group shifted from Bj11-1 to Bj11-2. The dominant group BeL7 increased from 53% in 2016 to 77% in 2017 as with the other study locations, while BjS10J, which was 23% in 2016, disappeared in 2017.

In the Miyazaki soil, the rhizobia consisted of BjS10J-2, BjS10J-4 and BeL7 (Table 3 and Figure 4). In the Miyazaki location, BeL7 was dominant at 77% in 2016 and completely eliminated BjS10J in 2017. In the Fukagawa and Matsue locations, BjS10J-2, which was dominant at 73% and 53% in 2016, decreased to 13% and 0% in 2017, respectively, while BeL7 increased from 13% and 33% in 2016 to 80% and 100% in 2017, respectively. BjS10J-4 also decreased from 13% in 2016 to 0–7% in 2017, respectively.

When the Fukagawa soil was moved to the Matsue and Miyazaki locations, the dominant rhizobia changed from Bj11 to BeL7 in the second year. For the Matsue and Miyazaki soils, BeL7 decreased in the Fukagawa and Matsue locations in the first year and recovered to the original level in the second year.

**Figure 4.** Relative abundance of the soybean rhizobial strains.

### **4. Discussion**

Although the fresh weight and number of nodules were not significantly different among soils at all study locations (Figure 1), the fresh weight increased from northern to southern study locations, while the number of nodules showed the opposite tendency depending on the study location (Figure 2). The cultivation temperature might be involved in the change in the parameters (Table 1), and a similar tendency of the temperaturedependent growth of soybean has been reported [12,23–25].

In the case of the number of nodules, previous reports showed opposite temperaturedependent tendencies from ours [23–25]. Reduction in nodules at higher temperature might be due to strain-specific properties. Shiro et al. [14] reported that the nodule numbers were different by about 10 times depending on the strains, and the temperature-dependent expression of the *nodC* gene was also strain-specific but it was not related to the nodule numbers of the corresponding strains. Hungria and Vargas [26] showed an example of adverse effects of high temperature on the soil population of bradyrhizobia and the nodule number of soybean. Strain-dependent tolerance against high temperature in soil was also reported [13]. Since the bradyrhizobial community structure was changed at the different study locations, the microbial transition might be a reason for the reduction in the nodules at the southern study location.

The phylogenetic analysis of the 16S rRNA genes of the *Bradyrhizobium* spp. isolates showed three clusters, Bj11, BjS10J and BeL, which mostly corresponded to the three clusters in the phylogeny of the ITS sequences. As the pHs of the soil samples were slightly acidic (Table S1, [15]), the dominant presence of *B. japonicum* and *B. elkanii* in the nodules is reasonable [11]. The three clusters were phylogenetically comparable with the results of Saeki et al. [27] and Willems et al. [28] (data not shown). Willems et al. [28] showed that each cluster had more than 95.5% similarity in ITS sequences, whereas the similarity within each cluster in this study ranged 95–97%, and those within the subclusters (Bj11-1-2 and BjS10J-1-4) were 98–99% (data not shown). These results suggest that the *Bradyrhizobium* spp. isolates in this study were phylogenetically positioned in the same groups as previously reported, and they were further grouped by physiological property (Bj11-1 and 2), phylogeny of 16S rRNA genes (BjS10J-1) and origin of the soil (BjS10J-1, 2 and 3).

The topology of the phylogenetic trees of 16S rDNA and the ITS region was almost the same except for BjS10J-1. The variable position of a subcluster of *B. japonicum* has also been reported in the reports of Saeki et al. [8] and Adhikari et al. [10]. The ITS nucleotide sequence similarity of the BjS10J-1 strains was more than 98% with those of the Bj11 strains, while it was 88 to 90% with those of the other BjS10J strains having the same 16S rRNA gene sequences. Horizontal gene transfer among them would be one of the possible reasons for the discrepancy in their topologies.

Each cluster of BjS10J was characterized by its origin; on the other hand, BeL7 originated from all soils having undistinguishable gene sequences, and Bj11 was isolated only from Fukagawa and Matsue soils. These results suggest that the range of distribution of the strains differed among the groups. As the wide range of distributions was generally reported in previous studies [10,29], the limited range of distribution of the BjS10J strains suggests that their presence might depend on soil characteristics.

It has been well known that the species-specific distribution of soybean-nodulating rhizobia in the field is temperature-dependent [8–10] and that the temperature effect is mainly due to their infection preference [14] and/or proliferation in soil [12,13]. However, it is uncertain which factor, temperature-dependent infection or proliferation in soil, contributes to the temperature-dependent distribution of the rhizobia in nodules.

In the case of the Fukagawa soil, *B. japonicum* was dominant in the nodules in the high-latitude Fukagawa location, and the dominancy was maintained for two years (Table 3 and Figure 4). This tendency is the same as the temperature-dependent nodule dominancy of *B. japonicum* as previously reported [8–10]. When the Fukagawa soil was moved to the warmer Matsue and Miyazaki locations, the nodule composition of *B. japonicum* and *B. elkanii* was not changed, suggesting an originally lower population of *B. elkanii* in the Fukagawa soil. If *B. elkanii* was present in the Fukagawa soil to a certain extent and low temperature prevented their infection, thus resulting in their lower nodule dominancy, the nodule dominancy of *B. elkanii* would increase when the temperature increased in the sub-tropical study locations. In the second year, however, the nodule dominancy of *B. elkanii* increased in the Matsue and Miyazaki locations, suggesting an increase in the soil population of *B. elkanii* in the warmer environment. Regarding *B. japonicum* Bj11 in the Fukagawa soil, the composition of Bj11-2 was maintained in both years in the Matsue and Miyazaki locations, while that of Bj11-1 decreased in the second year, suggesting a higher sensitivity of Bj11-1 to high temperature.

In the case of the Matsue soil, the dominancy of *B. elaknii* was observed in the Matsue and Miyazaki locations (Table 3 and Figure 4). Similar temperature-dependent nodule occupancy of *B. elaknii* has been reported [8–10]. The dominancy of *B. elkanii* increased in both study locations in the second year. When this soil was moved to the cooler Fukagawa location, the dominancy of *B. japonicum* increased in the first year, suggesting that *B. japonicum* was originally present in the Matsue soil and its nodule dominancy increased due to the lower temperature. The composition of *B. elkanii* increased in the second year, supposing a decrease in the population of *B. japonicum* and/or an increase in that of *B. elkanii*. Although the average minimum and maximun temperatures during the cultivation period were similar between both years, those during one month after sowing, when frequent nodulation would be expected, seemed to be a little higher in the second year (Table 1). It was supposed that the higher temperature in the second year might cause the increase in the relative dominancy of *B. elaknii*. Among the *B. japonicum* strains in the first year, Bj11-2 was dominant in the Matsue and Miyazaki locations, while Bj11-1 and BjS10J were dominant in the Fukagawa location. Along with the decrease in Bj11-1 and BjS10J in the second year, the relative dominancy of Bj11-2 increased. The transition of the *B. japonicum* strains might be due the difference in sensitivity to high temperature among them.

In the Miyazaki soil, BeL7 was dominant and BjS10J-4 was minor in the Miyazaki location in 2016, while in the Fukagawa and Matsue locations, BjS10J-2 appeared dominant (Table 3 and Figure 4), suggesting that *B. japonicum* was originally present in the Miyazaki soil and its nodulation increased due to the lower temperature in the cooler environments. In the second year, however, BeL7 recovered to 80–100%. A slightly higher temperature during one month after sowing in the second year might be the reason (Table 1), but the temperature in the Fukagawa location in 2017 was lower than that in the Matsue location in 2016; therefore, only the change in temperature could not explain the increase in BjS10J-2 and BeL7 in the Matsue (2016) and Fukagawa (2017) locations, respectively. The difference in rainfall that changed at all study locations each year might be another possible reason (Table 1). As the nodule occupancy of BeL7 increased in the Matsue and Miyazaki soils in 2017, but not in the Fukagawa soil in the Fukagawa location, BeL7 in the different soils might have different properties for a competitive relationship with the coexisting Bj strains.

### **5. Conclusions**

Various conditions of soil storage until the next year in the study location might be differentiated in environmental conditions even in the same temperature conditions as outdoors. Potentially other environmental factors and their correlation with temperature might also affect the microorganisms. In addition, rainfall might affect the soil conditions between outdoor and indoor storage during the winter. Although the effects could not be verified, the shift of the nodule composition in the second year showed the same tendency in all soils and study locations, suggesting that the effects of the difference in the soil storage conditions did not seem to be serious on the composition of rhizobia. Fluctuating rainfall over two successive years in the study locations (Table 1) also suggests that the difference in rainfall would not significantly affect the nodule composition.

By the novel methodology used in this study, we could assume that *B. japonicum* (Bj11-1) dominantly proliferated in the Fukagawa soil and led to its dominant nodule composition and that both *B. japonicum* (BjS10J-2) and *B. elkanii* (BeL7) existed in the Miyazaki soil and the dominant nodule composition of *B. elkanii* (BeL7) was due to the temperature-dependent infection.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2311 -7524/7/2/22/s1, Table S1: Soil property of the study sites [15], Table S2: PCR ingredients for amplification of 16S rRNA and 16S-23S rRNA ITS region, Table S3: PCR running conditions.

**Author Contributions:** M.H.R.H. and K.I. conceptualized the study and designed the experiments; M.H.R.H. performed the experiments; F.A., M.O., Y.S., A.Y. performed the field experiment; S.H., A.S. helped to conduct the experiment and data analysis; M.H.R.H. wrote the article, with a substantial contribution from K.I. All authors have read and agreed to the published version of the manuscript.

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

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

### **References**


## *Article* **A Novel Method for Estimating Nitrogen Stress in Plants Using Smartphones**

### **Ranjeeta Adhikari and Krishna Nemali \***

Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN 47907, USA; adhikar@purdue.edu

**\*** Correspondence: knemali@purdue.edu; Tel.: +1765-494-8179; Fax: +1765-494-0391

Received: 1 October 2020; Accepted: 26 October 2020; Published: 29 October 2020

**Abstract:** For profits in crop production, it is important to ensure that plants are not subjected to nitrogen stress (NS). Methods to detect NS in plants are either time-consuming (e.g., laboratory analysis) or require expensive equipment (e.g., a chlorophyll meter). In this study, a smartphone-based index was developed for detecting NS in plants. The index can be measured in real time by capturing images and processing them on a smartphone with network connectivity. The index is calculated as the ratio of blue reflectance to the combined reflectance of blue, green, and red wavelengths. Our results indicated that the index was specific to NS and decreased with increasing stress exposure in plants. Further, the index was related to photosynthesis based on the path analysis of several physiological traits. Our results further indicate that index decreased in the NS treatment due to increase in reflectance of red and green (or yellow) wavelengths, thus it is likely related to loss of chlorophyll in plants. The index response was further validated in strawberry and hydrangea plants, with contrasting plant architecture and N requirement than petunia.

**Keywords:** electrical conductivity; greenhouse; image processing; nutrient stress; remote sensing

### **1. Introduction**

Nitrogen (N) is one of the major elements essential for plant growth, development, and quality. Maintaining optimal N concentration in the plant tissue is essential for increasing productivity and profitability in controlled environment agriculture (CEA). In spite of supplying plants with optimal fertilizer solution concentration, plant N uptake can vary from pot to pot due to differences in substrate pH, leaching, water content, and crop growth. Therefore, monitoring N concentration of the plant tissue is more useful than measuring N concentration supplied to plants or present in the substrate, to ensure that plants are not exposed to N stress in CEA.

Nitrogen concentration in the tissue can be measured in a laboratory. However, plant sample analysis in a laboratory can be both expensive and time-consuming. Sensors recommended for indirect measurement of plant N status in CEA systems are expensive (e.g., chlorophyll meter, Soil Plant Analysis Development (SPAD), Normalized Deviation Vegetation Index (NDVI) sensor). Moreover, some sensor measurements (e.g., NDVI) can be potentially confounded by the signal from the background when the canopy is not fully closed [1], leading to errors. Other sensors for measuring crop N status including Cropscan, Greenseeker, Yara N-sensor and Fieldspec-Spectroradiometer [2] are more suitable for conventional agriculture and not CEA. Therefore, regularly monitoring plant N status in CEA can be challenging with available techniques.

Plant N status can be assessed using plant images. Chlorophyll pigment in the leaves absorb red, blue, and a small proportion of green wavelengths incident on plants [3]. Because tissue N concentration affects chlorophyll synthesis in plants [4,5], a deficiency of N in the tissue can decrease the concentration of chlorophyll and increase reflectance of red, blue, and green wavelengths from plants. Therefore, an indirect assessment of N stress experienced by plants can be made by measuring the reflectance of red, blue, and green wavelengths from a canopy [2,6–8]. Images are comprised of pixels that store information on the intensity of reflected light from an object. Reflectance from plants can be measured to estimate N status of plants by processing images using image analysis software [9].

Using this technique, hyper-spectral and multi-spectral imaging platforms are being developed for N stress assessments in plants [10–12]. Several indices for N stress have been developed using reflectance in the red, blue, green, red-edge, and near infrared regions of the light spectrum [12–19]. Although these platforms and indices are available, they are not widely used in academic research and industry. Some of the reasons for this include high equipment cost, complicated hardware and software, and the selective nature of developed applications. Smartphones can capture high quality images of plants. Color images captured by a smartphone can be separated into blue, green, and red channels. Thus, the images captured by smartphones can be processed to measure reflectance in the blue, green, and red wavebands. From this, it is possible to develop indices for N stress in plants. With advancements in cloud computing, software can be developed and made accessible on smartphones with network connectivity. The images can be captured, processed, and N stress index measured in real-time using smartphones, similar to other remote sensing platforms. Nitrogen stress assessments made using smartphones can be highly valuable as these devices are universally available and simple to use. However, there is limited research that has tested or developed smartphone- based applications for assessing N stress in plants.

It is well known that carotenoids in addition to chlorophyll affect blue light absorption [20–22], while mainly chlorophyll absorbs red light [20,21,23]. Furthermore, the xanthophyll (a carotenoid) pool can increase in response to N stress in plants [24,25]. Therefore, it is possible that the reflectance of blue light is relatively less (or absorption is relatively more) than other wavelengths under N stress, as blue light can be absorbed by carotenoids in addition to chlorophyll. Based on this, we hypothesized that the ratio of blue light reflectance to that of the combined blue, green, and red wavebands will decrease under N stress in plants. The objectives of this research were to (i) test the hypothesis that the ratio of blue light reflectance to that of combined reflectance in the visible band can be used as an index for N stress, (ii) study the association between the N stress index and the physiological pathways in plants, and (iii) develop a smartphone application to measure the N stress index in species with differences in plant architecture.

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

The study comprised of "proof-of-concept" and "product development" experiments. Hypothesis testing, associating index with physiological pathways, and testing index specificity to N stress were conducted in the proof-of-concept experiment using a multispectral image station with tight control on incident light intensity, spectrum, and distance between object and camera. The purpose of the product development experiment was to test a smartphone application for measuring the N stress index in two different species with contrasting plant architecture under real-world conditions in a greenhouse. Incident light intensity, spectral composition, and distance between the plant and camera were similar but not tightly controlled in the product development experiment.

### *2.1. Proof-of-Concept Experiment*

The experiment was conducted during July and August of 2017 in a temperature-controlled glass greenhouse at Purdue University, West Lafayette, IN using petunia (*Petunia* × *hybrida* L. var. "Easy Wave Red Velour"). It is well known that growth rate and nutrient requirements of petunia is higher than other herbaceous greenhouse crops [26]. Therefore, large effects can be observed in N stress index among treatments (see below) using petunia. Seeds were purchased from Ball Seed Company (West Chicago, IL, USA) and germinated in plug flats (72-cell, Landmark Plastics, Akron, OH, USA) filled with a propagation mix (Fafard®, germination mix, Sungro Horticulture, Agawam, MA, USA). The trays were placed in a mist until germination, after which, seedlings were transplanted

into 0.45 L containers (Hummert International, Earth City, MO, USA) filled with a peat-based soilless substrate (Sunshine mix #8, Sungro Horticulture) containing 75% peat, 20% perlite, and 5% vermiculite. Plants were fertilized with a solution made by mixing 15N-2.2P-12.5K and 21N-2.2P-16.6K commercial fertilizers (Peters Excel, ICL specialty fertilizer, UK) in a 3:1 ratio every alternate day. The electrical conductivity (EC) of the fertilizer solution (a measure of total fertilizer ions dissolved in the solution) was 2.0 dS·m−<sup>1</sup> and contained an N concentration of 198 mg N·L−1, except in the nitrogen stress treatment (see *Treatments* section below). The pH of the substrate was maintained between 6.0 to 6.5 during the study. Greenhouse was maintained at a day/night temperature of 26/20 ± 2.4/1.1 ◦C, daily light integral of 20 to 25 mol·m−2·d<sup>−</sup>1, and relative humidity close to 50% during the study.

Plants were grown under optimal conditions for two weeks after transplanting. After this, plants were subjected to three treatments including optimal or control (C), drought stress (DS), and nitrogen stress (NS). Drought stress was applied by maintaining a low substrate volumetric water content (θ) of 0.15 m3·m−<sup>3</sup> and supplying a fertilizer solution with EC of 2.0 dS·m<sup>−</sup>1. Nitrogen stress was provided by supplying a fertilizer solution with an EC of 0.75 dS·m−<sup>1</sup> and maintaining a <sup>θ</sup> level of 0.48 m3·m<sup>−</sup>3. Plants in the optimal treatment were grown at an <sup>θ</sup> level of 0.47 m3·m−<sup>3</sup> using a fertilizer solution with an EC of 2.0 dS·m<sup>−</sup>1. Plants were grown under different treatments for five weeks.

Solution EC, substrate EC (ECs), and θ were measured weekly using a dielectric sensor (ECHO 5TE, Meter Group, Pullman, WA, USA). A line quantum sensor (SQ-326-SS, Apogee instruments, Logan, UT, USA) was used to measure photosynthetic photon flux density (PPFD) at the canopy level during the middle of the day.

A custom measuring station with three quantum sensors (LI190, LI-COR Biosciences, Lincoln, NE, USA) was used to measure the light absorption fraction (*Iabs*) of plants. A group of four plants in a tray was moved from the main experiment to the station for measurement of incident, transmitted and reflected light intensity (*PPFDi, PPFDt* and *PPFDr,* respectively) in different treatments. We measured *PPFDi* by placing a quantum sensor horizontally on a flat surface at approximately canopy height. A second quantum sensor was placed at the bottom of the canopy to measure *PPFDt*. In addition, a third quantum sensor was placed upside down at an angle of 45◦ towards the canopy and 0.3 m above the plants was used to measure *PPFDr*. The intensity of light absorbed by the plants (*PPFDa*) was calculated as described by [27]:

$$PPFD\_a = PPFD\_i - \left(PPFD\_r + PPFD\_t\right) \tag{1}$$

Fraction of incident light absorbed by plants was calculated as follows:

$$I\_{\text{abs}} = \frac{PPFD\_a}{PPFD\_i} \tag{2}$$

Leaf photosynthetic rate (*A*) and quantum efficiency in light (φPSII) were measured according to the procedure described by [28] using a leaf chamber fluorometer with an LED light source attached to an open-flow leaf gas exchange system (LI-COR-6400XT, LI-COR Biosciences). Measurements were taken on three separate leaves belonging to different plants within each treatment at midday prior to harvest. Fully expanded new leaves were clamped and exposed to a reference CO2 concentration of 400 <sup>μ</sup>mol·mol−<sup>1</sup> and a light intensity of 400 <sup>μ</sup>mol·m−2·s−<sup>1</sup> inside the chamber. The proportion of red and blue light was 90 and 10%, respectively. Relative humidity and temperature inside the leaf chamber were maintained at 40–70% and 25 ◦C, respectively.

Canopy area (CA) and reflectance at 450 (blue), 521 (green), and 660 (red) nm were measured on the 4th, 8th, 16th, 22nd, 27th, and 34th day after imposing treatments using a multi-spectral image station (TopView, Aris, Eindhoven, The Netherlands). A group of four plants from each treatment were placed inside the image station and sequentially exposed to 450, 521, 625, and 660 nm of light using strobe light-emitting diodes (OSLON SSL80, Osram, Munich, Germany). A monochromatic camera (acA3800; Basler Ace, 10 MP with MT9J003 CMOS sensor, 8-bits resolution, ON Semiconductor, AZ, USA) inside the image station captured grayscale images (Figure 1) for each light exposure. The images were automatically stored with unique file names.

**Figure 1.** Procedure for estimating canopy area and N stress index (R) of petunia plants using multi-spectral image station. Original image, mask, and grayscale images captured by a monochromatic camera after sequentially exposing plants to 450 nm, 521 nm and 660 nm.

Captured images were processed automatically using built-in MultiSpec software V2.0 (Aris, The Netherlands). Image processing involved developing a mask of plant, separating plant pixels from the background by super-imposing a mask on the image, counting plant pixels, and measuring average gray value of plant pixels from each image exposed to 450, 521, and 660 nm wavelengths. The average gray value of a grayscale image is related to average reflectance of light from the objects (i.e., plants) captured in the image. As plants absorb more blue and red wavelengths in photosynthesis, images from blue (450 nm) and red (660 nm) exposures are less bright (lower gray value) than those from green (521 nm) exposure as relatively more green light is reflected by plants (Figure 1). From the gray values, N stress index (R) was calculated as the ratio of average gray value of 450 nm image to combined gray value of 450, 521, and 660 nm images.

$$R = \frac{Gray\ Value\_{450}}{Gray\ Value\_{(450+521+660)}}\tag{3}$$

Image-processing software automatically measured CA by counting the number of plant pixels, and multiplying the pixel number by the individual pixel area and magnification factor (specific to the camera inside the image station). Plants were harvested after five weeks of exposure to different treatments. Shoot material was dried in a forced oven maintained at 70 ◦C for one week. The dried samples were weighed to measure shoot dry weight (SDW).

### *2.2. Product Development Experiment*

The experiment used strawberry (*Fragaria* × *ananassa* var. "Quinault") and hydrangea (*Hydrangea paniculate* var. "Bobo"). These species were selected due to their differences in leaf shape, growth rate, N requirement, and architecture to petunia. Strawberry runners were separated from stock plants available with researchers. Hydrangea plants were purchased from Spring Meadow Nursery Inc. (MI, USA). Strawberry runners were transplanted in plastic containers (10 cm diameter, 0.45 L, Hummert International, Earth City, MO, USA) and hydrangea plants were transplanted in nursery containers (16 cm diameter, 3.78 L, Greenhouse Megastore, Danville, IL, USA). Containers for both strawberry and hydrangea plants were filled with the same media used in the proof-of-concept experiment. A fertilizer solution containing EC of 1.0 dS·m−<sup>1</sup> was supplied to strawberry plants during the establishment stage. After two weeks, strawberry plants were exposed to C and NS treatments. Plants in the C and NS treatments received fertilizer solutions containing an EC of 2.0 and 0.75 dS·m<sup>−</sup>1, respectively twice a week. The hydrangea plants were grown in four N fertilizer treatments containing 9, 15, 21 and 30 g·pot−<sup>1</sup> of 21N-2.2P-16.6K commercial fertilizer (Peters Excel, ICL specialty fertilizer, UK), respectively. The substrate water content and environmental conditions were similar to the proof-of-concept experiment.

Smartphone images of strawberry plants and hydrangea branches were captured after three and five weeks of exposure to treatments, respectively. In addition, hydrangea branches were imaged inside the multi-spectral image station used in the proof-of-concept experiment. This was done to compare the N stress indices measured by the smartphone (Rsp) and multi-spectral image station (R). Prior to capturing images, strawberry plants were placed on the greenhouse floor. The images of whole strawberry plants were captured by placing the smartphone approximately 60 cm above plants (Figure 2). A black plastic sheet (0.45 m × 0.45 m) was used as the background for hydrangea branches. Each branch, while attached to the mother plant, was inserted through a slit in the middle of the plastic sheet. The smartphone was placed approximately 30 cm above the plastic sheet for capturing images. After capturing smartphone images, the branch was cut and placed inside the image station. The images of hydrangea branches were captured inside the multispectral image station as described above for petunia plants in the proof-of-concept experiment. The time between cutting the branch from the mother plant and imaging the branch inside the image station was less than a minute.

The image-processing software for analyzing images of strawberry plants and hydrangea branches collected by the smartphone was developed using Matlab (R2017B, MathWorks, Natic, MA, USA). The image processing method used was similar to that described in other published works [19,29]. The developed software was loaded to an online drive (Matlab Drive, MathWorks) and accessed on the smartphone using an app (Matlab Mobile, MathWorks). The software controls the camera of the smartphone and displays a video of the plant on the screen to enable users to capture images from a preferred height. The software on the Matlab Drive automatically processed images after capture. Image processing involved separating the color image into red, green, and blue channels, enhancing green color and developing a mask, segmenting plant pixels by superimposing the mask on red, green, and blue channels, and measuring the average gray value of plant pixels in each channel (Figure 2). From the average gray values, software automatically calculated Rsp and stored the results of the analysis as a Microsoft excel file:

$$R\_{sp} = \frac{Gray\ Valuc\_{bluc}}{Gray\ Valuc\_{(bluc + grav + rad)}}\tag{4}$$

Equation (4) is similar to, but slightly different from Equation (3) used to measure R from images captured by the multi-spectral image station. Plants were exposed to narrow wavebands of 450, 521, and 660 nm in the multi-spectral image station using strobed LED lights. Such exposure to narrow wave bands is not possible using a smartphone. The images captured by the smartphone are based on the broadband blue (400 to 499 nm), green (500 to 599 nm) and red (600 to 700 nm) wavelengths in the

natural light. Therefore, average gray values of images captured by the multi-spectral image station and smartphone were based on narrow and broadband wavelengths, respectively.

**Figure 2.** Procedure for the estimation of N stress index by a smartphone (Rsp). Strawberry plant images are shown in the illustration. The green channel of the original image was enhanced to make mask, blue, green, and red channels were separated, and the mask was used to segment plants in three channels. Average gray values were calculated for each segmented channel to estimate Rsp.

### *2.3. Experimental Design and Data Analyses*

A randomized complete block design with four replications was used in both the proof-of-concept and product development experiments. Data were analyzed using a linear mixed model (Proc Mixed) procedure of statistical analysis software (SAS, SAS Institute, Cary, NC, USA) with repeated measures as needed. Tukey's honestly significant difference procedure was used to separate least square means. Path analyses in the main experiment was conducted using the "Proc Calis" procedure of SAS. For all analyses, a *p* ≤ 0.05 was considered statistically significant.

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

### *3.1. Proof-of-Concept Experiment*

Statistical analyses indicated that the environmental conditions were significantly different in the NS and DS compared to C treatment (Table 1). Photosynthetic photon flux density incident on plants was not significantly different among the treatments and averaged 417 <sup>μ</sup>mol·m−2·s−<sup>1</sup> (Table 1). However, θ was significantly lower in the DS compared to the other two treatments. A θ value of 0.15 m3·m−<sup>3</sup> was maintained in the DS treatment based on a previous work [30] that showed a decline in the growth of bedding plants including petunia at this level. Substrate EC was significantly lower in both the NS and the DS compared to the C treatment. In addition, ECs was significantly lower

in the DS compared to the NS treatment. The ECH2O-5TE sensor used in our experiment measures electrical resistance to calculate EC. Electrical resistance increases or conductivity decreases when the current flow through the solution decreases. The current flow can decrease significantly when θ is low, as in the DS treatment. In addition, the sensor measures bulk EC (influenced by dielectric permittivity of water, dissolved ions, substrate particles, and air), therefore the values are lower than other commonly used sensors measuring pore–water conductivity. In previous research, pore–water EC was 1.8 times higher than bulk water EC measurements for the substrate used in this experiment [31]. Based on this, the equivalent pore–water ECs in the C and NS treatments can be estimated as 1.3 and 0.6 dS·m<sup>−</sup>1, respectively.

**Table 1.** Photosynthetic photon flux density (PPFD), substrate electrical conductivity (ECs) and volumetric water content (θ) maintained in the control (C), nitrogen stress (NS), and drought stress (DS) treatments in the main experiment. Treatment means followed by the same letter are not statistically different (*p* ≤ 0.05). Values in parenthesis indicate standard error of mean.


Statistical analyses indicated that SDW was higher in the C compared to the other treatments. Furthermore, SDW was significantly higher in the NS than DS treatment (Table 2). This confirms that the stress treatments in our experiment decreased plant growth compared to C treatment. A significant decrease in CA, *Iabs* and *A* were observed in the DS whereas only *A* was significantly lower in the NS compared to the control (Table 2). This may suggest that nitrogen stress mainly affects plant growth by reducing *A*. Photosynthesis is affected by light absorption, generation of energy in the light-dependent reactions and utilization of energy in the Calvin cycle [32,33]. Nitrogen stress can reduce both light absorption (by decreasing chlorophyll concentration) [4,34] and utilization of energy in the Calvin cycle (due to decreased enzymatic activity) [35,36] in plants. There were no differences in φPSII among the treatments, although a numerically lower value was observed in the NS treatment. Lack of significance could be due to small effect size and/or large variability in the φPSII measurements. A similar decline φPSII (without statistical significance) of wheat plants under N stress was previously reported [37].

**Table 2.** Shoot dry weight (SDW), canopy area (CA), leaf photosynthesis (A), light absorption fraction (*Iabs*), N stress index (R) and quantum efficiency in light (φPSII) of petunia at harvest stage in the control (C), nitrogen stress (NS) and drought stress (DS) treatments. Treatment means followed by the same letter are not statistically different (*p* ≤ 0.05). Values in parenthesis indicate standard error of mean.


Nitrogen stress index was significantly lower in the NS than C, but not different between the DS and C treatments (Table 2). Reflectance-based measurements are mostly affected by chlorophyll concentration [3,38]. Nitrogen stress can significantly reduce the chlorophyll concentration [4,34], while DS may have a relatively smaller effect on chlorophyll in plants [39]. This may be the reason for the observed R differences in the NS than the DS compared to C treatment in our experiment (Table 2). Furthermore, the result supports our hypothesis that the ratio of blue light reflectance to that of combined reflectance in the visible band can be used as an index for N stress. In addition, the

index was specific to NS and was not affected by DS. Analyses of changes in R with time indicated no significant differences on any day between the DS and C treatments (Figure 3). However, a gradual decrease in R was observed with stress progression in the NS treatment. There were no differences in R on the 4th, 8th, 16th and 22nd day after imposing treatments, but the differences became gradually larger. By the 27th and 34th day of stress exposure, the decrease in R was large to significant in the NS compared to C. A significantly lower R was associated with a significantly lower *A* and a numerically lower φPSII in the NS compared to the C treatment (Table 2 and Figure 3). This may suggest that R measurements are related to photosynthetic pathways in plants.

**Figure 3.** Reflectance index (R) of petunia plants measured on different days after exposure to optimal (C), nitrogen stress (NS) and drought stress (DS) treatments in the main experiment. The R-value was measured based on images of petunia plants captured inside a multi-spectral image station. A. \* denotes statistical significance (*p* ≤ 0.05) between NS and C treatments on a given day.

The path analysis tested the model where SDW was considered as a primary response affected by several lower-order responses including *Iabs*, *A*, CA, φPSII and R. When the data were pooled from the C, NS and DS treatments, path analyses indicated that R, φPSII and CA were exogenous (not affected by other variables) and *A*, *Iabs* and SDW were endogenous (affected by other variables) in nature (Figure 4). Furthermore, the model indicated that SDW was dependent on both *A* and CA and the reliability of the effects on SDW was high (error was 0.32; Figure 3). Both CA and *A* are known to influence biomass production in plants [40–42]. This supports that *A* and CA are secondary responses affecting SDW. Furthermore, CA affected *Iabs*. Light absorption is proportional to light interception by the canopy, which in turn is proportional to CA [43,44]. The model also indicated that φPSII directly affected *A,* which is expected. Interestingly, the model indicated that R inversely affected *A*. This supports our finding that R measurements are related to the photosynthetic pathway, however the effects observed in the model are opposite to those observed between R and *A* in the NS treatment. This could be because the model included data from the DS and C treatments in addition to the NS treatment. Furthermore, the effects of *A* on SDW and that of R and φPSII on *A* were not significant when NS data were removed from the model. This may suggest that NS effects on plants are primarily due to the reduction in *A*. In addition, the model supports that R is related to photosynthesis pathways. Both R and φPSII showed covariance with CA, but there were no causal relationships among them. Based on the model, R and φPSII can be considered as independent tertiary responses affecting *A*.

**Figure 4.** Path analyses of physiological measurements associated with shoot dry weight (SDW). Other variables include leaf photosynthesis (A), canopy area (CA), light absorption (*Iabs*), reflectance index (R) and quantum efficiency in light (ΦPSII). Beta (or linear coefficient) and error values are shown for different effects. The model used terms that showed statistical significance (*p* ≤ 0.05).

### *3.2. Product Development Experiment*

Image analysis software effectively segmented strawberry plants from the background in the images captured by the smartphone (Figure 2). In spite of broadband wavelengths used in the smartphone method, Rsp of strawberry plants in the NS treatment was significantly lower than that of plants in the C treatment (Figure 5), similar to responses observed for petunia in the proof-of-concept experiment. This indicates that the broadband wavelengths used in the Rsp estimation were equally effective as narrowband wavelengths used in the proof-of-concept experiment.

**Figure 5.** Nitrogen stress index (R) assessment using a smartphone. Strawberry plants were exposed to nitrogen stress (NS) and optimal (C) treatments. Letters 'a' and 'b' indicate that the means are statistically different. Error bars represent standard error of the mean.

The decrease in R or Rsp in the NS treatment was related to an increase in the gray value (or reflectance) of combined blue, green and red wavelengths as opposed to decreases in the gray value of blue wavelength in both petunia and strawberry (Table 3). This indicates that N stress effects were more pronounced on the reflectance of red and green wavelengths than the blue wavelength. As described before, carotenoids and xanthophylls in addition to chlorophyll can absorb blue light [20–22]. While chlorophyll synthesis is affected by N stress, the xanthophyll (a carotenoid) pool can increase in response to N stress in plants [24,25]. Thus, reflectance (or gray value) of blue wavelengths is relatively less affected than green or red wavelengths under N stress. In addition, decrease in chlorophyll can expose xanthophyll pigments, which are yellow in color [25]. As yellow is a combination of red and green colors, an increase in yellow color on the leaf surface may result in increased gray values for red and green channels. Given this, the decrease in R-value in the NS treatment is likely due to a loss of chlorophyll or increased xanthophyll absorption (appearance of yellow coloration on the leaf).


**Table 3.** Average gray values of blue and combined wavelengths in petunia (proof-of-concept experiment) and strawberry (product development experiment) under control (C) and nitrogen stress (NS) treatments. Treatment means followed by the same letter are not statistically different (*p* ≤ 0.05). Values in parenthesis indicate standard error of mean.

There was a linear relationship between N stress indices measured using smartphone and multi-spectral image stations in hydrangea (Figure 6). This indicates that the N stress index estimated using a smartphone is comparable to the values estimated using a multi-spectral image station. Interestingly, Rsp value changed approximately by 1.7-folds for one-fold change in R-value. Furthermore, statistical analysis (data not shown) indicated that Rsp values of hydrangea plants grown at the two highest N fertilizer treatments (30 and 21 g·pot<sup>−</sup>1) were significantly higher than those in the two lowest N fertilizer treatments (3 and 9 g·pot<sup>−</sup>1), whereas R-values trended lower in the two lowest N fertilizer treatments compared to those in the two higher N fertilizer treatments. This may suggest that Rsp is more sensitive than R in the detection of differences between the treatments. One possible reason for this could be due to the broadband wavelengths used in measuring Rsp. The difference can be larger when multiple wavelengths are included in the estimation of an index, especially if the effects are spread across the broadband.

**Figure 6.** Linear relationship between N stress indices measured by smartphone (Rsp) and multi-spectral image station (R) in hydrangea.

### **4. Conclusions**

In this study, we tested an index for N stress based on the images of plants. The index was calculated as the ratio of reflectance of blue relative to the reflectance of combined wavelengths in the visible band. The index value decreased when plants were exposed to NS relative to optimal conditions. Furthermore, the index value decreased gradually with increasing N stress in plants. Therefore, the continuous measurement of index can aid in the timely detection of N stress in plants. The index can be estimated using images captured by smartphones and image processing software loaded on network drives. The smartphone-based approach can be attractive to users in academia and industry. It is possible to make image-processing software available to users on a webserver. Using the network connectivity on smartphones, users can connect to the webserver, capture images using a smartphone, and process images on the webserver in real time to estimate N stress index.

**Author Contributions:** R.A. conducted experiments, analyzed data, and helped in the drafting of an early version of the manuscript; K.N. was responsible for overall project management, provided resources and helped in data interpretation and the final draft of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received funding from Fred Gloeckner Foundation, American Floral Endowment and Horticultural Research Institute.

**Acknowledgments:** We thank Jacob Brasseur for helping with capturing images of plants.

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

### **References**


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