Physio-Morphological Traits Contributing to Genotypic Differences in Nitrogen Use Efficiency of Leafy Vegetable Species under Hydroponics

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On the other hand, the concerns are rising on environmental pollution of both air and water in high-input agriculture due to excessive use of N fertilizer in a short growing season of vegetable crops, which is directly linked with the health of human beings and environmental safety. The aim of the study was to determine genotypic differences in Nitrogen Use Efficiency (NUE) of different leafy vegetable species (Arugula, Spinach, Cress, Parsley and Dill) grown hydroponically under two different N-rates (Low N: 0.3 mM and High N: 3.0 mM) and to identify the plant traits which are contributing to NUE. The nutrient solution experiment was conducted between March – April in 2020 by using an aerated Deep-Water Culture (DWC) technique in a fully automated climate room with a completely randomized block design (CRBD) with three replications for five weeks. The results indicated that shoot growth, root morphological and leaf physiological responses were significantly ( p<0.001 ) affected by Genotype, N-Rate and Genotype x N-Rate interaction. Shoot growth of some vegetable species (Argula, Spinach and Cress) was significantly higher under low N than high N-rate, illustrating that they have a great capability for NUE under low N stress conditions. Similar results were also recorded for the root growth of the N-efficient species under low N-rate. The NUE of these species was closely associated with leaf physiological (leaf area, SPAD, photosynthesis, leaf chlorophyll (a+b) and carotenoid) and root morphological (root length, root volume and average rot diameter) characteristics. These physiological and morphological plant traits could be useful characters for the selection and breeding of ‘N-efficient’ leafy vegetable species for sustainable agriculture in the future. However, further investigation should be carried out at field level to confirm their commercial production. Leafy vegetables N-efficiency Photosynthesis Nitrogen Hydroponic culture Figures Figure 1 Introduction The world population is increasing at a rate of 80 million per year and expected to be around 9.0 billion for the year 2037 and 10.0 billion for 2058 (UN, 2022 ). A rapidly increasing world population demands ever-increasing food production. However, to keep food production at the same level as population growth without using up or devastate the non-renewable resources is not an easy task for sustainable agriculture (Adam and Ulas, 2023 ). Today, mineral fertilizers are still an important resource and essential input for crop growth and yield in both high-input and low-input agricultural systems. Particularly, nitrogen (N) is the most common and widely used essential fertilizer nutrient to increase the crop yield and plant biomass (Liu et al. 2014 ). To meet the demand of food for increasing world population more than 110 Tg of N fertilizers are applied annually to enhance crop production, where 1–2% of world energy is utilized to produce N fertilizers (Cho et al., 2017). However, the available N is more often a limiting factor influencing plant growth than any other nutrient in both high-input and low-input agriculture systems (Grindlay, 1997 ). Usually, in low-input agriculture systems, low amounts of N fertilizers are used mostly by peasant farmers, and thus the fertility of the soil is reducing. On the other hand, due to the overdosing of nitrogen fertilizers carried out by high-rate agriculture systems, concerns are rising on environmental pollution of both air and water (Brégard et al., 2000 ). For instance, there is a concept that more N, more yield can be obtained. And keeping this thought the farmers or growers apply more nitrogenous fertilizers to get the maximum yield of leafy vegetable crops in a short growing season. Leafy vegetable crops are consumed by the human beings in a large quantity throughout the world, since nutritionally and dietary vegetable is an important source of vitamins (A, C, and B-complex), minerals (especially calcium, iron, magnesium, and phosphorus) and fibers. Moreover, green leafy vegetables act as strong antioxidants that detoxify Reactive Oxygen Species (ROS) formed under stress or disease conditions (Slavin and Lloyd, 2012 ). Therefore, medical doctors advised to take the green vegetables more in our daily diet (Gupta et al., 2012 ; Singh et al., 2016 ). However, many leafy vegetable crops have shallow root systems (30–40 cm rooting depth) and thus N fertilization should be done carefully for the health of human beings and environmental safety. Because, the efficiency of N fertilizers is frequently low, since, plants take up often less than 50% of the applied N (Raun and Johnson, 1999), and the proportion of fertilizer N not utilized by the crop is left in the soil and/or lost from the plant/soil system through volatilization (i.e. ammoniac), leaching (i.e. nitrate) and denitrification (i.e. nitrous oxide/greenhouse gas) (Byrnes and Bumb, 1998; Laegreid et al., 1999). Many crop plants, especially leafy vegetables, accumulate excessive nitrate in expanded leaves under low light conditions when the uptake of nitrate exceeds reduction at a high transpiration rate. Usually when the plants use the solar energy sufficiently, the absorbed nitrate from the soil is reduced to nitrite by Nitrate Reductase (NR) in cytosol (Watanabe and Yamasaki, 2016 ). Nitrite translocated into the chloroplasts is converted to ammonium ion (NH 4 + ) by nitrite reductase (NiR) located in the chloroplasts and then ammonium ion is assimilated into amino acids (Watanabe and Yamasaki, 2016 ). However, particularly in winter season when duration and the intensity of solar energy reduces, excessive nitrate in expanded leaves may harm the health of the consumer as it can be converted to nitrite causing methaemoglobinaemia or carcinogenic nitrosamines. In food science, nitrite contained in human diet has long been recognized for its toxicity as the causative agent of methemoglobinemia (Yamasaki et al., 2014 ). Due to the formation of mutagenic nitrosamine it has also been presumed that nitrite is potentially carcinogenic (de González et al., 2015 ). Because of these two historical backgrounds, nitrite as well as nitrate content in foods and drinks has been strictly limited by regulations in many countries (Slavin and Lloyd, 2012 ). At the moment, there is a great interest in identifying the processes involved in the regulation of N uptake and N metabolism within the leafy vegetable crops, and also in developing N management strategies to reduce N fertilizer use and to improve nitrogen use efficiency (NUE). In plants NUE is a result of uptake efficiency (where N is taken from the soil or N fertilizer) and utilization efficiency (extracted N is converted into yield) (Schroeder et al., 2013 ). Many external environmental conditions are responsible for the efficient use of N within plants along with N-rates (Cho et al., 2017). Therefore, the aim of the study was to determine genotypic differences NUE among different leafy vegetable species (Arugula, Spinach, Cress, Parsley and Dill) grown hydroponically under two different N-rates (Low N: 0.3 mM and High N: 3.0 mM) and to identify the leaf and root traits which are contributing to NUE. Materials and Methods Plant material, treatments, and experimental design This study was carried out in the Plant Nutritional Physiology Laboratory of Erciyes University, Faculty of Agriculture, Central Anatolia in Turkey in between March – April in 2020. A hydroponic experiment was conducted by using an aerated Deep-Water Culture (DWC) (DWC) technique in a fully automated climate chamber. For the vegetative growth, the average day/night temperatures were 18/20°C while the relative humidity was 65–70%. Plants received approximately 400 µmol m − 2 S − 1 photon flux in a photoperiod of 10/14 h of light/dark regimes in the controlled growth chamber. Five leafy vegetable species (Parsley, Arugula, Dill, Spinach and Cress) were used as the plant materials. The seeds were sown in 72-cell polystyrene trays (W 280 × L 540 × H 45 mm, IBK Iklim Bahce Co., Ltd., Turkey) filled with a mixture of peat (pH: 6.0-6.5) and perlite (2v:1v). To promote germination, the plug trays were wrapped with vinyl chloride resin film and then placed in a germination chamber at 28°C. Seedlings were watered daily. When the seedlings developed three or four true leaves, they were transplanted to plastic pots after the root system of the plants was carefully washed in distilled water to clean the substrate. Three uniform seedlings of each vegetable were then transferred into 8 L container containing a modified Hoagland nutrient solution and grown for five weeks (Slide 1). Two different nitrogen concentrations (Low N: 0.3 mM N, High N: 3.0 mM N) were supplied and Ca(NO 3 ) 2 was used as N source. Each pot was filled with 8 L cultivation solution that was aerated by an air pump. The nutrient solution had the following composition (µM): K 2 SO 4 (500); KH 2 PO 4 (250); CaSO 4 (1000); MgSO 4 (325); NaCl (50); H 3 BO 3 (8); MnSO 4 (0.4); ZnSO 4 (0.4); CuSO 4 (0.4); MoNa 2 O 4 (0.4); Fe-EDDHA (80). Due to transplanting of small seedlings, solutions were replaced completely every week in the first two weeks. Complete renewals of the nutrient solution were done (at 7-day intervals) when the N concentration of the nutrient solution in the 3.0 mM N rate pots fell below 0.5 mM, as measured daily with nitrate test strips (Merck, Darmstadt, Germany) by using a NitracheckTM reflectometer. In hydroponics experiment, the total vegetative growth period from transplanting up to final harvest was almost five weeks. The experiment was in a completely randomized block design (CRBD) with three replicates, with six plants per replicate. Harvest, shoot, and root dry weight measurements At final harvest, five weeks after transplanting in 2020, the shoot biomass (the upper part of the plants) was determined. Plants were harvested by separating them into stems, leaves, and roots. Their tissues were dried in a forced-air oven at 80 ◦C for 72 h for biomass determination until the stable weight was reached. And then they were weighed on an electronic digital scale. Shoot biomass was equal to the sum of aerial vegetative plant parts (leaves + stems) and was expressed in g plant − 1 . Root to shoot ratio was calculated by dividing the root dry weight by the sum of leaf and stem dry weights. Plant height (cm) was measured as the distance between the pot surface and the tip of the plant by using a ruler. Each fully developed leaf was counted as leaf number and recorded as LN plant − 1 . Leaf physiological measurements In the hydroponics experiment, total leaf area of the plants was measured destructively at harvesting by using leaf-area meter (LI-COR Model 3100, LI-COR. Inc., Lincoln, NE, USA). Total leaf area was recorded in centimeter square (cm 2 ). Single-Photon Avalanche Diode (SPAD) was measured with a chlorophyll meter Minolta SPAD 502 Plus chlorophyll meter at the third week after plant establishment in hydroponics culture, and continued weekly till the end of the experiment. During the growth period, the leaf chlorophyll content measurement was performed on the youngest fully expanded leaves (3rd − 4th leaf from the apex) of whole plants, using four replicate leaves per treatment in the third and fifth week of the vegetative period. The measurements were carried out at the temperature of 18/20°C (average day/night temperatures), the relative humidity of 65–70%. The supplied photon flux in the growth chamber was almost 400 µmol m –2 S –1 with an intensity of 10/14 h (light/dark) photoperiod. Prior to harvest, non-destructive measurements of the leaf-level CO 2 gas exchange (µmol CO 2 m − 2 s − 1 ) were done in a controlled growth chamber by using a portable photosynthesis system (LI-6400XT; LI-COR Inc., Lincoln, NE, USA). The leaf net photosynthesis measurement was performed on the youngest fully expanded leaves (3rd − 4th leaf from the apex), using four replicate leaves per treatment in the third and fifth week of the vegetation period. The LI-6400 was equipped with a well-stirred 2.5 × 10–5 m 3 leaf chamber with constant area inserts (1.2 × 10–3 m 2 ) and fitted with a variable intensity red source (Model QB1205LI-670; Quantum Devices, Barneveld, WI) (Tennessen et al., 1994). Leaf temperature within the chamber was 30 ± 2°C while the vapor pressure difference between the leaf and air was 2.6 ± 0.3°C, and CO 2 concentration was 365 ± 10 µL L –1 . All gas exchange measurements were carried out between 09:00 and 12:00 hr. Leaf total chlorophyll (a + b) and carotenoid content measurements A day before harvesting, extraction of the photosynthetic pigments from 100 mg (0.1 g) of fresh leaf samples from each replicate of the treatment combinations were done by measuring the total chlorophyll content of the leaf and carotenoid contents, using UV-VIS Spectroscopy. The leaf samples used for chlorophyll and carotenoid determinations were of the same physiological age with those used for the leaf net photosynthesis measurements. The samples were put into 15 ml capped containers where 10 ml of ethylene alcohol of 95% concentration was added. They were then kept in darkness at room temperature for overnight, to allow the extraction of the leaf pigments. Measurements were done using the spectrometer (UV/VS T80 + of PG Instruments Limited, UK) at wavelengths of 470 nm, 648.6 nm, and 664.2 nm. Total chlorophyll (Total-Chlo) and total carotenoids (TC) were then estimated from the spectrometric readings using the formulae of Lichtenthaler ( 1987 ). Total-Chlo (mg/g plant sample) = [(5.24 WL664.2- 22.24 WL648.6 x 8.1]/ weight of plant sample (g) TC (mg/g plant sample) =[(4.785 WL470 + 3.657 WL664.2) -12.76 WL648.6) x 8.1]/ weight of plant sample (g) Note WL470, WL648.6 and WL664.2 refers to spectrometric readings at wavelength 470 nm, 648.6 nm and 664.2 nm respectively. Root morphological measurements The plant root morphological parameter of total root length (m plant − 1 ), total root volume (cm 3 plant − 1 ) and average root diameter (mm) were measured by using a special image analysis software program WinRHIZO (Win/Mac RHIZO Pro V. 2002c Regent Instruments Inc., Québec, QC G1V 1V4, Canada) in combination with a recording device of Epson Expression 11000XL scanner (Long Beach, CA, USA). It was recorded as cm plant − 1 , and then converted to m plant − 1 . Leaf nitrate reductase enzyme activity (NRA) measurement Leaf nitrate reductase enzyme activity (NRA) in the leaf was determined following the method proposed by Harley ( 1993 ) and Goltsev et al. ( 2016 ). During harvesting time leaf sample was taken and chopped into pieces; 2 grams of the latter were placed in each of two falcon tubes and labeled time-0 (T 0 ) and time-60 (T 60 ). The tubes were covered with aluminum foil to prevent exposer to light. It was then added with 10 ml of assay buffer solution [100 mM phosphate buffer, pH 7.5; 30 mM KNO 3 ; 5%(v/v) propanol] (T 0 and T 60 ). The T 0 container was immediately placed into boiling water for 5 minutes, removed and allowed to cool to room temperature. While the T 60 was kept for 60 minutes at room temperature before it was also placed in boiling water for 5 minutes and allowed to cool to room temperature. To detect nitrite in the assay tubes, the optical density (OD) of each standard tube was determined at 540 nm wavelength in the spectrometer. Statistical Analysis All measured physiological and morphological parameters were analyzed using SAS Statistical Software (SAS 9.0, SAS Institute Inc., Cary, NC, USA). A Two-factor Factorial analysis of variance was performed to study the effects of genotypes and N and their interactions. Levels of significance are represented by * P < 0.05, ** P < 0.01, *** P < 0.001, and ns means not significant. The mean separation of treatments was done using Duncan’s Multiple Test (P < 0.05). Results Biomass production and partitioning Results obtained from the hydroponic experiment demonstrated that shoot fresh weight and root fresh weight of different vegetable species were significantly ( p < 0.001 ) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table 1 ). Some of the vegetable species grown under low N-rate usually exhibited a higher shoot growth performance as compared to high N-rate. Among the vegetable species shoot fresh weight varied in between 19.1 and 4.10 g plant − 1 at low N-rate, 15.1 and 4.93 g plant − 1 at high N-rate. Significantly highest shoot fresh weight was observed in Arugula followed by Spinach and Cress under low N-rate, whereas Parsley and Dill showed the lowest shoot fresh weight at the same N-rate. At high N-rate, Arugula showed again the significantly highest shoot fresh weight and followed by Dill and Spinach while, Parsley and Cress showed the lowest shoot fresh weight at same N-rate. On the other hand, root growth responses of the vegetable species were not similar to shoot growth responses under low N-rate. Since vegetable species usually exhibited higher root fresh weight under high N-rate as compared to low N-rate. The root fresh weight among the vegetable species varied in between 5.57 and 2.34 g plant − 1 at low N-rate, 4.71 and 2.84 g plant − 1 at high N-rate. Among the species, significantly highest root fresh weight was shown by Arugula followed by Cress and Spinach under low N-rate, whereas Parsley and Dill showed the lowest root fresh weight at the same N-rate. All these indicated that shoot and root growth responses of vegetable species to low N-rate were matching. However, similar genotypic responses could not be recorded in root fresh weight under high N-rate, indicating a significant Genotype x N-rate interaction. Significantly highest root fresh weight was shown by Arugula followed by Dill and Spinach under high N-rate, whereas Parsley and Cress showed the lowest root fresh weigh. Although, highly significant genotypic variation ( p < 0.001 ) existed among vegetable species in plant height, however, effects of N-rate and Genotype x N-rate interaction were no significant. Averaged over N-rates, the highest plant height was shown by Dill, whereas the lowest plant height was shown by Spinach. However, regarding to the shoot fresh weight, both genotypes (Dill and Spinach) showed contrasted results, which clearly indicating that not always a positive relationship between shoot fresh weight and plant height existing. Among the species, the plant height varied in between 19.0 and 13.0 cm plant − 1 at low N-rate, 20.0 and 12.0 cm plant − 1 at high N-rate. Shoot dry matter production of different vegetable species was significantly affected by Genotype, N-rate, and Genotype x N-rate interaction (Table 2 ). As similar as in shoot fresh weight, vegetable species grown under low N-rate usually exhibited a higher shoot dry matter production as compared to high N-rate. The shoot dry matter among the species varied in between 1.60 and 0.42 g plant − 1 at low N-rate, 1.26 and 0.50 g plant − 1 at high N-rate. The best performance in shoot dry matter production was shown by Arugula followed by Spinach and Cress under low N-rate, whereas Parsley and Dill showed the lowest shoot dry matter production at the same N-rate. All these results clearly indicate that some of the vegetable species have a great capability for Nitrogen Use Efficiency (NUE) under stress conditions. Particularly three vegetable species; Arugula, Spinach and Cress can be characterized as ‘N-efficient’ genotypes due to exhibiting high shoot fresh (Table 1 ) and dry biomass (Table 2 ) production as compared to ‘N-ineficient’ genotypes Parsley and Dill under low N-rate. On the other hand, vegetable species Arugula and Spinach maintained high shoot dry matter production at high N-rate while Parsley and Dill also maintained to be the lowest in shoot dry matter production among other species at the same N-rate. Although, the root fresh weight of different vegetable species was significantly ( p < 0.001 ) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table 1 ), only significant difference existed among the genotypes in root dry matter (Table 2 ). The root dry weight among the species varied in between 0.25 and 0.09 g plant − 1 at low N-rate, 0.24 and 0.10 g plant − 1 at high N-rate. Averaged over N-rates, the highest root dry weight among the species was shown by Arugula followed by Spinach and Cress, whereas Parsley and Dill showed the lowest root dry weight. All these results clearly indicate that the ‘N-efficient’ characterized genotypes (Arugula, Spinach and Cress) which produced highest shoot fresh (Table 1 ) and shoot dry biomass (Table 2 ), also exhibited a higher root dry matter production as compared to ‘N-ineficient’ genotypes Parsley and Dill under low N-rate. Table 1 Shoot and root fresh weight and main stem length of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate. Shoot Fresh Weight [g plant − 1 ] Root Fresh Weight [g plant − 1 ] Plant Height [cm plant − 1 ] Genotypes Low-N High-N Low-N High-N Low-N High-N Parsley 4.10 d 4.93 D 2.34 e 2.84 D 13 c 14 BC Arugula 19.10 a 15.10 A 5.57 a 4.71 A 15 b 14 B Dill 7.50 c 9.00 B 3.14 d 4.28 B 19 a 20 A Spinach 11.09 b 8.70 B 3.31 c 3.65 C 13 c 12 CD Cress 11.11 b 7.30 C 3.38 b 2.87 D 13 c 12 D Mean 10.58 9.01 3.55 3.67 15 14 F test Genotype *** *** *** Nitrogen *** *** n.s. Genotype X Nitrogen *** *** n.s. 1 Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P < 0.05. ns, non-significant. * P < 0.05, ** P < 0.01 and *** P < 0.001. Biomass partitioning between shoot and root was significantly affected by N-rates and Genotypes. Irrespective of vegetable species, a higher assimilate allocation might be occurred from shoot to root and thus a high root:shoot ration was found under high N-rate as compared to low N-rate. Among the species, the root:shoot ratio varied in between 0.21 and 0.13 g g − 1 at low N-rate, 0.24 and 0.18 g g − 1 at high N-rates. The ‘N-inefficent’ genotype Parsley had the highest root:shoot ration while the lowest ratio was shown by ‘N-efficient’ genotype Spinach at low N. On the other hand, highest root:shoot ration was shown by ‘N-efficient’ genotype Cress, while the lowest ratio was shown by ‘N-efficient’ genotype Spinach at high N-rate. All these results clearly indicated that ‘N-efficient’ characterized genotypes (Arugula, Spinach and Cress) exhibited a balanced dry matter partitioning between shoot and root (Table 2 ). On the other hand, the ‘N-inefficient’ characterized genotypes (Parsley and Dill) exhibited a non-balanced dry matter partitioning between shoot and root and therefore showed a higher root:shoot ratio then ‘N-efficient’ characterized genotypes under low N-rate. Table 2 Shoot and root dry weight and root: shoot ratio of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate. Shoot Dry Weight [g plant − 1 ] Root Dry Weight [g plant − 1 ] Root:Shoot Ratio [g g − 1 ] Genotypes Low-N High-N Low-N High-N Low-N High-N Parsley 0.42 c 0.50 C 0.09 d 0.10 C 0.21 a 0.20 AB Arugula 1.60 a 1.26 A 0.25 a 0.24 A 0.16 c 0.19 BC Dill 0.60 c 0.67 BC 0.11 d 0.13 BC 0.18 bc 0.19 BC Spinach 1.10 b 0.90 B 0.15 c 0.17 B 0.13 d 0.18 C Cress 1.00 b 0.70 BC 0.19 b 0.17 B 0.20 ab 0.24 A F test Genotype *** *** ** Nitrogen *** n.s. ** Genotype X Nitrogen *** n.s. n.s. 1 Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P < 0.05. ns, non-significant. * P < 0.05, ** P < 0.01 and *** P < 0.001. Total Leaf Number, Total Leaf Area, Photosynthesis Activity and Leaf Chlorophyll Index (SPAD) Total leaf number per plant (Fig. 1 A) and total leaf area (Fig. 1 B) of different vegetable species were significantly ( p < 0.001 ) affected by Genotype, N-rate, and Genotype x N-rate interaction. Contrast to shoot fresh and dry matter (Table 1 and Table 2 ), total leaf number per plant was significantly higher under high N-rate than low N-rate, irrespective of vegetable species. The leaf number per plant varied among the genotypes in between 25 and 12 LN plant − 1 at low N-rate, 27 and 13 LN plant − 1 at high N-rate. The highest leaf number per plant was shown by the ‘N-efficient’ genotype Cress, while the lowest leaf number per plant was shown by the ‘N-inefficient’ genotype Parsley at both N-rates (Fig. 1 A). Contrast to total leaf number per plant, but similar with shoot fresh and dry matter (Table 1 and Table 2 ), total leaf area was significantly higher under low N-rate than high N-rate, irrespective of vegetable species (Fig. 1 B). Among five vegetable species total leaf area varied in between 550 and 164 cm 2 plant − 1 at low N-rate, 399 and 169 cm 2 plant − 1 at high N-rate. Significantly highest total leaf area was shown by the ‘N-efficient’ genotypes Arugula, Cress and Spinach under low N-rate, whereas Parsley and Dill which are characterized as ‘N-efficient’ genotypes, showed the lowest total leaf area at the same N-rate (Fig. 1 B). This result clearly indicated that ‘N-efficient’ genotypes have large sized but low number of leaves than ‘N-inefficient’ genotypes which characterized by small sized but high number of leaves at low N-rate. As a result of significant Genotype x Nitrogen interaction, Arugula and interestingly Dill showed higher leaf area than Spinach and Cress while the lowest total leaf area was shown by Parsley under high N-rate. Photosynthetic activity of leaves of different vegetable species were significantly ( p < 0.001 ) affected by Genotype, N-rate, and Genotype x N-rate interaction (Fig. 1 C). As similar as in shoot fresh and dry weight (Table 1 and Table 2 ), vegetable species exhibited a higher photosynthetic activity at low N-rate as compared to high N-rate. The photosynthesis among the species varied in between 11.5 and 6.9 µmol m 2 s − 1 g plant − 1 at low N-rate, 11.4 and 7.5 µmol m 2 s − 1 g plant − 1 at high N-rate. The best performance in photosynthesis was shown by Spinach followed by Cress and Parsley under low N-rate, whereas Arugula and Dill showed the lowest photosynthesis at the same N-rate. At high N-rate, Spinach showed again the significantly highest photosynthesis and followed by Dill and Parsley while, Arugula and Cress showed the lowest photosynthetic activity at the same N-rate. Although, no significant difference was found between two N-rates in leaf chlorophyll index (SPAD), some of the species (Arugula, Spinach and Cress) tended to be higher at low N-rate than at high N-rate (Fig. 1 C). May be therefore genotypic variation and Genotype x N-rate interaction was highly significant in SPAD. Among the species SPAD varied in between 56.6 and 10.0 at low N-rate, 55.8 and 10.3 at high N-rate. The best performance in SPAD was shown by Spinach followed by Arugula and Cress under low N-rate, whereas Parsley and Dill showed the lowest SPAD value at the same N-rate. Leaf Chlorophyll (a + b) Content, Leaf Carotenoid Content, and The Leaf Nitrate Reductase Enzyme Activity Results demonstrated that leaf chlorophyll (a + b) content, leaf carotenoid content, and the leaf nitrate reductase enzyme activity (NRA) of different vegetable species were significantly ( p < 0.001 ) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table 3 ). The examined vegetable species grown under low N-rate exhibited consistently a higher chlorophyll (a + b) content and leaf carotenoid content as compared to high N-rate. This results clearly explain why the photosynthetic activity of leaves (Fig. 1 C) was also significantly high at low N-rate than high-N rate. Among the species the leaf chlorophyll (a + b) content varied in between 2.65 and 1.73 µmol g − 1 plant − 1 at low N-rate, 2.70 and 1.54 µmol g − 1 plant − 1 at high N-rate. The variation in leaf carotenoid content was in between 0.34 and 0.22 µmol g − 1 plant − 1 at low N-rate, 0.34 and 0.21 µmol g − 1 plant − 1 at high N-rate. Responses of the genotypes to the both measured parameters were similar and thus the highest leaf chlorophyll (a + b) and carotenoid content was shown by Spinach, Dill and Parsley under low N-rate, whereas Cress and Arugula showed the lowest leaf chlorophyll (a + b) and carotenoid contents at the same N-rate. Almost similar genotypic ranking (Spinach > Dill > Cress > Parsley > Arugula) in leaf chlorophyll (a + b) and leaf carotenoid contents (Spinach > Dill > Parsley > Cress > Arugula) were recorded under high N-rate among the vegetable species. Interesting results were found in NRA, since vegetable species grown under low N-rate exhibited highly significant enzyme activity than at high N-rate. Among the species RNA varied in between 3.27 and 0.25 µmol s − 1 g − 1 at low N-rate, 2.39 and 0.29 µmol s − 1 g − 1 at high N-rate. Enormously highest performance in NRA was shown by Parsley and Dill under low N-rate, whereas Arugula, Spinach and Cress showed the lowest NRA at the same N-rate. At high N-rate, again Parsley showed significantly highest NRA and followed by Spinach and Dill while, Arugula and Cress showed the lowest NRA activity at the same N-rate. Table 3 Total leaf number, total leaf area and photosynthesis of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate. Total Leaf Chlorophyll (A + B) [µmol g − 1 ] Total Leaf Carotenoid [µmol g − 1 ] Leaf Nitrate Reductase Enzyme Activity [µmol s − 1 g − 1 ] Genotypes Low-N High-N Low-N High-N Low-N High-N Parsley 1.825 c 1.675 D 0.301 c 0.271 C 3.274 a 2.385 A Arugula 1.733 d 1.539 E 0.224 d 0.213 E 0.459 c 0.370 D Dill 2.065 b 2.159 B 0.309 b 0.289 B 2.257 b 0.627 C Spinach 2.653 a 2.703 A 0.337 a 0.344 A 0.433 d 0.771 B Cress 1.820 c 1.707 C 0.228 d 0.226 D 0.250 e 0.285 E F test Genotype *** *** *** Nitrogen *** *** *** Genotype X Nitrogen *** *** *** 1 Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P < 0.05. ns, non-significant. * P < 0.05, ** P < 0.01 and *** P < 0.001. Root morphology [Root Length (cm plant − 1 ), Root Volume (cm 3 plant − 1 ), Root Diameter (mm plant − 1 )] Root morphological results demonstrated that root length, root volume and averaged root diameter of different vegetable species were significantly ( p < 0.001 ) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table 4 ). In contrast to shoot growth (Table 1 and Table 2 ), the vegetable species grown under low N-rate usually exhibited a lower root growth performance as compared to high N-rate (Table 4 ). However, particularly three vegetable species; Arugula, Spinach and Cress which are characterized as ‘N-efficient’ genotypes due to exhibiting high shoot fresh (Table 1 ) and dry biomass (Table 2 ) production under low N-rate, also exhibited significantly higher root length as compared to ‘N-ineficient’ characterized genotypes Parsley and Dill under low N-rate (Table 4 ). This is also true for the root volume of these three vegetable species (Arugula, Spinach and Cress) treated under low N-rate. On the other hand, vegetable species Arugula and Spinach maintained high root length and root volume at high N-rate while Parsley and Dill also maintained to be the lowest in root length and root volume among other species at the same N-rate. Among the vegetable species root length varied in between 5840 and 1563 m plant − 1 at low N-rate, 5990 and 1732 m g plant − 1 at high N-rate. The variation in root volume among genotypes ranked in between 1.65 and 0.68 cm 3 plant − 1 at low N-rate, 2.35 and 0.80 cm 3 plant − 1 at high N-rate. As similar as root length and root volume, the vegetable species grown under low N-rate usually exhibited significantly lower root diameter as compared to high N-rate (Table 4 ). Table 4 Total root length, root volume and average root diameter of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate Total Root Length [cm plant − 1 ] Total Root Volume [cm 3 plant − 1 ] Av. Root Diameter [mm] Genotypes Low-N High-N Low-N High-N Low-N High-N Parsley 1563 e 1732 E 0.683 e 0.797 E 0.237 c 0.244 C Arugula 5840 a 5990 A 1.867 a 2.353 A 0.203 d 0.216 E Dill 1952 d 2959 C 0.982 d 1.514 C 0.264 a 0.264 A Spinach 2052 c 2585 D 1.462 c 1.558 B 0.251 b 0.256 B Cress 3717 b 3544 B 1.665 b 1.432 D 0.236 c 0.227 D F test Genotype *** *** *** Nitrogen *** *** ** Genotype X Nitrogen *** *** *** 1 Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P < 0.05. ns, non-significant. * P < 0.05, ** P < 0.01 and *** P < 0.001. However, some of the ‘N-efficient’ vegetable species (Arugula and Cress) exhibited significantly lower root diameter whereas some of the ‘N-ineficient’ genotypes (Dill) exhibited significantly higher root diameter under both low and high N-rate conditions (Table 4 ). This is a root morphological strategy of plants to adapt adverse growth conditions. Since, when the plants produce longer roots, they try to decrease the root diameter and usually exhibit thinner roots in growth medium. Opposite is also true when the plants produce shorter roots, they try to increase the root diameter and usually exhibit thicker roots. Among two N contrasting vegetable species averaged root diameter varied in between 0.26 (N-ineficient’ Dill) and 0.20 (N-eficient’ Arugula) mm at low N-rate, 0.26 (Dill) and 0.22 (Arugula) mm at high N-rate. Discussion Plant metabolism have always been under alteration under stress environment of growth, which may alter macromolecule structure and triggers the plant to become a reactive oxygen species. Therefore, plant biomass is always affected by the stress conditions (Watanabe and Yamasaki, 2016 ). The salad leafy vegetables that were used in this study are consumed daily in every household in the world, more or less by poor and rich. The results obtained from the hydroponic experiment clearly indicate that some of the vegetable species have a great capability for Nitrogen Use Efficiency (NUE) under stress conditions. Particularly three vegetable species; Arugula, Spinach and Cress can be characterized as ‘N-efficient’ genotypes due to realizing an above-average yield in shoot dry biomass (Table 2 ) under suboptimal (low) N supply (Graham, 1984 ). Based on this reference, the lowest shoot dry biomass yielding vegetable genotypes Parsley and Dill can be characterized as ‘N-inefficient’. As well, the response to supplied high N-rate was highly significant ( p < 0.001 ) among genotypes in shoot dry matter production (Table 2 ). At this N rate, the vegetable species Arugula and Spinach maintained high shoot dry matter production at high N-rate while Parsley and Dill also maintained to be the lowest in shoot dry matter production among other species at the same N-rate. Based on these results, Arugula and Spinach genotypes can be characterized as ‘N-responsive’ due to realizing an above-average yield (shoot dry biomass) under high N supply (Sattelmacher et al., 1994 ). On the other hand, the low shoot biomass yielding vegetable genotypes (Parsley and Dill) can be characterized as ‘N-irresponsive’. Nitrogen uptake is regulated by the demand of the growing crop if N supply is not limited (Clarkson 1985). In contrast, if the required N is limited in the soil, the N uptake depends on the extent and effectiveness of the root system (Jackson et al., 1986 ). However, without continuous assimilate allocation from the active leaves, root growth and thus N uptake activity cannot be maintained under low N condition. Roots are the first places where nutrients are absorbed and translocated into shoots for chlorophyll synthesis. More root formation depends on several factors (Watanabe and Yamasaki, 2016 ; Goltsev et al., 2016 ). Response to limiting mineral elements, plants have the ability to allocate a greater proportion of their biomass to the root system (Slavin and Lloyd, 2012 ). Regarding to our results, the ‘N-efficient’ characterized genotypes (Arugula, Spinach and Cress) which produced highest shoot fresh (Table 1 ) and shoot dry biomass (Table 2 ), also exhibited a higher root dry matter production as compared to ‘N-ineficient’ genotypes Parsley and Dill under low N-rate. In biomass partitioning the ‘N-inefficent’ genotype Parsley had the highest root:shoot ration while the lowest ratio was shown by ‘N-efficient’ genotype Spinach at low N-rate. This might be due to a higher partitioning of dry matter to the root system under low N condition (Levin et al., 1989). Furthermore, an increased carbohydrate sink strength of the roots under N deficiency usually leads to a greater allocation of photo assimilates to the roots (Merrill et al., 2002 ). On the other hand, highest root:shoot ration was shown by ‘N-efficient’ genotype Cress, while the lowest ratio was shown by ‘N-efficient’ genotype Spinach at high N-rate. All these results clearly indicated that ‘N-efficient’ characterized genotypes (Arugula, Spinach and Cress) exhibited a balanced dry matter partitioning between shoot and root (Table 2 ). On the other hand, the ‘N-inefficient’ characterized genotypes (Parsley and Dill) exhibited a non-balanced dry matter partitioning between shoot and root and therefore showed a higher root:shoot ratio then ‘N-efficient’ characterized genotypes under low N-rate. The response of root parameters might be related to overexpression of type I H+ -PPases under the control of either the 35S or Ubiquitin promoter which is responsible for root and shoot proliferation (Li et al., 2010 ; Pasapula et al., 2011 ). Biomass production and yield of a crop is strongly dependent on its leaf area as well as the rate of leaf photosynthesis (Hirasawa and Hsiao, 1999). Further, carbon and nitrogen (N) metabolisms are also interrelated for the sustained growth and development of plants (Zheng, 2009 ). Furthermore, an increased root capacity also depends on an ATP supply generated by the respiration of reduced carbon molecules. Deficiency of N leads to low output of photosynthetic activity in plants, this have been indicated by several physiological and biochemical studies (Gupta et al., 2012 ). Leaf area is assessed as the one-sided green leaf area per unit ground area in broadleaf canopies, i.e. leaf area index (LAI) (Mae, 1997 ). As a rule, enhancement in crop yield occurs when an optimal LAI value is reached which depends on plant species, light intensity, leaf shape or leaf angle (Marschner, 1995 ). Regarding to our results in number of leaves (Fig. 1 A) and total leaf area (Fig. 1 B), the ‘N-efficient’ genotypes Arugula, Cress and Spinach have significantly largest sized but low numbered of leaves than ‘N-inefficient’ genotypes Parsley and Dill which characterized by significantly smallest sized but high numbered of leaves at low N-rate. The chlorophyll concentration in the leaf is essential for crop growth and development (Singh et al., 2016 ) hence quantifying it makes available vital information about the effects of environment on plant growth (de González et al., 2015 ). Although, no significant difference was found between two N-rates in leaf chlorophyll index (SPAD), some of the species (Arugula, Spinach and Cress) tended to be higher at low N-rate than at high N-rate (Fig. 1 C). May be therefore vegetable species grown under low N-rate exhibited consistently a higher chlorophyll (a + b) content and leaf carotenoid content as compared to high N-rate. This results clearly explain why the photosynthetic activity of leaves (Fig. 1 C) was also significantly high at low N-rate than high-N rate. Carotenoids on the other hand, contributes to light harvesting whilst preventing photodamage to the photosynthetic systems (Ndukwe et al., 2016 ) through their interconversion with the xanthophyll molecules (Yamasaki et al., 2014 ). Our study is coinciding with the study of Chen et al., ( 2015 ) that, leaf chlorophyll and carotenoid content is dependent on the presence and ratio of mineral elements (especially nitrogen) in the substrate. The exclusive publications of Singh et al., ( 2016 ); Ndukwe et al., ( 2016 ); Hristov et al., ( 2019 ) indicate that SPAD and photosynthetic activity of the plant is not only limited with N-rate but several other factors like temperature and light intensity are involved for better build-up of plant mechanism under which their capability of utilizing more NUE is increased, even under stress N-rates. Our study indicates that these salad leafy vegetables can have more NUE under low stress conditions at low temperature. The enzyme nitrate reductase (NR) activity determination is one of the measures of N utilization efficiency in crops. Its activity in plants gives a good estimate of the plant’s N status and is usually correlated with growth and yield (Yamasaki et al., 2014 ). When plants take up nitrogen in the form of nitrates (NO 3 - ), it is first reduced to ammonium (NH 4 + ) by a NR enzyme before it is being incorporated into plant compounds. High NR activity in a plant implies that the plant has a greater ability to convert absorbed NO 3 - to usable forms. Interesting results were found in NRA in our present study, since vegetable species grown under low N-rate exhibited highly significant enzyme activity than at high N-rate (Table 3 ). Enormously highest performance in NRA was shown by N-inefficient genotypes Parsley and Dill under low N-rate, whereas the N-efficient genotypes Arugula, Spinach and Cress showed the lowest NRA at the same N-rate. This might be due to a high leaf area formation which usually leads to a reduction in the amount of N per unit of leaf area [Marschner, 1995 ). Also, corroborative results were demonstrated in the study of Ulas et al., ( 2019 ) who reported that the low yielding ‘N-inefficient’ genotypes showed substantially higher shoot N concentrations than the high yielding ‘N-efficient’ genotypes (Table 1 ). Conclusions The hydroponic study clearly indicated that shoot growth, root morphological and leaf physiological responses were significantly ( p < 0.001 ) affected by Genotype, N-Rate and Genotype x N-Rate interaction. Shoot growth of some vegetable species (Argula, Spinach and Cress) was significantly higher under low N than high N-rate, demonstrating that they have a great capability for Nitrogen Use Efficiency (NUE) under low N stress conditions. Similar responses were also recorded for the root growth of the N-efficient species under low N-rate. The NUE of these species was closely associated with leaf physiological (leaf area, SPAD, photosynthesis, leaf chlorophyll (a + b) and carotenoid) and root morphological (root length, root volume and average rot diameter) characteristics. These physiological and morphological plant traits could be useful characters for the selection and breeding of ‘N-efficient’ leafy vegetable species for sustainable agriculture in the future. However, further investigation should be carried out at field level to confirm their commercial production. Declarations Conflicts of interest: The author declare that she has no conflict of interest. Acknowledgements: I thank all staff members of the Plant Nutritional Physiology Laboratory of Erciyes University, Turkey for technical supports and for providing all the necessary facilities throughout the experiments. References Adam, M.B., A. Ulas. (2023). Vigorous rootstocks improve nitrogen efficiency of tomato by inducing morphological, physiological and biochemical responses. Gesunde Pflanzen 75:565–575. https://doi.org/10.1007/s10343-022-00819-8 Brégard, A.; Bélanger, G.; Michaud, R. Nitrogen use efficiency and morphological characteristics of timothy populations selected for low and high forage nitrogen concentrations. Crop Sci. 2000, 40, 422–429. Graham, R.D. Breeding characteristics in cereals. Adv. Plant Nutr. 1984, 1, 57–102. Chen, T.W., K. Kahlen, H. Stützel. (2015). Disentangling the contributions of osmotic and ionic effects of salinity on stomatal, mesophyll, biochemical and light limitations to photosynthesis. Plant Cell Environment 38:1528–1542. https ://doi.org/10.1111/pce.12504 de González, M.T.N., W.N. Osburn, M.D. Hardin, M. Longnecker, H.K. Garg. (2015). A Survey of nitrate and nitrite concentrations in conventional and organic-labeled raw vegetables at retail. Journal of Food Science 80: 942-949. Grindlay, D.J.C. Towards an explanation of crop nitrogen demand based on the optimization of leaf nitrogen per unit leaf area. J. Agric. Sci. 1997, 128, 377–396. Goltsev, V.N., H.M. Kalaji, M. Paunov, W. Bąba, T. Horaczek, J. Mojski, H. Kociel, S.I. Allakhverdiev. (2016). Variable chlorophyll fluorescence and its use for assessing physiological condition of plant photosynthetic apparatus. Russian Journal of Plant Physiology 63:869–893. https://doi.org/10.1134/S1021 44371 60500 58. Gupta, N., A. K. Gupta, V. S. Gaur, A. Kumar. (2012). Relationship of nitrogen use efficiency with the activities of enzymes involved in nitrogen uptake and assimilation of finger millet genotypes grown under different nitrogen inputs. The Scientific World Journal 2012, 625731. https://doi.org/10.1100/2012/625731. Harley, S. (1993). Use of a simple colorimetric assay to determine conditions for induction of nitrate reductase in plants. The American Biology Teacher55: 161-164. Hristov, N., A. Bannink, L. A. Crompton, P. Huhtanen, M. Kreuzer, M. McGee, P. Nozière, C. K. Reynolds, A. R. Bayat, D. R. Yáñez-Ruiz, J. Dijkstra, E. Kebreab, A. Schwarm, K. J. Shingfield, Z. Yu. (2019). Invited review: Nitrogen in ruminant nutrition: A review of measurement techniques. Journal of Dairy Sciences 102:5811–5852. https://doi.org/10.3168/jds.2018-15829.S Jackson, W.A.; Pan, W.L.; Moll, R.H.; Kamprath, E.J. Uptake, translocation, and reduction of nitrate. Biochem. Basis Plant Breed. 1986, 2, 73–108. Lewin, S.A.; Mooney, H.A.; Field, C. The dependence of plant root: Shoot ratios on internal nitrogen concentration. Ann. Botany 1989, 64, 71–75. Liu, C.W., Y. Sung, B.C. Chen, H.Y. Lai. (2014) Effects of nitrogen fertilizers on the growth and nitrate content of lettuce (Lactuca sativa L.). Int J Environ Res Public Health 11(4):4427–4440. https://doi.org/10.3390/ijerph110404427 Lichtenthaler, H. K. (1987). Chlorophylls and carotenoids: pigments of photosynthetic biomembranes. Methods in enzymology 148:350-382. Li, Z., C.M. Baldwin, Q. Hu, H. Liu, H. Luo. (2010). Heterologous expression of Arabidopsis H+-pyrophosphatase enhances salt tolerance in transgenic creeping bentgrass ( Agrostis stolonifera L.). Plant Cell Environment 33:272–289. Mae, T. (1997). Physiological nitrogen efficiency in rice: Nitrogen utilization, photosynthesis and yield potential. Plant Soil 1997, 196, 201–210. Marschner, H. Mineral Nutrition of Higher Plants, 2nd ed.; Academic Press: London, UK, 1995. Merrill, S.D.; Tanaka, D.L.; Hanson, J.D. Root length growth of eight crop species in haplustoll soils. Soil Sci. Soc. Am. J. 2002, 66, 913–923. Ndukwe, O. K., H.O. Edeoga, I. C. Okwulehie, G. Omosun. (2016). Variability in the chlorophyll and carotene composition of ten maize ( zea mays ) varieties. European Journal of Physical and Agricultural Sciences4(1): 1-6. Pasapula, V., G. Shen, S. Kuppu, J. Paez-Valencia, M. Mendoza, P. Hou, J. Chen, X. Qiu, L. Zhu, X. Zhang. (2011). Expression of an Arabidopsis vacuolar H+-pyrophosphatase gene (AVP1) in cotton improves drought and salt tolerance and increases fibre yield in the field conditions. Plant Biotechnology Journal 9: 88–99. Sattelmacher, B.; Horst, W.J.; Becker, H.C. Factors that contribute to genetic variation for nutrient efficiency of crop plants. Zeitschrift für Pflanzenernährung und Bodenkunde 1994, 157, 215–224. Schroeder, J.I., E. Delhaize, W.B. Frommer, M.L. Guerinot, M.J. Harrison, L. Herrera-Estrella, T. Horie, L.V. Kochian, R. Munns, N.K. Nishizawa, Y.F. Tsay, D. Sanders. (2013). Using membrane transporters to improve crops for sustainable food production. Nature 2;497(7447):60-6. doi: 10.1038/nature11909. PMID: 23636397; PMCID: PMC3954111. Singh, M., M. M. A. Khan, M. Naeem. (2016). Effect of nitrogen on growth, nutrient assimilation, essential oil content, yield and quality attributes in Zingiber officinale Rosc. Journal of the Saudi Society of Agricultural Sciences 15(2), 171-178. Slavin, J.L., B. Lloyd. (2012). Health benefits of fruits and vegetables. Advances in Nutrition 3:506-516. UN (2022). Future of the World, Policy Brief, United Nations Department of Economic and Social Affairs. https://www.un.org.development.desa.pd/files/undesa_pd_2022_pb_140.pdf Ulas A, Doganci E, Ulas F, Yetisir H (2019) Root-growth characteristics contributing to genotypic variation in nitrogen efficiency of bottle gourd and rootstock potential for watermelon. Plants (Basel) 8:77 Watanabe, N.S., H. Yamasaki. (2016). Dynamics of Nitrite Content in Fresh Spinach Leaves: Evidence for Nitrite Formation Caused by Microbial Nitrate Reductase Activity. Journal of Nutrition and Food Sciences 7: 572. doi: 10.4172/2155-9600.1000572. Yamasaki, H., Watanabe, N. S., Fukuto, J., & Cohen, M. F. (2014). Nitrite-dependent nitric oxide production pathway: Diversity of NO production systems. Studies on Pediatric Disorders, 35-54. Yamasaki, H., Watanabe, N.S., Fukuto, J., Cohen, M.F. (2014). Nitrite-Dependent Nitric Oxide Production Pathway: Diversity of NO Production Systems. In: Tsukahara, H., Kaneko, K. (eds) Studies on Pediatric Disorders. Oxidative Stress in Applied Basic Research and Clinical Practice. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0679-6_3 Zheng. Z.L. (2009). Carbon and nitrogen nutrient balance signaling in plants. Journal of Plant Signal Behaviour 4:584–591. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2710548/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3653783","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":252622365,"identity":"07e5bed3-d0c8-4dd5-9397-8feaa898f0a7","order_by":0,"name":"Firdes Ulas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBAC9gbmBgaGCiCLGSLA2EBIC88BkJozKFqYidDC2IYQIEKLRGLjx5/zbPL523kMH/5gsJHdcID/2AcCWpqlebelWc44zGNszMOQZrzhADPzDHxa7CUSG6QZtx02MGDmMZNmYDicCNJCyGHNP3/O+Q/SYv7zB8N/orS0SfA2HADbwsDDcIAILTwP26x5jiUbSBxmK5bmMUg2nnmY2Ri/Fvbkwzd/1NgZ8Pcf3vjxR4WdbN/xxsd4tSABDgMGBiAiFC3IgP0B8WpHwSgYBaNgRAEAKytCF2/33vYAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Firdes","middleName":"","lastName":"Ulas","suffix":""}],"badges":[],"createdAt":"2023-11-23 11:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3653783/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3653783/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47218582,"identity":"6b020c5d-ca9e-41cb-b32d-041751d5ddf2","added_by":"auto","created_at":"2023-11-28 18:46:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105106,"visible":true,"origin":"","legend":"\u003cp\u003eTotal leaf number (A), total leaf area (B), the intensity of net photosynthesis measurement (C) and SPAD (leaf chlorophyll index) (D) of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N supply. 1 Values denoted by different letters (lower and upper case letters for low and high N supply, respectively) are significantly different between genotypes within columns at P \u0026lt; 0.05. ns, non-significant. * P \u0026lt; 0.05, ** P \u0026lt; 0.01 and *** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3653783/v1/c72beb4806954d8691588e13.png"},{"id":47298459,"identity":"d74aea8a-0d2c-4acb-9230-f483106d88f2","added_by":"auto","created_at":"2023-11-29 14:59:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":547644,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3653783/v1/5f9820c7-bb33-4008-8be2-2bab40626114.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physio-Morphological Traits Contributing to Genotypic Differences in Nitrogen Use Efficiency of Leafy Vegetable Species under Hydroponics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe world population is increasing at a rate of 80\u0026nbsp;million per year and expected to be around 9.0\u0026nbsp;billion for the year 2037 and 10.0\u0026nbsp;billion for 2058 (UN, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A rapidly increasing world population demands ever-increasing food production. However, to keep food production at the same level as population growth without using up or devastate the non-renewable resources is not an easy task for sustainable agriculture (Adam and Ulas, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Today, mineral fertilizers are still an important resource and essential input for crop growth and yield in both high-input and low-input agricultural systems. Particularly, nitrogen (N) is the most common and widely used essential fertilizer nutrient to increase the crop yield and plant biomass (Liu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). To meet the demand of food for increasing world population more than 110 Tg of N fertilizers are applied annually to enhance crop production, where 1\u0026ndash;2% of world energy is utilized to produce N fertilizers (Cho et al., 2017). However, the available N is more often a limiting factor influencing plant growth than any other nutrient in both high-input and low-input agriculture systems (Grindlay, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsually, in low-input agriculture systems, low amounts of N fertilizers are used mostly by peasant farmers, and thus the fertility of the soil is reducing. On the other hand, due to the overdosing of nitrogen fertilizers carried out by high-rate agriculture systems, concerns are rising on environmental pollution of both air and water (Br\u0026eacute;gard et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). For instance, there is a concept that more N, more yield can be obtained. And keeping this thought the farmers or growers apply more nitrogenous fertilizers to get the maximum yield of leafy vegetable crops in a short growing season. Leafy vegetable crops are consumed by the human beings in a large quantity throughout the world, since nutritionally and dietary vegetable is an important source of vitamins (A, C, and B-complex), minerals (especially calcium, iron, magnesium, and phosphorus) and fibers. Moreover, green leafy vegetables act as strong antioxidants that detoxify Reactive Oxygen Species (ROS) formed under stress or disease conditions (Slavin and Lloyd, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, medical doctors advised to take the green vegetables more in our daily diet (Gupta et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, many leafy vegetable crops have shallow root systems (30\u0026ndash;40 cm rooting depth) and thus N fertilization should be done carefully for the health of human beings and environmental safety. Because, the efficiency of N fertilizers is frequently low, since, plants take up often less than 50% of the applied N (Raun and Johnson, 1999), and the proportion of fertilizer N not utilized by the crop is left in the soil and/or lost from the plant/soil system through volatilization (i.e. ammoniac), leaching (i.e. nitrate) and denitrification (i.e. nitrous oxide/greenhouse gas) (Byrnes and Bumb, 1998; Laegreid et al., 1999).\u003c/p\u003e \u003cp\u003eMany crop plants, especially leafy vegetables, accumulate excessive nitrate in expanded leaves under low light conditions when the uptake of nitrate exceeds reduction at a high transpiration rate. Usually when the plants use the solar energy sufficiently, the absorbed nitrate from the soil is reduced to nitrite by Nitrate Reductase (NR) in cytosol (Watanabe and Yamasaki, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nitrite translocated into the chloroplasts is converted to ammonium ion (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) by nitrite reductase (NiR) located in the chloroplasts and then ammonium ion is assimilated into amino acids (Watanabe and Yamasaki, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, particularly in winter season when duration and the intensity of solar energy reduces, excessive nitrate in expanded leaves may harm the health of the consumer as it can be converted to nitrite causing methaemoglobinaemia or carcinogenic nitrosamines. In food science, nitrite contained in human diet has long been recognized for its toxicity as the causative agent of methemoglobinemia (Yamasaki et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Due to the formation of mutagenic nitrosamine it has also been presumed that nitrite is potentially carcinogenic (de Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Because of these two historical backgrounds, nitrite as well as nitrate content in foods and drinks has been strictly limited by regulations in many countries (Slavin and Lloyd, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the moment, there is a great interest in identifying the processes involved in the regulation of N uptake and N metabolism within the leafy vegetable crops, and also in developing N management strategies to reduce N fertilizer use and to improve nitrogen use efficiency (NUE). In plants NUE is a result of uptake efficiency (where N is taken from the soil or N fertilizer) and utilization efficiency (extracted N is converted into yield) (Schroeder et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Many external environmental conditions are responsible for the efficient use of N within plants along with N-rates (Cho et al., 2017). Therefore, the aim of the study was to determine genotypic differences NUE among different leafy vegetable species (Arugula, Spinach, Cress, Parsley and Dill) grown hydroponically under two different N-rates (Low N: 0.3 mM and High N: 3.0 mM) and to identify the leaf and root traits which are contributing to NUE.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material, treatments, and experimental design\u003c/h2\u003e \u003cp\u003eThis study was carried out in the Plant Nutritional Physiology Laboratory of Erciyes University, Faculty of Agriculture, Central Anatolia in Turkey in between March \u0026ndash; April in 2020. A hydroponic experiment was conducted by using an aerated Deep-Water Culture (DWC) (DWC) technique in a fully automated climate chamber. For the vegetative growth, the average day/night temperatures were 18/20\u0026deg;C while the relative humidity was 65\u0026ndash;70%. Plants received approximately 400 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e S\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e photon flux in a photoperiod of 10/14 h of light/dark regimes in the controlled growth chamber. Five leafy vegetable species (Parsley, Arugula, Dill, Spinach and Cress) were used as the plant materials. The seeds were sown in 72-cell polystyrene trays (W 280 \u0026times; L 540 \u0026times; H 45 mm, IBK Iklim Bahce Co., Ltd., Turkey) filled with a mixture of peat (pH: 6.0-6.5) and perlite (2v:1v). To promote germination, the plug trays were wrapped with vinyl chloride resin film and then placed in a germination chamber at 28\u0026deg;C. Seedlings were watered daily. When the seedlings developed three or four true leaves, they were transplanted to plastic pots after the root system of the plants was carefully washed in distilled water to clean the substrate. Three uniform seedlings of each vegetable were then transferred into 8 L container containing a modified Hoagland nutrient solution and grown for five weeks (Slide 1). Two different nitrogen concentrations (Low N: 0.3 mM N, High N: 3.0 mM N) were supplied and Ca(NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e was used as N source. Each pot was filled with 8 L cultivation solution that was aerated by an air pump. The nutrient solution had the following composition (\u0026micro;M): K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e (500); KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (250); CaSO\u003csub\u003e4\u003c/sub\u003e (1000); MgSO\u003csub\u003e4\u003c/sub\u003e (325); NaCl (50); H\u003csub\u003e3\u003c/sub\u003eBO\u003csub\u003e3\u003c/sub\u003e (8); MnSO\u003csub\u003e4\u003c/sub\u003e (0.4); ZnSO\u003csub\u003e4\u003c/sub\u003e (0.4); CuSO\u003csub\u003e4\u003c/sub\u003e (0.4); MoNa\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e (0.4); Fe-EDDHA (80). Due to transplanting of small seedlings, solutions were replaced completely every week in the first two weeks. Complete renewals of the nutrient solution were done (at 7-day intervals) when the N concentration of the nutrient solution in the 3.0 mM N rate pots fell below 0.5 mM, as measured daily with nitrate test strips (Merck, Darmstadt, Germany) by using a NitracheckTM reflectometer. In hydroponics experiment, the total vegetative growth period from transplanting up to final harvest was almost five weeks. The experiment was in a completely randomized block design (CRBD) with three replicates, with six plants per replicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eHarvest, shoot, and root dry weight measurements\u003c/h2\u003e \u003cp\u003eAt final harvest, five weeks after transplanting in 2020, the shoot biomass (the upper part of the plants) was determined. Plants were harvested by separating them into stems, leaves, and roots. Their tissues were dried in a forced-air oven at 80 ◦C for 72 h for biomass determination until the stable weight was reached. And then they were weighed on an electronic digital scale. Shoot biomass was equal to the sum of aerial vegetative plant parts (leaves\u0026thinsp;+\u0026thinsp;stems) and was expressed in g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Root to shoot ratio was calculated by dividing the root dry weight by the sum of leaf and stem dry weights. Plant height (cm) was measured as the distance between the pot surface and the tip of the plant by using a ruler. Each fully developed leaf was counted as leaf number and recorded as LN plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLeaf physiological measurements\u003c/h2\u003e \u003cp\u003eIn the hydroponics experiment, total leaf area of the plants was measured destructively at harvesting by using leaf-area meter (LI-COR Model 3100, LI-COR. Inc., Lincoln, NE, USA). Total leaf area was recorded in centimeter square (cm\u003csup\u003e2\u003c/sup\u003e). Single-Photon Avalanche Diode (SPAD) was measured with a chlorophyll meter Minolta SPAD 502 Plus chlorophyll meter at the third week after plant establishment in hydroponics culture, and continued weekly till the end of the experiment. During the growth period, the leaf chlorophyll content measurement was performed on the youngest fully expanded leaves (3rd \u0026minus;\u0026thinsp;4th leaf from the apex) of whole plants, using four replicate leaves per treatment in the third and fifth week of the vegetative period. The measurements were carried out at the temperature of 18/20\u0026deg;C (average day/night temperatures), the relative humidity of 65\u0026ndash;70%. The supplied photon flux in the growth chamber was almost 400 \u0026micro;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e S\u003csup\u003e\u0026ndash;1\u003c/sup\u003e with an intensity of 10/14 h (light/dark) photoperiod. Prior to harvest, non-destructive measurements of the leaf-level CO\u003csub\u003e2\u003c/sub\u003e gas exchange (\u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were done in a controlled growth chamber by using a portable photosynthesis system (LI-6400XT; LI-COR Inc., Lincoln, NE, USA). The leaf net photosynthesis measurement was performed on the youngest fully expanded leaves (3rd \u0026minus;\u0026thinsp;4th leaf from the apex), using four replicate leaves per treatment in the third and fifth week of the vegetation period. The LI-6400 was equipped with a well-stirred 2.5 \u0026times; 10\u0026ndash;5 m\u003csup\u003e3\u003c/sup\u003e leaf chamber with constant area inserts (1.2 \u0026times; 10\u0026ndash;3 m\u003csup\u003e2\u003c/sup\u003e) and fitted with a variable intensity red source (Model QB1205LI-670; Quantum Devices, Barneveld, WI) (Tennessen et al., 1994). Leaf temperature within the chamber was 30\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C while the vapor pressure difference between the leaf and air was 2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u0026deg;C, and CO\u003csub\u003e2\u003c/sub\u003e concentration was 365\u0026thinsp;\u0026plusmn;\u0026thinsp;10 \u0026micro;L L\u003csup\u003e\u0026ndash;1\u003c/sup\u003e. All gas exchange measurements were carried out between 09:00 and 12:00 hr.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLeaf total chlorophyll (a\u0026thinsp;+\u0026thinsp;b) and carotenoid content measurements\u003c/h2\u003e \u003cp\u003eA day before harvesting, extraction of the photosynthetic pigments from 100 mg (0.1 g) of fresh leaf samples from each replicate of the treatment combinations were done by measuring the total chlorophyll content of the leaf and carotenoid contents, using UV-VIS Spectroscopy. The leaf samples used for chlorophyll and carotenoid determinations were of the same physiological age with those used for the leaf net photosynthesis measurements. The samples were put into 15 ml capped containers where 10 ml of ethylene alcohol of 95% concentration was added. They were then kept in darkness at room temperature for overnight, to allow the extraction of the leaf pigments. Measurements were done using the spectrometer (UV/VS T80\u0026thinsp;+\u0026thinsp;of PG Instruments Limited, UK) at wavelengths of 470 nm, 648.6 nm, and 664.2 nm. Total chlorophyll (Total-Chlo) and total carotenoids (TC) were then estimated from the spectrometric readings using the formulae of Lichtenthaler (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTotal-Chlo (mg/g plant sample)\u003c/b\u003e = [(5.24 WL664.2- 22.24 WL648.6 x 8.1]/ weight of plant sample (g)\u003c/p\u003e \u003cp\u003e \u003cb\u003eTC (mg/g plant sample)\u003c/b\u003e =[(4.785 WL470\u0026thinsp;+\u0026thinsp;3.657 WL664.2) -12.76 WL648.6) x 8.1]/ weight of plant sample (g)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eWL470, WL648.6 and WL664.2 refers to spectrometric readings at wavelength 470 nm, 648.6 nm and 664.2 nm respectively.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRoot morphological measurements\u003c/h2\u003e \u003cp\u003eThe plant root morphological parameter of total root length (m plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), total root volume (cm\u003csup\u003e3\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and average root diameter (mm) were measured by using a special image analysis software program WinRHIZO (Win/Mac RHIZO Pro V. 2002c Regent Instruments Inc., Qu\u0026eacute;bec, QC G1V 1V4, Canada) in combination with a recording device of Epson Expression 11000XL scanner (Long Beach, CA, USA). It was recorded as cm plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and then converted to m plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLeaf nitrate reductase enzyme activity (NRA) measurement\u003c/h2\u003e \u003cp\u003eLeaf nitrate reductase enzyme activity (NRA) in the leaf was determined following the method proposed by Harley (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and Goltsev et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). During harvesting time leaf sample was taken and chopped into pieces; 2 grams of the latter were placed in each of two falcon tubes and labeled time-0 (T\u003csub\u003e0\u003c/sub\u003e) and time-60 (T\u003csub\u003e60\u003c/sub\u003e). The tubes were covered with aluminum foil to prevent exposer to light. It was then added with 10 ml of assay buffer solution [100 mM phosphate buffer, pH 7.5; 30 mM KNO\u003csub\u003e3\u003c/sub\u003e; 5%(v/v) propanol] (T\u003csub\u003e0\u003c/sub\u003e and T\u003csub\u003e60\u003c/sub\u003e). The T\u003csub\u003e0\u003c/sub\u003e container was immediately placed into boiling water for 5 minutes, removed and allowed to cool to room temperature. While the T\u003csub\u003e60\u003c/sub\u003e was kept for 60 minutes at room temperature before it was also placed in boiling water for 5 minutes and allowed to cool to room temperature. To detect nitrite in the assay tubes, the optical density (OD) of each standard tube was determined at 540 nm wavelength in the spectrometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll measured physiological and morphological parameters were analyzed using SAS Statistical Software (SAS 9.0, SAS Institute Inc., Cary, NC, USA). A Two-factor Factorial analysis of variance was performed to study the effects of genotypes and N and their interactions. Levels of significance are represented by * P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and ns means not significant. The mean separation of treatments was done using Duncan\u0026rsquo;s Multiple Test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBiomass production and partitioning\u003c/h2\u003e \u003cp\u003eResults obtained from the hydroponic experiment demonstrated that shoot fresh weight and root fresh weight of different vegetable species were significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Some of the vegetable species grown under low N-rate usually exhibited a higher shoot growth performance as compared to high N-rate. Among the vegetable species shoot fresh weight varied in between 19.1 and 4.10 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 15.1 and 4.93 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. Significantly highest shoot fresh weight was observed in Arugula followed by Spinach and Cress under low N-rate, whereas Parsley and Dill showed the lowest shoot fresh weight at the same N-rate. At high N-rate, Arugula showed again the significantly highest shoot fresh weight and followed by Dill and Spinach while, Parsley and Cress showed the lowest shoot fresh weight at same N-rate. On the other hand, root growth responses of the vegetable species were not similar to shoot growth responses under low N-rate. Since vegetable species usually exhibited higher root fresh weight under high N-rate as compared to low N-rate. The root fresh weight among the vegetable species varied in between 5.57 and 2.34 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 4.71 and 2.84 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. Among the species, significantly highest root fresh weight was shown by Arugula followed by Cress and Spinach under low N-rate, whereas Parsley and Dill showed the lowest root fresh weight at the same N-rate. All these indicated that shoot and root growth responses of vegetable species to low N-rate were matching. However, similar genotypic responses could not be recorded in root fresh weight under high N-rate, indicating a significant Genotype x N-rate interaction. Significantly highest root fresh weight was shown by Arugula followed by Dill and Spinach under high N-rate, whereas Parsley and Cress showed the lowest root fresh weigh. Although, highly significant genotypic variation (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) existed among vegetable species in plant height, however, effects of N-rate and Genotype x N-rate interaction were no significant. Averaged over N-rates, the highest plant height was shown by Dill, whereas the lowest plant height was shown by Spinach. However, regarding to the shoot fresh weight, both genotypes (Dill and Spinach) showed contrasted results, which clearly indicating that not always a positive relationship between shoot fresh weight and plant height existing. Among the species, the plant height varied in between 19.0 and 13.0 cm plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 20.0 and 12.0 cm plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate.\u003c/p\u003e \u003cp\u003eShoot dry matter production of different vegetable species was significantly affected by Genotype, N-rate, and Genotype x N-rate interaction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As similar as in shoot fresh weight, vegetable species grown under low N-rate usually exhibited a higher shoot dry matter production as compared to high N-rate. The shoot dry matter among the species varied in between 1.60 and 0.42 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 1.26 and 0.50 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. The best performance in shoot dry matter production was shown by Arugula followed by Spinach and Cress under low N-rate, whereas Parsley and Dill showed the lowest shoot dry matter production at the same N-rate. All these results clearly indicate that some of the vegetable species have a great capability for Nitrogen Use Efficiency (NUE) under stress conditions. Particularly three vegetable species; Arugula, Spinach and Cress can be characterized as \u0026lsquo;N-efficient\u0026rsquo; genotypes due to exhibiting high shoot fresh (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and dry biomass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) production as compared to \u0026lsquo;N-ineficient\u0026rsquo; genotypes Parsley and Dill under low N-rate. On the other hand, vegetable species Arugula and Spinach maintained high shoot dry matter production at high N-rate while Parsley and Dill also maintained to be the lowest in shoot dry matter production among other species at the same N-rate.\u003c/p\u003e \u003cp\u003eAlthough, the root fresh weight of different vegetable species was significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), only significant difference existed among the genotypes in root dry matter (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The root dry weight among the species varied in between 0.25 and 0.09 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 0.24 and 0.10 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. Averaged over N-rates, the highest root dry weight among the species was shown by Arugula followed by Spinach and Cress, whereas Parsley and Dill showed the lowest root dry weight. All these results clearly indicate that the \u0026lsquo;N-efficient\u0026rsquo; characterized genotypes (Arugula, Spinach and Cress) which produced highest shoot fresh (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and shoot dry biomass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), also exhibited a higher root dry matter production as compared to \u0026lsquo;N-ineficient\u0026rsquo; genotypes Parsley and Dill under low N-rate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShoot and root fresh weight and main stem length of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eShoot Fresh Weight\u003c/p\u003e \u003cp\u003e[g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRoot Fresh Weight\u003c/p\u003e \u003cp\u003e[g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePlant Height\u003c/p\u003e \u003cp\u003e[cm plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParsley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.93 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.84 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 BC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArugula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.10 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.10 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.57 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.71 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.00 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.28 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.09 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.70 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.65 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 CD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.11 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.30 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.38 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.87 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eF test\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype X Nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ns, non-significant. * P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eBiomass partitioning between shoot and root was significantly affected by N-rates and Genotypes. Irrespective of vegetable species, a higher assimilate allocation might be occurred from shoot to root and thus a high root:shoot ration was found under high N-rate as compared to low N-rate. Among the species, the root:shoot ratio varied in between 0.21 and 0.13 g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 0.24 and 0.18 g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rates. The \u0026lsquo;N-inefficent\u0026rsquo; genotype Parsley had the highest root:shoot ration while the lowest ratio was shown by \u0026lsquo;N-efficient\u0026rsquo; genotype Spinach at low N. On the other hand, highest root:shoot ration was shown by \u0026lsquo;N-efficient\u0026rsquo; genotype Cress, while the lowest ratio was shown by \u0026lsquo;N-efficient\u0026rsquo; genotype Spinach at high N-rate. All these results clearly indicated that \u0026lsquo;N-efficient\u0026rsquo; characterized genotypes (Arugula, Spinach and Cress) exhibited a balanced dry matter partitioning between shoot and root (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On the other hand, the \u0026lsquo;N-inefficient\u0026rsquo; characterized genotypes (Parsley and Dill) exhibited a non-balanced dry matter partitioning between shoot and root and therefore showed a higher root:shoot ratio then \u0026lsquo;N-efficient\u0026rsquo; characterized genotypes under low N-rate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShoot and root dry weight and root: shoot ratio of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eShoot Dry Weight\u003c/p\u003e \u003cp\u003e[g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRoot Dry Weight\u003c/p\u003e \u003cp\u003e[g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRoot:Shoot Ratio\u003c/p\u003e \u003cp\u003e[g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParsley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20 AB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArugula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19 BC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19 BC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eF test\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype X Nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ns, non-significant. * P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTotal Leaf Number, Total Leaf Area, Photosynthesis Activity and Leaf Chlorophyll Index (SPAD)\u003c/h2\u003e \u003cp\u003eTotal leaf number per plant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and total leaf area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) of different vegetable species were significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) affected by Genotype, N-rate, and Genotype x N-rate interaction. Contrast to shoot fresh and dry matter (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), total leaf number per plant was significantly higher under high N-rate than low N-rate, irrespective of vegetable species. The leaf number per plant varied among the genotypes in between 25 and 12 LN plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 27 and 13 LN plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. The highest leaf number per plant was shown by the \u0026lsquo;N-efficient\u0026rsquo; genotype Cress, while the lowest leaf number per plant was shown by the \u0026lsquo;N-inefficient\u0026rsquo; genotype Parsley at both N-rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Contrast to total leaf number per plant, but similar with shoot fresh and dry matter (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), total leaf area was significantly higher under low N-rate than high N-rate, irrespective of vegetable species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Among five vegetable species total leaf area varied in between 550 and 164 cm\u003csup\u003e2\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 399 and 169 cm\u003csup\u003e2\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. Significantly highest total leaf area was shown by the \u0026lsquo;N-efficient\u0026rsquo; genotypes Arugula, Cress and Spinach under low N-rate, whereas Parsley and Dill which are characterized as \u0026lsquo;N-efficient\u0026rsquo; genotypes, showed the lowest total leaf area at the same N-rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This result clearly indicated that \u0026lsquo;N-efficient\u0026rsquo; genotypes have large sized but low number of leaves than \u0026lsquo;N-inefficient\u0026rsquo; genotypes which characterized by small sized but high number of leaves at low N-rate. As a result of significant Genotype x Nitrogen interaction, Arugula and interestingly Dill showed higher leaf area than Spinach and Cress while the lowest total leaf area was shown by Parsley under high N-rate.\u003c/p\u003e \u003cp\u003ePhotosynthetic activity of leaves of different vegetable species were significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) affected by Genotype, N-rate, and Genotype x N-rate interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). As similar as in shoot fresh and dry weight (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), vegetable species exhibited a higher photosynthetic activity at low N-rate as compared to high N-rate. The photosynthesis among the species varied in between 11.5 and 6.9 \u0026micro;mol m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 11.4 and 7.5 \u0026micro;mol m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. The best performance in photosynthesis was shown by Spinach followed by Cress and Parsley under low N-rate, whereas Arugula and Dill showed the lowest photosynthesis at the same N-rate. At high N-rate, Spinach showed again the significantly highest photosynthesis and followed by Dill and Parsley while, Arugula and Cress showed the lowest photosynthetic activity at the same N-rate.\u003c/p\u003e \u003cp\u003eAlthough, no significant difference was found between two N-rates in leaf chlorophyll index (SPAD), some of the species (Arugula, Spinach and Cress) tended to be higher at low N-rate than at high N-rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). May be therefore genotypic variation and Genotype x N-rate interaction was highly significant in SPAD. Among the species SPAD varied in between 56.6 and 10.0 at low N-rate, 55.8 and 10.3 at high N-rate. The best performance in SPAD was shown by Spinach followed by Arugula and Cress under low N-rate, whereas Parsley and Dill showed the lowest SPAD value at the same N-rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLeaf Chlorophyll (a\u0026thinsp;+\u0026thinsp;b) Content, Leaf Carotenoid Content, and The Leaf Nitrate Reductase Enzyme Activity\u003c/h2\u003e \u003cp\u003eResults demonstrated that leaf chlorophyll (a\u0026thinsp;+\u0026thinsp;b) content, leaf carotenoid content, and the leaf nitrate reductase enzyme activity (NRA) of different vegetable species were significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The examined vegetable species grown under low N-rate exhibited consistently a higher chlorophyll (a\u0026thinsp;+\u0026thinsp;b) content and leaf carotenoid content as compared to high N-rate. This results clearly explain why the photosynthetic activity of leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) was also significantly high at low N-rate than high-N rate. Among the species the leaf chlorophyll (a\u0026thinsp;+\u0026thinsp;b) content varied in between 2.65 and 1.73 \u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 2.70 and 1.54 \u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. The variation in leaf carotenoid content was in between 0.34 and 0.22 \u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 0.34 and 0.21 \u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. Responses of the genotypes to the both measured parameters were similar and thus the highest leaf chlorophyll (a\u0026thinsp;+\u0026thinsp;b) and carotenoid content was shown by Spinach, Dill and Parsley under low N-rate, whereas Cress and Arugula showed the lowest leaf chlorophyll (a\u0026thinsp;+\u0026thinsp;b) and carotenoid contents at the same N-rate. Almost similar genotypic ranking (Spinach\u0026thinsp;\u0026gt;\u0026thinsp;Dill\u0026thinsp;\u0026gt;\u0026thinsp;Cress\u0026thinsp;\u0026gt;\u0026thinsp;Parsley\u0026thinsp;\u0026gt;\u0026thinsp;Arugula) in leaf chlorophyll (a\u0026thinsp;+\u0026thinsp;b) and leaf carotenoid contents (Spinach\u0026thinsp;\u0026gt;\u0026thinsp;Dill\u0026thinsp;\u0026gt;\u0026thinsp;Parsley\u0026thinsp;\u0026gt;\u0026thinsp;Cress\u0026thinsp;\u0026gt;\u0026thinsp;Arugula) were recorded under high N-rate among the vegetable species. Interesting results were found in NRA, since vegetable species grown under low N-rate exhibited highly significant enzyme activity than at high N-rate. Among the species RNA varied in between 3.27 and 0.25 \u0026micro;mol s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 2.39 and 0.29 \u0026micro;mol s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. Enormously highest performance in NRA was shown by Parsley and Dill under low N-rate, whereas Arugula, Spinach and Cress showed the lowest NRA at the same N-rate. At high N-rate, again Parsley showed significantly highest NRA and followed by Spinach and Dill while, Arugula and Cress showed the lowest NRA activity at the same N-rate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal leaf number, total leaf area and photosynthesis of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal Leaf Chlorophyll (A\u0026thinsp;+\u0026thinsp;B) [\u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTotal Leaf Carotenoid [\u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLeaf Nitrate Reductase Enzyme Activity\u003c/p\u003e \u003cp\u003e[\u0026micro;mol s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParsley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.825 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.675 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.301 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.271 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.274 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.385 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArugula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.733 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.539 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.224 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.459 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.370 D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.065 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.159 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.309 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.257 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.627 C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.653 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.703 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.337 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.344 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.433 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.771 B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.820 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.707 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.228 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.226 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.250 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.285 E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eF test\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype X Nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ns, non-significant. * P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRoot morphology\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e[Root Length (cm plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Root Volume (cm\u003csup\u003e3\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Root Diameter (mm plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)]\u003c/h2\u003e \u003cp\u003eRoot morphological results demonstrated that root length, root volume and averaged root diameter of different vegetable species were significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) affected by Genotype, N-rate, and Genotype x N-rate interaction (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast to shoot growth (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the vegetable species grown under low N-rate usually exhibited a lower root growth performance as compared to high N-rate (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, particularly three vegetable species; Arugula, Spinach and Cress which are characterized as \u0026lsquo;N-efficient\u0026rsquo; genotypes due to exhibiting high shoot fresh (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and dry biomass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) production under low N-rate, also exhibited significantly higher root length as compared to \u0026lsquo;N-ineficient\u0026rsquo; characterized genotypes Parsley and Dill under low N-rate (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This is also true for the root volume of these three vegetable species (Arugula, Spinach and Cress) treated under low N-rate. On the other hand, vegetable species Arugula and Spinach maintained high root length and root volume at high N-rate while Parsley and Dill also maintained to be the lowest in root length and root volume among other species at the same N-rate. Among the vegetable species root length varied in between 5840 and 1563 m plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 5990 and 1732 m g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. The variation in root volume among genotypes ranked in between 1.65 and 0.68 cm\u003csup\u003e3\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at low N-rate, 2.35 and 0.80 cm\u003csup\u003e3\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at high N-rate. As similar as root length and root volume, the vegetable species grown under low N-rate usually exhibited significantly lower root diameter as compared to high N-rate (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal root length, root volume and average root diameter of edible leafy vegetable genotypes (Parsley, Arugula, Dill, Spinach and Cress) grown under low (0.3 mM) and high (3.0 mM) N-rate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal Root Length\u003c/p\u003e \u003cp\u003e[cm plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTotal Root Volume\u003c/p\u003e \u003cp\u003e[cm\u003csup\u003e3\u003c/sup\u003e plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAv. Root Diameter\u003c/p\u003e \u003cp\u003e[mm]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh-N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParsley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1563 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1732 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.683 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.797 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.237 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.244 C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArugula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5840 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5990 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.867 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.353 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.203 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.216 E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1952 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2959 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.514 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.264 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.264 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2052 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2585 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.462 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.558 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.251 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.256 B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3717 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3544 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.665 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.432 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.236 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.227 D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eF test\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype X Nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e Values denoted by different letters (lower and upper case letters for low and high N-rate, respectively) are significantly different between genotypes within columns at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ns, non-significant. * P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eHowever, some of the \u0026lsquo;N-efficient\u0026rsquo; vegetable species (Arugula and Cress) exhibited significantly lower root diameter whereas some of the \u0026lsquo;N-ineficient\u0026rsquo; genotypes (Dill) exhibited significantly higher root diameter under both low and high N-rate conditions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This is a root morphological strategy of plants to adapt adverse growth conditions. Since, when the plants produce longer roots, they try to decrease the root diameter and usually exhibit thinner roots in growth medium. Opposite is also true when the plants produce shorter roots, they try to increase the root diameter and usually exhibit thicker roots. Among two N contrasting vegetable species averaged root diameter varied in between 0.26 (N-ineficient\u0026rsquo; Dill) and 0.20 (N-eficient\u0026rsquo; Arugula) mm at low N-rate, 0.26 (Dill) and 0.22 (Arugula) mm at high N-rate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePlant metabolism have always been under alteration under stress environment of growth, which may alter macromolecule structure and triggers the plant to become a reactive oxygen species. Therefore, plant biomass is always affected by the stress conditions (Watanabe and Yamasaki, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The salad leafy vegetables that were used in this study are consumed daily in every household in the world, more or less by poor and rich. The results obtained from the hydroponic experiment clearly indicate that some of the vegetable species have a great capability for Nitrogen Use Efficiency (NUE) under stress conditions. Particularly three vegetable species; Arugula, Spinach and Cress can be characterized as \u0026lsquo;N-efficient\u0026rsquo; genotypes due to realizing an above-average yield in shoot dry biomass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) under suboptimal (low) N supply (Graham, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Based on this reference, the lowest shoot dry biomass yielding vegetable genotypes Parsley and Dill can be characterized as \u0026lsquo;N-inefficient\u0026rsquo;. As well, the response to supplied high N-rate was highly significant (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) among genotypes in shoot dry matter production (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At this N rate, the vegetable species Arugula and Spinach maintained high shoot dry matter production at high N-rate while Parsley and Dill also maintained to be the lowest in shoot dry matter production among other species at the same N-rate. Based on these results, Arugula and Spinach genotypes can be characterized as \u0026lsquo;N-responsive\u0026rsquo; due to realizing an above-average yield (shoot dry biomass) under high N supply (Sattelmacher et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). On the other hand, the low shoot biomass yielding vegetable genotypes (Parsley and Dill) can be characterized as \u0026lsquo;N-irresponsive\u0026rsquo;. Nitrogen uptake is regulated by the demand of the growing crop if N supply is not limited (Clarkson 1985). In contrast, if the required N is limited in the soil, the N uptake depends on the extent and effectiveness of the root system (Jackson et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). However, without continuous assimilate allocation from the active leaves, root growth and thus N uptake activity cannot be maintained under low N condition. Roots are the first places where nutrients are absorbed and translocated into shoots for chlorophyll synthesis. More root formation depends on several factors (Watanabe and Yamasaki, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Goltsev et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Response to limiting mineral elements, plants have the ability to allocate a greater proportion of their biomass to the root system (Slavin and Lloyd, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Regarding to our results, the \u0026lsquo;N-efficient\u0026rsquo; characterized genotypes (Arugula, Spinach and Cress) which produced highest shoot fresh (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and shoot dry biomass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), also exhibited a higher root dry matter production as compared to \u0026lsquo;N-ineficient\u0026rsquo; genotypes Parsley and Dill under low N-rate. In biomass partitioning the \u0026lsquo;N-inefficent\u0026rsquo; genotype Parsley had the highest root:shoot ration while the lowest ratio was shown by \u0026lsquo;N-efficient\u0026rsquo; genotype Spinach at low N-rate. This might be due to a higher partitioning of dry matter to the root system under low N condition (Levin et al., 1989). Furthermore, an increased carbohydrate sink strength of the roots under N deficiency usually leads to a greater allocation of photo assimilates to the roots (Merrill et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). On the other hand, highest root:shoot ration was shown by \u0026lsquo;N-efficient\u0026rsquo; genotype Cress, while the lowest ratio was shown by \u0026lsquo;N-efficient\u0026rsquo; genotype Spinach at high N-rate. All these results clearly indicated that \u0026lsquo;N-efficient\u0026rsquo; characterized genotypes (Arugula, Spinach and Cress) exhibited a balanced dry matter partitioning between shoot and root (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On the other hand, the \u0026lsquo;N-inefficient\u0026rsquo; characterized genotypes (Parsley and Dill) exhibited a non-balanced dry matter partitioning between shoot and root and therefore showed a higher root:shoot ratio then \u0026lsquo;N-efficient\u0026rsquo; characterized genotypes under low N-rate. The response of root parameters might be related to overexpression of type I H+ -PPases under the control of either the 35S or Ubiquitin promoter which is responsible for root and shoot proliferation (Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pasapula et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Biomass production and yield of a crop is strongly dependent on its leaf area as well as the rate of leaf photosynthesis (Hirasawa and Hsiao, 1999). Further, carbon and nitrogen (N) metabolisms are also interrelated for the sustained growth and development of plants (Zheng, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, an increased root capacity also depends on an ATP supply generated by the respiration of reduced carbon molecules. Deficiency of N leads to low output of photosynthetic activity in plants, this have been indicated by several physiological and biochemical studies (Gupta et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeaf area is assessed as the one-sided green leaf area per unit ground area in broadleaf canopies, i.e. leaf area index (LAI) (Mae, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). As a rule, enhancement in crop yield occurs when an optimal LAI value is reached which depends on plant species, light intensity, leaf shape or leaf angle (Marschner, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Regarding to our results in number of leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and total leaf area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), the \u0026lsquo;N-efficient\u0026rsquo; genotypes Arugula, Cress and Spinach have significantly largest sized but low numbered of leaves than \u0026lsquo;N-inefficient\u0026rsquo; genotypes Parsley and Dill which characterized by significantly smallest sized but high numbered of leaves at low N-rate.\u003c/p\u003e \u003cp\u003eThe chlorophyll concentration in the leaf is essential for crop growth and development (Singh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) hence quantifying it makes available vital information about the effects of environment on plant growth (de Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although, no significant difference was found between two N-rates in leaf chlorophyll index (SPAD), some of the species (Arugula, Spinach and Cress) tended to be higher at low N-rate than at high N-rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). May be therefore vegetable species grown under low N-rate exhibited consistently a higher chlorophyll (a\u0026thinsp;+\u0026thinsp;b) content and leaf carotenoid content as compared to high N-rate. This results clearly explain why the photosynthetic activity of leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) was also significantly high at low N-rate than high-N rate. Carotenoids on the other hand, contributes to light harvesting whilst preventing photodamage to the photosynthetic systems (Ndukwe et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) through their interconversion with the xanthophyll molecules (Yamasaki et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our study is coinciding with the study of Chen et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) that, leaf chlorophyll and carotenoid content is dependent on the presence and ratio of mineral elements (especially nitrogen) in the substrate. The exclusive publications of Singh et al., (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); Ndukwe et al., (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); Hristov et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicate that SPAD and photosynthetic activity of the plant is not only limited with N-rate but several other factors like temperature and light intensity are involved for better build-up of plant mechanism under which their capability of utilizing more NUE is increased, even under stress N-rates. Our study indicates that these salad leafy vegetables can have more NUE under low stress conditions at low temperature. The enzyme nitrate reductase (NR) activity determination is one of the measures of N utilization efficiency in crops. Its activity in plants gives a good estimate of the plant\u0026rsquo;s N status and is usually correlated with growth and yield (Yamasaki et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). When plants take up nitrogen in the form of nitrates (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), it is first reduced to ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) by a NR enzyme before it is being incorporated into plant compounds. High NR activity in a plant implies that the plant has a greater ability to convert absorbed NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e to usable forms. Interesting results were found in NRA in our present study, since vegetable species grown under low N-rate exhibited highly significant enzyme activity than at high N-rate (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Enormously highest performance in NRA was shown by N-inefficient genotypes Parsley and Dill under low N-rate, whereas the N-efficient genotypes Arugula, Spinach and Cress showed the lowest NRA at the same N-rate. This might be due to a high leaf area formation which usually leads to a reduction in the amount of N per unit of leaf area [Marschner, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Also, corroborative results were demonstrated in the study of Ulas et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) who reported that the low yielding \u0026lsquo;N-inefficient\u0026rsquo; genotypes showed substantially higher shoot N concentrations than the high yielding \u0026lsquo;N-efficient\u0026rsquo; genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe hydroponic study clearly indicated that shoot growth, root morphological and leaf physiological responses were significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) affected by Genotype, N-Rate and Genotype x N-Rate interaction. Shoot growth of some vegetable species (Argula, Spinach and Cress) was significantly higher under low N than high N-rate, demonstrating that they have a great capability for Nitrogen Use Efficiency (NUE) under low N stress conditions. Similar responses were also recorded for the root growth of the N-efficient species under low N-rate. The NUE of these species was closely associated with leaf physiological (leaf area, SPAD, photosynthesis, leaf chlorophyll (a\u0026thinsp;+\u0026thinsp;b) and carotenoid) and root morphological (root length, root volume and average rot diameter) characteristics. These physiological and morphological plant traits could be useful characters for the selection and breeding of \u0026lsquo;N-efficient\u0026rsquo; leafy vegetable species for sustainable agriculture in the future. However, further investigation should be carried out at field level to confirm their commercial production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe author declare that she has no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e I thank all staff members of the Plant Nutritional Physiology Laboratory of Erciyes University, Turkey for technical supports and for providing all the necessary facilities throughout the experiments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdam, M.B., A. Ulas. (2023). Vigorous rootstocks improve nitrogen efficiency of tomato by inducing morphological, physiological and biochemical responses. Gesunde Pflanzen 75:565\u0026ndash;575. https://doi.org/10.1007/s10343-022-00819-8\u003c/li\u003e\n\u003cli\u003eBr\u0026eacute;gard, A.; B\u0026eacute;langer, G.; Michaud, R. 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Journal of the Saudi Society of Agricultural Sciences 15(2), 171-178.\u003c/li\u003e\n\u003cli\u003eSlavin, J.L., B. Lloyd. (2012). Health benefits of fruits and vegetables. Advances in Nutrition 3:506-516.\u003c/li\u003e\n\u003cli\u003eUN (2022). Future of the World, Policy Brief, United Nations Department of Economic and Social Affairs. https://www.un.org.development.desa.pd/files/undesa_pd_2022_pb_140.pdf\u003c/li\u003e\n\u003cli\u003eUlas A, Doganci E, Ulas F, Yetisir H (2019) Root-growth characteristics contributing to genotypic variation in nitrogen efficiency of bottle gourd and rootstock potential for watermelon. Plants (Basel) 8:77\u003c/li\u003e\n\u003cli\u003eWatanabe, N.S., H. Yamasaki. (2016). Dynamics of Nitrite Content in Fresh Spinach Leaves: Evidence for Nitrite Formation Caused by Microbial Nitrate Reductase Activity. Journal of Nutrition and Food Sciences 7: 572. doi: 10.4172/2155-9600.1000572.\u003c/li\u003e\n\u003cli\u003eYamasaki, H., Watanabe, N. S., Fukuto, J., \u0026amp; Cohen, M. F. (2014). Nitrite-dependent nitric oxide production pathway: Diversity of NO production systems. Studies on Pediatric Disorders, 35-54.\u003c/li\u003e\n\u003cli\u003eYamasaki, H., Watanabe, N.S., Fukuto, J., Cohen, M.F. (2014). Nitrite-Dependent Nitric Oxide Production Pathway: Diversity of NO Production Systems. In: Tsukahara, H., Kaneko, K. (eds) Studies on Pediatric Disorders. Oxidative Stress in Applied Basic Research and Clinical Practice. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0679-6_3\u003c/li\u003e\n\u003cli\u003eZheng. Z.L. (2009). Carbon and nitrogen nutrient balance signaling in plants. Journal of Plant Signal Behaviour 4:584\u0026ndash;591. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2710548/ \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Leafy vegetables, N-efficiency, Photosynthesis, Nitrogen, Hydroponic culture","lastPublishedDoi":"10.21203/rs.3.rs-3653783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3653783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil fertility is declining in low-input agriculture due to insufficient fertilizer application by small-scale farmers. On the other hand, the concerns are rising on environmental pollution of both air and water in high-input agriculture due to excessive use of N fertilizer in a short growing season of vegetable crops, which is directly linked with the health of human beings and environmental safety. The aim of the study was to determine genotypic differences in Nitrogen Use Efficiency (NUE) of different leafy vegetable species (Arugula, Spinach, Cress, Parsley and Dill) grown hydroponically under two different N-rates (Low N: 0.3 mM and High N: 3.0 mM) and to identify the plant traits which are contributing to NUE. The nutrient solution experiment was conducted between March – April in 2020 by using an aerated Deep-Water Culture (DWC) technique in a fully automated climate room with a completely randomized block design (CRBD) with three replications for five weeks. The results indicated that shoot growth, root morphological and leaf physiological responses were significantly (\u003cem\u003ep\u0026lt;0.001\u003c/em\u003e) affected by Genotype, N-Rate and Genotype x N-Rate interaction. Shoot growth of some vegetable species (Argula, Spinach and Cress) was significantly higher under low N than high N-rate, illustrating that they have a great capability for NUE under low N stress conditions. Similar results were also recorded for the root growth of the N-efficient species under low N-rate. The NUE of these species was closely associated with leaf physiological (leaf area, SPAD, photosynthesis, leaf chlorophyll (a+b) and carotenoid) and root morphological (root length, root volume and average rot diameter) characteristics. These physiological and morphological plant traits could be useful characters for the selection and breeding of ‘N-efficient’ leafy vegetable species for sustainable agriculture in the future. However, further investigation should be carried out at field level to confirm their commercial production.\u003c/p\u003e","manuscriptTitle":"Physio-Morphological Traits Contributing to Genotypic Differences in Nitrogen Use Efficiency of Leafy Vegetable Species under Hydroponics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-28 18:46:54","doi":"10.21203/rs.3.rs-3653783/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc6f6184-64ab-4c1b-923a-7038137f803c","owner":[],"postedDate":"November 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-29T14:59:24+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-28 18:46:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3653783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3653783","identity":"rs-3653783","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}