INTRODUCTION

The extraction of Hypericum perforatum L. are commonly used in EU countries as a drug and food additive for the treatment of mild to moderate depression (Fiebich et al. 2011). Hypericum consists of 484 species in forms of small trees, shrubs and herbs (Crockett and Robson 2011). Turkey is one of the important country for the genus Hypericum and there are 46 endemic Hypericum species (Guner et al. 2012).

One of the endemic and perennial plant of Anatolian flora is Hypericum pruinatum Boiss (Camas et al. 2013), hyperforin, adhyperforin, chlorogenic acid, neochlorogenic acid, caffeic acid and 2,4-dihydroxybenzoic acid (Cirak et al. 2015), amentoflavone, hyperoside, isoquercitrin, quercitrin, quercetin, avicularin, rutin, (+)-catechin and (-)-epicatechin (Cirak et al. 2014). Hypericum extracts which are especially hypericins and hyperforins has antidepressant activities (Guedes and Eriksson 2005; Du et al. 2006). The pharmacological effects of Hypericum extracts have made also an important contribution to the antimicrobial (Zhao et al. 2010) and antidepressant (Butterweck et al. 2000) activities. This similarity of its chemical composition could be considered as potential cultivated plant being used as an alternative crop to H. pruinatum instead of H. perforatum.

Macro and micronutrients have proven effects on plant growth and development as well as substrate content and enzymes activity, thereby, chemical compound accumulations and finally on plant/plant derived product quality (Montoya-Garcia et al. 2018). Thereby, timely and sufficient supply of nutrients is the first practice of agricultural affecting both biomass production and quality of drug in medicinal plants (Odabas et al. 2016; Barroso et al. 2018). Considering the specific importance of edaphic and physiological factors with regards to plant production and key role of nitrogen availability in plant development and chemistry, we aimed to evaluate the effects of nitrogen, applied in different doses on growth and chemical accumulation levels of greenhouse-grown H. pruinatum plants in the current study.

Leaf is very important part of plants. Thus, leaf area has important role for researches where some physiological phenomenon such as light, photosynthesis, and respiration etc (Centritto et al. 2000). Also, leaf area is important for cultural practices. The estimation of leaf area that goal to calculate non-destructively of leaf area. It can be useful tool for researches with many advantages in agricultural experiments. Furthermore, such mathematical models reduce experimental variability by allowing researchers to make leaf area measurements on the same plants throughout a study (Oner et al. 2011).

2. MATERIAL AND METHODS

Seeds of Hypericum pruinatum L. were germinated in small viols and emerged seedlings were transplanted into pots. The pods were filled with the commercial peat. The seedlings were moved to greenhouse. The greenhouse temperature was 24 °C relative humidity 75% i and 400 μmol m^-2 s^-1 PAR (Parabolic Anodized Reflector). The pods were watered daily until they reached maturity, then three times a week. They were fertilized with five levels of nitrogen including 33% pure nitrogen as NH₄ (0, 3, 6, 9 and 12 kg da^-1) after plants reached average 20 cm length in the greenhouse. Experimental design was randomized block design with three replications. At the end of 56 days cultivation period, the tops 2 / 3 of plants were harvested, dried at 21°C.

The 500 leaf samples were used the validation of the estimation model. At first, they were placed on the scanner and scanned (300 dpi resolution) on A4 sheets (at 1:1 ratio). Then, the sheets were saved as jpg file. Image processing technique was used to measure actual leaf area of the image (Caliskan et al. 2017). The choice of leaf sizes determined for the measurement was determined according to the change in leaf features. (e.g., size, shape, and symmetry). Considering these factors, maximum leaf width (W) and length (L) were selected to correlate with leaf area (Oner et al. 2011; Odabas et al. 2017).

Then, the multiple linear correlation coefficient (r) and the coefficient of determination (R²) were calculated. For each model, the mean absolute error (MAE), the root of mean square error (RMSE), and the mean absolute percentage error (MAPE) were calculated by means of equations:

 

3. RESULTS AND DISCUSSION

The multiple regression analysis was used for determination of the best fitting equation. This equation was helped for estimation of leaf area. The goodness of fit statistic describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between actual values and the predicted values under the model (Table 1). When Table 1 is examined, all R² and adjusted R² values are higher than 0.86. Although MSE, RMSE, and MAPE values vary according to parameters, they were found to be 0.321, 0.567, and 2.397 for leaf width respectively. For the other parameters were shown in Table 1.

The R² values show us the relationship between actual values and predicted values. For instance, the R² of leaf area was found 0.904. That means, the model can estimate the leaf area 90% accuracy. The highest accuracies were found dry and fresh yield values (Table 1). According to analysis of variance, leaf width, leaf length, plant length, number of shoot, dry yield, fresh yield and leaf area were found statistically important (Table 2). Pandey and Singh (2011) found for individual species, the coefficient of determination between the two sets of estimates varied between 0.933 and 0.998.

The leaf area in a canopy is an important variable affecting light interception, photosynthesis and carbohydrate production (Landsberg and Sands 2011). The leaf area is estimated by equations that correlate a measured variable with the actual leaf area and it is performed indirectly or directly on the leaves or even using digital measuring instruments. The other parameters were modeled based on the change in nitrogen dose. The analysis of the data was performed for each parameter separately (Table 3).

The coefficients that for predicting the best model were found with various subsets of the independent variables. These variables are dose (D), height (H), and width (W). The best estimating equations for the parameters were tested and formulized (Table 4). The nitrogen in the soil is associated to organic matter. That’s why, this is one of the critically criteria considered in the current recommendation of nitrogen fertilization to define the amount to be applied. The amount of nitrogen may vary with species, amount of organic residue, temperature, and humidity. Nitrogen stands out among the essential nutrients for plants, depending on environmental conditions.

When the Table 4 examined, there are so close relationship between actual and predicted values. The selection of models requires a balance between predictive qualities and the including the least number of variables necessary to predict parameters.  Due to the simplicity and convenience of linear equations, they have been used to estimate the models. This close relationship shows that the obtained equations make accurate predictions. The equations of leaf width, leaf length, plant length, number of shoot, dry yield, and fresh yield can calculate different nitrogen doses. But, the equation of leaf area can only calculate leaf length and leaf width.

 

4. CONCLUSIONS

As a result of this research, it can be concluded that the mathematical equations (prediction models) are potentially an efficient and feasible tool for predicting of the dependent parameters. This approach is much simple than adopting a high dimensional polynomial regression. The order of polynomial increases due to accuracy and the number of terms in polynomial increases exponentially according to its degree. In this study, we developed the mathematical models for predicting parameters (leaf width, leaf length, plant length, number of shoot, dry yield, fresh yield, and leaf area) for the medicinal plant namely Hypericum pruinatum L. Such models would also allow researchers to estimate the parameters easily and high accuracy. The models that we found in this research can be used safely.

ACKNOWLEDGEMENTS

The authors are grateful to Aydan Ermis and Tuba Odabas (Ondokuz Mayis University, School of Foreign Languages) for editing English in this paper.

 AUTHOR CONTRIBUTIONS

D.K. conceived and designed the experiments, conducted the experiments, and collected the data. M.S.O. did the statistics evaluations. Authors wrote and approved the final manuscript.

 REFERENCES

1. Barroso BMR, Martins N, Barros L, Antonio AL, Rodriguesa MA, Sousa MJ, Santos-Buelga C, Ferreira ICFR, 2018. Assessment of the nitrogen fertilization effect on bioactive compounds of frozen fresh and dried samples of Stevia rebaudiana. Food Chemistry, 243: 208-213.
2. Butterweck V, Jurgenliemk G, Nahrstedt A, Winterhoff H, 2000. Flavonoids from Hypericum perforatum show antidepressant activity in the forced swimming test. Planta Medica, 66: 3-6.
3. Caliskan O, Kurt D, Temizel KE, Odabas MS, 2017. Effect of salt stress and irrigation water on growth and development of sweet basil (Ocimum basilicum). Open Agriculture, 2: 589-594.
4. Camas N, Radusiene J, Cirak C, Stanius Z, Odabas MS, Caliskan O, 2013. Chemical composition of Hypericum pruinatum and Bal. from wild populations of Northern Turkey. Research in Pharmacy, 3: 14-21.
5. Centritto M, Loreto F, Massacci A, Pietrini F, Villani MC, Zacchine M, 2000. Improved growth and water use efficiency of cherry saplings under reduced light intensity. Ecological Research, 15: 385-392.
6. Cirak C, Radusiene J, Ivanauskas L, Jakstas V, Camas N, 2014. Phenological changes in the chemical content of wild and greenhouse-grown Hypericum pruinatum: flavonoids. Turkish Journal of Agriculture and Forestry, 38: 362-370.
7. Cirak C, Radusiene J, Ivanauskas L, Jakstas V, Camas N, Kurt D, 2015. Phenological changes in the chemical content of wild and greenhouse-grown Hypericum pruinatum: hypericins, hyperforins and phenolic acids. Research & Reviews: Journal of Botany, 4:37-47.
8. Crockett SL, Robson NKB, 2011. Taxonomy and chemotaxonomy of the genus Hypericum. In: Odabas MS, Cirak C (Eds) Hypericum. Medicinal and Aromatic Plant Science and Biotechnology, 5(Special Issue 1): 1-13.
9. Du H, Bay BH, Mahendran R, 2006. Hypericin-mediated photodynamic therapy elicits differential interleukin-6 response in nasopharyngeal cancer. Cancer Letters, 235: 202-208.
10. Fiebich BL, Knorle R, Apel K, Kammler T, Weiss G, 2011. Pharmacological studies in an herbal drug combination of St. John’s wort (Hypericum perforatum) and passion flower (Passiflora incarnata): In vitro and in vivo evidence of synergy between Hypericum and Passiflora in antidepressant pharmacological models. Fitoterapia, 82: 474-480.
11. Guedes RC, Eriksson LA, 2005. Theoretical study of hypericin. J Photochem Photobiol A: Chemistry, 172: 293-299.
12. Guner A, Aslan S, Ekim T, Vural M, Babac MT, 2012. List of Turkish Flora (Vascular Plants). Publication of Nezahat Gökyiğit Botanical Garden and Flora Research Fondation, Istanbul. Available online at http://www.bizimbitkiler.org.tr.
13. Landsberg J, Sands P, 2011. Physiological Ecology of Forest Production. Terrestrial Ecology, 4: 81-114.
14. Montoya-Garcia CO, Volke-Haller VH, Trinidad-Santos A, Villanueva-Verduzco C, 2018. Change in the contents of fatty acids and antioxidant capacity of purslane in relation to fertilization. Scientia Horticulturae, 234: 152-159.
15. Odabas MS, Radusiene J, Ivanauskas L, Jakstas V, Camas N, Kayikci S, 2016. Secondary metabolites in selected plants from Hypericum species and their distributions in different plant parts. Zemdirbyste-Agriculture, 103(2): 193-198.
16. Odabas MS, Simsek H, Lee WC, Iseri I, 2017. Multilayer perceptron neural network approach to estimate chlorophyll concentration index of lettuce (Lactuca sativa). Communications in Soil Science and Plant Analysis, 48(2): 162-169.
17. Oner F, Odabas MS, Sezer I, Odabas F, 2011. Leaf area prediction for corn (Zea mays) cultivars with multiregression analysis. Photosynthetica, 49(4): 637-640.
18. Pandey SK, Singh H, 2011. A simple, cost-effective method for leaf area estimation. Journal of Botany, 4: 1-6.
19. Zhao M, Wang H, Yang B, 2010. Identification of cyclodextrin inclusion complex of chlorogenic acid and its antimicrobial activity. Food Chemistry, 120: 1138-1142.

 

 

How to cite this article

KURT, D., & ODABAS, MS (2020). Modeling of the Effects of Nitrogen Doses on Agronomic Characteristics and Leaf Area of Hypericum pruinatum L. Int J Agric Life Sci, 6(2), 288-292. doi:10.22573/spg.ijals.020.s12200099.

CONFLICTS OF INTEREST

“The authors declare no conflict of interest”.