Determination of the relationship between plant nutrient concentration and spectral reflection in Rocket (Eruca vesicaria) cultivation

Abstract

The spectral reflection characteristics of the plants vary during the vegetation period with the effect of plant nutrient concentrations and other factors. The aim of this study was to investigate the relationship between nutrient concentration and spectral reflection (RF) in the maturity and harvest periods of the rocket (Eruca sativa L.) plant. The study was conducted using a completely randomized block design with three replications under controlled greenhouse conditions. The treatments were solid fertilizers applied before planting at two different rates (0,  300  and  600 kg da^-1), and liquid organic fertilizer applied by drip (20-40 lt da-¹) throughout the growing period. Within this period, spectroradiometric measurements were taken with a hand spectroradiometer (plant probe and leaf clips) in the range of 330-1075 nm wavelength of the electromagnetic spectrum. In the study, leaf samples were taken simultaneously with spectroradiometric measurements and plant nutrient analyzes were performed in these samples. According to the results,  at the maturity stage; between blue band and Cu (0.621**), red band and potassium K (0.554**), harvest stage; green band and P (0.559**), red band and C (-0.581**) there was a statistically significant relationship at P≤0.01 level. With these results, fast, economical and reliable results were obtained in the estimation of some important plant nutrient concentrations affecting yield and quality in rocket cultivation especially in winter in greenhouse conditions without damaging the plant.

Keywords

• Rocket,Spectral reflectance,Plant nutrients concentration

Roka (Eruca vesicaria) yetiştiriciliğinde spektral yansıma ile bitki besin maddesi konsantrasyonu arasındaki ilişkinin belirlenmesi

Abstract

Bitkilerin spektral yansıma karakteristikleri bitki besin maddesi konsantrasyonları ve diğer faktörlerin etkisi ile vejetasyon periyodu boyunca farklılık göstermektedir. Bu çalışmanın amacı, roka (Eruca sativa L.) bitkisinin olgunluk ve hasat dönemlerinde spektral yansıması (RF) ile besin maddesi konsantrasyonu arasındaki ilişkiyi incelemektir. Çalışma kontrollü sera ortamında tesadüf parselleri deneme desenine göre yürütülmüştür. Denemede bitki yetişme periyodu süresince roka bitkisinin bitki besin maddesi konsantrasyonunda varyasyon oluşturmak amacıyla tabandan katı organik gübre (0, 300 ve 600 kg da^-1) ve damlamadan sıvı organik gübre (20 ve 40 lt da^-1) uygulanmıştır. Ayrıca olgunluk ve hasat dönemlerinde elektromanyetik spektrumun (EMS) 330-1075 nm dalga boyu aralığında el spektroradyometresi ile bitki probu kullanılarak spektroradyometrik ölçümler gerçekleştirilmiştir. Aynı zamanda spektral yansıma alınan yaprak örneklerinde bitki besin elementi konsantrasyonlarını belirlemek amacıyla kimyasal analizler yapılmıştır. Araştırma sonuçlarına göre, roka bitkisinin olgunluk döneminde; EMS’un mavi bandı ile bakır (Cu) (0.621**), kırmızı bandı ile potasyum (K) (0.554**) hasat döneminde ise; yeşil bandı ile fosfor (P) (0.559**), kırmızı bandı ile kalsiyum (Ca) (-0.581**) arasında P≤0.01 seviyesinde istatistiksel olarak önemli ilişkiler bulunmuştur. Araştırma sonuçları, roka bitkisinin bitki besin maddesi konsantrasyonun tahmin edilmesinde olgunluk dönemine göre hasat döneminde daha yüksek korelasyon olduğunu göstermiştir. Bu sonuçlar ile birlikte özelikle kışın sera koşullarında roka yetiştiriciliğinde verimi ve kaliteyi etkileyen bazı önemli bitki besin elementi konsantrasyonlarının tahmin edilmesinde bitkiye zarar vermeden hızlı, ekonomik ve güvenilir sonuçlar elde edilmiştir.

Keywords

• Roka,Spektral yansıma,Bitki besin elementi konsantrasyonu

References

1. Abadia J, Vázquez S, Álvarez RR, Jendoubi H, Abadía A, Fernández AA, Millán AFL (2011) Towards a knowledge-based correction of iron chlorosis. Plant Physiology and Biochem 49: 471–482.
2. Albayrak S, Başayiğit L, Türk M (2011) Use of canopy- and leafreflectance indices for the detection of quality variables of Vicia species. International Journal of Remote Sensing 32: 1199–1211.
3. Altunbas S, Gozukara G, Sonmez NK, Maltaş AŞ, Kaplan M (2018a) Relationship between spectral reflectance and plant nutrient-chlorophyll content in lettuce (Lactuca Sativa L.) growing. Fresenius Environmental Bulletin 27(5A): 3624-3632.
4. Altunbas S, Sonmez NK, Gozukara G, Maltaş AŞ, Kaplan M (2018b) Relationship between solid-liquid organic fertilization and spectral reflectance in lettuce (Lactuca Sativa L.) growing. Fresenius Environmental Bulletin 27(8): 5355-5362.
5. Anonim (1988) Meyve, sebze ve mamulleri-nitrit ve nitrat tayinimoleküler absorpsiyon spektrofotometrik metot. Türk Standardı, ICS 67.080, TS 6183/Aralık 1988.
6. Anonim (2018) T.C. Başbakanlık Türkiye İstatistik Kurumu, TÜİK verileri.
7. Başayiğit L, Dedeoğlu M, Akgül H (2015) The prediction of iron contents in orchards using VNIR spectroscopy. Turkish Journal of Agriculture and Forestry 39: 123-134.
8. Black CA (1957) Soil-plant relationships. John Wiley and Sons, Inc., New York.

9. Black CA (1965) Methods of Soil Analysis. Part 2, Amer. Society of Agronomy Inc., Publisher Madisson, Wilconsin, U.S.A., 1372-1376.
10. Bouyoucos GJ (1955) A recalibration of the hydrometer method for making mechanical analysis of the soils. Agronomy Journal 4(9): 434.
11. Cao H, Zhan Y (2014) Near-infrared spectra quantitative analysis for flue gas of thermal power plant based on wavelength selection. Scientific Research and Essay 9: 288–292.
12. Ebtsam M, Morsy AA, Mahmoud AA, Khali AA (2006) Influence of bio-inoculation and various potassium application rates on soil fertilitiy, maize productivity and yield components. Egyptian Journal of Applied Sciences 21: 255-267.
13. Evliya H (1964) Kültür bitkilerinin beslenmesi. Ankara. Üniv. Ziraat Fak. Yayınları, Yayın no: 36, 292-294, Ankara.
14. Gamon JA, Qiu H (1999) Ecological applications of remote sensing at multiple scales. In: F. I. Pugnaire, & F. Valladares (Eds.), Handbook of functional plant ecology, New York: Marcel Dekker, pp. 805–846.
15. Gitelsan AA, Gritz Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physilogy 160: 271-281.
16. Gözükara G, Kalkan H, Kaplan M (2014) Evaluation of differences in fertilizer consumption of autumn tomato production in greenhouse. 9 th International soil science congress, Antalya, s. 685-689.
17. Gözükara G, Kaplan M, Kalkan H (2016) Evaluation of Soil Analysis Results and Fertilizer Consumption in Autumn Greenhouse Tomato Cultivation. 2. International Conference on Science, Ecology and Technology, Barcelona. s. 721-726.
18. Güneş A, Alpaslan M, İnal A (2000) Bitki besleme ve gübreleme, A.Ü. Ziraat Fakültesi, Yayın No: 1514.
19. Güzel N, Gülüt KY, Büyük G (2002) Toprak verimliliği ve gübreler. Ç.Ü. Ziraat Fakültesi, Genel Yayın No: 246.
20. Kalkan H, Gözükara G, Kaplan M (2017) Sera güzlük domates yetiştiriciliğinde yeni eğilim: sıvı organik gübre tüketimi. Academia Journal of Engineering and Applied Sciences 2(3): 92-100.
21. IBM SPSS Statistics (2014) IBM SPSS statistics software version 22. SPSS Inc., Chicago.
22. Jackson MC (1967) Soil chemical analysis. Prentice Hall of India Private’Limited, New Delhi.
23. Kacar B (1972) Bitki ve toprağın kimyasal analizleri. II. Bitki Analizleri, A.Ü. Ziraat Fak. Yayınları: 453, Ankara.
24. Kacar B, İnal A (2008) Bitki analizleri. Nobel Yayın Dağıtım, Ankara.
25. Kacar B, Katkat V (2010) Bitki besleme. Nobel yayınları, s. 660, Ankara.
26. Kellog CE (1952) Our garden soils. The Macmillan Company, Newyork.
27. Lê S, Josse J, Husson F (2008) FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software. 25(1). pp. 1-18.
28. Lennartsson M, Ögren E (2003) Predicting the cold hardiness of willow stems using visible and near-infrared spectra and sugar concentrations. Trees-Structure Function 17: 463–470.
29. Leone AP, Menenti M, Buondonno A, Letizia A, Maffei C, Sorrentino G (2007) A field experiment on spectrometry of crop response to soil salinity. Agricultural Water Management 89: 39-48.
30. Lillhonga T, Geladi P (2011) Three-way analysis of a designed compost experiment using near-infrared spectroscopy and laboratory measurements. Journals of Chemometrics 25: 193–200.
31. Lindsay WL, Norvell WA (1978) Development of a DTPA soil test for Zinc, Iron, Manganese and Copper. Soil Science American Journal 42(3): 421-428.
32. Loue A (1968) Diagnostic petiolaire de prospection etudes sur la nutrition et al. fertilisation potassiques de la vigne. Societe Commerciale des Potasses d’ Alsace Services Agronomiques 31-41.
33. Marschner H (2003) Mineral nutrition of higher plants, Academic Press, London.
34. Marschner H (2008) Mineral nutrition of higher plants, Academic Press, Digital Print. Academic Press., pp. 889.
35. Mengel K, Kirkby EA (2001) Principles of plant nutrition. 5th Edition. Kluwer Academic Publishers. ISBN: 1-4020-0008-1, Dordrecht, The Netherlands.
36. Olsen SR, Sommers EL (1982) Phosphorus soluble in sodium bicarbonate, methods of soil analysis, Part 2, Chemical and Microbiological Properties. Edit: A.L. Page, P.H. Miller, D.R. Keeney, pp. 404-430.
37. Peng Y, Gitelson AA (2011) Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agriculturel and Forest Meteorlogy 151: 1267-1276.
38. Pizer NH (1967) Some advisory aspect soil potassium and magnesium. Tech. Bull No: 14-184.
39. Richardson AD, Duigan SP, Berlyn GP (2002) An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol 153: 185–194.
40. Sari M, Sonmez NK, Karaca M (2005a) Relationship between chlorophyll content and canopy reflectance in washington navel orange trees (cıtrus sınensıs (L.) osbeck). Pakistan Journal of Botany 37(4): 1093-1102.
41. Sari M, Sonmez NK, Kurklu A (2005b) Determination of seasonal variation of solar energy utilization by the leaves of washington navel orange trees (Citrus sinensis L. Osbeck). International Journal of Remote Sensing 26: 3295-3307.
42. Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 8: 337– 354.
43. Slaton MR, Hunt ER, Smith WK (2001) Estimating near infrared leaf reflectance from leaf structural characteristics. American Journal of Botany 88(2): 278-284.
44. Shedeed SI, Nasef MA, Abo-Basha DM (2011) A comparative study on responce of lettuce plants to different K-fertilizer sources through applying fertigation system. Research Journal of Agriculture and Biological Sciences 7(1): 68-78.
45. Soil Survey Staff (1951) Soil survey manuel. Agricultural Research Administration, U.S Depth. Agriculture, Handbook No: 18.
46. Soltanpour PN, Workman SM (1981) Use of inductively-coupled plasma spectroscopy for the simultaneous determination of macro and micro nutrients in NH4HCO3-DTPA extracts of soils. In Barnes R.M. (ed). Developments in Atomic Plasma Analysis, USA, pp. 673-680.
47. Sonmez NK, Emekli Y, Sari M, Bastug R (2008a) Relationship between spectral reflectance and water stress conditions of Bermuda grass (Cynodon dactylon L.). New Zealand Journal of Agriculturel Research 51: 223-233.
48. Sonmez NK, Sari M, Sonmez S (2008b) Relations mineral content and canopy reflectance in Washington navel orange trees. Asian Journal of Chemistry 20(6): 4760-4772.
49. Sonmez NK, Aslan GE, Kurunc A (2015) Relationship spectral reflectance under different salt stress conditions of tomato. Jorunal of Agriculturel Sciences 21: 585-595.
50. Thun R, Hermann R, Knıckman E (1955) Die untersuchung von boden neuman verlag, Radelbeul und Berlin, s. 48-48.
51. Vural H, Eşiyok D, Duman İ (2000) Kültür Sebzeleri (Sebze Yetiştirme). Ege Üniversitesi Ziraat Fakültesi Bahçe Bitkileri Bölümü, Bornova, İzmir, s. 440.
52. Zhao X, Hui B, Hu L, Cheng Q, Via BK, Nadel R, Starkey T, Enebak S (2017) Potential of near infrared spectroscopy to monitor variations insoluble sugars in Loblolly pine seedlings after cold acclimation. Agricultural and Forest Meteorology 232: 536–542.