Abstract
Bu çalışmada, yonca (Medicago
sativa L.) bitkisinde uzaktan algılama yöntemi ile spektral yansıma
değerlerinin elde edilmesi ve bu değerlerin kullanılmasıyla azot düzeylerinin
tahmin edilmesi amaçlanmıştır. Çalışmalar hem tarla hem de sera koşullarında
yürütülmüştür. Tarla koşullarında parsellere ve sera koşullarında saksılara
farklı oranlarda azot, fosfor ve potasyum uygulaması yapılmıştır. Spektral
yansıma ölçümleri bitkilerin çiçeklenmeye başladığı dönemde kanopi (genel) ve
tek yaprak düzeyinde yapılmıştır ve yansıma ölçümleri elektromanyetik
spektrumun 325-1075 nm dalga boyları arasında yansıma ölçümleri yapabilen
taşınabilir bir spektroradyometre kullanılarak yapılmıştır. Yapraktan yapılan
ölçümler için yapay ışık kaynağı bulunan yaprak ölçüm cihazı (plant probe) ve
yaprak tutucu (leaf clip) kullanılmıştır. Elde edilen verilerin istatistiksel analizi
MINITAB 13 istatistik programında stepwise (değişken ekleme-çıkarma) regresyon
analizi kullanılarak yapılmıştır. Çalışma sonucunda, bitkideki azot düzeyleri
ile yansıma değerleri arasında önemli (Tarla-genel: 0.94, Tarla-yaprak: 0.23,
Sera-genel: 0.55, Sera-yaprak:0.92) ilişkiler belirlenmiştir. Sonuçlar ayrıca,
azot düzeylerindeki değişimlerin özellikle spektrumun görünür bölgesindeki
(400-700 nm) yansımaları etkilediğini göstermiştir. Dalga boyları ayrı ayrı
değerlendirildiğinde ise özellikle fotosentez için önemli olan mavi, kırmızı ve
yakın kızılötesi bölgelere ait dalga boylarının azot seviyelerinin tahmininde
kullanılabileceğini göstermiştir.
References
- Açıkgöz E 2001. Yem Bitkileri (3. Baskı). Uludağ Üniversitesi Güçlendirme Vakfı Yayın No: 182. VİPAŞ A.Ş. Yayın No: 58, Bursa, ss. 584.
- Açıkgöz E, Hatipoğlu R, Altınok S, Sancak C, Tan A, Uraz D 2005. Yem bitkileri üretimi ve sorunları, Türkiye Ziraat Mühendisliği VI. Teknik Kongresi, 3-7 Ocak 2005, Ankara, s. 503-518, 2005.
- Albayrak S. 2008. Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in sainfoin pasture. Sensors, 8 (11): 7275-7286.
- Anonymous 2018. https://www.tarim.gov.tr/sgb/Belgeler/SagMenuVeriler/BUGEM.pdf (Access date: 10.05.2018)
- Ball DM, Hoveland CS, Lacefıeld GD 1996. Forage Quality” (Chapter 16) Southern Forages. Publ. By the Williams Printing Company, pp: 124-132.
- Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S 2007. Estimating forage quantity and quality using aerial hyperspectral imagery for Northern mixed-grass prairie. Remote Sensing of Environment, 110(2): 216–225.
- Brink GE, Rowe DE, Sistani KR, Adeli A 2003. Bermudagrass cultivar response to swine effluent application. Agronomy Journal, 95(3): 597–601.
- Daughtry CST, Walthall CL, Kim MS, De-Colstoun EB, McMurtrey JE 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2): 229-239.
- Delalieux S, Somers B, Verstraeten WW, Keulemans W, Coppin P 2008. Hyperspectral canopy measurements under artificial illumination. International Journal of Remote Sensing, 29(20): 6051-6058.
- Elçi Ş 2005. Baklagil ve Buğdaygil Yem Bitkileri. T.C. Tarım ve Köyişleri Bakanlığı. ISBN 975-407-189-6. Mart Matbaası- İstanbul. Ankara, 486 s.
- Graeff S, Steffens D, Schubert S 2001. Use of reflectance measurements for the early detection of N, P, Mg, and Fe deficiencies in corn (Zea mays L.). Journal of Plant Nutrition and Soil Science, 164: 445–450.
- Han L, Rundquist DC 2003. The spectral responses of Ceratophyllum demersum at varying depths in an experimental tank. International Journal of Remote Sensing, 24(4): 859-864.
- Johan F, MatJafri MZ, Lim HS, Sim CK 2013. Preliminary study: Spectral reflectance properties of microalgae in freshwater. Proceeding of the 2013 IEEE International Conference on Space Science and Communication (IconSpace), 1-3 July 2013, Melaka, Malaysia, pp. 337-340.
- Kacar B, Katkat VA 2007. Bitki Besleme” (Genişletilmiş ve Güncellenmiş 3. Baskı). Nobel Yayınları ISBN: 978-975-591-834-1, ss. 975.
- Kahriman F, Demirel K, Inalpulat M, Egesel CO, Genç L 2016. Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize. Journal of Agricultural Science and Technology, 18 (6): 1705-1718
- Karaca S, Çimrin KM 2002. Effects of the Nitrogen and Phosphorus Fertilization on the Yield and Quality of the Common Vetch (Vicia sativa L.) and Barley (Hordeum vulgare L.) Mixture. Yüzüncü Yıl University Journal of Agricultural Sciences, 12(1): 47-52.
- Kokaly RF, Clark RN 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3): 267-287.
- Li B, Liew OW, Asundi AK 2006. Pre-visual detection of iron and phosphorus deficiency by transformed reflectance spectra. Journal of Photochemistry and Photobiology. B: Biology, 85: 131–139
- Lin Y, Liquan Z 2006. Identification of the spectral characteristics of submerged plant Vallisneria spiralis. Acta Ecologica Sinica, 26 (4): 1005–1011.
- Manga İ, Acar Z, Ayan İ 1995. Baklagil Yembitkileri. Ondokuz Mayıs Üniversitesi, Ziraat Fakültesi, Ders notu No: 7
- Schlemmer MR, Francis DD, Shanahan JF, Schepers JS 2005. Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agronomy Journal, 97(1): 106–112.
- St-Jacques C, Bellefleur P 1991. Determining leaf nitrogen concentration of broadleaf tree seedlings by reflectance measurements. Tree Physiology, 8(4): 391-398.
- Sulak M, Aydın İ 2005. Nitrate accumulation in forage. Journal of Agriculture Faculty, OMU, 20(2): 106-109.
- Summy KR, Little CR, Mazariegos RA, Everitt JH, Davis MR, French JV, Scott AW 2003. Detecting stress in glasshouse plants using color infrared imagery: a potential new application for remote sensing. Subtropical Plant Science 55(1): 51–58.
- Thomas JR, Oerther GF 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal, 64(1): 11-13.
- Walburg G, Bauer ME, Daughtry CST, Housley TL 1982. Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies. Agronomy Journal, 74(4): 677-683.
- Wright DL, Rasmussen VP, Ramsey RD 2005. Comparing the Use of Remote Sensing with Traditional Techniques to Detect Nitrogen Stress in Wheat. Geocarto International, 20 (1): 63-68.
- Yılmaz M, Albayrak S 2016. Determination of Forage Yield and Quality of Some Alfalfa (Medicago sativa L.) Cultivars under Isparta Ecological Conditions. Journal of Field Crops Central Research Institute, 25(1): 42-47.
- Zhao D, Starks PJ, Brown MA, Phillips WA, Coleman SW 2007. Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassland Science, 53(1): 39-49.
Abstract
The aim of this study was to obtain spectral reflectance values by using
remote sensing method in alfalfa plant and to estimate nitrogen levels by using
these values. The treatments were carried out in both field and greenhouse
conditions. Different nitrogen, phosphorus and potassium
doses were applied to plots in the field and pots in greenhouse. Spectral
reflectance measurements were made at both the canopy (general) level and the
single leaf level when the plants were in pre-flowering stage. Reflectance measurements were undertaken using a portable
spectroradiometer measuring the wavelength range of 325-1075 nm of the
electromagnetic spectrum. Plant probe with artificial light source and leaf
clip were used for leaf measurements. Statistical analyses were conducted using
stepwise regression analysis
implemented in MINITAB-13 statistical program. As a result of the study,
significant relationships (Field-canopy: 0.94, Field-leaf: 0.23,
Greenhouse-canopy: 0.55, Greenhouse-leaf: 0.92) were determined between
the nitrogen levels in the plant and the reflectance values. Also results showed that
changes in nitrogen levels affect reflectance in the visible region (400-700
nm) of the spectrum. When the wavelengths were evaluated separately, it was observed
that the wavelengths of the blue, red and near infrared regions, which are
important for photosynthesis, can be used to estimate the nitrogen levels.
References
- Açıkgöz E 2001. Yem Bitkileri (3. Baskı). Uludağ Üniversitesi Güçlendirme Vakfı Yayın No: 182. VİPAŞ A.Ş. Yayın No: 58, Bursa, ss. 584.
- Açıkgöz E, Hatipoğlu R, Altınok S, Sancak C, Tan A, Uraz D 2005. Yem bitkileri üretimi ve sorunları, Türkiye Ziraat Mühendisliği VI. Teknik Kongresi, 3-7 Ocak 2005, Ankara, s. 503-518, 2005.
- Albayrak S. 2008. Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in sainfoin pasture. Sensors, 8 (11): 7275-7286.
- Anonymous 2018. https://www.tarim.gov.tr/sgb/Belgeler/SagMenuVeriler/BUGEM.pdf (Access date: 10.05.2018)
- Ball DM, Hoveland CS, Lacefıeld GD 1996. Forage Quality” (Chapter 16) Southern Forages. Publ. By the Williams Printing Company, pp: 124-132.
- Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S 2007. Estimating forage quantity and quality using aerial hyperspectral imagery for Northern mixed-grass prairie. Remote Sensing of Environment, 110(2): 216–225.
- Brink GE, Rowe DE, Sistani KR, Adeli A 2003. Bermudagrass cultivar response to swine effluent application. Agronomy Journal, 95(3): 597–601.
- Daughtry CST, Walthall CL, Kim MS, De-Colstoun EB, McMurtrey JE 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2): 229-239.
- Delalieux S, Somers B, Verstraeten WW, Keulemans W, Coppin P 2008. Hyperspectral canopy measurements under artificial illumination. International Journal of Remote Sensing, 29(20): 6051-6058.
- Elçi Ş 2005. Baklagil ve Buğdaygil Yem Bitkileri. T.C. Tarım ve Köyişleri Bakanlığı. ISBN 975-407-189-6. Mart Matbaası- İstanbul. Ankara, 486 s.
- Graeff S, Steffens D, Schubert S 2001. Use of reflectance measurements for the early detection of N, P, Mg, and Fe deficiencies in corn (Zea mays L.). Journal of Plant Nutrition and Soil Science, 164: 445–450.
- Han L, Rundquist DC 2003. The spectral responses of Ceratophyllum demersum at varying depths in an experimental tank. International Journal of Remote Sensing, 24(4): 859-864.
- Johan F, MatJafri MZ, Lim HS, Sim CK 2013. Preliminary study: Spectral reflectance properties of microalgae in freshwater. Proceeding of the 2013 IEEE International Conference on Space Science and Communication (IconSpace), 1-3 July 2013, Melaka, Malaysia, pp. 337-340.
- Kacar B, Katkat VA 2007. Bitki Besleme” (Genişletilmiş ve Güncellenmiş 3. Baskı). Nobel Yayınları ISBN: 978-975-591-834-1, ss. 975.
- Kahriman F, Demirel K, Inalpulat M, Egesel CO, Genç L 2016. Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize. Journal of Agricultural Science and Technology, 18 (6): 1705-1718
- Karaca S, Çimrin KM 2002. Effects of the Nitrogen and Phosphorus Fertilization on the Yield and Quality of the Common Vetch (Vicia sativa L.) and Barley (Hordeum vulgare L.) Mixture. Yüzüncü Yıl University Journal of Agricultural Sciences, 12(1): 47-52.
- Kokaly RF, Clark RN 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3): 267-287.
- Li B, Liew OW, Asundi AK 2006. Pre-visual detection of iron and phosphorus deficiency by transformed reflectance spectra. Journal of Photochemistry and Photobiology. B: Biology, 85: 131–139
- Lin Y, Liquan Z 2006. Identification of the spectral characteristics of submerged plant Vallisneria spiralis. Acta Ecologica Sinica, 26 (4): 1005–1011.
- Manga İ, Acar Z, Ayan İ 1995. Baklagil Yembitkileri. Ondokuz Mayıs Üniversitesi, Ziraat Fakültesi, Ders notu No: 7
- Schlemmer MR, Francis DD, Shanahan JF, Schepers JS 2005. Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agronomy Journal, 97(1): 106–112.
- St-Jacques C, Bellefleur P 1991. Determining leaf nitrogen concentration of broadleaf tree seedlings by reflectance measurements. Tree Physiology, 8(4): 391-398.
- Sulak M, Aydın İ 2005. Nitrate accumulation in forage. Journal of Agriculture Faculty, OMU, 20(2): 106-109.
- Summy KR, Little CR, Mazariegos RA, Everitt JH, Davis MR, French JV, Scott AW 2003. Detecting stress in glasshouse plants using color infrared imagery: a potential new application for remote sensing. Subtropical Plant Science 55(1): 51–58.
- Thomas JR, Oerther GF 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal, 64(1): 11-13.
- Walburg G, Bauer ME, Daughtry CST, Housley TL 1982. Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies. Agronomy Journal, 74(4): 677-683.
- Wright DL, Rasmussen VP, Ramsey RD 2005. Comparing the Use of Remote Sensing with Traditional Techniques to Detect Nitrogen Stress in Wheat. Geocarto International, 20 (1): 63-68.
- Yılmaz M, Albayrak S 2016. Determination of Forage Yield and Quality of Some Alfalfa (Medicago sativa L.) Cultivars under Isparta Ecological Conditions. Journal of Field Crops Central Research Institute, 25(1): 42-47.
- Zhao D, Starks PJ, Brown MA, Phillips WA, Coleman SW 2007. Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassland Science, 53(1): 39-49.
Details
| Primary Language | English |
|---|---|
| Journal Section | RESEARCH ARTICLE |
| Authors | |
| Publication Date | November 30, 2018 |
| Submission Date | February 20, 2018 |
| Acceptance Date | June 7, 2018 |
| Published in Issue | Year 2018Volume: 21 Issue: 6 |
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KSU Journal of Agriculture and Nature
e-ISSN: 2619-9149