Research Article
BibTex RIS Cite

Uydu Verisi ve AgrometShell Modülü ile Işık Kullanım Etkinliği LUE Modeli Kullanarak Çankırı İli Meralarının Birincil Üretim Tahmini

Year 2016, Volume: 22 Issue: 4, 555 - 565, 01.09.2016
https://doi.org/10.1501/Tarimbil_0000001414

Abstract

Bu çalışmada, Çankırı meralarının 2000-2009 arasındaki aylık ve yıllık toplam birincil üretimleri ışık kullanım etkinliği modeli ile hesaplanmıştır. Elde edilen bulgulara göre il sınırları içinde kalan meraların son on yıllık ortalama birincil üretimi yaklaşık 17877 tondur ve bu üretim hem mevsimsel hem de yıllık olarak 12630-37701 ton arası değişkenlik göstermektedir. Bu değişkenliğin ana sebepleri içinde bölgeye düşen yağış miktarı ve otlayan hayvan sayısındaki değişimler gösterilebilir. Model performansı, toplanmış normalize edilmiş farklılık indeksi INDVI ile test edilmiştir. Test sonucuna göre, INDVI ve toplam birincil üretim arasında orta seviyede bir ilişki R2 = 0.69, P0.05, 2008; r= 0.41, P>0.05, 2009 . Örneklenen bitki türleri, kişisel örnekleme hataları ve uydu verileri ile örnekleme alanı arasındaki ölçek farklılığı ilişki çıkmamasının ana sebepleri olarak gösterilebilir. Bu çalışma, AgrometShell girdilerini kullanan LUE modelinin meralarda birincil üretim miktarının tahmin edilmesinde iyi bir araç olduğunu ortaya koymaktadır.

References

  • Allen R G, Pereira L S, Raes D & Smith M (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. United Nations Food and Agriculture Organization (FAO), Irrigation and Drainage Paper 56, Rome, Italy
  • Anonymous (2001). Risk assessment guidance for superfund (RAGS). In: Volume III-Part A. Process for Conducting Probabilistic Risk Assessment. U.S. Environmental Protection Agency Washington, DC, pp. 385-387
  • Anonymous (2012). Ulusal Mera Kullanım ve Yönetim Projesi sonuç raporu. Proje No: 106G017. TÜBİTAK Kamu Kurumları Araştırma ve Geliştirme Projelerini Destekleme Programı (1007 Programı)
  • Brogaard S, Runnström M & Seaquist J A (2004). Primary production of inner Mongolia, China between 1982 and 1999 estimated by a satellite-driven light use efficiency model. Global and Planetary Change 45: 313-332
  • Goetz S J, Prince S D, Goward S N, Thawley M M & Small J (1999). Satellite remote sensing of primary production: an improved production efficiency modelling approach. Ecological Modelling 122: 239- 255
  • Ha W, Gowda P, Oommen T, Marek T, Porter D & Howell T (2011). Spatial interpolation of daily reference evapotranspiration in the Texas High Plains. In: World Environmental and Water Resources Congress, May 22-26, Palm Springs, CA, pp. 2796-2804
  • Hilker T, Nicholas C C, Wulder M A, Black T A & Guy R D (2008). The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements. Science of the Total Environment 404: 411-423
  • Holben B N (1986). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing 7: 1417- 1434
  • Huete A, Didan K, Miura T & Rodriguez E (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195-213
  • Ketenoğlu O, Quezel P, Akman Y & Aydoğdu M (1983). New syntaxa on the gypsaceous formation in the Central Anatolia. Ecologia Mediterranea 9(3-4): 211- 221
  • Kurt L, Tuğ G N & Ketenoğlu O (2006). Synoptic view of the steppe vegetation of Central Anatolia (Turkey). Asian Journal of Plant Sciences 5(4): 733-739
  • Le Roux H X, Gauthier A, Begue H & Sinoquet H (1997). Radiation absorption and use by humid savannah grassland: assessment using remote sensing and modelling. Agricultural and Forest Meteorology 85: 117-132
  • Los S O, North P R J, Grey W M F & Barnsley M J (2005). A method to convert AVHRR normalized difference vegetation index time series to a standard viewing and illumination geometry. Remote Sensing of Environment 99(4): 400-411
  • Mermer A, Ünal E, Aydoğdu M, Urla Ö, Yıldız H & Torunlar H (2012). Determining rangeland areas by satellite images. TABAD-Research Journal of Agricultural Sciences 5: 107-110
  • Monteith J L (1972). Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9: 747-766
  • Morgan G M & Henrion M (1990). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press, NY
  • Mukhala E & Hoefsloot P (2004). AgrometShell Manual. Agrometeorology Group, Environment and Natural Resources Service, Food and Agricultural Organization Rome, Italy
  • Prince S D (1991). Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981-1988. International Journal of Remote Sensing 12: 1301-1311
  • Seaquist J W, Olsson L & Ardö J (2003). A remote sensing based primary production model for grassland biomes. Ecological Modelling 169: 131-155
  • Shevenell L & Hoffman F O (1993). Necessity of uncertainty analyses in risk assessment. Journal of Hazardous Materials 35: 369-385
  • Sims P L & Singh J S (1978). The structure and function of ten western North America grasslands. II. Intra- seasonal dynamics in primary producer compartments. Journal of Ecology 66: 251-285
  • Tucker C J, Vanpraet C, Boerwinkel E & Gaston A (1983). Satellite remote sensing of total dry matter production in the Senegalese Sahel. Remote Sensing of Environment 13: 461-474
  • TUIK (2009). Livestock statistics. Retrieved in July, 18 2009 from http://www.tuik.gov.tr
  • Uzun B & Demir V (2012). Fotosentetik aktif radyasyon (FAR) ölçümlerinde LED ve foto diyotların hassas tarım açısından kullanılabilirliği üzerine bir araştırma. Tarım Bilimleri Dergisi-Journal of Agricultural Sciences 18 (3): 214-225
  • Wight J R & Hanks R J (1981). A water-balance, climate model for range herbage production. Journal of Range Management 4: 307-311

Primary Production Estimation of Çankırı Province’s Rangelands Using Light Use Efficiency LUE Model with Satellite Data and AgrometShell Module

Year 2016, Volume: 22 Issue: 4, 555 - 565, 01.09.2016
https://doi.org/10.1501/Tarimbil_0000001414

Abstract

In this study, monthly and annual gross primary production GPP of rangelands in Çankırı province for the period of 2000-2009 was calculated using light use efficiency LUE model with the inputs of satellite data and AgrometShell module. The average production of rangelands varied seasonally and annually from 12630 to 37701 tons and was approximately 17800 tons for the last ten years. The amount of rainfall and changing number of animal grazing in the region probably led to the variation. Model performance was tested with integrated normalized difference vegetation index INDVI approach which produced a moderate significant correlation R2= 0.69, P0.05 for 2008, r= 0.41, P>0.05 for 2009 due to some factors such as sampled plant type, scale differences between satellite data and ground sample size, and subjective sampling errors. This study indicates that LUE Model together with the inputs of AgrometShell module is suitable tool for estimation of rangeland primary production

References

  • Allen R G, Pereira L S, Raes D & Smith M (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. United Nations Food and Agriculture Organization (FAO), Irrigation and Drainage Paper 56, Rome, Italy
  • Anonymous (2001). Risk assessment guidance for superfund (RAGS). In: Volume III-Part A. Process for Conducting Probabilistic Risk Assessment. U.S. Environmental Protection Agency Washington, DC, pp. 385-387
  • Anonymous (2012). Ulusal Mera Kullanım ve Yönetim Projesi sonuç raporu. Proje No: 106G017. TÜBİTAK Kamu Kurumları Araştırma ve Geliştirme Projelerini Destekleme Programı (1007 Programı)
  • Brogaard S, Runnström M & Seaquist J A (2004). Primary production of inner Mongolia, China between 1982 and 1999 estimated by a satellite-driven light use efficiency model. Global and Planetary Change 45: 313-332
  • Goetz S J, Prince S D, Goward S N, Thawley M M & Small J (1999). Satellite remote sensing of primary production: an improved production efficiency modelling approach. Ecological Modelling 122: 239- 255
  • Ha W, Gowda P, Oommen T, Marek T, Porter D & Howell T (2011). Spatial interpolation of daily reference evapotranspiration in the Texas High Plains. In: World Environmental and Water Resources Congress, May 22-26, Palm Springs, CA, pp. 2796-2804
  • Hilker T, Nicholas C C, Wulder M A, Black T A & Guy R D (2008). The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements. Science of the Total Environment 404: 411-423
  • Holben B N (1986). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing 7: 1417- 1434
  • Huete A, Didan K, Miura T & Rodriguez E (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195-213
  • Ketenoğlu O, Quezel P, Akman Y & Aydoğdu M (1983). New syntaxa on the gypsaceous formation in the Central Anatolia. Ecologia Mediterranea 9(3-4): 211- 221
  • Kurt L, Tuğ G N & Ketenoğlu O (2006). Synoptic view of the steppe vegetation of Central Anatolia (Turkey). Asian Journal of Plant Sciences 5(4): 733-739
  • Le Roux H X, Gauthier A, Begue H & Sinoquet H (1997). Radiation absorption and use by humid savannah grassland: assessment using remote sensing and modelling. Agricultural and Forest Meteorology 85: 117-132
  • Los S O, North P R J, Grey W M F & Barnsley M J (2005). A method to convert AVHRR normalized difference vegetation index time series to a standard viewing and illumination geometry. Remote Sensing of Environment 99(4): 400-411
  • Mermer A, Ünal E, Aydoğdu M, Urla Ö, Yıldız H & Torunlar H (2012). Determining rangeland areas by satellite images. TABAD-Research Journal of Agricultural Sciences 5: 107-110
  • Monteith J L (1972). Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9: 747-766
  • Morgan G M & Henrion M (1990). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press, NY
  • Mukhala E & Hoefsloot P (2004). AgrometShell Manual. Agrometeorology Group, Environment and Natural Resources Service, Food and Agricultural Organization Rome, Italy
  • Prince S D (1991). Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981-1988. International Journal of Remote Sensing 12: 1301-1311
  • Seaquist J W, Olsson L & Ardö J (2003). A remote sensing based primary production model for grassland biomes. Ecological Modelling 169: 131-155
  • Shevenell L & Hoffman F O (1993). Necessity of uncertainty analyses in risk assessment. Journal of Hazardous Materials 35: 369-385
  • Sims P L & Singh J S (1978). The structure and function of ten western North America grasslands. II. Intra- seasonal dynamics in primary producer compartments. Journal of Ecology 66: 251-285
  • Tucker C J, Vanpraet C, Boerwinkel E & Gaston A (1983). Satellite remote sensing of total dry matter production in the Senegalese Sahel. Remote Sensing of Environment 13: 461-474
  • TUIK (2009). Livestock statistics. Retrieved in July, 18 2009 from http://www.tuik.gov.tr
  • Uzun B & Demir V (2012). Fotosentetik aktif radyasyon (FAR) ölçümlerinde LED ve foto diyotların hassas tarım açısından kullanılabilirliği üzerine bir araştırma. Tarım Bilimleri Dergisi-Journal of Agricultural Sciences 18 (3): 214-225
  • Wight J R & Hanks R J (1981). A water-balance, climate model for range herbage production. Journal of Range Management 4: 307-311
There are 25 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Ediz Ünal This is me

İlhami Bayramin This is me

Publication Date September 1, 2016
Submission Date January 1, 2016
Published in Issue Year 2016 Volume: 22 Issue: 4

Cite

APA Ünal, E., & Bayramin, İ. (2016). Primary Production Estimation of Çankırı Province’s Rangelands Using Light Use Efficiency LUE Model with Satellite Data and AgrometShell Module. Journal of Agricultural Sciences, 22(4), 555-565. https://doi.org/10.1501/Tarimbil_0000001414

Journal of Agricultural Sciences is published open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).