Theoretical and Applied Climatology

, Volume 128, Issue 1–2, pp 1–11 | Cite as

Spatial analysis of climate factors used to determine suitability of greenhouse production in Turkey

  • Bilal CemekEmail author
  • Mustafa Güler
  • Hakan Arslan
Original Paper


This study aimed to identify the most suitable growing periods for greenhouse production in Turkey in order to make valuable contribution to economic viability. Data collected from the meteorological databases of 81 provinces was used to determine periodic climatological requirements of greenhouses in terms of cooling, heating, natural ventilation, and lighting. Spatial distributions of mean daily outside temperatures and greenhouse heating requirements were derived using ordinary co-kriging (OCK) supported by Geographical Information System (GIS). Mean monthly temperatures throughout the country were found to decrease below 12 °C in January, February, March, and December, indicating heating requirements, whereas temperatures in 94.46 % of the country rose above 22 °C in July, indicating cooling requirements. Artificial lighting is not a requirement in Turkey except for November, December, and January. The Mediterranean, Aegean, Marmara, and Black Sea Regions are more advantageous than the Central, East, and Southeast Anatolia Regions in terms of greenhouse production because the Mediterranean and Aegean Regions are more advantageous in terms of heating, and the Black Sea Region is more advantageous in terms of cooling. Results of our study indicated that greenhouse cultivation of winter vegetables is possible in certain areas in the north of the country. Moreover, greenhouses could alternatively be used for drying fruits and vegetables during the summer period which requires uneconomical cooling systems due to high temperatures in the Mediterranean and Southeastern Anatolian Regions.


Geographical Information System Kriging Natural Ventilation Cool Requirement Lighting Requirement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Alsamamra H, Ruiz-Arias JA, Pozo-Va’zquez D, Tovar-Pescador J (2009) A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain. Agr Forest Meteorol 149(8):1343–1357CrossRefGoogle Scholar
  2. ASHRAE (2005) Fundamentals SI. Environmental control for animals and plants—physiological considerations. ASHRAE, Atlanta, GAGoogle Scholar
  3. Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman Higher Education, HarlowGoogle Scholar
  4. Baudoin W, Grafiadellis M, Jiminez R, La Malfa G, Martinez-Garcia PF, Garnaud JC, Montero AA, Nisen A, Verlodt H, de Villele O, von Zabeltitz C (1991) Protected cultivation in the Mediterranean climate. FAO plant production and protection paper no. 90Google Scholar
  5. Baytorun N, Abak K, Üstün S, İkiz Ö (1996) GAP alanında sera tarımı potansiyeli sahil bölgeleri ile karşılaştırılması. GAP 1.Sebze Tarımısempozyumu ŞanlıurfaGoogle Scholar
  6. Cemek B (2005) Samsun İl ve ilçelerinde seraların iklimsel ihtiyaçlarının belirlenmesi. OMÜ. Zir Fak 20(3):34–43Google Scholar
  7. Chuanyan Z, Zhongren N, Guodong C (2005) Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains. China Ecol Model 189:209–220CrossRefGoogle Scholar
  8. Coops N, Loughhead A, Ryan P, Hutton R (2001) Development of daily spatial heat unit mapping from monthly climatic surfaces for the Australian continent. Int J Geogr Inf Sci 15:345–361CrossRefGoogle Scholar
  9. Daly C, Neilson RP, Phillips D (1994) A statistical-topographical model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158CrossRefGoogle Scholar
  10. Diodato N, Ceccarelli M (2005) Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the Sannio mountains (southern Italy). Earth Surf Proc Land 30(3):259–268CrossRefGoogle Scholar
  11. Gomez KA, Gomez AA (1984) Statistical procedures for agricultural research, 2nd edn. John Wiley and Sons, SingaporeGoogle Scholar
  12. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New YorkGoogle Scholar
  13. Goovaerts P (1999) Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 34:227–242CrossRefGoogle Scholar
  14. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129CrossRefGoogle Scholar
  15. Güler M, Cemek B, Günal H (2007) Assessment of some spatial climatic layers through GIS and statistical analysis techniques in Samsun Turkey. Meteorol Appl 14(2):163–169CrossRefGoogle Scholar
  16. Kendirli B, Çakmak B, Gökalp Z (2007) Analysis of climate factors for the development of greenhouses in eastern Black Sea region. Build Environ 42(7):4072–4078CrossRefGoogle Scholar
  17. Krug H, Liebig HP, Stutzel H (2002) Gemüseproduktion. Ulmer Verlag, StuttgartGoogle Scholar
  18. Li J, Andrew DH (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol Inform 6:228–241CrossRefGoogle Scholar
  19. MGM (2010) Meteorology bulletin reports, TurkeyGoogle Scholar
  20. Moral FJ (2010) Comparison of different geostatistical approaches to map climate variables: application to precipitation. Int J Climatol 30:620–631Google Scholar
  21. Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92:211–225CrossRefGoogle Scholar
  22. Phillips DL, Dolph J, Marks D (1992) A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain. Agric For Meteorol 58:119–141CrossRefGoogle Scholar
  23. Spadavecchia L, Williams M (2009) Can spatio-temporal geostatistical methods improve high resolution regionalisation of meteorological variables? Agric For Meteorol 149(6–7):1105–1117CrossRefGoogle Scholar
  24. Stefanescu V, Stefan S, Georgescu F (2013) Spatial distribution of heavy precipitation events in Romania between 1980 and 2009. Meteorol Appl. doi: 10.1002/met.1391 Google Scholar
  25. Toy S, Yilmaz S, Yilmaz H (2007) Determination of bioclimatic comfort in three different land uses in the city of Erzurum, Turkey. Build Environ 42:1315–1518CrossRefGoogle Scholar
  26. TUIK (2012) Available at 2012. (accessed 01.02.2014)
  27. Tuzel Y, Gul A, Dasgan HY, Oztekin GB, Engindeniz S, Boyacı HF, Ersoy A, Tepe A, Ugur A (2010) Örtüaltı Yetiştiriciliğinin gelişimi. TMMOB Ziraat Mühendisleri Odası Türkiye Ziraat Mühendisliği VII. TeknikKongresi Bildiriler Kitabı: 559–576,11–15, AnkaraGoogle Scholar
  28. von Elsner B, Briassoulis D, Waaijenberg D, Mistriotis A, von ZabeltitzChr GJ, Russo G, Suay-Cortes R (2000) Review of structural and functional characteristics of greenhouses in European Union countries, part I: design requirements. J Agr Eng Res 75(1):1–16CrossRefGoogle Scholar
  29. Von Zabeltitz C (1999) Greenhouse structures. In: Stanhill G, Zvi Enoch H (eds) Greenhouse ecosystems, Ecosystems of the world, vol 20. Elsevier, Amsterdam, pp. 17–69Google Scholar
  30. Von Zabeltitz C (2011) Integrated greenhouse systems for mild winter climates: climatic conditions, design, construction, maintenance and climate control. Springer-Verlag, BerlinCrossRefGoogle Scholar
  31. Von Zabeltitz C, Baudoin W (1999) Greenhouses and shelter structures for tropical regions. FAO plant production and protection paper no. 154Google Scholar
  32. Wan KKW, Danny HWL, Wenyan P, Joseph CL (2012) Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications. Appl Energ 97:274–282CrossRefGoogle Scholar
  33. Yıldırım D, Meral R (2010) Güneydoğu anadolu projesi (GAP) bölgesi ve civari illerde seraların iklimlendirme gereksinimleri. Harran Zir Fak 14(4):13–22Google Scholar
  34. Yurekli K, Simsek H, Cemek B, Karaman S (2007) Simulating climatic variables by using stochastic approach. Build Environ 42:3493–3499CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  1. 1.Department of Agricultural EngineeringOndokuz Mayis UniversitySamsunTurkey
  2. 2.Agrobigen Ltd. Co., Samsun Teknopark, Ondokuz Mayis UniversitySamsunTurkey
  3. 3.Middle Black Sea Development AgencySamsunTurkey

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