Estimation of Wheat Yield for Turkey Using AgroMetShell Model


Simsek O., Mermer A., Yildiz H., Oezaydin K. A., ÇAKMAK B.

JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI, cilt.13, sa.3, ss.299-307, 2007 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 3
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1501/tarimbil_0000000552
  • Dergi Adı: JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.299-307
  • Anahtar Kelimeler: Yield forecast, AgrometShell model, wheat, meteorological data
  • Ankara Üniversitesi Adresli: Evet

Özet

Agricultural policy plays a major role in effective management of natural resources of a country. It is important that the acreage and condition of the various crop production sites, amounts and yields should be determined and forecast on time for a complete set up of agricultural planning. Otherwise, the development of agricultural policies in compliance with the realities of the country, and coinciding with development objectives beco me difficult. Most cereal production in the Anatolian Plateau is rainfed and therefore, exposed to the interannual variability of rainfall which directly affects the variability of the main cereals and food security as well. Yield forecasts were made by using AgroMetShell in the scope of FAO Technical Cooperation Project (TCP). Meteorological data, crop coefficients, phenological observations, soil characteristics and NDVI data are prepared in order to run the model. Model was run at 265 stations for unirrigated conditions. Water Satisfaction Index (WSI) values and graphics were obtained. By using NDVI data, average values of WSI calculated city by city. Statistical analysis were made between WSI values and statistics. As a result yield forecasts of cities were obtained for wheat in 2005 and 2006. Maps were prepared by using statistics and yield forecasts for 2005 and 2006. A relation of r(2) = 0.9067 calculated between yield forecasts and statistics.