Wheat Yield Prediction through Agro Meteorological Indices for Ardebil District
Wheat prediction was carried out using different meteorological variables together with agro meteorological indices in Ardebil district for the years 2004-2005 & 2005–2006. On the basis of correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agro meteorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapor pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (5th of June). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agro meteorological indices.
[1] Baier, W. (1977). World Meteorological Organization, Rome. Crop-
Weather models and their use in yield assessments. Tech Note. 151. pp:
1-48.
[2] Bazgeer, Saeed. (2005). Land use change analysis in the sub
mountainous region of Punjab using remote sensing, GIS & agro
meteorological parameters. Ph. D. Dissertation, Punjab Agricultural
University (PAU), Ludhiana, India. pp: 128.
[3] Nuttonson, M. Y. (1955). Wheat climate relationships and use of
phonology in ascertaining the thermal and photo-thermal requirements
of wheat. pp: 54-55. American Institute of Crop Ecology, Washing DC.
[4] Hundal, S. S. Singh, R. and Dhaliwal, L. K. (1997). Agro-climatic
indices for predicting phonology of wheat (Tritium aestivum) in Punjab.
J. Agric Sci. 67: 265-268.
[5] Sharma, A. Sood, R. K. and Kalubarme, M. H. (2004).
Agrometeorological wheat yield forecast in Himachal Pradesh. J
Agromet 6: 153-160.
[6] Dubey, R. P. Kalubarme, M. H. Jhorar, O. P. and Cheema, S. S. (1987).
Wheat yield models and production estimates for Patiala and Ludhiana
districts based on Landsat - MSS and Agro meteorological data.
Scientific Note. IRS-UP/SAC/CPF/SN/08/87. pp 1-34. Space
Applications Center, Ahmadabad.
[7] Reddy, T. Y. and Reddi, G. H. S. (2003). Principles of Agronomy. pp:
48-77. Kalyani Publishers, Ludhiana, India.
[8] Rao, G. S. L. H. V. P. (2003). Agricultural meteorology. pp: 95-112.
Director of Extension, Kerala Agricultural University, Thrissur, Kerala,
India.
[9] Kramer, P. J. (1997) Plant and Soil water Relationships: A Modern
Synthesis. pp: 269-345.Tata McGraw Hill Publishing Company Ltd,
New Delhi.
[10] Baier, W. and Robertson, G. W. (1967). Estimating yield components of
wheat from calculated soil moisture. Canadian J Plant Sci. 47: 617-630.
[11] Clarke, D.Smith, M. and EL-Askari, K. (1998). Food and Agriculture
Organization of United Nations, Rome. CropWat for windows package,
version 4.2.
[12] Doorenbos, J. and Pruitt, W. O. (1975). Food and Agriculture
Organization of United Nations, Rome. Guidelines for predicting crop
water requirements. Tech Note 24. pp:124.
[13] Marcellos, H. and Single, W. V. (1972). The influence of cultivar,
temperature and photoperiod on post-flowering development of wheat.
Australia J Agric Res. 23: 533-540.
[14] Asana, R. D. and Williams, R. F. (1965). The effect of temperature
stress on grain development in wheat. Aust J Agric Res. 16: 1-13.
[15] Abbate, P. E. Dardanelli, J. L. Cantarero, M. G. Maturano, M. Mechiori,
R. J. M. and Suero, E. E. (2004). Climatic & water availability effects on
water use efficiency in wheat. Crop Sci. 44: 474-483.
[16] Musick, J. T. Jones, O. R. Stewart, B. and Dusek, D. A. 1994. Wateryield
relations for irrigated & dry land wheat in the US Southern plains.
Argon J. 86: 980-986.
[17] Mahey, R. K. (1999). Acreage estimation and technique development for
yield prediction through remote sensing. Final technical report. pp: 73-
92.Depth of Agronomy, Punjab Agricultural University, Ludhiana.
[1] Baier, W. (1977). World Meteorological Organization, Rome. Crop-
Weather models and their use in yield assessments. Tech Note. 151. pp:
1-48.
[2] Bazgeer, Saeed. (2005). Land use change analysis in the sub
mountainous region of Punjab using remote sensing, GIS & agro
meteorological parameters. Ph. D. Dissertation, Punjab Agricultural
University (PAU), Ludhiana, India. pp: 128.
[3] Nuttonson, M. Y. (1955). Wheat climate relationships and use of
phonology in ascertaining the thermal and photo-thermal requirements
of wheat. pp: 54-55. American Institute of Crop Ecology, Washing DC.
[4] Hundal, S. S. Singh, R. and Dhaliwal, L. K. (1997). Agro-climatic
indices for predicting phonology of wheat (Tritium aestivum) in Punjab.
J. Agric Sci. 67: 265-268.
[5] Sharma, A. Sood, R. K. and Kalubarme, M. H. (2004).
Agrometeorological wheat yield forecast in Himachal Pradesh. J
Agromet 6: 153-160.
[6] Dubey, R. P. Kalubarme, M. H. Jhorar, O. P. and Cheema, S. S. (1987).
Wheat yield models and production estimates for Patiala and Ludhiana
districts based on Landsat - MSS and Agro meteorological data.
Scientific Note. IRS-UP/SAC/CPF/SN/08/87. pp 1-34. Space
Applications Center, Ahmadabad.
[7] Reddy, T. Y. and Reddi, G. H. S. (2003). Principles of Agronomy. pp:
48-77. Kalyani Publishers, Ludhiana, India.
[8] Rao, G. S. L. H. V. P. (2003). Agricultural meteorology. pp: 95-112.
Director of Extension, Kerala Agricultural University, Thrissur, Kerala,
India.
[9] Kramer, P. J. (1997) Plant and Soil water Relationships: A Modern
Synthesis. pp: 269-345.Tata McGraw Hill Publishing Company Ltd,
New Delhi.
[10] Baier, W. and Robertson, G. W. (1967). Estimating yield components of
wheat from calculated soil moisture. Canadian J Plant Sci. 47: 617-630.
[11] Clarke, D.Smith, M. and EL-Askari, K. (1998). Food and Agriculture
Organization of United Nations, Rome. CropWat for windows package,
version 4.2.
[12] Doorenbos, J. and Pruitt, W. O. (1975). Food and Agriculture
Organization of United Nations, Rome. Guidelines for predicting crop
water requirements. Tech Note 24. pp:124.
[13] Marcellos, H. and Single, W. V. (1972). The influence of cultivar,
temperature and photoperiod on post-flowering development of wheat.
Australia J Agric Res. 23: 533-540.
[14] Asana, R. D. and Williams, R. F. (1965). The effect of temperature
stress on grain development in wheat. Aust J Agric Res. 16: 1-13.
[15] Abbate, P. E. Dardanelli, J. L. Cantarero, M. G. Maturano, M. Mechiori,
R. J. M. and Suero, E. E. (2004). Climatic & water availability effects on
water use efficiency in wheat. Crop Sci. 44: 474-483.
[16] Musick, J. T. Jones, O. R. Stewart, B. and Dusek, D. A. 1994. Wateryield
relations for irrigated & dry land wheat in the US Southern plains.
Argon J. 86: 980-986.
[17] Mahey, R. K. (1999). Acreage estimation and technique development for
yield prediction through remote sensing. Final technical report. pp: 73-
92.Depth of Agronomy, Punjab Agricultural University, Ludhiana.
@article{"International Journal of Biological, Life and Agricultural Sciences:54968", author = "Fariba Esfandiary and Ghafoor Aghaie and Ali Dolati Mehr", title = "Wheat Yield Prediction through Agro Meteorological Indices for Ardebil District", abstract = "Wheat prediction was carried out using different meteorological variables together with agro meteorological indices in Ardebil district for the years 2004-2005 & 2005–2006. On the basis of correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agro meteorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapor pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (5th of June). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agro meteorological indices.
", keywords = "Wheat yields prediction, agro meteorological
indices, statistical models", volume = "3", number = "1", pages = "49-4", }
