National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Yield forecasting regression models utilise data on yield and weather variables for past several years pertaining to locations under consideration. By studying the relationship of yield with different weather elements, predictors are identified. Generally, rainfall, temperature, humidity, rainy days, dry days and cloud amount etc., during critical phases of crop growth fulfill the criteria to be predictors. The weather variability both within and between seasons is unmanageable source of variability in yield. The weather variables affect the crop differently during various stages of development. Thus, extent of weather influence on crop yield depends not only on the magnitude but also on the distribution pattern of weather variables over the crop season. Statistical method has been performed for forecasting crop yield for Rice crop at Cauvery delta zone of Tamil Nadu. The forecast has been developed by using crop yield data considering four weather variables (Maximum and Minimum temperature, Rainfall, Morning and Evening Relative Humidity) simultaneously. Long term weather (1995-2016) and historical crop yield (1999-2016) data were utilized in the model. District level rice yield forecast for delta districts viz., Thanjavur, Thiruvarur, Nagapattinam, Trichy, Perambalur, Ariyalur and Cuddalore was issued by using the statistical model at mid-season (F2) and pre-harvest stage (F3) during Kharif, 2017 and Rabi, 2017-18.
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