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Assessment of Crop Weather Relations in Wheat (Triticum aestivum) in Western Maharashtra Plain Zone
Article | IMSEAR | ID: sea-229313
Wheat (Triticum aestivum L.) is a thermo-sensitive long-day crop. Temperature is a major determinant of its growth and productivity. Late sown wheat exposes preanthesis phenological events to high temperature that influence grain development and ultimately the yield [1]. Comprehensive assessments of the influence of climate variability on crop yields at local and regional scales can be highly beneficial. With an aim to assess the weather influences on wheat at local scale this study was taken up. An experiment was conducted at Department of Agricultural Meteorology Farm, College of Agriculture, Pune, Maharashtra State (India) in a split-plot design with three replications and sixteen treatment combinations of four different varieties and four sowing windows. Four varieties used were NIAW-301 (Trymbak ), NIAW-917 (Tapovan), NIAW-1415 (Netravati) and NIAW-1994 (Phule Samadhan). Four sowings were taken up on 43rd MW (22-28 October), 45th MW (5-11November), 47th MW (19-25 November) and 49th MW (3-9 December). The grain yield of wheat was influenced significantly by wheat varieties. The grain yields were significantly higher in NIAW-1994 (51.07 and 48.52 qha-1) and significantly superior to the rest of the wheat varieties. This was followed by NIAW-917(45.72 and 43.43qha-1), NIAW-301(43.57 and 41.27 q ha-1). The variety NIAW-1415 recorded significantly lower grain yield (40.89 and 38.84 qha-1) during 2016 and 2017, respectively. Correlation analysis with weather parameters e.g. Temperature (Maximum and Minimum), Relative humidity (Morning and Evening), Rainfall and bright sunshine hours and yield showed that from tillering to 50% flowering stage, maximum temperature (-0.962*) was significantly negatively correlated with grain yield (r = -0.980**), (r =-0.950**) during 2016 and 2017, respectively in NIAW-301 (Trymbak ). The same trend was observed in the remaining varieties also. Regression equations were developed to predict the yield.
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Texte intégral: 1 Indice: IMSEAR Année: 2023 Type: Article
Texte intégral: 1 Indice: IMSEAR Année: 2023 Type: Article