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Formulation of weekly and monthly thumb rule models for prediction of potential productivity of sunflower in Punjab
Author(s) -
Jaspal Singh,
Prabhjyot Kaur
Publication year - 2021
Publication title -
journal of agrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 11
eISSN - 2583-2980
pISSN - 0972-1665
DOI - 10.54386/jam.v23i2.64
Subject(s) - sunflower , crop , crop yield , yield (engineering) , environmental science , sunshine duration , relative humidity , productivity , rule of thumb , helianthus annuus , automatic weather station , agronomy , mathematics , meteorology , geography , biology , materials science , macroeconomics , economics , metallurgy , algorithm
A study was conducted to evaluate the effect of meteorological parameters on Sunflower crop by analyzing meteorological and crop data (2003-2017) for three locations (Ludhiana, Ballowal Saunkhari and Amritsar) and to develop weather based “Weekly and Monthly Thumb Rule Models” for predicting the potential yield of sunflower crop in Punjab. These climatic normals were used for comparing the actual data to evaluate the effect of meteorological parameters on the yield of sunflower. In Punjab, ideally humid (maximum relative humidity between 77% - 94%) weather from mid-February to mid-March is favourable for optimum growth and development of vegetative stage in crop. The warm temperature (>35 ºC) during the seed development period after the flowering stage of sunflower is favourable for seed yield. However, heavy rainfall in the months of April and May with cloudy weather (sunshine hour < 9.2 hour) are not favourable for its productivity. The actual meteorological data of high yield crop years over the past 15 years were analyzed for different growth stages of sunflower to work out the critical ranges of meteorological parameters. Weather based “Thumb Rule Models” using the weekly and monthly meteorological data for different growth stages were formulated for use in developing the crop weather insurance term sheets and also predicting the potential yield of sunflower crop.

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