Development of Yield Forecast Model in Bread Wheat Using Regression Analysis

Background: Studies highlighted the possibilities of simultaneous crop failures in the world’s “breadbaskets” (wheat) due to heat and 40% of the variability in inter-annual wheat production is already related to temperature extremes. The global yield numbers hide the degree of variability of wheat production, yet several environmental conditions pose a threat to wheat production.

Objective: The main objective of the study was to develop a regression model that fitted the dependent variable sufficiently well to account for the total variability.

Method: For this, sixty advance lines along with four standard checks were evaluated for fifteen yield-associated traits and eight quality traits during Rabi 2020-21 at the research area of Wheat and Barley section, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar. Multiple regression analysis revealed that 98.5% of the variability is explained by the studied morphological and quality traits.

Result: The stepwise regression analysis retained a total of seven traits (six morphological and one quality) viz. biological yield per plot, harvest index, grain weight per spike, flag leaf length, main spike weight, number of spikelets per spike and grain appearance score; explaining 97.8 % of the total variability.

Conclusion: The seventh model among all, indicated good yield predicting performance without modifying the traits.