Development of Yield Forecast Model in Bread Wheat Using Regression Analysis

Karuna *

Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar-125001, India.

Y. P. S. Solanki

Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar-125001, India.

Vikram Singh

Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar-125001, India.

Navreet Kaur Rai

Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar-125001, India.

Nikhil Gangadhar

Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar-125001, India.

*Author to whom correspondence should be addressed.


Abstract

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.

Keywords: Regression model, multiple regression, stepwise regression, riability


How to Cite

Karuna, Y. P. S. Solanki, Vikram Singh, Navreet Kaur Rai, and Nikhil Gangadhar. 2024. “Development of Yield Forecast Model in Bread Wheat Using Regression Analysis”. International Journal of Plant & Soil Science 36 (7):875-81. https://doi.org/10.9734/ijpss/2024/v36i74799.

Downloads

Download data is not yet available.