Markov Network Model for Predicting Thousand Seed Weight in Chickpea Genotypes

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Abstract

Predicting yield-related traits such as thousand seed weight (TSW) allows researchers to develop varieties that achieve maximum efficiency and value under changing climate conditions. In this paper, we propose a Markov network model for predicting the important phenotypic trait TSW in chickpea genotypes using pre-selected single nucleotide polymorphisms and weather data for 5 days before and 20 days after sowing, such as minimum and maximum temperatures, precipitation, humidity, infrared radiation, and daylength. The constructed model predicts the TSW trait with high accuracy – the Pearson correlation coefficient is 0.83.

About the authors

D. D Maltsov

Peter the Great St.-Petersburg Polytechnic University

St.-Petersburg, Russia

M. G Samsonova

Peter the Great St.-Petersburg Polytechnic University

St.-Petersburg, Russia

K. N Kozlov

Peter the Great St.-Petersburg Polytechnic University

Email: kozlov_kn@spbstu.ru
St.-Petersburg, Russia

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