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<p>Predicting yield-related traits such as the 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 TSW trait 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.</p>

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A Markov Network Model for Predicting Thousand Seed Weight in Chickpea Genotypes

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • D. D. Maltsov,
  • M. G. Samsonova,
  • K. N. Kozlov

摘要

Predicting yield-related traits such as the 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 TSW trait 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.